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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

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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Subplots with Matplotlib

As already discussed in some of my previous articles good visualisation of data is essential to getting the associated message across. One aspect of this is the need to plot multiple data sets or visualise the same data set in different ways on the same figure. For example we may wish to illustrate our data and the residuals after we subtract a fit to that data all in the same figure. This can be effectively done using matplotlib and the associated subplots environments. Mastery of these tools is something that comes very much with practice and I do not claim to be an expert. However, I have some experience with the environment and I will share with you the basics in this article. In order to use the matplotlib environment we will need to begin by importing matplotlib via, import matplotlib.pyplot as plt We can then proceed to explore what is on offer. plt.subplot() and plt.subplots() So plt.subplots() and plt.subplot() are probably where most people begin to learn about the idea of c...
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Random Number Generation: Box-Muller Transform

Knowing how to generate random numbers is a key tool in any data scientists tool box. They appear in multiple different optimisation routines and machine learning processes. However, we often use random number generators built into programming languages without thinking about what is happening below the surface. For example in Python if I want to generate a random number uniformally distributed between 0 and 1 all I need to do is import numpy and use the np.random.uniform() function. Similarly if I want gaussian random numbers to for example simulate random noise in an experiment all I need to do is use np.random.normal(). But what is actually happening when I call these functions? and how do I go about generating random numbers from scratch? This is the first of hopefully a number of blog posts on the subject of random numbers and generating random numbers. There are multiple different methods that can be used in order to do this such as the inverse probability transform method and I ...
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