Promene u dizajnu dokumenata, controla, residual graph, residual tabele...

backend/core/tables.py

@@ -3,14 +3,6 @@ from backend.core.utils import calculate_aggregate_likelihood, calculate_aggrega
 
 
 def risk_matrix_table():
-    likelihood_labels = [
-        "Almost Certain (90-100%) (5)",
-        "Probable (51-89%) (4)",
-        "Possible (25-50%) (3)",
-        "Unlikely (11-24%) (2)",
-        "Rare (0-10%) (1)"
-    ]
-
     impact_labels = [
         "Insignificant (1)",
         "Significant (2)",
@@ -18,39 +10,28 @@ def risk_matrix_table():
         "Material (4)",
         "Major (5)"
     ]
+    header = ["Likelihood ↓ / Impact →"] + impact_labels
 
-    color_mapping = {
-        "Very Low": "lightgreen",
-        "Low": "green",
-        "Medium": "yellow",
-        "High": "orange",
-        "Critical": "red"
-    }
+    matrix = [
+        ["Almost Certain (5)",
+            (5, "bg-medium"), (10, "bg-high"), (15, "bg-critical"), (20, "bg-critical"), (25, "bg-critical")
+        ],
+        ["Likely (4)",
+            (4, "bg-low"), (8, "bg-medium"), (12, "bg-high"), (16, "bg-high"), (20, "bg-critical")
+        ],
+        ["Probable (3)",
+            (3, "bg-low"), (6, "bg-low"), (9, "bg-medium"), (12, "bg-high"), (15, "bg-high")
+        ],
+        ["Unlikely (2)",
+            (2, "bg-very-low"), (4, "bg-low"), (6, "bg-medium"), (8, "bg-medium"), (10, "bg-medium")
+        ],
+        ["Rare (1)",
+            (1, "bg-very-low"), (2, "bg-very-low"), (3, "bg-low"), (4, "bg-low"), (5, "bg-medium")
+        ],
+    ]
 
-    def get_label(score):
-        if score <= 2:
-            return "Very Low"
-        elif score <= 4:
-            return "Low"
-        elif score <= 10:
-            return "Medium"
-        elif score <= 16:
-            return "High"
-        else:
-            return "Critical"
-
-    table_matrix_risk = [["Likelihood ↓ / Impact →"] + impact_labels]
-
-    for likelihood in range(5, 0, -1):
-        row = [likelihood_labels[5 - likelihood]]
-        for impact in range(1, 6):
-            score = likelihood * impact
-            label = get_label(score)
-            color_class = color_mapping[label]
-            row.append((score, label, color_class))
-        table_matrix_risk.append(row)
-
-    return table_matrix_risk
+    table = [header] + matrix
+    return table
 
 def get_risk_table(document):
     risks = (
@@ -98,3 +79,34 @@ def get_risk_table(document):
     risks_with_controls.sort(key=lambda x: x['risk_score'], reverse=True)
 
     return risks_with_controls
+
+def get_safeguard_summary_table(risks_with_controls):
+    from collections import Counter
+    from backend.core.models import Control
+
+    safeguard_counter = Counter()
+    safeguard_names = {}
+
+    for risk in risks_with_controls:
+        for control in risk.get('controls', []):
+            control_id = control.get('control')
+            control_name = control.get('control__name')
+            if control_id:
+                safeguard_counter[control_id] += 1
+                safeguard_names[control_id] = control_name
+
+    summary = []
+    controls = Control.objects.filter(id__in=safeguard_counter.keys())
+    controls_map = {c.id: c for c in controls}
+
+    for control_id, count in safeguard_counter.items():
+        control = controls_map.get(control_id)
+        summary.append({
+            'id': control_id,
+            'safeguard_id': control.safeguard_id if control else '',
+            'name': safeguard_names.get(control_id, ''),
+            'description': control.description if control else '',
+            'count': count,
+        })
+    summary.sort(key=lambda x: x['count'], reverse=True)
+    return summary