title: Generate data with normally distributed noise and mean function
body: <p>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. </p>
  
  <p>So i figured I need to do something like this: <code>t = sin(x) + noise</code>. Where the noise must be something like this: <code>noise = np.random.randn(mean, std)</code>.</p>
  
  <p>However, I do not know how I can calculate the noise when my mean is sin(x) (and not a constant).</p>