.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` 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. .. 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) print(viridis) .. rst-class:: sphx-glr-script-out Out: .. code-block:: none The object ``viridis`` is a callable, that when passed a float between 0 and 1 returns an RGBA value from the colormap: .. 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) 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. .. 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. ]] The colormap is a lookup table, so "oversampling" the colormap returns nearest-neighbor interpolation (note the repeated colors in the list below) .. 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. ]] 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. .. code-block:: default copper = cm.get_cmap('copper', 8) print(copper) 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. ]] 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. .. 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() In the simplest case we might type in a list of color names to create a colormap from those. .. code-block:: default cmap = ListedColormap(["darkorange", "gold", "lawngreen", "lightseagreen"]) plot_examples([cmap]) .. image:: /tutorials/colors/images/sphx_glr_colormap-manipulation_001.png :class: sphx-glr-single-img In fact, that list may contain any valid :doc:`matplotlib color specification `. 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: .. 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:: /tutorials/colors/images/sphx_glr_colormap-manipulation_002.png :class: sphx-glr-single-img We can easily reduce the dynamic range of a colormap; here we choose the middle 0.5 of the colormap. However, we need to interpolate from a larger colormap, otherwise the new colormap will have repeated values. .. code-block:: default viridisBig = cm.get_cmap('viridis', 512) newcmp = ListedColormap(viridisBig(np.linspace(0.25, 0.75, 256))) plot_examples([viridis, newcmp]) .. image:: /tutorials/colors/images/sphx_glr_colormap-manipulation_003.png :class: sphx-glr-single-img and we can easily concatenate two colormaps: .. 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:: /tutorials/colors/images/sphx_glr_colormap-manipulation_004.png :class: sphx-glr-single-img 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). .. 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:: /tutorials/colors/images/sphx_glr_colormap-manipulation_005.png :class: sphx-glr-single-img Creating linear segmented colormaps =================================== `.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]``: .. 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:: /tutorials/colors/images/sphx_glr_colormap-manipulation_006.png :class: sphx-glr-single-img 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] is the value to the left of 0, and red[2, 2] is the value to the right of 1.0. .. 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:: /tutorials/colors/images/sphx_glr_colormap-manipulation_007.png :class: sphx-glr-single-img Directly creating a segmented colormap from a list -------------------------------------------------- The above described is a very versatile approach, but admitedly 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. .. code-block:: default colors = ["darkorange", "gold", "lawngreen", "lightseagreen"] cmap1 = LinearSegmentedColormap.from_list("mycmap", colors) If desired, the nodes of the colormap can be given as numbers between 0 and 1. E.g. one could have the reddish part take more space in the colormap. .. 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:: /tutorials/colors/images/sphx_glr_colormap-manipulation_008.png :class: sphx-glr-single-img ------------ References """""""""" The use of the following functions, methods, classes and modules is shown in this example: .. code-block:: default import matplotlib 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-script-out Out: .. code-block:: none .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 1.017 seconds) .. _sphx_glr_download_tutorials_colors_colormap-manipulation.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: colormap-manipulation.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: colormap-manipulation.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature Keywords: matplotlib code example, codex, python plot, pyplot `Gallery generated by Sphinx-Gallery `_