Shown in the ( Surface from irregular data example). The required nearest-neighbor matching, and interpolation, as The data points lie on a surface, and we want to Quite often, the connectivity is not regular, but is not known inĪdvance either. Triangular_mesh(), connectivity is explicitly specified. Information is implicitly extracted from the shape of the inputĪrrays: neighboring data points in the 2D input arrays areĬonnected, and the data lies on a grid. With theįunctions surf() and mesh(), this connectivity Surface, connectivity information is also required. Knowing the positions of data points is not enough to define a This behavior can beĬontrolled by specifying the “warp_scale=’auto’”. By default the z-axis is supposed toīe in the same units as the x and y axis, but it can beĪuto-scaled to give a 2/3 aspect ratio. Surf() and contour_surf() can be used as 3D Vertical scale of surf() and contour_surf() Vertices, and the (n, 3) array of the indices of Plot a triangular mesh, fully specified by This function is very versatile and will accept 2D orģD arrays, but also clouds of points, to position the X, y and z coordinates with no privilegedĭirection. Unlike surf(), the surface is defined by its Y, z giving the coordinates of the data points Plot a surface described by three 2D arrays, x, View a 2D array as line contours, elevatedĪccording to the value of the array points. Representation through elevation the value of the View a 2D array as a carpet plot, with the z axis Plots line between the supplied data, described by x, Plots glyphs (like points) at the position of the
The user guide, with figures and examples. Is described in detail in the MLab reference, at the end of In this section, we only list the different functions. Visualization can also be modified by changing the attributes of this In addition, they all return the visualization module created, thus
Their behavior, and thus the visualizationĬreated, can be fine-tuned through keyword arguments, similarly to pylab. Visualizations: they create the data source, filters if necessary, andĪdd the visualization modules. The mlab plotting functions take numpy arrays as input, describing the Visualization can be created in mlab by a set of functions operating on IPython via the handy mlab.test_contour3d?. Test_contour3d, test_points3d, test_plot3d_anim etc.). Several examples of this kind are provided with mlab (see The visualization is created by the single function mesh() in the above. This produces the following visualization:
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show ()īulk of the code in the above example is to create the data. from numpy import pi, sin, cos, mgrid dphi, dtheta = pi / 250.0, pi / 250.0 = mgrid m0 = 4 m1 = 3 m2 = 2 m3 = 3 m4 = 6 m5 = 2 m6 = 6 m7 = 4 r = sin ( m0 * phi ) ** m1 + cos ( m2 * phi ) ** m3 + sin ( m4 * theta ) ** m5 + cos ( m6 * theta ) ** m7 x = r * sin ( phi ) * cos ( theta ) y = r * cos ( phi ) z = r * sin ( phi ) * sin ( theta ) # View it. To get you started, here is a pretty example showing a spherical harmonic Some detailed examples of applying these tools to visualizing volumetricĬhanging the looks of the visual objects created
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In which full visualization pipeline are built in scripts, and we give Finally, we expose a more advanced use of mlab Represent data (2) how the visualization created through mlab canīe modified interactively with dialogs, (3) how scripts andĪnimations can be ran. Properties such as color or glyph size can be modified or used to In this section, we first introduce simple plotting functions, to createģD objects as representations of numpy arrays. For more details on using mlab and running scripts, read the section plot ( x, x + 15, color = "purple", lw = 1, ls = '-', marker = 'o', markersize = 8, markerfacecolor = "red" ) ax. plot ( x, x + 14, color = "purple", lw = 1, ls = '-', marker = 'o', markersize = 4 ) ax.
plot ( x, x + 13, color = "purple", lw = 1, ls = '-', marker = 'o', markersize = 2 ) ax. plot ( x, x + 12, color = "green", lw = 2, ls = '-', marker = '1' ) # marker size and color ax. set_dashes () # format: line length, space length. plot ( x, x + 8, color = "black", lw = 1.50 ) line. plot ( x, x + 7, color = "red", lw = 2, ls = ':' ) # custom dash line, = ax. plot ( x, x + 5, color = "red", lw = 2, linestyle = '-' ) ax. plot ( x, x + 3, color = "blue", linewidth = 1.00 ) ax. plot ( x, x + 2, color = "blue", linewidth = 0.50 ) ax. plot ( x, x + 1, color = "blue", linewidth = 0.25 ) ax.
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