""" Matplotlib based plotting utilities """ import math import matplotlib.pyplot as plt import numpy as np from mpl_toolkits.mplot3d import art3d from matplotlib.patches import FancyArrowPatch from mpl_toolkits.mplot3d.proj3d import proj_transform from mpl_toolkits.mplot3d import Axes3D def plot_arrow(x, y, yaw, arrow_length=1.0, origin_point_plot_style="xr", head_width=0.1, fc="r", ec="k", **kwargs): """ Plot an arrow or arrows based on 2D state (x, y, yaw) All optional settings of matplotlib.pyplot.arrow can be used. - matplotlib.pyplot.arrow: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.arrow.html Parameters ---------- x : a float or array_like a value or a list of arrow origin x position. y : a float or array_like a value or a list of arrow origin y position. yaw : a float or array_like a value or a list of arrow yaw angle (orientation). arrow_length : a float (optional) arrow length. default is 1.0 origin_point_plot_style : str (optional) origin point plot style. If None, not plotting. head_width : a float (optional) arrow head width. default is 0.1 fc : string (optional) face color ec : string (optional) edge color """ if not isinstance(x, float): for (i_x, i_y, i_yaw) in zip(x, y, yaw): plot_arrow(i_x, i_y, i_yaw, head_width=head_width, fc=fc, ec=ec, **kwargs) else: plt.arrow(x, y, arrow_length * math.cos(yaw), arrow_length * math.sin(yaw), head_width=head_width, fc=fc, ec=ec, **kwargs) if origin_point_plot_style is not None: plt.plot(x, y, origin_point_plot_style) def plot_curvature(x_list, y_list, heading_list, curvature, k=0.01, c="-c", label="Curvature"): """ Plot curvature on 2D path. This plot is a line from the original path, the lateral distance from the original path shows curvature magnitude. Left turning shows right side plot, right turning shows left side plot. For straight path, the curvature plot will be on the path, because curvature is 0 on the straight path. Parameters ---------- x_list : array_like x position list of the path y_list : array_like y position list of the path heading_list : array_like heading list of the path curvature : array_like curvature list of the path k : float curvature scale factor to calculate distance from the original path c : string color of the plot label : string label of the plot """ cx = [x + d * k * np.cos(yaw - np.pi / 2.0) for x, y, yaw, d in zip(x_list, y_list, heading_list, curvature)] cy = [y + d * k * np.sin(yaw - np.pi / 2.0) for x, y, yaw, d in zip(x_list, y_list, heading_list, curvature)] plt.plot(cx, cy, c, label=label) for ix, iy, icx, icy in zip(x_list, y_list, cx, cy): plt.plot([ix, icx], [iy, icy], c) class Arrow3D(FancyArrowPatch): def __init__(self, x, y, z, dx, dy, dz, *args, **kwargs): super().__init__((0, 0), (0, 0), *args, **kwargs) self._xyz = (x, y, z) self._dxdydz = (dx, dy, dz) def draw(self, renderer): x1, y1, z1 = self._xyz dx, dy, dz = self._dxdydz x2, y2, z2 = (x1 + dx, y1 + dy, z1 + dz) xs, ys, zs = proj_transform((x1, x2), (y1, y2), (z1, z2), self.axes.M) self.set_positions((xs[0], ys[0]), (xs[1], ys[1])) super().draw(renderer) def do_3d_projection(self, renderer=None): x1, y1, z1 = self._xyz dx, dy, dz = self._dxdydz x2, y2, z2 = (x1 + dx, y1 + dy, z1 + dz) xs, ys, zs = proj_transform((x1, x2), (y1, y2), (z1, z2), self.axes.M) self.set_positions((xs[0], ys[0]), (xs[1], ys[1])) return np.min(zs) def _arrow3D(ax, x, y, z, dx, dy, dz, *args, **kwargs): '''Add an 3d arrow to an `Axes3D` instance.''' arrow = Arrow3D(x, y, z, dx, dy, dz, *args, **kwargs) ax.add_artist(arrow) def plot_3d_vector_arrow(ax, p1, p2): setattr(Axes3D, 'arrow3D', _arrow3D) ax.arrow3D(p1[0], p1[1], p1[2], p2[0]-p1[0], p2[1]-p1[1], p2[2]-p1[2], mutation_scale=20, arrowstyle="-|>", ) def plot_triangle(p1, p2, p3, ax): ax.add_collection3d(art3d.Poly3DCollection([[p1, p2, p3]], color='b')) def set_equal_3d_axis(ax, x_lims, y_lims, z_lims): """Helper function to set equal axis Args: ax (Axes3DSubplot): matplotlib 3D axis, created by `ax = fig.add_subplot(projection='3d')` x_lims (np.array): array containing min and max value of x y_lims (np.array): array containing min and max value of y z_lims (np.array): array containing min and max value of z """ x_lims = np.asarray(x_lims) y_lims = np.asarray(y_lims) z_lims = np.asarray(z_lims) # compute max required range max_range = np.array([x_lims.max() - x_lims.min(), y_lims.max() - y_lims.min(), z_lims.max() - z_lims.min()]).max() / 2.0 # compute mid-point along each axis mid_x = (x_lims.max() + x_lims.min()) * 0.5 mid_y = (y_lims.max() + y_lims.min()) * 0.5 mid_z = (z_lims.max() + z_lims.min()) * 0.5 # set limits to axis ax.set_xlim(mid_x - max_range, mid_x + max_range) ax.set_ylim(mid_y - max_range, mid_y + max_range) ax.set_zlim(mid_z - max_range, mid_z + max_range)