spectra | A header-only C++ library for large scale eigenvalue

There's no need to compare the result of re.search with True. From documentation you can see that search returns a match object when a match is found:

Scan through string looking for the first location where the regular expression pattern produces a match, and return a corresponding match object. Return None if no position in the string matches the pattern; note that this is different from finding a zero-length match at some point in the string.

So, when comparing a match object with True the return is False and your else condition is executed.

The load_workbook() function will accept a WindowsPath as an arugment.

Try something like this:

You are almost there. Would you please try:

You can wrap up the functions of file 1 in a class and in file 2 you can create object and can call the specific function. However if you can share the file 1's code then it would be clear. For your reference find below...

File-1

I think what you want to achieve can be done with interp2d:

The error you are referring is when you call the function load_data_from_file().It seems that you are not passing the filename correctly in the parameters. The correct way to call it should be something like:

Try this, it worked in my colab:

I think you are conflating several things here; The by trick to turn off random effects only works for bs = "re" smooths. Locality is a factor (otherwise your random effect isn't a random intercept) and setting it to 0 is creating a new level (although it could be creating an NA as 0 isn't among the original levels.

If what you want to do is turn off anything to do with Locality, you should use exclude; however you have the invocation wrong. The reason why it's not working is because you are creating a character vector with a single element "c(Locality)". This fails for obvious reasons once you realize that c(Locality) doesn't related to anything in your model. What you need to provide here is a vector of smooth names as printed by summary(). For example, to exclude the smooth s(Locality, bs = "re"), {mgcv} knows this as s(Locality), so you would use exclude = "s(Locality)".

In your case, it is tedious to type out all the "s(wl):LocalityLevelX" labels for each smooth. As you have only two taxa, it would be easier to use the complimentary argument terms, where you list smooth labels that you want to include in the model. So you could do terms = c("s(wl):TaxonSpeciesB", "s(wl):TaxonSpeciesC") or whatever summary() displays for these smooths.

You also need to include the Taxon term in terms, which I think needs to be:

no need for a loop here simply use lapply.