This repo is a collection of all codes I have written for the IUCAA-NCRA Graduate School 2018-19. Some of the folders contain codes as well as assignment solutions.
These codes in no way show the whole of the graduate school, but comprise of a small part of the whole curriculum. Each folder contains:
- Astro1: This is basically an 'ASTRO 101' taken by Prof. Raghunathan Srianand at IUCAA. The assignments contained, among other things, calculation of flux from magnitudes (from SDSS data), computation of spectrum from a Quasar as it gets redshifted and also undergoes Ly-$\alpha$ absorption, etc.
- AstroTech1: This was a course on Astronomical techniques (Optical astronomy) taken by Prof. AN Ramaprakash at IUCAA. The assignments in this course were highly complementary to what was being taught in the class, with many questions containing error propagation requirement. At one stage, I thought it best to have Tensorflow do the backpropagation for me and compute the errors. Also, we had to look at device charachteristics for which plots, rather than tables were available. To get accurate data, I wrote another code Graph2Data, which enables one to select points on the plot and produce corresponding values.
- FluidMechanics: Taken by Prof. Sukanta Bose at IUCAA, Fluid mechanics started off from Boltzmann equation and then proceeded to self gravitating fluid systems, instabilities,etc. One of our exercises was to understand the use of Lane-Emden equation in deriving the Chandrasekhar limit. Thus, I have simulated the equation in the notebook file of the repo. The notebook can be run on Google Colab by the interested user. I have also written a semi-technical blog article on Chandrasekhar limit - which can be found on Medium.
- Polarimetry: Polarimetry experiment was a part of the Astronomical Techniques course of Prof. AN Ramaprakash. Siddharth Maharana, a graduate student at IUCAA was our TA for the experiment. We were supposed to use IRAF for this work, but it is easy enough with Python. The data analysis is primarily sky subtraction and error propagation, where again I have used tensorflow!