# title = r'Argon ionic abundance versus $S^{+2}$ temperature in Cloudy models' # print len(Temps), len(logArII_ArIII) # dz.data_plot(nominal_values(Temps), nominal_values(logArII_ArIII), color=dz.ColorVector[1], label='Observations', markerstyle='o', x_error=std_devs(Temps), y_error=std_devs(logArII_ArIII)) # dz.FigWording(xtitle, ytitle, title, axis_Size = 20.0, title_Size = 20.0, legend_size=20.0, legend_loc='upper right') # 'ArIons_vs_TSIII_Obs' # # #Display figure # dz.display_fig() # # dz.savefig(output_address = '/home/vital/Dropbox/Astrophysics/Papers/Elemental_RegressionsSulfur/Cloudy_Models/ArIons_vs_TSIII_Ionization_Obs') # # print 'Data treated' #----------------------Plotting abundances #Perform linear regression zero_vector = zeros(len(list_xvalues_clean_greater)) m ,n, m_err, n_err, covab = bces(list_xvalues_clean_greater, zero_vector, list_yvalues_clean_greater, zero_vector, zero_vector) x_regresion = linspace(0, max(list_xvalues_clean_greater), 50) y_regression = m[0] * x_regresion + n[0] LinearRegression_Label = r'Linear fitting'.format(n = round(n[0],2) ,nerr = round(n_err[0],2)) dz.data_plot(x_regresion, y_regression, label=LinearRegression_Label, linestyle='--', color=dz.ColorVector[1]) logSII_SIII_theo = m[0] * logArII_ArIII + n[0] dz.data_plot(nominal_values(logArII_ArIII), nominal_values(logSII_SIII_theo), color=dz.ColorVector[1], label='Observations', markerstyle='o', x_error=std_devs(logArII_ArIII), y_error=std_devs(logSII_SIII_theo)) # #Plot fitting formula formula = r"$log\left(Ar^{{+2}}/Ar^{{+3}}\right) = {m} \cdot log\left(S^{{+2}}/S^{{+3}}\right) + {n}$".format(m='m', n='n') formula2 = r"$m = {m} \pm {merror}; n = {n} \pm {nerror}$".format(m=round(m[0],3), merror=round(m_err[0],3), n=round(n[0],3), nerror=round(n_err[0],3)) dz.Axis.text(0.50, 0.15, formula, transform=dz.Axis.transAxes, fontsize=20)
# #----------------------Plotting temperatures # #Plot wording # xtitle = r'$log(S^{+2}/S^{+3})$' # ytitle = r'$T[SIII] (K)$' # title = 'Temperature - Sulfur ionic abundance relation in Cloudy photoionization models' # dz.FigWording(xtitle, ytitle, title, axis_Size = 20.0, title_Size = 20.0, legend_size=20.0, legend_loc='upper right') # # #Display figure # dz.display_fig() # # print 'Data treated' #----------------------Plotting abundances #Perform linear regression zero_vector = zeros(len(list_xvalues_clean_greater)) m ,n, m_err, n_err, covab = bces(list_xvalues_clean_greater, zero_vector, list_yvalues_clean_greater, zero_vector, zero_vector) # x_regresion = linspace(0, max(list_xvalues_clean_greater), 50) y_regression = m[0] * x_regresion + n[0] LinearRegression_Label = r'Linear Regression: $n = {n} \pm {nerr}$'.format(n = round(n[0],2) ,nerr = round(n_err[0],2)) dz.data_plot(x_regresion, y_regression, label=LinearRegression_Label, linestyle='--', color=dz.ColorVector[1]) #----Observations data FilesList = pv.Folder_Explorer(Pattern, Catalogue_Dic['Obj_Folder'], CheckComputer=False) Abundances_Matrix = import_data_from_objLog_triple(FilesList, pv) Objects = Abundances_Matrix[:,0] ArIII_HII_array = Abundances_Matrix[:,1] ArIV_HII_array = Abundances_Matrix[:,2]
Obj_vector, Elem, Y = list(values[0]), values[1], values[2] Method_index = 0 if len(Obj_vector) > 2: x_regresion_range = linspace(0.0, max(nominal_values(Elem))*1.10, 20) #Plotting the data pv.DataPloter_One(nominal_values(Elem), nominal_values(Y), Titles_wording[Keys][0], pv.Color_Vector[2][colors_dict[Keys]], LineStyle=None, XError=std_devs(Elem), YError=std_devs(Y)) pv.TextPlotter(nominal_values(Elem), nominal_values(Y), Obj_vector, x_pad = 0.95, y_pad = 1) pv.DataPloter_One(Y_WMAP_coord[0].nominal_value, Y_WMAP_coord[1].nominal_value, 'WMAP prediction', pv.Color_Vector[2][0], LineStyle=None) #Regression: cov_matrix = zeros(len(Obj_vector)) m_bces ,n_bces, m_err, n_err, covab = bces(nominal_values(Elem), std_devs(Elem), nominal_values(Y), std_devs(Y), cov_matrix) # if Keys == 'S_ArCorr_Regression_Inference': # print '\nThe key is\n', Keys # print m_bces[Method_index] ,n_bces[Method_index], m_err[Method_index], n_err[Method_index] y_trend = m_bces[Method_index] * x_regresion_range + n_bces[Method_index] pv.DataPloter_One(x_regresion_range, y_trend, 'Nemmer regression ' + str(0), pv.Color_Vector[2][colors_dict[Keys]], LineStyle=':') #Plot labels pv.Labels_Legends_One(Plot_Title = Titles_wording[Keys][1], Plot_xlabel = Titles_wording[Keys][3], Plot_ylabel = Titles_wording[Keys][2], LegendLocation=4) pv.Axis1.set_xlim(0, max(nominal_values(Elem))*1.10) #Save the figure SavingName = pv.ScriptCode + '_' + Titles_wording[Keys][4] pv.SaveManager(SavingName = SavingName, SavingFolder = Catalogue_Dic['Data_Folder'], ForceSave=True, savevectorfile=False) #Save PlotVector