interp | Testing dispatch methods | Interpreter library

I would like to do a linear interpolation of my data as np.interp() does but the machine I am using does not have numpy or scipy (and I don't have the rights to install them). Does anyone know a way to manually code this interp function please?

My idea is to give as an input: x and y and interp_points:

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 plotted a data with np.NaN. And I also want to change the center value of the colorbar due to the distribution of original data. But when I change the Vmin, Vmax and vcenter value of the colorbar, the color of np.NaN value changes to other colors other than white. So how can I fix that? Here follows the codes:

Edit: Still No answer works, the output is:

I read up some threads about scipy.misc.imresize being removed in early versions, and how you have to use Pillow instead. The following is my line of code which is producing the error.

I'm facing a problem while trying to implement the coupled differential equation below (also known as single-mode coupling equation) in Python 3.8.3. As for the solver, I am using Scipy's function scipy.integrate.solve_bvp, whose documentation can be read here. I want to solve the equations in the complex domain, for different values of the propagation axis (z) and different values of beta (beta_analysis).

The problem is that it is extremely slow (not manageable) compared with an equivalent implementation in Matlab using the functions bvp4c, bvpinit and bvpset. Evaluating the first few iterations of both executions, they return the same result, except for the resulting mesh which is a lot greater in the case of Scipy. The mesh sometimes even saturates to the maximum value.

The equation to be solved is shown here below, along with the boundary conditions function.

I finished my command handler but I got an error and was wondering if anyone in this community could help me fix the issue?

my code:

I built an API with FastAPI, then I want to call it by PHP (I ran PHP by Docker, expose port 80 to 80), but It give always boolean(False). However this API works very well with JavaScript, Postman, Firefox.(I want to give results from this API to externals users so my ideal is using PHP to bring the results from this API, then give it to Front-end, I don't know how to give this API directly from FastAPI to externals users). So you can see my code for FastAPI below:

I have a dataframe with three columns Time, observed value (Obs.Value), and an interpolated value (Interp.Value). If the value of Obs.Value is NA then the value of Interp.Value should also be NA. I can make the whole row NA but I need to keep the Time value.

Here is the repex: