![]() ![]() Therefore, if we do the following: fig = plt.figure(figsize=(3.54,3.54), dpi=300) However, font size and thickness is related to the size of the figure, not to the number of pixels. If we used bigger image sizes, we would get more pixels. Font SizeĪt some point, you must wonder why we are specifying both the figure size and the dpi. If we make the math, this means the final figure has 2124 pixels on each axis. We can specify this directly on matplotlib: fig = plt.figure(figsize=(3.54,3.54), dpi=600) A typical dpi value for printing is 600dpi. Therefore, we must match that value when we prepare our images, or they would look terrible. If we are preparing a paper, the dpi is fixed by the printer. If we are looking at the image on a screen instead of printing, the dots become pixels. The terminology comes from the printer process of making dots of ink. The other way around, if we know the final size of an image and the dpi, we can calculate the number of pixels we need. We can calculate the dots per inch if we know the total number of pixels and the physical size we expect for the image. The resolution of an image can be calculated, counting the total number of pixels it contains or defining the dots per inch: DPI. We can make a plot of the same size, but with different resolution, which is the second parameter we need to discuss. However, knowing the size is only half the problem. With matplotlib, defining the size of a figure is straightforward: fig = plt.figure(figsize=(3.54,3.54)) For simplicity, I will assume a square image, so we have 3.54x3.54 inches. The height of the image will depend on the content we are displaying. Science uses a width of 9cm, or 3.54 inches. If we were preparing a plot for a paper, for example, we should first check the column width. Size is the physical dimensions of an image. Sizeįirst, we need to discuss size and resolution, two independent, but related magnitudes. We will focus mostly on plots for papers, but once you get the gist, you can extrapolate to other formats such as posters, books, or websites. Once you understand the options that Matplotlib offers, and what they mean, you will realize that generating publication-ready figures can be a breeze. You won't have to tinker ever again with random parameters until the result is right. We will take care of the most important aspects: shape, font sizes, and resolution. In this Python Tip, we will see how to create images using Matplotlib that are ready to be embedded. Saving figures for publications, presentations, books, or websites can be a cumbersome task but doesn't need to be. ![]() 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.By Aquiles Carattino figure resolution saving matplotlib plot #matplotlib #resolution #figure #plot #saving tip #tip, tips 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), If you change your code to: fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) The problem is that the plot region height is too large and this is leaving empty place in the image. 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: ![]() 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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