Implement mode of transport

3 files changed, 14 insertions, 13 deletions

diff --git a/algorithms.py b/algorithms.py
index 3bd9f5e..df2f874 100644
--- a/algorithms.py
+++ b/algorithms.py
@@ -77,8 +77,6 @@ def get_closest_node(nodes, source_node):
     min_value = None
 
     for node in nodes:
-        print(source_node)
-        print(node)
         length = length_haversine(source_node, node)
 
         if (min_node is None or length < min_value):
@@ -88,7 +86,7 @@ def get_closest_node(nodes, source_node):
     return min_node
 
 
-def find_shortest_path(nodes, source_id, target_id, transport_mode: str):
+def find_shortest_path(nodes, source_id, target_id):
     """ Return the shortest path using Dijkstra's algortihm. """
     # queue contains multiple (walk_dist, (node_0, node_1, ... node_n))-tuples
     # where (node_0, node_1, ... node_n) is a walk to node_n
@@ -112,9 +110,7 @@ def find_shortest_path(nodes, source_id, target_id, transport_mode: str):
         # consider all our neighbours
         end_node = nodes[walk_end]
 
-        relevant_neighbours = get_relevant_neighbours(end_node, transport_mode)
-
-        for neighbour in relevant_neighbours:
+        for neighbour in end_node.neighbours:
             if neighbour in visited:
                 # there exists a shorter walk to neighbour
                 continue
diff --git a/server.py b/server.py
index 5992aa2..15145e5 100644
--- a/server.py
+++ b/server.py
@@ -52,15 +52,19 @@ def shortest_path(body):
     body = json.loads(body)
     transport_mode = body['transport_mode']
 
-    source_id = algorithms.get_closest_node(grids[transport_mode],
-                                            store.Node(-1, body['lat1'],
-                                                           body['lng1']))
-    target_id = algorithms.get_closest_node(grids[transport_mode],
-                                            store.Node(-1, body['lat2'],
-                                                           body['lng2']))
+    relevant_grid = grids[transport_mode]
+
+    source_id = algorithms.grid_search(relevant_grid,
+                                       store.Node(-1, body['lat1'],
+                                                      body['lng1']))
+    target_id = algorithms.grid_search(relevant_grid,
+                                       store.Node(-1, body['lat2'],
+                                                      body['lng2']))
+
+    relevant_nodes = nodes[transport_mode]
 
     path = algorithms.find_shortest_path(nodes[transport_mode], source_id, target_id)
-    response = {"path": [(nodes[node].lat, nodes[node].lng) for node in path]}
+    response = {"path": [(relevant_nodes[node].lat, relevant_nodes[node].lng) for node in path]}
 
     return json.dumps(response)
 
diff --git a/store.py b/store.py
index 4821072..34c0f0b 100644
--- a/store.py
+++ b/store.py
@@ -56,6 +56,7 @@ def add_neighbours(nodes):
             node1 = road[i]
             node2 = road[i + 1]
 
+            # FIXME: lots of repeated code here
             if suitable_bike(way):
                 bike_nodes = nodes['bike']