add sample data

nfopt/utils/data.py

@@ -134,8 +134,7 @@ def readLinks():
 
         logger.info("Reading links...")
         dataCapacity = pd.read_csv(
-            f"{DATASET_PATH}/{DATASET_LINKS_NAME}.csv.gz",
-            compression="gzip",
+            f"{DATASET_PATH}/{DATASET_LINKS_NAME}.csv",
             names=["linkStart", "linkEnd", "capacity"],
             skiprows=1,
             engine="pyarrow",

sample-data/flow-path-day1.csv

@@ -0,0 +1,5 @@
+Mon 00:00:00,A,G,[A;B;D;G]
+Mon 00:00:00,A,G,[A;B;E;G]
+Mon 00:00:00,A,G,[A;C;F;G]
+Mon 00:00:00,X,G,[X;C;F;G]
+Mon 00:00:00,X,G,[X;Y;F;G]

sample-data/flow-traffic-day1.csv

@@ -0,0 +1,2 @@
+Mon 00:00:00,A,G,50.00
+Mon 00:00:00,X,G,75.00

sample-data/links.csv

@@ -0,0 +1,12 @@
+linkStart,linkEnd,capacity
+A,B,100
+A,C,100
+B,D,100
+B,E,100
+C,F,100
+D,G,100
+E,G,100
+F,G,100
+X,C,100
+X,Y,100
+Y,F,100

test.py

@@ -97,10 +97,7 @@ def runLinearOptimizationModel(
     }
 
 
-    with gp.Env(params=options) as env, gp.Model(env=env) as m:
-        # Create optimization model based on the input model
-        m = gp.Model("network_optimization", env=env)
-
+    with gp.Env(params=options) as env, gp.Model("network_optimization", env=env) as m:
         # Decision variables for path ratios for each source-destination pair
         path_ratios = m.addVars(
             [

test2.py

@@ -52,8 +52,7 @@ def build_graph(edges):
 
 
 def optMC(parserArgs, links, flowTraffic, flows, timestamp):
-    with gp.Env(params=options) as env, gp.Model(env=env) as m:
-        m = gp.Model("netflow", env=env)
+    with gp.Env(params=options) as env, gp.Model("netflow", env=env) as m:
         # example of link data
         links = {
             "A;B": {"capacity": 100},