How to use the rampy.baseline function in rampy

sp = np.genfromtxt(path_in+data_liste["Name"][i],delimiter=delim,skip_header=1)

        # constructing an interpolator: this will allow an output of all data with the same X axis
        f = scipy.interpolate.interp1d(sp[:,0], sp[:,1],fill_value="extrapolate")

        # temperature and excitation line correction (see Rameau help)
        x, y_all[:,i], sdf = rp.tlcorrection(x,f(x),23.0,laser,normalisation='intensity')

        # getting the roi
        roi = np.array(rois.loc[i]).reshape(int(len(rois.loc[i])/2),2)

        # calculating baseline
        if method == "LL2012": # spline

            try:
                c_hf, b_hf = rp.baseline(x,y_all[:,i],roi,"gcvspline",s=spline_coeff)
            except:
                break

            y_all_corr[:,i]=c_hf[:,0]
            y_all_base[:,i]=b_hf[:,0]

        elif method == "DG2017": # polynomial 3 following DG2017 method

            # getting portion of interrest
            x_lf = x[np.where(x<2000.)].reshape(-1)
            x_hf = x[np.where(x>2000.)].reshape(-1)

            y_lf = y_all[np.where(x<2000.),i].reshape(-1)
            y_hf = y_all[np.where(x>2000.),i].reshape(-1)

            c_lf, b_lf = rp.baseline(x_lf,y_lf,np.array([[0,200],[1240,1500]]),"poly",polynomial_order = poly_coeff)

break

            y_all_corr[:,i]=c_hf[:,0]
            y_all_base[:,i]=b_hf[:,0]

        elif method == "DG2017": # polynomial 3 following DG2017 method

            # getting portion of interrest
            x_lf = x[np.where(x<2000.)].reshape(-1)
            x_hf = x[np.where(x>2000.)].reshape(-1)

            y_lf = y_all[np.where(x<2000.),i].reshape(-1)
            y_hf = y_all[np.where(x>2000.),i].reshape(-1)

            c_lf, b_lf = rp.baseline(x_lf,y_lf,np.array([[0,200],[1240,1500]]),"poly",polynomial_order = poly_coeff)
            c_hf, b_hf = rp.baseline(x_hf,y_hf,np.array([[2500,3100],[3750,3900]]),"poly",polynomial_order = poly_coeff)

            y_all_corr[:,i] = np.hstack((c_lf.reshape(-1),c_hf.reshape(-1)))
            y_all_base[:,i] = np.hstack((b_lf.reshape(-1),b_hf.reshape(-1)))

        else:
            raise TypeError('method should be set to LL2012 or DG2017')

        # Area / Integrated Intensity calculation
        S = np.trapz(y_all_corr[np.where((x>150)&(x<1250)),i],x[np.where((x>150)&(x<1250))])
        W = np.trapz(y_all_corr[np.where((x>3100)&(x<3750)),i],x[np.where((x>3100)&(x<3750))])

        # updating the Pandas dataframe rws
        rs[i] = S[0]
        rw[i] = W[0]
        rws[i] = W[0]/S[0]

except:
                break

            y_all_corr[:,i]=c_hf[:,0]
            y_all_base[:,i]=b_hf[:,0]

        elif method == "DG2017": # polynomial 3 following DG2017 method

            # getting portion of interrest
            x_lf = x[np.where(x<2000.)].reshape(-1)
            x_hf = x[np.where(x>2000.)].reshape(-1)

            y_lf = y_all[np.where(x<2000.),i].reshape(-1)
            y_hf = y_all[np.where(x>2000.),i].reshape(-1)

            c_lf, b_lf = rp.baseline(x_lf,y_lf,np.array([[0,200],[1240,1500]]),"poly",polynomial_order = poly_coeff)
            c_hf, b_hf = rp.baseline(x_hf,y_hf,np.array([[2500,3100],[3750,3900]]),"poly",polynomial_order = poly_coeff)

            y_all_corr[:,i] = np.hstack((c_lf.reshape(-1),c_hf.reshape(-1)))
            y_all_base[:,i] = np.hstack((b_lf.reshape(-1),b_hf.reshape(-1)))

        else:
            raise TypeError('method should be set to LL2012 or DG2017')

        # Area / Integrated Intensity calculation
        S = np.trapz(y_all_corr[np.where((x>150)&(x<1250)),i],x[np.where((x>150)&(x<1250))])
        W = np.trapz(y_all_corr[np.where((x>3100)&(x<3750)),i],x[np.where((x>3100)&(x<3750))])

        # updating the Pandas dataframe rws
        rs[i] = S[0]
        rw[i] = W[0]
        rws[i] = W[0]/S[0]