From 9616c2c31c870163a36c333022eb76d65170d275 Mon Sep 17 00:00:00 2001
From: Federico Catalano <44501803+PyMap@users.noreply.github.com>
Date: Tue, 25 Jul 2023 18:44:33 -0300
Subject: [PATCH] Informative warning for shortest path unsigned integer (#169)

---
 pandana/network.py | 13 +++++++++++++
 pandana/utils.py   |  7 +++++++
 2 files changed, 20 insertions(+)

diff --git a/pandana/network.py b/pandana/network.py
index 61303ea6..fe3086cb 100644
--- a/pandana/network.py
+++ b/pandana/network.py
@@ -282,6 +282,12 @@ def shortest_path_length(self, node_a, node_b, imp_name=None):
 
         len = self.net.shortest_path_distance(node_a, node_b, imp_num)
 
+        if len == 4294967.295:
+            warnings.warn(
+            "Unsigned integer: shortest path distance is trying to be calculated between\
+            external %s and %s unconntected nodes"%(node_a, node_b)
+            )
+
         return len
 
     def shortest_path_lengths(self, nodes_a, nodes_b, imp_name=None):
@@ -322,6 +328,13 @@ def shortest_path_lengths(self, nodes_a, nodes_b, imp_name=None):
 
         lens = self.net.shortest_path_distances(nodes_a_idx, nodes_b_idx, imp_num)
 
+        if 4294967.295 in lens:
+             unconnected_idx = [i for i,v in enumerate(lens) if v == 4294967.295]
+             unconnected_nodes = [(nodes_a[i],nodes_b[i]) for i in unconnected_idx]
+             warnings.warn(
+             "Unsigned integer: shortest path distance is trying to be calculated \
+             between the following external unconnected nodes: %s"%(unconnected_nodes))
+
         return lens
 
     def set(self, node_ids, variable=None, name="tmp"):
diff --git a/pandana/utils.py b/pandana/utils.py
index 10e0885d..b3ef06fc 100644
--- a/pandana/utils.py
+++ b/pandana/utils.py
@@ -25,3 +25,10 @@ def reindex(series1, series2):
                   right_index=True,
                   how="left")
     return df.right
+
+def adjacency_matrix(edges_df, plot_matrix=False):
+    df = pd.crosstab(edges_df['from'], edges_df['to'])
+    idx = df.columns.union(df.index)
+    df = df.reindex(index = idx, columns=idx, fill_value=0)
+
+    return df