├── examples
    ├── CMakeLists.txt
    ├── icp.cpp
    └── robust_pgo_mc.cpp
├── CMakeLists.txt
├── .gitignore
├── README.md
├── include
    ├── PosePointFactor.h
    └── pgo_utils.h
└── data
    └── pointclouds
        ├── bunny_sparse100_part1.xyz
        ├── bunny_sparse100_part2.xyz
        ├── dragon_sparse100_part1.xyz
        ├── dragon_sparse100_part2.xyz
        └── bunny_sparse10_part1.xyz


/examples/CMakeLists.txt:
--------------------------------------------------------------------------------
1 | add_executable(icp icp.cpp)
2 | target_link_libraries(icp dcsam gtsam)
3 | 
4 | add_executable(robust_pgo_mc robust_pgo_mc.cpp)
5 | target_link_libraries(robust_pgo_mc dcsam gtsam)
6 | 


--------------------------------------------------------------------------------
/CMakeLists.txt:
--------------------------------------------------------------------------------
 1 | cmake_minimum_required(VERSION 3.15)
 2 | project(dcsam_examples)
 3 | 
 4 | # Set some compilation options.
 5 | set(CMAKE_CXX_STANDARD 17)
 6 | add_compile_options(-Wall -Wpedantic -Wextra)
 7 | 
 8 | # External package dependencies.
 9 | find_package(GTSAM 4.2 REQUIRED)
10 | find_package(Eigen3 3.3 REQUIRED)
11 | find_package(dcsam REQUIRED)
12 | 
13 | include_directories(include)
14 | add_subdirectory(examples)
15 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
 1 | # OS generated files #
 2 | ######################
 3 | .DS_Store
 4 | .DS_Store?
 5 | ._*
 6 | .Spotlight-V100
 7 | .Trashes
 8 | ehthumbs.db
 9 | [Tt]humbs.db
10 | 
11 | # Jupyter Notebooks #
12 | #####################
13 | .ipynb_checkpoints
14 | 
15 | # Binaries #
16 | ############
17 | *.pyc
18 | 
19 | # Latex #
20 | #########
21 | *.aux
22 | *.fdb_latexmk
23 | *.fls
24 | *.log
25 | *.out
26 | *.synctex.gz
27 | *.bbl
28 | *.blg
29 | *.toc
30 | *.dvi
31 | *.lof
32 | *.lot
33 | main.pdf
34 | 
35 | # spacemacs #
36 | #############
37 | auto/
38 | .#*
39 | 
40 | # Python #
41 | ##########
42 | __pycache__/
43 | 
44 | 
45 | # PyTorch #
46 | ###########
47 | *.pt
48 | 
49 | # Executables/C++ #
50 | ###################
51 | build/
52 | 
53 | # Tensorflow #
54 | ##############
55 | *.tfrecord
56 | *.pb
57 | 
58 | # Temporary Files #
59 | ###################
60 | *.*~
61 | *.swp
62 | 
63 | # Large Files #
64 | ###############
65 | *.png
66 | *.mp4
67 | *.jpg
68 | 
69 | # Blender #
70 | ###########
71 | *.blend
72 | 
73 | # MATLAB #
74 | ##########
75 | *.m~
76 | 
77 | # ROS #
78 | #######
79 | *.bag
80 | 
81 | # Jekyll #
82 | ##########
83 | .jekyll-metadata
84 | .sass-cache/
85 | Gemfile.lock
86 | _site/
87 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # dcsam-examples
 2 | 
 3 | This repository contains example code for the experiments provided in
 4 | our paper "[Discrete-Continuous Smoothing and Mapping](https://arxiv.org/abs/2204.11936)."
 5 | 
 6 | **NOTE: As of 1/30/2023 the latest version of DC-SAM (and dcsam-examples) on `main` depends on GTSAM release 4.2a8.** If you are using GTSAM 4.1.1, check out our [pre-4.2 release tag](https://github.com/MarineRoboticsGroup/dcsam-examples/releases/tag/pre-4.2) and the [corresponding pre-4.2 version of DC-SAM](https://github.com/MarineRoboticsGroup/dcsam/releases/tag/pre-4.2). This is the version of DC-SAM (and dcsam-examples) you would have used if you cloned the repository prior to 1/30/2023. Many thanks to [Parker Lusk](https://github.com/plusk01) for bringing us into the future.
 7 | 
 8 | The file `icp.cpp` implements the iterative closest point (ICP) method for point
 9 | cloud registration.
10 | 
11 | The file `robust_pgo_mc.cpp` implements robust pose-graph optimization by
12 | introducing inlier/outlier discrete hypotheses for untrusted loop closure edges.
13 | 
14 | To build the ICP and robust PGO examples:
15 | 
16 | ```bash
17 | ~ $ mkdir build
18 | ~ $ cd build
19 | ~/build $ cmake ..
20 | ~/build $ make -j
21 | ```
22 | 
23 | To run the ICP example:
24 | ```bash
25 | ~/build/examples $ ./icp (path to source cloud) (path to target cloud)
26 | ```
27 | 
28 | To run the robust pose-graph optimization example:
29 | ```bash
30 | ~/build/examples $ ./robust_pgo_mc [.g2o file] [outlier rate (int >= 0; default 0)] [is3D (0/1; default 1)] [num trials (default 1)]
31 | ```
32 | 


--------------------------------------------------------------------------------
/include/PosePointFactor.h:
--------------------------------------------------------------------------------
 1 | /**
 2 |  * PosePointFactor.h
 3 |  * A simple factor representing the cost:
 4 |  *    f(T) : = ||Tp - q||^2
 5 |  * where p,q \in R^3 and T \in SE(3)
 6 |  */
 7 | #pragma once
 8 | 
 9 | #include <gtsam/geometry/Point3.h>
10 | #include <gtsam/geometry/Pose3.h>
11 | #include <gtsam/nonlinear/NonlinearFactor.h>
12 | 
13 | class PosePointFactor : public gtsam::NonlinearFactor {
14 |  private:
15 |   gtsam::Point3 p_;
16 |   gtsam::Point3 q_;
17 | 
18 |  public:
19 |   PosePointFactor(const gtsam::Key &k, const gtsam::Point3 &p,
20 |                   const gtsam::Point3 &q)
21 |       : p_(p), q_(q) {
22 |     keys_.push_back(k);
23 |   }
24 | 
25 |   gtsam::Vector error_vector(const gtsam::Values &values) const {
26 |     gtsam::Pose3 T = values.at<gtsam::Pose3>(keys_[0]);
27 |     gtsam::Point3 transformed_p = T.transformFrom(p_);
28 |     gtsam::Point3 delta = transformed_p - q_;
29 |     return gtsam::Vector3(delta.x(), delta.y(), delta.z());
30 |   }
31 | 
32 |   double error(const gtsam::Values &values) const override {
33 |     gtsam::Pose3 T = values.at<gtsam::Pose3>(keys_[0]);
34 |     gtsam::Point3 transformed_p = T.transformFrom(p_);
35 |     gtsam::Point3 delta = transformed_p - q_;
36 | 
37 |     // Return the squared norm of the error.
38 |     return pow(delta.norm(), 2);
39 |   }
40 | 
41 |   size_t dim() const override { return 3; };
42 | 
43 |   boost::shared_ptr<gtsam::GaussianFactor> linearize(
44 |       const gtsam::Values &values) const override {
45 |     gtsam::Pose3 T = values.at<gtsam::Pose3>(keys_[0]);
46 |     Eigen::Matrix<double, 3, 6> H;
47 |     gtsam::Point3 transformed_p = T.transformFrom(p_, &H);
48 |     return boost::make_shared<gtsam::JacobianFactor>(keys_[0], H,
49 |                                                      -error_vector(values));
50 |   }
51 | };
52 | 


--------------------------------------------------------------------------------
/examples/icp.cpp:
--------------------------------------------------------------------------------
  1 | #include <dcsam/DCMixtureFactor.h>
  2 | #include <dcsam/DCSAM.h>
  3 | #include <dcsam/HybridFactorGraph.h>
  4 | 
  5 | #include <fstream>
  6 | #include <iostream>
  7 | #include <sstream>
  8 | 
  9 | #include "PosePointFactor.h"
 10 | 
 11 | std::vector<gtsam::Point3> read_xyz_file(const std::string& path_to_file) {
 12 |   // Vector of points to output.
 13 |   std::vector<gtsam::Point3> points;
 14 | 
 15 |   // A string containing the elements of each line.
 16 |   std::string line;
 17 | 
 18 |   // Preallocate x, y, z coordinates for each point.
 19 |   double x, y, z;
 20 | 
 21 |   // Open file for reading.
 22 |   std::ifstream infile(path_to_file);
 23 | 
 24 |   size_t i = 0;
 25 |   while (std::getline(infile, line)) {
 26 |     // Construct a stream from line.
 27 |     std::stringstream strstrm(line);
 28 | 
 29 |     // Extract formatted point.
 30 |     strstrm >> x >> y >> z;
 31 | 
 32 |     // Convert x, y, z to gtsam::Point3.
 33 |     gtsam::Point3 point(x, y, z);
 34 | 
 35 |     points.push_back(point);
 36 |   }
 37 | 
 38 |   return points;
 39 | }
 40 | 
 41 | int main(int argc, char** argv) {
 42 |   if (argc != 3) {
 43 |     std::cout << "Usage: " << argv[0] << " [source .xyz file]"
 44 |               << " [target .xyz file]" << std::endl;
 45 |     exit(1);
 46 |   }
 47 | 
 48 |   // Read source and target point clouds
 49 |   std::vector<gtsam::Point3> source = read_xyz_file(argv[1]);
 50 |   std::vector<gtsam::Point3> target = read_xyz_file(argv[2]);
 51 | 
 52 |   std::cout << "Loaded " << source.size()
 53 |             << " points from source cloud in file " << argv[1] << " and \n"
 54 |             << target.size() << " points from target cloud in file " << argv[2]
 55 |             << std::endl;
 56 | 
 57 |   if (source.size() == 0 || target.size() == 0) {
 58 |     std::cout
 59 |         << "Error: no measurements found in one or more input pointclouds."
 60 |         << std::endl;
 61 |     exit(1);
 62 |   }
 63 | 
 64 |   std::cout << source[0].x() << ", " << source[0].y() << ", " << source[0].z()
 65 |             << std::endl;
 66 | 
 67 |   gtsam::KeyVector poseKey{gtsam::Symbol('x', 1)};
 68 | 
 69 |   // Create factor graph
 70 |   dcsam::HybridFactorGraph hfg;
 71 |   for (size_t i = 0; i < source.size(); i++) {
 72 |     std::vector<PosePointFactor> components;
 73 |     for (size_t j = 0; j < target.size(); j++) {
 74 |       components.push_back(PosePointFactor(poseKey[0], source[i], target[j]));
 75 |     }
 76 |     gtsam::DiscreteKey dk(gtsam::Symbol('d', i), target.size());
 77 |     dcsam::DCMixtureFactor<PosePointFactor> dcm(poseKey, dk, components, true);
 78 |     hfg.push_dc(dcm);
 79 |   }
 80 | 
 81 |   std::cout << "Built HybridFactorGraph with " << hfg.size() << " factors."
 82 |             << std::endl;
 83 | 
 84 |   // Create DCSAM instance.
 85 |   dcsam::DCSAM dcsam;
 86 |   gtsam::Values initial_values;
 87 |   // Currently DCSAM requires that we provide initial discrete values, but they
 88 |   // aren't actually used since we perform the discrete solve first, so we
 89 |   // just set them all to 0.
 90 |   dcsam::DiscreteValues initial_discrete;
 91 |   for (size_t i = 0; i < source.size(); i++) {
 92 |     initial_discrete[gtsam::Symbol('d', i)] = 0;
 93 |   }
 94 |   initial_values.insert(poseKey[0], gtsam::Pose3::Identity());
 95 | 
 96 |   // Compute the first iterate.
 97 |   dcsam.update(hfg, initial_values, initial_discrete);
 98 |   dcsam::DCValues estimate = dcsam.calculateEstimate();
 99 |   std::cout << "pose out: " << std::endl;
100 |   estimate.continuous.at<gtsam::Pose3>(poseKey[0]).print();
101 |   std::ofstream file1("out1.txt");
102 |   file1 << estimate.continuous.at<gtsam::Pose3>(poseKey[0]).matrix();
103 | 
104 |   // Perform a few more iterations, writing to file each time:
105 |   dcsam.update();
106 |   estimate = dcsam.calculateEstimate();
107 |   std::cout << "pose out 2: " << std::endl;
108 |   estimate.continuous.at<gtsam::Pose3>(poseKey[0]).print();
109 |   std::ofstream file2("out2.txt");
110 |   file2 << estimate.continuous.at<gtsam::Pose3>(poseKey[0]).matrix();
111 | 
112 |   dcsam.update();
113 |   estimate = dcsam.calculateEstimate();
114 |   std::cout << "pose out 3: " << std::endl;
115 |   estimate.continuous.at<gtsam::Pose3>(poseKey[0]).print();
116 |   std::ofstream file3("out3.txt");
117 |   file3 << estimate.continuous.at<gtsam::Pose3>(poseKey[0]).matrix();
118 | 
119 |   dcsam.update();
120 |   estimate = dcsam.calculateEstimate();
121 |   std::cout << "pose out 4: " << std::endl;
122 |   estimate.continuous.at<gtsam::Pose3>(poseKey[0]).print();
123 |   std::ofstream file4("out4.txt");
124 |   file4 << estimate.continuous.at<gtsam::Pose3>(poseKey[0]).matrix();
125 | 
126 |   dcsam.update();
127 |   estimate = dcsam.calculateEstimate();
128 |   std::cout << "pose out 5: " << std::endl;
129 |   estimate.continuous.at<gtsam::Pose3>(poseKey[0]).print();
130 |   std::ofstream file5("out5.txt");
131 |   file5 << estimate.continuous.at<gtsam::Pose3>(poseKey[0]).matrix();
132 | }
133 | 


--------------------------------------------------------------------------------
/include/pgo_utils.h:
--------------------------------------------------------------------------------
  1 | #pragma once
  2 | 
  3 | #include <dcsam/DCMixtureFactor.h>
  4 | #include <dcsam/DCSAM.h>
  5 | #include <dcsam/DiscretePriorFactor.h>
  6 | #include <dcsam/HybridFactorGraph.h>
  7 | #include <gtsam/geometry/Pose3.h>
  8 | #include <gtsam/geometry/Pose2.h>
  9 | #include <gtsam/nonlinear/GncOptimizer.h>
 10 | #include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
 11 | #include <gtsam/nonlinear/NonlinearFactorGraph.h>
 12 | #include <gtsam/slam/dataset.h>
 13 | 
 14 | #include <chrono>
 15 | #include <iostream>
 16 | #include <random>
 17 | 
 18 | // Generate incorrect loop closures
 19 | 
 20 | /**
 21 |  * A factor is considered a loop closure if it is between two non-consecutive
 22 |  * poses.
 23 |  */
 24 | bool isLoopClosure(const gtsam::NonlinearFactor& factor) {
 25 |   // Convert key indices to int to avoid wraparound issues
 26 |   gtsam::KeyVector keys = factor.keys();
 27 |   if (keys.size() != 2) {
 28 |     return false;
 29 |   }
 30 |   int i = static_cast<int>(gtsam::Symbol(keys[0]).index());
 31 |   int j = static_cast<int>(gtsam::Symbol(keys[1]).index());
 32 |   return (abs(i - j) > 1);
 33 | }
 34 | 
 35 | gtsam::NonlinearFactorGraph generateRandomLoopClosures(
 36 |     const gtsam::KeyVector& keys, size_t num,
 37 |     const gtsam::SharedNoiseModel& inlier_model, bool is3D = true,
 38 |     size_t random_seed = 7) {
 39 |   // Set up random generator for selecting indices
 40 |   // We fix the random seed at 7 (selected arbitrarily) so that results can be
 41 |   // reproduced in a deterministic manner, but any random seed will be fine :)
 42 |   std::mt19937 rng(random_seed);
 43 | 
 44 |   // Set up random generator for poses (translation + rotation)
 45 |   std::uniform_real_distribution<double> t_distribution(-5.0, 5.0);
 46 |   std::uniform_real_distribution<double> u_distribution(0.0, 1.0);
 47 |   std::uniform_real_distribution<double> R_distribution(0.0, 6.28);
 48 | 
 49 |   gtsam::NonlinearFactorGraph graph;
 50 |   for (size_t k = 0; k < num; k++) {
 51 |     // Randomly select i
 52 |     std::uniform_int_distribution<int> uniform(0, keys.size());
 53 | 
 54 |     auto i = uniform(rng);
 55 | 
 56 |     // Randomly select j
 57 |     auto j = uniform(rng);
 58 | 
 59 |     // Ensure the edge (i,j) is a loop closure.
 60 |     while (abs(j - i) <= 1) {
 61 |       j = uniform(rng);
 62 |     }
 63 | 
 64 |     /// Generate the measurement
 65 |     // Translation part
 66 |     double dx = t_distribution(rng);
 67 |     double dy = t_distribution(rng);
 68 |     double dz = t_distribution(rng);
 69 | 
 70 |     // Quaternion random sampling http://planning.cs.uiuc.edu/node198.html
 71 |     double u1 = u_distribution(rng);
 72 |     double u2 = u_distribution(rng);
 73 |     double u3 = u_distribution(rng);
 74 | 
 75 |     double qx = sqrt(1.0 - u1) * sin(2.0 * M_PI * u2);
 76 |     double qy = sqrt(1.0 - u1) * cos(2.0 * M_PI * u2);
 77 |     double qz = sqrt(u1) * sin(2.0 * M_PI * u3);
 78 |     double qw = sqrt(u1) * cos(2.0 * M_PI * u3);
 79 | 
 80 |     if (is3D) {
 81 |       gtsam::Point3 t(dx, dy, dz);
 82 |       gtsam::Rot3 R(
 83 |           Eigen::Quaternion<double>(qw, qx, qy, qz).toRotationMatrix());
 84 | 
 85 |       gtsam::Pose3 meas(R, t);
 86 |       // gtsam::noiseModel::Diagonal::shared_ptr noise =
 87 |       //     gtsam::noiseModel::Diagonal::Sigmas(sigmas);
 88 | 
 89 |       gtsam::BetweenFactor<gtsam::Pose3> lc(keys[i], keys[j], meas,
 90 |                                             inlier_model);
 91 | 
 92 |       graph.add(lc);
 93 |     } else {
 94 |       gtsam::Point2 t(dx, dy);
 95 |       // Sample rotation from -pi to pi
 96 |       gtsam::Rot2 R(M_PI * (2.0 * u1 - 1.0));
 97 | 
 98 |       gtsam::Pose2 meas(R, t);
 99 | 
100 |       gtsam::BetweenFactor<gtsam::Pose2> lc(keys[i], keys[j], meas,
101 |                                             inlier_model);
102 |       graph.add(lc);
103 |     }
104 |   }
105 | 
106 |   return graph;
107 | }
108 | 
109 | double hybrid_error(const dcsam::HybridFactorGraph& hfg,
110 |                     const dcsam::DCValues& values) {
111 |   double dcError = 0.0;
112 |   for (auto dcfactor : hfg.dcGraph()) {
113 |     dcError += dcfactor->error(values.continuous, values.discrete);
114 |   }
115 |   double discreteError = 0.0;
116 |   for (auto dfactor : hfg.discreteGraph()) {
117 |     discreteError -= log(dfactor->operator()(values.discrete));
118 |   }
119 |   return log(hfg.nonlinearGraph().error(values.continuous)) + dcError +
120 |          discreteError;
121 | }
122 | 
123 | void writeDiscreteMarginals(const gtsam::KeySet& discreteKeys,
124 |                             const gtsam::DiscreteMarginals& discreteMarg,
125 |                             const std::string& fname) {
126 |   std::ofstream file(fname);
127 |   for (const gtsam::Key& key : discreteKeys) {
128 |     // Construct a discrete key from gtsam::Key plus cardinality (2 for
129 |     // inlier/outlier)
130 |     gtsam::DiscreteKey dk(key, 2);
131 |     // Recover probabilities from the marginal conditioned on continuous
132 |     // states.
133 |     gtsam::Vector probabilities = discreteMarg.marginalProbabilities(dk);
134 | 
135 |     // We have probabilities[0] = p(inlier | C, meas) and probabilities[1] =
136 |     // p(outlier | . )
137 |     double outlier_prob = probabilities[1];
138 |     file << outlier_prob << std::endl;
139 |   }
140 | }
141 | 


--------------------------------------------------------------------------------
/data/pointclouds/bunny_sparse100_part1.xyz:
--------------------------------------------------------------------------------
  1 | 0.6500 -4.5800 3.8600
  2 | -1.3400 -0.8600 12.8900
  3 | -3.8600 -4.6300 4.2400
  4 | -2.2500 -0.6100 12.6200
  5 | -3.5800 -4.4400 4.5100
  6 | -3.9800 -4.4400 4.6500
  7 | -4.1900 -4.3500 4.7800
  8 | -4.4000 -4.2500 4.9100
  9 | -3.6400 -2.5300 12.5400
 10 | 2.4400 -2.5800 12.2900
 11 | -6.2000 -4.3500 14.9700
 12 | 4.4200 -2.8700 5.2800
 13 | -2.8400 -0.8800 12.5400
 14 | -1.4500 -3.4600 12.0900
 15 | -1.1000 -4.0600 11.4100
 16 | -1.9700 -3.2500 12.0800
 17 | 0.6400 -5.6300 6.2000
 18 | -0.6600 -5.6000 6.3300
 19 | -2.5100 -4.0000 6.3100
 20 | -3.7700 -4.4800 6.4800
 21 | 0.9200 -2.0200 13.0700
 22 | 4.4800 -2.3700 6.5100
 23 | -9.1800 -2.1300 12.6900
 24 | 1.9600 -3.4700 11.6800
 25 | 0.8400 -5.5500 7.1700
 26 | 0.1600 -5.8700 7.3300
 27 | -2.4900 -4.3100 7.3200
 28 | -4.8600 -4.6700 7.4800
 29 | 4.1600 -1.0600 10.5600
 30 | -7.3700 -5.8900 12.4600
 31 | 1.4300 -5.4400 7.8600
 32 | -0.6400 -5.8600 8.0100
 33 | -3.2600 -4.6200 8.0500
 34 | -5.3400 -4.9000 8.1900
 35 | -6.1600 -3.5000 15.4700
 36 | -2.4700 -3.3500 11.8000
 37 | 1.2400 -5.5400 8.5600
 38 | -0.6500 -5.7600 8.7000
 39 | -2.6500 -4.6300 8.7500
 40 | -4.6400 -4.9500 8.9000
 41 | -0.1700 -4.6300 10.0800
 42 | -6.6500 -4.9200 14.5600
 43 | 0.4400 -5.5500 9.2500
 44 | -2.7500 -4.7000 9.3200
 45 | -5.4400 -4.9800 9.4600
 46 | -0.0400 -5.2600 9.7800
 47 | 1.5800 -4.8400 9.6300
 48 | -1.1200 -5.0100 9.7700
 49 | -3.3700 -4.5500 9.8700
 50 | -5.2500 -4.7900 10.0200
 51 | -0.6700 -0.9000 13.0400
 52 | 0.7600 -3.5300 12.1000
 53 | 0.1300 -4.4000 10.3100
 54 | -2.5800 -4.3800 10.4300
 55 | -4.8800 -4.4700 10.5700
 56 | -6.9000 -4.3600 10.6900
 57 | 4.4000 -0.6400 10.0100
 58 | 1.3000 -3.9300 10.8500
 59 | -0.0700 -4.3100 11.0000
 60 | -0.5800 -4.2900 11.1400
 61 | -4.5900 -1.9700 13.5100
 62 | -1.5300 -5.4800 3.9000
 63 | -0.1000 -4.8600 4.1000
 64 | 2.1900 -4.1900 4.3600
 65 | -6.2200 -4.8600 13.8300
 66 | 5.2600 -1.7800 4.6600
 67 | -4.3700 -1.1400 15.5100
 68 | -6.3000 -4.9200 14.1100
 69 | -3.7900 -0.4200 16.0900
 70 | 1.7200 -4.8800 5.3500
 71 | 2.3800 -4.5800 5.5000
 72 | 2.9600 -4.0300 5.6400
 73 | 3.9400 -3.0200 5.7700
 74 | 4.0700 -2.8600 5.9000
 75 | 4.2800 -2.6400 6.0300
 76 | -5.8300 -4.7000 12.8300
 77 | 5.5800 -2.3400 6.3500
 78 | 6.0200 -1.7700 6.4800
 79 | 1.8800 -4.7900 9.5300
 80 | 3.7900 -3.4900 9.5500
 81 | -5.4600 -3.7400 14.5300
 82 | 1.7100 -3.3300 12.0600
 83 | 3.2900 -3.9800 9.5600
 84 | 1.8800 -5.2400 7.4000
 85 | 2.1600 -5.1100 7.5400
 86 | 2.2700 -5.0900 7.6800
 87 | 2.0100 -5.2400 7.8100
 88 | 1.5700 -5.3500 7.9300
 89 | -6.4600 -5.8200 11.7200
 90 | -5.3900 -4.2800 11.6900
 91 | -5.2400 -4.3100 12.6900
 92 | -5.1800 -4.2300 12.6900
 93 | -4.2800 -0.9300 15.9400
 94 | -4.7800 -3.7700 12.5500
 95 | -4.4700 -3.0500 12.8200
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 279 | -1.6623 2.0730 12.4825
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 293 | 0.9627 2.0117 15.8075
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 300 | -6.9823 1.9753 17.4875
 301 | 6.3758 1.9188 6.0425
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 320 | 4.6279 1.8138 8.7725
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 323 | 1.2764 1.8138 15.5975
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 329 | 0.4027 1.7787 15.1505
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 367 | -3.1290 1.5461 12.0194
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 379 | -5.3040 1.4640 14.2717
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 381 | -6.9123 1.4288 5.6932
 382 | 2.0839 1.4354 9.9429
 383 | -3.3980 1.4283 11.6198
 384 | 8.7677 1.4288 13.2432
 385 | -0.0523 1.4288 14.6541
 386 | -6.5973 1.4288 17.8479
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 388 | 2.5557 1.4011 10.7351
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 391 | 2.0127 1.4120 15.5975
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 395 | 1.8727 1.3588 12.2912
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 400 | 7.3327 1.3450 10.8375
 401 | 6.2827 1.3358 12.6575
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 414 | 8.4877 1.2650 14.5125
 415 | -2.3273 1.2538 16.5285
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 419 | 9.0477 1.2442 13.3575
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 450 | 7.6127 1.0690 12.5175
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 512 | 7.2627 0.7614 14.3375
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 541 | -1.9073 0.6069 15.5275
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 563 | -9.2795 0.4837 15.1075
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 570 | -8.6049 0.4647 15.0603
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 577 | -5.5473 0.4138 14.5251
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 580 | 1.9077 0.3826 6.0775
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 585 | -5.5439 0.3976 15.6258
 586 | -4.5652 0.3788 17.7675
 587 | 2.0127 0.3440 5.8675
 588 | 4.5941 0.3339 7.5482
 589 | 0.3326 0.3438 10.0675
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 600 | 3.1677 0.3209 14.5825
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 615 | 1.4527 0.2390 13.6025
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 617 | -7.7202 0.2479 16.5600
 618 | -6.8773 0.2038 5.4178
 619 | 5.8627 0.2043 7.3025
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 621 | -4.3573 0.2138 11.2925
 622 | 4.1127 0.2068 13.2525
 623 | -6.7723 0.2038 14.3267
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 626 | 3.2936 0.1748 5.6359
 627 | 4.7301 0.1516 7.6072
 628 | -8.5398 0.1688 9.7525
 629 | 4.7427 0.1749 11.9225
 630 | 4.3227 0.1775 13.1475
 631 | -10.2023 0.1872 14.5475
 632 | -9.3273 0.1688 16.7900
 633 | 3.4330 0.1580 5.4383
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 637 | -5.0573 0.1512 13.4975
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 639 | -3.0973 0.1338 16.8216
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1001 | 


--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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  89 | -2.7733 3.3892 8.0582
  90 | -0.8522 3.4525 9.9940
  91 | -0.4976 3.4630 12.1891
  92 | 1.4238 3.4736 15.0822
  93 | -3.4576 3.1621 18.5220
  94 | 4.6683 3.7492 7.7929
  95 | 2.6972 3.6085 9.7333
  96 | 1.4783 3.5070 12.6112
  97 | -3.3591 3.1846 15.2656
  98 | -3.5199 3.1273 18.6987
  99 | -1.4794 3.3863 8.0889
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 169 | -8.5780 2.5447 7.2363
 170 | -7.0333 2.5943 9.4249
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 183 | 2.2254 2.8848 16.0769
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 190 | 5.4964 3.1502 7.4909
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 194 | -7.8860 2.4105 6.7514
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 201 | 2.9296 2.8645 10.9731
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 330 | -6.8451 1.4892 17.3616
 331 | -1.3785 1.9497 6.5191
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 381 | -6.5747 1.3614 6.5509
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 402 | -7.2069 1.0750 15.3590
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 411 | 5.7160 1.7686 9.7322
 412 | -3.3464 1.2599 11.5676
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 414 | 9.1112 1.8592 14.8378
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 421 | -8.3643 0.8394 17.8472
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 479 | -5.5779 0.7196 15.2685
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 493 | -6.5893 0.5865 15.7843
 494 | -6.8497 0.5315 17.9290
 495 | 2.0783 1.1835 6.5602
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 497 | -3.3718 0.8025 12.2080
 498 | 9.7300 1.4443 13.8225
 499 | -1.2970 0.8414 15.5158
 500 | -9.0829 0.4090 17.9348
 501 | 4.0409 1.2721 6.0690
 502 | 5.2275 1.2695 8.8335
 503 | -3.8141 0.7447 11.4862
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 505 | -8.3642 0.4317 15.3532
 506 | 8.7735 1.3588 15.4713
 507 | -8.3401 0.3805 18.0761
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 509 | 5.1895 1.2324 8.8342
 510 | 5.7438 1.2233 10.8112
 511 | -5.7826 0.5627 13.7591
 512 | 7.9089 1.2946 14.6948
 513 | -0.7281 0.7896 15.7232
 514 | -0.6863 0.7596 17.8929
 515 | -1.0473 0.9160 6.4890
 516 | 2.6280 1.0733 9.6214
 517 | -1.4076 0.7895 12.0691
 518 | 8.4180 1.2876 14.1865
 519 | 8.5239 1.2611 15.1981
 520 | -6.1598 0.4403 17.1527
 521 | -2.7579 0.5994 18.7533
 522 | 6.4153 1.2766 7.2506
 523 | -8.0352 0.4326 10.7532
 524 | -6.4673 0.4699 13.0809
 525 | -2.2256 0.6550 15.0026
 526 | -7.8169 0.3823 15.8892
 527 | -0.9162 0.6814 17.1990
 528 | -2.5754 0.5770 18.9009
 529 | 7.4517 1.2790 7.1097
 530 | 2.8472 1.0149 9.7878
 531 | -3.7875 0.6018 11.6936
 532 | 3.7146 0.9729 13.5747
 533 | 8.8589 1.2130 14.6602
 534 | 3.1256 0.8963 15.7306
 535 | -8.0102 0.2644 18.3152
 536 | -4.2834 0.6414 6.4869
 537 | 2.7720 0.9525 9.5442
 538 | -1.5989 0.6980 11.5838
 539 | -2.4970 0.6189 13.9256
 540 | 9.8795 1.2233 14.7825
 541 | -1.1934 0.6342 16.1921
 542 | -7.5515 0.2686 18.4043
 543 | 3.8112 1.0113 6.1084
 544 | 5.2791 1.0695 8.4429
 545 | 6.9005 1.1132 10.7818
 546 | 2.2746 0.8430 13.3075
 547 | -8.9149 0.1937 15.2982
 548 | 3.7539 0.8704 15.6031
 549 | -8.3932 0.1643 18.2422
 550 | 3.7199 1.0018 6.0908
 551 | 5.7590 1.0710 7.8442
 552 | -2.1400 0.6204 10.7568
 553 | 1.6593 0.7641 12.6229
 554 | -4.4180 0.4344 14.9002
 555 | -7.5647 0.2202 15.7056
 556 | -7.9448 0.1818 17.0154
 557 | -4.3192 0.3341 18.6805
 558 | -0.6707 0.7166 6.9396
 559 | 4.6102 0.9429 9.5499
 560 | 1.2745 0.7268 11.6941
 561 | -5.0330 0.3497 14.1145
 562 | 9.6394 1.1148 14.7713
 563 | -8.5592 0.1282 16.0202
 564 | -8.3153 0.0958 18.3616
 565 | 2.4176 0.8583 6.1924
 566 | 2.0131 0.7857 8.0350
 567 | -2.2448 0.5265 10.5575
 568 | 1.7746 0.7298 12.8676
 569 | 1.4640 0.6392 14.9369
 570 | -7.8866 0.1458 15.9498
 571 | -6.8127 0.1413 18.2750
 572 | -1.4276 0.6214 6.1880
 573 | 4.9287 0.8751 7.7313
 574 | 4.7226 0.8806 10.0703
 575 | 4.0900 0.8313 12.0180
 576 | 2.7940 0.6970 13.9173
 577 | -4.8510 0.2662 15.3102
 578 | -1.2407 0.4504 16.5758
 579 | -5.9212 0.1553 18.7307
 580 | 2.2960 0.7770 6.6155
 581 | 5.0111 0.8499 7.9971
 582 | 7.0635 0.9423 10.7952
 583 | -0.3386 0.5316 13.3233
 584 | -6.0100 0.1741 15.1164
 585 | -4.8084 0.2310 16.4097
 586 | -3.7559 0.2266 18.5162
 587 | 2.3955 0.7477 6.4014
 588 | 5.0310 0.8449 7.9930
 589 | 0.8653 0.5853 10.6552
 590 | -6.6099 0.1502 12.9077
 591 | 0.5704 0.5312 14.2723
 592 | -9.1055 -0.0118 15.5815
 593 | -8.5851 -0.0405 17.4338
 594 | -2.8646 0.2294 18.9755
 595 | 4.2347 0.8062 6.7543
 596 | 0.2150 0.5556 8.6108
 597 | 4.8189 0.7574 10.6496
 598 | -6.1398 0.1323 13.2971
 599 | 1.4845 0.5068 14.5902
 600 | 3.8536 0.6337 15.0701
 601 | -6.7049 0.0106 17.3533
 602 | -2.3373 0.2209 19.0347
 603 | 4.9667 0.8210 7.3192
 604 | -8.2024 0.0585 9.7017
 605 | 4.7536 0.7194 10.7319
 606 | -0.3749 0.4189 13.1124
 607 | 2.8507 0.5790 14.3686
 608 | 3.5677 0.6059 14.8694
 609 | -4.7418 0.0917 16.3301
 610 | -2.6319 0.1955 18.5815
 611 | -1.1634 0.4589 6.4856
 612 | 0.2045 0.5179 8.6455
 613 | 4.3342 0.6612 10.6844
 614 | -0.5651 0.3900 12.6282
 615 | 2.1120 0.4783 14.1475
 616 | -2.1838 0.2304 15.3140
 617 | -6.9400 -0.0500 17.4141
 618 | -6.4853 0.1449 6.2510
 619 | 6.2953 0.7870 7.7019
 620 | 8.5209 0.8714 9.3421
 621 | -3.7658 0.1858 12.0359
 622 | 4.7563 0.5930 13.7068
 623 | -6.0696 -0.0049 15.1495
 624 | -5.3496 0.0296 16.2856
 625 | -2.9850 0.1136 18.3822
 626 | 3.6746 0.6506 6.1232
 627 | 5.1783 0.6691 8.0438
 628 | -7.9914 -0.0536 10.6381
 629 | 5.3403 0.6177 12.3559
 630 | 4.9637 0.5767 13.5942
 631 | -9.4842 -0.2069 15.4862
 632 | -8.5317 -0.2181 17.6970
 633 | 3.8077 0.6446 5.9207
 634 | 4.6650 0.6425 7.4669
 635 | 8.4360 0.8043 8.9585
 636 | 7.3090 0.6945 11.5877
 637 | -4.3841 0.0478 14.2617
 638 | -8.0860 -0.1932 15.8012
 639 | -2.3111 0.0762 17.5165
 640 | -2.9465 0.2262 6.1935
 641 | 2.1225 0.4718 7.2242
 642 | 7.8654 0.7798 8.4876
 643 | 8.5438 0.7262 11.4753
 644 | -9.6317 -0.2964 15.1327
 645 | -5.0922 -0.0822 16.1930
 646 | -3.0886 -0.0134 18.2315
 647 | 7.3273 0.7331 5.9965
 648 | 4.7883 0.6092 7.6447
 649 | 7.0693 0.6496 9.6238
 650 | 0.0121 0.2510 12.8858
 651 | -7.9465 -0.2161 15.7996
 652 | -6.0832 -0.1693 17.1527
 653 | -5.8152 0.0067 6.1469
 654 | 4.3387 0.5323 6.8877
 655 | 7.7318 0.6707 8.5146
 656 | 7.2294 0.5952 11.0635
 657 | -9.5939 -0.3641 15.1082
 658 | -4.4588 -0.1437 16.4637
 659 | -3.2174 -0.0913 18.2817
 660 | 7.3328 0.6650 6.0523
 661 | -0.1509 0.2523 8.0929
 662 | -2.0980 0.0837 10.4347
 663 | -3.8517 -0.0642 14.2762
 664 | -8.8683 -0.3944 16.2929
 665 | -3.7784 -0.1570 18.3500
 666 | -0.9298 0.1794 6.8228
 667 | 8.2804 0.6389 8.0153
 668 | -7.8120 -0.2607 10.9505
 669 | -5.3435 -0.1475 13.5913
 670 | -9.0290 -0.4181 16.1672
 671 | -3.3291 -0.1645 18.1442
 672 | -1.5750 0.1220 6.2319
 673 | 1.9358 0.2728 7.9503
 674 | -1.5566 0.0360 10.7652
 675 | -2.9932 -0.1313 14.8265
 676 | -5.5680 -0.2447 16.1194
 677 | -8.5990 -0.4771 18.2177
 678 | -3.3770 -0.0059 6.2658
 679 | 8.2340 0.5719 7.7355
 680 | -3.9804 -0.1195 10.4052
 681 | -5.4490 -0.2285 13.4535
 682 | -5.4950 -0.3079 16.0658
 683 | -8.7697 -0.5258 18.4680
 684 | -3.4779 -0.0486 6.2951
 685 | -0.4304 0.0813 7.8194
 686 | 7.0614 0.4287 10.1558
 687 | -5.5918 -0.2688 13.3176
 688 | -8.9870 -0.5285 16.2002
 689 | -8.7660 -0.5617 18.5211
 690 | 1.1960 0.1727 6.2739
 691 | 7.2830 0.4401 8.3619
 692 | 0.2028 0.0197 10.9136
 693 | -8.2916 -0.5047 15.0284
 694 | -3.9415 -0.2895 16.7985
 695 | 3.1049 0.2345 5.8369
 696 | 1.7479 0.1551 6.9007
 697 | 0.0242 0.0109 9.0633
 698 | -0.3008 -0.0342 11.4907
 699 | -7.6959 -0.5275 16.0110
 700 | -8.4953 -0.6091 18.0717
 701 | 1.2488 0.0943 6.2549
 702 | 6.4749 0.3503 8.1784
 703 | -3.9410 -0.2560 10.3196
 704 | -4.0048 -0.2928 13.5420
 705 | -3.4588 -0.3424 16.1141
 706 | -7.5885 -0.6151 19.0338
 707 | 2.0167 0.0777 6.8059
 708 | 7.0581 0.3467 8.8235
 709 | -7.1000 -0.4877 11.9376
 710 | -9.0167 -0.6691 16.1387
 711 | -4.8394 -0.4933 18.3663
 712 | 2.0177 0.0798 6.6540
 713 | 7.0160 0.3012 8.5440
 714 | 7.5127 0.2552 11.2929
 715 | -5.7158 -0.5242 15.9629
 716 | -6.6732 -0.6192 18.0971
 717 | 4.7110 0.1689 6.4359
 718 | 3.7041 0.0707 8.6906
 719 | 7.5621 0.2226 11.2906
 720 | -9.0137 -0.7096 16.1616
 721 | -4.6715 -0.5530 18.2812
 722 | 4.7146 0.1514 6.4597
 723 | 6.5725 0.1824 8.8726
 724 | -6.4443 -0.5434 12.5720
 725 | -8.8295 -0.7474 16.3646
 726 | -2.5077 -0.2735 6.0325
 727 | -0.5109 -0.2090 8.1324
 728 | -1.1705 -0.2516 10.3264
 729 | -5.2937 -0.5996 15.3861
 730 | -8.3460 -0.8122 18.1067
 731 | -1.5446 -0.2431 6.5567
 732 | 3.4729 -0.0527 9.0118
 733 | 4.3295 -0.1056 12.6120
 734 | -8.8218 -0.8401 16.4461
 735 | -8.5273 -0.8714 18.8691
 736 | 2.8326 -0.0772 6.7220
 737 | -0.4859 -0.3093 9.7426
 738 | 4.3575 -0.1136 12.8303
 739 | -4.0029 -0.6189 17.2141
 740 | 6.8590 0.1445 5.7446
 741 | -7.7894 -0.6886 7.8529
 742 | -1.9828 -0.3967 9.9319
 743 | -4.0295 -0.6388 15.1644
 744 | -4.0339 -0.6742 17.2492
 745 | -6.3358 -0.6205 6.3599
 746 | -0.5509 -0.3591 8.5165
 747 | 8.8124 0.0929 10.5127
 748 | -9.2719 -0.9167 15.4664
 749 | -4.6798 -0.7527 17.7257
 750 | 4.2175 -0.0495 6.0922
 751 | 3.9429 -0.1598 8.5732
 752 | 7.5721 -0.0093 11.0060
 753 | -4.4642 -0.7403 15.8356
 754 | -7.2757 -0.9412 18.6187
 755 | 4.7211 -0.1394 6.7569
 756 | -1.0713 -0.4811 9.7917
 757 | -4.2231 -0.7531 15.0757
 758 | -5.4419 -0.8457 16.8253
 759 | -1.9589 -0.4963 6.3529
 760 | 3.9963 -0.2284 8.6387
 761 | -6.7307 -0.8333 11.9465
 762 | -9.2822 -1.0653 15.9637
 763 | -7.3241 -1.0139 18.6191
 764 | -0.9568 -0.4691 6.9876
 765 | -0.8869 -0.5448 9.9578
 766 | -4.6684 -0.8566 15.0139
 767 | -9.3601 -1.1116 16.6920
 768 | -1.3919 -0.5293 6.4763
 769 | 8.5950 -0.0459 8.0291
 770 | 8.2840 -0.1185 10.9442
 771 | -6.1936 -0.9509 15.1832
 772 | -5.1669 -0.9501 17.5305
 773 | 2.8996 -0.3514 6.5868
 774 | -1.5690 -0.6344 9.3529
 775 | 4.6527 -0.3665 12.2576
 776 | -9.0422 -1.1715 16.6665
 777 | -5.5902 -0.8259 6.3025
 778 | -0.7036 -0.5780 7.0469
 779 | -3.1628 -0.7410 9.7569
 780 | -4.8314 -0.9548 15.0100
 781 | -4.7237 -0.9696 17.1280
 782 | 3.3198 -0.3923 6.2091
 783 | 2.2020 -0.4674 7.0678
 784 | -7.3534 -1.0396 10.8438
 785 | -4.3695 -0.9559 15.1894
 786 | -5.3373 -1.0641 17.5331
 787 | -1.2121 -0.6405 6.4331
 788 | 6.1392 -0.3231 8.4586
 789 | 8.6863 -0.2278 10.4379
 790 | -6.1863 -1.0981 15.1673
 791 | -7.3976 -1.2072 17.5338
 792 | -1.2856 -0.6878 6.2597
 793 | 4.5847 -0.4325 8.0941
 794 | 4.4960 -0.4446 10.9518
 795 | -6.5009 -1.1614 15.7654
 796 | 4.5436 -0.4250 5.7805
 797 | 4.7667 -0.4241 6.9177
 798 | -7.6107 -1.1413 9.9053
 799 | -4.9853 -1.1035 15.0797
 800 | -7.8059 -1.3029 17.7547
 801 | 1.3441 -0.6435 6.7212
 802 | -2.0589 -0.8484 9.0510
 803 | 4.0929 -0.5999 11.0351
 804 | -7.1301 -1.2557 16.0234
 805 | 5.7030 -0.4369 5.9068
 806 | -1.6900 -0.8649 8.0220
 807 | -1.1700 -0.8640 9.3843
 808 | -5.9719 -1.2255 15.0871
 809 | -7.8399 -1.3733 17.6751
 810 | -0.7118 -0.8068 6.4477
 811 | 4.1972 -0.5989 8.0250
 812 | -7.2149 -1.2136 10.5207
 813 | -5.5697 -1.2440 15.5416
 814 | -6.1254 -1.2858 17.6969
 815 | 1.1672 -0.7652 6.9783
 816 | -2.2431 -0.9705 8.6000
 817 | 8.4218 -0.4527 10.4782
 818 | -8.2199 -1.4333 16.1610
 819 | 6.9624 -0.4693 5.7653
 820 | 2.8948 -0.7037 6.9595
 821 | -1.5080 -0.9382 8.6099
 822 | 7.4712 -0.5344 10.3139
 823 | -7.6693 -1.4435 15.8540
 824 | -1.8767 -0.9752 5.9617
 825 | 1.8307 -0.8005 6.9720
 826 | 2.9155 -0.7818 8.9485
 827 | 4.8283 -0.7363 11.7897
 828 | -9.3327 -1.5744 16.7773
 829 | -2.5506 -1.0541 6.4026
 830 | 4.3818 -0.7210 7.9055
 831 | 4.0656 -0.7398 9.3970
 832 | 4.1522 -0.7650 11.3574
 833 | -8.4915 -1.5562 16.3037
 834 | 0.7650 -0.9036 6.2100
 835 | -7.2600 -1.3424 7.9631
 836 | 4.1797 -0.8025 9.4684
 837 | 4.1054 -0.8146 11.0303
 838 | -9.9912 -1.6782 16.7392
 839 | -4.2994 -1.2161 6.5309
 840 | -7.2209 -1.3747 8.0312
 841 | 4.3982 -0.8220 9.6501
 842 | -5.8495 -1.4569 15.2324
 843 | -5.7102 -1.4828 17.2236
 844 | -4.0144 -1.2402 6.6238
 845 | -7.1672 -1.4378 8.3084
 846 | 4.5062 -0.8617 9.7169
 847 | -5.9849 -1.4773 15.3418
 848 | -7.0087 -1.5833 18.0728
 849 | 2.6758 -0.9295 6.8707
 850 | 8.7778 -0.6385 8.3551
 851 | 4.1579 -0.9351 10.8963
 852 | -7.0691 -1.6203 17.2686
 853 | -0.5392 -1.1266 6.5412
 854 | 5.3404 -0.8470 8.0134
 855 | 4.7072 -0.9346 9.8843
 856 | -7.3264 -1.6288 15.8764
 857 | 2.8646 -0.9761 6.1643
 858 | 4.4942 -0.9035 7.1627
 859 | -6.9599 -1.5502 9.2449
 860 | 5.1603 -0.9556 10.3469
 861 | -7.8163 -1.7261 17.8183
 862 | -3.7777 -1.3752 7.0002
 863 | 4.7534 -0.9437 8.3151
 864 | 4.8315 -0.9653 9.8625
 865 | -7.9126 -1.7701 16.1143
 866 | 2.2085 -1.0548 6.3414
 867 | -6.7173 -1.5807 8.0801
 868 | -3.2429 -1.4285 9.3975
 869 | 5.1938 -1.0101 10.3306
 870 | -6.5190 -1.7613 17.7574
 871 | 7.8617 -0.8280 6.6033
 872 | 4.8161 -1.0108 7.8092
 873 | 7.1307 -0.9221 9.4993
 874 | -9.1771 -1.8958 15.6912
 875 | -4.9474 -1.5335 6.5757
 876 | -6.3459 -1.6267 7.6109
 877 | -6.5071 -1.6596 8.9820
 878 | 4.0862 -1.1405 10.6768
 879 | -8.0716 -1.9224 18.2553
 880 | -3.5525 -1.5068 7.1652
 881 | 3.0867 -1.1777 8.1300
 882 | -4.2646 -1.6007 10.0956
 883 | -6.5194 -1.8579 17.5956
 884 | 3.8434 -1.1252 6.5253
 885 | 4.5780 -1.1558 7.9085
 886 | 5.0004 -1.1438 9.5400
 887 | -8.0743 -1.9505 16.0708
 888 | -2.9809 -1.5370 6.6463
 889 | 3.4947 -1.2203 7.8119
 890 | -5.8162 -1.7132 9.6225
 891 | -9.0619 -1.9999 15.8255
 892 | -3.0133 -1.5742 6.6668
 893 | -1.4346 -1.5021 7.8269
 894 | 8.6741 -0.9895 8.8838
 895 | -8.1692 -2.0097 15.2481
 896 | 7.6078 -1.0150 6.3637
 897 | 4.7495 -1.1996 7.6162
 898 | -3.5148 -1.6904 9.5075
 899 | -9.0726 -2.0986 15.5789
 900 | 3.8556 -1.2874 6.8626
 901 | -6.2405 -1.8496 8.5491
 902 | 5.3029 -1.2853 10.6427
 903 | 7.2928 -1.1304 6.1994
 904 | -6.1223 -1.8797 8.6123
 905 | 5.3372 -1.2908 9.9159
 906 | -5.9518 -1.8720 6.8573
 907 | 5.6443 -1.2868 8.0127
 908 | 7.4087 -1.2104 9.4846
 909 | 7.0836 -1.2098 6.1120
 910 | 8.4422 -1.1699 7.7329
 911 | 8.1770 -1.1900 9.4231
 912 | 3.7242 -1.4195 6.0369
 913 | -0.7608 -1.6604 7.8006
 914 | -5.2600 -1.9581 9.5637
 915 | -0.9257 -1.6971 5.9885
 916 | 7.9170 -1.2492 6.8392
 917 | -5.4530 -1.9502 8.5899
 918 | -4.7761 -1.9730 9.8969
 919 | 4.8225 -1.4048 6.3491
 920 | -6.2362 -2.0488 7.9850
 921 | -3.5352 -1.9299 9.3289
 922 | -4.7618 -1.9721 6.1925
 923 | -3.9193 -1.9490 7.2832
 924 | 8.4525 -1.2924 8.0807
 925 | 5.0984 -1.5468 10.9255
 926 | 1.2936 -1.6993 6.6773
 927 | -6.1680 -2.1056 7.9466
 928 | -4.8543 -2.0440 9.5481
 929 | -5.9043 -2.0750 6.4668
 930 | -0.8647 -1.8418 7.5559
 931 | 6.6709 -1.4896 8.5948
 932 | -8.7427 -2.4257 15.2662
 933 | -3.2956 -2.0153 6.9573
 934 | 5.9007 -1.5518 7.9201
 935 | 7.9665 -1.4316 9.0409
 936 | -6.0005 -2.1768 6.5735
 937 | -2.3261 -2.0124 7.6273
 938 | -3.6878 -2.0994 8.7008
 939 | -8.4869 -2.4828 15.4817
 940 | 2.4059 -1.7852 6.8806
 941 | 6.1537 -1.5883 8.0857
 942 | -7.8890 -2.4798 14.9355
 943 | 7.1823 -1.5606 6.4040
 944 | 8.1561 -1.5328 7.5965
 945 | -8.2936 -2.5383 15.0537
 946 | 7.3220 -1.5905 6.5037
 947 | -3.5866 -2.1990 8.4265
 948 | -4.6805 -2.2369 6.1060
 949 | 1.2914 -1.9483 6.8535
 950 | -5.0488 -2.2879 8.6049
 951 | 6.0642 -1.7049 6.3005
 952 | -5.8008 -2.3770 8.0069
 953 | 4.5840 -1.8795 10.9726
 954 | 1.2872 -2.0177 6.8121
 955 | -5.2273 -2.3827 8.3643
 956 | 3.6467 -1.9143 6.0413
 957 | -2.1611 -2.2444 7.3839
 958 | 5.1586 -1.9237 10.6366
 959 | -1.8754 -2.2579 7.0935
 960 | 3.7847 -1.9871 8.3717
 961 | 1.2930 -2.1192 6.6352
 962 | 6.5797 -1.8536 7.8210
 963 | -4.2401 -2.4390 6.3664
 964 | 6.0712 -1.9151 7.2096
 965 | 4.7639 -2.0535 10.8359
 966 | -2.9538 -2.4206 7.0962
 967 | -3.5043 -2.4738 8.3558
 968 | -1.4344 -2.3472 6.5161
 969 | 4.2664 -2.0537 7.5481
 970 | 1.6721 -2.2273 6.2696
 971 | 4.0524 -2.1606 9.3400
 972 | -3.8773 -2.5848 8.5588
 973 | 3.9315 -2.2131 7.7637
 974 | -5.8185 -2.7353 7.9585
 975 | -3.2342 -2.6585 7.7614
 976 | -3.3619 -2.6896 8.0836
 977 | 4.0908 -2.3509 8.1094
 978 | -3.6621 -2.7719 8.4079
 979 | 5.2571 -2.3545 9.3997
 980 | -3.9709 -2.8807 6.3759
 981 | 4.4315 -2.5182 8.5859
 982 | -5.8963 -3.0553 6.2043
 983 | -3.7099 -3.0045 7.6700
 984 | -4.3111 -3.1125 8.3861
 985 | -5.8349 -3.2494 7.7066
 986 | -3.9438 -3.2184 7.6043
 987 | -3.9904 -3.2392 6.2746
 988 | -4.2980 -3.3279 7.4046
 989 | -4.5121 -3.3979 8.2165
 990 | -4.5508 -3.4683 8.0010
 991 | -4.9866 -3.5443 6.4091
 992 | -4.8463 -3.6613 8.1185
 993 | -4.7784 -3.7229 7.0993
 994 | -5.7749 -3.9079 7.4811
 995 | -5.6000 -4.1520 7.8911
 996 | -1.6056 0.9585 15.4081
 997 | 4.0272 0.4909 6.4176
 998 | -1.3233 -0.6706 6.2088
 999 | 4.6087 -1.0100 8.3717
1000 | 4.9524 -1.5333 8.3707
1001 | 


--------------------------------------------------------------------------------
/examples/robust_pgo_mc.cpp:
--------------------------------------------------------------------------------
  1 | #include <dcsam/DCMixtureFactor.h>
  2 | #include <dcsam/DCSAM.h>
  3 | #include <dcsam/DiscretePriorFactor.h>
  4 | #include <dcsam/HybridFactorGraph.h>
  5 | #include <gtsam/geometry/Pose2.h>
  6 | #include <gtsam/geometry/Pose3.h>
  7 | #include <gtsam/nonlinear/GncOptimizer.h>
  8 | #include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
  9 | #include <gtsam/nonlinear/NonlinearFactorGraph.h>
 10 | #include <gtsam/slam/dataset.h>
 11 | 
 12 | #include <chrono>
 13 | #include <iostream>
 14 | #include <random>
 15 | 
 16 | #include "pgo_utils.h"
 17 | 
 18 | /**
 19 |  * graph - the uncorrupted factor graph
 20 |  * initial_values - the initial guess (usually odometry)
 21 |  * outlier_pct - fraction in [0,1] of outliers
 22 |  * random_seed - the random seed to use
 23 |  * is3D - true if the dataset is 3D
 24 |  * dataset_name - the name of the dataset
 25 |  */
 26 | void run_experiment3D(const gtsam::NonlinearFactorGraph& graph,
 27 |                       const gtsam::Values& initial_values, double outlier_pct,
 28 |                       size_t random_seed, std::string dataset_name) {
 29 |   /// 1. Build outlier graph.
 30 |   // Add prior on the pose having index (key) = 0
 31 |   gtsam::NonlinearFactorGraph graphWithOutliers = graph;
 32 | 
 33 |   gtsam::Vector6 prior_sigmas;
 34 |   prior_sigmas << 1e-8, 1e-8, 1e-8, 1e-6, 1e-6, 1e-6;
 35 |   gtsam::noiseModel::Diagonal::shared_ptr priorModel =
 36 |       gtsam::noiseModel::Diagonal::Sigmas(prior_sigmas);
 37 |   graphWithOutliers.add(
 38 |       gtsam::PriorFactor<gtsam::Pose3>(0, gtsam::Pose3(), priorModel));
 39 |   std::cout << "Adding prior on pose 0 " << std::endl;
 40 | 
 41 |   // Give a very coarse estimate for outlier sigmas. Derived from Olson and
 42 |   // Agarwal 2013 who recommend 10^7 for outlier variance, we conservatively
 43 |   // round up to 4000 for sigmas.
 44 |   gtsam::Vector6 outlier_sigmas;
 45 |   outlier_sigmas << 4000, 4000, 4000, 4000, 4000, 4000;
 46 |   gtsam::noiseModel::Diagonal::shared_ptr outlierModel =
 47 |       gtsam::noiseModel::Diagonal::Sigmas(outlier_sigmas);
 48 | 
 49 |   double outlier_prob = 0.5;
 50 |   std::vector<double> lc_probabilities{(1.0 - outlier_prob), outlier_prob};
 51 | 
 52 |   // We'll use this to save the inlier model so we can create the inlier
 53 |   // hypotheses.
 54 |   gtsam::SharedNoiseModel inlier_model;
 55 | 
 56 |   // Make a HybridFactorGraph to store the problem data.
 57 |   dcsam::HybridFactorGraph hfg;
 58 | 
 59 |   // Keep track of loop closure index.
 60 |   size_t k = 0;
 61 | 
 62 |   // Keep track of known inliers (odometry) for GNC.
 63 |   size_t known_inlier_idx = 0;
 64 |   gtsam::GncParams<gtsam::LevenbergMarquardtParams>::IndexVector knownInliers;
 65 | 
 66 |   // Add all good measurements.
 67 |   for (const auto& factor : graphWithOutliers) {
 68 |     // Ensure that we correctly retrieve a BetweenFactor
 69 |     boost::shared_ptr<gtsam::BetweenFactor<gtsam::Pose3>> bwFactor =
 70 |         boost::dynamic_pointer_cast<gtsam::BetweenFactor<gtsam::Pose3>>(factor);
 71 |     if (bwFactor && isLoopClosure(*bwFactor)) {
 72 |       // If the meaasurement is a loop closure, we add inlier and outlier
 73 |       // hypotheses.
 74 |       auto keys = bwFactor->keys();
 75 | 
 76 |       // We'll save the last inlier model to use for all our random
 77 |       // measuremenets.
 78 |       inlier_model = bwFactor->noiseModel();
 79 |       // std::cout << inlier_model->sigmas() << std::endl;
 80 | 
 81 |       gtsam::Vector6 outlier_sigmas = 4000.0 * inlier_model->sigmas();
 82 | 
 83 |       gtsam::noiseModel::Diagonal::shared_ptr outlierModel =
 84 |         gtsam::noiseModel::Diagonal::Sigmas(outlier_sigmas);
 85 | 
 86 |       // Build a vector of components factors (inlier model, outlier model)
 87 |       std::vector<gtsam::BetweenFactor<gtsam::Pose3>> components;
 88 |       components.push_back(*bwFactor);
 89 |       components.push_back(gtsam::BetweenFactor<gtsam::Pose3>(
 90 |           keys[0], keys[1], bwFactor->measured(), outlierModel));
 91 | 
 92 |       // Create a discrete key to index into components. Cardinality is 2
 93 |       // since a loop closure is either an inlier (0) or outlier (1).
 94 |       gtsam::DiscreteKey dk(gtsam::Symbol('d', k), 2);
 95 |       dcsam::DCMixtureFactor dcmf(bwFactor->keys(), dk, components, false);
 96 |       hfg.push_dc(dcmf);
 97 | 
 98 |       dcsam::DiscretePriorFactor dpf(dk, lc_probabilities);
 99 |       hfg.push_discrete(dpf);
100 |       k++;
101 | 
102 |     } else {
103 |       hfg.push_nonlinear(factor);
104 |       // if (bwFactor) {
105 |         knownInliers.push_back(known_inlier_idx);
106 |       // }
107 |     }
108 |     // Skip the prior factor when counting odometry measurements
109 |     // if (bwFactor) {
110 |       known_inlier_idx++;
111 |     // }
112 |   }
113 | 
114 |   /// 2. For each method: solve graph.
115 | 
116 |   size_t num_original_lc = k;
117 | 
118 |   std::cout << "Processed " << num_original_lc << " loop closures."
119 |             << std::endl;
120 | 
121 |   // We want num_outliers such that:
122 |   //    outlier_pct = num_outliers / (num_outliers + num_original)
123 |   //
124 |   // A little algebra reveals:
125 |   //    num_outliers = num_original_lc * (outlier_pct / (1 - outlier_pct))
126 |   //
127 |   // Cast is kosher since we know outlier_pct >= 0 and num_original_lc is
128 |   // of type size_t.
129 |   size_t num_outliers =
130 |       (size_t)((outlier_pct / (1.0 - outlier_pct)) * num_original_lc);
131 | 
132 |   gtsam::NonlinearFactorGraph outlierGraph = generateRandomLoopClosures(
133 |       graph.keyVector(), num_outliers, inlier_model, true, random_seed);
134 | 
135 |   // Add all outlier measurements.
136 |   for (const auto& factor : outlierGraph) {
137 |     // Ensure that we correctly retrieve a BetweenFactor
138 |     boost::shared_ptr<gtsam::BetweenFactor<gtsam::Pose3>> bwFactor =
139 |         boost::dynamic_pointer_cast<gtsam::BetweenFactor<gtsam::Pose3>>(factor);
140 |     if (bwFactor && isLoopClosure(*bwFactor)) {
141 |       // If the meaasurement is a loop closure, we add inlier and outlier
142 |       // hypotheses.
143 |       auto keys = bwFactor->keys();
144 | 
145 |       inlier_model = bwFactor->noiseModel();
146 | 
147 |       gtsam::Vector6 outlier_sigmas = 4000.0 * inlier_model->sigmas();
148 | 
149 |       gtsam::noiseModel::Diagonal::shared_ptr outlierModel =
150 |         gtsam::noiseModel::Diagonal::Sigmas(outlier_sigmas);
151 | 
152 |       // Build a vector of components factors (inlier model, outlier model)
153 |       std::vector<gtsam::BetweenFactor<gtsam::Pose3>> components;
154 |       components.push_back(*bwFactor);
155 |       components.push_back(gtsam::BetweenFactor<gtsam::Pose3>(
156 |           keys[0], keys[1], bwFactor->measured(), outlierModel));
157 | 
158 |       // Create a discrete key to index into components. Cardinality is 2
159 |       // since a loop closure is either an inlier (0) or outlier (1).
160 |       gtsam::DiscreteKey dk(gtsam::Symbol('d', k), 2);
161 |       dcsam::DCMixtureFactor dcmf(bwFactor->keys(), dk, components, false);
162 |       hfg.push_dc(dcmf);
163 | 
164 |       // Set up the discrete weights for each factor.
165 |       dcsam::DiscretePriorFactor dpf(dk, lc_probabilities);
166 |       hfg.push_discrete(dpf);
167 | 
168 |       // Add outlier factor to graph so we can visualize later.
169 |       graphWithOutliers.add(*bwFactor);
170 |       k++;
171 |     } else {
172 |       hfg.push_nonlinear(factor);
173 |     }
174 |   }
175 |   std::cout << "HFG size: " << hfg.size() << std::endl;
176 | 
177 |   // Currently DCSAM requires that we provide initial discrete values, but
178 |   // they aren't actually used since we perform the discrete solve first, so
179 |   // we just set them all to 0.
180 |   dcsam::DiscreteValues initial_discrete;
181 |   for (size_t i = 0; i < k; i++) {
182 |     initial_discrete[gtsam::Symbol('d', i)] = 0;
183 |   }
184 | 
185 |   // Create a solver instance and use it to optimize the graph.
186 |   dcsam::DCSAM dcsam;
187 | 
188 |   using std::chrono::duration;
189 |   using std::chrono::duration_cast;
190 |   using std::chrono::milliseconds;
191 | 
192 |   std::vector<double> dcsam_compute_times, dcsam_costs, dcsam_hybrid_costs;
193 |   auto t1 = std::chrono::high_resolution_clock::now();
194 |   dcsam.update(hfg, initial_values, initial_discrete);
195 |   auto t2 = std::chrono::high_resolution_clock::now();
196 |   // Get milliseconds as int
197 |   auto ms_int = std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1);
198 |   // Convert to double
199 |   double ms_double = static_cast<double>(ms_int.count());
200 |   // Get compute time in sec
201 |   dcsam_compute_times.push_back(ms_double / 1000.0);
202 | 
203 |   // Perform a few more iterations to converge.
204 |   dcsam::DCValues result;
205 |   std::vector<dcsam::DCValues> dc_results;
206 |   result = dcsam.calculateEstimate();
207 |   dc_results.push_back(result);
208 |   dcsam_costs.push_back(graph.error(result.continuous));
209 |   dcsam_hybrid_costs.push_back(hybrid_error(hfg, result));
210 |   gtsam::writeG2o(graphWithOutliers, result.continuous, "out_robust0.g2o");
211 |   for (size_t iter = 0; iter < 49; iter++) {
212 |     t1 = std::chrono::high_resolution_clock::now();
213 |     dcsam.update();
214 |     t2 = std::chrono::high_resolution_clock::now();
215 |     ms_int = std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1);
216 |     ms_double = static_cast<double>(ms_int.count());
217 |     dcsam_compute_times.push_back(ms_double / 1000.0);
218 | 
219 |     result = dcsam.calculateEstimate();
220 |     dc_results.push_back(result);
221 |     dcsam_costs.push_back(graph.error(result.continuous));
222 |     dcsam_hybrid_costs.push_back(hybrid_error(hfg, result));
223 | 
224 |     std::string fname = "out_robust" + std::to_string(iter + 1) + ".g2o";
225 |     gtsam::writeG2o(graphWithOutliers, result.continuous, fname);
226 |     // dcsam::DCMarginals marg =
227 |     //     dcsam.getMarginals(dcsam.getNonlinearFactorGraph(),
228 |     //     result.continuous,
229 |     //                        dcsam.getDiscreteFactorGraph());
230 |     // std::string marg_fname = "marg_robust" + std::to_string(iter + 1) +
231 |     // ".txt"; writeDiscreteMarginals(dcsam.getDiscreteFactorGraph().keys(),
232 |     // marg.discrete,
233 |     //                        marg_fname);
234 | 
235 |     if (dc_results[iter + 1].continuous.equals(dc_results[iter].continuous) &&
236 |         dc_results[iter + 1].discrete.equals(dc_results[iter].discrete)) {
237 |       // We have reached convergence
238 |       std::cout << "We have reached convergence!" << std::endl;
239 |       break;
240 |     }
241 |   }
242 | 
243 |   // Perform a few more iterations
244 |   // for (size_t j = 0; j < 10; j++) {
245 |   //   t1 = std::chrono::high_resolution_clock::now();
246 |   //   dcsam.update();
247 |   //   t2 = std::chrono::high_resolution_clock::now();
248 |   //   ms_int = std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1);
249 |   //   ms_double = static_cast<double>(ms_int.count());
250 |   //   dcsam_compute_times.push_back(ms_double / 1000.0);
251 |   //   result = dcsam.calculateEstimate();
252 |   //   dcsam_costs.push_back(graph.error(result.continuous));
253 |   //   dcsam_hybrid_costs.push_back(hybrid_error(hfg, result));
254 |   // }
255 |   // auto t2 = std::chrono::high_resolution_clock::now();
256 | 
257 |   // Get milliseconds as int
258 |   // auto ms_int = std::chrono::duration_cast<std::chrono::milliseconds>(t2 -
259 |   // t1);
260 | 
261 |   // Convert to double
262 |   // double ms_double = static_cast<double>(ms_int.count());
263 | 
264 |   // Total time in seconds:
265 |   // double total_time = ms_double / 1000.0;
266 | 
267 |   // std::cout << "Total time [DCSAM]: " << total_time << " seconds." <<
268 |   // std::endl; std::cout << "Avg. time/iter [DCSAM]: " << total_time / 7.0 << "
269 |   // seconds."
270 |   //           << std::endl;
271 |   assert(dcsam_costs.size() == dcsam_compute_times.size());
272 |   assert(dcsam_costs.size() == dcsam_hybrid_costs.size());
273 |   double total_time_dcsam = 0.0;
274 |   for (size_t idx = 0; idx < dcsam_costs.size(); idx++) {
275 |     total_time_dcsam += dcsam_compute_times[idx];
276 |     std::cout << "idx, time, cost, dcCost" << std::endl;
277 |     std::cout << idx << ", " << dcsam_compute_times[idx] << ", "
278 |               << dcsam_costs[idx] << ", " << dcsam_hybrid_costs[idx]
279 |               << std::endl;
280 |     std::cout << "total time: " << total_time_dcsam << std::endl;
281 |   }
282 | 
283 |   double total_time_lm, total_time_gnc;
284 | 
285 |   result = dcsam.calculateEstimate();
286 |   std::cout << "Initial cost: " << graph.error(initial_values) << std::endl;
287 |   std::cout << "Final cost [DCSAM]: " << graph.error(result.continuous)
288 |             << std::endl;
289 | 
290 |   // Make sure we add the prior factor to the outlier graph before passing to LM
291 |   // and GNC. This addresses gauge ambiguity.
292 |   gtsam::NonlinearFactorGraph graphWithOutliersAndPrior =
293 |       graphWithOutliers.clone();
294 | 
295 |   // graphWithOutliersAndPrior.add(
296 |   //     gtsam::PriorFactor<gtsam::Pose3>(0, gtsam::Pose3(), priorModel));
297 | 
298 |   // Solve the non-robust version of the graph
299 |   gtsam::LevenbergMarquardtOptimizer lm(graphWithOutliersAndPrior,
300 |                                         initial_values);
301 | 
302 |   t1 = std::chrono::high_resolution_clock::now();
303 |   gtsam::Values lm_result = lm.optimize();
304 |   t2 = std::chrono::high_resolution_clock::now();
305 | 
306 |   std::cout << "Final cost [LM]: " << graph.error(lm_result) << std::endl;
307 | 
308 |   // Get milliseconds as int
309 |   ms_int = std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1);
310 | 
311 |   // Convert to double
312 |   ms_double = static_cast<double>(ms_int.count());
313 |   total_time_lm = ms_double / 1000.0;
314 | 
315 |   std::cout << "Total time [LM]: " << total_time_lm << " seconds." << std::endl;
316 | 
317 |   /// Solve with GNC
318 |   gtsam::GncParams<gtsam::LevenbergMarquardtParams> gncParams;
319 | 
320 |   // Let GNC know that odometry measurements are inliers:
321 |   std::cout << "Known inliers size: " << knownInliers.size() << std::endl;
322 |   gncParams.knownInliers = knownInliers;
323 |   gncParams.lossType = gtsam::GncLossType::TLS;
324 |   // Collect summary stats from GNC
325 |   // gncParams.verbosity =
326 |   //     gtsam::GncParams<gtsam::LevenbergMarquardtParams>::Verbosity::SUMMARY;
327 |   gncParams.maxIterations = 50;
328 | 
329 |   // Make the optimizer and solve.
330 |   auto gnc =
331 |       gtsam::GncOptimizer<gtsam::GncParams<gtsam::LevenbergMarquardtParams>>(
332 |           graphWithOutliersAndPrior, initial_values, gncParams);
333 | 
334 |   std::cout << "Default inlier cost threshold = "
335 |             << gnc.getInlierCostThresholds()[0] << std::endl;
336 | 
337 |   // We found that setting the inlier cost threshold to 0.5 gave us the best
338 |   // results on the `parking-garage.g2o` dataset, and setting the to 4 gave the
339 |   // best results on `sphere2500.g2o`
340 |   // gnc.setInlierCostThresholds(4.0);
341 |   gnc.setInlierCostThresholds(0.5);
342 | 
343 |   t1 = std::chrono::high_resolution_clock::now();
344 |   gtsam::Values gnc_result = gnc.optimize();
345 |   t2 = std::chrono::high_resolution_clock::now();
346 | 
347 |   std::cout << "Final cost [GNC]: " << graph.error(gnc_result) << std::endl;
348 | 
349 |   // Get milliseconds as int
350 |   ms_int = std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1);
351 | 
352 |   // Convert to double
353 |   ms_double = static_cast<double>(ms_int.count());
354 |   total_time_gnc = ms_double / 1000.0;
355 | 
356 |   std::cout << "Total time [GNC]: " << total_time_gnc << " seconds."
357 |             << std::endl;
358 | 
359 |   std::cout << "OUTLIER PCT" << std::to_string((int)(100 * outlier_pct))
360 |             << std::endl;
361 |   std::string path_prefix = "../../output/robust_pgo_vanilla/" + dataset_name +
362 |                             "/" + std::to_string((int)(100 * outlier_pct)) +
363 |                             "/" + std::to_string(random_seed) + "/";
364 | 
365 |   std::cout << "path_prefix: " << path_prefix << std::endl;
366 | 
367 |   // use "writeG2o" to save file.
368 |   gtsam::writeG2o(graphWithOutliers, lm_result,
369 |                   path_prefix + "out_nonrobust.g2o");
370 |   gtsam::writeG2o(graphWithOutliers, result.continuous,
371 |                   path_prefix + "out_robust.g2o");
372 |   gtsam::writeG2o(graphWithOutliers, gnc_result, path_prefix + "out_gnc.g2o");
373 | 
374 |   // All loop closures _after_ this number should be visualized as outliers.
375 |   std::ofstream file(path_prefix + "orig_graph_size_out.txt");
376 |   file << graph.size();
377 | 
378 |   // Write timing info
379 |   std::ofstream time_file(path_prefix + "times.txt");
380 |   time_file << "DCSAM: " << std::to_string(total_time_dcsam) << "\n"
381 |             << "GNC: " << std::to_string(total_time_gnc) << "\n"
382 |             << "LM: " << total_time_lm << "\n";
383 | 
384 |   /// 3. Collect stats, final trajectory, and output to file.
385 |   return;
386 | }
387 | 
388 | /**
389 |  * graph - the uncorrupted factor graph
390 |  * initial_values - the initial guess (usually odometry)
391 |  * outlier_pct - fraction in [0,1] of outliers
392 |  * random_seed - the random seed to use
393 |  * dataset_name - the name of the dataset
394 |  */
395 | void run_experiment2D(const gtsam::NonlinearFactorGraph& graph,
396 |                       const gtsam::Values& initial_values, double outlier_pct,
397 |                       size_t random_seed, std::string dataset_name) {
398 |   /// 1. Build outlier graph.
399 |   // Add prior on the pose having index (key) = 0
400 |   gtsam::NonlinearFactorGraph graphWithOutliers = graph;
401 | 
402 |   gtsam::Vector3 prior_sigmas;
403 |   prior_sigmas << 1e-6, 1e-6, 1e-8;
404 |   gtsam::noiseModel::Diagonal::shared_ptr priorModel =
405 |       gtsam::noiseModel::Diagonal::Sigmas(prior_sigmas);
406 |   graphWithOutliers.add(
407 |       gtsam::PriorFactor<gtsam::Pose2>(0, gtsam::Pose2(), priorModel));
408 |   std::cout << "Adding prior on pose 0 " << std::endl;
409 | 
410 |   // Give a very coarse estimate for outlier sigmas. Derived from Olson and
411 |   // Agarwal 2013 who recommend 10^7 for outlier variance, we conservatively
412 |   // round up to 4000 for sigmas.
413 |   gtsam::Vector3 outlier_sigmas;
414 |   outlier_sigmas << 4000, 4000, 4000;
415 |   gtsam::noiseModel::Diagonal::shared_ptr outlierModel =
416 |       gtsam::noiseModel::Diagonal::Sigmas(outlier_sigmas);
417 | 
418 |   double outlier_prob = 0.5;
419 |   std::vector<double> lc_probabilities{(1.0 - outlier_prob), outlier_prob};
420 | 
421 |   // double outlier_prob = 0.5;
422 |   // std::vector<double> lc_probabilities{(1.0 - outlier_prob), outlier_prob};
423 | 
424 |   // We'll use this to save the inlier model so we can create the inlier
425 |   // hypotheses.
426 |   gtsam::SharedNoiseModel inlier_model;
427 | 
428 |   // Make a HybridFactorGraph to store the problem data.
429 |   dcsam::HybridFactorGraph hfg;
430 | 
431 |   // Keep track of loop closure index.
432 |   size_t k = 0;
433 | 
434 |   // Keep track of known inliers (odometry) for GNC.
435 |   size_t known_inlier_idx = 0;
436 |   gtsam::GncParams<gtsam::LevenbergMarquardtParams>::IndexVector knownInliers;
437 | 
438 |   // Add all good measurements.
439 |   for (const auto& factor : graphWithOutliers) {
440 |     // Ensure that we correctly retrieve a BetweenFactor
441 |     boost::shared_ptr<gtsam::BetweenFactor<gtsam::Pose2>> bwFactor =
442 |         boost::dynamic_pointer_cast<gtsam::BetweenFactor<gtsam::Pose2>>(factor);
443 |     if (bwFactor && isLoopClosure(*bwFactor)) {
444 |       // If the meaasurement is a loop closure, we add inlier and outlier
445 |       // hypotheses.
446 |       auto keys = bwFactor->keys();
447 | 
448 |       // We'll save the last inlier model to use for all our random
449 |       // measuremenets.
450 |       inlier_model = bwFactor->noiseModel();
451 |       // std::cout << inlier_model->sigmas() << std::endl;
452 | 
453 |       // Build a vector of components factors (inlier model, outlier model)
454 |       std::vector<gtsam::BetweenFactor<gtsam::Pose2>> components;
455 |       components.push_back(*bwFactor);
456 | 
457 |       gtsam::Vector3 outlier_sigmas = 4000.0 * inlier_model->sigmas();
458 | 
459 |       gtsam::noiseModel::Diagonal::shared_ptr outlierModel =
460 |           gtsam::noiseModel::Diagonal::Sigmas(outlier_sigmas);
461 | 
462 |       components.push_back(gtsam::BetweenFactor<gtsam::Pose2>(
463 |           keys[0], keys[1], bwFactor->measured(), outlierModel));
464 | 
465 |       // Create a discrete key to index into components. Cardinality is 2
466 |       // since a loop closure is either an inlier (0) or outlier (1).
467 |       gtsam::DiscreteKey dk(gtsam::Symbol('d', k), 2);
468 |       dcsam::DCMixtureFactor dcmf(bwFactor->keys(), dk, components, false);
469 |       hfg.push_dc(dcmf);
470 | 
471 |       dcsam::DiscretePriorFactor dpf(dk, lc_probabilities);
472 |       hfg.push_discrete(dpf);
473 |       k++;
474 | 
475 |     } else {
476 |       hfg.push_nonlinear(factor);
477 |       // if (bwFactor) {
478 |       knownInliers.push_back(known_inlier_idx);
479 |       // }
480 |     }
481 |     // Skip the prior factor when counting odometry measurements
482 |     // if (bwFactor) {
483 |     known_inlier_idx++;
484 |     // }
485 |   }
486 | 
487 |   /// 2. For each method: solve graph.
488 | 
489 |   size_t num_original_lc = k;
490 | 
491 |   std::cout << "Processed " << num_original_lc << " loop closures."
492 |             << std::endl;
493 | 
494 |   // We want num_outliers such that:
495 |   //    outlier_pct = num_outliers / (num_outliers + num_original)
496 |   //
497 |   // A little algebra reveals:
498 |   //    num_outliers = num_original_lc * (outlier_pct / (1 - outlier_pct))
499 |   //
500 |   // Cast is kosher since we know outlier_pct >= 0 and num_original_lc is
501 |   // of type size_t.
502 |   size_t num_outliers =
503 |       (size_t)((outlier_pct / (1.0 - outlier_pct)) * num_original_lc);
504 | 
505 |   gtsam::NonlinearFactorGraph outlierGraph = generateRandomLoopClosures(
506 |       graph.keyVector(), num_outliers, inlier_model, false, random_seed);
507 | 
508 |   // Add all outlier measurements.
509 |   for (const auto& factor : outlierGraph) {
510 |     // Ensure that we correctly retrieve a BetweenFactor
511 |     boost::shared_ptr<gtsam::BetweenFactor<gtsam::Pose2>> bwFactor =
512 |         boost::dynamic_pointer_cast<gtsam::BetweenFactor<gtsam::Pose2>>(factor);
513 |     if (bwFactor && isLoopClosure(*bwFactor)) {
514 |       // If the meaasurement is a loop closure, we add inlier and outlier
515 |       // hypotheses.
516 |       auto keys = bwFactor->keys();
517 | 
518 |       inlier_model = bwFactor->noiseModel();
519 | 
520 |       // Build a vector of components factors (inlier model, outlier model)
521 |       std::vector<gtsam::BetweenFactor<gtsam::Pose2>> components;
522 |       components.push_back(*bwFactor);
523 | 
524 |       gtsam::Vector3 outlier_sigmas = 4000.0 * inlier_model->sigmas();
525 | 
526 |       gtsam::noiseModel::Diagonal::shared_ptr outlierModel =
527 |           gtsam::noiseModel::Diagonal::Sigmas(outlier_sigmas);
528 |       components.push_back(gtsam::BetweenFactor<gtsam::Pose2>(
529 |           keys[0], keys[1], bwFactor->measured(), outlierModel));
530 | 
531 |       // Create a discrete key to index into components. Cardinality is 2
532 |       // since a loop closure is either an inlier (0) or outlier (1).
533 |       gtsam::DiscreteKey dk(gtsam::Symbol('d', k), 2);
534 |       dcsam::DCMixtureFactor dcmf(bwFactor->keys(), dk, components, false);
535 |       hfg.push_dc(dcmf);
536 | 
537 |       // Set up the discrete weights for each factor.
538 |       dcsam::DiscretePriorFactor dpf(dk, lc_probabilities);
539 |       hfg.push_discrete(dpf);
540 | 
541 |       // Add outlier factor to graph so we can visualize later.
542 |       graphWithOutliers.add(*bwFactor);
543 |       k++;
544 |     } else {
545 |       hfg.push_nonlinear(factor);
546 |     }
547 |   }
548 |   std::cout << "HFG size: " << hfg.size() << std::endl;
549 | 
550 |   // Currently DCSAM requires that we provide initial discrete values, but
551 |   // they aren't actually used since we perform the discrete solve first, so
552 |   // we just set them all to 0.
553 |   dcsam::DiscreteValues initial_discrete;
554 |   for (size_t i = 0; i < k; i++) {
555 |     initial_discrete[gtsam::Symbol('d', i)] = 0;
556 |   }
557 | 
558 |   // Create a solver instance and use it to optimize the graph.
559 |   dcsam::DCSAM dcsam;
560 | 
561 |   using std::chrono::duration;
562 |   using std::chrono::duration_cast;
563 |   using std::chrono::milliseconds;
564 | 
565 |   std::vector<double> dcsam_compute_times, dcsam_costs, dcsam_hybrid_costs;
566 |   auto t1 = std::chrono::high_resolution_clock::now();
567 |   dcsam.update(hfg, initial_values, initial_discrete);
568 |   auto t2 = std::chrono::high_resolution_clock::now();
569 |   // Get milliseconds as int
570 |   auto ms_int = std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1);
571 |   // Convert to double
572 |   double ms_double = static_cast<double>(ms_int.count());
573 |   // Get compute time in sec
574 |   dcsam_compute_times.push_back(ms_double / 1000.0);
575 | 
576 |   // Perform a few more iterations to converge.
577 |   dcsam::DCValues result;
578 |   std::vector<dcsam::DCValues> dc_results;
579 |   result = dcsam.calculateEstimate();
580 |   dc_results.push_back(result);
581 |   dcsam_costs.push_back(graph.error(result.continuous));
582 |   dcsam_hybrid_costs.push_back(hybrid_error(hfg, result));
583 |   gtsam::writeG2o(graphWithOutliers, result.continuous, "out_robust0.g2o");
584 |   for (size_t iter = 0; iter < 49; iter++) {
585 |     t1 = std::chrono::high_resolution_clock::now();
586 |     dcsam.update();
587 |     t2 = std::chrono::high_resolution_clock::now();
588 |     ms_int = std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1);
589 |     ms_double = static_cast<double>(ms_int.count());
590 |     dcsam_compute_times.push_back(ms_double / 1000.0);
591 | 
592 |     result = dcsam.calculateEstimate();
593 |     dc_results.push_back(result);
594 |     dcsam_costs.push_back(graph.error(result.continuous));
595 |     dcsam_hybrid_costs.push_back(hybrid_error(hfg, result));
596 | 
597 |     std::string fname = "out_robust" + std::to_string(iter + 1) + ".g2o";
598 |     gtsam::writeG2o(graphWithOutliers, result.continuous, fname);
599 | 
600 |     if (dc_results[iter + 1].continuous.equals(dc_results[iter].continuous) &&
601 |         dc_results[iter + 1].discrete.equals(dc_results[iter].discrete)) {
602 |       // We have reached convergence
603 |       std::cout << "We have reached convergence!" << std::endl;
604 |       break;
605 |     }
606 |     // dcsam::DCMarginals marg =
607 |     //     dcsam.getMarginals(dcsam.getNonlinearFactorGraph(),
608 |     //     result.continuous,
609 |     //                        dcsam.getDiscreteFactorGraph());
610 |     // std::string marg_fname = "marg_robust" + std::to_string(iter + 1) +
611 |     // ".txt"; writeDiscreteMarginals(dcsam.getDiscreteFactorGraph().keys(),
612 |     // marg.discrete,
613 |     //                        marg_fname);
614 |   }
615 | 
616 |   // Perform a few more iterations
617 |   // for (size_t j = 0; j < 100; j++) {
618 |   //   t1 = std::chrono::high_resolution_clock::now();
619 |   //   dcsam.update();
620 |   //   t2 = std::chrono::high_resolution_clock::now();
621 |   //   ms_int = std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1);
622 |   //   ms_double = static_cast<double>(ms_int.count());
623 |   //   dcsam_compute_times.push_back(ms_double / 1000.0);
624 |   //   result = dcsam.calculateEstimate();
625 |   //   dc_results.push_back(result);
626 |   //   dcsam_costs.push_back(graph.error(result.continuous));
627 |   //   dcsam_hybrid_costs.push_back(hybrid_error(hfg, result));
628 |   // }
629 |   // auto t2 = std::chrono::high_resolution_clock::now();
630 | 
631 |   // Get milliseconds as int
632 |   // auto ms_int = std::chrono::duration_cast<std::chrono::milliseconds>(t2 -
633 |   // t1);
634 | 
635 |   // Convert to double
636 |   // double ms_double = static_cast<double>(ms_int.count());
637 | 
638 |   // Total time in seconds:
639 |   // double total_time = ms_double / 1000.0;
640 | 
641 |   // std::cout << "Total time [DCSAM]: " << total_time << " seconds." <<
642 |   // std::endl; std::cout << "Avg. time/iter [DCSAM]: " << total_time / 7.0 << "
643 |   // seconds."
644 |   //           << std::endl;
645 |   assert(dcsam_costs.size() == dcsam_compute_times.size());
646 |   assert(dcsam_costs.size() == dcsam_hybrid_costs.size());
647 |   double total_time_dcsam = 0.0;
648 |   for (size_t idx = 0; idx < dcsam_costs.size(); idx++) {
649 |     total_time_dcsam += dcsam_compute_times[idx];
650 |     std::cout << "idx, time, cost, dcCost" << std::endl;
651 |     std::cout << idx << ", " << dcsam_compute_times[idx] << ", "
652 |               << dcsam_costs[idx] << ", " << dcsam_hybrid_costs[idx]
653 |               << std::endl;
654 |     std::cout << "total time: " << total_time_dcsam << std::endl;
655 |     if (idx > 0) {
656 |       std::cout << "stationary?: "
657 |                 << (dc_results[idx].continuous.equals(
658 |                         dc_results[idx - 1].continuous) &&
659 |                     dc_results[idx].discrete.equals(
660 |                         dc_results[idx - 1].discrete))
661 |                 << std::endl;
662 |     }
663 |   }
664 | 
665 |   double total_time_lm, total_time_gnc;
666 | 
667 |   result = dcsam.calculateEstimate();
668 |   std::cout << "Initial cost: " << graph.error(initial_values) << std::endl;
669 |   std::cout << "Final cost [DCSAM]: " << graph.error(result.continuous)
670 |             << std::endl;
671 | 
672 |   // Make sure we add the prior factor to the outlier graph before passing to LM
673 |   // and GNC. This addresses gauge ambiguity.
674 |   gtsam::NonlinearFactorGraph graphWithOutliersAndPrior =
675 |       graphWithOutliers.clone();
676 | 
677 |   // graphWithOutliersAndPrior.add(
678 |   //     gtsam::PriorFactor<gtsam::Pose2>(0, gtsam::Pose2(), priorModel));
679 | 
680 |   // Solve the non-robust version of the graph
681 |   gtsam::LevenbergMarquardtOptimizer lm(graphWithOutliersAndPrior,
682 |                                         initial_values);
683 | 
684 |   t1 = std::chrono::high_resolution_clock::now();
685 |   gtsam::Values lm_result = lm.optimize();
686 |   t2 = std::chrono::high_resolution_clock::now();
687 | 
688 |   std::cout << "Final cost [LM]: " << graph.error(lm_result) << std::endl;
689 | 
690 |   // Get milliseconds as int
691 |   ms_int = std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1);
692 | 
693 |   // Convert to double
694 |   ms_double = static_cast<double>(ms_int.count());
695 |   total_time_lm = ms_double / 1000.0;
696 | 
697 |   std::cout << "Total time [LM]: " << total_time_lm << " seconds." << std::endl;
698 | 
699 |   /// Solve with GNC
700 |   gtsam::GncParams<gtsam::LevenbergMarquardtParams> gncParams;
701 | 
702 |   // Let GNC know that odometry measurements are inliers:
703 |   std::cout << "Known inliers size: " << knownInliers.size() << std::endl;
704 |   gncParams.knownInliers = knownInliers;
705 |   gncParams.lossType = gtsam::GncLossType::TLS;
706 |   // Collect summary stats from GNC
707 |   // gncParams.verbosity =
708 |   //     gtsam::GncParams<gtsam::LevenbergMarquardtParams>::Verbosity::SUMMARY;
709 |   gncParams.maxIterations = 50;
710 | 
711 |   // Make the optimizer and solve.
712 |   auto gnc =
713 |       gtsam::GncOptimizer<gtsam::GncParams<gtsam::LevenbergMarquardtParams>>(
714 |           graphWithOutliersAndPrior, initial_values, gncParams);
715 | 
716 |   std::cout << "Default inlier cost threshold = "
717 |             << gnc.getInlierCostThresholds()[0] << std::endl;
718 | 
719 |   // We found that setting the inlier cost threshold to 0.5 gave us the best
720 |   // results on the `parking-garage.g2o` dataset, and setting the to 4 gave the
721 |   // best results on `sphere2500.g2o`
722 |   // gnc.setInlierCostThresholds(4.0);
723 | 
724 |   t1 = std::chrono::high_resolution_clock::now();
725 |   gtsam::Values gnc_result = gnc.optimize();
726 |   t2 = std::chrono::high_resolution_clock::now();
727 | 
728 |   std::cout << "Final cost [GNC]: " << graph.error(gnc_result) << std::endl;
729 | 
730 |   // Get milliseconds as int
731 |   ms_int = std::chrono::duration_cast<std::chrono::milliseconds>(t2 - t1);
732 | 
733 |   // Convert to double
734 |   ms_double = static_cast<double>(ms_int.count());
735 |   total_time_gnc = ms_double / 1000.0;
736 | 
737 |   std::cout << "Total time [GNC]: " << total_time_gnc << " seconds."
738 |             << std::endl;
739 | 
740 |   std::cout << "OUTLIER PCT" << std::to_string((int)(100 * outlier_pct))
741 |             << std::endl;
742 |   std::string path_prefix = "../../output/robust_pgo_vanilla/" + dataset_name +
743 |                             "/" + std::to_string((int)(100 * outlier_pct)) +
744 |                             "/" + std::to_string(random_seed) + "/";
745 | 
746 |   std::cout << "path_prefix: " << path_prefix << std::endl;
747 | 
748 |   // use "writeG2o" to save file.
749 |   gtsam::writeG2o(graphWithOutliers, lm_result,
750 |                   path_prefix + "out_nonrobust.g2o");
751 |   gtsam::writeG2o(graphWithOutliers, result.continuous,
752 |                   path_prefix + "out_robust.g2o");
753 |   gtsam::writeG2o(graphWithOutliers, gnc_result, path_prefix + "out_gnc.g2o");
754 | 
755 |   // All loop closures _after_ this number should be visualized as outliers.
756 |   std::ofstream file(path_prefix + "orig_graph_size_out.txt");
757 |   file << graph.size();
758 | 
759 |   // Write timing info
760 |   std::ofstream time_file(path_prefix + "times.txt");
761 |   time_file << "DCSAM: " << std::to_string(total_time_dcsam) << "\n"
762 |             << "GNC: " << std::to_string(total_time_gnc) << "\n"
763 |             << "LM: " << total_time_lm << "\n";
764 | 
765 |   /// 3. Collect stats, final trajectory, and output to file.
766 |   return;
767 | }
768 | 
769 | int main(int argc, char** argv) {
770 |   if (argc < 2) {
771 |     std::cout << "Usage: " << argv[0] << " [.g2o file]"
772 |               << " [outlier rate (int >= 0; default 0)]"
773 |               << " [is3D (0/1; default 1)]"
774 |               << " [num trials (default 1)]" << std::endl;
775 |     exit(1);
776 |   }
777 | 
778 |   // Defaults
779 |   double outlier_pct = 0.0;
780 |   bool is3D = true;
781 |   size_t num_trials = 1;
782 | 
783 |   // Begin parsing input
784 |   std::string path = argv[1];
785 | 
786 |   std::string base_filename = path.substr(path.find_last_of("/\\") + 1);
787 |   std::string::size_type const p(base_filename.find_last_of('.'));
788 |   std::string file_without_extension = base_filename.substr(0, p);
789 | 
790 |   if (argc >= 3) {
791 |     std::cout << "outlier percent " << atoi(argv[2]) << std::endl;
792 |     outlier_pct = (double)(atoi(argv[2])) / 100.0;
793 |     std::cout << "outlier percent " << outlier_pct << std::endl;
794 |   }
795 |   if ((argc >= 4) && (atoi(argv[3]) != 1)) {
796 |     is3D = false;
797 |   }
798 |   if ((argc >= 5)) {
799 |     assert(atoi(argv[4]) > 0);
800 |     num_trials = atoi(argv[4]);
801 |   }
802 | 
803 |   std::cout << "Running robust PGO experiment\n"
804 |             << "=============================\n"
805 |             << "dataset:\t" << file_without_extension << "\n"
806 |             << "outlier_pct:\t" << outlier_pct << "\n"
807 |             << "is3D?:\t\t" << is3D << "\n"
808 |             << "num_trials:\t" << num_trials << "\n"
809 |             << std::endl;
810 | 
811 |   gtsam::NonlinearFactorGraph::shared_ptr graph;
812 |   gtsam::Values::shared_ptr initial;
813 | 
814 |   boost::tie(graph, initial) = gtsam::readG2o(path, is3D);
815 | 
816 |   std::cout << "Loaded a graph of size: " << graph->size() << std::endl;
817 | 
818 |   for (size_t i = 1; i < num_trials + 1; i++) {
819 |     if (is3D) {
820 |       run_experiment3D(*graph, *initial, outlier_pct, i,
821 |                        file_without_extension);
822 |     } else {
823 |       run_experiment2D(*graph, *initial, outlier_pct, i,
824 |                        file_without_extension);
825 |     }
826 |   }
827 | }
828 | 


--------------------------------------------------------------------------------
/data/pointclouds/bunny_sparse10_part1.xyz:
--------------------------------------------------------------------------------
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   5 | 3.4300 -2.4300 11.4700
   6 | -6.8000 -2.6100 6.3100
   7 | 1.0000 -3.1700 12.5100
   8 | -2.9900 -0.5500 12.5300
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  16 | -3.7300 -0.4100 17.1200
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 151 | -3.7200 -0.4800 16.9700
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 157 | -1.0900 -5.3600 6.0200
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 161 | -2.1000 -2.9600 12.2100
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 165 | -1.9900 -0.6800 12.7000
 166 | -3.4800 -4.4200 6.0600
 167 | -2.5200 -3.9400 6.0300
 168 | 3.4000 -1.7300 11.7000
 169 | -0.3600 -5.6500 6.2000
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 171 | 0.1800 -3.8100 11.9700
 172 | -0.6300 -3.6800 12.1000
 173 | 4.3600 -2.2400 9.7200
 174 | 5.0100 -2.7100 5.9700
 175 | 1.7800 -3.7300 11.2600
 176 | 1.7800 -2.8100 12.4800
 177 | -3.8800 -4.4300 6.2000
 178 | -2.9000 -4.1300 6.1800
 179 | 1.7200 -1.2700 12.8900
 180 | -0.6600 -5.6000 6.3300
 181 | 0.3400 -5.6600 6.3400
 182 | 1.5600 -3.5000 11.8200
 183 | 0.4800 -3.7500 11.9700
 184 | -3.5100 -0.6900 12.7200
 185 | 4.6000 -2.6300 6.1100
 186 | 2.1700 -3.5800 11.2600
 187 | 1.6100 -1.7900 12.9300
 188 | -2.7300 -0.3400 12.4700
 189 | -3.4700 -4.4700 6.3500
 190 | -2.5100 -4.0000 6.3100
 191 | -4.4900 -1.5700 13.2800
 192 | -0.3600 -5.6400 6.4700
 193 | 0.6400 -5.6300 6.4800
 194 | -7.1200 -5.2500 14.2800
 195 | -1.7100 -3.9600 11.4100
 196 | -9.2400 -2.3400 12.2800
 197 | 5.1900 -2.5500 6.2500
 198 | 0.3900 -1.8800 13.1200
 199 | -4.9300 -3.8100 6.4200
 200 | -3.7700 -4.4800 6.4800
 201 | -2.8000 -4.1400 6.4600
 202 | 3.6700 -1.8100 11.3600
 203 | -0.6700 -5.5500 6.6000
 204 | 0.3500 -5.6600 6.6200
 205 | 1.3300 -5.4100 6.6000
 206 | -1.5400 -3.6000 11.9500
 207 | -4.4800 -3.2400 12.0700
 208 | 4.3100 -2.6100 6.3800
 209 | 5.2900 -2.4600 6.3800
 210 | 0.9200 -2.0200 13.0700
 211 | -4.6000 -4.1000 6.5800
 212 | -3.5700 -4.5100 6.6300
 213 | -2.6000 -4.0900 6.6000
 214 | 4.7200 -2.0800 6.8500
 215 | -0.4600 -5.6000 6.7500
 216 | 0.5400 -5.6100 6.7500
 217 | 1.5200 -5.3200 6.7400
 218 | 0.7400 -2.6800 12.8700
 219 | -4.8900 -1.5700 14.4700
 220 | 4.4800 -2.3700 6.5100
 221 | 5.4800 -2.3300 6.5100
 222 | 0.8100 -2.0000 13.0900
 223 | -6.9900 -2.6300 6.4600
 224 | -3.5700 -4.5100 6.7700
 225 | -2.6000 -4.1300 6.7400
 226 | -1.2700 -5.5200 6.8700
 227 | -0.2600 -5.6400 6.8900
 228 | 0.7400 -5.5400 6.8900
 229 | 0.3100 -1.4400 13.1700
 230 | -9.1800 -2.1300 12.6900
 231 | 2.3000 -1.9800 12.5800
 232 | 5.1700 -2.3200 6.6500
 233 | 1.7500 -2.2500 12.7800
 234 | -5.6500 -5.0100 3.6200
 235 | -3.3700 -4.4900 6.9100
 236 | -2.4000 -4.2100 6.8900
 237 | -1.0600 -5.6100 7.0200
 238 | -0.0500 -5.7600 7.0400
 239 | 0.9400 -5.5200 7.0300
 240 | 1.9600 -3.4700 11.6800
 241 | -0.2100 -3.9100 11.8300
 242 | -8.5900 -4.4800 13.7900
 243 | 4.9600 -2.2000 6.7800
 244 | -5.4900 -3.0900 6.1300
 245 | -8.3600 -3.2400 15.2200
 246 | -3.4700 -4.5100 7.0500
 247 | -2.4900 -4.2100 7.0300
 248 | -1.1700 -5.6300 7.1600
 249 | -0.1400 -5.8300 7.1800
 250 | 0.8400 -5.5500 7.1700
 251 | 0.0000 -4.0000 11.6900
 252 | -0.4100 -3.8800 11.8300
 253 | -8.3900 -1.1700 10.8900
 254 | 5.1500 -2.0900 6.9100
 255 | -5.3400 -1.3100 15.8600
 256 | -4.2700 -4.5400 7.1900
 257 | -3.2700 -4.5100 7.1900
 258 | -0.6400 -3.1700 12.5300
 259 | -0.8600 -5.6700 7.3100
 260 | 0.1600 -5.8700 7.3300
 261 | 1.1400 -5.5700 7.3100
 262 | 0.6000 -4.0000 11.5500
 263 | -3.6700 -3.3600 11.9400
 264 | 4.6200 -1.4200 9.1800
 265 | 4.5600 -0.6200 9.4600
 266 | 3.4400 -2.5800 11.3400
 267 | -5.4700 -4.6000 7.3200
 268 | -4.4700 -4.5700 7.3300
 269 | -3.4700 -4.5200 7.3300
 270 | -2.4900 -4.3100 7.3200
 271 | -1.1600 -5.6800 7.4400
 272 | -0.1400 -5.8700 7.4600
 273 | 0.8400 -5.5900 7.4500
 274 | -1.3100 -3.9300 11.5400
 275 | -7.0600 -5.9200 11.9000
 276 | 3.2800 -1.9800 11.8100
 277 | 4.5000 -0.9300 9.7300
 278 | 1.2400 -2.9500 12.6300
 279 | -5.8700 -4.6100 7.4600
 280 | -4.8600 -4.6700 7.4800
 281 | -3.8600 -4.6300 7.4800
 282 | -2.8800 -4.3600 7.4600
 283 | -1.5400 -1.5100 12.8600
 284 | -0.7500 -5.7900 7.5900
 285 | 0.2600 -5.8800 7.6100
 286 | 1.2400 -5.5500 7.5900
 287 | -3.0600 -3.4900 11.6600
 288 | -3.8700 -3.3500 11.9400
 289 | -5.8300 -4.8600 3.5800
 290 | 4.1600 -1.0600 10.5600
 291 | -1.5700 -0.6500 12.8100
 292 | -5.1600 -4.7400 7.6200
 293 | -4.1600 -4.6000 7.6200
 294 | -3.1700 -4.4800 7.6100
 295 | -1.5600 -5.6500 7.7200
 296 | -0.5400 -5.8400 7.7400
 297 | 0.4600 -5.8100 7.7400
 298 | 1.4300 -5.4600 7.7200
 299 | -2.2600 -3.5000 11.6700
 300 | -7.3700 -5.8900 12.4600
 301 | -8.5800 -0.7900 9.5400
 302 | 0.5600 -1.3700 13.1700
 303 | -5.8600 -4.7200 7.7500
 304 | -4.8500 -4.7600 7.7700
 305 | -3.8600 -4.6600 7.7600
 306 | -2.8800 -4.3900 7.7500
 307 | -1.5500 -5.7300 7.8600
 308 | -0.5400 -5.8600 7.8800
 309 | 0.4600 -5.7800 7.8800
 310 | 1.4300 -5.4400 7.8600
 311 | -2.1500 -3.5600 11.6700
 312 | 4.0100 -1.5700 10.8400
 313 | -1.6800 -0.4800 12.7400
 314 | -7.4800 -4.1500 10.0800
 315 | -5.9500 -4.7800 7.9000
 316 | -4.9500 -4.8400 7.9100
 317 | -3.9600 -4.6200 7.9000
 318 | -2.9800 -4.4300 7.8900
 319 | -8.7500 -1.7100 10.1000
 320 | -0.6400 -5.8600 8.0100
 321 | 0.3500 -5.7600 8.0100
 322 | 1.3300 -5.4300 8.0000
 323 | -2.3600 -3.4700 11.6600
 324 | -6.9100 -5.4000 14.0100
 325 | -7.7000 -4.3100 14.9800
 326 | -0.0700 -2.8900 12.8000
 327 | -6.2600 -4.7900 8.0500
 328 | -5.2400 -4.9300 8.0500
 329 | -4.2600 -4.7300 8.0500
 330 | -3.2600 -4.6200 8.0500
 331 | -8.3500 -2.9500 15.3000
 332 | -0.9500 -5.8000 8.1500
 333 | 0.0500 -5.7600 8.1500
 334 | 1.0400 -5.5500 8.1400
 335 | -2.1500 -3.6600 11.5400
 336 | -0.9700 -3.0800 12.5300
 337 | -8.7600 -2.5900 15.1600
 338 | -7.0000 -2.9900 15.6000
 339 | -2.3900 -2.1400 12.4700
 340 | -5.3400 -4.9000 8.1900
 341 | -4.3500 -4.7900 8.1900
 342 | -3.3600 -4.6800 8.1900
 343 | -8.5200 -1.3800 10.6300
 344 | -1.2400 -5.9000 8.2900
 345 | -0.2500 -5.7900 8.2900
 346 | 0.7500 -5.6800 8.2900
 347 | 4.1900 -0.3500 10.4200
 348 | -2.4600 -0.2700 12.4900
 349 | -6.8600 -5.9800 12.1800
 350 | -6.1600 -3.5000 15.4700
 351 | -0.0100 -4.5300 10.1100
 352 | -5.3400 -4.9000 8.3300
 353 | -4.3400 -4.8400 8.3300
 354 | -3.3500 -4.6900 8.3300
 355 | -8.7300 -1.6100 10.0900
 356 | -1.1400 -5.8400 8.4300
 357 | -0.1500 -5.7300 8.4200
 358 | 0.8500 -5.6800 8.4300
 359 | -8.7100 -1.3900 11.3100
 360 | -2.4700 -3.3500 11.8000
 361 | -7.0200 -0.8600 15.5800
 362 | 1.5500 -1.6400 12.9600
 363 | -6.0400 -4.9800 8.4700
 364 | -5.0300 -4.9800 8.4800
 365 | -4.0500 -4.7100 8.4700
 366 | -3.0700 -4.5600 8.4600
 367 | -0.2200 -0.6000 13.0800
 368 | -0.7500 -5.8100 8.5700
 369 | 0.2500 -5.7300 8.5700
 370 | 1.2400 -5.5400 8.5600
 371 | 2.2800 -4.8700 8.5200
 372 | -3.5400 -3.6600 11.5300
 373 | -8.8500 -4.5100 12.7000
 374 | -2.3300 -0.3000 12.5300
 375 | -5.7400 -4.9900 8.6200
 376 | -4.7300 -4.9900 8.6200
 377 | -3.7600 -4.6100 8.6000
 378 | -2.7700 -4.5200 8.6000
 379 | 0.4800 -0.8000 13.1600
 380 | -0.6500 -5.7600 8.7000
 381 | 0.3500 -5.6800 8.7000
 382 | 1.3300 -5.4900 8.6900
 383 | 2.3700 -4.7800 8.6500
 384 | 3.1000 -2.3400 11.8800
 385 | -7.6400 -4.0200 10.0700
 386 | -9.1700 -3.1400 12.6900
 387 | -5.6300 -5.0300 8.7600
 388 | -4.6300 -4.9800 8.7600
 389 | -3.6600 -4.6300 8.7400
 390 | -2.6500 -4.6300 8.7500
 391 | 1.5700 -1.0900 12.9500
 392 | -0.5500 -5.7500 8.8400
 393 | 0.4400 -5.6400 8.8400
 394 | 1.4300 -5.4000 8.8300
 395 | 2.4700 -4.7100 8.7900
 396 | -2.0600 -0.9400 12.7100
 397 | 0.7600 -1.8400 13.1300
 398 | -8.9100 -4.0200 12.5600
 399 | -5.6300 -5.0400 8.9000
 400 | -4.6400 -4.9500 8.9000
 401 | -3.6600 -4.6200 8.8800
 402 | -2.6600 -4.6300 8.8900
 403 | 2.1100 -1.2400 12.7200
 404 | -0.4500 -5.7600 8.9800
 405 | 0.5400 -5.5900 8.9800
 406 | 1.5200 -5.3200 8.9600
 407 | 2.6600 -4.6300 8.9300
 408 | 4.4800 -1.5300 9.7300
 409 | 2.4300 -2.1600 12.4700
 410 | -0.1700 -4.6300 10.0800
 411 | -6.0300 -5.0200 9.0400
 412 | -5.0400 -4.9600 9.0400
 413 | -4.0600 -4.6600 9.0300
 414 | -3.0500 -4.7100 9.0400
 415 | 2.4500 -1.3300 12.5300
 416 | -0.9500 -5.7400 9.1200
 417 | 0.0500 -5.6700 9.1200
 418 | 1.0300 -5.4100 9.1100
 419 | -6.6600 -5.9000 11.7600
 420 | -6.6500 -4.9200 14.5600
 421 | -6.6400 -5.0300 14.4200
 422 | 2.5300 -1.7700 12.4700
 423 | -6.6600 -4.7900 9.1600
 424 | -5.6300 -5.0400 9.1800
 425 | -4.6500 -4.8500 9.1800
 426 | -3.6600 -4.6000 9.1700
 427 | -2.6500 -4.6600 9.1800
 428 | 2.9200 -1.4600 12.1700
 429 | -0.5600 -5.6800 9.2600
 430 | 0.4400 -5.5500 9.2500
 431 | 1.4100 -5.2000 9.2300
 432 | 2.3600 -4.5900 9.2000
 433 | -7.3800 -5.6400 13.7200
 434 | -3.7600 -0.3400 12.7400
 435 | 1.1500 -2.9300 12.6600
 436 | -6.7600 -4.7800 9.3000
 437 | -5.7300 -5.0200 9.3200
 438 | -4.7500 -4.8300 9.3200
 439 | -3.7700 -4.5800 9.3100
 440 | -2.7500 -4.7000 9.3200
 441 | 3.8100 -1.7100 11.1500
 442 | -0.6600 -5.6300 9.3900
 443 | 0.3300 -5.5300 9.3900
 444 | 1.3000 -5.1500 9.3700
 445 | 2.2600 -4.5800 9.3400
 446 | -7.4800 -5.6300 13.7200
 447 | -5.5400 -3.9500 14.8200
 448 | 3.8500 -2.1300 11.0100
 449 | -6.4500 -4.8100 9.4500
 450 | -5.4400 -4.9800 9.4600
 451 | -4.4400 -4.8700 9.4600
 452 | -3.4600 -4.6400 9.4500
 453 | -2.4500 -4.7400 9.4700
 454 | 0.9800 -3.0200 12.6200
 455 | -0.3700 -5.5100 9.5300
 456 | 0.6200 -5.3900 9.5300
 457 | 1.5800 -4.9500 9.4900
 458 | 2.5500 -4.5300 9.4800
 459 | -0.2500 -3.4700 12.3700
 460 | -0.0400 -5.2600 9.7800
 461 | 1.3300 -1.3000 13.0400
 462 | -6.4600 -4.8000 9.5900
 463 | -5.4400 -4.9000 9.6000
 464 | -4.4400 -4.8500 9.6000
 465 | -3.4600 -4.6000 9.5900
 466 | -2.4500 -4.7100 9.6000
 467 | 0.4400 -5.2300 9.7700
 468 | -0.3800 -5.4000 9.6600
 469 | 0.6100 -5.2900 9.6600
 470 | 1.5800 -4.8400 9.6300
 471 | 2.5500 -4.4900 9.6200
 472 | 0.5300 -1.5000 13.1700
 473 | -6.5500 -2.2600 15.6300
 474 | -8.6300 -2.8000 10.1000
 475 | -6.1600 -4.7700 9.7300
 476 | -5.1500 -4.7900 9.7400
 477 | -4.1600 -4.6800 9.7300
 478 | -3.1600 -4.6200 9.7300
 479 | -2.1600 -4.6200 9.7400
 480 | -1.1200 -5.0100 9.7700
 481 | 0.3100 -5.2400 9.7800
 482 | 1.2700 -4.8300 9.7700
 483 | 2.2500 -4.5300 9.7600
 484 | -4.0800 -3.1500 12.0700
 485 | -7.1600 -5.9100 13.1700
 486 | -2.2000 -2.7600 12.2700
 487 | -6.3600 -4.7400 9.8700
 488 | -5.3500 -4.7800 9.8800
 489 | -4.3600 -4.7100 9.8800
 490 | -3.3700 -4.5500 9.8700
 491 | -2.3600 -4.5800 9.8800
 492 | 1.0300 -2.7500 12.7900
 493 | -7.2500 -3.0900 6.9100
 494 | 1.3700 -4.7200 9.9100
 495 | 2.3500 -4.4700 9.9000
 496 | -1.8500 -1.1400 12.7700
 497 | 0.4000 -2.4500 12.9900
 498 | -0.3100 -1.0000 13.1100
 499 | -6.2600 -4.7200 10.0100
 500 | -5.2500 -4.7900 10.0200
 501 | -4.2600 -4.6100 10.0100
 502 | -3.2700 -4.5200 10.0100
 503 | -2.2600 -4.6100 10.0200
 504 | -1.2600 -4.5200 10.0300
 505 | -8.9400 -1.3100 11.7300
 506 | 0.8500 -4.5800 10.0300
 507 | 1.8600 -4.6000 10.0400
 508 | 3.7100 -2.3700 11.1000
 509 | -1.8000 -1.0100 12.7800
 510 | -0.6700 -0.9000 13.0400
 511 | -6.0600 -4.7400 10.1500
 512 | -5.0600 -4.7000 10.1600
 513 | -4.0700 -4.5800 10.1500
 514 | -3.0700 -4.4600 10.1500
 515 | -2.0600 -4.5800 10.1600
 516 | -1.0600 -4.4800 10.1600
 517 | -8.5800 -2.7900 10.2300
 518 | 1.0600 -4.5400 10.1700
 519 | 2.0500 -4.4900 10.1800
 520 | 0.7600 -3.5300 12.1000
 521 | -6.4600 -5.9000 12.1800
 522 | 1.3700 -1.5900 13.0300
 523 | -6.8900 -4.4200 10.2700
 524 | -5.8600 -4.6900 10.2900
 525 | -4.8700 -4.5600 10.2900
 526 | -3.8700 -4.4900 10.2900
 527 | -2.8800 -4.4300 10.2900
 528 | -1.8600 -4.5400 10.3000
 529 | -0.8600 -4.4700 10.3000
 530 | 0.1300 -4.4000 10.3100
 531 | 1.1500 -4.4500 10.3000
 532 | 2.1400 -4.3600 10.3100
 533 | 4.5800 -1.8200 9.3100
 534 | -8.2300 -3.0600 10.7700
 535 | 0.0700 -2.6400 12.9200
 536 | -6.5900 -4.4600 10.4100
 537 | -5.5700 -4.6400 10.4300
 538 | -4.5800 -4.4400 10.4200
 539 | -3.5900 -4.2900 10.4200
 540 | -2.5800 -4.3800 10.4300
 541 | -1.5700 -4.4500 10.4400
 542 | -0.5600 -4.4300 10.4400
 543 | 0.4300 -4.3400 10.4400
 544 | 1.4400 -4.3700 10.4400
 545 | -8.8900 -1.1100 11.9900
 546 | -0.5100 -1.2200 13.0700
 547 | 0.4400 -1.7600 13.1400
 548 | -6.9000 -4.3600 10.5500
 549 | -5.8800 -4.5500 10.5600
 550 | -4.8800 -4.4700 10.5700
 551 | -3.8800 -4.4100 10.5700
 552 | -2.8900 -4.2700 10.5700
 553 | -1.8700 -4.4100 10.5800
 554 | -0.8700 -4.4100 10.5800
 555 | 0.1300 -4.4200 10.5900
 556 | 1.1100 -4.1300 10.5700
 557 | 2.1200 -4.1300 10.5800
 558 | -0.0400 -0.9300 13.1300
 559 | -5.5100 -4.1100 11.3900
 560 | -6.9000 -4.3600 10.6900
 561 | -5.8800 -4.4700 10.7000
 562 | -4.8800 -4.4200 10.7100
 563 | -3.8900 -4.3400 10.7100
 564 | -2.9000 -4.2000 10.7000
 565 | -1.8800 -4.3400 10.7200
 566 | -0.8700 -4.4000 10.7200
 567 | 0.1300 -4.3800 10.7300
 568 | 1.1100 -4.0500 10.7100
 569 | 2.1000 -3.9900 10.7100
 570 | 4.4000 -0.6400 10.0100
 571 | 1.2500 -1.9800 13.0200
 572 | -6.7000 -4.3300 10.8300
 573 | -5.6900 -4.3800 10.8400
 574 | -4.6900 -4.2800 10.8400
 575 | -3.7000 -4.1500 10.8400
 576 | -2.7000 -4.1300 10.8400
 577 | -1.6800 -4.2900 10.8500
 578 | -0.6700 -4.3700 10.8600
 579 | 0.3200 -4.2900 10.8600
 580 | 1.3000 -3.9300 10.8500
 581 | 2.2900 -3.7900 10.8500
 582 | -4.9200 -3.9400 11.3900
 583 | -6.9400 -3.4400 15.5100
 584 | -6.1100 -4.3300 10.9800
 585 | -5.1000 -4.2500 10.9700
 586 | -4.1000 -4.2000 10.9800
 587 | -3.1100 -4.0100 10.9800
 588 | -2.1000 -4.1600 10.9900
 589 | -1.0800 -4.3100 11.0000
 590 | -0.0700 -4.3100 11.0000
 591 | 0.9100 -4.1000 11.0000
 592 | 1.9900 -3.8200 10.9800
 593 | 2.8500 -2.1300 12.1600
 594 | -6.8100 -1.3400 15.5600
 595 | -5.6100 -4.1800 11.1100
 596 | -4.6100 -4.1200 11.1200
 597 | -3.6200 -3.9600 11.1200
 598 | -2.6200 -3.9200 11.1100
 599 | -1.5900 -4.1600 11.1300
 600 | -0.5800 -4.2900 11.1400
 601 | 0.4200 -4.2300 11.1400
 602 | 1.3900 -3.8300 11.1300
 603 | 2.3700 -3.5800 11.1200
 604 | 4.1500 -0.0500 10.4300
 605 | 4.0600 -1.8700 10.7000
 606 | -5.4100 -4.0900 11.2500
 607 | -4.4200 -3.9800 11.2500
 608 | -3.4300 -3.8200 11.2500
 609 | -2.4300 -3.8300 11.2500
 610 | -4.5900 -1.9700 13.5100
 611 | -4.7800 -3.7700 11.9800
 612 | -2.8000 -0.7400 12.5300
 613 | -0.8700 -5.1700 3.7800
 614 | 0.1900 -4.7300 3.8300
 615 | 1.3400 -4.4100 3.8900
 616 | 2.3300 -4.0300 3.9400
 617 | -5.6400 -4.4600 12.9700
 618 | -4.8100 -5.0800 3.6100
 619 | -4.1900 -1.5100 12.9400
 620 | -1.5300 -5.4800 3.9000
 621 | -0.5900 -5.0400 3.9300
 622 | 0.3800 -4.6600 3.9800
 623 | 1.4600 -4.4000 4.0400
 624 | 2.4300 -4.0000 4.0800
 625 | 2.9400 -2.8900 4.0300
 626 | -5.1300 -5.1600 3.7200
 627 | -4.1100 -4.8400 3.7700
 628 | -2.0700 -5.6200 4.0100
 629 | -1.0600 -5.2600 4.0600
 630 | -0.1000 -4.8600 4.1000
 631 | 2.2600 -3.2100 11.9300
 632 | 2.2800 -4.1400 4.2200
 633 | 4.0000 -3.1200 9.5500
 634 | -5.3200 -3.6000 14.3900
 635 | -4.7000 -5.0700 3.8900
 636 | 0.5500 -4.8200 9.9800
 637 | -1.1500 -5.3100 4.1900
 638 | -0.2100 -4.8900 4.2300
 639 | -4.9400 -4.0500 12.2700
 640 | 2.1900 -4.1900 4.3600
 641 | 2.9100 -3.4800 4.3500
 642 | -6.1500 -4.4600 11.2400
 643 | -5.4500 -3.7500 14.9600
 644 | -4.0600 -4.7500 4.0500
 645 | -1.6400 -5.5100 4.3100
 646 | -4.5700 -3.0900 13.1000
 647 | -8.9500 -4.7300 12.0200
 648 | 2.3500 -4.0600 4.5000
 649 | 3.0600 -3.3500 4.4900
 650 | -6.2200 -4.8600 13.8300
 651 | -4.5400 -1.2400 15.5000
 652 | -3.9800 -4.6900 4.1900
 653 | -1.5600 -5.4500 4.4500
 654 | -4.1700 -1.0600 15.5100
 655 | -6.5500 -5.4100 13.5500
 656 | 2.0400 -4.1600 4.6200
 657 | 2.8800 -3.6100 4.6300
 658 | 3.4900 -2.7800 4.6100
 659 | 4.6200 -2.5700 4.6800
 660 | 5.2600 -1.7800 4.6600
 661 | 2.5200 -3.8400 10.6000
 662 | -5.9900 -4.3500 14.6800
 663 | 3.2100 -3.4200 10.6100
 664 | 3.3600 -3.2800 10.6100
 665 | 2.1200 -4.1100 4.7600
 666 | 2.9700 -3.5600 4.7700
 667 | 3.4900 -3.1300 10.6100
 668 | 4.7700 -2.6000 4.8200
 669 | 5.4000 -1.7900 4.8000
 670 | -4.3700 -1.1400 15.5100
 671 | -5.6100 -3.8700 13.8100
 672 | 4.0700 -0.0600 10.5600
 673 | 4.1200 -0.3600 10.5600
 674 | 2.0900 -4.2200 4.9000
 675 | 2.9000 -3.6300 4.9100
 676 | 4.1400 -2.9800 9.4100
 677 | 4.7500 -2.7300 4.9700
 678 | 5.4200 -1.9700 4.9600
 679 | 3.0400 -3.3700 11.0100
 680 | -6.3000 -4.9200 14.1100
 681 | -2.7500 -0.2700 15.9600
 682 | -5.5500 -3.9500 14.9700
 683 | 2.0800 -4.3700 5.0500
 684 | 2.8200 -3.7000 5.0500
 685 | 3.5600 -3.0200 5.0500
 686 | 4.7000 -2.8300 5.1100
 687 | 5.4200 -2.1300 5.1100
 688 | -9.2500 -2.7400 12.1500
 689 | -6.9300 -5.7300 13.4000
 690 | -3.7900 -0.4200 16.0900
 691 | -6.7200 -5.3500 13.9700
 692 | 2.1600 -4.4600 5.2000
 693 | 2.8700 -3.7600 5.2000
 694 | 3.6200 -3.0900 5.2000
 695 | 4.8100 -2.8100 5.2600
 696 | 5.5500 -2.1300 5.2600
 697 | -4.8500 -0.9700 14.3100
 698 | -6.8400 -5.6800 13.4100
 699 | -3.7500 -0.4600 15.0700
 700 | 1.7200 -4.8800 5.3500
 701 | 2.5500 -4.3200 5.3600
 702 | 3.2600 -3.6000 5.3500
 703 | 2.3500 -3.1600 11.9400
 704 | 5.1500 -2.6000 5.4000
 705 | 5.8200 -1.8400 5.3900
 706 | 4.5300 -0.7200 9.6000
 707 | -3.8100 -0.8700 15.6600
 708 | -5.0500 -4.0700 12.6900
 709 | 1.5300 -5.1200 5.4900
 710 | 2.3800 -4.5800 5.5000
 711 | 3.0700 -3.8500 5.4900
 712 | 3.7600 -3.1100 5.4900
 713 | 3.1700 -3.0500 11.2800
 714 | 5.6300 -2.2300 5.5400
 715 | 1.6500 -3.2600 12.1900
 716 | -5.9200 -4.2800 14.8200
 717 | -6.0400 -4.4400 13.6700
 718 | -4.6100 -3.5000 12.6900
 719 | 2.2100 -4.6800 5.6400
 720 | 2.9600 -4.0300 5.6400
 721 | 3.7000 -3.3500 5.6400
 722 | -4.5500 -3.4100 12.4000
 723 | 5.7600 -2.0800 5.6700
 724 | -5.4500 -4.5200 12.1200
 725 | -4.6900 -3.4100 13.1100
 726 | -5.2600 -3.6600 13.6700
 727 | 1.7000 -5.0200 5.7700
 728 | 2.5800 -4.5100 5.7800
 729 | 3.2900 -3.8000 5.7800
 730 | 3.9400 -3.0200 5.7700
 731 | -6.2200 -4.8600 13.6800
 732 | 5.7800 -2.1000 5.8100
 733 | 4.5300 -0.8200 9.6000
 734 | -4.4200 -1.0700 15.7900
 735 | -5.9800 -4.6700 13.1100
 736 | 2.7700 -3.5100 11.0100
 737 | 1.8400 -4.8800 5.9000
 738 | 2.7200 -4.3700 5.9200
 739 | 3.4800 -3.7200 5.9200
 740 | 4.0700 -2.8600 5.9000
 741 | -8.9600 -4.2300 12.1500
 742 | 5.9000 -1.9400 5.9400
 743 | 4.2000 -2.5700 9.8000
 744 | 4.6600 -1.1100 8.7600
 745 | -8.9600 -3.7200 12.1500
 746 | 1.2900 -5.3100 6.0300
 747 | 2.1300 -4.7500 6.0400
 748 | 2.9700 -4.1900 6.0500
 749 | 3.7400 -3.5600 6.0600
 750 | 4.2800 -2.6400 6.0300
 751 | 5.4400 -2.4800 6.0900
 752 | 6.0700 -1.6700 6.0700
 753 | -6.6800 -5.2500 14.1100
 754 | -9.2300 -2.7400 12.0100
 755 | -6.8300 -4.3000 7.5700
 756 | 1.5500 -5.1500 6.1700
 757 | 2.4200 -4.6300 6.1900
 758 | 3.2100 -4.0200 6.1900
 759 | 3.9400 -3.3300 6.1900
 760 | -5.8300 -4.7000 12.8300
 761 | 5.6700 -2.2900 6.2200
 762 | -4.4000 -1.0900 14.9200
 763 | -6.0800 -5.1900 11.5700
 764 | -5.1800 -3.4500 13.8200
 765 | 1.2700 -5.4400 6.3100
 766 | 2.0900 -4.8600 6.3200
 767 | 2.8800 -4.2500 6.3200
 768 | 3.6900 -3.6700 6.3300
 769 | 4.2500 -2.7600 6.3100
 770 | 5.5800 -2.3400 6.3500
 771 | 4.0900 -2.2800 10.4600
 772 | 1.2500 -3.7100 11.6400
 773 | -5.7500 -4.1700 14.2500
 774 | 2.2700 -4.1600 10.4700
 775 | 2.1000 -4.8800 6.4500
 776 | 2.8800 -4.2500 6.4600
 777 | 3.7000 -3.6700 6.4700
 778 | 4.2600 -2.7900 6.4400
 779 | -6.0000 -4.3300 13.8200
 780 | 6.0200 -1.7700 6.4800
 781 | -5.5900 -4.0500 14.6800
 782 | -4.0100 -0.6200 15.9400
 783 | 3.4000 -3.1700 10.7400
 784 | 1.8200 -5.1600 6.5900
 785 | 2.5700 -4.5000 6.5900
 786 | 3.3700 -3.9000 6.6000
 787 | 4.0900 -3.2000 6.5900
 788 | -6.3500 -5.4700 11.4200
 789 | 5.8300 -2.0100 6.6200
 790 | 1.8800 -4.7900 9.5300
 791 | 3.0500 -4.1700 9.5600
 792 | 4.6200 -0.6200 9.1800
 793 | -6.2900 -5.7100 11.7200
 794 | 2.1400 -4.9200 6.7300
 795 | 2.8600 -4.2300 6.7200
 796 | 3.6800 -3.6600 6.7300
 797 | 4.2800 -2.8000 6.7200
 798 | 3.0900 -4.2000 9.4200
 799 | 6.0700 -1.5200 6.7300
 800 | 3.7900 -3.4900 9.5500
 801 | -6.2600 -4.8100 14.2500
 802 | 2.0800 -3.1500 12.0600
 803 | 1.5600 -5.3200 6.8500
 804 | 2.3900 -4.7600 6.8600
 805 | 3.1100 -4.0600 6.8600
 806 | 3.9000 -3.4500 6.8700
 807 | 4.4100 -2.4800 6.8300
 808 | 5.8700 -1.7600 6.8700
 809 | -5.2600 -4.2800 11.8400
 810 | -5.4600 -3.7400 14.5300
 811 | -6.7700 -5.4500 13.8300
 812 | 1.5700 -5.3400 6.9900
 813 | 2.4100 -4.7800 7.0000
 814 | 3.1000 -4.0500 6.9900
 815 | 3.8900 -3.4400 7.0000
 816 | 4.4200 -2.5000 6.9700
 817 | 2.2700 -3.3800 11.6600
 818 | -6.2400 -5.7700 12.0000
 819 | -3.8100 -0.3900 16.3800
 820 | 1.7100 -3.3300 12.0600
 821 | 1.5800 -5.3400 7.1200
 822 | 2.4400 -4.8200 7.1400
 823 | 3.1200 -4.0700 7.1300
 824 | 3.9000 -3.4500 7.1400
 825 | 4.4400 -2.5200 7.1100
 826 | 2.0500 -4.8300 9.2700
 827 | 2.9100 -3.6800 10.4700
 828 | -6.2000 -5.2000 12.9800
 829 | -4.6000 -3.5200 12.5500
 830 | 3.2900 -3.9800 9.5600
 831 | 2.0900 -5.0200 7.2700
 832 | 2.8800 -4.4200 7.2700
 833 | 3.6200 -3.7300 7.2700
 834 | 4.2500 -2.9300 7.2600
 835 | -8.9000 -2.1100 13.5100
 836 | 4.2100 -2.4200 9.9300
 837 | -3.2200 -0.3300 15.5100
 838 | 3.0000 -3.6300 10.4700
 839 | 4.6400 -0.5100 8.9000
 840 | 1.8800 -5.2400 7.4000
 841 | 2.7000 -4.6700 7.4100
 842 | 3.4200 -3.9700 7.4100
 843 | 4.0900 -3.2000 7.4000
 844 | 4.5700 -2.2200 7.3700
 845 | -4.9300 -3.7400 13.1100
 846 | -3.4500 -0.5100 15.5100
 847 | 2.8900 -3.3400 11.1400
 848 | -8.7100 -3.3900 13.5100
 849 | -5.4100 -3.6500 15.1100
 850 | 2.1600 -5.1100 7.5400
 851 | 2.9400 -4.4800 7.5500
 852 | 3.6600 -3.7800 7.5500
 853 | 4.2600 -2.9400 7.5300
 854 | 4.6800 -1.8700 7.4800
 855 | 3.0600 -3.2300 11.1400
 856 | -5.3800 -3.6700 13.9600
 857 | -4.7900 -3.4500 13.2500
 858 | -5.4200 -3.9400 13.5300
 859 | 4.4000 -1.0400 10.0100
 860 | 2.2700 -5.0900 7.6800
 861 | 3.0100 -4.4200 7.6800
 862 | 3.7400 -3.7200 7.6800
 863 | 4.3100 -2.8500 7.6600
 864 | 4.7200 -1.7600 7.6100
 865 | 3.1400 -3.1700 11.1400
 866 | -5.7800 -4.2900 13.3900
 867 | -5.1600 -3.3000 14.1000
 868 | -4.9500 -4.0300 12.1200
 869 | 4.4800 0.3700 8.8900
 870 | 2.0100 -5.2400 7.8100
 871 | 2.8300 -4.6700 7.8200
 872 | 3.5500 -3.9800 7.8200
 873 | 4.1700 -3.1400 7.8100
 874 | 4.6200 -2.1200 7.7700
 875 | 4.1600 -0.7600 10.5600
 876 | -5.8300 -4.0700 13.9600
 877 | 4.4200 0.0700 9.5900
 878 | -5.5500 -4.2500 13.2500
 879 | -5.9900 -5.1500 11.7100
 880 | 1.5700 -5.3500 7.9300
 881 | 2.5000 -4.9100 7.9500
 882 | 3.2400 -4.2300 7.9500
 883 | 3.9300 -3.4900 7.9500
 884 | 4.4500 -2.5500 7.9200
 885 | 4.7500 -1.3200 7.8600
 886 | -0.2400 -4.4100 10.2400
 887 | 3.8400 -3.2300 9.8100
 888 | -5.2000 -3.4200 14.3900
 889 | -4.6000 -1.3100 14.9200
 890 | -6.4600 -5.8200 11.7200
 891 | 2.0600 -5.1500 8.0800
 892 | 2.9100 -4.6100 8.0900
 893 | 3.6300 -3.9100 8.0900
 894 | 4.2300 -3.0700 8.0700
 895 | 4.6600 -2.0100 8.0400
 896 | 3.7000 -3.2300 10.0800
 897 | 2.5000 -4.1300 10.3400
 898 | 4.2200 -1.3500 10.4200
 899 | -7.6200 -5.4300 11.4900
 900 | -5.3900 -4.2800 11.6900
 901 | 1.8300 -5.1800 8.2000
 902 | 2.7400 -4.7300 8.2300
 903 | 3.4600 -4.0200 8.2200
 904 | 4.1000 -3.2300 8.2100
 905 | 4.6000 -2.2300 8.1800
 906 | -4.4200 -3.1000 12.2500
 907 | -5.0200 -3.6400 13.3900
 908 | 2.5600 -4.3600 9.9500
 909 | 2.6900 -3.2600 11.4000
 910 | -5.2400 -4.3100 12.6900
 911 | 1.6400 -5.2700 8.3300
 912 | 2.5600 -4.8200 8.3600
 913 | 3.2700 -4.1100 8.3500
 914 | 3.9700 -3.3900 8.3500
 915 | 4.5100 -2.4600 8.3200
 916 | 2.7200 -4.3900 9.6900
 917 | -9.1300 -3.3400 12.4200
 918 | 3.5300 -3.1700 10.4800
 919 | -3.7700 -0.4500 16.5300
 920 | -5.1800 -4.2300 12.6900
 921 | 1.3100 -5.5100 8.4600
 922 | 2.1500 -4.9500 8.4800
 923 | 3.0300 -4.4500 8.4900
 924 | 3.7500 -3.7400 8.4900
 925 | 4.3300 -2.8800 8.4700
 926 | -7.4400 -4.0900 10.3500
 927 | 2.9000 -3.9900 10.0800
 928 | -4.6600 -3.5900 12.4000
 929 | -3.2400 -0.4600 15.6600
 930 | -4.2800 -0.9300 15.9400
 931 | 1.5700 -5.3500 8.6000
 932 | -5.9600 -5.3300 12.1400
 933 | 3.2000 -4.1900 8.6200
 934 | 3.9300 -3.4900 8.6200
 935 | 4.4500 -2.5500 8.5900
 936 | 2.6500 -4.4700 9.5500
 937 | -3.8800 -0.6200 15.0700
 938 | 3.3800 -3.7800 9.8200
 939 | -4.3000 -1.0600 15.0700
 940 | -4.7800 -3.7700 12.5500
 941 | -5.7300 -4.2000 14.5400
 942 | 1.6600 -5.3000 8.7400
 943 | -6.2800 -4.9300 11.2600
 944 | 3.2700 -4.1100 8.7500
 945 | 3.9800 -3.4000 8.7500
 946 | 4.4800 -2.4300 8.7200
 947 | 3.8600 -3.1000 9.9500
 948 | -5.6800 -4.7300 12.4100
 949 | -5.5100 -3.8400 15.1100
 950 | -4.4700 -3.0500 12.8200
 951 | -5.1900 -4.3700 12.4100
 952 | 1.7400 -5.2400 8.8700
 953 | -4.5000 -3.3200 12.4000
 954 | 3.3500 -4.0600 8.8900
 955 | 4.0400 -3.3100 8.8800
 956 | 4.5000 -2.3000 8.8500
 957 | 2.6000 -4.2400 10.0800
 958 | -5.1300 -4.1200 11.8400
 959 | -9.0300 -3.5300 12.1500
 960 | -5.8700 -4.9700 12.5500
 961 | -6.8700 -5.6400 13.5400
 962 | 1.9500 -5.0300 9.0100
 963 | 2.9100 -4.4600 9.0300
 964 | 3.6900 -3.8400 9.0300
 965 | 4.2300 -2.9300 9.0100
 966 | 2.0100 -4.7900 9.4000
 967 | -8.5400 -1.0800 11.3000
 968 | -5.6000 -4.6600 12.4100
 969 | -6.4700 -5.8100 12.5700
 970 | -5.6400 -3.9900 14.2500
 971 | 4.6700 -0.8100 8.6200
 972 | 1.9400 -5.0100 9.1400
 973 | 2.8900 -4.4400 9.1600
 974 | 3.6700 -3.8100 9.1600
 975 | 4.2200 -2.9100 9.1400
 976 | -5.3500 -3.5600 14.1000
 977 | -5.1000 -2.7900 14.4700
 978 | -8.6800 -1.2100 9.5500
 979 | 2.6500 -3.3600 11.2700
 980 | -5.6100 -3.8700 14.2400
 981 | 1.3700 -3.3800 12.1800
 982 | 2.4600 -3.6200 3.7500
 983 | 4.1700 -0.2600 3.9400
 984 | 2.5800 -3.6200 3.8900
 985 | -8.5600 -0.9900 10.0800
 986 | 4.3800 -0.0400 4.1100
 987 | 2.6500 -3.7100 4.0500
 988 | -4.6800 -2.4600 13.5500
 989 | 4.7900 -0.2100 4.2900
 990 | -9.0000 -1.4200 12.0000
 991 | -4.8300 -1.1700 13.8600
 992 | 5.2000 -0.8700 4.4600
 993 | 4.8500 0.1000 4.4500
 994 | -5.1200 -3.2000 14.1600
 995 | 5.3800 -1.1600 4.6200
 996 | 5.1900 -0.1700 4.6100
 997 | -5.0200 -2.2900 14.7800
 998 | -4.6400 -2.2400 13.5600
 999 | 5.5700 -0.9400 4.7900
1000 | 5.2800 0.0400 4.7700
1001 | -5.0500 -2.3900 14.7800
1002 | 5.5900 -1.6400 4.9200
1003 | 5.6900 -0.6300 4.9400
1004 | 5.2300 0.3300 4.9100
1005 | -8.4600 -2.9800 11.1700
1006 | -4.9100 -2.6800 13.8500
1007 | 5.8100 -0.7100 5.0900
1008 | 5.4200 0.2500 5.0600
1009 | -6.9000 -2.6800 6.4200
1010 | -4.7800 -1.2600 13.7100
1011 | 5.9100 -1.2100 5.2400
1012 | 5.7700 -0.2200 5.2300
1013 | 4.2900 -1.0500 10.2900
1014 | -4.7900 -1.1600 13.7100
1015 | 5.9700 -1.1000 5.3800
1016 | 5.7900 -0.1100 5.3700
1017 | -4.8000 -0.9600 13.5500
1018 | -4.9200 -2.9800 13.7000
1019 | 5.9800 -1.6100 5.5200
1020 | 5.9900 -0.6000 5.5300
1021 | 5.5400 0.3600 5.4900
1022 | -4.6900 -1.7600 13.7100
1023 | -5.1800 -2.6000 15.2400
1024 | 6.0600 -0.6900 5.6700
1025 | 5.6600 0.2700 5.6400
1026 | -5.1000 -2.5900 14.6200
1027 | -9.2200 -2.1400 12.2800
1028 | 6.1200 -0.6900 5.8200
1029 | 5.7000 0.2800 5.7800
1030 | -8.7200 -4.8800 11.5500
1031 | 4.6100 -1.1200 9.3200
1032 | 6.1600 -0.9800 5.9600
1033 | 5.9400 0.0000 5.9400
1034 | -8.3200 -1.1500 14.6000
1035 | -8.7900 -2.0200 9.5600
1036 | 6.1400 -1.2900 6.0900
1037 | 6.0900 -0.2900 6.0900
1038 | -9.2100 -2.4400 11.8800
1039 | -9.1600 -2.9400 11.8800
1040 | -4.7500 -1.5600 13.8600
1041 | 6.1900 -1.0800 6.2300
1042 | 6.0100 -0.0900 6.2300
1043 | 4.6200 -0.5200 9.1800
1044 | -7.1100 -2.3000 6.4300
1045 | 6.1700 -1.2900 6.3700
1046 | 6.0800 -0.2900 6.3700
1047 | -8.7100 -1.4100 9.2800
1048 | -8.9400 -4.7300 12.1600
1049 | -7.6900 -3.1400 7.3900
1050 | 6.1900 -0.8800 6.5100
1051 | 5.8600 0.1000 6.4900
1052 | -8.1400 -0.8300 14.7300
1053 | -9.1200 -3.1400 11.8800
1054 | -4.7100 -2.0600 13.7100
1055 | 6.1500 -1.2900 6.6500
1056 | 6.0200 -0.2900 6.6400
1057 | -8.7500 -3.6100 11.6000
1058 | -3.0700 -5.0600 3.7500
1059 | -4.7000 -1.9600 13.7100
1060 | 6.0900 -1.3900 6.7800
1061 | 6.0400 -0.3900 6.7800
1062 | -8.8400 -4.6100 12.7000
1063 | -7.1500 -2.5300 6.5600
1064 | -8.6700 -3.2300 11.4100
1065 | 6.0800 -1.0900 6.9200
1066 | 5.8500 -0.1000 6.9100
1067 | -9.2000 -2.0400 12.0100
1068 | -9.1900 -2.7400 11.8800
1069 | -4.7500 -1.7600 13.8600
1070 | 6.0100 -0.8800 7.0600
1071 | -7.9800 -3.4400 10.9000
1072 | -8.7800 -0.8000 12.1300
1073 | -6.1500 -4.7000 7.8300
1074 | -4.7300 -1.2600 13.5600
1075 | -8.5200 -2.3900 10.6400
1076 | -8.8900 -1.1100 12.1300
1077 | -5.1800 -3.1000 14.6200
1078 | 4.6500 -1.0200 8.9000
1079 | -8.5000 -2.4900 10.6400
1080 | -8.9300 -3.7200 12.0100
1081 | 4.6500 -1.6200 8.9000
1082 | -5.0600 -2.4900 14.9300
1083 | -8.7900 -0.9000 11.9900
1084 | -8.7300 -3.1900 13.9300
1085 | 4.6200 -1.9200 8.8900
1086 | -8.5400 -1.0900 10.2200
1087 | -7.2900 -2.4000 6.6900
1088 | 4.4600 0.3700 7.4900
1089 | 4.5700 -1.2200 9.4600
1090 | -5.0100 -1.9800 14.9300
1091 | 4.3800 -0.4400 10.0100
1092 | 4.5900 -2.0200 8.8900
1093 | 4.5800 0.0800 7.6400
1094 | -8.9600 -4.0200 12.0200
1095 | 4.5600 -2.1300 8.8900
1096 | 4.2600 -0.3500 10.2900
1097 | 4.7500 -1.0100 7.7900
1098 | 4.6100 -0.0200 7.7800
1099 | -8.4400 -2.5800 10.7700
1100 | -4.9300 -2.2800 14.1600
1101 | -8.9000 -1.2100 12.5400
1102 | 4.7300 -0.8100 7.9300
1103 | 4.5500 0.1800 7.9200
1104 | -4.6600 -1.5500 13.5600
1105 | -4.6300 -1.3500 13.4100
1106 | -8.8800 -2.4000 13.7800
1107 | 4.7100 -1.8100 8.0600
1108 | 4.7200 -0.8100 8.0600
1109 | 4.5500 0.1800 8.0600
1110 | -8.7700 -2.6200 9.3000
1111 | -5.0700 -2.2900 14.4700
1112 | 4.7100 -1.7100 8.2000
1113 | 4.7100 -0.7100 8.2000
1114 | 4.5000 0.2800 8.1900
1115 | -8.9600 -4.4300 12.0200
1116 | -4.9200 -0.8700 14.1700
1117 | 4.6900 -1.8100 8.3400
1118 | 4.6900 -0.8100 8.3400
1119 | 4.5100 0.1700 8.3300
1120 | -8.9600 -4.5300 12.0200
1121 | -5.0600 -2.7900 14.3200
1122 | 4.6900 -1.7100 8.4800
1123 | 4.6800 -0.7100 8.4800
1124 | 4.4700 0.2700 8.4700
1125 | -7.3000 -4.6800 14.8400
1126 | -8.9400 -4.5200 12.2900
1127 | -3.4800 -2.8600 12.3600
1128 | -1.0700 -2.0600 12.9100
1129 | -6.5100 -3.5500 15.4900
1130 | -7.5900 -4.3100 15.0100
1131 | -7.2400 -3.8000 15.3600
1132 | -4.4700 -1.1400 13.1100
1133 | -8.6200 -1.3800 13.9100
1134 | 2.1900 -2.5100 12.4300
1135 | 0.2200 -5.0300 9.9300
1136 | -6.4400 -2.2900 15.6300
1137 | -7.7100 -1.9600 15.5300
1138 | -8.1700 -2.5600 15.4200
1139 | -0.8700 -5.1900 9.7500
1140 | -0.1800 -4.9200 9.9900
1141 | -8.6000 -2.4600 15.2500
1142 | -0.9600 -5.3200 9.6500
1143 | -8.0700 -3.0200 15.3900
1144 | -4.8700 -3.3300 13.4700
1145 | -8.1500 -3.3600 11.0300
1146 | -0.0700 -4.6600 10.0600
1147 | -2.0800 -1.7900 12.6600
1148 | -6.6700 -4.0900 15.2600
1149 | -7.9100 -2.2000 15.5100
1150 | -1.7700 -2.7300 12.4100
1151 | -3.1300 -2.9600 12.1900
1152 | -7.4900 -4.1900 15.1200
1153 | 1.2600 -2.2600 12.9500
1154 | -2.4500 -1.6900 12.5800
1155 | -2.5700 -1.6600 12.5700
1156 | -7.2000 -3.9600 15.3000
1157 | -4.4200 -1.4500 13.1400
1158 | 1.9000 -2.5700 12.5700
1159 | -8.6000 -1.5800 14.0500
1160 | -6.7800 -3.6200 15.4600
1161 | -8.4800 -2.3800 10.9100
1162 | 1.0200 -2.3300 12.9900
1163 | -8.4700 -1.6400 15.1700
1164 | -2.5600 -1.9500 12.5200
1165 | 0.5100 -2.2000 13.0700
1166 | -8.8800 -3.8200 11.7400
1167 | -6.6200 -1.5400 15.5700
1168 | -6.2800 -4.1900 15.1700
1169 | -9.1100 -1.8300 12.8200
1170 | -6.4600 -3.1300 15.6000
1171 | -8.9200 -4.1200 11.7500
1172 | -7.6100 -1.5600 15.5100
1173 | -8.7900 -1.8200 9.5600
1174 | -8.7500 -2.1100 10.1000
1175 | -4.1700 -3.1100 12.0900
1176 | -1.0800 -1.7700 12.9600
1177 | -6.4200 -3.5700 15.4600
1178 | -8.4800 -2.4800 10.9100
1179 | -7.3700 -4.2200 15.1400
1180 | 0.0900 -3.5000 12.3200
1181 | -2.0900 -1.5000 12.7100
1182 | -8.7600 -1.8100 10.1000
1183 | -2.7800 -2.6100 12.2300
1184 | -2.6800 -3.0700 12.0000
1185 | -2.8200 -1.3100 12.5700
1186 | -8.4300 -1.1600 14.1800
1187 | -4.6700 -2.6600 13.4900
1188 | 0.1700 -3.3800 12.4000
1189 | 3.6000 0.3100 11.0500
1190 | -8.5600 -1.3900 10.4900
1191 | -7.2400 -1.6600 15.5800
1192 | -8.6500 -3.0100 9.2900
1193 | -3.9800 -0.9900 12.8700
1194 | 5.5400 -0.3100 7.2300
1195 | -0.2300 -4.7700 10.0500
1196 | -4.5400 -0.8400 13.0500
1197 | -3.3300 -1.7400 12.7200
1198 | -8.7700 -1.7100 9.4200
1199 | -8.8500 -2.2000 13.7800
1200 | -8.6000 -1.8900 15.1700
1201 | -6.7000 -1.9400 15.6200
1202 | -5.2600 -2.6000 15.3600
1203 | 5.9600 -0.5700 7.0500
1204 | -9.2300 -2.9400 12.5600
1205 | -2.9800 -1.5500 12.6100
1206 | -2.8900 -2.4400 12.3700
1207 | 3.5400 0.0400 11.2800
1208 | 2.8000 0.2400 11.9600
1209 | -8.8900 -3.0000 14.3400
1210 | -5.8100 -1.0400 15.7300
1211 | 1.6200 -2.3500 12.8000
1212 | -7.7400 -2.1000 15.5400
1213 | 5.6100 -0.6200 7.3000
1214 | -6.5400 -4.5700 14.8900
1215 | -5.1700 -1.7800 15.3500
1216 | 3.9300 -0.2000 10.8300
1217 | 3.2700 -0.0300 11.6800
1218 | 2.4600 0.1900 12.2200
1219 | -8.6200 -2.9000 9.9700
1220 | -2.6600 -1.9200 12.5200
1221 | -0.3200 -1.8300 13.0800
1222 | -0.4400 -1.8000 13.0700
1223 | 5.0300 -0.6000 7.3100
1224 | -3.0100 -2.7000 12.2800
1225 | -5.2500 -2.8900 15.3200
1226 | 3.7300 -0.2900 11.1500
1227 | 3.0200 -0.1000 11.9000
1228 | 2.2000 0.1200 12.4300
1229 | -8.5500 -0.9800 11.4400
1230 | -3.8200 -1.6100 12.8200
1231 | -4.6100 -2.3900 13.4700
1232 | -3.1400 -1.7900 12.6600
1233 | 5.9100 -0.9900 7.1600
1234 | 4.9000 -0.7000 7.3200
1235 | -7.4800 -3.7400 15.3400
1236 | -4.3800 -2.7500 12.8100
1237 | 3.6500 -0.4100 11.2700
1238 | 2.9400 -0.2200 12.0100
1239 | 2.1100 0.0100 12.5300
1240 | -8.6100 -4.1800 14.0700
1241 | -5.1300 -2.2200 15.2000
1242 | -8.6700 -2.5900 15.2100
1243 | -3.2900 -2.4700 12.5000
1244 | -4.0400 -2.5700 12.4800
1245 | 5.4200 -1.0000 7.3600
1246 | -3.3500 -1.4500 12.7400
1247 | -4.1500 -1.9600 12.8200
1248 | 3.9600 -0.6300 10.8800
1249 | 3.3000 -0.4600 11.7400
1250 | 2.5200 -0.2400 12.3300
1251 | 1.6500 0.0000 12.7600
1252 | -8.7800 -2.2100 9.9700
1253 | -8.3600 -2.6600 15.3600
1254 | -3.2300 -2.0600 12.6300
1255 | -4.1100 -1.5300 12.9000
1256 | -0.6800 -1.7400 13.0300
1257 | 5.1700 -1.0700 7.3600
1258 | -0.2300 -3.1300 12.6300
1259 | 0.6200 -3.0700 12.6300
1260 | 3.7500 -0.7200 11.1900
1261 | 3.0700 -0.5300 11.9800
1262 | 2.2600 -0.3100 12.5200
1263 | 1.3600 -0.0600 12.9000
1264 | -8.4300 -2.9800 10.3600
1265 | -3.4900 -1.5600 12.7600
1266 | -8.4400 -1.9200 15.2800
1267 | 1.7700 -2.6700 12.6000
1268 | 5.7300 -1.3700 7.2300
1269 | 4.7700 -1.1000 7.5000
1270 | -7.3400 -1.0600 15.5200
1271 | -4.7100 -3.2300 13.2800
1272 | 3.5400 -0.8000 11.4800
1273 | 2.8200 -0.6000 12.2000
1274 | 1.9800 -0.3700 12.6900
1275 | 1.0400 -0.1100 12.9900
1276 | -8.4800 -2.8800 10.3600
1277 | -6.6400 -1.6700 15.5900
1278 | -5.9100 -2.0000 15.5900
1279 | -3.4700 -2.4200 12.5800
1280 | -5.8700 -3.0000 15.5400
1281 | 5.4900 -1.4500 7.2800
1282 | -9.2200 -2.5400 11.8800
1283 | -1.0600 -3.2000 12.4400
1284 | 0.3500 -2.2900 13.0400
1285 | 3.3200 -0.8800 11.7700
1286 | 2.5600 -0.6700 12.4000
1287 | 1.6900 -0.4300 12.8400
1288 | 0.7100 -0.1600 13.0400
1289 | -8.8900 -4.6200 12.5600
1290 | -8.5700 -2.7600 15.2400
1291 | -6.1200 -1.9500 15.6000
1292 | 0.6400 -4.8500 9.9500
1293 | -3.6000 -2.6800 12.4700
1294 | 5.2400 -1.5200 7.2700
1295 | -2.5400 -2.3900 12.3300
1296 | -3.9300 -2.1600 12.6900
1297 | 3.8900 -1.1700 11.0300
1298 | 3.2300 -0.9900 11.8700
1299 | 2.4600 -0.7800 12.4900
1300 | 1.5800 -0.5400 12.9100
1301 | 0.5700 -0.2600 13.0700
1302 | -8.5200 -2.5900 10.5000
1303 | -1.0600 -2.3400 12.8500
1304 | -8.1200 -3.6000 15.1900
1305 | -1.6400 -1.7700 12.8100
1306 | -2.0200 -1.6600 12.7000
1307 | 4.9200 -1.5700 7.1500
1308 | -1.0200 -5.0100 9.8000
1309 | -4.0700 -1.7000 12.8600
1310 | 3.7500 -1.2700 11.2400
1311 | 3.0600 -1.0800 12.0400
1312 | 2.2700 -0.8700 12.6100
1313 | 1.3700 -0.6200 12.9900
1314 | 0.3400 -0.3400 13.1000
1315 | -9.2100 -2.2400 12.5500
1316 | -9.1800 -2.8400 12.8300
1317 | 0.4800 -4.9600 9.9400
1318 | -2.0400 -5.1800 9.0500
1319 | -4.1800 -2.9700 12.2000
1320 | 5.2000 -1.7900 7.1300
1321 | -4.4400 -1.7300 13.2400
1322 | -7.6900 -0.6900 15.3800
1323 | 3.9200 -1.4600 11.0000
1324 | 3.2600 -1.2800 11.8500
1325 | 2.5000 -1.0700 12.4800
1326 | 1.6300 -0.8300 12.9200
1327 | 0.6500 -0.5600 13.1300
1328 | -0.5300 -0.2400 12.9800
1329 | -8.6400 -2.0000 10.5000
1330 | -6.7600 -0.6500 15.5300
1331 | -7.7000 -3.1100 15.4900
1332 | -3.7900 -2.7800 12.3900
1333 | 5.4100 -1.9900 6.9800
1334 | -7.2200 -4.5500 14.9400
1335 | 1.0100 -3.3100 12.3900
1336 | -0.5300 -2.6200 12.8800
1337 | 3.4500 -1.4700 11.6500
1338 | 2.7100 -1.2600 12.3400
1339 | 1.8700 -1.0300 12.8200
1340 | 0.9300 -0.7800 13.1200
1341 | -0.1900 -0.4700 13.0800
1342 | -6.1300 -2.3200 3.4600
1343 | -6.1200 -4.6000 3.6500
1344 | -5.1300 -5.1500 3.6600
1345 | -7.1100 -1.4400 3.5300
1346 | -6.4000 -2.1600 3.5600
1347 | -6.3900 -3.6900 3.7200
1348 | -5.9800 -4.7500 3.8000
1349 | -7.7100 -5.5600 11.7500
1350 | -3.2100 -4.8900 3.6600
1351 | -2.1200 -5.6100 3.6700
1352 | -7.1700 -1.3600 3.6700
1353 | -6.4800 -2.0900 3.7000
1354 | -6.3400 -3.4700 3.8300
1355 | -6.0100 -4.6200 3.9300
1356 | -8.0000 -3.5000 10.6000
1357 | -3.3000 -4.8300 3.8000
1358 | -2.5500 -5.4900 3.8200
1359 | -7.9600 -4.7300 14.4700
1360 | -7.0200 -1.4900 3.8200
1361 | -6.2800 -2.1600 3.8400
1362 | -6.2500 -3.7000 3.9900
1363 | -5.8700 -4.7700 4.0700
1364 | -6.6200 -4.5800 7.9100
1365 | -3.3000 -4.8300 3.9400
1366 | -2.5400 -5.4800 3.9600
1367 | -8.9100 -4.8300 12.2800
1368 | -7.0700 -0.8100 3.8800
1369 | -6.6100 -1.7700 3.9600
1370 | -5.8300 -2.4000 3.9700
1371 | -6.0700 -4.3900 4.1900
1372 | -5.3300 -5.0600 4.2100
1373 | -3.4100 -4.6500 4.0700
1374 | -2.7100 -5.3600 4.1000
1375 | -7.5800 -3.9400 10.4800
1376 | -7.9200 -3.9100 9.6700
1377 | -7.0300 -1.1900 4.0600
1378 | -6.3600 -1.9300 4.1000
1379 | -6.9800 -4.6500 9.5500
1380 | -5.9200 -4.5000 4.3300
1381 | -2.2100 -4.8100 7.5100
1382 | -3.2600 -4.7700 4.2100
1383 | -2.4800 -5.3900 4.2300
1384 | -8.1700 -5.1500 13.7900
1385 | -6.9600 -4.6200 9.6900
1386 | -6.8900 -1.0100 4.1900
1387 | -6.2900 -1.8500 4.2300
1388 | -6.7900 -4.7400 9.5500
1389 | -5.7600 -4.6200 4.4700
1390 | -7.1400 -4.3600 10.0800
1391 | -2.9400 -5.0100 4.3600
1392 | -1.6700 -5.5200 9.0600
1393 | -6.9200 -4.1900 7.4900
1394 | -7.3100 -4.4600 8.6000
1395 | -6.6900 -1.3900 4.3600
1396 | -5.8500 -1.9400 4.3600
1397 | -8.4800 -5.4100 12.1600
1398 | -7.8600 -5.7500 12.4400
1399 | -3.2400 -4.5800 4.4800
1400 | -1.7100 -5.4000 9.2000
1401 | -2.3400 -4.9200 8.9100
1402 | -0.9600 -4.6500 9.9800
1403 | -2.0800 -4.9100 9.3200
1404 | -6.1100 -1.6300 4.4900
1405 | -8.6100 -3.0400 9.0900
1406 | -5.6400 -4.3200 4.7100
1407 | -7.7000 -5.8600 13.1300
1408 | -2.8900 -4.7800 4.6100
1409 | -8.3000 -5.0000 13.7900
1410 | -6.6200 -4.7300 8.3300
1411 | -7.9300 -5.0100 14.2000
1412 | -6.0800 -1.4400 4.6100
1413 | -5.2300 -1.9700 4.6100
1414 | -5.3900 -4.1400 4.8200
1415 | -7.4100 -4.4200 8.7400
1416 | -2.9100 -4.6600 4.7500
1417 | -8.8600 -3.3200 14.5700
1418 | -7.1600 -4.1600 7.7700
1419 | -7.5800 -5.6000 11.9000
1420 | -1.9000 -5.6600 8.2500
1421 | -5.4000 -1.7100 4.7300
1422 | -8.3100 -3.2600 10.3200
1423 | -7.5300 -5.5500 11.6300
1424 | -3.0300 -4.3200 4.8600
1425 | -2.1000 -4.6100 9.5800
1426 | -8.5900 -5.2200 12.5600
1427 | -8.2500 -4.6100 14.3300
1428 | -8.5200 -3.2400 9.3700
1429 | -5.6200 -1.3500 4.8500
1430 | -2.3500 -4.5200 7.3500
1431 | -4.7100 -3.9900 5.0600
1432 | -6.9900 -4.6700 9.4200
1433 | -2.4200 -4.6800 5.0000
1434 | -8.1500 -3.5900 8.8400
1435 | -7.8000 -5.6900 12.3100
1436 | -2.0000 -5.1800 7.5300
1437 | -5.6400 -1.2100 4.9800
1438 | -4.9300 -1.9200 5.0100
1439 | -4.8400 -3.6600 5.1700
1440 | -7.6500 -5.5600 11.7600
1441 | -2.6200 -4.2900 5.1100
1442 | -8.7600 -3.5100 14.5800
1443 | -8.0700 -5.6700 12.9900
1444 | -8.2400 -4.7600 14.2000
1445 | -1.7800 -5.6600 8.6600
1446 | -5.1400 -1.7000 5.1400
1447 | -6.3700 -4.9000 8.4700
1448 | -8.3600 -5.0700 13.5100
1449 | -7.8900 -4.3200 14.8700
1450 | -8.2200 -3.3100 10.4500
1451 | -7.8000 -5.8400 12.5800
1452 | -8.7200 -5.0900 11.7500
1453 | -7.9800 -5.5600 13.4000
1454 | -8.2700 -3.2700 8.5500
1455 | -4.9100 -1.8900 5.2900
1456 | -4.7500 -3.7000 5.4400
1457 | -7.2300 -4.5100 8.6100
1458 | -2.3700 -3.9900 5.3600
1459 | -7.1500 -4.5800 8.7400
1460 | -8.4900 -3.2100 8.9600
1461 | -8.8200 -3.4200 14.5700
1462 | -2.4300 -4.8900 8.3500
1463 | -4.9900 -1.8200 5.4300
1464 | -8.3700 -3.2300 8.6900
1465 | -4.2500 -4.1900 5.6000
1466 | -7.0100 -4.4400 8.0500
1467 | -1.8500 -4.6100 5.5300
1468 | -7.0000 -4.6800 9.2800
1469 | -7.7300 -3.8900 8.0400
1470 | -8.6200 -3.0200 9.7700
1471 | -1.7800 -5.6900 7.9700
1472 | -5.0900 -1.7800 5.5700
1473 | -4.9600 -3.1700 5.6800
1474 | -4.4100 -4.0700 5.7400
1475 | -2.0100 -5.3100 8.9200
1476 | -1.9800 -4.4400 5.6600
1477 | -1.2900 -5.1800 5.6900
1478 | -8.1700 -5.4700 13.2500
1479 | -8.1300 -3.6800 9.7900
1480 | -7.9100 -3.8900 9.8000
1481 | -7.5300 -4.2300 9.5400
1482 | -5.0700 -2.6800 5.7900
1483 | -4.6900 -3.7700 5.8600
1484 | -8.0200 -3.6100 8.1600
1485 | -2.2400 -4.1300 5.7800
1486 | -1.6000 -4.9300 5.8200
1487 | -0.7100 -5.4200 5.8300
1488 | -8.3100 -4.6900 14.1900
1489 | -1.2600 -4.5200 10.1200
1490 | -5.3000 -2.7900 5.9400
1491 | -4.7600 -3.6900 5.9900
1492 | -4.0000 -4.3500 6.0200
1493 | -2.0600 -4.9500 7.3800
1494 | -1.7300 -4.7700 5.9500
1495 | -0.8600 -5.4300 5.9700
1496 | -7.7900 -5.3200 13.9400
1497 | -7.8200 -5.8500 12.7200
1498 | -5.1300 -3.2000 6.1000
1499 | -4.5000 -4.0000 6.1500
1500 | -1.9500 -5.1300 7.3800
1501 | -2.1300 -4.3000 6.0700
1502 | -1.4900 -5.0900 6.1100
1503 | -7.7800 -4.0500 9.5400
1504 | -1.8000 -5.6900 8.5300
1505 | -1.9900 -5.1200 9.1900
1506 | -5.0200 -3.5300 6.2600
1507 | -4.2800 -4.2100 6.2900
1508 | -2.2700 -5.1700 8.3700
1509 | -1.9300 -4.5300 6.2200
1510 | -1.2900 -5.3100 6.2600
1511 | -8.6800 -3.2400 14.9800
1512 | -8.0300 -4.0200 15.0000
1513 | -8.4000 -3.4000 9.5100
1514 | -1.5700 -4.7600 9.7100
1515 | -4.0400 -4.3900 6.4400
1516 | -2.1500 -4.3100 6.3500
1517 | -1.5200 -5.1200 6.3900
1518 | -7.1700 -4.5800 9.1500
1519 | -8.0100 -5.2700 13.8000
1520 | -6.2800 -4.9200 9.1500
1521 | -2.1300 -5.3000 8.6500
1522 | -8.5200 -4.7900 13.5100
1523 | -8.1400 -5.6100 12.9800
1524 | -2.1700 -4.3400 6.5000
1525 | -1.5600 -5.1700 6.5400
1526 | -8.5800 -3.1500 9.3600
1527 | -7.8100 -3.6800 7.8900
1528 | -2.0100 -4.9900 9.3200
1529 | -1.4200 -5.3600 9.4700
1530 | -4.2700 -4.3400 6.7100
1531 | -8.6200 -3.0500 9.2200
1532 | -1.8400 -4.8700 6.6600
1533 | -8.3400 -5.3800 12.8400
1534 | -8.8200 -5.0500 12.0200
1535 | -6.9000 -4.7200 9.4200
1536 | -8.1900 -3.4900 8.7000
1537 | -2.4100 -4.8800 8.0700
1538 | -8.5600 -3.7200 14.7200
1539 | -1.9900 -4.7400 6.8000
1540 | -1.3200 -5.4900 6.8300
1541 | -8.2300 -3.4900 10.0600
1542 | -7.8100 -5.8400 12.9900
1543 | -6.9600 -4.5300 8.1900
1544 | -7.5200 -5.6800 12.0400
1545 | -8.4200 -4.9900 13.5100
1546 | -8.3800 -3.3500 9.9200
1547 | -1.7300 -5.2000 6.9600
1548 | -8.5800 -3.1400 9.5000
1549 | -8.0800 -3.6700 8.7100
1550 | -5.7800 -0.8700 15.8000
1551 | -7.3200 -4.2700 10.0800
1552 | -1.6100 -5.7900 8.1100
1553 | -1.9500 -5.5700 7.9700
1554 | -2.2800 -4.4400 7.0600
1555 | -1.7000 -5.3100 7.1100
1556 | -1.8100 -5.7100 8.1100
1557 | -7.2500 -4.5400 8.8800
1558 | -6.4600 -4.7100 8.0500
1559 | -2.1000 -5.4300 8.3800
1560 | -5.4500 -1.5900 5.7000
1561 | -8.0300 -4.8100 14.3400
1562 | -8.1400 -5.4300 13.3900
1563 | -2.0800 -4.8200 7.2300
1564 | -1.4000 -5.5700 7.2600
1565 | -7.7900 -3.2800 7.6300
1566 | -9.0000 -3.4200 12.9700
1567 | -8.8400 -3.9100 12.9700
1568 | -9.0400 -2.6300 11.4700
1569 | -5.8000 -2.6900 3.9900
1570 | -4.9900 -0.4400 16.5600
1571 | -6.9100 -0.7700 4.2200
1572 | -5.6400 -2.3700 4.1100
1573 | -6.1700 -3.4200 4.1800
1574 | -8.6800 -2.2000 10.3700
1575 | -8.8600 -1.2000 13.0900
1576 | -5.8700 -3.0900 4.2900
1577 | -6.1100 -4.1200 4.3300
1578 | -8.9300 -4.3200 12.4200
1579 | -9.1500 -2.6300 12.9600
1580 | -5.4500 -2.7500 4.3800
1581 | -6.0500 -3.8100 4.4600
1582 | -8.7700 -3.0900 13.9300
1583 | -6.4100 -0.8300 4.5800
1584 | -8.8700 -3.2000 14.7500
1585 | -5.2600 -2.7400 4.5000
1586 | -5.9000 -3.8000 4.5800
1587 | -8.8800 -3.6100 13.1000
1588 | -6.1500 -0.9100 4.7000
1589 | -4.9700 -2.3100 4.6100
1590 | -5.5000 -3.3600 4.6700
1591 | -5.1400 -0.5500 16.4100
1592 | -5.8400 -0.6700 4.8100
1593 | -8.8400 -3.4000 14.4800
1594 | -5.0900 -2.9200 4.7700
1595 | -5.4300 -3.9500 4.8100
1596 | -5.7500 -0.5700 4.9400
1597 | -8.9500 -2.4000 14.4700
1598 | -4.9300 -2.5000 4.8900
1599 | -5.1000 -3.5200 4.9100
1600 | -5.7200 -0.6600 5.0800
1601 | -8.9800 -2.4100 14.3300
1602 | -4.9900 -2.8100 5.0400
1603 | -8.8500 -4.1100 12.8300
1604 | -5.6100 -1.0600 5.2100
1605 | -4.9600 -2.7100 5.1700
1606 | -8.9200 -1.3100 12.8100
1607 | -8.8200 -3.4000 14.2000
1608 | -4.9700 -2.8100 5.3200
1609 | 4.5100 0.3100 6.7100
1610 | -5.4500 -1.3500 5.4800
1611 | -4.9300 -2.5000 5.4500
1612 | -4.8500 -3.5000 5.4500
1613 | -5.6600 -0.7600 5.6300
1614 | -5.1600 -1.7200 5.6000
1615 | -8.9900 -1.6100 13.2300
1616 | -8.6100 -1.5800 14.7400
1617 | -5.7000 -0.8600 5.7700
1618 | -8.8200 -2.0000 13.6400
1619 | -9.0400 -2.6200 13.2400
1620 | -5.7500 -0.5600 5.9000
1621 | -8.8800 -3.4100 13.2400
1622 | -8.7300 -2.0800 15.1500
1623 | -8.6600 -4.2900 13.2400
1624 | -8.6600 -4.4900 13.2400
1625 | -8.3000 -1.2500 15.1400
1626 | -5.7400 -0.8100 15.8400
1627 | -8.6500 -4.1900 13.6600
1628 | -5.7300 -0.9100 15.8500
1629 | -7.5200 -1.3200 6.7600
1630 | -8.8700 -2.8000 13.9200
1631 | -7.5800 -1.2200 6.8900
1632 | -8.5600 -1.5700 14.4700
1633 | -7.8300 -1.5400 7.0600
1634 | -8.9600 -2.5000 14.8800
1635 | -7.9400 -1.2500 7.2000
1636 | -7.9200 -2.2500 7.2100
1637 | -8.8000 -3.1900 14.8900
1638 | -7.9000 -0.8400 7.3200
1639 | -8.0400 -1.8600 7.3500
1640 | -5.2700 -4.2500 6.8500
1641 | -8.6100 -1.6800 15.0100
1642 | -8.1300 -1.5700 7.4900
1643 | -7.9400 -2.5600 7.4800
1644 | -8.8600 -3.1100 13.3800
1645 | -8.2700 -1.1800 7.6300
1646 | -8.1300 -2.1700 7.6300
1647 | -8.1900 -0.8400 14.5900
1648 | -8.1900 -0.7700 7.7500
1649 | -8.3200 -1.7800 7.7800
1650 | -7.9800 -3.0600 7.7600
1651 | -8.8900 -2.3000 15.0200
1652 | -8.4100 -1.2900 7.9100
1653 | -8.4000 -2.2900 7.9200
1654 | -8.0200 -3.2600 7.9000
1655 | -8.3700 -1.0800 8.0400
1656 | -8.5100 -2.1000 8.0600
1657 | -8.1300 -3.0700 8.0500
1658 | -8.9000 -2.6000 15.0200
1659 | -8.4500 -1.2900 8.1800
1660 | -8.5000 -2.3000 8.2000
1661 | -8.1700 -3.2700 8.1900
1662 | -9.2000 -2.0400 12.4100
1663 | -8.5200 -1.6000 8.3300
1664 | -8.4500 -2.5900 8.3400
1665 | -8.6500 -4.3900 13.3800
1666 | -8.7800 -2.9900 15.0200
1667 | -8.6000 -1.5000 8.4700
1668 | -8.5000 -2.5000 8.4700
1669 | -8.6200 -4.5900 13.3800
1670 | -8.5200 -1.4700 14.8800
1671 | -8.6400 -1.0000 8.6000
1672 | -8.6700 -2.0100 8.6100
1673 | -8.4600 -2.9900 8.6100
1674 | -8.9500 -2.7100 14.6100
1675 | -9.1600 -3.2400 12.5600
1676 | -8.7400 -1.5200 8.7500
1677 | -8.6700 -2.5100 8.7500
1678 | -8.0800 -0.8200 14.8700
1679 | -8.5600 -0.9700 13.7700
1680 | -8.7400 -1.2100 8.8700
1681 | -8.7500 -2.2200 8.8900
1682 | -8.8900 -2.9000 14.0600
1683 | -8.9700 -2.5100 14.7500
1684 | -8.6500 -1.0100 9.0000
1685 | -8.8100 -2.0200 9.0200
1686 | -8.6300 -3.0000 9.0200
1687 | -8.8700 -3.0000 14.8900
1688 | -8.6500 -1.1000 9.1400
1689 | -8.8200 -2.1200 9.1600
1690 | -8.6600 -1.7800 14.0500
1691 | -8.9100 -2.3000 14.6100
1692 | -9.0400 -1.5200 12.6800
1693 | 5.6500 0.2500 6.7500
1694 | 5.4800 -0.1400 7.1600
1695 | 5.7400 0.2100 6.7000
1696 | -6.1300 -0.9200 15.4800
1697 | -6.1200 -0.7800 15.4200
1698 | 4.7500 0.1600 6.9300
1699 | 4.6900 -0.2900 7.4700
1700 | 5.4700 0.2900 6.8000
1701 | 4.6800 -0.0200 7.1500
1702 | -8.4600 -3.0700 11.2000
1703 | -8.3100 -5.1200 11.3200
1704 | -4.7400 -0.6800 13.2100
1705 | 4.7400 0.2500 6.8200
1706 | -4.6700 -0.5300 12.9600
1707 | -1.0100 -0.2500 12.8800
1708 | 0.1000 0.0000 12.9900
1709 | 1.1200 0.0200 12.9300
1710 | -1.9100 -0.3800 12.6600
1711 | -2.2300 -0.3200 12.5600
1712 | -2.7800 -0.2900 12.4400
1713 | 5.0800 0.1000 7.0300
1714 | -6.4200 -3.8000 6.8000
1715 | -7.1300 -0.6900 3.8300
1716 | -8.6500 -5.0400 11.5800
1717 | -7.4100 -2.9900 7.0000
1718 | -5.2600 -5.1100 3.5800
1719 | -6.0800 -4.5200 7.4300
1720 | -4.5400 -4.2500 6.7500
1721 | -7.4900 -3.6000 7.4900
1722 | -5.0400 -4.4000 7.1100
1723 | -7.7200 -5.1600 11.1500
1724 | -7.0100 -5.2700 11.1300
1725 | -6.2700 -2.9100 6.2400
1726 | -7.4000 -1.3000 6.6500
1727 | -6.6300 -2.6800 6.2500
1728 | -5.6900 -3.0500 6.1600
1729 | 1.7300 0.1300 3.4300
1730 | 2.8800 0.2300 3.7000
1731 | 1.4700 -0.0400 3.4200
1732 | 2.5300 -0.1000 3.4800
1733 | 3.7300 0.1000 3.8600
1734 | 1.1100 -0.1600 3.4400
1735 | 2.1400 -0.2800 3.4400
1736 | 3.2600 -0.2400 3.6200
1737 | 0.2700 -0.1900 3.5100
1738 | 1.3200 -0.2700 3.5500
1739 | 2.3000 -0.4800 3.4400
1740 | 3.4100 -0.4500 3.6100
1741 | -8.8400 -3.1400 11.5000
1742 | 0.6000 -0.4600 3.4500
1743 | 2.4500 -0.6900 3.4300
1744 | 3.5700 -0.6400 3.6300
1745 | -7.6300 -5.2700 11.2400
1746 | -0.0900 -0.2000 3.8600
1747 | 0.8400 -0.5100 3.6300
1748 | 3.0400 -0.8900 3.4900
1749 | 4.2900 -0.5900 3.9700
1750 | -3.8700 -0.3700 3.4000
1751 | -1.0700 -0.2800 3.8600
1752 | -0.0800 -0.4600 3.7800
1753 | 2.3900 -0.9400 3.5500
1754 | 3.4400 -1.0400 3.5700
1755 | -6.4400 -3.3300 6.5300
1756 | -4.5500 -0.5000 3.3600
1757 | -2.7900 -0.2900 3.8300
1758 | -1.7500 -0.4000 3.8400
1759 | -0.7100 -0.4900 3.8600
1760 | 0.1300 -0.9400 3.4600
1761 | 2.7200 -1.2300 3.4700
1762 | 3.8900 -1.0900 3.7700
1763 | -6.1100 -3.6800 6.5800
1764 | -5.0300 -0.6400 3.3400
1765 | -3.9900 -0.7500 3.3600
1766 | -2.7400 -0.4800 3.8200
1767 | -1.7000 -0.5800 3.8400
1768 | -0.6600 -0.6800 3.8600
1769 | 0.1800 -1.1400 3.4500
1770 | 2.6600 -1.4100 3.4600
1771 | 3.8300 -1.2700 3.7600
1772 | -7.3900 -2.4600 6.8100
1773 | -6.1100 -0.7000 3.3500
1774 | -5.0800 -0.8100 3.3500
1775 | -4.0400 -0.9200 3.3600
1776 | -2.8000 -0.6600 3.8100
1777 | -1.7600 -0.7600 3.8300
1778 | -0.7200 -0.8600 3.8500
1779 | 0.1400 -1.2900 3.4800
1780 | 2.5100 -1.5600 3.4800
1781 | 3.6600 -1.4600 3.7300
1782 | -6.0700 -3.4500 6.4400
1783 | -6.6700 -0.8100 3.3500
1784 | -5.6400 -0.9200 3.3500
1785 | -4.6000 -1.0200 3.3800
1786 | -3.5700 -1.1400 3.3700
1787 | -2.3300 -0.8900 3.8100
1788 | -1.2900 -0.9900 3.8300
1789 | -0.2700 -1.1200 3.8200
1790 | 0.5700 -1.5800 3.4100
1791 | 2.7900 -1.7300 3.5200
1792 | 3.9800 -1.5600 3.8700
1793 | -8.9400 -3.2000 11.6300
1794 | -6.6200 -0.9900 3.3500
1795 | -5.5900 -1.1100 3.3500
1796 | -4.5000 -1.1200 3.4800
1797 | -3.5200 -1.3300 3.3700
1798 | -2.2800 -1.0700 3.8200
1799 | -1.2400 -1.1700 3.8400
1800 | -0.2200 -1.3100 3.8100
1801 | 0.6200 -1.7600 3.4100
1802 | 2.4100 -1.9000 3.4800
1803 | 3.5700 -1.7800 3.7700
1804 | 4.8400 -1.4700 4.2700
1805 | -6.9600 -1.0900 3.3900
1806 | -5.9500 -1.2400 3.3600
1807 | -4.8900 -1.3000 3.4300
1808 | -3.8100 -1.3300 3.5300
1809 | -2.6500 -1.2300 3.7900
1810 | -1.6100 -1.3200 3.8200
1811 | -0.5700 -1.4200 3.8500
1812 | 0.3000 -1.8200 3.5100
1813 | 1.3200 -1.9600 3.4800
1814 | 2.3600 -2.0600 3.5000
1815 | 3.5300 -1.9300 3.8000
1816 | 4.7900 -1.6300 4.2900
1817 | -7.0100 -1.2400 3.4100
1818 | -6.0000 -1.4100 3.3600
1819 | -4.9100 -1.4200 3.4900
1820 | -3.1100 -1.7300 3.3700
1821 | -1.8700 -1.4800 3.8100
1822 | -0.8300 -1.5800 3.8400
1823 | 0.1200 -1.8400 3.6600
1824 | 1.0500 -2.1300 3.4500
1825 | 2.1000 -2.2300 3.4800
1826 | 3.2600 -2.1000 3.7700
1827 | 4.4900 -1.8600 4.2000
1828 | -5.8800 -3.5000 6.4100
1829 | -6.3500 -1.5300 3.3800
1830 | -5.3400 -1.6700 3.3500
1831 | -2.9400 -1.9000 3.4000
1832 | -1.7200 -1.6700 3.8200
1833 | -0.6700 -1.7700 3.8500
1834 | 0.2700 -2.0400 3.6500
1835 | 1.2100 -2.3200 3.4600
1836 | 2.2600 -2.4000 3.5100
1837 | 3.4700 -2.2000 3.8900
1838 | 4.7100 -1.9500 4.3300
1839 | -4.6300 -4.2100 6.7800
1840 | -6.2000 -1.7200 3.3800
1841 | -5.1800 -1.8600 3.3600
1842 | -2.4100 -1.8000 3.7800
1843 | -1.3500 -1.8800 3.8300
1844 | -0.3100 -1.9800 3.8500
1845 | 0.6100 -2.3000 3.6100
1846 | 1.5700 -2.5300 3.4700
1847 | 2.7200 -2.4400 3.7200
1848 | 3.9200 -2.2600 4.0800
1849 | -7.2400 -2.5700 6.7100
1850 | -6.8200 -1.7300 3.4900
1851 | -5.8400 -1.9400 3.3700
1852 | -3.1500 -2.2400 3.3900
1853 | -1.9300 -2.0100 3.8100
1854 | -0.8900 -2.1100 3.8300
1855 | 0.1500 -2.2100 3.8500
1856 | 1.0500 -2.5600 3.5700
1857 | 2.0500 -2.7400 3.4900
1858 | 3.3100 -2.4400 4.0000
1859 | 4.5200 -2.2400 4.3900
1860 | -7.3700 -2.3400 6.7500
1861 | -6.1900 -2.0600 3.4000
1862 | -5.2100 -2.2600 3.3000
1863 | -2.4100 -2.1600 3.7700
1864 | -1.3500 -2.2400 3.8300
1865 | -0.3100 -2.3300 3.8600
1866 | 0.6900 -2.5100 3.7800
1867 | 1.5800 -2.8800 3.4800
1868 | 2.7600 -2.7400 3.8300
1869 | 4.0000 -2.4800 4.2500
1870 | 3.9000 0.1300 3.9400
1871 | -7.4100 -4.6800 10.9200
1872 | -5.9300 -2.2600 3.4000
1873 | -3.3300 -2.5100 3.4600
1874 | -2.1400 -2.3500 3.8000
1875 | -1.0900 -2.4300 3.8400
1876 | -0.0500 -2.5300 3.8600
1877 | 0.9500 -2.7100 3.8000
1878 | 1.8400 -3.0800 3.4900
1879 | 3.1400 -2.7100 4.0800
1880 | 4.3400 -2.5200 4.4500
1881 | -8.7400 -3.8500 11.5100
1882 | -8.6100 -4.7100 11.3400
1883 | -6.1400 -3.8900 6.7400
1884 | -5.1700 -2.5400 3.3900
1885 | -2.5100 -2.5100 3.7700
1886 | -1.4500 -2.5700 3.8400
1887 | -0.4100 -2.6700 3.8700
1888 | 0.6400 -2.7500 3.9100
1889 | 1.4800 -3.2100 3.5000
1890 | 2.6700 -3.0500 3.8300
1891 | 3.9800 -2.6600 4.4400
1892 | -6.8300 -4.0600 7.2500
1893 | -7.2700 -4.5500 10.8800
1894 | -8.6200 -4.3900 11.3000
1895 | -5.8300 -2.6200 3.4000
1896 | -3.5000 -2.7800 3.5400
1897 | -2.3400 -2.6800 3.8100
1898 | -1.2900 -2.7600 3.8500
1899 | -0.2500 -2.8600 3.8800
1900 | 0.8000 -2.9400 3.9200
1901 | 1.6200 -3.4300 3.4700
1902 | -6.7400 -4.4100 7.6800
1903 | 4.2000 -2.7400 4.5900
1904 | -8.0200 -4.6700 11.0500
1905 | -7.4800 -3.5100 7.4000
1906 | -6.3100 -4.7800 11.2100
1907 | -5.4700 -2.8400 3.3900
1908 | -3.8200 -3.0300 3.4000
1909 | -2.6100 -2.8400 3.7800
1910 | -1.5500 -2.9100 3.8400
1911 | -0.5100 -3.0000 3.8800
1912 | 0.5500 -3.0800 3.9300
1913 | 1.3600 -3.5800 3.4700
1914 | -6.8500 -2.9200 6.5500
1915 | -6.5900 -3.0500 6.4400
1916 | -8.6100 -2.8600 11.2200
1917 | -6.2500 -4.6000 7.7200
1918 | -5.8400 -3.2700 6.2900
1919 | -5.5200 -3.0000 3.4000
1920 | -4.5000 -3.1400 3.3800
1921 | -3.4500 -3.2300 3.4300
1922 | -2.2300 -3.0300 3.8200
1923 | -1.1900 -3.1200 3.8600
1924 | -0.1300 -3.2000 3.9100
1925 | 0.9100 -3.2800 3.9500
1926 | 1.7300 -3.7900 3.4800
1927 | -7.9200 -3.0500 7.6700
1928 | -4.4200 -4.4600 7.1300
1929 | -6.9500 -5.5200 11.2600
1930 | -6.5300 -0.6000 6.2200
1931 | -7.1500 -3.6100 7.1200
1932 | -5.7700 -3.1400 3.4100
1933 | -4.7300 -3.2500 3.4300
1934 | -3.7000 -3.3700 3.4300
1935 | -2.4900 -3.1800 3.8200
1936 | -1.4400 -3.2500 3.8800
1937 | -0.4000 -3.3500 3.9000
1938 | 0.6600 -3.4300 3.9500
1939 | 1.4600 -3.9600 3.4600
1940 | -4.7200 -3.9500 6.4700
1941 | -8.7600 -3.1900 11.4500
1942 | -6.2600 -4.0500 6.9300
1943 | -6.8300 -0.6100 6.3600
1944 | -6.0000 -1.1000 5.9300
1945 | -6.1100 -3.2400 3.4400
1946 | -5.0900 -3.3700 3.4300
1947 | -4.0600 -3.4900 3.4500
1948 | -2.8900 -3.3700 3.7500
1949 | -1.8000 -3.3900 3.8700
1950 | -0.7500 -3.4800 3.9100
1951 | 0.3000 -3.5600 3.9600
1952 | 1.1000 -4.0800 3.4700
1953 | 2.2700 -3.9700 3.7500
1954 | -6.9800 -4.7100 10.9500
1955 | -8.4100 -5.2700 11.5500
1956 | -8.2500 -2.4200 7.7500
1957 | -6.6500 -0.9600 6.1900
1958 | -5.7900 -1.3900 5.8200
1959 | -5.7600 -3.4700 3.4300
1960 | -4.7300 -3.5800 3.4400
1961 | -3.6900 -3.7100 3.4500
1962 | -2.4800 -3.5000 3.8600
1963 | -1.4300 -3.5900 3.9000
1964 | -0.3900 -3.6800 3.9300
1965 | 0.4300 -4.1900 3.4600
1966 | 1.4900 -4.2500 3.5300
1967 | -8.2700 -4.8800 11.1800
1968 | -7.1700 -5.6900 11.4600
1969 | -7.7400 -5.4400 11.4600
1970 | -7.0200 -0.9200 6.3900
1971 | -6.2000 -1.4200 5.9400
1972 | -6.3000 -3.5200 3.4900
1973 | -5.2900 -3.7000 3.4300
1974 | -4.2600 -3.8000 3.4500
1975 | -3.2400 -3.9500 3.4400
1976 | -2.0000 -3.7100 3.8800
1977 | -0.9500 -3.7900 3.9300
1978 | -0.1300 -4.2800 3.4800
1979 | 0.9100 -4.3800 3.5000
1980 | -5.7700 -4.1600 6.8800
1981 | -7.9800 -3.3800 7.8900
1982 | -8.6800 -4.6600 11.4000
1983 | -7.6400 -3.3900 7.5200
1984 | -6.8600 -1.3000 6.1900
1985 | -5.9900 -1.7100 5.8400
1986 | -6.0600 -3.7700 3.4300
1987 | -5.0300 -3.8900 3.4500
1988 | -4.0000 -4.0000 3.4600
1989 | -2.9700 -4.1400 3.4500
1990 | -1.7300 -3.8900 3.9100
1991 | -0.8900 -4.3500 3.5100
1992 | 0.1200 -4.5000 3.4600
1993 | 1.2400 -4.4500 3.6700
1994 | -6.0700 -4.3600 7.2100
1995 | -8.4000 -3.9500 11.2300
1996 | -7.3900 -0.9800 6.7000
1997 | -6.6600 -1.6000 6.0700
1998 | -5.7500 -1.9500 5.8000
1999 | -5.8000 -3.9700 3.4400
2000 | -4.7700 -4.0800 3.4600
2001 | -3.7400 -4.2100 3.4600
2002 | -2.7100 -4.3400 3.4500
2003 | -1.4700 -4.0800 3.9300
2004 | -0.6500 -4.5800 3.4700
2005 | 0.4200 -4.6300 3.5700
2006 | -7.9500 -5.3400 11.3300
2007 | -8.8200 -3.0200 11.4300
2008 | -6.5100 -3.7700 6.8200
2009 | -7.2300 -1.3500 6.4900
2010 | -6.4400 -1.8900 5.9800
2011 | -8.6600 -4.5300 11.3400
2012 | -5.5400 -4.1700 3.4400
2013 | -4.5100 -4.2900 3.4600
2014 | -3.4800 -4.4100 3.4600
2015 | -2.4500 -4.5300 3.4700
2016 | -1.4100 -4.6500 3.4800
2017 | -0.3800 -4.7700 3.4900
2018 | -7.0700 -5.8600 11.7000
2019 | -7.5100 -3.8700 7.7900
2020 | -4.7100 -4.3100 7.0600
2021 | -6.6200 -3.8400 6.9300
2022 | -7.1400 -1.6500 6.3700
2023 | -6.3000 -2.1000 5.9600
2024 | -6.3300 -4.1100 3.6100
2025 | -5.3900 -4.3600 3.4400
2026 | -4.3500 -4.4700 3.4600
2027 | -3.3100 -4.5800 3.4900
2028 | -2.2900 -4.7200 3.4800
2029 | -1.2600 -4.8500 3.4700
2030 | -0.1900 -4.8900 3.5800
2031 | -6.5400 -4.5300 7.7600
2032 | -5.9600 -4.1200 6.8900
2033 | -8.2900 -2.2300 7.7800
2034 | -6.6900 -3.1200 6.5500
2035 | -7.0100 -1.8900 6.3100
2036 | -6.1400 -2.2900 5.9600
2037 | -6.2200 -4.3500 3.5400
2038 | -5.2300 -4.5500 3.4500
2039 | -4.1800 -4.6400 3.4900
2040 | -3.1500 -4.7600 3.5000
2041 | -2.1300 -4.9000 3.4800
2042 | -1.1000 -5.0300 3.4900
2043 | -6.1500 -3.3100 6.4000
2044 | -5.9600 -3.3700 6.3600
2045 | -8.1600 -4.4600 11.0700
2046 | -5.6500 -3.9900 6.6800
2047 | -7.6900 -1.4200 6.9600
2048 | -6.9700 -2.0900 6.2900
2049 | -6.0800 -2.4500 5.9900
2050 | -6.1500 -4.5100 3.5600
2051 | -5.1700 -4.7200 3.4500
2052 | -4.1000 -4.7700 3.5500
2053 | -3.0900 -4.9100 3.5400
2054 | -2.0800 -5.0800 3.4900
2055 | -1.0200 -5.1500 3.5600
2056 | -8.0400 -2.7100 7.6500
2057 | -6.0700 -4.2700 7.1100
2058 | -6.9900 -3.8000 7.1300
2059 | -7.6900 -4.6000 10.9700
2060 | -7.5100 -1.7700 6.7900
2061 | -6.7500 -2.3600 6.2000
2062 | -5.8100 -2.6500 6.0000
2063 | -5.9200 -4.7400 3.5100
2064 | -4.9000 -4.8900 3.4900
2065 | -7.7900 -5.0200 11.0900
2066 | -2.8300 -5.1200 3.5200
2067 | -1.8200 -5.2800 3.4900
2068 | -5.0700 -0.6200 13.9900
2069 | -4.3500 -0.5100 14.6900
2070 | -4.8400 -0.6800 14.4200
2071 | 


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