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About

I am a first year PhD student at University of Cambridge studying and working on data analysis for 21-cm Cosmology experiments. I previously did an integrated Masters Degree at the University of Manchester in Physics with Astrophysics.

I am interested in Cosmology, Data Analysis, Galaxies, Radio Astronomy.

All of the codes I am writing for this blog are written in Python3 (unless otherwise stated) on a Ubuntu Linux system. They are available at: https://github.com/htjb/Blog

You can follow updates on my blog on Instagram at @astroanddata.

Opinions are all my own.

Popular posts from this blog

Random Number Generation: Inverse Transform Sampling with Python

Following on from my previous post, in which I showed how to generate random normally distributed numbers using the Box-Muller Transform, I want to demonstrate how Inverse Transform Sampling(ITS) can be used to generate random exponentially distributed numbers. The description of the Box-Muller Transform can be found here:  https://astroanddata.blogspot.com/2020/06/random-number-generation-box-muller.html . As discussed in my previous post random numbers appear everywhere in data analysis and knowing how to generate them is an important part of any data scientists tool box. ITS takes a sample of uniformly distributed numbers and maps them onto a chosen probability density function via the cumulative distribution function (CDF). In our case the chosen probability density function is for an exponential distribution given by, $P_d(x) = \lambda \exp(-\lambda x)$. This is a common distribution that describes events that occur independently, continuously and with an average constant rate...
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LDL Decomposition with Python

I recently wrote a post on the Cholesky decomposition of a matrix. You can read more about the Cholesky decomposition here;  https://harrybevins.blogspot.com/2020/04/cholesky-decomposition-and-identity.html . A closely related and more stable decomposition is the LDL decomposition which has the form, $\textbf{Q} = \textbf{LDL*}$, where $\textbf{L}$ is a lower triangular matrix with diagonal entries equal to 1, $\textbf{L*}$ is it's complex conjugate and $\textbf{D}$ is a diagonal matrix. Again an LDL decomposition can be performed using the Scipy or numpy linear algebra packages but it is a far more rewarding experience to write the code. This also often leads to a better understanding of what is happening during this decomposition. The relationship between the two decompositions, Cholesky and LDL, can be expressed like so, $\textbf{Q} = \textbf{LDL*} = \textbf{LD}^{1/2}(\textbf{D})^{1/2}\textbf{*L*} = \textbf{LD}^{1/2}(\textbf{LD}^{1/2})\textbf{*}$. A simple way to calcu...
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5 Great Popular Science Books: Get started with Physics and Astrophysics

Popular science books are a great way to alleviate your curiosity in a relaxing way! I have spent many an hour reading some fantastic books and I want to share a few of these with you now. The following books serve as a good introduction to Physics, Astrophysics and some of the more complex concepts in the subject areas. I consider these 'must reads' for every enthusiast and professional. I hope you can enjoy them as much as I have! #1 - The Character of Physical Law By Richard Feynman Okay you should read as much of Feynman's writing as you can get you hands on. That goes without saying and explains why two of his books appear on this list! However, 'The Character of Physical Law' is a great place to start not only with Feynman's writing but also with Physics based popular science books in general. The book itself is based on a series of seven guest lectures given by Feynman at Cornell University in 1964. You can watch recordings of these lectures here:  https:...
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