out = odr.run() Pv.Axis1.set_ylim(0.10, 0.50) Pv.Axis1.errorbar( Metal_Abund, Y_Mass, xerr=Metal_Error, yerr=Y_Mass_Error, linestyle="None", marker="x", color=Pv.Color_Vector[2][k + 1], ) # New method Regression_Fit, Uncertainty_Matrix, Red_Chi_Sq, Residuals = linfit( array(Metal_Abund), array(Y_Mass), array(Y_Mass_Error), cov=True, relsigma=False, chisq=True, residuals=True ) m_n_error = [sqrt(Uncertainty_Matrix[t, t]) for t in range(2)] R_Factor = Uncertainty_Matrix[0, 1] / (m_n_error[0] * m_n_error[1]) cHbeta, cHbeta_error = Regression_Fit[0], m_n_error[0] n, n_error = Regression_Fit[1], m_n_error[1] TrendLine2 = Pv.Axis1.plot( TrendX, Regression_Fit[0] * TrendX + Regression_Fit[1], color=Pv.Color_Vector[2][k + 1], label=ListofElements[k][3], linestyle="--", ) for m in range(len(Object_Vector)): Label_point = Object_Vector[m]
AxHor2.tick_params(axis='x', length=7, width=2, labelsize=20, colors=Pv.Color_Vector[1]) AxHor2.set_frame_on(True) AxHor2.patch.set_visible(False) AxHor2.xaxis.set_ticks_position('bottom') AxHor2.xaxis.set_label_position('bottom') AxHor2.spines['bottom'].set_position(('outward', 100)) AxHor2.spines['bottom'].set_color(Pv.Color_Vector[1]) AxHor2.xaxis.get_offset_text().set_color(Pv.Color_Vector[1]) OxygenLine = Pv.Axis1.errorbar(ElementsResults[0][0], ElementsResults[0][2], xerr=ElementsResults[0][1], yerr=ElementsResults[0][3], linestyle='None', marker='x', color = Pv.Color_Vector[2][1]) NitrogenLine = AxHor1.errorbar(ElementsResults[1][0], ElementsResults[1][2], xerr=ElementsResults[1][1], yerr=ElementsResults[1][3], linestyle='None', marker='x', color = Pv.Color_Vector[2][2]) SulphurLine = AxHor2.errorbar(ElementsResults[2][0], ElementsResults[2][2], xerr=ElementsResults[2][1], yerr=ElementsResults[2][3], linestyle='None', marker='x', color =Pv.Color_Vector[2][3]) # Oxygen O_Regression_Fit, O_Uncertainty_Matrix, Red_Chi_Sq, Residuals = linfit(array(ElementsResults[0][0]), array(ElementsResults[0][2]), array(ElementsResults[0][3]), cov=True, relsigma=False, chisq=True, residuals=True) O_m_n_error = [sqrt(O_Uncertainty_Matrix[t,t]) for t in range(2)] O_n, O_n_error = O_Regression_Fit[1], O_m_n_error[1] # Nitrogen N_Regression_Fit, N_Uncertainty_Matrix, Red_Chi_Sq, Residuals = linfit(array(ElementsResults[1][0]), array(ElementsResults[1][2]), array(ElementsResults[1][3]), cov=True, relsigma=False, chisq=True, residuals=True) N_m_n_error = [sqrt(N_Uncertainty_Matrix[t,t]) for t in range(2)] N_n, N_n_error = N_Regression_Fit[1], N_m_n_error[1] # Sulphur S_Regression_Fit, S_Uncertainty_Matrix, Red_Chi_Sq, Residuals = linfit(array(ElementsResults[2][0]), array(ElementsResults[2][2]), array(ElementsResults[2][3]), cov=True, relsigma=False, chisq=True, residuals=True) S_m_n_error = [sqrt(S_Uncertainty_Matrix[t,t]) for t in range(2)] S_n, S_n_error = S_Regression_Fit[1], S_m_n_error[1] Pv.Axis1.set_ylim(0.10, 0.50) Pv.Axis1.set_xlim(0, max(ElementsResults[0][0])*1.10)