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)
