

.. _sphx_glr_gallery_mplot3d_trisurf3d_2.py:


===========================
More triangular 3D surfaces
===========================

Two additional examples of plotting surfaces with triangular mesh.

The first demonstrates use of plot_trisurf's triangles argument, and the
second sets a Triangulation object's mask and passes the object directly
to plot_trisurf.




.. image:: /gallery/mplot3d/images/sphx_glr_trisurf3d_2_001.png
    :align: center





.. code-block:: python


    import numpy as np
    import matplotlib.pyplot as plt
    from mpl_toolkits.mplot3d import Axes3D
    import matplotlib.tri as mtri


    fig = plt.figure(figsize=plt.figaspect(0.5))

    #============
    # First plot
    #============

    # Make a mesh in the space of parameterisation variables u and v
    u = np.linspace(0, 2.0 * np.pi, endpoint=True, num=50)
    v = np.linspace(-0.5, 0.5, endpoint=True, num=10)
    u, v = np.meshgrid(u, v)
    u, v = u.flatten(), v.flatten()

    # This is the Mobius mapping, taking a u, v pair and returning an x, y, z
    # triple
    x = (1 + 0.5 * v * np.cos(u / 2.0)) * np.cos(u)
    y = (1 + 0.5 * v * np.cos(u / 2.0)) * np.sin(u)
    z = 0.5 * v * np.sin(u / 2.0)

    # Triangulate parameter space to determine the triangles
    tri = mtri.Triangulation(u, v)

    # Plot the surface.  The triangles in parameter space determine which x, y, z
    # points are connected by an edge.
    ax = fig.add_subplot(1, 2, 1, projection='3d')
    ax.plot_trisurf(x, y, z, triangles=tri.triangles, cmap=plt.cm.Spectral)
    ax.set_zlim(-1, 1)


    #============
    # Second plot
    #============

    # Make parameter spaces radii and angles.
    n_angles = 36
    n_radii = 8
    min_radius = 0.25
    radii = np.linspace(min_radius, 0.95, n_radii)

    angles = np.linspace(0, 2*np.pi, n_angles, endpoint=False)
    angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)
    angles[:, 1::2] += np.pi/n_angles

    # Map radius, angle pairs to x, y, z points.
    x = (radii*np.cos(angles)).flatten()
    y = (radii*np.sin(angles)).flatten()
    z = (np.cos(radii)*np.cos(angles*3.0)).flatten()

    # Create the Triangulation; no triangles so Delaunay triangulation created.
    triang = mtri.Triangulation(x, y)

    # Mask off unwanted triangles.
    xmid = x[triang.triangles].mean(axis=1)
    ymid = y[triang.triangles].mean(axis=1)
    mask = np.where(xmid**2 + ymid**2 < min_radius**2, 1, 0)
    triang.set_mask(mask)

    # Plot the surface.
    ax = fig.add_subplot(1, 2, 2, projection='3d')
    ax.plot_trisurf(triang, z, cmap=plt.cm.CMRmap)


    plt.show()

**Total running time of the script:** ( 0 minutes  0.039 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: trisurf3d_2.py <trisurf3d_2.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: trisurf3d_2.ipynb <trisurf3d_2.ipynb>`

.. rst-class:: sphx-glr-signature

    `Generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_
