text: # From https://github.com/felixpatzelt/colorednoise
  # MIT license
  """Generate colored noise."""
  
  from numpy import sqrt, newaxis
  from numpy.fft import irfft, rfftfreq
  from numpy.random import normal
  from numpy import sum as npsum
  
  
  def powerlaw_psd_gaussian(exponent, size, fmin=0):
      """Gaussian (1/f)**beta noise.
  
      Based on the algorithm in:
      Timmer, J. and Koenig, M.:
      On generating power law noise.
      Astron. Astrophys. 300, 707-710 (1995)
  
      Normalised to unit variance
  
      Parameters:
      -----------
  
      exponent : float
          The power-spectrum of the generated noise is proportional to
  
          S(f) = (1 / f)**beta
          flicker / pink noise:   exponent beta = 1
          brown noise:            exponent beta = 2
  
          Furthermore, the autocorrelation decays proportional to lag**-gamma
          with gamma = 1 - beta for 0 < beta < 1.
          There may be finite-size issues for beta close to one.
  
      shape : int or iterable
          The output has the given shape, and the desired power spectrum in
          the last coordinate. That is, the last dimension is taken as time,
          and all other components are independent.
  
      fmin : float, optional
          Low-frequency cutoff.
          Default: 0 corresponds to original paper. It is not actually
          zero, but 1/samples.
  
      Returns
      -------
      out : array
          The samples.
  
  
      Examples:
      ---------
  
      # generate 1/f noise == pink noise == flicker noise
      >>> import colorednoise as cn
      >>> y = cn.powerlaw_psd_gaussian(1, 5)
      """
      
      # Make sure size is a list so we can iterate it and assign to it.
      try:
          size = list(size)
      except TypeError:
          size = [size]
      
      # The number of samples in each time series
      samples = size[-1]
      
      # Calculate Frequencies (we asume a sample rate of one)
      # Use fft functions for real output (-> hermitian spectrum)
      f = rfftfreq(samples)
      
      # Build scaling factors for all frequencies
      s_scale = f
      fmin = max(fmin, 1./samples) # Low frequency cutoff
      ix   = npsum(s_scale < fmin)   # Index of the cutoff
      if ix and ix < len(s_scale):
          s_scale[:ix] = s_scale[ix]
      s_scale = s_scale**(-exponent/2.)
      
      # Calculate theoretical output standard deviation from scaling
      w      = s_scale[1:].copy()
      w[-1] *= (1 + (samples % 2)) / 2. # correct f = +-0.5
      sigma = 2 * sqrt(npsum(w**2)) / samples
      
      # Adjust size to generate one Fourier component per frequency
      size[-1] = len(f)
  
      # Add empty dimension(s) to broadcast s_scale along last
      # dimension of generated random power + phase (below)
      dims_to_add = len(size) - 1
      s_scale     = s_scale[(newaxis,) * dims_to_add + (Ellipsis,)]
      
      # Generate scaled random power + phase
      sr = normal(scale=s_scale, size=size)
      si = normal(scale=s_scale, size=size)
      
      # If the signal length is even, frequencies +/- 0.5 are equal
      # so the coefficient must be real.
      if not (samples % 2): si[...,-1] = 0
      
      # Regardless of signal length, the DC component must be real
      si[...,0] = 0
      
      # Combine power + corrected phase to Fourier components
      s  = sr + 1J * si
      
      # Transform to real time series & scale to unit variance
      y = irfft(s, n=samples, axis=-1) / sigma
      
      return y
  