.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/Geometry/plot_brainStyle.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_Geometry_plot_brainStyle.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_Geometry_plot_brainStyle.py:


Stylized brain
========================
Multiresolution meshing from an existing surface mesh.

Original mesh, generated from MRI data, is available at `<https://3dprint.nih.gov/discover/3DPX-000320>`_

.. GENERATED FROM PYTHON SOURCE LINES 11-47

.. code-block:: default


    import numpy as np
    import xcell
    import pyvista as pv
    import tqdm
    from vtk import VTK_QUAD

    stl = pv.read("full_oriented_simpler.stl")

    # pv.set_jupyter_backend(None)

    pts = stl.points
    ax = 2

    bbox = np.hstack((np.min(pts, axis=0), np.max(pts, axis=0)))
    xmax = np.max(np.abs(bbox)) * 1.5

    mindepth = 2
    max_depth = 6

    setup = xcell.Simulation("brain", xmax * np.sign(bbox), True)

    # interpolate by bounds
    idealDepth = mindepth + (max_depth - mindepth) * (pts[:, 2] - bbox[2]) / (bbox[5] - bbox[2])
    metfun = xcell.general_metric

    setup.make_adaptive_grid(
        ref_pts=pts,
        max_depth=idealDepth.astype(int),
        min_l0_function=metfun,
        coefs=0.2 * np.ones_like(idealDepth),
        coarsen=False,
    )

    setup.finalize_mesh()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Make elements starting
    Make elements: 34.2734s [CPU], 34.2656s [wall]
    Finalize mesh starting
    Finalize mesh: 64.389 ms [CPU], 64.2962 ms [wall]
    Calculate conductances starting
    Calculate conductances: 116.174 ms [CPU], 116.174 ms [wall]
    Renumber nodes starting
    Renumber nodes: 321.12 ms [CPU], 244.579 ms [wall]




.. GENERATED FROM PYTHON SOURCE LINES 48-55

.. code-block:: default

    adj = setup.mesh.get_element_adjacencies()
    # reg = xcell.io.Regions()
    # stl.cell_data['sigma'] = 1.
    # reg['Conductors'].append(stl)

    # reg.assign_sigma(setup.mesh, default_sigma=0)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Calculating adjacency:   0%|          | 0/22905 [00:00<?, ?it/s]
    Calculating adjacency:  28%|██▊       | 6332/22905 [00:00<00:00, 63313.12it/s]
    Calculating adjacency:  56%|█████▌    | 12718/22905 [00:00<00:00, 63632.60it/s]
    Calculating adjacency:  83%|████████▎ | 19082/22905 [00:00<00:00, 63241.25it/s]
    Calculating adjacency: 100%|██████████| 22905/22905 [00:00<00:00, 63372.19it/s]




.. GENERATED FROM PYTHON SOURCE LINES 56-66

.. code-block:: default


    xmesh = xcell.io.to_vtk(setup.mesh)


    xmesh.save("xmesh%d%d.vtk" % (mindepth, max_depth))
    inside = xmesh.cell_centers().select_enclosed_points(stl, tolerance=1e-9)


    for el, s in zip(setup.mesh.elements, inside["SelectedPoints"]):
        el.sigma = s







.. GENERATED FROM PYTHON SOURCE LINES 67-112

.. code-block:: default

    okfaces = []

    quadorders = np.array([[0, 4, 6, 2], [1, 3, 7, 5], [0, 1, 5, 4], [2, 6, 7, 3], [0, 2, 3, 1], [4, 5, 7, 6]])

    for ii in tqdm.trange(len(setup.mesh.elements), desc="Checking faces"):
        el = setup.mesh.elements[ii]
        if el.sigma > 0:
            neighbors = adj[ii]

            for jj in range(6):
                neighbor = neighbors[jj]

                for nei in neighbor:
                    if nei.sigma == 0:
                        if nei.depth > el.depth:
                            otherFaceInd = jj + (-1) ** jj
                            inds = np.flip(nei.vertices[quadorders[otherFaceInd]])
                        else:
                            inds = el.vertices[quadorders[jj]]

                        globalind = [setup.mesh.inverse_index_map[idx] for idx in inds]
                        okfaces.append(globalind)


    fc = np.array(okfaces)

    usedNodes = np.unique(fc.ravel())
    revNodes = {}
    for ii, n in enumerate(usedNodes):
        revNodes[n] = ii

    newpts = setup.mesh.node_coords[usedNodes]

    newFaces = np.array([revNodes[n] for n in fc.ravel()]).reshape((fc.shape[0], 4))

    cells = np.hstack((4 * np.ones((newFaces.shape[0], 1), dtype=np.uint64), newFaces.astype(np.uint64))).ravel()
    cellTypes = [VTK_QUAD] * newFaces.shape[0]

    qmesh = pv.UnstructuredGrid(cells, cellTypes, newpts)
    qmesh.save("quad.vtk")

    qg = qmesh.compute_cell_sizes(length=False, volume=False)
    qg.set_active_scalars("Area")
    # qg.plot(style='wireframe')





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    Checking faces:   0%|          | 0/22905 [00:00<?, ?it/s]
    Checking faces: 100%|██████████| 22905/22905 [00:00<00:00, 562401.41it/s]

    (<FieldAssociation.CELL: 1>, pyvista_ndarray([14.62477487, 14.6247767 , 14.62477852, ...,  3.65619691,
                      3.65619691,  3.65619509]))



.. GENERATED FROM PYTHON SOURCE LINES 113-116

Segment mesh and visualize
-------------------------------


.. GENERATED FROM PYTHON SOURCE LINES 116-127

.. code-block:: default


    fstem = "xcell%d-%d" % (mindepth, max_depth)

    result = inside.threshold(value=0.5, scalars="SelectedPoints").extract_largest()
    result.save(fstem + ".vtk")

    # obj = result.extract_surface()
    # pv.save_meshio(fstem+'.obj', obj)
    pv.save_meshio("quads.obj", qmesh)









.. GENERATED FROM PYTHON SOURCE LINES 128-131

Generate animation
------------------------------


.. GENERATED FROM PYTHON SOURCE LINES 131-158

.. code-block:: default


    # to generate from premeshed file
    # result = pv.read('xcell2-9.vtk')

    thm = pv.themes.DarkTheme()
    thm.background = pv.Color(xcell.colors.DARK, opacity=0)
    offwhite = pv.Color(xcell.colors.OFFWHITE, opacity=1.0)


    pv.set_plot_theme(thm)

    p = pv.Plotter(off_screen=True)
    p.add_mesh(stl, color="blue", opacity=0.75)

    viewup = [0.2, -1.0, 0.0]

    p.add_mesh(result, show_edges=False, color=offwhite)

    p.enable_eye_dome_lighting()

    p.show(auto_close=False)

    path = p.generate_orbital_path(factor=1.5, n_points=32, viewup=viewup, shift=-0.2)

    p.open_movie("orbit.mp4")

    p.orbit_on_path(path, write_frames=True, viewup=viewup, step=0.1, progress_bar=True)



.. image-sg:: /auto_examples/Geometry/images/sphx_glr_plot_brainStyle_001.png
   :alt: plot brainStyle
   :srcset: /auto_examples/Geometry/images/sphx_glr_plot_brainStyle_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


      0%|          | 0/32 [00:00<?, ?it/s]
      3%|▎         | 1/32 [00:00<00:04,  7.03it/s]
     41%|████      | 13/32 [00:00<00:00, 61.94it/s]
     78%|███████▊  | 25/32 [00:00<00:00, 83.70it/s]
    100%|██████████| 32/32 [00:00<00:00, 78.71it/s]





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 36.324 seconds)


.. _sphx_glr_download_auto_examples_Geometry_plot_brainStyle.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example




    .. container:: sphx-glr-download sphx-glr-download-python

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

    .. container:: sphx-glr-download sphx-glr-download-jupyter

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


.. only:: html

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

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_