diff --git a/python/gtsam/examples/SFMExample_bal.py b/python/gtsam/examples/SFMExample_bal.py
index 65b9e1334..77c186bd3 100644
--- a/python/gtsam/examples/SFMExample_bal.py
+++ b/python/gtsam/examples/SFMExample_bal.py
@@ -16,36 +16,37 @@ import sys
 
 import gtsam
 from gtsam import (GeneralSFMFactorCal3Bundler,
-                   PriorFactorPinholeCameraCal3Bundler, PriorFactorPoint3,
-                   readBal)
+                   PriorFactorPinholeCameraCal3Bundler, PriorFactorPoint3)
 from gtsam.symbol_shorthand import C, P
-from gtsam.utils.plot import plot_3d_points, plot_trajectory
+from gtsam.utils import plot
+from matplotlib import pyplot as plt
 
 logging.basicConfig(stream=sys.stdout, level=logging.INFO)
 
+DEFAULT_BAL_DATASET = "dubrovnik-3-7-pre"
 
-def plot(scene_data: gtsam.SfmData, result: gtsam.Values):
-    """Plot the trajectory."""
+
+def plot_scene(scene_data: gtsam.SfmData, result: gtsam.Values):
+    """Plot the SFM results."""
     plot_vals = gtsam.Values()
     for cam_idx in range(scene_data.number_cameras()):
         plot_vals.insert(C(cam_idx),
                          result.atPinholeCameraCal3Bundler(C(cam_idx)).pose())
-    for t_idx in range(scene_data.number_tracks()):
-        plot_vals.insert(P(t_idx), result.atPoint3(P(t_idx)))
+    for j in range(scene_data.number_tracks()):
+        plot_vals.insert(P(j), result.atPoint3(P(j)))
 
-    plot_3d_points(0, plot_vals, linespec="g.")
-    plot_trajectory(0, plot_vals, show=True)
+    plot.plot_3d_points(0, plot_vals, linespec="g.")
+    plot.plot_trajectory(0, plot_vals, title="SFM results")
+
+    plt.show()
 
 
 def run(args: argparse.Namespace):
     """ Run LM optimization with BAL input data and report resulting error """
-    if args.input_file:
-        input_file = args.input_file
-    else:
-        input_file = gtsam.findExampleDataFile("dubrovnik-3-7-pre")
+    input_file = args.input_file
 
     # Load the SfM data from file
-    scene_data = readBal(input_file)
+    scene_data = gtsam.readBal(input_file)
     logging.info("read %d tracks on %d cameras\n", scene_data.number_tracks(),
                  scene_data.number_cameras())
 
@@ -56,16 +57,14 @@ def run(args: argparse.Namespace):
     noise = gtsam.noiseModel.Isotropic.Sigma(2, 1.0)  # one pixel in u and v
 
     # Add measurements to the factor graph
-    j = 0
-    for t_idx in range(scene_data.number_tracks()):
-        track = scene_data.track(t_idx)  # SfmTrack
+    for j in range(scene_data.number_tracks()):
+        track = scene_data.track(j)  # SfmTrack
         # retrieve the SfmMeasurement objects
         for m_idx in range(track.number_measurements()):
             # i represents the camera index, and uv is the 2d measurement
             i, uv = track.measurement(m_idx)
             # note use of shorthand symbols C and P
             graph.add(GeneralSFMFactorCal3Bundler(uv, noise, C(i), P(j)))
-        j += 1
 
     # Add a prior on pose x1. This indirectly specifies where the origin is.
     graph.push_back(
@@ -89,9 +88,9 @@ def run(args: argparse.Namespace):
         i += 1
 
     # add each SfmTrack
-    for t_idx in range(scene_data.number_tracks()):
-        track = scene_data.track(t_idx)
-        initial.insert(P(t_idx), track.point3())
+    for j in range(scene_data.number_tracks()):
+        track = scene_data.track(j)
+        initial.insert(P(j), track.point3())
 
     # Optimize the graph and print results
     try:
@@ -107,7 +106,7 @@ def run(args: argparse.Namespace):
     logging.info("initial error: %f", graph.error(initial))
     logging.info("final error: %f", graph.error(result))
 
-    plot(scene_data, result)
+    plot_scene(scene_data, result)
 
 
 def main():
@@ -116,7 +115,7 @@ def main():
     parser.add_argument('-i',
                         '--input_file',
                         type=str,
-                        default="",
+                        default=gtsam.findExampleDataFile(DEFAULT_BAL_DATASET),
                         help="""Read SFM data from the specified BAL file.
         The data format is described here: https://grail.cs.washington.edu/projects/bal/.
         BAL files contain (nrPoses, nrPoints, nrObservations), followed by (i,j,u,v) tuples,