

.. _sphx_glr_gallery_images_contours_and_fields_contour_demo.py:


============
Contour Demo
============

Illustrate simple contour plotting, contours on an image with
a colorbar for the contours, and labelled contours.

See also contour_image.py.




.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /gallery/images_contours_and_fields/images/sphx_glr_contour_demo_001.png
            :scale: 47

    *

      .. image:: /gallery/images_contours_and_fields/images/sphx_glr_contour_demo_002.png
            :scale: 47

    *

      .. image:: /gallery/images_contours_and_fields/images/sphx_glr_contour_demo_003.png
            :scale: 47

    *

      .. image:: /gallery/images_contours_and_fields/images/sphx_glr_contour_demo_004.png
            :scale: 47

    *

      .. image:: /gallery/images_contours_and_fields/images/sphx_glr_contour_demo_005.png
            :scale: 47

    *

      .. image:: /gallery/images_contours_and_fields/images/sphx_glr_contour_demo_006.png
            :scale: 47





.. code-block:: python

    import matplotlib
    import numpy as np
    import matplotlib.cm as cm
    import matplotlib.mlab as mlab
    import matplotlib.pyplot as plt

    matplotlib.rcParams['xtick.direction'] = 'out'
    matplotlib.rcParams['ytick.direction'] = 'out'

    delta = 0.025
    x = np.arange(-3.0, 3.0, delta)
    y = np.arange(-2.0, 2.0, delta)
    X, Y = np.meshgrid(x, y)
    Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
    Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
    # difference of Gaussians
    Z = 10.0 * (Z2 - Z1)


    # Create a simple contour plot with labels using default colors.  The
    # inline argument to clabel will control whether the labels are draw
    # over the line segments of the contour, removing the lines beneath
    # the label
    plt.figure()
    CS = plt.contour(X, Y, Z)
    plt.clabel(CS, inline=1, fontsize=10)
    plt.title('Simplest default with labels')


    # contour labels can be placed manually by providing list of positions
    # (in data coordinate). See ginput_manual_clabel.py for interactive
    # placement.
    plt.figure()
    CS = plt.contour(X, Y, Z)
    manual_locations = [(-1, -1.4), (-0.62, -0.7), (-2, 0.5), (1.7, 1.2), (2.0, 1.4), (2.4, 1.7)]
    plt.clabel(CS, inline=1, fontsize=10, manual=manual_locations)
    plt.title('labels at selected locations')


    # You can force all the contours to be the same color.
    plt.figure()
    CS = plt.contour(X, Y, Z, 6,
                     colors='k',  # negative contours will be dashed by default
                     )
    plt.clabel(CS, fontsize=9, inline=1)
    plt.title('Single color - negative contours dashed')

    # You can set negative contours to be solid instead of dashed:
    matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
    plt.figure()
    CS = plt.contour(X, Y, Z, 6,
                     colors='k',  # negative contours will be dashed by default
                     )
    plt.clabel(CS, fontsize=9, inline=1)
    plt.title('Single color - negative contours solid')


    # And you can manually specify the colors of the contour
    plt.figure()
    CS = plt.contour(X, Y, Z, 6,
                     linewidths=np.arange(.5, 4, .5),
                     colors=('r', 'green', 'blue', (1, 1, 0), '#afeeee', '0.5')
                     )
    plt.clabel(CS, fontsize=9, inline=1)
    plt.title('Crazy lines')


    # Or you can use a colormap to specify the colors; the default
    # colormap will be used for the contour lines
    plt.figure()
    im = plt.imshow(Z, interpolation='bilinear', origin='lower',
                    cmap=cm.gray, extent=(-3, 3, -2, 2))
    levels = np.arange(-1.2, 1.6, 0.2)
    CS = plt.contour(Z, levels,
                     origin='lower',
                     linewidths=2,
                     extent=(-3, 3, -2, 2))

    # Thicken the zero contour.
    zc = CS.collections[6]
    plt.setp(zc, linewidth=4)

    plt.clabel(CS, levels[1::2],  # label every second level
               inline=1,
               fmt='%1.1f',
               fontsize=14)

    # make a colorbar for the contour lines
    CB = plt.colorbar(CS, shrink=0.8, extend='both')

    plt.title('Lines with colorbar')
    #plt.hot()  # Now change the colormap for the contour lines and colorbar
    plt.flag()

    # We can still add a colorbar for the image, too.
    CBI = plt.colorbar(im, orientation='horizontal', shrink=0.8)

    # This makes the original colorbar look a bit out of place,
    # so let's improve its position.

    l, b, w, h = plt.gca().get_position().bounds
    ll, bb, ww, hh = CB.ax.get_position().bounds
    CB.ax.set_position([ll, b + 0.1*h, ww, h*0.8])


    plt.show()

**Total running time of the script:** ( 0 minutes  0.426 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

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