"""
GTSAM Copyright 2010-2018, Georgia Tech Research Corporation,
Atlanta, Georgia 30332-0415
All Rights Reserved
Authors: Frank Dellaert, et al. (see THANKS for the full author list)

See LICENSE for the license information

Simple robotics example using odometry measurements and bearing-range (laser) measurements
Author: Alex Cunningham (C++), Kevin Deng & Frank Dellaert (Python)
"""
# pylint: disable=invalid-name, E1101

from __future__ import print_function

import math

import numpy as np

import gtsam

import matplotlib.pyplot as plt
import gtsam.utils.plot as gtsam_plot


def vector3(x, y, z):
    """Create 3d double numpy array."""
    return np.array([x, y, z], dtype=np.float)

# Create noise models
PRIOR_NOISE = gtsam.noiseModel.Diagonal.Sigmas(vector3(0.3, 0.3, 0.1))
ODOMETRY_NOISE = gtsam.noiseModel.Diagonal.Sigmas(vector3(0.2, 0.2, 0.1))

# 1. Create a factor graph container and add factors to it
graph = gtsam.NonlinearFactorGraph()

# 2a. Add a prior on the first pose, setting it to the origin
# A prior factor consists of a mean and a noise ODOMETRY_NOISE (covariance matrix)
graph.add(gtsam.PriorFactorPose2(1, gtsam.Pose2(0, 0, 0), PRIOR_NOISE))

# 2b. Add odometry factors
# Create odometry (Between) factors between consecutive poses
graph.add(gtsam.BetweenFactorPose2(1, 2, gtsam.Pose2(2, 0, 0), ODOMETRY_NOISE))
graph.add(gtsam.BetweenFactorPose2(
    2, 3, gtsam.Pose2(2, 0, math.pi / 2), ODOMETRY_NOISE))
graph.add(gtsam.BetweenFactorPose2(
    3, 4, gtsam.Pose2(2, 0, math.pi / 2), ODOMETRY_NOISE))
graph.add(gtsam.BetweenFactorPose2(
    4, 5, gtsam.Pose2(2, 0, math.pi / 2), ODOMETRY_NOISE))

# 2c. Add the loop closure constraint
# This factor encodes the fact that we have returned to the same pose. In real
# systems, these constraints may be identified in many ways, such as appearance-based
# techniques with camera images. We will use another Between Factor to enforce this constraint:
graph.add(gtsam.BetweenFactorPose2(
    5, 2, gtsam.Pose2(2, 0, math.pi / 2), ODOMETRY_NOISE))
print("\nFactor Graph:\n{}".format(graph))  # print

# 3. Create the data structure to hold the initial_estimate estimate to the
# solution. For illustrative purposes, these have been deliberately set to incorrect values
initial_estimate = gtsam.Values()
initial_estimate.insert(1, gtsam.Pose2(0.5, 0.0, 0.2))
initial_estimate.insert(2, gtsam.Pose2(2.3, 0.1, -0.2))
initial_estimate.insert(3, gtsam.Pose2(4.1, 0.1, math.pi / 2))
initial_estimate.insert(4, gtsam.Pose2(4.0, 2.0, math.pi))
initial_estimate.insert(5, gtsam.Pose2(2.1, 2.1, -math.pi / 2))
print("\nInitial Estimate:\n{}".format(initial_estimate))  # print

# 4. Optimize the initial values using a Gauss-Newton nonlinear optimizer
# The optimizer accepts an optional set of configuration parameters,
# controlling things like convergence criteria, the type of linear
# system solver to use, and the amount of information displayed during
# optimization. We will set a few parameters as a demonstration.
parameters = gtsam.GaussNewtonParams()

# Stop iterating once the change in error between steps is less than this value
parameters.setRelativeErrorTol(1e-5)
# Do not perform more than N iteration steps
parameters.setMaxIterations(100)
# Create the optimizer ...
optimizer = gtsam.GaussNewtonOptimizer(graph, initial_estimate, parameters)
# ... and optimize
result = optimizer.optimize()
print("Final Result:\n{}".format(result))

# 5. Calculate and print marginal covariances for all variables
marginals = gtsam.Marginals(graph, result)
for i in range(1, 6):
    print("X{} covariance:\n{}\n".format(i, marginals.marginalCovariance(i)))

fig = plt.figure(0)
for i in range(1, 6):
    gtsam_plot.plot_pose2(0, result.atPose2(i), 0.5, marginals.marginalCovariance(i))

plt.axis('equal')
plt.show()
