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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

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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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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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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