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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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Contour Plotting with Matplotlib

Visualising data well is an important part of any analysis and a good handle on the Python package Matplotlib is essential for any Python data analyst. I hope to provide a few tutorials on some of the more complex concepts in data visualisation and how to produce clear and tidy graphs with Matplotlib. I will assume some basic knowledge of the Matplotlib package but will try and explain the code as clearly as possible. Comments are always welcome! I am going to begin with this piece on contour plotting which is an area I have a particular interest in. Specifically I am interested in plotting parameter spaces for fitted functions with contours defined by an objective function like $\chi^2$. We will get to an example of this shortly but I first want to look at how we make contour plots with a simpler example. Basic Example with Radii For our simple example we will define variables $x$ and $y$ over a given range and plot the corresponding radius, $Z$ from 0 for each data point $(x, y)$ as ...
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