title: Using scipy.signal.spectral.lombscargle for period discovery
body: <p>The new Scipy v0.11 offers a package for spectral analysis. Unfortunately the documentation is sparse and there aren't many available examples.</p>
  
  <p>As a baby example, I'm trying to do period discovery of a sine wave. Unfortunately it predicts a period of <code>1</code> instead of the expected <code>2pi</code>. Any ideas?</p>
  
  <pre><code># imports the numerical array and scientific computing packages
  import numpy as np
  import scipy as sp
  from scipy.signal import spectral
  
  # generates 100 evenly spaced points between 1 and 1000
  time = np.linspace(1, 1000, 100)
  
  # computes the sine value of each of those points
  mags = np.sin(time)
  
  # scales the sine values so that the mean is 0 and the variance is 1 (the documentation specifies that this must be done)
  scaled_mags = (mags-mags.mean())/mags.std()
  
  # generates 1000 frequencies between 0.01 and 1
  freqs = np.linspace(0.01, 1, 1000)
  
  # computes the Lomb Scargle Periodogram of the time and scaled magnitudes using each frequency as a guess
  periodogram = spectral.lombscargle(time, scaled_mags, freqs)
  
  # returns the inverse of the frequence (i.e. the period) of the largest periodogram value
  1/freqs[np.argmax(periodogram)]
  </code></pre>
  
  <p>This returns <code>1</code> instead of the expected period of <code>2pi ~= 1/0.6366</code>. Any ideas?</p>