robustness fixes

deconvolution/main/d2_path_evaluation.py

@@ -85,6 +85,12 @@ def is_coherent_path(central_nodes,path_len):
     assert(len(mols_in_central_nodes) == path_len+1)
     return is_there_path_acc(mols_in_central_nodes)
 
+def get_barcode(x):
+    if 'udg' in graph.nodes[x]:
+        return graph.nodes[x]['udg'].split()[0]
+    else:
+        return graph.nodes[x]['label']
+
 """ the main function that tests for accuracy of random paths """
 graph = None
 def evaluate_accuracy_paths(path_len,max_paths_per_node=100):
@@ -101,7 +107,7 @@ def evaluate_accuracy_paths(path_len,max_paths_per_node=100):
             seen_paths.add(tuple(sorted(path)))
             assert(len(path) == path_len+1)
             #print("path",path)
-            central_nodes = [graph.nodes[x]['udg'].split()[0] for x in path]
+            central_nodes = [get_barcode(x) for x in path]
             assert(len(central_nodes) == path_len+1)
             #print(path,central_nodes)
             if is_coherent_path(central_nodes,path_len):
@@ -131,7 +137,7 @@ def evaluate_sensitivity_paths(path_len,overlap_length=7000):
     from collections import defaultdict
     molecules_to_nodes = defaultdict(list)
     for node in graph.nodes():
-        central_node = graph.nodes[node]['udg'].split()[0]
+        central_node = get_barcode(node)
         mols =  central_node_to_molecules(central_node)
         all_molecules |= set(mols)
         for mol in mols:

deconvolution/main/evaluate.py

@@ -184,7 +184,10 @@ def str_to_udg_lists(s):
 
 def parse_dg_name(gr, name):
     udg = nx.get_node_attributes(gr, 'udg')[name]
-    central, h1, h2 = str_to_udg_lists(udg)
+    res = str_to_udg_lists(udg)
+    if len(res) != 3:
+        print("parsing problem:",res)
+    central, h1, h2 = res
     
     idx = name
     score = nx.get_node_attributes(gr, 'score')[name]