I have been scratching my head over this for the last hour.

What does the NumPy array[1,2] operation do?
This is an image of me printing the output of this operations
This is an image of the output

I have a simulation of particles moving on a grid, created with numpy.meshgrid, and I want to determine the distance of every point on the grid to the closest particle. I’ve already found the closest particles, and stored them in a 2d array closest which has the same dimensions as the grid and contains the index of the particle closest to the corresponding point. I also have the 3d array distances with dimensions (number of parties, x size, y size) and which holds every point’s distance to every particle.

I am working with PyTorch and the Hugging Face Transformers library to fine-tune a BERT model (UFNLP/gatortron-base) for a downstream task. I encountered issue:

My numpy is currently 1.24. However, some packages (like sktime) require a lower version or the error message pops out

Suppose I have an array like this:

I have implemented a Sokoban level generator in Python using numpy, but when I run the code, it doesn’t print anything or generate the expected Sokoban level. The generator is supposed to go through several steps:

I wish to fill all zero valued elements of a three-dimensional numpy array with the value of the “nearest” non-zero valued element (at the point of running the program.) I do not mind which is used when there are multiple with the same distance.

Let’s model parallel parking of cars. Suppose there is a street, on which if cars parked in the most efficient manner with no unnecessary gaps, the street would fit n cars (we need only concern ourselves with one side of the street). We can consider this street to be n car-lengths long (where the minimum necessary gap between cars gets incorporated into the unit of “one car length”). For ease, we can assume that all the cars we consider take up the same space along a street, i.e. 1 car-length.
Suppose the street is initially empty. Then a car parks, and given the entire street is available, the car parks randomly anywhere along the street — the car has an equal probability of selecting any position along the street. Then a second car comes along, and also parks randomly, with the only constraint that the space the second car occupies cannot overlap with the space occupied by the first car (a physical impossibility). Thus it has an equal probability of selecting any position along the street provided its viable, i.e. it doesn’t overlap with the already parked car. The third car also parks randomly, but this time it must not overlap with either of the previous two cars and so on … until there are no gaps large enough to fit one more car.

Say I have a 3D (dimension order = zyx) numpy array of objects where each z dimension represents a set of 2D objects and the first z position is all zeros (reason for this will be explained soon):