To view all colormaps available in Matplotlib, we can take the following steps −
- Set the figure size and adjust the padding between and around the subplots.
- Create a new figure or activate an existing figure.
- Add an '~.axes.Axes' to the figure as part of a subplot arrangement
- Make an axis that is divider on the existing axes.
- Create random data using numpy.
- Display the data as an image, i.e., on a 2D regular raster.
- Create a colorbar for a ScalarMappable instance, im.
- Set a title for the current figure.
- Animate the image with all colormaps available in matplotlib.
- Make an animation by repeatedly calling a function.
- To display the figure, use show() method.
Example
import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation from mpl_toolkits.axes_grid1 import make_axes_locatable plt.rcParams["figure.figsize"] = [7.50, 3.50] plt.rcParams["figure.autolayout"] = True fig = plt.figure() ax = fig.add_subplot(111) div = make_axes_locatable(ax) cax = div.append_axes('right', '5%', '5%') data = np.random.rand(5, 5) im = ax.imshow(data) cb = fig.colorbar(im, cax=cax) cmap = plt.colormaps() tx = ax.set_title('Color map: {0}'.format(cmap[0])) def animate(i): cax.cla() data = np.random.rand(5, 5) im = ax.imshow(data, cmap=cmap[i]) fig.colorbar(im, cax=cax) tx.set_text('Color map: {0}'.format(cmap[i])) ani = animation.FuncAnimation(fig, animate, frames=166) plt.show()