Formatting

README.md

@@ -118,7 +118,7 @@ can be constructed using NumPy arrays and vectors. Utilizing NumPy and matrix
 operations allows for a much faster construction of the BQM than building it
 with for-loops. As problem instances become larger and larger, it becomes more
 and more important to efficiently build the BQM to save time in the
-initilization and setup of the model. The chart below demonstrates the savings
+initialization and setup of the model. The chart below demonstrates the savings
 in classical compute time when setting up the BQM for this problem using
 for-loops versus using efficient NumPy operations in the Leap IDE.
 

demo.py

@@ -28,7 +28,7 @@
     import matplotlib.pyplot as plt
 
 def read_in_args():
-    """ Read in user specified parameters or use defaults."""
+    """Read in user specified parameters or use defaults."""
 
     # Set up user-specified optional arguments
     parser = argparse.ArgumentParser()
@@ -67,22 +67,17 @@ def set_up_scenario(w, h, num_poi, num_cs):
     """Build scenario set up with specified parameters.
     
     Args:
-        w (int):
-            Width of grid
-        h (int):
-            Height of grid
-        num_poi (int):
-            Number of points of interest
-        num_cs (int):
-            Number of existing charging stations
+        w (int): Width of grid
+        h (int): Height of grid
+        num_poi (int): Number of points of interest
+        num_cs (int): Number of existing charging stations
+    
     Returns:
-        G (networkx graph):
-            Grid graph of size w by h
-        pois (list of tuples of ints):
-            A fixed set of points of interest
-        charging_stations (list of tuples of ints):
-            A fixed set of current charging locations
-        potential_new_cs_nodes (list of tuples of ints):
+        G (networkx graph): Grid graph of size w by h
+        pois (list of tuples of ints): A fixed set of points of interest
+        charging_stations (list of tuples of ints): 
+            Set of current charging locations
+        potential_new_cs_nodes (list of tuples of ints): 
             Potential new charging locations
     """
 
@@ -109,19 +104,15 @@ def build_bqm(potential_new_cs_nodes, num_poi, pois, num_cs, charging_stations,
     Args:
         potential_new_cs_nodes (list of tuples of ints):
             Potential new charging locations
-        num_poi (int):
-            Number of points of interest
-        pois (list of tuples of ints):
-            A fixed set of points of interest
-        num_cs (int):
-            Number of existing charging stations
-        charging_stations (list of tuples of ints):
-            A fixed set of current charging locations
-        num_new_cs (int):
-            Number of new charging stations desired
+        num_poi (int): Number of points of interest
+        pois (list of tuples of ints): A fixed set of points of interest
+        num_cs (int): Number of existing charging stations
+        charging_stations (list of tuples of ints): 
+            Set of current charging locations
+        num_new_cs (int): Number of new charging stations desired
+    
     Returns:
-        bqm_np (BinaryQuadraticModel):
-            QUBO model for the input scenario
+        bqm_np (BinaryQuadraticModel): QUBO model for the input scenario
     """
 
     # Tunable parameters
@@ -169,16 +160,14 @@ def run_bqm_and_collect_solutions(bqm, sampler, potential_new_cs_nodes, **kwargs
     """Solve the bqm with the provided sampler to find new charger locations. 
     
     Args:
-        bqm (BinaryQuadraticModel):
-            The QUBO model for the problem instance
-        sampler:
-            Sampler or solver to be used
-        potential_new_cs_nodes (list of tuples of ints):
+        bqm (BinaryQuadraticModel): The QUBO model for the problem instance
+        sampler: Sampler or solver to be used
+        potential_new_cs_nodes (list of tuples of ints): 
             Potential new charging locations
-        **kwargs:
-            Sampler-specific parameters to be used
+        **kwargs: Sampler-specific parameters to be used
+    
     Returns:
-        new_charging_nodes (list of tuples of ints):
+        new_charging_nodes (list of tuples of ints): 
             Locations of new charging stations
     """
 
@@ -195,18 +184,15 @@ def printout_solution_to_cmdline(pois, num_poi, charging_stations, num_cs, new_c
     """Print solution statistics to command line.
     
     Args:
-        pois (list of tuples of ints):
-            A fixed set of points of interest
-        num_poi (int):
-            Number of points of interest
-        charging_stations (list of tuples of ints):
+        pois (list of tuples of ints): A fixed set of points of interest
+        num_poi (int): Number of points of interest
+        charging_stations (list of tuples of ints): 
             A fixed set of current charging locations
-        num_cs (int):
-            Number of existing charging stations
-        new_charging_nodes (list of tuples of ints):
+        num_cs (int): Number of existing charging stations
+        new_charging_nodes (list of tuples of ints): 
             Locations of new charging stations
-        num_new_cs (int):
-            Number of new charging stations desired
+        num_new_cs (int): Number of new charging stations desired
+    
     Returns:
         None.
     """
@@ -241,14 +227,13 @@ def save_output_image(G, pois, charging_stations, new_charging_nodes):
             - Blue nodes: new charger locations
 
     Args:
-        G (networkx graph):
-            Grid graph of size w by h
-        pois (list of tuples of ints):
-            A fixed set of points of interest
-        charging_stations (list of tuples of ints):
+        G (networkx graph): Grid graph of size w by h
+        pois (list of tuples of ints): A fixed set of points of interest
+        charging_stations (list of tuples of ints): 
             A fixed set of current charging locations
-        new_charging_nodes (list of tuples of ints):
+        new_charging_nodes (list of tuples of ints): 
             Locations of new charging stations
+    
     Returns:
         None. Output saved to file "map.png".
     """

demo_numpy.py

@@ -22,21 +22,17 @@ def build_bqm(potential_new_cs_nodes, num_poi, pois, num_cs, charging_stations,
     """Build bqm that models our problem scenario using NumPy. 
 
     Args:
-        potential_new_cs_nodes (list of tuples of ints):
+        potential_new_cs_nodes (list of tuples of ints): 
             Potential new charging locations
-        num_poi (int):
-            Number of points of interest
-        pois (list of tuples of ints):
-            A fixed set of points of interest
-        num_cs (int):
-            Number of existing charging stations
-        charging_stations (list of tuples of ints):
+        num_poi (int): Number of points of interest
+        pois (list of tuples of ints): A fixed set of points of interest
+        num_cs (int): Number of existing charging stations
+        charging_stations (list of tuples of ints): 
             A fixed set of current charging locations
-        num_new_cs (int):
-            Number of new charging stations desired
+        num_new_cs (int): Number of new charging stations desired
+    
     Returns:
-        bqm_np (BinaryQuadraticModel):
-            QUBO model for the input scenario
+        bqm_np (BinaryQuadraticModel): QUBO model for the input scenario
     """
 
     # Tunable parameters
@@ -56,35 +52,45 @@ def build_bqm(potential_new_cs_nodes, num_poi, pois, num_cs, charging_stations,
     # Constraint 1: Min average distance to POIs
     if num_poi > 0:
 
-        ct_matrix = np.matmul(nodes_array, pois_array.T)*(-2.) + np.sum(np.square(pois_array), axis=1).astype(float) + np.sum(np.square(nodes_array), axis=1).reshape(-1,1).astype(float)
+        ct_matrix = (np.matmul(nodes_array, pois_array.T)*(-2.) 
+                    + np.sum(np.square(pois_array), axis=1).astype(float) 
+                    + np.sum(np.square(nodes_array), axis=1).reshape(-1,1).astype(float))
 
         linear += np.sum(ct_matrix, axis=1) / num_poi * gamma1
 
     # Constraint 2: Max distance to existing chargers
     if num_cs > 0:    
 
-        dist_mat = np.matmul(nodes_array, cs_array.T)*(-2.) + np.sum(np.square(cs_array), axis=1).astype(float) + np.sum(np.square(nodes_array), axis=1).reshape(-1,1).astype(float)
+        dist_mat = (np.matmul(nodes_array, cs_array.T)*(-2.) 
+                    + np.sum(np.square(cs_array), axis=1).astype(float) 
+                    + np.sum(np.square(nodes_array), axis=1).reshape(-1,1).astype(float))
 
         linear += -1 * np.sum(dist_mat, axis=1) / num_cs * gamma2 
 
     # Constraint 3: Max distance to other new charging locations
     if num_new_cs > 1:
 
-        dist_mat = ((np.matmul(nodes_array, nodes_array.T)*(-2.) + np.sum(np.square(nodes_array), axis=1)).astype(float) + np.sum(np.square(nodes_array), axis=1).reshape(-1,1).astype(float)) * -gamma3
+        dist_mat = -gamma3*((np.matmul(nodes_array, nodes_array.T)*(-2.) 
+                    + np.sum(np.square(nodes_array), axis=1)).astype(float) 
+                    + np.sum(np.square(nodes_array), axis=1).reshape(-1,1).astype(float))
 
     # Constraint 4: Choose exactly num_new_cs new charging locations
     linear += (1-2*num_new_cs)*gamma4
     dist_mat += 2*gamma4
     dist_mat = np.triu(dist_mat, k=1).flatten()
 
     quad_col = np.tile(np.arange(len(potential_new_cs_nodes)), len(potential_new_cs_nodes))
-    quad_row = np.tile(np.arange(len(potential_new_cs_nodes)), (len(potential_new_cs_nodes),1)).flatten('F')
+    quad_row = np.tile(np.arange(len(potential_new_cs_nodes)), 
+                (len(potential_new_cs_nodes),1)).flatten('F')
 
     q2 = quad_col[dist_mat != 0]
     q1 = quad_row[dist_mat != 0]
     q3 = dist_mat[dist_mat != 0]
 
-    bqm_np = dimod.BinaryQuadraticModel.from_numpy_vectors(linear=linear, quadratic=(q1, q2, q3), offset=0, vartype=dimod.BINARY)
+    bqm_np = dimod.BinaryQuadraticModel.from_numpy_vectors(linear=linear, 
+                                                            quadratic=(q1, q2, q3), 
+                                                            offset=0, 
+                                                            vartype=dimod.BINARY)
 
     return bqm_np
 
@@ -94,10 +100,18 @@ def build_bqm(potential_new_cs_nodes, num_poi, pois, num_cs, charging_stations,
     args = demo.read_in_args()
 
     # Build large grid graph for city
-    G, pois, charging_stations, potential_new_cs_nodes = demo.set_up_scenario(args.width, args.height, args.poi, args.chargers)
+    G, pois, charging_stations, potential_new_cs_nodes = demo.set_up_scenario(args.width, 
+                                                                            args.height, 
+                                                                            args.poi, 
+                                                                            args.chargers)
 
     # Build BQM
-    bqm = build_bqm(potential_new_cs_nodes, args.poi, pois, args.chargers, charging_stations, args.new_chargers)
+    bqm = build_bqm(potential_new_cs_nodes, 
+                    args.poi, 
+                    pois, 
+                    args.chargers, 
+                    charging_stations, 
+                    args.new_chargers)
 
     # Run BQM on HSS
     sampler = LeapHybridSampler()
@@ -106,7 +120,12 @@ def build_bqm(potential_new_cs_nodes, num_poi, pois, num_cs, charging_stations,
     new_charging_nodes = demo.run_bqm_and_collect_solutions(bqm, sampler, potential_new_cs_nodes)
 
     # Print results to commnand-line for user
-    demo.printout_solution_to_cmdline(pois, args.poi, charging_stations, args.chargers, new_charging_nodes, args.new_chargers)
+    demo.printout_solution_to_cmdline(pois, 
+                                    args.poi, 
+                                    charging_stations, 
+                                    args.chargers, 
+                                    new_charging_nodes, 
+                                    args.new_chargers)
 
     # Create scenario output image
     demo.save_output_image(G, pois, charging_stations, new_charging_nodes)