4 ms·

I'm confused on the need for cytpes/etc here. You can directly modify somearr.strides and somearr.shape. And if you need to set them both together, then there

by jofer 2y ago

I'm confused on the need for cytpes/etc here. You can directly modify somearr.strides and somearr.shape. And if you need to set them both together, then there's numpy.lib.stride_tricks.as_strided. Unless I'm missing something, you can do the same assignment that ctypes is used for here directly in numpy. But I might also be missing a lot - I'm still pre-coffee. On a different note, I'm surprised no one has mentioned Cython in this context. This is distinct from, but broadly related to things like @cython.boundscheck(False). Cython is _really_ handy, and it's a shame that it's kinda fallen out of favor in some ways lately.

yosefk 2y ago
You need to modify base pointer in this example, specifically to point 2 bytes before the current base pointer (moving back from the first red pixel value to the first blue value.) I don't think you can do it without ctypes, maybe I'm wrong. What does Cython do better than numbs except static compilation? Honest q, I know little about both
- jofer 2y ago
  You actually can do the "offset by 2 bytes back" with a reshape + indexing + reshape back. But I suspect I'm still missing something. I need to read things in more depth and try it out locally. By "numbs" here, I'm assuming you mean numba. If that's not correct, I apologize in advance! There are several things where Cython is a better fit than numba (and to be fair, several cases where the opposite is true). There are two that stick out in my mind: First off, the optimizations for cython are explicit and a matter of how you implement the actual code. It's a separate language (technically a superset of python). There's no "magic". That's often a significant advantage in and of itself. Personally, I often find larger speedups with Cython, but then again, I'm more familiar with it, and understand what settings to turn on/off in different situations. Numba is much more of a black box given than it's a JIT compiler. With that said, it can also do things that Cython can't _because_ it's a JIT compiler. If you want to run python code as-is, then yeah, numba is the better choice. You won't see a speedup at all with Cython unless you change the way you've written things. The second key thing is one that's likely the most overlooked. Cython is arguably the best way to write a python wrapper around C code where you want to expose things as numpy arrays. It takes a _huge_ amount of the boilerplate out. That alone makes it worth learning, i.m.o.
  - jofer 2y ago
    Ah, right, you mean _outside_ the memory block of the array! Sorry, my mind was just foggy this morning. That's not strictly possible, but "circular" references are with "stride tricks". Those can accomplish similar things in some circumstances. But with that said, I don't think that would work in this case.

by jofer 2y ago

yosefk 2y ago

You need to modify base pointer in this example, specifically to point 2 bytes before the current base pointer (moving back from the first red pixel value to the first blue value.) I don't think you can do it without ctypes, maybe I'm wrong. What does Cython do better than numbs except static compilation? Honest q, I know little about both

jofer 2y ago
You actually can do the "offset by 2 bytes back" with a reshape + indexing + reshape back. But I suspect I'm still missing something. I need to read things in more depth and try it out locally. By "numbs" here, I'm assuming you mean numba. If that's not correct, I apologize in advance! There are several things where Cython is a better fit than numba (and to be fair, several cases where the opposite is true). There are two that stick out in my mind: First off, the optimizations for cython are explicit and a matter of how you implement the actual code. It's a separate language (technically a superset of python). There's no "magic". That's often a significant advantage in and of itself. Personally, I often find larger speedups with Cython, but then again, I'm more familiar with it, and understand what settings to turn on/off in different situations. Numba is much more of a black box given than it's a JIT compiler. With that said, it can also do things that Cython can't _because_ it's a JIT compiler. If you want to run python code as-is, then yeah, numba is the better choice. You won't see a speedup at all with Cython unless you change the way you've written things. The second key thing is one that's likely the most overlooked. Cython is arguably the best way to write a python wrapper around C code where you want to expose things as numpy arrays. It takes a _huge_ amount of the boilerplate out. That alone makes it worth learning, i.m.o.
- jofer 2y ago
  Ah, right, you mean _outside_ the memory block of the array! Sorry, my mind was just foggy this morning. That's not strictly possible, but "circular" references are with "stride tricks". Those can accomplish similar things in some circumstances. But with that said, I don't think that would work in this case.