### A dynamic terminal prompt in linux.

A while back, I was frustrated while working on the terminal. I have nested folders which go down two or more levels. And the folders are explicitly named and therefore when I access them in a terminal, the prompt ends up taking more than half of the window width and the commands end up being wrapped around. There should be a better way.

I tried creating a dynamic prompt that would check the length of the prompt and compare it with the window width. If the length of the prompt was greater than half of the window width, it would reduce the prompt to just username@host:$instead of the usual username@host:/.../dir/.../$ saving on space.

So, far I've only been able to add a couple of commands in by .bashrc that will check the length of the prompt, when prompted, and change the prompt if the length of the prompt is greater than a certain value, in this specific case more than 40 characters in length.

if ["$(expr length rahulporuri@astronut:$(pwd))" -gt 40]; then
PS1 = '${debian_chroot:+(debian_chroot)}\u@\h\$\'
else
PS1 = '${debian_chroot:+(debian_chroot)}\u@\h:\w\$\'
fi

is the snippet of code i added in my .bashrc. Everytime i want to change the length of the prompt, I do "\$ source .bashrc" and the prompt length is corrected appropriately. the \u refers to the username, \h for host machine and \w for working directory.

I did try to compare this value with the current window width, the command to which I don't remember now, but it wasn't working. I should've been better at documenting stuff. Or maybe I did document it but forgot to add the reference in my .bashrc. I need to go back and check my notebooks now...

### 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…

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 …