Address comments

python/gtsam/tests/test_SfmData.py

@@ -14,10 +14,9 @@ from __future__ import print_function
 
 import unittest
 
-import numpy as np
-
 import gtsam
-from gtsam import SfmData, SfmTrack, Point2, Point3
+import numpy as np
+from gtsam import Point2, Point3, SfmData, SfmTrack
 from gtsam.utils.test_case import GtsamTestCase
 
 
@@ -34,7 +33,7 @@ class TestSfmData(GtsamTestCase):
         """Test functions in SfmTrack"""
         # measurement is of format (camera_idx, imgPoint)
         # create arbitrary camera indices for two cameras
-        i1, i2 = 4,5
+        i1, i2 = 4, 5
 
         # create arbitrary image measurements for cameras i1 and i2
         uv_i1 = Point2(12.6, 82)
@@ -53,15 +52,14 @@ class TestSfmData(GtsamTestCase):
         cam_idx, img_measurement = self.tracks.measurement(0)
         self.assertEqual(cam_idx, i1)
         np.testing.assert_array_almost_equal(
-            Point3(0.,0.,0.), 
+            Point3(0., 0., 0.),
             self.tracks.point3()
         )
 
-
     def test_data(self):
         """Test functions in SfmData"""
         # Create new track with 3 measurements
-        i1, i2, i3 = 3,5,6
+        i1, i2, i3 = 3, 5, 6
         uv_i1 = Point2(21.23, 45.64)
 
         # translating along X-axis
@@ -86,8 +84,11 @@ class TestSfmData(GtsamTestCase):
         self.assertEqual(cam_idx, i1)
 
     def test_Balbianello(self):
+        """ Check that we can successfully read a bundler file and create a 
+            factor graph from it
+        """
         # The structure where we will save the SfM data
-        filename = gtsam.findExampleDataFile("Balbianello")
+        filename = gtsam.findExampleDataFile("Balbianello.out")
         sfm_data = SfmData.FromBundlerFile(filename)
 
         # Check number of things
@@ -104,7 +105,8 @@ class TestSfmData(GtsamTestCase):
         self.gtsamAssertEquals(expected, actual, 1)
 
         # We share *one* noiseModel between all projection factors
-        model = gtsam.noiseModel.Isotropic.Sigma(2, 1.0)  # one pixel in u and v
+        model = gtsam.noiseModel.Isotropic.Sigma(
+            2, 1.0)  # one pixel in u and v
 
         # Convert to NonlinearFactorGraph
         graph = sfm_data.sfmFactorGraph(model)

python/gtsam/utils/visual_data_generator.py

@@ -1,12 +1,12 @@
 from __future__ import print_function
-from typing import Tuple
 
 import math
-import numpy as np
 from math import pi
+from typing import Tuple
 
 import gtsam
-from gtsam import Point3, Pose3, PinholeCameraCal3_S2, Cal3_S2
+import numpy as np
+from gtsam import Cal3_S2, PinholeCameraCal3_S2, Point3, Pose3
 
 
 class Options:
@@ -36,7 +36,7 @@ class GroundTruth:
         self.cameras = [Pose3()] * nrCameras
         self.points = [Point3(0, 0, 0)] * nrPoints
 
-    def print(self, s="") -> None:
+    def print(self, s: string = "") -> None:
         print(s)
         print("K = ", self.K)
         print("Cameras: ", len(self.cameras))
@@ -88,7 +88,8 @@ def generate_data(options) -> Tuple[Data, GroundTruth]:
         r = 10
         for j in range(len(truth.points)):
             theta = j * 2 * pi / nrPoints
-            truth.points[j] = Point3(r * math.cos(theta), r * math.sin(theta), 0)
+            truth.points[j] = Point3(
+                r * math.cos(theta), r * math.sin(theta), 0)
     else:  # 3D landmarks as vertices of a cube
         truth.points = [
             Point3(10, 10, 10), Point3(-10, 10, 10),