My Customized Pattern Bracelet

thingiverse

Based on the input provided, it seems that you're trying to create a 3D model with multiple layers and specific transformations applied. I'll guide you through creating this structure using Python with libraries like NumPy and Matplotlib for visualisation. ```python import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # Define the initial coordinates of points on a sphere (you may adjust them) r = 10.0 theta = np.linspace(0, np.pi, 100) phi = np.linspace(0, 2 * np.pi, 100) x = r * np.outer(np.cos(phi), np.sin(theta)) y = r * np.outer(np.sin(phi), np.sin(theta)) z = r * np.outer(np.ones(np.size(phi)), np.cos(theta)) # Function to rotate a point def rotate(point): rotation_matrix_x = np.array([[1, 0, 0], [0, np.cos(np.radians(pattern_rotation)), -np.sin(np.radians(pattern_rotation))], [0, np.sin(np.radians(pattern_rotation)), np.cos(np.radians(pattern_rotation))]]) rotation_matrix_y = np.array([[np.cos(np.radians(pattern_rotation)), 0, np.sin(np.radians(pattern_rotation))], [0, 1, 0], [-np.sin(np.radians(pattern_rotation)), 0, np.cos(np.radians(pattern_rotation))]]) point = np.dot(rotation_matrix_x, point) return np.dot(point, rotation_matrix_y) # Perform rotations on all points all_points = np.array([point for sublist in pattern_layers for point in sublist]) for point in all_points: point[:3] = rotate(point[:3]) # Function to calculate distance from a line def distance_point_to_line(point, start_line, end_line): return abs(np.cross(start_line - point[:2], end_line - point[:2])) / np.linalg.norm(end_line - start_line) # Identify and filter layers by pattern_clip (not actually used in your code) # If there's no need for clipping as per your requirement, this part can be removed # Visualize fig = plt.figure() ax = fig.add_subplot(111, projection='3d') for layer_index, layer_points in enumerate(pattern_layers): # To make different points have the same color colors = np.array([1] * len(layer_points)) + layer_index # Layer points share the same color ax.scatter(*zip(*(point[:3] for point in layer_points)), c=colors) # Add line visualization ax.set_xlabel('X Axis') ax.set_ylabel('Y Axis') ax.set_zlabel('Z Axis') plt.show() ``` This code will produce a plot where all points belonging to the same pattern are shown as dots of the same color. However, without an actual 3D visualization requirement (only mentioned in context of showing how Python and Matplotlib can be used for it), this example doesn't necessarily address all specifics you'd require like filtering or adjusting line parameters. Make sure your pattern layers follow the structure that `pattern_layers = [[[...]]]`. If the depth or width differs from what I've assumed, adjust `all_points` creation accordingly. This solution primarily aims at visualizing patterns in 3D space by demonstrating how points could be rotated according to given angles and plotted on a grid. Keep in mind, Matplotlib isn't the most ideal tool for interactive or high-speed rendering. Depending on the size of your data and the details needed from the final visualization (for example, detailed shapes with many lines), you might need other libraries like VTK (Visualization Toolkit). However, it works fine here to demonstrate rotation transformation as asked. Please replace pattern\_rotation = 180 with any valid angle that suits your purpose, such as \[-90.0] for 135 degrees, and change the radius r if the circles appear too small or large in your actual scenario.