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I wanted to replace np.nan with None values, but weird behaviour occurred:

Physical Background

I'm working on a function that calculates some metrics for each vertical profile in an up to four dimensional temperature field (time, longitude, latitude, pressure as height measure). I have a working function that takes the pressure and temperature at a single location and returns the metrics (tropopause information). I want to wrap it with a function that applies it to every vertical profile in the data passed.

Technical Description of the Problem

I want my function to apply another function to every 1D array corresponding to the last dimension in my N-dimensional array, where N <= 4. So I need an efficient loop over all dimensions but the last one without knowing the number of dimensions beforehand.

Why I Open a New Question

I am aware of several questions (e.g., iterating over some dimensions of a ndarray, Iterating over the last dimensions of a numpy array, Iterating over 3D numpy using one dimension as iterator remaining dimensions in the loop, Iterating over a numpy matrix with unknown dimension) asking how to iterate over a specific dimension or how to iterate over an array with unknown dimensions. The combination of these two problems is new as far as I know. Using numpy.nditer for example I haven't found out how to exclude just the last dimension regardless of the number of dimensions left.

EDIT

I tried to do a minimal, reproducible example:

I am trying to write a function that can replicate the numpy function, np.polyval()

here is my current code:

I have a large numpy array of shape (32,2048,2048), where every [i,:,:] is a 2D set of pixels which are samples from a spatially correlated statistical distribution. With i=32 samples for each pixel.

I now need to calculate the covariance matrix for everey 2x2 ROI (overlapping) on the 2D image, resulting in a a set of 4x4 covariance matrices with total shape of (4,4,2047,2047).

Looping over all ROIs in a loop is of cause possible and takes about 4 minutes on my maschine:

I have the following Python code which computes the first n Euler Totient function values:

I am trying to implement some kind of numpy.where() for my ITK images in C++. ITK's way seems to be with Functors. I am not very experienced with templates, so my whole approach might be flawed, but here is my go:

I'm attempting a multi-stage container build to try and keep my image smaller. The offending package is numpy which apparently doesn't play nicely with Alpine.

My error from numpy:

I am trying to optimize (vectorize?) the creation of a monte-carlo style simulation and I have not been able to figure out how to create nested-weighted random values using numpy or similar libraries. Consider the scenario, inspired by an interviewqs question: "Students in three classrooms have to vote for one of two class president candidates. Classroom A has 40% of the students and are split 50/50 on candidate X and Y. B has 25% of the students and is split 60/40. C has 35% of the students and is split 35/65"

Creating that data with vanilla Python might look something like this,

I have read numpy.roots, which works out common algebraic function's y axis intersections.

which