support pythonic print for gtsam objects.

cython/gtsam/examples/OdometryExample.py

@@ -35,7 +35,7 @@ odometry = gtsam.Pose2(2.0, 0.0, 0.0)
 # Create odometry (Between) factors between consecutive poses
 graph.add(gtsam.BetweenFactorPose2(1, 2, odometry, ODOMETRY_NOISE))
 graph.add(gtsam.BetweenFactorPose2(2, 3, odometry, ODOMETRY_NOISE))
-graph.print_("\nFactor Graph:\n")
+print("\nFactor Graph:\n{}".format(graph))
 
 # Create the data structure to hold the initialEstimate estimate to the solution
 # For illustrative purposes, these have been deliberately set to incorrect values
@@ -43,10 +43,10 @@ initial = gtsam.Values()
 initial.insert(1, gtsam.Pose2(0.5, 0.0, 0.2))
 initial.insert(2, gtsam.Pose2(2.3, 0.1, -0.2))
 initial.insert(3, gtsam.Pose2(4.1, 0.1, 0.1))
-initial.print_("\nInitial Estimate:\n")
+print("\nInitial Estimate:\n{}".format(initial))
 
 # optimize using Levenberg-Marquardt optimization
 params = gtsam.LevenbergMarquardtParams()
 optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial, params)
 result = optimizer.optimize()
-result.print_("\nFinal Result:\n")
+print("\nFinal Result:\n{}".format(result))

cython/gtsam/examples/PlanarSLAMExample.py

@@ -50,7 +50,7 @@ graph.add(gtsam.BearingRangeFactor2D(
     X3, L2, gtsam.Rot2.fromDegrees(90), 2.0, MEASUREMENT_NOISE))
 
 # Print graph
-graph.print_("Factor Graph:\n")
+print("Factor Graph:\n{}".format(graph))
 
 # Create (deliberately inaccurate) initial estimate
 initial_estimate = gtsam.Values()
@@ -61,7 +61,7 @@ initial_estimate.insert(L1, gtsam.Point2(1.80, 2.10))
 initial_estimate.insert(L2, gtsam.Point2(4.10, 1.80))
 
 # Print
-initial_estimate.print_("Initial Estimate:\n")
+print("Initial Estimate:\n{}".format(initial_estimate))
 
 # Optimize using Levenberg-Marquardt optimization. The optimizer
 # accepts an optional set of configuration parameters, controlling
@@ -72,7 +72,7 @@ initial_estimate.print_("Initial Estimate:\n")
 params = gtsam.LevenbergMarquardtParams()
 optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initial_estimate, params)
 result = optimizer.optimize()
-result.print_("\nFinal Result:\n")
+print("\nFinal Result:\n{}".format(result))
 
 # Calculate and print marginal covariances for all variables
 marginals = gtsam.Marginals(graph, result)

cython/gtsam/examples/Pose2SLAMExample.py

@@ -52,7 +52,7 @@ graph.add(gtsam.BetweenFactorPose2(
 # techniques with camera images. We will use another Between Factor to enforce this constraint:
 graph.add(gtsam.BetweenFactorPose2(
     5, 2, gtsam.Pose2(2, 0, math.pi / 2), ODOMETRY_NOISE))
-graph.print_("\nFactor Graph:\n")  # print
+print("\nFactor Graph:\n{}".format(graph))  # print
 
 # 3. Create the data structure to hold the initial_estimate estimate to the
 # solution. For illustrative purposes, these have been deliberately set to incorrect values
@@ -62,7 +62,7 @@ initial_estimate.insert(2, gtsam.Pose2(2.3, 0.1, -0.2))
 initial_estimate.insert(3, gtsam.Pose2(4.1, 0.1, math.pi / 2))
 initial_estimate.insert(4, gtsam.Pose2(4.0, 2.0, math.pi))
 initial_estimate.insert(5, gtsam.Pose2(2.1, 2.1, -math.pi / 2))
-initial_estimate.print_("\nInitial Estimate:\n")  # print
+print("\nInitial Estimate:\n{}".format(initial_estimate))  # print
 
 # 4. Optimize the initial values using a Gauss-Newton nonlinear optimizer
 # The optimizer accepts an optional set of configuration parameters,
@@ -79,7 +79,7 @@ parameters.setMaxIterations(100)
 optimizer = gtsam.GaussNewtonOptimizer(graph, initial_estimate, parameters)
 # ... and optimize
 result = optimizer.optimize()
-result.print_("Final Result:\n")
+print("Final Result:\n{}".format(result))
 
 # 5. Calculate and print marginal covariances for all variables
 marginals = gtsam.Marginals(graph, result)

cython/gtsam/examples/README.md

@@ -1,4 +1,4 @@
 These examples are almost identical to the old handwritten python wrapper
-examples. However, there are just some slight name changes, for example .print
-becomes .print_, and noiseModel.Diagonal becomes noiseModel_Diagonal etc...
+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(ord('b'), 0))

wrap/Method.cpp

@@ -106,7 +106,7 @@ void Method::emit_cython_pyx_no_overload(FileWriter& file,
 
   // leverage python's special treatment for print
   if (funcName == "print_") {
-    file.oss << "    def __str__(self):\n";
+    file.oss << "    def __repr__(self):\n";
     file.oss << "        strBuf = RedirectCout()\n";
     file.oss << "        self.print_('')\n";
     file.oss << "        return strBuf.str()\n";