![]() ![]() Below is the full code with some added bits to get some of the other visual elements working (colors, labels, etc.). We should now have our figure completely filled with subplots. Let’s take another look at the plot planner to see how this would work.įig.add_subplot(8,2,14) fig.add_subplot(8,2,16) To do this, we want to break up the figure space into 4 quadrants and select the top right quadrant. So again, we’ll forget about all other subplots (even the one we already made), and we’ll just focus on making a new subplot in the upper right corner. Again, looking at the image, it appears as if ax2 takes up the top right quadrant of the figure. With this in mind, let’s create the ax2 subplot (blue). Essentially, every new subplot will happily go exactly where you tell it to go regardless of what other subplot already exist. This is not the case.Įach new subplot that you define doesn’t care about any of the other subplots that you already made. You might think that since you just divided your figure into two parts, left and right, that your only other option now is to leave the right half blank or plot something in that subplot. Then, highlighted in green, we can see that the cell indexed with number 1 is our selected subplot. I find this visualization much clearer than any of the explanations I saw. But here is an image from the plot planner app that may make this whole thing a little more explicit. ![]() This is because each subplot is independent, and we aren’t ever shown the subplots that aren’t selected. This is a pretty simple subplot, but more complex ones can become difficult to keep track of in your head. So when we say use subplot 1, we are telling our graph to go in the space of the first subplot. The odd thing here is that subplots are indexed starting at 1, not 0 as you might expect. The last number indicates which of those cells to use. In this case, these numbers mean - take my figure and divide it in such a way that there is 1 row and 2 columns. Then, ignoring all the other subplots, lets just split our figure into two portions, left and right. First, we’ll define our figure and make it an 8x8 square (the figure size is arbitrary, but works fine for this example). ![]() Just looking over the image, it appears that ax1 takes up the left half of the figure area. We’ll start with the one labeled ax1 (red). We’re going to make the example shown below with 5 subplots of varying sizes. With all this in mind, let’s try our hand at it. add_subplot() in the matplotlib documentation. So if you define a subplot as (2,3,1), that means to break the subplot into a 2 x 3 grid, and place the new subplot in the first cell of that grid. The key is to understand that the first two integers define the division of the figure, and the last number is the one that actually says where in that division the subplot should go. But what do those numbers actually mean? If the several Stack Overflow posts on the topic, there seems to be some confusion here. add_subplot(1, 2, 3) can be simplified as. If each of those integers are a single digit, they can be simplified into a single three digit integer. It returns an axis object, and takes in three integers. The figure.add_subplot() method is one of the easiest ways to divide an existing figure object into distinct regions of various sizes. Specifically, I’ll be going over two methods. Just tweak a few parameters and see how they change the subplot you’re dealing with.įor this article, I’ll be using my plot planner tool to explain how some features of matplotlib’s subplot system work. I couldn’t find quite what I was looking for, so I went ahead and built my own little web app that I call the plot planner! It’s a pretty straight forward tool. I had to get back to basics, and spent some time reading through the docs and scouring Stack Overflow for examples and clear explanations.Īs I began to understand how all the intricacies of mateplotlib’s subplot system worked, I realized that it would be a lot easier to learn if there was a simple UI tool where you could test out your code and see exactly what was happening in your figure. Though I felt comfortable with making basic visualizations, I found out pretty quickly that my understanding of the subplot system was not up to par. I recently worked on a project that required some fine tuned subplotting and overlaying in matplotlib. Subplots in Matplotlib: A guide and tool for planning your plots ![]()
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