def cnvlGauss2D(idxPrc, aryBoxCar, aryMdlParamsChnk, tplPngSize, varNumVol,<EOL>queOut):
<EOL>varChnkSze = np.size(aryMdlParamsChnk, axis=<NUM_LIT:0>)<EOL>varNumMtnDrtn = aryBoxCar.shape[<NUM_LIT:2>]<EOL>aryOut = np.zeros([varChnkSze, varNumMtnDrtn, varNumVol])<EOL>for idxMtn in range(<NUM_LIT:0>, varNumMtnDrtn):<EOL><INDENT>for idxMdl in range(<NUM_LIT:0>, varChnkSze):<EOL><INDENT>varTmpX = aryMdlParamsChnk[idxMdl, <NUM_LIT:1>]<EOL>varTmpY = aryMdlParamsChnk[idxMdl, <NUM_LIT:2>]<EOL>varTmpSd = aryMdlParamsChnk[idxMdl, <NUM_LIT:3>]<EOL>aryGauss = crtGauss2D(tplPngSize[<NUM_LIT:0>],<EOL>tplPngSize[<NUM_LIT:1>],<EOL>varTmpX,<EOL>varTmpY,<EOL>varTmpSd)<EOL>aryPrfTcTmp = np.multiply(aryBoxCar[:, :, idxMtn, :],<EOL>aryGauss[:, :, None])<EOL>aryPrfTcTmp = np.sum(aryPrfTcTmp, axis=(<NUM_LIT:0>, <NUM_LIT:1>))<EOL>aryOut[idxMdl, idxMtn, :] = aryPrfTcTmp<EOL><DEDENT><DEDENT>lstOut = [idxPrc,<EOL>aryOut]<EOL>queOut.put(lstOut)<EOL>
Spatially convolve boxcar functions with 2D Gaussian.

    Parameters
    ----------
    idxPrc : 2d numpy array, shape [n_samples, n_measurements]
        Description of input 1.
    aryBoxCar : float, positive
      Description of input 2.
    aryMdlParamsChnk : 2d numpy array, shape [n_samples, n_measurements]
        Description of input 1.
    tplPngSize : float, positive
      Description of input 2.
    varNumVol : 2d numpy array, shape [n_samples, n_measurements]
        Description of input 1.
    queOut : float, positive
      Description of input 2.
    Returns
    -------
    data : 2d numpy array, shape [n_samples, n_measurements]
        Closed data.
    Reference
    ---------
    [1]