u

      return self._freq

   @property

   def ps_shape(self) :

      """the spectral shape: ps_shape=P(f)**0.5"""

      return self._S

      """the spectral shape: ps_shape=P(f)"""

      return self._S2

   @property

   def ps_shape_integral(self) :

      """the spectral shape integral  (excluding the freq=0 sample)"""

      return self._ps_shape_integral

   @property

   def seed(self) :

      """the seed of the random number generator"""

      pl=self.alpha

      with np.errstate(divide='ignore'):

         x=self.fknee/np.where(self._freq==0, np.inf , self._freq)

         self._S=(1+x**pl)**0.5

         self._S2=(1+x**pl)

      #

      if self._zero_policy=='0' :

         self._S[0]=0.

         self._S2[0]=0.

      elif self._zero_policy=='1' :

         self._S[0]=1.

         self._S2[0]=1.

      elif self._zero_policy=='M' :

         self._S[0]=self.wn_mean

         self._S2[0]=self.wn_mean

      elif self._zero_policy=='100x1' :

         self._S[0]=1 if fknee>ff[1] else (1+100*(self.fknee/self._freq[1])**pl)**0.5

         self._S2[0]=(1+100*(self.fknee/self._freq[1])**pl)

      else : # self._zero_policy=='I' :

         # left things as they are

         pass

      #

      self._S=self._S2**0.5

      #

      x=self.freq[1:]

      y=self._S2[1:]

      self._ps_shape_integral=((y[1:]+y[:-1])*(x[1:]-x[:-1])).sum()/2*2

      #

   def __len__(self) :

      return self._N

   def __str__(self) :

      return str((self.N,self.wn_mean,self.wn_sigma,self.fknee,self.alpha,self.seed,self._zero_policy))

   def __call__(self,OutAll=False) :

      """computes the colored noise, if OutAll == False returns just the cn if True returns 

      (colored_noise,white_noise,colored_noise_rfft, colored_noise_shape, white_noise_rfft, original_white_noise)