A blog of Python-related topics and code.
Langton's ant on a hexagonal plane
Posted by: christian on 22 Nov 2020
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
Posted by: christian in Featured on frontpage on 7 Oct 2020
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
Posted by: christian in Featured on frontpage on 29 Sep 2020
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
Posted by: christian in Featured on frontpage on 18 Sep 2020
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
Posted by: christian in Featured on frontpage on 17 Sep 2020
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.