How to use the gtsam.noiseModel_Diagonal.Sigmas function in gtsam

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))

def __init__(self, K=gtsam.Cal3_S2(), nrCameras=3, nrPoints=4):
        self.K = K
        self.Z = [x[:] for x in [[gtsam.Point2()] * nrPoints] * nrCameras]
        self.J = [x[:] for x in [[0] * nrPoints] * nrCameras]
        self.odometry = [gtsam.Pose3()] * nrCameras

        # Set Noise parameters
        self.noiseModels = Data.NoiseModels()
        self.noiseModels.posePrior = gtsam.noiseModel_Diagonal.Sigmas(
            np.array([0.001, 0.001, 0.001, 0.1, 0.1, 0.1]))
        # noiseModels.odometry = gtsam.noiseModel_Diagonal.Sigmas(
        #    np.array([0.001,0.001,0.001,0.1,0.1,0.1]))
        self.noiseModels.odometry = gtsam.noiseModel_Diagonal.Sigmas(
            np.array([0.05, 0.05, 0.05, 0.2, 0.2, 0.2]))
        self.noiseModels.pointPrior = gtsam.noiseModel_Isotropic.Sigma(3, 0.1)
        self.noiseModels.measurement = gtsam.noiseModel_Isotropic.Sigma(2, 1.0)

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(