matplotlib: coloring line plots by iteration-dependent gray scale

Question

Relative programming newbie here. I have trouble figuring out how to plot interpolated functions over a series of iterations, where as the iteration index increases, the plot would go from black to gradually lighter shades of grey.

For example,

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d

for t in np.arange(0.,2., 0.4):
    x = np.linspace(0.,4, 100)
    y = np.sin(x-2*t) + 0.01 * np.random.normal(size=x.shape)
    yint = interp1d(x, y)
    plt.plot(x, yint(x))

plt.show()

produces

I would like the blue sinusoidal function to be black, and the rest becomes lighter and greyer as t increases (to the right). How would I do that?

Thank you all for your generous help!

Answer 1

See: http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.plot

E.g. you can set plt.plot(x, yint(x), color=(0.5, 0.5, 0.5)) for a gray line. You can set the values up however you like (0.0 is black, 1.0 is white). A simple example:

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d

for t in np.arange(0.,2., 0.4):
    x = np.linspace(0.,4, 100)
    y = np.sin(x-2*t) + 0.01 * np.random.normal(size=x.shape)
    yint = interp1d(x, y)
    print t
    col = (t/2.0, t/2.0, t/2.0)
    plt.plot(x, yint(x), color=col)

plt.show()