

.. _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.



.. 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



.. code-block:: python


    plt.figure()
    CS = plt.contour(X, Y, Z)
    plt.clabel(CS, inline=1, fontsize=10)
    plt.title('Simplest default with labels')





.. image:: /gallery/images_contours_and_fields/images/sphx_glr_contour_demo_001.png
    :align: center




contour labels can be placed manually by providing list of positions
(in data coordinate). See ginput_manual_clabel.py for interactive
placement.



.. code-block:: python


    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')





.. image:: /gallery/images_contours_and_fields/images/sphx_glr_contour_demo_002.png
    :align: center




You can force all the contours to be the same color.



.. code-block:: python


    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')




.. image:: /gallery/images_contours_and_fields/images/sphx_glr_contour_demo_003.png
    :align: center




You can set negative contours to be solid instead of dashed:



.. code-block:: python


    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')





.. image:: /gallery/images_contours_and_fields/images/sphx_glr_contour_demo_004.png
    :align: center




And you can manually specify the colors of the contour



.. code-block:: python


    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')





.. image:: /gallery/images_contours_and_fields/images/sphx_glr_contour_demo_005.png
    :align: center




Or you can use a colormap to specify the colors; the default
colormap will be used for the contour lines



.. code-block:: python


    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()



.. image:: /gallery/images_contours_and_fields/images/sphx_glr_contour_demo_006.png
    :align: center




**Total running time of the script:** ( 0 minutes  0.833 seconds)



.. only :: html

 .. 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>`


.. only:: html

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

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