def crt_prf_ftr_tc(aryMdlRsp, aryTmpExpInf, varNumVol, varTr, varTmpOvsmpl,<EOL>switchHrfSet, tplPngSize, varPar, dctPrm=None,<EOL>lgcPrint=True):
<EOL>vecFeat = np.unique(aryTmpExpInf[:, <NUM_LIT:3>])<EOL>vecFeat = vecFeat[np.nonzero(vecFeat)[<NUM_LIT:0>]]<EOL>aryPrfTc = np.zeros((aryMdlRsp.shape[<NUM_LIT:0>], <NUM_LIT:0>, varNumVol),<EOL>dtype=np.float32)<EOL>for indFtr, ftr in enumerate(vecFeat):<EOL><INDENT>if lgcPrint:<EOL><INDENT>print('<STR_LIT>' +<EOL>str(ftr))<EOL><DEDENT>aryTmpCnd = aryTmpExpInf[aryTmpExpInf[:, <NUM_LIT:3>] == ftr, <NUM_LIT:0>]<EOL>aryTmpOns = aryTmpExpInf[aryTmpExpInf[:, <NUM_LIT:3>] == ftr, <NUM_LIT:1>]<EOL>aryTmpDrt = aryTmpExpInf[aryTmpExpInf[:, <NUM_LIT:3>] == ftr, <NUM_LIT:2>]<EOL>aryNrlTcTmp = crt_nrl_tc(aryMdlRsp, aryTmpCnd, aryTmpOns, aryTmpDrt,<EOL>varTr, varNumVol, varTmpOvsmpl,<EOL>lgcPrint=lgcPrint)<EOL>aryPrfTcTmp = crt_prf_tc(aryNrlTcTmp, varNumVol, varTr, varTmpOvsmpl,<EOL>switchHrfSet, tplPngSize, varPar,<EOL>dctPrm=dctPrm, lgcPrint=lgcPrint)<EOL>aryPrfTc = np.concatenate((aryPrfTc, aryPrfTcTmp), axis=<NUM_LIT:1>)<EOL><DEDENT>return aryPrfTc<EOL>
Create all spatial x feature prf time courses.

    Parameters
    ----------
    aryMdlRsp : 2d numpy array, shape [n_x_pos * n_y_pos * n_sd, n_cond]
        Responses of 2D Gauss models to spatial conditions
    aryTmpExpInf: 2d numpy array, shape [unknown, 4]
        Temporal information about conditions
    varNumVol : float, positive
        Number of volumes of the (fMRI) data.
    varTr : float, positive
        Time to repeat (TR) of the (fMRI) experiment.
    varTmpOvsmpl : int, positive
        Factor by which the data hs been temporally upsampled.
    switchHrfSet : int, (1, 2, 3)
        Switch to determine which hrf basis functions are used
    tplPngSize : tuple
        Pixel dimensions of the visual space (width, height).
    varPar : int, positive
        Description of input 1.
    dctPrm : dictionary, default None
        Dictionary with customized hrf parameters. If this is None, default
        hrf parameters will be used.
    lgcPrint: boolean, default True
        Should print messages be sent to user?

    Returns
    -------
    aryNrlTcConv : 3d numpy array,
                   shape [nr of models, nr of unique feautures, varNumVol]
        Prf time course models