### field's L2-norm generalized to Lq-norm.

parent 0c38434c
 ... @@ -5897,17 +5897,27 @@ class field(object): ... @@ -5897,17 +5897,27 @@ class field(object): x = self.domain.calc_weight(x,power=1) x = self.domain.calc_weight(x,power=1) return self.domain.calc_dot(self.val,x) return self.domain.calc_dot(self.val,x) def norm(self): ## TODO: extend to L^q norm def norm(self,q=None): """ """ Computes the L2-norm of the field values. Computes the Lq-norm of the field values. Parameters ---------- q : scalar Parameter q of the Lq-norm (default: 2). Returns Returns ------- ------- norm : scalar norm : scalar The L2-norm of the field values. The Lq-norm of the field values. """ """ return np.sqrt(self.dot(x=self.val)) if(q is None): return np.sqrt(self.dot(x=self.val)) else: return self.dot(x=self.val**(q-1))**(1/q) ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ def pseudo_dot(self,x=1,**kwargs): def pseudo_dot(self,x=1,**kwargs): """ """ ... ...
 ... @@ -36,11 +36,8 @@ ... @@ -36,11 +36,8 @@ homogeneity and isotropy. Fields which are only statistically homogeneous homogeneity and isotropy. Fields which are only statistically homogeneous can also be created using the diagonal operator routine. can also be created using the diagonal operator routine. At the moment, NIFTy offers one additional routine for power spectrum At the moment, NIFTY offers several additional routines for power spectrum manipulation, the smooth_power function to smooth a power spectrum with a manipulation. Gaussian convolution kernel. This can be necessary in cases where power spectra are reconstructed and reused in an iterative algorithm, where too much statistical variation might severely effect the results. """ """ ... ...
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