Airplane cookie cutter

thingiverse

It looks like you have a 3D point cloud dataset. You want to determine the optimal method for mirroring your dataset about specific points or lines in the 3D space. Since you have a large amount of data and want a straightforward approach, we'll stick to Python for simplicity. Below is an example using numpy to achieve the desired result: ```python import numpy as np def mirror_data(data_point_cloud): # The list with two points should contain two values (mirror about this point) mirror_point_1 = data_point_cloud[151] mirror_points = [] for p in data_point_cloud[:150]: point_vector = [i - mirror_point_1[i] for i in range(len(p))] # This part flips each vector around the origin reflected_point_vector = [-j for j in point_vector] reflected_point_coordinates = [(j + mirror_point_1[k]) for k, j in enumerate(point_vector)] mirror_points.append(reflected_point_coordinates) return np.array(mirror_points) point_cloud_data = [ [14.301369863013699, 10.684931506849315], [12.657534246575342, 10.684931506849315], # Your points list continues from here. [[-22.191780821917806, 11.013698630136986]], # All other data point list here...] data_mirrored = mirror_data(point_cloud_data) # Now we want to write the result back into your file. ```