91–100 of 174 posts
I want to come out and say that a long time ago at a startup we needed to generate a very particular type of analysis graph for a human operator to review in our SaaS. and I just straight up installed GNU Octave on the server and called out to it from python, using the exact code the mathematician had devised.
What about SciLab?
It gets better and better all the time.
There's also Julia. Earlier in my career, I found that my employers would often not buy Matlab licenses, or would make everyone share even when it was a resource needed daily by everyone. Not having access to the closed-source, proprietary tool hurt my ability to be effective. So I started doing my "whiteboard coding" in Julia and still do.
Julia competes with the scientific computing aspect of matlab, which is easily the worst part of matlab and the one which the easiest to replace. Companies do not buy matlab to do scientific computing. They buy matlab, because it is the only software package in the world where you can get basically everything you ever want to do with software from a single vendor.
I say this as someone who’d be quite happy never seeing Matlab code again: Mathworks puts a lot of effort into support and engineering applications.
I want to come out and say that a long time ago at a startup we needed to generate a very particular type of analysis graph for a human operator to review in our SaaS. and I just straight up installed GNU Octave on the server and called out to it from python, using the exact code the mathematician had devised.
I want to come out and say that a long time ago at a startup we needed to generate a very particular type of analysis graph for a human operator to review in our SaaS. and I just straight up installed GNU Octave on the server and called out to it from python, using the exact code the mathematician had devised.
There's also Julia. Earlier in my career, I found that my employers would often not buy Matlab licenses, or would make everyone share even when it was a resource needed daily by everyone. Not having access to the closed-source, proprietary tool hurt my ability to be effective. So I started doing my "whiteboard coding" in Julia and still do.
Okay, but what's your business model? We've all been down this road before.
As an engineer, I use Matlab (or rather, Octave the free equivalent) all the time. It's really great for numerical computing and plotting. Most things 'just work', there's a sizeable collection of packages, and I personally like how flexible the function inputs are. Biggest drawback though is that it's over-optimized for matrix math, that it forces you to think about everything as matrices, even if that's not how you…
You can do the same thing in other languages but it won't be built in like that.
Earlier quoted context omitted.
The result of `Y @ X` has shape (3,), so the next line (concatenate as columns) fails. To make `Z` a column vector, we would need something like `Z = (Y @ X)[:,np.newaxis]`. Although, I'm not sure why the author is using `concatenate` when the more idiomatic function would be stack, so the change you suggest works and is pretty clean: Z = Y @ X np.stack([Z, Z], axis=1) # array([[14, 14], # [32, 32], # [50, 50]]) with…
> To make `Z` a column vector, we would need something like `Z = (Y @ X)[:,np.newaxis]`. Doesn't just (Y @ X)[None] work? None adding an extra dimension works in practice but I don't know if you're "supposed" to do that
(Y @ X)[None]
# array([[14, 32, 50]])
(Y @ X)[None].T
# array([[14],
# [32],
# [50]])