Clean up day 25 and add explanation.

.gitignore

@@ -1,2 +1,3 @@
+Pipfile
 __pycache__
 *.txt

d25.py

@@ -1,58 +1,23 @@
 from lib import *
-
-
-def plot(graph):
-    import networkx as nx
-    import matplotlib
-    import matplotlib.pyplot as plt
-    G = nx.Graph()
-    for node, connected_nodes in graph.items():
-        for connected_node in connected_nodes:
-            G.add_edge(node, connected_node)
-    # pos = nx.spring_layout(G, k=2.0, iterations=20)  # Adjust k as needed
-    pos = nx.shell_layout(G)
-    nx.draw(G, with_labels=True, node_color='lightblue', edge_color='gray', node_size=2000, font_size=15, font_weight='bold')
-    matplotlib.use('qtagg')
-    plt.show()
-
-
-def solve_non_hands_free(input: Input):
-    graph = {}
-    for line in input.lines():
-        source, targets = line.split(":")
-        targets = targets.strip()
-        targets = targets.split(" ")
-
-        for target in targets:
-            if not source in graph:
-                graph[source] = [target]
-            else:
-                graph[source].append(target)
-            if not target in graph:
-                graph[target] = [source]
-            else:
-                graph[target].append(source)
-
-    # plot(graph) # I used this to find the nodes that have to be removed.
-    to_remove = (("plt", "mgb"), ("jxm", "qns"), ("dbt", "tjd"))
-    for a, b in to_remove:
-        graph[a].remove(b)
-        graph[b].remove(a)
-
-    to_visit = ["plt"]
-    seen = set(to_visit)
-    while to_visit:
-        node = to_visit.pop()
-        for nb in graph[node]:
-            if not nb in seen:
-                seen.add(nb)
-                to_visit.append(nb)
-
-    return len(seen) * (len(graph) - len(seen))
-
 from random import choice
 from collections import deque
 
+
+# def plot(graph):
+#     import networkx as nx
+#     import matplotlib
+#     import matplotlib.pyplot as plt
+#     G = nx.Graph()
+#     for node, connected_nodes in graph.items():
+#         for connected_node in connected_nodes:
+#             G.add_edge(node, connected_node)
+#     # pos = nx.spring_layout(G, k=2.0, iterations=20)  # Adjust k as needed
+#     pos = nx.shell_layout(G)
+#     nx.draw(G, with_labels=True, node_color='lightblue', edge_color='gray', node_size=2000, font_size=15, font_weight='bold')
+#     matplotlib.use('qtagg')
+#     plt.show()
+
+
 def solve(input: Input):
     graph = {}
     edges = {}
@@ -73,7 +38,7 @@ def solve(input: Input):
             edge = tuple(sorted([src, dst]))
             edges[edge] = 0
 
-    for i in range (1000):
+    for _ in range(100):
         first_node = choice(list(graph.keys()))
         seen = set([first_node])
         visit = deque([first_node])
@@ -86,9 +51,16 @@ def solve(input: Input):
                     edge = tuple(sorted([node, nb]))
                     edges[edge] += 1
 
+    # Orignally, I used `plot(graph)` to visually find the nodes that have to
+    # be removed. I then came up with this heuristic approach. The idea is that
+    # we have to cross one of the three nodes when we do a breadth first
+    # search. By repeatedly doing that we can identify the "bridges" as the
+    # three edges that are used the most often.
     most_visited = sorted(edges.items(), key=lambda t: t[1], reverse=True)[:3]
+
+    # to_remove = (("plt", "mgb"), ("jxm", "qns"), ("dbt", "tjd"))  # found visually
     # for node, count in most_visited:
-    #     print(node, count)
+    #     print(node, count) # should print the same as `to_remove`
 
     for (a, b), _ in most_visited:
         graph[a].remove(b)
@@ -108,10 +80,8 @@ def solve(input: Input):
 
 def main():
     DAY_INPUT = "i25.txt"
-    print("Solution 1:", solve_non_hands_free(Input(DAY_INPUT)))
     print("Solution 1:", solve(Input(DAY_INPUT)), "(hands-free)")
 
 
-
 if __name__ == "__main__":
     main()