Update to match cython

python/gtsam_py/python/examples/ImuFactorExample.py

@@ -17,10 +17,7 @@ import math
 import gtsam
 import matplotlib.pyplot as plt
 import numpy as np
-from gtsam import symbol_shorthand
+from gtsam.gtsam.symbol_shorthand import B, V, X
-B = symbol_shorthand.B
-V = symbol_shorthand.V
-X = symbol_shorthand.X
 
 from gtsam.utils.plot import plot_pose3
 from mpl_toolkits.mplot3d import Axes3D

python/gtsam_py/python/examples/ImuFactorExample2.py

@@ -17,10 +17,7 @@ from gtsam import (ISAM2, BetweenFactorConstantBias, Cal3_S2,
                    PinholeCameraCal3_S2, Point3, Pose3,
                    PriorFactorConstantBias, PriorFactorPose3,
                    PriorFactorVector, Rot3, Values)
-from gtsam import symbol_shorthand
+from gtsam.gtsam.symbol_shorthand import B, V, X
-B = symbol_shorthand.B
-V = symbol_shorthand.V
-X = symbol_shorthand.X
 from mpl_toolkits.mplot3d import Axes3D  # pylint: disable=W0611
 
 

python/gtsam_py/python/examples/PlanarSLAMExample.py

@@ -16,6 +16,7 @@ from __future__ import print_function
 import numpy as np
 
 import gtsam
+from gtsam.gtsam.symbol_shorthand import X, L
 
 # Create noise models
 PRIOR_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([0.3, 0.3, 0.1]))
@@ -26,11 +27,11 @@ MEASUREMENT_NOISE = gtsam.noiseModel.Diagonal.Sigmas(np.array([0.1, 0.2]))
 graph = gtsam.NonlinearFactorGraph()
 
 # Create the keys corresponding to unknown variables in the factor graph
-X1 = gtsam.symbol('x', 1)
+X1 = X(1)
-X2 = gtsam.symbol('x', 2)
+X2 = X(2)
-X3 = gtsam.symbol('x', 3)
+X3 = X(3)
-L1 = gtsam.symbol('l', 4)
+L1 = L(4)
-L2 = gtsam.symbol('l', 5)
+L2 = L(5)
 
 # Add a prior on pose X1 at the origin. A prior factor consists of a mean and a noise model
 graph.add(gtsam.PriorFactorPose2(X1, gtsam.Pose2(0.0, 0.0, 0.0), PRIOR_NOISE))

python/gtsam_py/python/examples/Pose2SLAMExample_g2o.py

@@ -83,7 +83,7 @@ else:
     print ("Done!")
 
 if args.plot:
-    resultPoses = gtsam.extractPose2(result)
+    resultPoses = gtsam.utilities.extractPose2(result)
     for i in range(resultPoses.shape[0]):
         plot.plot_pose2(1, gtsam.Pose2(resultPoses[i, :]))
     plt.show()

python/gtsam_py/python/examples/Pose3SLAMExample_g2o.py

@@ -44,7 +44,7 @@ priorModel = gtsam.noiseModel.Diagonal.Variances(vector6(1e-6, 1e-6, 1e-6,
 
 print("Adding prior to g2o file ")
 graphWithPrior = graph
-firstKey = initial.keys().at(0)
+firstKey = initial.keys()[0]
 graphWithPrior.add(gtsam.PriorFactorPose3(firstKey, gtsam.Pose3(), priorModel))
 
 params = gtsam.GaussNewtonParams()
@@ -66,7 +66,7 @@ else:
     print ("Done!")
 
 if args.plot:
-    resultPoses = gtsam.allPose3s(result)
+    resultPoses = gtsam.utilities.allPose3s(result)
     for i in range(resultPoses.size()):
         plot.plot_pose3(1, resultPoses.atPose3(i))
     plt.show()

python/gtsam_py/python/examples/PreintegrationExample.py

@@ -111,7 +111,7 @@ class PreintegrationExample(object):
         actualPose = self.scenario.pose(t)
         plot_pose3(POSES_FIG, actualPose, 0.3)
         t = actualPose.translation()
-        self.maxDim = max([max(t), self.maxDim])
+        self.maxDim = max([max(np.abs(t)), self.maxDim])
         ax = plt.gca()
         ax.set_xlim3d(-self.maxDim, self.maxDim)
         ax.set_ylim3d(-self.maxDim, self.maxDim)

python/gtsam_py/python/examples/README.md

@@ -1,18 +1,12 @@
-# THIS FILE IS OUTDATED!
-
-~~These examples are almost identical to the old handwritten python wrapper examples. However, there are just some slight name changes, for example `noiseModel.Diagonal` becomes `noiseModel.Diagonal` etc...~~
-
-~~Also, annoyingly, instead of `gtsam.Symbol('b', 0)` we now need to say `gtsam.symbol('b', 0))`~~
-
 # Porting Progress
 
 | C++ Example Name                                      | Ported |
 |-------------------------------------------------------|--------|
 | CameraResectioning                                    |        |
 | CreateSFMExampleData                                  |        |
-| DiscreteBayesNet_FG                                   | none of the required discrete functionality is exposed through cython |
+| DiscreteBayesNet_FG                                   | none of the required discrete functionality is exposed through Python |
-| easyPoint2KalmanFilter                                | ExtendedKalmanFilter not exposed through cython |
+| easyPoint2KalmanFilter                                | ExtendedKalmanFilter not exposed through Python |
-| elaboratePoint2KalmanFilter                           | GaussianSequentialSolver not exposed through cython |
+| elaboratePoint2KalmanFilter                           | GaussianSequentialSolver not exposed through Python |
 | ImuFactorExample2                                     | X      |
 | ImuFactorsExample                                     |        |
 | ISAM2Example_SmartFactor                              |        |
@@ -26,7 +20,7 @@
 | Pose2SLAMExample_g2o                                  | X      |
 | Pose2SLAMExample_graph                                |        |
 | Pose2SLAMExample_graphviz                             |        |
-| Pose2SLAMExample_lago                                 | lago not exposed through cython |
+| Pose2SLAMExample_lago                                 | lago not exposed through Python |
 | Pose2SLAMStressTest                                   |        |
 | Pose2SLAMwSPCG                                        |        |
 | Pose3SLAMExample_changeKeys                           |        |

python/gtsam_py/python/examples/SFMExample.py

@@ -10,24 +10,21 @@ A structure-from-motion problem on a simulated dataset
 """
 from __future__ import print_function
 
+import gtsam
 import matplotlib.pyplot as plt
 import numpy as np
+from gtsam import symbol_shorthand
+L = symbol_shorthand.L
+X = symbol_shorthand.X
 
-import gtsam
 from gtsam.examples import SFMdata
-from gtsam.gtsam import (Cal3_S2, DoglegOptimizer,
+from gtsam import (Cal3_S2, DoglegOptimizer,
                          GenericProjectionFactorCal3_S2, Marginals,
-                         NonlinearFactorGraph, Point3, Pose3,
+                         NonlinearFactorGraph, PinholeCameraCal3_S2, Point3,
-                         PriorFactorPoint3, PriorFactorPose3, Rot3,
+                         Pose3, PriorFactorPoint3, PriorFactorPose3, Rot3, Values)
-                         SimpleCamera, Values)
 from gtsam.utils import plot
 
 
-def symbol(name: str, index: int) -> int:
-    """ helper for creating a symbol without explicitly casting 'name' from str to int """
-    return gtsam.symbol(name, index)
-
-
 def main():
     """
     Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).
@@ -74,23 +71,23 @@ def main():
     # Add a prior on pose x1. This indirectly specifies where the origin is.
     # 0.3 rad std on roll,pitch,yaw and 0.1m on x,y,z
     pose_noise = gtsam.noiseModel.Diagonal.Sigmas(np.array([0.3, 0.3, 0.3, 0.1, 0.1, 0.1]))
-    factor = PriorFactorPose3(symbol('x', 0), poses[0], pose_noise)
+    factor = PriorFactorPose3(X(0), poses[0], pose_noise)
     graph.push_back(factor)
 
     # Simulated measurements from each camera pose, adding them to the factor graph
     for i, pose in enumerate(poses):
-        camera = SimpleCamera(pose, K)
+        camera = PinholeCameraCal3_S2(pose, K)
         for j, point in enumerate(points):
             measurement = camera.project(point)
             factor = GenericProjectionFactorCal3_S2(
-                measurement, measurement_noise, symbol('x', i), symbol('l', j), K)
+                measurement, measurement_noise, X(i), L(j), K)
             graph.push_back(factor)
 
     # Because the structure-from-motion problem has a scale ambiguity, the problem is still under-constrained
     # Here we add a prior on the position of the first landmark. This fixes the scale by indicating the distance
     # between the first camera and the first landmark. All other landmark positions are interpreted using this scale.
     point_noise = gtsam.noiseModel.Isotropic.Sigma(3, 0.1)
-    factor = PriorFactorPoint3(symbol('l', 0), points[0], point_noise)
+    factor = PriorFactorPoint3(L(0), points[0], point_noise)
     graph.push_back(factor)
     graph.print_('Factor Graph:\n')
 
@@ -99,10 +96,10 @@ def main():
     initial_estimate = Values()
     for i, pose in enumerate(poses):
         transformed_pose = pose.retract(0.1*np.random.randn(6,1))
-        initial_estimate.insert(symbol('x', i), transformed_pose)
+        initial_estimate.insert(X(i), transformed_pose)
     for j, point in enumerate(points):
-        transformed_point = Point3(point.vector() + 0.1*np.random.randn(3))
+        transformed_point = point + 0.1*np.random.randn(3)
-        initial_estimate.insert(symbol('l', j), transformed_point)
+        initial_estimate.insert(L(j), transformed_point)
     initial_estimate.print_('Initial Estimates:\n')
 
     # Optimize the graph and print results
@@ -121,6 +118,5 @@ def main():
     plot.set_axes_equal(1)
     plt.show()
 
-
 if __name__ == '__main__':
     main()

python/gtsam_py/python/utils/plot.py

@@ -221,9 +221,9 @@ def plot_3d_points(fignum, values, linespec="g*", marginals=None,
     keys = values.keys()
 
     # Plot points and covariance matrices
-    for i in range(keys.size()):
+    for i in range(len(keys)):
         try:
-            key = keys.at(i)
+            key = keys[i]
             point = values.atPoint3(key)
             if marginals is not None:
                 covariance = marginals.marginalCovariance(key)
@@ -319,19 +319,19 @@ def plot_trajectory(fignum, values, scale=1, marginals=None,
         title (string): The title of the plot.
         axis_labels (iterable[string]): List of axis labels to set.
     """
-    pose3Values = gtsam.utilities_allPose3s(values)
+    pose3Values = gtsam.utilities.allPose3s(values)
     keys = gtsam.KeyVector(pose3Values.keys())
     lastIndex = None
 
-    for i in range(keys.size()):
+    for i in range(len(keys)):
-        key = keys.at(i)
+        key = keys[i]
         try:
             pose = pose3Values.atPose3(key)
         except:
             print("Warning: no Pose3 at key: {0}".format(key))
 
         if lastIndex is not None:
-            lastKey = keys.at(lastIndex)
+            lastKey = keys[lastIndex]
             try:
                 lastPose = pose3Values.atPose3(lastKey)
             except:
@@ -350,7 +350,7 @@ def plot_trajectory(fignum, values, scale=1, marginals=None,
 
     # Draw final pose
     if lastIndex is not None:
-        lastKey = keys.at(lastIndex)
+        lastKey = keys[lastIndex]
         try:
             lastPose = pose3Values.atPose3(lastKey)
             if marginals: