1/17/2024 0 Comments Matplotlib subplot sizesIn this article, we need some basic concept of two python module named as “Matplotlib” and “Numpy”. The main motto of this article is how to change or set the size of a figure in Matplotlib using Python. Setting or Changing the Size of a Figure in Matplotlib Python The matplotlib module is used to create a figure, and we can do different types of experiment with that figure such as “changing the axis of the graph”, “changing the geometric shape”, “changing the background colour of the figure” and many more. In this article, we have to only focus on changing the size of the figure. There is a method of changing the size of a figure in matplotlib by using “ figsize=(a,b)” attribute, where “a = width of the figure in unit inches” and “b = height of the figure in unit inches”. Improve subplot size/spacing with many subplots. In this example we see the default drawn figure(width = height). Calling a function of a module by using its name (a string) 901.Square size figure in Matplotlib with Python import matplotlib.pyplot as plt If we don’t use the property to change or set the size of figure, then it takes width and height both same and the result will be a square type figure. labelouter is a handy method to remove labels and ticks from subplots that are not at the edge of. For example, we can reduce the height between vertical subplots using addgridspec(hspace0). The explanation is same as stated in the above examples, the only thing that is changed. Now, you can see that the width and height of the figure are equal. To precisely control the positioning of the subplots, one can explicitly create a GridSpec with Figure.addgridspec, and then call its subplots method. In this example, width 2 inch and height 6 inch. Here, the first thing we have to do is to import two python module “ matplotlib” and “ numpy” by these line of codes:-Īnd then we created a numpy array and then established the relation between X and Y i.e. In this example again we change the width and height of the figure(width height):–.Let say we want to set the width of the figure to 2 inches and height to 6 inches. Again this change will make the figure in the shape of something like a rectangular shape. Now, you can see that the width of the figure is 1/3rd the height of the figure. The explanation is same as stated in the above examples, the only thing that is changed is the value of width and height. The figure size is a specific ratio which forces the spacing.įor those sharing the y-axis across both plots, setting constrained_layout to True may help.In this example, width = 2 inch and height = 6 inch. I'm explicitly selecting that there will be 2 sub plots in my figure, and that the figure will be chosen_value tall and each subplot will be about half that size wide, and that the subplots will have an aspect ratio of 1 (i.e., they will both be square). Here's some code I use: fig, axis_array = plt.subplots(1, 2, figsize=(chosen_value, 1.05 * chosen_value / 2), and learn how to customize its location, color, font size and style. If you change your code to: fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) Learn how to add titles to plots, subplots and figures with the title, settitle and. The problem is that the plot region height is too large and this is leaving empty place in the image. As every dimension in generated graphs is adjusted by the library, it can be quite difficult to visualize data in a proper format. The solution proposed by worked like a charm for me, but for completeness, I'd like to mention a pretty simple workaround I was suggested to apply (credit to Zhang) before my question was marked as an exact duplicate of this one: What is Figsize in Python Matplotlib Figsize is a method from the pyplot class which allows you to change the dimensions of the graph. I had the same problem and asked a very similar question in SO. If you want to use mpl_toolkits and make your hands dirty, this answer would be a good read. This answer for using the subplot parameters to achieve a certain aspect. If the image does not have equal limits (is not square), one still needs to divide by the aspect of the image: asp = np.diff(ax2.get_xlim()) / np.diff(ax2.get_ylim())Īsp /= np.abs(np.diff(ax1.get_xlim()) / np.diff(ax1.get_ylim())) Or you may set the aspect of the line plot depending on its axis limits such that it gets the same size as the image (in case the image has equal x and y sizes) asp = np.diff(ax2.get_xlim()) / np.diff(ax2.get_ylim())Īsp = np.diff(ax2.get_xlim()) / np.diff(ax2.get_ylim()) You may use automatic aspect on the image ax.imshow(z, aspect="auto") It's not perfectly clear what your desired outcome is.
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