![]() ![]() Matplotlib has many colormaps to chooseįrom ( Choosing Colormaps in Matplotlib) you can make your They all can set a linear mapping between vmin and vmax into These are all examples of Artists that derive from ScalarMappable colorbar ( pc, ax = axs, extend = 'both' ) axs. scatter ( data1, data2, c = data3, cmap = 'RdBu_r' ) fig. set_title ( 'imshow() with LogNorm()' ) pc = axs. imshow ( Z ** 2 * 100, cmap = 'plasma', norm = mpl. pcolormesh ( X, Y, Z, vmin =- 1, vmax = 1, cmap = 'RdBu_r' ) fig. subplots ( 2, 2, layout = 'constrained' ) pc = axs. exp ( - X ** 2 - Y ** 2 ) fig, axs = plt. Is to convert these to numpy.array objects prior to plotting. Input, or objects that can be passed to numpy.asarray.Ĭlasses that are similar to arrays ('array-like') such as pandasĭata objects and numpy.matrix may not work as intended. Plotting functions expect numpy.array or numpy.ma.masked_array as Most Artists are tied to an Axes suchĪn Artist cannot be shared by multiple Axes, or moved from one to another. When the Figure is rendered, all of theĪrtists are drawn to the canvas. Text objects, Line2D objects, collections objects, Patch Artist #īasically, everything visible on the Figure is an Artist (evenįigure, Axes, and Axis objects). ![]() ![]() TheĬombination of the correct Locator and Formatter gives very fineĬontrol over the tick locations and labels. Ticklabel strings are formatted by a Formatter. Of the ticks is determined by a Locator object and the On the Axis) and ticklabels (strings labeling the ticks). These objects set the scale and limits and generate ticks (the marks The Axes class and its member functions are the primaryĮntry point to working with the OOP interface, and have most of the (set via set_title()), an x-label (set via Plotting data, and usually includes two (or three in the case of 3D)īetween Axes and Axis) that provide ticks and tick labels to Axes #Īn Axes is an Artist attached to a Figure that contains a region for Note that manyįor more on Figures, see Creating, viewing, and saving Matplotlib Figures. It is often convenient to create the Axes together with the Figure, but youĬan also manually add Axes later on. subplots ( 2, 2 ) # a figure with a 2x2 grid of Axes # a figure with one axes on the left, and two on the right: fig, axs = plt. subplots () # a figure with a single Axes fig, axs = plt. ![]() figure () # an empty figure with no Axes fig, ax = plt. Please direct your usage questions to Discourse and not to Slack, to make questions and answers accessible to everybody.įor technical issues and bug reports, open an issue in the Makie.jl repository which serves as the central hub for Makie and backend issues.Fig = plt. If you do this manually, please use the category Domain/Visualization and tag questions with Makie to increase their visibility.įor casual conversation about Makie and its development, have a look at the Makie Discord Server. You can use the following BibTeX entry: = Ĭlick this link to open a preformatted topic on the Julia Discourse Page. Journal of Open Source Software, 6(65), 3349, Makie.jl: Flexible high-performance data visualization for Julia. If you use Makie for a scientific publication, please cite our JOSS paper the following way:ĭanisch & Krumbiegel, (2021). Geographic plotting utilities including projections. To switch to a different backend, for example CairoMakie, call CairoMakie.activate!(). Each backend re-exports Makie so there's no need to install it separately. Record(fig, "lorenz.mp4", 1:120) do frameĪx.azimuth = 1.7pi + 0.3 * sin(2pi * frame / 120)Īdd one or more of the Makie backend packages GLMakie.jl (OpenGL), CairoMakie.jl (Cairo), or WGLMakie.jl (WebGL), RPRMakie (RadeonProRender) using Julia's inbuilt package manager. Example using mutable struct Lorenzįig, ax, l = lines(points, color = colors,Ĭolormap = :inferno, transparency = true,Īxis = ( type = Axis3, protrusions = (0, 0, 0, 0), It is available for Windows, Mac and Linux. Makie is a data visualization ecosystem for the Julia programming language, with high performance and extensibility. ![]()
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