Conway's Game of Life in Python
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Top Comments (10)
Thank you so much dude for making this video! Excellent explanation I shall say, I could follow along with each of the steps in almost no time. I haven't used numpy so it did took me a while to understood the lines using numpy methods, ( guess shall be learning that soon as well :p) Proud seeing my own Conway Game of Life :) Thanks!!
When you declare your numpy arrays, you use the default type of nd.float64. This is over the top, as each item in the array will be 0 or 1. It would be better to use nd.int8
4:19 Why not just np.zeros(cells.shape) ?
How does your alive calculation handle corner cases, for example, the (0,0) cell? Won't it go out of bound?
Incredible! Thanks for your work!
Recently, I have noticed that an rgb tuple is way faster in rendering than a #
Thanks for sharing
That's so amazing 😍
Thank you so much for this video. You are just amazing.
Awesome, thanks so much for this clear and consise explanation with all the steps. It works! Very helpful for beginners like me.
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Top Comments (10)
Thank you so much dude for making this video! Excellent explanation I shall say, I could follow along with each of the steps in almost no time. I haven't used numpy so it did took me a while to understood the lines using numpy methods, ( guess shall be learning that soon as well :p) Proud seeing my own Conway Game of Life :) Thanks!!
When you declare your numpy arrays, you use the default type of nd.float64. This is over the top, as each item in the array will be 0 or 1. It would be better to use nd.int8
4:19 Why not just np.zeros(cells.shape) ?
How does your alive calculation handle corner cases, for example, the (0,0) cell? Won't it go out of bound?
Incredible! Thanks for your work!
Recently, I have noticed that an rgb tuple is way faster in rendering than a #
Thanks for sharing
That's so amazing 😍
Thank you so much for this video. You are just amazing.
Awesome, thanks so much for this clear and consise explanation with all the steps. It works! Very helpful for beginners like me.