Yesterday I have found an amazing audio comment on Nature’s Arts and Books blog that was discussing a possible influence of music on the development of modern science. Among the many connections we may find between science and music, the one I am going to propose today turns out quite unexpected.

I understand that pipeline development is overtaking the discussion in this blog, and this could actually result quite boring. That’s because I have to face my very first big project in genomics, and I am in the need to explore the best solutions and strategies to manage complex workflows. So, as I already discussed some Python solutions for pipelines and the NextFlow DSL Project, let me take some lines to talk about Luigi.

Luigi is a Python package to build complex pipelines of batch jobs. Long batch processes can be easily managed, and there are pre-designed templates. For instance, there is a full support for MapReduce development in Python, that is the only language used in Luigi. All the workflow can be monitored with a very useful graphical interface, providing a graph representing the structure of your pipeline and the status of data processing. More information and downloads are available on the Luigi GitHub page.

How is this related with music? Well, the picture above displays a romantic view of what music was in the past. Nowadays, anything is managed as a big data thing, tunes and chords are transformed into bit, with a cruel disregard for any romance. Luigi was developed in the context of the very famous (and my favourite) music application Spotify. The main developer, Erik Bernhardsson, is a NYC-based computer scientist who headed the Machine Learning Division at Spotify for six years long.

So, we can actually agree with Kerri Smith’s point on Nature: music influences scientific production. Sometimes is a matter of cultural environment, sometimes is a matter of data science.

Post-pub integration. I was informed on twitter about this page with examples of Luigi usage. Think is worth to be mentioned. Thanks to @smllmp


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