Room Z533 in the Kruyt building in Utrecht can have a wonderful view, especially at sunset. Not too long ago, I took the following photograph.

Having a love-hate relationship with colormaps, and in particular choosing a good one, I INSTANTLY noticed the beauty of the gradient of the sky, and hence, its application as a colormap.

In Python, it is easy to import figures as a matrix, and then extract the RGB values along a line. To make things easier, I first cropped out the part of the sky I wanted:

Then, I had a look at this website.

Let's import the "slice of sky" in Python and have a look:

This results in the following figure:

Now let's make the actual colormap.
Having a love-hate relationship with colormaps, and in particular choosing a good one, I INSTANTLY noticed the beauty of the gradient of the sky, and hence, its application as a colormap.

In Python, it is easy to import figures as a matrix, and then extract the RGB values along a line. To make things easier, I first cropped out the part of the sky I wanted:

Then, I had a look at this website.

Let's import the "slice of sky" in Python and have a look:

import matplotlib.pyplot as plt import numpy as np from matplotlib.colors import LinearSegmentedColormap from PIL import Image im = Image.open("/path/to/sunset.jpg") pix = im.load() xs = range(im.size[1]) y = im.size[0]/2 rs = [pix[y,x][0] for x in xs] gs = [pix[y,x][1] for x in xs] bs = [pix[y,x][2] for x in xs] fig = plt.figure(figsize=(5,2)) ax = fig.add_subplot(111) ax.plot(xs, rs, color='red') ax.plot(xs, gs, color='green') ax.plot(xs, bs, color='blue') ax.set_xlabel("pixel") ax.set_ylabel('r/g/b value') plt.savefig('rgb-plot.png', dpi=300, bboxinches='tight')

This results in the following figure:

Dp = 1 ## determines the number of pixels between "nodes" xs = np.linspace(0,1,im.size[1]/Dp + 1) ## points between 0 and 1 idxs = range(0,im.size[1],Dp) ## indices in the original picture matrix redlist = [rs[idx]/255. for idx in idxs] + [rs[-1]/255., 0] greenlist = [gs[idx]/255. for idx in idxs] + [gs[-1]/255., 0] bluelist = [bs[idx]/255. for idx in idxs] + [bs[-1]/255., 0] ## LinearSegmentedColormap wants these weird triples, ## where some end points are ignored... redtuples = [(x, redlist[i], redlist[i+1]) for i, x in enumerate(xs)] greentuples = [(x, greenlist[i], greenlist[i+1]) for i, x in enumerate(xs)] bluetuples = [(x, bluelist[i], bluelist[i+1]) for i, x in enumerate(xs)] cdict = {'red' : redtuples, 'green' : greentuples, 'blue' : bluetuples} cmap = LinearSegmentedColormap('tbz533', cdict) ## choose a name plt.register_cmap(cmap=cmap) ## now we can use our new colormap!

OK. Let's make some 70s wallpaper to celebrate the new colormap:

xs = np.linspace(-5,5,1000) ys = np.linspace(-5,5,1000) zs = np.array([[np.sin(x)*np.cos(y) for x in xs] for y in ys]) fig = plt.figure(figsize=(5.5,5)) ax1 = fig.add_subplot(1,1,1) C = ax1.pcolormesh(xs, ys, zs, cmap='tbz533') fig.colorbar(C) fig.savefig('my70swallpaper.png', dpi=200, bboxinches='tight')

This results in the following figure:

That's it! Please leave your own favorite colormap in the comments.

One of the great article, I have seen yet. Waiting for more updates.

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