Lola'S BEST SNOWFLAKE SO FAR

thingiverse

I can help with that. Given this output: `[[-0.00684931506849315,0.1958904109589041],[-0.00410958904109589,0.17671232876712328],[...` This looks like a 3D point list output by the program using PyOpenGL or Pygame and NumPy. We can easily clean this data into X and Y coordinates in Python and then we'll convert it to PNG format which is what matplotlib needs for making graphs. ```python import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import numpy as np def data_preparation(list_3d_points): X, Y = [], [] # Separate into X and Y coordinate lists and add zeros for points without depth if len(list_3d_points[0]) != 2: list_3d_points_without_depth = [[point[0], point[1]] for point in list_3d_points] for pnt in list_3d_points_without_depth: X.append(pnt[0]) Y.append(pnt[1]) # Calculate number of rows based on size parameter N = len(X) # If N % 3 isn't zero then add random values for those missing Z coordinate data if(N % 3 != 0): for _ in range((N%3)-int(N%3)): X.append(np.random.rand() * np.array([-10, 15]).max() -np.array([[-2], [-1]])) list_3d_points_with_zeros_added_for_depth = [list(p) +[0] for p in list_3d_points_without_depth] list_all_data_point_without_dopth_to_plot= np.split(list_3d_points_with_zeros_added_for_depth,int(len(list_3d_points)/len(X))) # Return X and Y coordinate lists as numpy arrays else: return(np.array(X), np.array(Y)) if(int(len(list_3d_points_without_depth)) % 4 == 0): X_arr, Y_arr = [], [] list_2d_with_points_X_Y_and_color_for_4_colours_in_sequence_order_list=[[points[0], points[1], color[1] + '\033['+ str(38)+ ';5;' +str(points[1]+16)+'m']+color[0]+'\033[0m' for points, color in zip(list_of_two_2d_lists , list_all_colours)] list_final_with_all_points_for_one_figure_together=[(i for sublist in [[item[i] for item in x] for x in zip(*[[a, b], [c,d]] if (b%4) == (i) else None for i,(a,b),(c,d) in enumerate(zip(list_all_data_point_without_dopth_to_plot[::1],list_all_data_point_without_dopth_to_plot[::2])))] for sublist in [[subitem[i] for subitem in sublist ] for sublist in zip(*[ [t, r] if isinstance(t,tuple) and (t%2) == (0 )else None for t,r in enumerate(sublist)])]) list_of_final_with_four_4_points_data = list(zip(X_arr,Y_arr)) +list_final_with_all_points_for_one_figure_together return(np.array(X), np.array(Y)) data_file = open('data.txt','r') file_contents = data_file.read().replace("][", "\n][").split('\n')[:-2] # Strip off newlines and parse 3D points list_3d_points=[] for item in file_contents: if(item!='size'and item!='thickness'= loop): list_3d_points.append([float(n) for n in item.strip("[]").replace('], [','').split(',')]) X,Y=data_preparation(list_3d_points) fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(X, Y) ax.set_xlabel('X axis') ax.set_ylabel('Y axis') ax.set_zlabel('Z axis') plt.show() import imageio size_of_graphic=1000 # pixels scale_around_point_to_fit_in_pictureBox(size_of_graphic,size_of_graphic) graph='output.png' if __name__ == '__main__': if graph is not None: img = imageio.imread(graph,as_gray=True) def scale_image(scale): height=img.shape[0]*scale width= int(1/img.shape[1])*int(height)*scale img_reshape=newsize(img, (height,width)) fig.set_size_inches(3.6,7) # Remove white borders from axes imageio.mimsave("graphics_with_scaled_down.png", [img]*8)