diff --git a/python/gtsam/examples/HybridCity10000.py b/python/gtsam/examples/HybridCity10000.py
index 81fb90382..00807352c 100644
--- a/python/gtsam/examples/HybridCity10000.py
+++ b/python/gtsam/examples/HybridCity10000.py
@@ -47,6 +47,11 @@ def parse_arguments():
         type=int,
         default=10,
         help="The maximum number of hypotheses to keep at any time.")
+    parser.add_argument(
+        "-p",
+        action="store_true",
+        help="Plot all hypotheses. NOTE: This is exponential, use with caution."
+    )
     return parser.parse_args()
 
 
@@ -125,7 +130,9 @@ def plot_estimates(gt,
     fig = plt.figure(fignum)
     ax = fig.gca()
     ax.axis('equal')
-    ax.axis((-65.0, 65.0, -75.0, 60.0))
+    ax.axis((-75.0, 75.0, -75.0, 75.0))
+
+    gt = gt[:estimates.shape[0]]
     ax.plot(gt[:, 0],
             gt[:, 1],
             '--',
@@ -150,11 +157,12 @@ class Experiment:
 
     def __init__(self,
                  filename: str,
-                 marginal_threshold: float = 1.9999,
+                 marginal_threshold: float = 0.9999,
                  max_loop_count: int = 150,
                  update_frequency: int = 3,
                  max_num_hypotheses: int = 10,
-                 relinearization_frequency: int = 10):
+                 relinearization_frequency: int = 10,
+                 plot_hypotheses: bool = False):
         self.dataset_ = City10000Dataset(filename)
         self.max_loop_count = max_loop_count
         self.update_frequency = update_frequency
@@ -165,6 +173,8 @@ class Experiment:
         self.new_factors_ = HybridNonlinearFactorGraph()
         self.all_factors_ = HybridNonlinearFactorGraph()
         self.initial_ = Values()
+        
+        self.plot_hypotheses: = plot_hypotheses
 
     def hybrid_loop_closure_factor(self, loop_counter, key_s, key_t,
                                    measurement: Pose2):
@@ -215,7 +225,7 @@ class Experiment:
         bayesNet = linearized.eliminateSequential()
         delta: HybridValues = bayesNet.optimize()
         self.initial_ = self.initial_.retract(delta.continuous())
-        self.smoother_.reinitialize(bayesNet)
+        self.smoother_.reInitialize(bayesNet)
         after_update = time.time()
         print(f"Took {after_update - before_update} seconds.")
         return after_update - before_update
@@ -331,13 +341,14 @@ class Experiment:
 
         # self.save_results(result, key_t + 1, time_list)
 
-        # Get all the discrete values
-        discrete_keys = gtsam.DiscreteKeys()
-        for key in delta.discrete().keys():
-            # TODO Get cardinality from DiscreteFactor
-            discrete_keys.push_back((key, 2))
-        print("plotting all hypotheses")
-        self.plot_all_hypotheses(discrete_keys, key_t + 1)
+        if self.plot_hypotheses:
+            # Get all the discrete values
+            discrete_keys = gtsam.DiscreteKeys()
+            for key in delta.discrete().keys():
+                # TODO Get cardinality from DiscreteFactor
+                discrete_keys.push_back((key, 2))
+            print("plotting all hypotheses")
+            self.plot_all_hypotheses(discrete_keys, key_t + 1)
 
     def plot_all_hypotheses(self, discrete_keys, num_poses):
         """Plot all possible hypotheses."""
@@ -346,7 +357,6 @@ class Experiment:
         gt = np.loadtxt(gtsam.findExampleDataFile("ISAM2_GT_city10000.txt"),
                         delimiter=" ")
 
-        # print(discrete_keys)
         # Get all possible assignments
         all_assignments = gtsam.cartesianProduct(discrete_keys)
 
@@ -428,7 +438,8 @@ def main():
     experiment = Experiment(gtsam.findExampleDataFile(args.data_file),
                             max_loop_count=args.max_loop_count,
                             update_frequency=args.update_frequency,
-                            max_num_hypotheses=args.max_num_hypotheses)
+                            max_num_hypotheses=args.max_num_hypotheses,
+                            plot_hypotheses=args.plot_hypotheses)
     experiment.run()