July 29, 2026 - Full list of matplotlib colormaps (viridis, plasma, coolwarm, tab10...) grouped by type. Click on a colormap to copy its name and use it with the cmap argument.

cmaps[category] = cmap_list · For the Sequential plots, the lightness value increases monotonically through the colormaps. This is good. Some of the \(L^*\) values in the colormaps span from 0 to 100 (binary and the other grayscale), and others start around \(L^*=20\).

December 23, 2024 - Python · from matplotlib import colormaps print(list(colormaps)) Output: ['magma', 'inferno', 'plasma', 'viridis', 'cividis', 'twilight', 'twilight_shifted', 'turbo', 'Blues', 'BrBG', 'BuGn', 'BuPu', 'CMRmap', 'GnBu', 'Greens', 'Greys', 'OrRd', 'Oranges', 'PRGn', 'PiYG'] ..

import matplotlib.pyplot as plt import colormaps as cmaps import numpy as np x = y = np.arange(-3.0, 3.01, 0.05) X, Y = np.meshgrid(x, y) sigmax = sigmay = 1.0 mux = muy = sigmaxy=0.0 Xmu = X-mux Ymu = Y-muy rho = sigmaxy/(sigmax*sigmay) z = Xmu**2/sigmax**2 + Ymu**2/sigmay**2 - 2*rho*Xmu*Ymu/(sigmax*sigmay) denom = 2*np.pi*sigmax*sigmay*np.sqrt(1-rho**2) Z = np.exp(-z/(2*(1-rho**2))) / denom plt.pcolormesh(X,Y,Z,cmap=cmaps.cubehelix3_16_r) plt.colorbar()

Matplotlib colormaps with erratic color transitions like 'jet' are still registered, but they are hidden from this table by default, and their usage is discouraged. If you need a list of colors associated with a registered or on-the-fly colormap, simply use get_colors.

Reference for colormaps included with Matplotlib.

January 2, 2026 - Matplotlib, a widely-used data visualization library, offers numerous built-in colormaps. It also empowers users to craft custom colormaps, granting enhanced control and flexibility over the color schemes in their visualizations, a valuable feature when considering cmap in python colormaps.

Colormaps is a library of collection of colormaps or color palettes for Python. It’s written in Python with matplotlib and numpy as dependencies.

Update: These colormaps have been merged into the development version of Matplotlib, all of them will be included in matplotlib 1.5, and "option D" (now called "viridis") will be the new default colormap in matplotlib 2.0. If you just want the colormaps themselves, they're available in a single file here under a CC0 "no rights reserved" license.

This example generates side-by-side visualizations of the original colormap and its reversed version. from matplotlib.colors import ListedColormap import matplotlib.pyplot as plt import numpy as np # Define a list of color values in hexadecimal format colors = ["#bbffcc", "#a1fab4", "#41b6c4", "#2c7fb8", "#25abf4"] # Create a ListedColormap with the specified colors my_cmap = ListedColormap(colors, name="my_cmap") # Create a reversed version of the colormap my_cmap_r = my_cmap.reversed() # Define a helper function to plot data with associated colormap def plot_examples(colormaps): np.random.se

May 25, 2022 - Matplotlib has support for visualizing information with a wide array of colors and colormaps.

ListedColormaps 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.

September 6, 2025 - Colormaps in Python Here are some alternative color maps (cmaps) you can use in your maps to convey different visual styles or highlight specific features. These are built into Matplotlib, and you …

import matplotlib.pyplot as plt import colormaps as cmaps import numpy as np x = y = np.arange(-3.0, 3.01, 0.05) X, Y = np.meshgrid(x, y) sigmax = sigmay = 1.0 mux = muy = sigmaxy=0.0 Xmu = X-mux Ymu = Y-muy rho = sigmaxy/(sigmax*sigmay) z = Xmu**2/sigmax**2 + Ymu**2/sigmay**2 - 2*rho*Xmu*Ymu/(sigmax*sigmay) denom = 2*np.pi*sigmax*sigmay*np.sqrt(1-rho**2) Z = np.exp(-z/(2*(1-rho**2))) / denom plt.pcolormesh(X,Y,Z,cmap=cmaps.cubehelix3_16_r) plt.colorbar()

Colormap("viridis").to_gee() Returns a list of hexadecimal color strings (without a # prefix).

July 3, 2023 - In the left side you can see the name, and next to this the colormap. EDIT: A couple more. ... To get a list of all registered colormaps, you can do:: from matplotlib import colormaps list(colormaps)

July 28, 2020 - Let’s create a continuous colormap containing all of the colors above. We’ll be using the matplotlib.colors function called LinearSegmentedColormap. This function accepts a dictionary with a red, green and blue entries. Each entry should be a list of x, y0, y1 tuples, forming rows in a table.

"""Python colormaps demo includes: examples for registering own color maps utility for showing all or selected named colormaps including self-defined ones""" import matplotlib import matplotlib.colors as col import matplotlib.cm as cm import matplotlib.pyplot as plt import numpy as np def register_own_cmaps(): """define two example colormaps as segmented lists and register them""" # a good guide for choosing colors is provided at # http://geography.uoregon.edu/datagraphics/color_scales.htm # # example 1: # create own colormap from purple, blue, green, orange to red # cdict contains a tuple structure for 'red', 'green', and 'blue'.

First, we’ll show the range of each colormap. Note that some seem to change more “quickly” than others. nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps.items()) gradient = np.linspace(0, 1, 256) gradient = np.vstack((gradient, gradient)) def plot_color_gradients(cmap_category, cmap_list, nrows): fig, axes = plt.subplots(nrows=nrows) fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99) axes[0].set_title(cmap_category + ' colormaps', fontsize=14) for ax, name in zip(axes, cmap_list): ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name)) pos = list(ax.get_position().bounds) x_text = pos[0] - 0.01 y_text = pos[1] + pos[3]/2.