My Customized Bat Signal for your phone

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This is a complex dataset that represents a 3D shape, specifically a torus. The coordinates are given in three arrays of floats: `xs`, `ys`, and `zs`. The structure of the dataset can be seen from the code itself, which seems to represent each vertex of the mesh as a pair of floating point numbers in the `[x, y]` and `[y, z]` planes respectively. Each element in these arrays represents the coordinate values at that specific vertex. To better visualize this structure, consider the following: * **2D Surface Plotting:** The dataset has both `[xs, ys]` pairs to describe a 2D curve embedded within a plane parallel to `y=-1`. * **Implicit Function:** Another interesting way of looking at this data is to represent the set where \(\{(x,y,z)\in R^3:y+z=f(x)+g(x).\}\), with appropriate choices for $f$ and $g$, as in your data. There's also a separate dataset `vs`, which seems like a 1D array that describes a sequence or an index of some sort. The length of the 2D surface (number of rows in `ys`) matches the size of the one-dimensional dataset `vs`. Below is an example using Python's NumPy library to display the dataset in its simplest form. ```python import numpy as np # Given Data xs = [[-0.998824, -0.993511], [-0.983213, -0.980351], ..., [-1.008109, -1.006135]] ys = np.array([[1,-0.997142],[-0.996111,-0.995143],[-0.992969,-0.991893],[ ... ]]) zs = np.array([[0.011424, 0.009956 ],[ 0.007983, 0.006517 ],[0.004044, 0.002587], [ ...]]) vs = np.arange(201) # Select a subset of the data for visualization subset_xs = xs[:100] subset_ys = ys[:100,:2] # Select the first two elements from each row in `ys` subset_zs = zs[:100] # First 100 elements from `zs` print("Subsetting:") print("xs", subset_xs) print("vs:", vs[:100]) # This will output the same values as zs if you assume the sequence of vs ``` Remember, this simplification ignores several factors and complexities in the given data. ### Displaying The Data Here is an example to display `ys` or similar datasets: ```python import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # Create a 3D subplot figure. fig = plt.figure(figsize=(10,5)) ax = fig.add_subplot(111, projection='3d') xs= xs[0][:100] # Taking one of the curves ys= ys[:100 , :1 ] zs= zs[:100 ] ax.plot(xs, ys[:, 0], zs) # plot it on our created axes # We have an 'Axes' type named ax which represents the axes for us, we now have 3D representation plt.show() ```