hanging dependency in zero-dependency mode fixed

.gitignore

@@ -71,3 +71,17 @@ site/museum/
 museum/
 updated_results_expanded_story/
 updated_results_2/
+gitgalaxy/README gitgalaxy.md
+gitgalaxy/core/README core.md
+gitgalaxy/physics/README physics.md
+gitgalaxy/recorders/README recorder.md
+gitgalaxy/security/README security.md
+gitgalaxy/standards/README standards.md
+gitgalaxy/tools/README tools.md
+gitgalaxy/tools/ai_guardrails/README ai.md
+gitgalaxy/tools/cobol_to_cobol/README c2c.md
+gitgalaxy/tools/cobol_to_java/README c2j.md
+gitgalaxy/tools/compliance/README sbom.md
+gitgalaxy/tools/network_auditing/README network.md
+gitgalaxy/tools/supply_chain_security/README supply_chain.md
+gitgalaxy/tools/terabyte_log_scanning/README terabyte.md

gitgalaxy/core/network_risk_sensor.py

@@ -275,16 +275,16 @@ def _fallback_map_ecosystem(self, stars: List[Dict[str, Any]]) -> Tuple[List[Dic
 
             if "telemetry" not in s: s["telemetry"] = {}
             s["telemetry"]["network_metrics"] = {
-                "pagerank_score": None,
-                "normalized_blast_radius": None, 
-                "betweenness_score": None,
-                "closeness_score": None,
+                "pagerank_score": 0.0,
+                "normalized_blast_radius": 0.0, 
+                "betweenness_score": 0.0,
+                "closeness_score": 0.0,
                 "in_degree": in_d,
                 "out_degree": out_d,
                 "producer_ratio": round(producer_ratio, 3),
                 "ecosystem_role": ecosystem_role,
-                "systemic_threat_vector": None,
-                "is_algorithmic_bottleneck": None
+                "systemic_threat_vector": [],
+                "is_algorithmic_bottleneck": False
             }
             s["telemetry"]["popularity"] = in_d
 

gitgalaxy/physics/signal_processor.py

@@ -837,13 +837,13 @@ def get_avg(metric_name):
             # ---> N-DIMENSIONAL AI NETWORK POSTURE <---
             if ai_files:
                 # Find the most heavily relied-upon AI node in the graph
-                ai_files.sort(key=lambda x: x.get("telemetry", {}).get("network_metrics", {}).get("pagerank_score", 0.0), reverse=True)
+                ai_files.sort(key=lambda x: x.get("telemetry", {}).get("network_metrics", {}).get("pagerank_score") or 0.0, reverse=True)
                 primary_ai_node = ai_files[0]
                 net_mets = primary_ai_node.get("telemetry", {}).get("network_metrics", {})
 
                 role = net_mets.get("ecosystem_role", "Unknown")
-                pr = net_mets.get("normalized_blast_radius", 0.0)
-                btw = net_mets.get("betweenness_score", 0.0)
+                pr = net_mets.get("normalized_blast_radius") or 0.0
+                btw = net_mets.get("betweenness_score") or 0.0
 
                 ai_topology["insights"].append(f"Structural Posture: The primary AI integration acts as a '{role}' within the repository.")
 
@@ -1675,9 +1675,9 @@ def get_cumulative_risk(f):
             rv = file_data.get("risk_vector", [])
             p = file_data.get("path", "")
 
-            btw = net.get("betweenness_score", 0.0)
-            close = net.get("closeness_score", 0.0)
-            pr = net.get("normalized_blast_radius", 0.0)
+            btw = net.get("betweenness_score") or 0.0
+            close = net.get("closeness_score") or 0.0
+            pr = net.get("normalized_blast_radius") or 0.0
 
             flux_risk = rv[flux_idx] if flux_idx >= 0 and len(rv) > flux_idx else 0.0
             err_risk = rv[err_idx] if err_idx >= 0 and len(rv) > err_idx else 0.0

tests/test_zero_dependency.py

@@ -0,0 +1,49 @@
+import unittest
+from unittest.mock import patch
+
+from gitgalaxy.core.network_risk_sensor import NetworkRiskSensor
+from gitgalaxy.physics.signal_processor import SignalProcessor
+
+class TestZeroDependencyMode(unittest.TestCase):
+
+    @patch('gitgalaxy.core.network_risk_sensor.HAS_NETWORKX', False)
+    def test_fallback_does_not_crash_signal_processor(self):
+        """
+        Simulates a user running GalaxyScope without 'networkx' installed.
+        Ensures that the None-type fallbacks don't crash Phase 6 Synthesis.
+        """
+        # 1. Initialize the blinded sensor
+        sensor = NetworkRiskSensor()
+
+        # 2. Create a mock star (file) with some basic AI topology hits to trigger that specific math
+        mock_stars = [{
+            "path": "src/ai_agent.py",
+            "lang_id": "python",
+            "coding_loc": 100,
+            "raw_imports": [],
+            "hit_vector": [1, 0, 0, 0, 0, 0, 0, 0, 0], # Trigger AI logic
+            "risk_vector": [0.0] * 18,
+            "telemetry": {}
+        }]
+
+        # 3. Force the sensor to use the fallback method
+        mapped_stars, macro_metrics = sensor.map_ecosystem(mock_stars)
+
+        # 4. Pass the resulting payload into the Signal Processor
+        processor = SignalProcessor()
+
+        try:
+            # If the bug exists, this will throw a TypeError due to NoneType math
+            processor.summarize_galaxy_metrics(mapped_stars, unparsable_files=[])
+            processor.generate_forensic_report(mapped_stars)
+
+            # 5. Assert the fallback values populated safely as 0.0 floats
+            network_metrics = mapped_stars[0]["telemetry"]["network_metrics"]
+            self.assertEqual(network_metrics["betweenness_score"], 0.0)
+            self.assertEqual(network_metrics["normalized_blast_radius"], 0.0)
+
+        except TypeError as e:
+            self.fail(f"Zero-Dependency Mode crashed the Signal Processor! Error: {e}")
+
+if __name__ == '__main__':
+    unittest.main()