Certainly, you can find more than one way to run a function for multiple times to perform a task in Python (and other languages). Here I am just going to use the same approach as in [Calling a function for multiple times in Python (I)] (https://hhsieh.github.io/calling-function-multiple-times/).

Today I am going to blog a little about Python, a language that I have found very helpful in producing clean and productive code.

Here are some books that I have found helpful and interesting as learning ecological staistics and theoretical modeling.

For now, I am done with blogging the series of Unlimited Networks. The script in this series in fact comes from my Github repo ulnet, from which I am also developing a R package. This repo and the upcoming R package include more functions than there exists on the blog. Please refer to ulnet if interested and come back for any new release in the near future. I also appreciate any comments on the blog posts as well as functions to include in the package.

Now we need to do something a little bit more challenging. It is not too bad, however. The idea is really logical and may even come intuitively.

Now it comes a bigger problem. Due to budget constraint, you only have sampled all of the nodes within a rectangular plot. They are the total 100 nodes you have in Unlimited-Networks (I).

We can edit the nt function to make graphs and compute nodes and network properties.

Suppose you have 100 nodes; each of them has its own x and y spatial coordinates. You are interested in learning how the spatial structure of the network composed of the nodes and the edges forming between them affects the system dynamics. Unfortunately, you have no clue whether there is an edge between any two nodes and, therefore, cannot conclude the configuration of the spatial network. What should you do to generate testable/hypothetical networks in order to answer the interesting question you have in mind?