Changes to coordinates

PatrickOHara · Aug 22, 2024 · 062f7f5 · 062f7f5
1 parent 2c1fcc0
commit 062f7f5
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Showing 2 changed files with 37 additions and 30 deletions.
diff --git a/tspwplib/problem.py b/tspwplib/problem.py
@@ -273,32 +273,36 @@ def from_tsplib95(cls, problem: tsplib95.models.StandardProblem):
             if problem.node_coord_type
             else NodeCoordType.NO_COORDS
         )
-        if node_coord_type == NodeCoordType.NO_COORDS:
+        # do verifcation on node coords
+        node_coords = {i: problem.node_coords.get(i) for i in problem.get_nodes()}
+        key_error = False
+        no_coords = False
+        try:
+            coord = node_coords.get(1)
+            if not coord:
+                no_coords = True
+        except KeyError:
+            key_error = True
+        if key_error or len(node_coords) == 0 or no_coords:
+            node_coord_type == NodeCoordType.NO_COORDS
             node_coords = None
-        else:
-            # do verifcation on node coords
-            node_coords = {i: problem.node_coords.get(i) for i in problem.get_nodes()}
-            if len(node_coords) == 0:
-                node_coord_type == NodeCoordType.NO_COORDS
-                node_coords = None
-                raise RuntimeWarning(f"Problem {problem.name} has no node co-ordinates.")
-            elif (
-                len(node_coords.get(1)) == 3
-                or node_coord_type == NodeCoordType.THREED_COORDS
-            ):
-                raise NotImplementedError("3D coords not yet supported")
-            elif (
-                len(node_coords.get(1)) == 2
-                and node_coord_type == NodeCoordType.THREED_COORDS
-            ) or (
-                len(node_coords.get(1)) == 3
-                and node_coord_type == NodeCoordType.TWOD_COORDS
-            ):
-                raise ValueError(
-                    f"Problem {problem.name} has NODE_COORD_TYPE {node_coord_type.value}, but the length of the co-ordinates for node 1 is {len(node_coords.get(1))}."
-                )
-            elif len(node_coords.get(1)) == 2:
-                node_coord_type = NodeCoordType.TWOD_COORDS
+        elif (
+            len(node_coords.get(1)) == 3
+            or node_coord_type == NodeCoordType.THREED_COORDS
+        ):
+            raise NotImplementedError("3D coords not yet supported")
+        elif (
+            len(node_coords.get(1)) == 2
+            and node_coord_type == NodeCoordType.THREED_COORDS
+        ) or (
+            len(node_coords.get(1)) == 3
+            and node_coord_type == NodeCoordType.TWOD_COORDS
+        ):
+            raise ValueError(
+                f"Problem {problem.name} has NODE_COORD_TYPE {node_coord_type.value}, but the length of the co-ordinates for node 1 is {len(node_coords.get(1))}."
+            )
+        elif len(node_coords.get(1)) == 2:
+            node_coord_type = NodeCoordType.TWOD_COORDS
 
         return cls(
             capacity=problem.capacity,

diff --git a/tspwplib/sparse.py b/tspwplib/sparse.py
@@ -1,16 +1,18 @@
 """Functions for creating a sparse graph from a complete graph"""
 
+from typing import Optional
 from copy import deepcopy
 import random
 import networkx as nx
+import numpy as np
 from .types import Vertex
 
 
 def sparsify_uid(
     G: nx.Graph,
     kappa: int,
     remove_self_loops: bool = False,
-    seed: int = 0,
+    generator: Optional[np.random.Generator] = None,
 ) -> nx.Graph:
     """Remove edges with uniform and independent (uid) probability until
     the number of edges equals kappa * number of nodes
@@ -19,23 +21,24 @@ def sparsify_uid(
         G: Graph
         kappa: Parameter independent of the input size
         remove_self_loops: Should self loops have a change of being removed?
-        seed: Set the random seed
+        generator: Pass an optional random generator
 
     Returns:
         Graph with kappa * V edges where V is the number of nodes
 
     Notes:
         A copy of the graph is made and returned. The original graph is unedited.
     """
-    random.seed(seed)
+    if not generator:
+        generator = np.random.default_rng()
     graph_copy = deepcopy(G)
     vertex_list = list(graph_copy.nodes())
     while graph_copy.number_of_edges() > graph_copy.number_of_nodes() * kappa:
         # choose vertex randomly
-        u = random.choice(vertex_list)
+        u = generator.choice(vertex_list)
         if graph_copy.degree(u) > 0:
             # choose a neighbor randomly
-            v = random.choice(list(graph_copy.neighbors(u)))
+            v = generator.choice(list(graph_copy.neighbors(u)))
             # remove edge if it is not a self loop
             if remove_self_loops or u != v:
                 graph_copy.remove_edge(u, v)