Functions | |
def | ellipsoid |
def | plot_3d_points (fignum, values, linespec="g*", marginals=None, title="3D Points", axis_labels=('X axis', 'Y axis', 'Z axis')) |
def | plot_covariance_ellipse_2d |
def | plot_covariance_ellipse_3d |
def | plot_incremental_trajectory |
def | plot_point2 |
def | plot_point2_on_axes |
def | plot_point3 |
def | plot_point3_on_axes |
def | plot_pose2 |
def | plot_pose2_on_axes |
def | plot_pose3 |
def | plot_pose3_on_axes (axes, pose, axis_length=0.1, P=None, scale=1) |
def | plot_trajectory |
def | set_axes_equal |
Various plotting utlities.
def gtsam.utils.plot.plot_3d_points | ( | fignum, | |
values, | |||
linespec = "g*" , |
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marginals = None , |
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title = "3D Points" , |
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axis_labels = ('X axis', 'Y axis', 'Z axis') |
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) |
Plots the Point3s in `values`, with optional covariances. Finds all the Point3 objects in the given Values object and plots them. If a Marginals object is given, this function will also plot marginal covariance ellipses for each point. Args: fignum (int): Integer representing the figure number to use for plotting. values (gtsam.Values): Values dictionary consisting of points to be plotted. linespec (string): String representing formatting options for Matplotlib. marginals (numpy.ndarray): Marginal covariance matrix to plot the uncertainty of the estimation. title (string): The title of the plot. axis_labels (iterable[string]): List of axis labels to set.
def gtsam.utils.plot.plot_covariance_ellipse_2d | ( | axes, | |
origin | |||
) |
def gtsam.utils.plot.plot_covariance_ellipse_3d | ( | axes, | |
origin | |||
) |
def gtsam.utils.plot.plot_pose3_on_axes | ( | axes, | |
pose, | |||
axis_length = 0.1 , |
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P = None , |
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scale = 1 |
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) |
Plot a 3D pose on given axis `axes` with given `axis_length`. The uncertainty ellipse (if covariance is given) is scaled in such a way that 95% of drawn samples are inliers, see `plot_covariance_ellipse_3d`. Args: axes (matplotlib.axes.Axes): Matplotlib axes. point (gtsam.Point3): The point to be plotted. linespec (string): String representing formatting options for Matplotlib. P (numpy.ndarray): Marginal covariance matrix to plot the uncertainty of the estimation.