You could use colorbar‘s format parameter: import matplotlib.pyplot as plt import numpy as np import matplotlib.ticker as ticker img = np.random.randn(300,300) myplot = plt.imshow(img) def fmt(x, pos): a, b = ‘{:.2e}’.format(x).split(‘e’) b = int(b) return r’${} \times 10^{{{}}}$’.format(a, b) plt.colorbar(myplot, format=ticker.FuncFormatter(fmt)) plt.show()
I think the problem is that with del you cancel the variable, but not the referenced object colorbar. If you want the colorbar to be removed from plot and disappear, you have to use the method remove of the colorbar instance and to do this you need to have the colorbar in a variable, for … Read more
Here a step by step explanation. First consider the following example: [X,Y,Z1] = peaks; figure(1) [~,h1] = contourf(X,Y,Z1,20); a1 = colorbar; colormap(jet) caxis([-6,6]) which will give you the following plot: It has linear data and a linear colormap. Now I want to scale the Z-Data to get it non-linear like in your case. I chose … Read more
Not to steal @ianilis’s answer, but I wanted to add an example… There are multiple ways, but the simplest is just to specify the vmin and vmax kwargs to imshow. Alternately, you can make a matplotlib.cm.Colormap instance and specify it, but that’s more complicated than necessary for simple cases. Here’s a quick example with a … Read more
While I’m not sure how to do this specifically using an ArtistAnimation, using a FuncAnimation is fairly straightforward. If I make the following modifications to your “naive” version 1 it works. Modified Version 1 import numpy as np import matplotlib.pyplot as plt import matplotlib.animation as animation from mpl_toolkits.axes_grid1 import make_axes_locatable fig = plt.figure() ax = … Read more
(I know this is an old question but…) Colorbars require a matplotlib.cm.ScalarMappable, plt.plot produces lines which are not scalar mappable, therefore, in order to make a colorbar, we are going to need to make a scalar mappable. Ok. So the constructor of a ScalarMappable takes a cmap and a norm instance. (norms scale data to … Read more
import matplotlib.pyplot as plt import numpy as np from matplotlib.colors import ListedColormap #discrete color scheme cMap = ListedColormap([‘white’, ‘green’, ‘blue’,’red’]) #data np.random.seed(42) data = np.random.rand(4, 4) fig, ax = plt.subplots() heatmap = ax.pcolor(data, cmap=cMap) #legend cbar = plt.colorbar(heatmap) cbar.ax.get_yaxis().set_ticks([]) for j, lab in enumerate([‘$0$’,’$1$’,’$2$’,’$>3$’]): cbar.ax.text(.5, (2 * j + 1) / 8.0, lab, ha=”center”, va=”center”) … Read more
(I know this is an old question but…) Colorbars require a matplotlib.cm.ScalarMappable, plt.plot produces lines which are not scalar mappable, therefore, in order to make a colorbar, we are going to need to make a scalar mappable. Ok. So the constructor of a ScalarMappable takes a cmap and a norm instance. (norms scale data to … Read more
Using vmin and vmax forces the range for the colors. Here’s an example: import matplotlib as m import matplotlib.pyplot as plt import numpy as np cdict = { ‘red’ : ( (0.0, 0.25, .25), (0.02, .59, .59), (1., 1., 1.)), ‘green’: ( (0.0, 0.0, 0.0), (0.02, .45, .45), (1., .97, .97)), ‘blue’ : ( (0.0, … Read more
Just place the colorbar in its own axis and use subplots_adjust to make room for it. As a quick example: import numpy as np import matplotlib.pyplot as plt fig, axes = plt.subplots(nrows=2, ncols=2) for ax in axes.flat: im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1) fig.subplots_adjust(right=0.8) cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7]) fig.colorbar(im, cax=cbar_ax) plt.show() Note that the … Read more