Conveniently, this data is now freely available from the National Oceanic and Atmospheric Administration (NOAA). The figure that we will be generating will display some of the paleo-climate data stretching back over 400kyr, taken from the famous Vostok ice core and first published by Petit et al. OK, so first up we are going to have to download some data. Import the packages (using the import command) that will be needed for this exercise: To begin, create a new, empty python file to write this code. This index varies between zero (optimal health condition, bliss) and 10 (extreme health issues due to stress, eventually leading to premature death). We want to know if this variable is correlated to the Health Index of Gardeners, commonly denoted HIG. In the following, av_mole refers to the average number of moles per square meter of garden. So we will just assume that we did the study and fake the data instead.Īt this point, I should probably point out that “fake data” can be a very serious topic and that is is the basis of many useful and relevant research branches, like stochastic hydrology for instance. Collecting this type of data can be very complex and will surely take a lot of time and money. Let’s say I want to study the influence of moles on stress-related health issues in a part of the human population, gardeners for instance. Check out the Matplotlib gallery for inspiration source code is provided with each figure just click on the one you want to replicate there is also extensive online documentation, a whole load of other websites and blogs from which you can get more information, or if you are really stuck, an active community of Matplotlib users on Stack Exchange. On a final note, one of the wonderful things about Matplotlib is the wealth of information out there to give you a head start. You will never look at an Excel plot in the same way again! The best way of becoming the proficient Matplotlib wizard that you aspire to be is to practice making your own. The aim is to give you sufficient knowledge of the commonly used commands for plotting in Matplotlib, so that you can quickly move on to producing your own scripts. With this in mind, we will try to introduce you to as many different options as is feasible within the confines of this course. All of this is available on the University of Edinburgh servers, but if you need to install it, we’d recommend installing Spyder via pythonxy, as for the same effort the latter will also install a whole bunch of useful libraries for scientific computing.īefore we begin, it is worth pointing out that there are many ways of producing the same figure, and there are an infinite number of possible permutations with regards to what kind of figure you might want to produce. In this class, we will be using a python GUI called Spyder, which also gets installed by default with pythonxy. Fortunately, Inkscape provides a nice package that deals with Scalar Vector Graphics (SVG) files, which enables it to sync nicely with your Matplotlib output. For some annotations, such as more complex annotations, it may still be necessary/easier for you to modify the figures in a graphics package. Whilst a little awkward to start with, if you tenderly nourish your nascent relationship with Matplotlib, you will find that it will provide a comprehensive range of options for plotting and annotating, with the advantage that if you need to reproduce figures, you can do so at the touch of a button (or a single command for all you Linux types). Python is your friend in this regard, providing a powerful library of tools Matplotlib. In almost all cases, figures may need to be revised, and you may need to produce plots of multiple datasets that have consistent formatting. One requirement ubiquitous to all research is the production of figures that can effectively relay your results to the research community, policy makers and/or general public (this fact is unfortunately lost on some people!).
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