blckbird blckbird - 1 year ago 127
Python Question

Generate data with normally distributed noise and mean function

I created a numpy array with n values from 0 to 2pi. Now, I want to generate n test data points deviating from sin(x) normally distributed.

So i figured I need to do something like this:

t = sin(x) + noise
. Where the noise must be something like this:
noise = np.random.randn(mean, std)

However, I do not know how I can calculate the noise when my mean is sin(x) (and not a constant).

Answer Source

The arguments to numpy.random.randn are not the mean and standard deviation. For that, you want numpy.random.normal. Its signature is

normal(loc=0.0, scale=1.0, size=None)

To add noise to your sin function, simply use a mean of 0 in the call of normal(). The mean corresponds to the loc argument (i.e. "location"), which by default is 0. So, given that x is something like np.linspace(0, 2*np.pi, n), you can do this:

t = np.sin(x) + np.random.normal(scale=std, size=n)

You could use numpy.random.randn, but you have to scale it by std, because randn returns samples from the standard normal distribution, with mean 0 and standard deviation 1. To use randn, you would write:

t = np.sin(x) + std * np.random.randn(n)
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