Clear visual to explain the signal vs noise

[SJaffa]

programming_with_python/_episodes/03-numpy_essential.md

Line 220 in 19edfb7

To identify any signal in the data we can use the standard deviation as an estimate of the noise around the mean value of the data.

This might not be clear for people who have not done this sort of signal processing. We should explain what is the "signal" - the bump - and what is the "noise" - the data that is around zero but not exactly zero because of the random numbers.

I made this plot to try to visualise the filtering we are doing:

plt.plot(x, noisy)
plt.plot(x, [mean_noisy]*len(x), 'g-')
plt.plot(x, [mean_noisy+stddev_noisy]*len(x), 'r-')
plt.plot(x, [mean_noisy-stddev_noisy]*len(x), 'r-')