# Blog

A blog of Python-related topics and code.

## Langton's ant on a hexagonal plane

Langton's ant is a two-dimensional cellular automaton usually implemented on a square grid in which each cell has one of two states (white or black). The ant moves according to the simple rules:

## Visualizing vibronic transitions in a diatomic molecule

The Morse oscillator code introduced in a previous blog post can be used to visualize the vibronic transitions in a diatomic molecule by creating two Morse objects (one for each electronic state) and plotting their potential energy curves and energy levels on the same Matplotlib Axes object.

## Plotting nuclide halflives

More than 3000 nuclides (atomic species characterised by the number of neutrons and protons in their nuclei) are known, most of them radioactive with a half-life of less than an hour. About 250 or so of them are stable (not observed to decay using presently-available instruments). The IAEA has an interactive online browser of the nuclides.

## A shallow neural network for simple nonlinear classification

Classification problems are a broad class of machine learning applications devoted to assigning input data to a predefined category based on its features. If the boundary between the categories has a linear relationship to the input data, a simple logistic regression algorithm may do a good job. For more complex groupings, such as in classifying the points in the diagram below, a neural network can often give good results.

## Plotting the decision boundary of a logistic regression model

In the notation of this previous post, a logistic regression binary classification model takes an input feature vector, $\boldsymbol{x}$, and returns a probability, $\hat{y}$, that $\boldsymbol{x}$ belongs to a particular class: $\hat{y} = P(y=1|\boldsymbol{x})$. The model is trained on a set of provided example feature vectors, $\boldsymbol{x}^{(i)}$, and their classifications, $y^{(i)} = 0$ or $1$, by finding the set of parameters that minimize the difference between $\hat{y}^{(i)}$ and $y^{(i)}$ in some sense.