The final Python-based automation of laboratory data analysis topic to discuss is that of generating and validating regressions from the stored data. This is typically the ultimate goal of laboratory data analysis projects, and there are still several things to think through before declaring the project completed. This post will introduce and discuss topics such as identifying the best regression form, different tools for generating regressions, and validating models.
So far, all of the discussion has been in analyzing results from individual tests. The next step is to begin to think bigger picture, and create ways to combine those individual test results into data sets describing the results from the entire project. The first step is storing the individual test results in a logical manner, which facilitates later analysis. This post provides guidance on how to do that.
One challenge of automated data analysis is that of checking the results. There is potential for errors in testing, and in data analysis which can both be caught quickly when manually analyzing data. This post provides some methods of doing the same error checking with automated processes, and provides example Python code.
Automating analysis of each individual test relies on the capabilities of several available packages. These packages include glob, pandas, bokeh, and matplotlib. This post provides an introduction to these packages, and future posts will provide a much more thorough description of individual capabilities.