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Given the fact that infinity is expressed as sympy.oo in sympy makes the use of sympy.zoo somewhat consistent/unsuprising.

Maybe the OP of the tweet was/is not aware of this use. I wonder if his next tweet will be about sympy using oo for infinity.


The audio also reminded me of the Fallout games.


Works fine w/ steam and "Proton Experimental" under "Linux 5.10.154-1-MANJARO #1 SMP PREEMPT Thu Nov 10 20:51:28 UTC 2022 x86_64 GNU/Linux". I had to install the package "sdl_ttf" though; without it, the game just hung at the inital screen that says "Loading".


If this update is like the old version, it has bundled dependencies on glibc and SDL 1.2. You can overwrite the outdated glibc libraries with symlinks to the system glibc. SDL, SDL_image, and SDL_ttf can be relinked to sdl12-compat, which implements the SDL1.2 ABI using the maintained SDL2 libraries. (edit: I forgot this is a Windows-only release, so maybe disregard this.)


You can use variables in the formats.

  >>> flt = 1.2345
  >>> two = 2
  >>> f"{flt:.{two}f}"
  '1.23'


That is the opposite of the problem being posed. The question is how you define `greet` using f-strings.


  greet = lambda x: f"hello {x}"


As someone from Germany it seems very strange how casually he talks about being shot at, as if this is a normal thing.


Check out Zenodo [1]. It lets you generate DOIs for Github releases.

[1] https://zenodo.org/


Actual atoms in molecules also vibrate all the time, so the provided bond length he talked about should be seen as an equilibrium bond length.


I'm a computational/theoretical chemist and use python extensivly.

To me te question seems a bit poorly phrased. What is the goal of the course? Are they supposed to learn how to program? Then it's of course fundamentals first, especially the stuff you cited (variables, functions, scoping). You can't solve any problem without knowing this stuff.

If they are supposed to learn a specific library/tool then, in my opinion, it depends on the libs/tools you want to teach them. Maybe this libary is not that relevant for comp. biologists, but take scikit-learn. You still have to know how to program when you wan't to use this library properly. On the other hand if they shall learn some GUI-tools then I guess they don't have to know how to program. You can ask yourself: Can you anticipate what tools/libraries your students will be using in a few years? If you can answer yes to this, then teach them a bit about these tools. If your field is constantly evolving/changing then it would be better to invest in the basics, so your students can adapt easier.

Considering the fact that you are (hopefully) and have to be constantly learning as a practicing scientist it is of great importance to get the basics right.


Interesting article but just skimming through it some things stand out immediately: 1.) The first snippet isn't even valid python code as floats don't have a shape attribute.

  s = 0. 
  n = s.shape
2.) The inline latex math isn't rendered properly.


The first snippet also doesn't balance the parenthesis

    s += 100. * x[i + 1] - x[i] ** 2.) ** 2. + (1 - x[i]) ** 2


It should be the shape of x (actually, the zero'th element of the shape), but this is also a tad odd because this would assume that x is a numpy array, which isn't introduced until after this 'naive pure python' code block (i.e., before numpy is even introduced in the text).


I believe the input should be a numpy array of floats which has a shape attribute


(shameful author here)

One should read

  def rosen_explicit_loop(x): 
    s = 0. 
    n = x.shape[0] 
    for i in range(0, n - 1): 
      s += 100. * (x[i + 1] - x[i] ** 2.) ** 2. + (1 - x[i]) ** 2 
    return s
(edited)


...

    n = x.shape[0]


Or just len(x). This works perfectly well on numpy arrays and has the bonus that it works on regular lists/tuples of floats, so the first snippet doesn't rely on numpy.


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