code: def stdDev(self):
          '''
          get the standard deviation 
          from the PSF is evaluated as 2d Gaussian
          '''
          if self._corrPsf is None:
              self.psf()
          p = self._corrPsf.copy()
          mn = p.min()
          p[p<0.05*p.max()] = mn
          p-=mn
          p/=p.sum()
          
          x,y = self._psfGridCoords()
          x = x.flatten()
          y = y.flatten()
  
          guess = (1,1,0)
  
          param, _ = curve_fit(self._fn, (x,y), p.flatten(), guess)
  
          self._fitParam = param 
          stdx,stdy =  param[:2]
          self._std = (stdx+stdy) / 2
          
          return self._stf stdDev(self):
          '''
          get the standard deviation 
          from the PSF is evaluated as 2d Gaussian
          '''
          if self._corrPsf is None:
              self.psf()
          p = self._corrPsf.copy()
          mn = p.min()
          p[p<0.05*p.max()] = mn
          p-=mn
          p/=p.sum()
          
          x,y = self._psfGridCoords()
          x = x.flatten()
          y = y.flatten()
  
          guess = (1,1,0)
  
          param, _ = curve_fit(self._fn, (x,y), p.flatten(), guess)
  
          self._fitParam = param 
          stdx,stdy =  param[:2]
          self._std = (stdx+stdy) / 2
          
          return self._std
docs: get the standard deviation 
          from the PSF is evaluated as 2d Gaussian