peri/interpolation.py
def _newcall(self, rvecs):
        """Correct, normalized version of Barnes"""
        # 1. Initial guess for output:
        sigma = 1*self.filter_size
        out = self._eval_firstorder(rvecs, self.d, sigma)
        # 2. There are differences between 0th order at the points and
        #    the passed data, so we iterate to remove:
        ondata = self._eval_firstorder(self.x, self.d, sigma)
        for i in range(self.iterations):
            out += self._eval_firstorder(rvecs, self.d-ondata, sigma)
            ondata += self._eval_firstorder(self.x, self.d-ondata, sigma)
            sigma *= self.damp
        return out
def _newcall(self, rvecs):
        """Correct, normalized version of Barnes"""
        # 1. Initial guess for output:
        sigma = 1*self.filter_size
        out = self._eval_firstorder(rvecs, self.d, sigma)
        # 2. There are differences between 0th order at the points and
        #    the passed data, so we iterate to remove:
        ondata = self._eval_firstorder(self.x, self.d, sigma)
        for i in range(self.iterations):
            out += self._eval_firstorder(rvecs, self.d-ondata, sigma)
            ondata += self._eval_firstorder(self.x, self.d-ondata, sigma)
            sigma *= self.damp
        return out
def _newcall ( self , rvecs ) : # 1. Initial guess for output: sigma = 1 * self . filter_size out = self . _eval_firstorder ( rvecs , self . d , sigma ) # 2. There are differences between 0th order at the points and #    the passed data, so we iterate to remove: ondata = self . _eval_firstorder ( self . x , self . d , sigma ) for i in range ( self . iterations ) : out += self . _eval_firstorder ( rvecs , self . d - ondata , sigma ) ondata += self . _eval_firstorder ( self . x , self . d - ondata , sigma ) sigma *= self . damp return out
Correct, normalized version of Barnes
Correct normalized version of Barnes