From f4c198981bab55ea7d8af7d271d449706adfce03 Mon Sep 17 00:00:00 2001
From: jebus <lorsbach@envipath.com>
Date: Thu, 23 Jul 2026 07:20:26 +1200
Subject: [PATCH] [Feature] SPathway to_json outputs Bayes Probabilities (#430)

Co-authored-by: Tim Lorsbach <tim@lorsba.ch>
Reviewed-on: https://git.envipath.com/enviPath/enviPy/pulls/430
---
 epdb/logic.py | 40 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)

diff --git a/epdb/logic.py b/epdb/logic.py
index f11e6858..c2a41a59 100644
--- a/epdb/logic.py
+++ b/epdb/logic.py
@@ -1805,12 +1805,51 @@ class SPathway(object):
 
         logger.info("Update done!")
 
+    def compute_bayes_probabilities(self) -> Dict[SEdge, float]:
+        """
+        Computes Bayes-adjusted probabilities for all edges in the pathway
+        by iterating level by level from depth 0 upwards, keyed on educt depth.
+
+        Returns:
+            A dict mapping each SEdge to its Bayes-adjusted probability.
+        """
+        bayes_probs: Dict[SEdge, float] = {}
+
+        # Group edges by their educt depth
+        edges_by_depth: Dict[int, List[SEdge]] = {}
+        for edge in self.edges:
+            d = edge.educts[0].depth
+            edges_by_depth.setdefault(d, []).append(edge)
+
+        for depth in sorted(edges_by_depth.keys()):
+            for edge in edges_by_depth[depth]:
+                if depth == 0:
+                    bayes_probs[edge] = edge.probability
+                else:
+                    predecessor_edges = [e for e in self.edges if edge.educts[0] in e.products]
+
+                    if not predecessor_edges or not all(
+                        e in bayes_probs for e in predecessor_edges
+                    ):
+                        # Predecessor not computed yet (e.g. same-depth product),
+                        # fall back to raw probability
+                        bayes_probs[edge] = edge.probability
+                    else:
+                        predecessor_avg = sum(bayes_probs[e] for e in predecessor_edges) / len(
+                            predecessor_edges
+                        )
+                        bayes_probs[edge] = predecessor_avg * edge.probability
+
+        return bayes_probs
+
     def to_json(self):
         nodes = []
         edges = []
 
         idx_lookup = {}
 
+        bayes_probs = self.compute_bayes_probabilities()
+
         for i, smiles in enumerate(self.smiles_to_node):
             n = self.smiles_to_node[smiles]
             idx_lookup[smiles] = i
@@ -1831,6 +1870,7 @@ class SPathway(object):
 
             if edge.probability:
                 e["probability"] = edge.probability
+                e["multiGenProbability"] = bayes_probs[edge]
 
             edges.append(e)