.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorial/04_filters/solutions/d_contouring.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_tutorial_04_filters_solutions_d_contouring.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_tutorial_04_filters_solutions_d_contouring.py:


.. _contouring_example:

Contouring
~~~~~~~~~~

Generate iso-lines or -surfaces for the scalars of a surface or volume.

3D meshes can have 2D iso-surfaces of a scalar field extracted and 2D surface
meshes can have 1D iso-lines of a scalar field extracted.

.. GENERATED FROM PYTHON SOURCE LINES 12-17

.. code-block:: Python


    import numpy as np
    import pyvista as pv
    from pyvista import examples








.. GENERATED FROM PYTHON SOURCE LINES 18-22

Iso-Lines
+++++++++

Let's extract 1D iso-lines of a scalar field from a 2D surface mesh.

.. GENERATED FROM PYTHON SOURCE LINES 22-24

.. code-block:: Python

    mesh = examples.load_random_hills()








.. GENERATED FROM PYTHON SOURCE LINES 25-27

.. code-block:: Python

    help(mesh.contour)





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

 .. code-block:: none

    Help on method contour in module pyvista.core.filters.data_set:

    contour(isosurfaces=10, scalars=None, compute_normals=False, compute_gradients=False, compute_scalars=True, rng=None, preference='point', method='contour', progress_bar=False) method of pyvista.core.pointset.PolyData instance
        Contour an input self by an array.
    
        ``isosurfaces`` can be an integer specifying the number of
        isosurfaces in the data range or a sequence of values for
        explicitly setting the isosurfaces.
    
        Parameters
        ----------
        isosurfaces : int | sequence[float], optional
            Number of isosurfaces to compute across valid data range or a
            sequence of float values to explicitly use as the isosurfaces.
    
        scalars : str | array_like[float], optional
            Name or array of scalars to threshold on. If this is an array, the
            output of this filter will save them as ``"Contour Data"``.
            Defaults to currently active scalars.
    
        compute_normals : bool, default: False
            Compute normals for the dataset.
    
        compute_gradients : bool, default: False
            Compute gradients for the dataset.
    
        compute_scalars : bool, default: True
            Preserves the scalar values that are being contoured.
    
        rng : sequence[float], optional
            If an integer number of isosurfaces is specified, this is
            the range over which to generate contours. Default is the
            scalars array's full data range.
    
        preference : str, default: "point"
            When ``scalars`` is specified, this is the preferred array
            type to search for in the dataset.  Must be either
            ``'point'`` or ``'cell'``.
    
        method : str, default:  "contour"
            Specify to choose which vtk filter is used to create the contour.
            Must be one of ``'contour'``, ``'marching_cubes'`` and
            ``'flying_edges'``.
    
        progress_bar : bool, default: False
            Display a progress bar to indicate progress.
    
        Returns
        -------
        pyvista.PolyData
            Contoured surface.
    
        Examples
        --------
        Generate contours for the random hills dataset.
    
        >>> from pyvista import examples
        >>> hills = examples.load_random_hills()
        >>> contours = hills.contour()
        >>> contours.plot(line_width=5)
    
        Generate the surface of a mobius strip using flying edges.
    
        >>> import pyvista as pv
        >>> a = 0.4
        >>> b = 0.1
        >>> def f(x, y, z):
        ...     xx = x * x
        ...     yy = y * y
        ...     zz = z * z
        ...     xyz = x * y * z
        ...     xx_yy = xx + yy
        ...     a_xx = a * xx
        ...     b_yy = b * yy
        ...     return (
        ...         (xx_yy + 1) * (a_xx + b_yy)
        ...         + zz * (b * xx + a * yy)
        ...         - 2 * (a - b) * xyz
        ...         - a * b * xx_yy
        ...     ) ** 2 - 4 * (xx + yy) * (a_xx + b_yy - xyz * (a - b)) ** 2
        ...
        >>> n = 100
        >>> x_min, y_min, z_min = -1.35, -1.7, -0.65
        >>> grid = pv.ImageData(
        ...     dimensions=(n, n, n),
        ...     spacing=(
        ...         abs(x_min) / n * 2,
        ...         abs(y_min) / n * 2,
        ...         abs(z_min) / n * 2,
        ...     ),
        ...     origin=(x_min, y_min, z_min),
        ... )
        >>> x, y, z = grid.points.T
        >>> values = f(x, y, z)
        >>> out = grid.contour(
        ...     1,
        ...     scalars=values,
        ...     rng=[0, 0],
        ...     method='flying_edges',
        ... )
        >>> out.plot(color='lightblue', smooth_shading=True)
    
        See :ref:`common_filter_example` or
        :ref:`marching_cubes_example` for more examples using this
        filter.





.. GENERATED FROM PYTHON SOURCE LINES 28-30

.. code-block:: Python

    contours = mesh.contour()








.. GENERATED FROM PYTHON SOURCE LINES 31-37

.. code-block:: Python

    p = pv.Plotter()
    p.add_mesh(mesh, opacity=0.85)
    p.add_mesh(contours, color="white", line_width=5)
    p.show()





.. image-sg:: /tutorial/04_filters/solutions/images/sphx_glr_d_contouring_001.png
   :alt: d contouring
   :srcset: /tutorial/04_filters/solutions/images/sphx_glr_d_contouring_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 38-42

Iso-Surfaces
++++++++++++

Let's extract 2D iso-surfaces of a scalar field from a 3D mesh.

.. GENERATED FROM PYTHON SOURCE LINES 42-46

.. code-block:: Python


    mesh = examples.download_embryo()
    mesh






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <table style='width: 100%;'><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>
    <table style='width: 100%;'>
    <tr><th>ImageData</th><th>Information</th></tr>
    <tr><td>N Cells</td><td>16581375</td></tr>
    <tr><td>N Points</td><td>16777216</td></tr>
    <tr><td>X Bounds</td><td>0.000e+00, 2.550e+02</td></tr>
    <tr><td>Y Bounds</td><td>0.000e+00, 2.550e+02</td></tr>
    <tr><td>Z Bounds</td><td>0.000e+00, 2.550e+02</td></tr>
    <tr><td>Dimensions</td><td>256, 256, 256</td></tr>
    <tr><td>Spacing</td><td>1.000e+00, 1.000e+00, 1.000e+00</td></tr>
    <tr><td>N Arrays</td><td>1</td></tr>
    </table>

    </td><td>
    <table style='width: 100%;'>
    <tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>
    <tr><td><b>SLCImage</b></td><td>Points</td><td>uint8</td><td>1</td><td>0.000e+00</td><td>1.970e+02</td></tr>
    </table>

    </td></tr> </table>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 47-49

For this example dataset, let's create 5 contour levels between the values
of 50 and 200

.. GENERATED FROM PYTHON SOURCE LINES 49-52

.. code-block:: Python


    contours = mesh.contour(np.linspace(50, 200, 5))








.. GENERATED FROM PYTHON SOURCE LINES 53-63

.. code-block:: Python

    p = pv.Plotter()
    p.add_mesh(mesh.outline(), color="k")
    p.add_mesh(contours, opacity=0.25, clim=[0, 200])
    p.camera_position = [
        (-130.99381142132086, 644.4868354828589, 163.80447435848686),
        (125.21748748157661, 123.94368717158413, 108.83283586619626),
        (0.2780372840777734, 0.03547871361794171, 0.9599148553609699),
    ]
    p.show()




.. image-sg:: /tutorial/04_filters/solutions/images/sphx_glr_d_contouring_002.png
   :alt: d contouring
   :srcset: /tutorial/04_filters/solutions/images/sphx_glr_d_contouring_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 64-71

.. raw:: html

    <center>
      <a target="_blank" href="https://colab.research.google.com/github/pyvista/pyvista-tutorial/blob/gh-pages/notebooks/tutorial/04_filters/solutions/d_contouring.ipynb">
        <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/ width="150px">
      </a>
    </center>


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

   **Total running time of the script:** (0 minutes 8.970 seconds)


.. _sphx_glr_download_tutorial_04_filters_solutions_d_contouring.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/pyvista/pyvista-tutorial/gh-pages?urlpath=lab/tree/notebooks/tutorial/04_filters/solutions/d_contouring.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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