Implementing global optimisation is not uncommon but is sufficiently complex to not want too roll your own solution, especially for more complex cases like quadratic or mixed integer programming. Fortunately, there are many packages available that provide this implementation and Python has wrappers for most of them. Unfortunately, there doesn’t seem to be a beginners guid for choosing which package to use. That’s what this article intends to provide.
Apparently there is some discussion somewhere about being able to import from parent directories. Long story short, I think, is that it was deemed that this is a poor way to structure modules, however, for work which contains run scripts in folder within the working directory this would be a useful functionality.
Call ‘git fetch’ if necessary to fetch refs from other repositories, then
so the current branch is the one that will persist.
Creating a Branch Locally
This will create a new local branch called ‘localbranchname’ which will be a copy of the currently checkedout branch.
Jekyll was something I was told about from a friend of mine from College, James Black, and appealed to me for a few reasons. The increased security, lack of databases and lack of updates all appealed. My recent love affair with git makes having everything in text files quite attractive and coding in vim is always better. But my main reason for exploring this was the ease of customising behaviour without the wordpress framework monolith looming over you.
I have recently been thoroughly sold by the benefits of Git. It is a revelation that came slower to me than many of you, probably because I haven’t worked on projects large enough or high risk enough to warrant that sort versioning and I have never seriously been in a situation with that level of collaboration.