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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_tutorials_colors_colormap-manipulation.py>`
        to download the full example code

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

.. _sphx_glr_tutorials_colors_colormap-manipulation.py:


********************************
Creating Colormaps in Matplotlib
********************************

Matplotlib has a number of built-in colormaps accessible via
`.matplotlib.cm.get_cmap`.  There are also external libraries like
palettable_ that have many extra colormaps.

.. _palettable: https://jiffyclub.github.io/palettable/

However, we often want to create or manipulate colormaps in Matplotlib.
This can be done using the class `.ListedColormap` or
`.LinearSegmentedColormap`.
Seen from the outside, both colormap classes map values between 0 and 1 to
a bunch of colors. There are, however, slight differences, some of which are
shown in the following.

Before manually creating or manipulating colormaps, let us first see how we
can obtain colormaps and their colors from existing colormap classes.


Getting colormaps and accessing their values
============================================

First, getting a named colormap, most of which are listed in
:doc:`/tutorials/colors/colormaps`, may be done using
`.matplotlib.cm.get_cmap`, which returns a colormap object.
The second argument gives the size of the list of colors used to define the
colormap, and below we use a modest value of 8 so there are not a lot of
values to look at.

.. GENERATED FROM PYTHON SOURCE LINES 33-41

.. code-block:: default


    import numpy as np
    import matplotlib.pyplot as plt
    from matplotlib import cm
    from matplotlib.colors import ListedColormap, LinearSegmentedColormap

    viridis = cm.get_cmap('viridis', 8)








.. GENERATED FROM PYTHON SOURCE LINES 42-44

The object ``viridis`` is a callable, that when passed a float between
0 and 1 returns an RGBA value from the colormap:

.. GENERATED FROM PYTHON SOURCE LINES 44-47

.. code-block:: default


    print(viridis(0.56))





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

 Out:

 .. code-block:: none

    (0.122312, 0.633153, 0.530398, 1.0)




.. GENERATED FROM PYTHON SOURCE LINES 48-57

ListedColormap
--------------

`.ListedColormap`\s store their color values in a ``.colors`` attribute.
The list of colors that comprise the colormap can be directly accessed using
the ``colors`` property,
or it can be accessed indirectly by calling  ``viridis`` with an array
of values matching the length of the colormap.  Note that the returned list
is in the form of an RGBA Nx4 array, where N is the length of the colormap.

.. GENERATED FROM PYTHON SOURCE LINES 57-62

.. code-block:: default


    print('viridis.colors', viridis.colors)
    print('viridis(range(8))', viridis(range(8)))
    print('viridis(np.linspace(0, 1, 8))', viridis(np.linspace(0, 1, 8)))





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

 Out:

 .. code-block:: none

    viridis.colors [[0.267004 0.004874 0.329415 1.      ]
     [0.275191 0.194905 0.496005 1.      ]
     [0.212395 0.359683 0.55171  1.      ]
     [0.153364 0.497    0.557724 1.      ]
     [0.122312 0.633153 0.530398 1.      ]
     [0.288921 0.758394 0.428426 1.      ]
     [0.626579 0.854645 0.223353 1.      ]
     [0.993248 0.906157 0.143936 1.      ]]
    viridis(range(8)) [[0.267004 0.004874 0.329415 1.      ]
     [0.275191 0.194905 0.496005 1.      ]
     [0.212395 0.359683 0.55171  1.      ]
     [0.153364 0.497    0.557724 1.      ]
     [0.122312 0.633153 0.530398 1.      ]
     [0.288921 0.758394 0.428426 1.      ]
     [0.626579 0.854645 0.223353 1.      ]
     [0.993248 0.906157 0.143936 1.      ]]
    viridis(np.linspace(0, 1, 8)) [[0.267004 0.004874 0.329415 1.      ]
     [0.275191 0.194905 0.496005 1.      ]
     [0.212395 0.359683 0.55171  1.      ]
     [0.153364 0.497    0.557724 1.      ]
     [0.122312 0.633153 0.530398 1.      ]
     [0.288921 0.758394 0.428426 1.      ]
     [0.626579 0.854645 0.223353 1.      ]
     [0.993248 0.906157 0.143936 1.      ]]




.. GENERATED FROM PYTHON SOURCE LINES 63-65

The colormap is a lookup table, so "oversampling" the colormap returns
nearest-neighbor interpolation (note the repeated colors in the list below)

.. GENERATED FROM PYTHON SOURCE LINES 65-68

.. code-block:: default


    print('viridis(np.linspace(0, 1, 12))', viridis(np.linspace(0, 1, 12)))





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

 Out:

 .. code-block:: none

    viridis(np.linspace(0, 1, 12)) [[0.267004 0.004874 0.329415 1.      ]
     [0.267004 0.004874 0.329415 1.      ]
     [0.275191 0.194905 0.496005 1.      ]
     [0.212395 0.359683 0.55171  1.      ]
     [0.212395 0.359683 0.55171  1.      ]
     [0.153364 0.497    0.557724 1.      ]
     [0.122312 0.633153 0.530398 1.      ]
     [0.288921 0.758394 0.428426 1.      ]
     [0.288921 0.758394 0.428426 1.      ]
     [0.626579 0.854645 0.223353 1.      ]
     [0.993248 0.906157 0.143936 1.      ]
     [0.993248 0.906157 0.143936 1.      ]]




.. GENERATED FROM PYTHON SOURCE LINES 69-74

LinearSegmentedColormap
-----------------------
`.LinearSegmentedColormap`\s do not have a ``.colors`` attribute.
However, one may still call the colormap with an integer array, or with a
float array between 0 and 1.

.. GENERATED FROM PYTHON SOURCE LINES 74-80

.. code-block:: default


    copper = cm.get_cmap('copper', 8)

    print('copper(range(8))', copper(range(8)))
    print('copper(np.linspace(0, 1, 8))', copper(np.linspace(0, 1, 8)))





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

 Out:

 .. code-block:: none

    copper(range(8)) [[0.         0.         0.         1.        ]
     [0.17647055 0.1116     0.07107143 1.        ]
     [0.35294109 0.2232     0.14214286 1.        ]
     [0.52941164 0.3348     0.21321429 1.        ]
     [0.70588219 0.4464     0.28428571 1.        ]
     [0.88235273 0.558      0.35535714 1.        ]
     [1.         0.6696     0.42642857 1.        ]
     [1.         0.7812     0.4975     1.        ]]
    copper(np.linspace(0, 1, 8)) [[0.         0.         0.         1.        ]
     [0.17647055 0.1116     0.07107143 1.        ]
     [0.35294109 0.2232     0.14214286 1.        ]
     [0.52941164 0.3348     0.21321429 1.        ]
     [0.70588219 0.4464     0.28428571 1.        ]
     [0.88235273 0.558      0.35535714 1.        ]
     [1.         0.6696     0.42642857 1.        ]
     [1.         0.7812     0.4975     1.        ]]




.. GENERATED FROM PYTHON SOURCE LINES 81-91

Creating listed colormaps
=========================

Creating a colormap is essentially the inverse operation of the above where
we supply a list or array of color specifications to `.ListedColormap` to
make a new colormap.

Before continuing with the tutorial, let us define a helper function that
takes one of more colormaps as input, creates some random data and applies
the colormap(s) to an image plot of that dataset.

.. GENERATED FROM PYTHON SOURCE LINES 91-108

.. code-block:: default



    def plot_examples(colormaps):
        """
        Helper function to plot data with associated colormap.
        """
        np.random.seed(19680801)
        data = np.random.randn(30, 30)
        n = len(colormaps)
        fig, axs = plt.subplots(1, n, figsize=(n * 2 + 2, 3),
                                constrained_layout=True, squeeze=False)
        for [ax, cmap] in zip(axs.flat, colormaps):
            psm = ax.pcolormesh(data, cmap=cmap, rasterized=True, vmin=-4, vmax=4)
            fig.colorbar(psm, ax=ax)
        plt.show()









.. GENERATED FROM PYTHON SOURCE LINES 109-111

In the simplest case we might type in a list of color names to create a
colormap from those.

.. GENERATED FROM PYTHON SOURCE LINES 111-115

.. code-block:: default


    cmap = ListedColormap(["darkorange", "gold", "lawngreen", "lightseagreen"])
    plot_examples([cmap])




.. image-sg:: /tutorials/colors/images/sphx_glr_colormap-manipulation_001.png
   :alt: colormap manipulation
   :srcset: /tutorials/colors/images/sphx_glr_colormap-manipulation_001.png, /tutorials/colors/images/sphx_glr_colormap-manipulation_001_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 116-125

In fact, that list may contain any valid
:doc:`Matplotlib color specification </tutorials/colors/colors>`.
Particularly useful for creating custom colormaps are Nx4 numpy arrays.
Because with the variety of numpy operations that we can do on a such an
array, carpentry of new colormaps from existing colormaps become quite
straight forward.

For example, suppose we want to make the first 25 entries of a 256-length
"viridis" colormap pink for some reason:

.. GENERATED FROM PYTHON SOURCE LINES 125-134

.. code-block:: default


    viridis = cm.get_cmap('viridis', 256)
    newcolors = viridis(np.linspace(0, 1, 256))
    pink = np.array([248/256, 24/256, 148/256, 1])
    newcolors[:25, :] = pink
    newcmp = ListedColormap(newcolors)

    plot_examples([viridis, newcmp])




.. image-sg:: /tutorials/colors/images/sphx_glr_colormap-manipulation_002.png
   :alt: colormap manipulation
   :srcset: /tutorials/colors/images/sphx_glr_colormap-manipulation_002.png, /tutorials/colors/images/sphx_glr_colormap-manipulation_002_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 135-140

We can reduce the dynamic range of a colormap; here we choose the
middle half of the colormap.  Note, however, that because viridis is a
listed colormap, we will end up with 128 discrete values instead of the 256
values that were in the original colormap. This method does not interpolate
in color-space to add new colors.

.. GENERATED FROM PYTHON SOURCE LINES 140-145

.. code-block:: default


    viridis_big = cm.get_cmap('viridis')
    newcmp = ListedColormap(viridis_big(np.linspace(0.25, 0.75, 128)))
    plot_examples([viridis, newcmp])




.. image-sg:: /tutorials/colors/images/sphx_glr_colormap-manipulation_003.png
   :alt: colormap manipulation
   :srcset: /tutorials/colors/images/sphx_glr_colormap-manipulation_003.png, /tutorials/colors/images/sphx_glr_colormap-manipulation_003_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 146-147

and we can easily concatenate two colormaps:

.. GENERATED FROM PYTHON SOURCE LINES 147-156

.. code-block:: default


    top = cm.get_cmap('Oranges_r', 128)
    bottom = cm.get_cmap('Blues', 128)

    newcolors = np.vstack((top(np.linspace(0, 1, 128)),
                           bottom(np.linspace(0, 1, 128))))
    newcmp = ListedColormap(newcolors, name='OrangeBlue')
    plot_examples([viridis, newcmp])




.. image-sg:: /tutorials/colors/images/sphx_glr_colormap-manipulation_004.png
   :alt: colormap manipulation
   :srcset: /tutorials/colors/images/sphx_glr_colormap-manipulation_004.png, /tutorials/colors/images/sphx_glr_colormap-manipulation_004_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 157-160

Of course we need not start from a named colormap, we just need to create
the Nx4 array to pass to `.ListedColormap`. Here we create a colormap that
goes from brown (RGB: 90, 40, 40) to white (RGB: 255, 255, 255).

.. GENERATED FROM PYTHON SOURCE LINES 160-169

.. code-block:: default


    N = 256
    vals = np.ones((N, 4))
    vals[:, 0] = np.linspace(90/256, 1, N)
    vals[:, 1] = np.linspace(40/256, 1, N)
    vals[:, 2] = np.linspace(40/256, 1, N)
    newcmp = ListedColormap(vals)
    plot_examples([viridis, newcmp])




.. image-sg:: /tutorials/colors/images/sphx_glr_colormap-manipulation_005.png
   :alt: colormap manipulation
   :srcset: /tutorials/colors/images/sphx_glr_colormap-manipulation_005.png, /tutorials/colors/images/sphx_glr_colormap-manipulation_005_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 170-183

Creating linear segmented colormaps
===================================

The `.LinearSegmentedColormap` class specifies colormaps using anchor points
between which RGB(A) values are interpolated.

The format to specify these colormaps allows discontinuities at the anchor
points. Each anchor point is specified as a row in a matrix of the
form ``[x[i] yleft[i] yright[i]]``, where ``x[i]`` is the anchor, and
``yleft[i]`` and ``yright[i]`` are the values of the color on either
side of the anchor point.

If there are no discontinuities, then ``yleft[i] == yright[i]``:

.. GENERATED FROM PYTHON SOURCE LINES 183-211

.. code-block:: default


    cdict = {'red':   [[0.0,  0.0, 0.0],
                       [0.5,  1.0, 1.0],
                       [1.0,  1.0, 1.0]],
             'green': [[0.0,  0.0, 0.0],
                       [0.25, 0.0, 0.0],
                       [0.75, 1.0, 1.0],
                       [1.0,  1.0, 1.0]],
             'blue':  [[0.0,  0.0, 0.0],
                       [0.5,  0.0, 0.0],
                       [1.0,  1.0, 1.0]]}


    def plot_linearmap(cdict):
        newcmp = LinearSegmentedColormap('testCmap', segmentdata=cdict, N=256)
        rgba = newcmp(np.linspace(0, 1, 256))
        fig, ax = plt.subplots(figsize=(4, 3), constrained_layout=True)
        col = ['r', 'g', 'b']
        for xx in [0.25, 0.5, 0.75]:
            ax.axvline(xx, color='0.7', linestyle='--')
        for i in range(3):
            ax.plot(np.arange(256)/256, rgba[:, i], color=col[i])
        ax.set_xlabel('index')
        ax.set_ylabel('RGB')
        plt.show()

    plot_linearmap(cdict)




.. image-sg:: /tutorials/colors/images/sphx_glr_colormap-manipulation_006.png
   :alt: colormap manipulation
   :srcset: /tutorials/colors/images/sphx_glr_colormap-manipulation_006.png, /tutorials/colors/images/sphx_glr_colormap-manipulation_006_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 212-230

In order to make a discontinuity at an anchor point, the third column is
different than the second.  The matrix for each of "red", "green", "blue",
and optionally "alpha" is set up as::

  cdict['red'] = [...
                  [x[i]      yleft[i]     yright[i]],
                  [x[i+1]    yleft[i+1]   yright[i+1]],
                 ...]

and for values passed to the colormap between ``x[i]`` and ``x[i+1]``,
the interpolation is between ``yright[i]`` and ``yleft[i+1]``.

In the example below there is a discontinuity in red at 0.5.  The
interpolation between 0 and 0.5 goes from 0.3 to 1, and between 0.5 and 1
it goes from 0.9 to 1.  Note that ``red[0, 1]``, and ``red[2, 2]`` are both
superfluous to the interpolation because ``red[0, 1]`` (i.e., ``yleft[0]``)
is the value to the left of 0, and ``red[2, 2]`` (i.e., ``yright[2]``) is the
value to the right of 1, which are outside the color mapping domain.

.. GENERATED FROM PYTHON SOURCE LINES 230-236

.. code-block:: default


    cdict['red'] = [[0.0,  0.0, 0.3],
                    [0.5,  1.0, 0.9],
                    [1.0,  1.0, 1.0]]
    plot_linearmap(cdict)




.. image-sg:: /tutorials/colors/images/sphx_glr_colormap-manipulation_007.png
   :alt: colormap manipulation
   :srcset: /tutorials/colors/images/sphx_glr_colormap-manipulation_007.png, /tutorials/colors/images/sphx_glr_colormap-manipulation_007_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 237-244

Directly creating a segmented colormap from a list
--------------------------------------------------

The approach described above is very versatile, but admittedly a bit
cumbersome to implement. For some basic cases, the use of
`.LinearSegmentedColormap.from_list` may be easier. This creates a segmented
colormap with equal spacings from a supplied list of colors.

.. GENERATED FROM PYTHON SOURCE LINES 244-248

.. code-block:: default


    colors = ["darkorange", "gold", "lawngreen", "lightseagreen"]
    cmap1 = LinearSegmentedColormap.from_list("mycmap", colors)








.. GENERATED FROM PYTHON SOURCE LINES 249-252

If desired, the nodes of the colormap can be given as numbers between 0 and
1. For example, one could have the reddish part take more space in the
colormap.

.. GENERATED FROM PYTHON SOURCE LINES 252-258

.. code-block:: default


    nodes = [0.0, 0.4, 0.8, 1.0]
    cmap2 = LinearSegmentedColormap.from_list("mycmap", list(zip(nodes, colors)))

    plot_examples([cmap1, cmap2])




.. image-sg:: /tutorials/colors/images/sphx_glr_colormap-manipulation_008.png
   :alt: colormap manipulation
   :srcset: /tutorials/colors/images/sphx_glr_colormap-manipulation_008.png, /tutorials/colors/images/sphx_glr_colormap-manipulation_008_2_0x.png 2.0x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 259-271

.. admonition:: References

   The use of the following functions, methods, classes and modules is shown
   in this example:

   - `matplotlib.axes.Axes.pcolormesh`
   - `matplotlib.figure.Figure.colorbar`
   - `matplotlib.colors`
   - `matplotlib.colors.LinearSegmentedColormap`
   - `matplotlib.colors.ListedColormap`
   - `matplotlib.cm`
   - `matplotlib.cm.get_cmap`


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

   **Total running time of the script:** ( 0 minutes  2.090 seconds)


.. _sphx_glr_download_tutorials_colors_colormap-manipulation.py:


.. only :: html

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



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

     :download:`Download Python source code: colormap-manipulation.py <colormap-manipulation.py>`



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

     :download:`Download Jupyter notebook: colormap-manipulation.ipynb <colormap-manipulation.ipynb>`


.. only:: html

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

    Keywords: matplotlib code example, codex, python plot, pyplot
    `Gallery generated by Sphinx-Gallery
    <https://sphinx-gallery.readthedocs.io>`_
