One of the typical challenges when exporting numerical computations is how to include the way the calculations were achieved. Typically, if results should be in an Excel sheet, you want to see a formula if you click on each cell. A naive method is using some Excel driver (like XlsxWriter) directly. But if you are dealing with a big Excel sheet (or multiple sheets in one file), this approach becomes quite inconvenient. Therefore, this article presents a library called Portable Spreadsheet - it can easily export defined formulas in Python to many formats, including Excel and JSON.

Many open-source tools can help with the prediction of renewable energy production. For example, the Python library PVLIB for computation of production solar of photovoltaic panels implements essential scientific methods. However, if it comes to predicting energy from wind turbines, there is no similar library - but the computation itself is sufficiently simple - so everyone can write their methods. There is also a question of how precise predictions are, which requires a probabilistic approach.

Code profiling is one of the most important parts of the code optimisation process. Generally speaking, it is the program analysis where we are mainly interested in memory usage and time complexity of the program (or each of its parts). We can also be interested in how often we use some function (or how often we call it, what is the latency). There are a lot of principles and tools to perform these tasks. This article is mainly focused on stack tracing, line-by-line code analysis tools, and function call graphs.