remove bg analysis

deconvolution/main/evaluate.py

@@ -12,7 +12,7 @@ def parse_args():
     parser = argparse.ArgumentParser(description='Process a d2 graph (complete graph or path) to evaluate its quality.')
     parser.add_argument('filename', type=str,
                         help='The file to evalute')
-    parser.add_argument('--type', '-t', choices=["d2", "path", "bg", "d2-2annotate", "dgraphs"], default="path", required=True,
+    parser.add_argument('--type', '-t', choices=["d2", "path", "d2-2annotate", "dgraphs"], default="path", required=True,
                         help="Define the data type to evaluate. Must be 'd2' or 'path' or 'd2-2annotate' (Rayan's hack).")
     parser.add_argument('--light-print', '-l', action='store_true',
                         help='Print only wrong nodes and paths')
@@ -259,8 +259,9 @@ def print_d2_summary(connected_components, longest_path, coverage_vars=(0, 0), l
         print("Longest path for increasing molecule number:")
         print(path_dg_idx)
     print(f"Size of the longest path: {len(longest_path)}")
-    print("Jumps in central nodes:")
-    print(path_to_jumps(longest_path))
+    if not light_print:
+        print("Jumps in central nodes:")
+        print(path_to_jumps(longest_path))
 
     print(f"Number of usable coverage variables: {len(coverage_vars[1])}")
     print(f"Coverage: {len(coverage_vars[0])}/{len(coverage_vars[1])}")
@@ -324,6 +325,7 @@ def compute_next_nodes(d2_component, max_jumps=0):
 
 def compute_longest_increasing_paths(d2_component, max_gap=0):
     next_nodes = compute_next_nodes(d2_component, max_jumps=max_gap)
+    sys.setrecursionlimit(len(d2_component.nodes)*2)
 
     # Compute the longest path for each node
     longest_paths = {}
@@ -489,39 +491,6 @@ def recursive_longest_path(current_node, current_molecule, next_nodes, longest_p
     return longest_paths[current_node][current_molecule]
 
 
-# def longest_common_subsequence(barcode_true_path, barcoded_graph):
-#     """ Assume that the two graphs have an attribute barcode for each node and a unique node name"""
-#     path_nodes = []
-#     path_nodes_barcodes = []
-#     for node, data in barcode_true_path.nodes(data=True):
-#         path_nodes.append(node)
-#         path_nodes_barcodes.append(data["barcode"])
-#     path_nodes_to_idx = {n: idx for idx, n in enumerate(path_nodes)}
-#
-#     graph_nodes = []
-#     graph_nodes_barcodes = []
-#     for node, data in barcoded_graph.nodes(data=True):
-#         graph_nodes.append(node)
-#         graph_nodes_barcodes.append(data["barcode"])
-#     graph_nodes_to_idx = {n: idx for idx, n in enumerate(graph_nodes)}
-#
-#     dynamic_array = [[0 for _ in range(len(graph_nodes)+1)] for _ in range(len(path_nodes)+1)]
-#     for row in range(1, len(path_nodes)+1):
-#         path_node = path_nodes[row-1]
-#         path_barcode = path_nodes_barcodes[row-1]
-#
-#         for column in range(1, len(graph_nodes)):
-#             graph_node = graph_nodes[column-1]
-#             graph_barcode = graph_nodes_barcodes[column-1]
-#
-#             prev_scores = [dynamic_array[row-1][column]]
-#             for neighbor_node in barcoded_graph[graph_node]:
-#                 neighbor_idx = graph_nodes_to_idx[neighbor_node]
-#                 prev_scores.append(dynamic_array[row-1][neighbor_idx])
-#
-#             match_point = 1 if path_barcode == graph_barcode else 0
-#             dynamic_array[row][column] = max(prev_scores) + match_point
-
 
 def compute_covered_variables(graph, path):
     path_nodes = set()
@@ -669,10 +638,7 @@ def main():
     elif args.type == "dgraphs":
         udg_per_node = parse_udg_qualities(graph)
         # print(udg_per_node)
-    elif args.type == "bg" or args.type == "d2":
-
-        if args.type == "bg":
-            graph = transform_bg(graph)
+    elif args.type == "d2":
 
         components = list(nx.connected_components(graph))
         components.sort(key=lambda x: -len(x))