clean up to support other python versions!

Author: PooriaNamyar

README.md

@@ -2,19 +2,19 @@
 
 
 ## Code Structure
-- ```topo_repo/``` contains a set of topologies (Jellyfish, Xpander, FatClique, Clos)
 - ```metric/``` code for computing throughput upper bound.
+- ```topo_repo/``` contains a set of topologies (Jellyfish, Xpander, FatClique, Clos)
 - ```utils/``` code for loading the topologies from file and generating maximal permutation traffic matrix.
 
-##Dependencies
+## Dependencies
 (1) Clone this repo<br>
 ```bash
 $ git clone https://github.com/USC-NSL/TUB.git
 ```
 
 (2) Python version<br>
-This repo is tested with;
-- Python 2.7.17 + pip 20.2.3.
+This repo is tested with the following two combinations;
+- Python 2.7.17 + pip 20.2.3 (used to generate results of the paper)
 - Python 3.7.10 + pip 21.1.1
 
 (3) Install dependencies<br>
@@ -30,5 +30,5 @@ Required dependencies:<br>
 - matplotlib
 
 
-##Example
+## Example
 

metric/__init__.py

@@ -2,12 +2,10 @@
 from __future__ import division
 from __future__ import print_function
 
-import networkx as nx
 from utils import near_wc_tm
-from typing import List, Dict
 
 
-def get_throughput_upper_bound(topo: nx.Graph, tor_list: List, demand_dict: Dict) -> float:
+def get_throughput_upper_bound(topo, tor_list, demand_dict):
     print("** Computing maximal permutation traffic matrix...")
     _, sum_weight_matching = near_wc_tm.get_longest_matching_traffic_matrix(topo, tor_list, demand_dict)
     print("** Computing TUB...")

metric/tub.py

@@ -1,5 +1,5 @@
 networkx
-python-igraph
+python-igraph==0.8.0
 numpy
 scipy
 matplotlib

requirements.txt

@@ -8,10 +8,9 @@
 
 from networkx import nx
 from utils import shortest_path
-from typing import List, Dict, Tuple
 
 
-def get_longest_matching_traffic_matrix(topology: nx.Graph, tor_list: List, demand_dict: Dict) -> Tuple[Dict, int]:
+def get_longest_matching_traffic_matrix(topology, tor_list, demand_dict):
     """ Generates maximal permutation traffic matrix that results in near worst-case throughput
 
             This is a generalization to: Measuring and Understanding Throughput of Network Topologies

topo_repo/__init__.py

@@ -6,10 +6,8 @@
 import networkx as nx
 import scipy
 
-from typing import List
 
-
-def sum_all_pair_shortest_path_length_adjacency_matrix(g: nx.Graph) -> int:
+def sum_all_pair_shortest_path_length_adjacency_matrix(g):
     """ Computes the sum of shortest path length over all the pairs of nodes in g
 
     Args:
@@ -36,7 +34,7 @@ def sum_all_pair_shortest_path_length_adjacency_matrix(g: nx.Graph) -> int:
     return total_sp
 
 
-def all_pair_shortest_path_length_adjacency_matrix(g: nx.Graph, tor_list: List = None) -> np.array:
+def all_pair_shortest_path_length_adjacency_matrix(g, tor_list=None):
     """ Returns the length of the shortest path between all pairs of ToRs
 
         Args:

utils/__init__.py

@@ -3,24 +3,26 @@
 from __future__ import print_function
 
 import os
+import sys
 import pickle
 
-from topo_repo import topology
 
-
-def get_model(filename: str) -> topology.Topology:
+def get_model(filename):
     print("** Loading Topology...")
     with open(filename, "rb") as file:
-        model = pickle.load(file, encoding="latin1")
+        if sys.version_info >= (3, 0):
+            model = pickle.load(file, encoding="latin1")
+        else:
+            model = pickle.load(file)
     return model
 
 
-def delete_file(file_path: str) -> None:
+def delete_file(file_path):
     if os.path.exists(file_path):
         os.remove(file_path)
 
 
-def store_model(model, file_path: str) -> None:
+def store_model(model, file_path):
     delete_file(file_path)
     with open(file_path, "wb") as f:
         pickle.dump(model, f)