Відео

manim? blitting? vpython?

​@@kellyaquinastom there is actually a way to make the animation smoother by setting the `blit` parameter within the `FuncAnimation` function to `True` this will allow matplotlib to optimize in a way the animation by ignoring some aspects that might slow it down (for example updating each time the title, position, and so on...) I made recently a git-hub repo that contain some animations, by saving them as .gif / or using blitting it results to a very satisfying visual 🤔feel free to check them about! :) Git-Hub Link: github.com/Younes-Toumi/Modeling-and-Simulating-Complex-Chaotic-Systems This video might also help: ua-cam.com/video/nT16-yQrnFk/v-deo.html&ab_channel=YounesLab By using manim and saving the results as an .mp4, (or .gif?) might also make the animation seems very smooth.

Thank you! :) Yes, you can convert a binary STL file to an ASCII STL file using the numpy-stl library in Python. You can read the binary file with mesh.Mesh.from_file(), then save it as an ASCII file using mesh_data.save(output_file, mode=mesh.Mesh.ASCII). ```python def binary_to_ascii_stl(binary_file, ascii_file): # Read the binary STL file using numpy-stl mesh_data = mesh.Mesh.from_file(binary_file) # Write the mesh data to an ASCII STL file mesh_data.save(ascii_file, mode=mesh.Mesh.ASCII) ```

@ Thank you so much. Really appreciate it. I saw that u mentioned for larger data it’s better to go with binary form. But with the birnary form it’s not easy to get the coordinates. Is there any way to do that ?

Wish you all the best in your learning journey! :)

Thank you for your feedback @aliffahrizi 😄 There actually is a trick to make the animation go faster! Here the trick: (I discovered it recently 😅) The main idea is to tweak the `frames` argument within the `FuncAnimation` function: - Setting `frames = len(t_points)` will render every frame (slower) you can also set it to an array `frames = range(1, len(t_points))`. Now both of these are equivalent, meaning in both cases we are considereing all the frames. - However we can also do something like `frames = range(1, len(t_points), n)` saying we want to render a portion of it, skipping every `n` frame. This should make the animation n-times faster! Note: The classical way the range function works: range object range(start, stop, step)

Yes exactly! Chaos theory revolves around dynamical systems that are highly sensitive to initial conditions. Some other popular example include: - Lorenz Attractor - The double pendulum If you want to know more about it, I recommend checking my Git-Hub repo: github.com/Younes-Toumi/Modeling-and-Simulating-Complex-Chaotic-Systems

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Thank you!! It's all thanks to the quality feedback I am getting from all of you! 😄

Thank you I am glad to hear it Sharing Knowledge is honorable, I encourage you to continue doing it! 👏 And all the best in your video making ^^

I appreciate it! Thank you I did somewhat of a simple 3d animation with the lorenz attractor you can check it out: ua-cam.com/video/GdNjTJZnTmM/v-deo.html&ab_channel=YounesLab And I am currently working on the 3 body problem so be sure to stick around!

That true thank you for letting me knoy, I will fix it in the Git-Hub code. If we have `n` interval we always have `n+1` nodes. When considering the simple example of a 1 meter long rod with two nodes, lenght = 1, and dx = 1 meaning nodes = lenght//dx + 1

True, 'axis' refers to a single object, where 'axes' refer to multiple ones within one figure. Thank you for pointing this one out! :)

This might be happening in a near future hopefully 😁

@@YounesLab will it be a separate course or integrated modules within an existing course?

It will likely be a seperate course. However, I might include a small recap (summary) and integrate a small portion as a module in my Python existing course (to give an overall tool kit for engineers) Being a seperate course, it will have some prerequisites such as a deep understanding of Python when it comes to computational physics, calculus, and so on. More about it soon! 😄

Thank you for your support 👏