def crtPrfNrlTc(aryBoxCar, varNumMtDrctn, varNumVol, tplPngSize, varNumX,<EOL>varExtXmin,  varExtXmax, varNumY, varExtYmin, varExtYmax,<EOL>varNumPrfSizes, varPrfStdMin, varPrfStdMax, varPar):
print('<STR_LIT>')<EOL>vecX = np.linspace(<NUM_LIT:0>, (tplPngSize[<NUM_LIT:0>] - <NUM_LIT:1>), varNumX, endpoint=True)<EOL>vecY = np.linspace(<NUM_LIT:0>, (tplPngSize[<NUM_LIT:1>] - <NUM_LIT:1>), varNumY, endpoint=True)<EOL>varDgr2PixX = tplPngSize[<NUM_LIT:0>] / (varExtXmax - varExtXmin)<EOL>varDgr2PixY = tplPngSize[<NUM_LIT:1>] / (varExtYmax - varExtYmin)<EOL>strErrMsg = '<STR_LIT>' +'<STR_LIT>' +'<STR_LIT>'<EOL>assert <NUM_LIT:0.5> > np.absolute((varDgr2PixX - varDgr2PixY)), strErrMsg<EOL>vecPrfSd = np.linspace(varPrfStdMin, varPrfStdMax, varNumPrfSizes,<EOL>endpoint=True)<EOL>vecPrfSd = np.multiply(vecPrfSd, varDgr2PixX)<EOL>varNumMdls = varNumX * varNumY * varNumPrfSizes<EOL>aryMdlParams = np.zeros((varNumMdls, <NUM_LIT:4>))<EOL>varCntMdlPrms = <NUM_LIT:0><EOL>for idxX in range(<NUM_LIT:0>, varNumX):<EOL><INDENT>for idxY in range(<NUM_LIT:0>, varNumY):<EOL><INDENT>for idxSd in range(<NUM_LIT:0>, varNumPrfSizes):<EOL><INDENT>aryMdlParams[varCntMdlPrms, <NUM_LIT:0>] = varCntMdlPrms<EOL>aryMdlParams[varCntMdlPrms, <NUM_LIT:1>] = vecX[idxX]<EOL>aryMdlParams[varCntMdlPrms, <NUM_LIT:2>] = vecY[idxY]<EOL>aryMdlParams[varCntMdlPrms, <NUM_LIT:3>] = vecPrfSd[idxSd]<EOL>varCntMdlPrms = varCntMdlPrms + <NUM_LIT:1><EOL><DEDENT><DEDENT><DEDENT>lstMdlParams = np.array_split(aryMdlParams, varPar)<EOL>queOut = mp.Queue()<EOL>lstPrfTc = [None] * varPar<EOL>lstPrcs = [None] * varPar<EOL>print('<STR_LIT>')<EOL>for idxPrc in range(<NUM_LIT:0>, varPar):<EOL><INDENT>lstPrcs[idxPrc] = mp.Process(target=cnvlGauss2D,<EOL>args=(idxPrc, aryBoxCar,<EOL>lstMdlParams[idxPrc], tplPngSize,<EOL>varNumVol, queOut)<EOL>)<EOL>lstPrcs[idxPrc].Daemon = True<EOL><DEDENT>for idxPrc in range(<NUM_LIT:0>, varPar):<EOL><INDENT>lstPrcs[idxPrc].start()<EOL><DEDENT>for idxPrc in range(<NUM_LIT:0>, varPar):<EOL><INDENT>lstPrfTc[idxPrc] = queOut.get(True)<EOL><DEDENT>for idxPrc in range(<NUM_LIT:0>, varPar):<EOL><INDENT>lstPrcs[idxPrc].join()<EOL><DEDENT>print('<STR_LIT>')<EOL>lstPrfTc = sorted(lstPrfTc)<EOL>aryPrfTc = np.empty((<NUM_LIT:0>, varNumMtDrctn, varNumVol))<EOL>for idx in range(<NUM_LIT:0>, varPar):<EOL><INDENT>aryPrfTc = np.concatenate((aryPrfTc, lstPrfTc[idx][<NUM_LIT:1>]), axis=<NUM_LIT:0>)<EOL><DEDENT>assert aryPrfTc.shape[<NUM_LIT:0>] == varNumMdls<EOL>del(aryMdlParams)<EOL>del(lstMdlParams)<EOL>del(lstPrfTc)<EOL>aryNrlTc = np.zeros([varNumX, varNumY, varNumPrfSizes, varNumMtDrctn,<EOL>varNumVol])<EOL>varCntMdlPrms = <NUM_LIT:0><EOL>for idxX in range(<NUM_LIT:0>, varNumX):<EOL><INDENT>for idxY in range(<NUM_LIT:0>, varNumY):<EOL><INDENT>for idxSd in range(<NUM_LIT:0>, varNumPrfSizes):<EOL><INDENT>aryNrlTc[idxX, idxY, idxSd, :, :] = aryPrfTc[<EOL>varCntMdlPrms, :, :]<EOL>varCntMdlPrms = varCntMdlPrms + <NUM_LIT:1><EOL><DEDENT><DEDENT><DEDENT>return aryNrlTc<EOL>
Create neural model time courses from pixel-wise boxcar functions.

    Parameters
    ----------
    aryBoxCar : 4d numpy array, shape [n_x_pix, n_y_pix, n_mtn_dir, n_vol]
        Description of input 1.
    varNumMtDrctn : float, positive
        Description of input 2.
    varNumVol : float, positive
        Description of input 2.
    tplPngSize : tuple
        Description of input 2.
    varNumX : float, positive
        Description of input 2.
    varExtXmin : float, positive
        Description of input 2.
    varExtXmax : float, positive
        Description of input 2.
    varNumY : float, positive
        Description of input 2.
    varExtYmin : float, positive
        Description of input 2.
    varExtYmax : float, positive
        Description of input 2.
    varNumPrfSizes : float, positive
        Description of input 2.
    varPrfStdMin : float, positive
        Description of input 2.
    varPrfStdMax : float, positive
        Description of input 2.
    varPar : float, positive
        Description of input 2.
    Returns
    -------
    aryNrlTc : 5d numpy array, shape [n_x_pos, n_y_pos, n_sd, n_mtn_dir, n_vol]
        Closed data.
    Reference
    ---------
    [1]