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# 3D Plotting functions for numpy arrays

Visualization can be created in mlab by a set of functions operating on numpy arrays.

The mlab plotting functions take numpy arrays as input, describing the `x`, `y`, and `z` coordinates of the data. They build full-blown visualizations: they create the data source, filters if necessary, and add the visualization modules. Their behavior, and thus the visualization created, can be fine-tuned through keyword arguments, similarly to pylab. In addition, they all return the visualization module created, thus visualization can also be modified by changing the attributes of this module.

Note

In this section, we only list the different functions. Each function is described in details in the MLab reference, at the end of the user guide, with figures and examples. Please follow the links.

## 0D and 1D data

| `points3d()` Plots glyphs (like points) at the position of the supplied data, described by `x`, `y`, `z` numpy arrays of the same shape.
| `plot3d()` Plots line between the supplied data, described by `x`, `y`, `z` 1D numpy arrays of the same length.

## 2D data

| `imshow()` View a 2D array as an image.
| `surf()` View a 2D array as a carpet plot, with the z axis representation through elevation the value of the array points.
| `contour_surf()` View a 2D array as line contours, elevated according to the value of the array points.
| `mesh()` Plot a surface described by three 2D arrays, `x`, `y`, `z` giving the coordinnates of the data points as a grid. Unlike `surf()`, the surface is defined by its `x`, `y` and `z` coordinates with no privileged direction. More complex surfaces can be created.
| `barchart()` Plot an array `s`, or a set of points with explicite coordinnates arrays, `x`, `y` and `z`, as a bar chart, eg for histograms. This function is very versatile and will accept 2D or 3D arrays, but also clouds of points, to position the bars.
| `triangular_mesh()` Plot a triangular mesh, fully specified by `x`, `y` and `z` coordinnates of its vertices, and the (n, 3) array of the indices of the triangles.

Vertical scale of `surf()` and `contour_surf()`

`surf()` and `contour_surf()` can be used as 3D representation of 2D data. By default the z-axis is supposed to be in the same units as the x and y axis, but it can be auto-scaled to give a 2/3 aspect ratio. This behavior can be controlled by specifying the “warp_scale=’auto’”.

From data points to surfaces.

Knowing the positions of data points is not enough to define a surface, connectivity information is also required. With the functions `surf()` and `mesh()`, this connectivity information is implicitely extracted from the shape of the input arrays: neighbooring data points in the 2D input arrays are connected, and the data lies on a grid. With the function `triangular_mesh()`, connectivity is explicitely specified. Quite often, the connectivity is not regular, but is not known in advance either. The data points lie on a surface, and we want to plot the surface implicitely defined. The delaunay2d filter does the required nearest-neighboor matching, and interpolation, as shown in the surface_from_irregular_data example (Surface from irregular data example).

## 3D data

| `contour3d()` Plot isosurfaces of volumetric data defined as a 3D array.
| `quiver3d()` Plot arrows to represent vectors at data points. The `x`, `y`, `z` position are specified by numpy arrays, as well as the `u`, `v`, `w` components of the vectors.
| `flow()` Plot trajectories of particles along a vector field described by three 3D arrays giving the `u`, `v`, `w` components on a grid.

Structured or unstructured data

`contour3d()` and `flow()` require ordered data (to be able to interpolate between the points), whereas `quiver3d()` works with any set of points. The required structure is detailed in the functions’ documentation.

Note

Many richer visualisations can be created by assembling data sources filters and modules. See the Assembling pipelines with mlab and the Case studies of some visualizations sections.

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