Skip to main content

Pocket reading list : Week 1.1 of May

What North Dakota Would Look Like if Its Oil Drilling Lines Were Aboveground : I'm fascinated by beautiful visualisations and I want to learn how to make them sometime in the near future. This is one such visualisation, that appeared on New York Times' The Upshot, where they depict the oil wells drilled to extract shale oil/gas in southern US but instead of drawing the wells underground, where they actually are, the authors drew them above ground, to provide a scale as to how deep/tall they are. What is also surprising is the sheer number of bore wells and how close they are to one another. No wonder extracting shale oil/gas at this level is causing serious environmental harm!

Seven Features You’ll Want In Your Next Charting Tool : There are lot of tools out there, offline and online, language dependent and language agnostic, that let you convert a dataset into a meaningful chart/plot/visualisation that will help you convey the message in the data a little easier. This is a brilliant article on features that one might consider essential in a tool if they wants widespread adoption and ease-of-use!

Why Should Engineers and Scientists Be Worried About Color? : You will have to incorporate color into your visualisation if you have data that has more than 2 dimensions. Now, the color scale you choose to display your dataset can have repercussions, such as making you see patterns where there aren't any or worse, not conveying a real pattern in the data! Now you decide which is worse. This article shows how beautiful a dataset can look if the right color palette is used and how easily one can convey the underlying structure in the dataset with the right palette. Another account of how choosing the wrong color palette can lead to bad things can be read here - Why rainbow colour scales can be misleading where the author shows how the data was misrepresented by the visualization/color palette leading the original authors of a paper to see a pattern where there wasnt any!

Why You Hate Comic Sans : Everyone hates Comic Sans. Heck, I did too at a certain point in time. But the weird thing is that I don't know why I hated it! I was just going along with every once else. This article makes an attempt to clear the misunderstanding between the font's intended usage and what it's legacy ended up being, thanks to the Digital revolution!

As you can see, the overall theme of the articles so far was visualisation, plotting, colors, interpretation and what not. So I thought that i'd end on a different note so here's a hip-hop-ified account of one of the founding fathers of USA, Alexander Hamilton, which eventually became a big broadway musical that is taking the country by surprise - Lin-Manuel Miranda Performs at the White House Poetry Jam.

Popular posts from this blog

Animation using GNUPlot

Animation using GNUPlotI've been trying to create an animation depicting a quasar spectrum moving across the 5 SDSS pass bands with respect to redshift. It is important to visualise what emission lines are moving in and out of bands to be able to understand the color-redshift plots and the changes in it.
I've tried doing this using the animate function in matplotlib, python but i wasn't able to make it work - meaning i worked on it for a couple of days and then i gave up, not having found solutions for my problems on the internet.
And then i came across this site, where the gunn-peterson trough and the lyman alpha forest have been depicted - in a beautiful manner. And this got me interested in using js and d3 to do the animations and make it dynamic - using sliders etc.
In the meanwhile, i thought i'd look up and see if there was a way to create animations in gnuplot and whoopdedoo, what do i find but nirvana!

In the image, you see 5 static curves and one dynam…

Pandas download statistics, PyPI and Google BigQuery - Daily downloads and downloads by latest version

Inspired by this blog post : https://langui.sh/2016/12/09/data-driven-decisions/, I wanted to play around with Google BigQuery myself. And the blog post is pretty awesome because it has sample queries. I mix and matched the examples mentioned on the blog post, intent on answering two questions - 
1. How many people download the Pandas library on a daily basis? Actually, if you think about it, it's more of a question of how many times was the pandas library downloaded in a single day, because the same person could've downloaded multiple times. Or a bot could've.
This was just a fun first query/question.
2. What is the adoption rate of different versions of the Pandas library? You might have come across similar graphs which show the adoption rate of various versions of Windows.
Answering this question is actually important because the developers should have an idea of what the most popular versions are, see whether or not users are adopting new features/changes they provide…

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 …