There's no need to manually set the colors. Instead, specify a grayscale colormap...

import numpy as np
import matplotlib.pyplot as plt

# Generate data...
x = np.random.random(10)
y = np.random.random(10)

# Plot...
plt.scatter(x, y, c=y, s=500) # s is a size of marker 
plt.gray()

plt.show()

Or, if you'd prefer a wider range of colormaps, you can also specify the cmap kwarg to scatter. To use the reversed version of any of these, just specify the "_r" version of any of them. E.g. gray_r instead of gray. There are several different grayscale colormaps pre-made (e.g. gray, gist_yarg, binary, etc).

import matplotlib.pyplot as plt
import numpy as np

# Generate data...
x = np.random.random(10)
y = np.random.random(10)

plt.scatter(x, y, c=y, s=500, cmap='gray')
plt.show()

This is what matplotlib.pyplot.scatter is for.

If no colormap is specified, scatter will use whatever the default colormap is set to. To specify which colormap scatter should use, use the cmap kwarg (e.g. cmap="jet").

As a quick example:

import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np

# Generate data...
t = np.linspace(0, 2 * np.pi, 20)
x = np.sin(t)
y = np.cos(t)

plt.scatter(t, x, c=y, ec='k')
plt.show()

One may specify a custom color map and norm

cmap, norm = mcolors.from_levels_and_colors([0, 2, 5, 6], ['red', 'green', 'blue'])
plt.scatter(x, y, c=t, cmap=cmap, norm=norm)

The plot you've shown doesn't have the color set by the vertical axis of the plot (which is what I would consider the y-value). Instead, it just has 8 different plots overlain, each with a different color, without stating what the color means.

Here's an example of something that looks like your plot:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm

# some fake data:
x = np.linspace(0, 2*np.pi, 1000)
fs = np.arange(1, 5.)
ys = np.sin(x*fs[:, None])

for y, f in zip(ys, fs):
    plt.plot(x, y, lw=3, c=cm.hot(f/5))

If you actually want the color of one line to change with respect to its value, you have to kind of hack it, because any given Line2D object can only have one color, as far as I know. One way to do this is to make a scatter plot, where each dot can have any color.

x = np.linspace(0, 2*np.pi, 1000)
y = np.sin(2*x)
plt.scatter(x,y, c=cm.hot(np.abs(y)), edgecolor='none')

Notes:

• The color vector should range between 0 and 1, so if y.max() > 1, then normalize by it: c=cm.hot(y/y.max()) and make sure it's all positive.
• I used edgecolor='none' because by default the scatter markers have a black outline which makes the it look less like a uniform line.
• If your data is spaced too far, you'll have to interpolate the data if you don't want gaps between markers.

You could look into using a qualitative colormap from https://matplotlib.org/tutorials/colors/colormaps.html instead of one based on a gradient. They might fit your type of data more effectively.

Looking something like