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Arxiv author affiliations using Python

So, I wanted to get author affiliation information from papers on arXiv. arXiv provides with an API to bulk query their database and get information. Following that, I look for the attribute 'arxiv:affiliation' in the html data. Here's the code - import urllib from BeautifulSoup import BeautifulStoneSoup   url = 'http://export.arxiv.org/api/query?search_query=all:astro&start=0&max_results=1000'   data = urllib.urlopen(url).read() soup = BeautifulStoneSoup(data) #print(soup.prettify()) #list = soup.findAll('arxiv:affiliation') #for i in range(len(list)): #        print list[i].contents   test = [tag.string for tag in soup.findAll('arxiv:aiffiliation')] Now, the problem I'm having is that I'm getting affiliation of all authors which I want to split into sets of affiliations of authors of a paper, which I'm stuck on at the moment. Once I get that part, I can move on to the next part of this pet project, displaying these...
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You need to start writing Architecture Decision Records

If you write software, you need to start maintaining Architecture Decision Records (ADRs). ADRs roughly fall into the category of Developer Documentation. They aren't aimed at the Users of the software. Instead, they are aimed at the developers and maintainers of the software. They usually contain information about the layout of the package and what the code does. ADRs, in this context, are an absolute necessity. By definition, ADRs are simply a Record of a Decision that has an impact of the Architecture of the application. In fact, Any meaningful technical decision made in a software project, irrespective of whether it has an impact on the architecture of the application, needs to be documented. I first came across ADRs in Chapter 17 of Fundamentals of Software Architecture book. A few of us (at Enthought ) read this book earlier in the year and ADRs are one of the things that we took away from the book. You can find more information on ADRs here and here . Here's what the f...
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Adaptive step size Runge-Kutta method

I am still trying to implement an adaptive step size RK routine. So far, I've been able to implement the step-halving method but not the RK-Fehlberg. I am not able to figure out how to increase the step size after reducing it initially. To give some background on the topic, Runge-Kutta methods are used to solve ordinary differential equations, of any order. For example, in a first order differential equation, it uses the derivative of the function to predict what the function value at the next step should be. Euler's method is a rudimentary implementation of RK. Adaptive step size RK is changing the step size depending on how fastly or slowly the function is changing. If a function is rapidly rising or falling, it is in a region that we should sample carefully and therefore, we reduce the step size and if the rate of change of the function is small, we can increase the step size. I've been able to implement a way to reduce the step size depending on the rate of change of...
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