mathtocode

Skulpt does support the Python 3.7.3 grammar [0], but it's a work in progress. I have it enabled here: https://github.com/vthommeret/mathtocode/blob/0d5f780be4f218... Is there something specific that did work for you? I tried all the examples on Python 3.7.3 and they worked for me. One notable thing that's missing is that the @ operator for matrix multiplication, but I hope to add that in the future. [0] http://skulpt.org Python 3 Grammar. The master branch is now building and running using the grammar for Python 3.7.3. There are still lots of things to implement under the hood, but we have made a huge leap forward in Python 3 compatibility. We will still support Python 2 as an option going forward for projects that rely on it.

Good idea! I just added it: https://github.com/vthommeret/mathtocode/blob/master/LICENSE

>It's open source here: https://github.com/vthommeret/mathtocode Any chance for a license like MIT?

That's close, but basically you need to take the square of each element first, e.g. `m * * 2` [0]. This keeps the shape of the matrix while `m.prod()` returns a single number (multiplying each element together). So it should look something like this: `np.sqrt((m * * 2).sum())` Re: The error message, it looks like it's occurring because `sum` doesn't normally take any parameters and interprets the argument as an "axis" parameter — https://numpy.org/doc/1.18/reference/generated/numpy.sum.htm... Order of operations is tricky and I could do a better job breaking it down. Still plan to add the Show Solution button but need to get some sleep :-). In the meantime you can see all the possible solutions in the repo! https://github.com/vthommeret/mathtocode/tree/master/questio... [0] Remove the space between the asterisks / I had to add it since HN

Thanks for the feedback. This is definitely oriented towards beginners but also love some more advanced examples. Math to Code is open source, so if you can express a solution with NumPy you can easily add new questions / I'm happy to take pull requests — https://github.com/vthommeret/mathtocode/blob/master/questio...

Math to Code is an interactive Python tutorial (all client-side) to teach engineers how to implement math in papers. I was inspired to create it while taking the Fast.ai course and seeing Jeremy Howard share [0] how a "complicated" Frobenius norm equation could be implemented in a single line of Python. Math to Code uses the Skulpt library to interpret Python in JavaScript. It's open source here: https://github.com/vthommeret/mathtocode I would appreciate any and all feedback! [0] https://youtu.be/4u8FxNEDUeg?t=1390 > It's time to start reading papers. And papers look something like this, which if you're anything like me, that's terrifying. And I'm not going to lie, it's still the case when I start looking at a new paper, every single time, I think, I'm not smart enough to understand this. I just can't get past that immediate reaction. So