Add SSVM working code (no explanation)

solution.py

@@ -920,7 +920,7 @@ def solve_drift_optimization(cand_graph):
     solver.add_cost(
         motile.costs.EdgeSelection(weight=1.0, constant=-30, attribute="drift_dist")
     )
-    solver.add_cost(motile.costs.Appear(constant=0, ignore_attribute="ignore_appear"))
+    solver.add_cost(motile.costs.Appear(constant=100, ignore_attribute="ignore_appear"))
 
     solver.add_constraint(motile.constraints.MaxParents(1))
     solver.add_constraint(motile.constraints.MaxChildren(2))
@@ -941,6 +941,7 @@ def solve_drift_optimization(cand_graph):
 # %%
 get_metrics(gt_tracks, gt_dets, solution_graph, solution_seg)
 
+
 # %% [markdown]
 # ## Checkpoint 4
 # <div class="alert alert-block alert-success"><h3>Checkpoint 4</h3>
@@ -954,7 +955,61 @@ def solve_drift_optimization(cand_graph):
 #
 
 # %%
-# get a ground truth track
-# match it to the candidate graph and annotate TP nodes and edges
+def get_cand_id(gt_node, gt_track, cand_segmentation):
+    data = gt_track.nodes[gt_node]
+    return cand_segmentation[data["t"], int(data["x"])][int(data["y"])]
+
+for gt_node in gt_tracks.nodes():
+    cand_id = get_cand_id(gt_node, gt_tracks, segmentation)
+    if cand_id != 0:
+        cand_graph.nodes[cand_id]["gt"] = True
+        succs = gt_tracks.successors(gt_node)
+        for succ in succs:
+            succ_id = get_cand_id(succ, gt_tracks, segmentation)
+            if succ_id != 0:
+                cand_graph.edges[(cand_id, succ_id)]["gt"] = True
+
+
+# %%
+import logging
 
-# find a negative node in napari 
+logging.basicConfig(level=logging.INFO)
+
+
+def solve_SSVM_optimization(cand_graph):
+    """Set up and solve the network flow problem.
+
+    Args:
+        cand_graph (nx.DiGraph): The candidate graph.
+
+    Returns:
+        nx.DiGraph: The networkx digraph with the selected solution tracks
+    """
+
+    cand_trackgraph = motile.TrackGraph(cand_graph, frame_attribute="t")
+    solver = motile.Solver(cand_trackgraph)
+
+    solver.add_cost(
+        motile.costs.EdgeSelection(weight=1.0, constant=-30, attribute="drift_dist")
+    )
+    solver.add_cost(motile.costs.Appear(constant=0, ignore_attribute="ignore_appear"))
+
+    solver.add_constraint(motile.constraints.MaxParents(1))
+    solver.add_constraint(motile.constraints.MaxChildren(2))
+
+    solver.fit_weights(gt_attribute="gt", regularizer_weight=0.00001, max_iterations=1000)
+    print(solver.weights)
+    solver.solve()
+    solution_graph = graph_to_nx(solver.get_selected_subgraph())
+    return solution_graph
+
+
+# %%
+solution_graph = solve_SSVM_optimization(cand_graph)
+
+# %%
+
+solution_seg = relabel_segmentation(solution_graph, segmentation)
+get_metrics(gt_tracks, gt_dets, solution_graph, solution_seg)
+
+# %%