class_docstr: Use a bivariate Gaussian as a distribution model
class_docstr_tok: ["Use","a","bivariate","Gaussian","as","a","distribution","model"]
code: def __call__(self, H):
      npts = nonzero(self.npoints(H))
      mI = np.sum(H * self.I) / npts
      mJ = np.sum(H * self.J) / npts
      vI = np.sum(H * (self.I) ** 2) / npts - mI**2
      vJ = np.sum(H * (self.J) ** 2) / npts - mJ**2
      cIJ = np.sum(H * self.J * self.I) / npts - mI * mJ
      rho2 = (cIJ / nonzero(np.sqrt(vI * vJ))) ** 2
      if self.renormalize:
          rho2 = correlation2loglikelihood(rho2, npts)
      return rho2
  
imports: import numpy
  from scipy.ndimage import gaussian_filter
  from ._registration import _L1_moments
label_desc: Use image node_id 2 for calling the CorrelationCoefficient obj's underlying member method code with example usage: obj.__call__(H) and returns: rho2