Continuous Integration (CI) is not a tool but a practice of continually merging in new behaviour/features into a released product. To facilitate this practice without exposing end users to unstable behaviour and bugs, testing needs to be standardised and automated.
It’s taking a long time to run my genetic algorithm optimisation models recently. So much so that I’ve been looking at offloading processes to other computers lying idle on the network.
About six months ago I was bitten by the mechanical keyboard bug and made a numpad (a yampad to be precise). Too much time and money later, I have modified a pcb to make a custom split keyboard and also tackled an ortholinear keyboard called the plaid, made using through-hole components.
Jupyter notebooks are great for experimentation, reporting and sharing. In a project there are often times when you need to transition from this activity to production ready code. The easiest first step is to convert your notebook to a script.
What is LHS? Latin hypercube sampling aims to bring the best of both worlds: the unbiased random sampling of monte carlo simulation; and the even coverage of a grid search over the decision space.
Rmarkdown was a revelation to me when I was first introduced to it in SEAMS (now Arcadis Gen). I’d used Jupyter notebooks before for Python and loved the live lab notebook feel of them.
Ever since a week before lockdown in mid-March, I’ve been holed up in my conservatory working from home. The wild swings in temperature have provided ample motivation to build a temperature probe and live dashboard to track patterns, open windows in good time or cope with the lead time that my pitiful electric heater requires.
When I began learning about how to use Docker I stumbled on an excellent project called Rocker. For anyone with an x86 machine these Rocker images allow them to run R and most of its dependencies in a containerised environment.