#Plot wording
xtitle = r'$n_{e}$ $(cm^{-3})$'
# ytitle = r'j(T) [erg cm$^{-3}$ s${-1}$]'
ytitle = 'Relative emissivity'
title = 'HeI emissivities @ $T_e$={:.0f}'.format(tem)
dz.FigWording(xtitle,
ytitle,
title,
axis_Size=20.0,
title_Size=20.0,
legend_size=20.0)
#Display figure
# dz.display_fig()
dz.savefig(
'/home/vital/Dropbox/Astrophysics/Lore/PopStar_SEDs/SIV_Emissivity_den',
extension='.png',
reset_fig=True)
#--------------------------Temperature case----------------------------------
#Plot the lines
for line in S_Lines:
y = S4.getEmissivity(tem_range, den, wave=line)
y_1000_100 = S4.getEmissivity(tem, den, wave=line)
dz.data_plot(tem_range,
y / y_1000_100,
label=str(line) + r' $\AA$ line',
linestyle='--',
linewidth=2)
# dz.Axis.set_xscale('log')
dz.Axis.tick_params(axis='both', labelsize=20.0)
dz.data_plot(x_BlueRed, unumpy.nominal_values(Trendline_all), label='Trend line: Including errors', linestyle=':')
dz.data_plot(x_Red, unumpy.nominal_values(y_Red), label='Red arm emission lines', markerstyle='o', y_error=unumpy.std_devs(y_Red))
dz.data_plot(x_BlueRed, unumpy.nominal_values(y_NoError_trend), label='Trend line: Without including error', linestyle='--')
dz.text_plot(EmLine_blue, x_Blue, unumpy.nominal_values(y_Blue), x_pad = 0.95, y_pad = 1.10, fontsize=10)
dz.text_plot(EmLine_Red, x_Red, unumpy.nominal_values(y_Red), x_pad = 0.95, y_pad = 1.10, fontsize=10)
#Plot the data
#Points not used for the treatment
# d.TextPlotter(x_outbound, unumpy.nominal_values(y_outbound), EmLine_outBound, x_pad = 0.95, y_pad = 1)
dz.text_plot(EmLine_outBound, x_outbound, unumpy.nominal_values(y_outbound), fontsize=10)
#--Blue arm
# dz.data_plot(x_Blue, unumpy.nominal_values(y_Blue), 'Blue arm', pv.Color_Vector[2][2], YError=unumpy.std_devs(y_Blue))
# dz.data_plot(x_Blue, unumpy.nominal_values(cHbeta_blue_MagEr * x_Blue + n_blue_MagEr), label='Trend line blue', linestyle=':')
# #--Red arm
#Increase the display range
# dz.Axis.set_ylim(-0.4,0.4)
#Insert labels and legends
Title = "HII galaxy " + CodeName + " " + r'$c(H\beta)$' + ' coefficient calculation'
y_Title = r'$log(I/I_{H\beta})_{th}-log(F/F_{H\beta})_{Obs}$'
x_Title = r'$f(\lambda)-f(\lambda_{H\beta})$'
dz.FigWording(x_Title, y_Title, Title)
#Save data
# pv.SaveManager(SavingName = pv.ScriptCode + '_' + CodeName + '_IntrinsicReddening', SavingFolder = FileFolder, ForceSave=True)
# dz.display_fig()
dz.savefig('/home/vital/Dropbox/Astrophysics/Papers/Elemental_RegressionsSulfur/Images/' + 'SHOC579_cHbeta')
print 'All data treated', pv.display_errors()
fontsize=10)
#Plot the data
#Points not used for the treatment
# d.TextPlotter(x_outbound, unumpy.nominal_values(y_outbound), EmLine_outBound, x_pad = 0.95, y_pad = 1)
dz.text_plot(EmLine_outBound,
x_outbound,
unumpy.nominal_values(y_outbound),
fontsize=10)
#--Blue arm
# dz.data_plot(x_Blue, unumpy.nominal_values(y_Blue), 'Blue arm', pv.Color_Vector[2][2], YError=unumpy.std_devs(y_Blue))
# dz.data_plot(x_Blue, unumpy.nominal_values(cHbeta_blue_MagEr * x_Blue + n_blue_MagEr), label='Trend line blue', linestyle=':')
# #--Red arm
#Increase the display range
# dz.Axis.set_ylim(-0.4,0.4)
#Insert labels and legends
Title = "HII galaxy " + CodeName + " " + r'$c(H\beta)$' + ' coefficient calculation'
y_Title = r'$log(I/I_{H\beta})_{th}-log(F/F_{H\beta})_{Obs}$'
x_Title = r'$f(\lambda)-f(\lambda_{H\beta})$'
dz.FigWording(x_Title, y_Title, Title)
#Save data
# pv.SaveManager(SavingName = pv.ScriptCode + '_' + CodeName + '_IntrinsicReddening', SavingFolder = FileFolder, ForceSave=True)
# dz.display_fig()
dz.savefig(
'/home/vital/Dropbox/Astrophysics/Papers/Elemental_RegressionsSulfur/Images/'
+ 'SHOC579_cHbeta')
print 'All data treated', pv.display_errors()
#----------------------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)
dz.Axis.text(0.50, 0.08, formula2, transform=dz.Axis.transAxes, fontsize=20)
#Plot wording
xtitle = r'$log(S^{+2}/S^{+3})$'
ytitle = r'$log(Ar^{+2}/Ar^{+3})$'
title = 'Argon - Sulfur ionic relation in Cloudy photoionization models'
dz.FigWording(xtitle, ytitle, title, axis_Size = 20.0, title_Size = 20.0, legend_size=20.0, legend_loc='best')
#Display figure
# dz.display_fig()
dz.savefig(output_address = '/home/vital/Dropbox/Astrophysics/Papers/Elemental_RegressionsSulfur/Cloudy_Models/ArIons_vs_SIons_Ionization_Obs')
print 'Data treated'
line = H_Lines[k]
label = line.replace('_',' ')
dz.data_plot(Age_dict[line], Eqw_dict[line], label=label, markerstyle='o')
#Define figure wording
xtitle = r'Age $(Myr)$'
ytitle = r'Ew $(\AA)$'
title = 'Starburst recombination lines absorption Ew evolution'
dz.FigWording(xtitle, ytitle, title, axis_Size=30, title_Size=30, legend_size=25, legend_loc='best')
dz.Axis.set_xlim(0, 120)
#save_fig
dz.display_fig()
dz.savefig('/home/vital/Dropbox/Astrophysics/Seminars/GTC_conference_2015/EWevolutin', extension='.eps')
# #-----------------------------------------STARBURST EQUIVALENT WIDTH Normalized Hydrogen EVOLUTION----------------------------------------
# FilesFolder = '/home/vital/Dropbox/Astrophysics/Data/Starburst_Spectra_z0.004/'
# FilesPattern = '_txt_LinesLog_v3.txt'
#
# #Locate files on hard drive
# FilesList = pv.Folder_Explorer(FilesPattern, FilesFolder, CheckComputer=False)
#
# # #Define figure format
# dz.FigConf()
#
# #Lines to plot
# H_Lines = ['H1_6563A', 'H1_3970A','H1_4102A','H1_4340A', 'H1_4861A']
# # H_Lines = ['He1_3188A','He1_4026A','He2_4686A','He1_5016A','He1_5876A','He1_6678A']
dz.data_plot(nominal_values(Metal_abun), nominal_values(Ymass_Metal), color = dz.ColorVector[2][j], label=element_label, markerstyle='o', x_error=std_devs(Metal_abun), y_error=std_devs(Ymass_Metal))
dz.data_plot(Y_WMAP_coord[0].nominal_value, Y_WMAP_coord[1].nominal_value, color = dz.ColorVector[2][len(Regression_list) + 1], label='WMAP prediction', markerstyle='o', x_error=Y_WMAP_coord[0].std_dev, y_error=Y_WMAP_coord[1].std_dev)
dz.text_plot(Obj_Elem, nominal_values(Metal_abun), nominal_values(Ymass_Metal), color = dz.ColorVector[1], x_pad = 0.95, y_pad = 1)
#Plot the linear trendlines
x_regression_range = linspace(0.0, max(Metal_matrix[:,0])*1.10, 20)
y_regression_range = m_Median * x_regression_range + n_Median
label_S = r'Median value: $' + round_sig(n_Median, n=4, scien_notation=False) + '_{-' + round_sig(n_Median - n_16th, n=4, scien_notation=False) + '}^{+' + round_sig(n_84th - n_Median, n=4, scien_notation=False) + '}$'
dz.data_plot(x_regression_range, y_regression_range, label = 'Linear regression', color = dz.ColorVector[2][j], linestyle = '--')
dz.FigWording(Titles_wording[regression][2], Titles_wording[regression][3], Titles_wording[regression][1],legend_loc='best')
#Set legends
# dz.Axis.set_xlim(0.0, x_regression_range[-1]*1.10)
print 'bien'
#Save the data
SavingName = regression + '_MC_pyneb'
dz.savefig(Catalogue_Dic['Data_Folder'] + SavingName, reset_fig = True)
# pv.SaveManager(SavingName = SavingName, SavingFolder = Catalogue_Dic['Data_Folder'], ForceSave=True, savevectorfile=False) #Save PlotVector
pv.ResetPlot()
# #Plot the figure
# from Plotting_Libraries.bayesian_data import bayes_plotter
# bp = bayes_plotter()
# bp.plot_Ownposteriors_histagram(n_dict.values(), ['Yp Sulfur', 'Yp Oxygen', 'Yp Nitrogen'], xlim=[0.22, 0.28]) #
# bp.savefig(Catalogue_Dic['Data_Folder'] + 'Yp_Distribution')
print 'Data treated'
xtitle = r'Age $(Myr)$'
ytitle = r'Ew $(\AA)$'
title = 'Starburst recombination lines absorption Ew evolution'
dz.FigWording(xtitle,
ytitle,
title,
axis_Size=30,
title_Size=30,
legend_size=25,
legend_loc='best')
dz.Axis.set_xlim(0, 120)
#save_fig
dz.display_fig()
dz.savefig(
'/home/vital/Dropbox/Astrophysics/Seminars/GTC_conference_2015/EWevolutin',
extension='.eps')
# #-----------------------------------------STARBURST EQUIVALENT WIDTH Normalized Hydrogen EVOLUTION----------------------------------------
# FilesFolder = '/home/vital/Dropbox/Astrophysics/Data/Starburst_Spectra_z0.004/'
# FilesPattern = '_txt_LinesLog_v3.txt'
#
# #Locate files on hard drive
# FilesList = pv.Folder_Explorer(FilesPattern, FilesFolder, CheckComputer=False)
#
# # #Define figure format
# dz.FigConf()
#
# #Lines to plot
# H_Lines = ['H1_6563A', 'H1_3970A','H1_4102A','H1_4340A', 'H1_4861A']
# # H_Lines = ['He1_3188A','He1_4026A','He2_4686A','He1_5016A','He1_5876A','He1_6678A']
Lineal_parameters = lineal_mod.guess(y_linealFitting, x=x_linealFitting)
x_lineal = linspace(0, np_max(x_linealFitting), 100)
y_lineal = Lineal_parameters[
'lineal_slope'].value * x_lineal + Lineal_parameters[
'lineal_intercept'].value
dz.data_plot(x_lineal,
y_lineal,
label='Linear fitting',
color='black',
linestyle='-')
# #Plot fitting formula
formula = r"$log\left(Ar^{{+2}}/Ar^{{+3}}\right) = {m} \cdot log\left(S^{{+2}}/S^{{+3}}\right) + {n}$".format(
m=round(Lineal_parameters['lineal_slope'].value, 3),
n=round(Lineal_parameters['lineal_intercept'].value, 3))
dz.Axis.text(0.35, 0.15, formula, transform=dz.Axis.transAxes, fontsize=20)
#Plot wording
xtitle = r'$log\left(S^{{+2}}/S^{{+3}}\right)$'
ytitle = r'$log\left(Ar^{{+2}}/Ar^{{+3}}\right)$'
title = 'Argon - Sulfur ionic abundances\nfor a Z, Mass, log(t) cluster grid'
dz.FigWording(xtitle, ytitle, title, loc='upper left')
#Display figure
# dz.display_fig()
dz.savefig(
output_address=
'/home/vital/Dropbox/Astrophysics/Data/WHT_observations/data/sulfur_argon_ionicAbundances',
extension='.png')
print 'Data treated otro'
xybox=(10, -10),
xycoords='figure fraction',
boxcoords="offset points")
dz.Axis.add_artist(ab)
dz.Axis.set_xlim(3600.0, 3900)
dz.Axis.set_ylim(0, 1e-15)
#Set plot labels
title = r'SHOC579 spectrum components$'
dz.FigWording(r'Wavelength $(\AA)$',
'Flux' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$', title)
#Display figure
# dz.display_fig()
dz.savefig(
'/home/vital/Dropbox/Astrophysics/Papers/Elemental_RegressionsSulfur/Images/'
+ 'SHOC579_spectralComponents',
extension='.png')
# #---------------------Zanstra calibration------------------------------
#
# pv = myPickle()
# dz = Plot_Conf()
# nebCalc = NebularContinuumCalculator()
# nebCalc.DataRoot = '/home/vital/Dropbox/Astrophysics/Lore/NebularContinuum/'
#
# #Define operation
# Catalogue_Dic = DataToTreat()
# Pattern = Catalogue_Dic['Datatype'] + '_dered.fits'
# Lineslog_extension = Catalogue_Dic['Datatype'] + '_dered_LinesLog_v3.txt'
#
# #Find and organize files from terminal command or .py file
dz.Axis.tick_params(axis='both', labelsize=20.0)
dz.Axis.set_ylim(0.0,2.5)
# dz.Axis.patch.set_facecolor('white')
# dz.Fig.set_facecolor('black')
# dz.Fig.set_edgecolor('black')
#Plot wording
xtitle = r'$n_{e}$ $(cm^{-3})$'
# ytitle = r'j(T) [erg cm$^{-3}$ s${-1}$]'
ytitle = 'Relative emissivity'
title = 'HeI emissivities @ $T_e$={:.0f}'.format(tem)
dz.FigWording(xtitle, ytitle, title, axis_Size = 20.0, title_Size = 20.0, legend_size=20.0)
#Display figure
# dz.display_fig()
dz.savefig('/home/vital/Dropbox/Astrophysics/Lore/PopStar_SEDs/SIV_Emissivity_den', extension = '.png', reset_fig=True)
#--------------------------Temperature case----------------------------------
#Plot the lines
for line in S_Lines:
y = S4.getEmissivity(tem_range, den, wave=line)
y_1000_100 = S4.getEmissivity(tem, den, wave=line)
dz.data_plot(tem_range, y/y_1000_100, label=str(line) + r' $\AA$ line', linestyle='--', linewidth=2)
# dz.Axis.set_xscale('log')
dz.Axis.tick_params(axis='both', labelsize=20.0)
# dz.Axis.set_ylim(0.0,2.5)
# dz.Axis.patch.set_facecolor('white')
# dz.Fig.set_facecolor('black')
# dz.Fig.set_edgecolor('black')
x_values = array(r_mags) y_values = array(g_mags) dz.Axis.set_xlim(12 , 22) dz.Axis.set_ylim(12 , 22) dz.data_plot(x_values, y_values, color = dz.ColorVector[2][0], label='Candidate objects', markerstyle='o') dz.text_plot(names, x_values, y_values, color = dz.ColorVector[1], fontsize = 11) dz.Axis.axhline(y = 20, color=dz.ColorVector[2][1]) dz.Axis.axvline(x = 19, color=dz.ColorVector[2][1]) Title = r'Sample SDSS model magnitudes' Title_X = r'r $(model)$' Title_Y = r'g $(model)$' dz.FigWording(Title_X, Title_Y, Title, legend_loc='best') dz.savefig(output_address = Catalogue_Dic['Data_Folder'] + 'g_r_magnitudes', reset_fig = True) #------Plot magnitudes x_values = array(Hbeta_values) y_values = array(Declination_values) dz.data_plot(x_values, y_values, color = dz.ColorVector[2][0], label='Candidate objects', markerstyle='o') dz.text_plot(names, x_values, y_values, color = dz.ColorVector[1], fontsize = 11) Title = r'Sample declination versus equivalent width' Title_X = r'$Eqw$ $(H\beta)$' Title_Y = r'dec $(deg)$' dz.FigWording(Title_X, Title_Y, Title, legend_loc='best')
# dz.InsertFigure(FileFolder, CodeName + '.png')
arr_hand = read_png(FileFolder + CodeName + '.png')
Image_Frame = OffsetImage(arr_hand, zoom=3)
ab = AnnotationBbox(Image_Frame, [0.865,0.8], xybox=(10,-10), xycoords='figure fraction', boxcoords="offset points")
dz.Axis.add_artist(ab)
dz.Axis.set_xlim(3600.0, 3900)
dz.Axis.set_ylim(0, 1e-15)
#Set plot labels
title = r'SHOC579 spectrum components$'
dz.FigWording(r'Wavelength $(\AA)$', 'Flux' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$', title)
#Display figure
# dz.display_fig()
dz.savefig('/home/vital/Dropbox/Astrophysics/Papers/Elemental_RegressionsSulfur/Images/' + 'SHOC579_spectralComponents', extension='.png')
# #---------------------Zanstra calibration------------------------------
#
# pv = myPickle()
# dz = Plot_Conf()
# nebCalc = NebularContinuumCalculator()
# nebCalc.DataRoot = '/home/vital/Dropbox/Astrophysics/Lore/NebularContinuum/'
#
# #Define operation
# Catalogue_Dic = DataToTreat()
# Pattern = Catalogue_Dic['Datatype'] + '_dered.fits'
# Lineslog_extension = Catalogue_Dic['Datatype'] + '_dered_LinesLog_v3.txt'
#
# #Find and organize files from terminal command or .py file
# FilesList = pv.Folder_Explorer(Pattern, Catalogue_Dic['Obj_Folder'], CheckComputer=False)
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"
dz.Axis.set_xlim(5000, 20000)
# Display figure
# dz.display_fig()
dz.savefig(
output_address="/home/vital/Dropbox/Astrophysics/Papers/Elemental_RegressionsSulfur/Cloudy_Models/ArIons_vs_TSIII_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])
dz.Axis.set_ylim(12, 22)
dz.data_plot(x_values,
y_values,
color=dz.ColorVector[2][0],
label='Candidate objects',
markerstyle='o')
dz.text_plot(names, x_values, y_values, color=dz.ColorVector[1], fontsize=11)
dz.Axis.axhline(y=20, color=dz.ColorVector[2][1])
dz.Axis.axvline(x=19, color=dz.ColorVector[2][1])
Title = r'Sample SDSS model magnitudes'
Title_X = r'r $(model)$'
Title_Y = r'g $(model)$'
dz.FigWording(Title_X, Title_Y, Title, legend_loc='best')
dz.savefig(output_address=Catalogue_Dic['Data_Folder'] + 'g_r_magnitudes',
reset_fig=True)
#------Plot magnitudes
x_values = array(Hbeta_values)
y_values = array(Declination_values)
dz.data_plot(x_values,
y_values,
color=dz.ColorVector[2][0],
label='Candidate objects',
markerstyle='o')
dz.text_plot(names, x_values, y_values, color=dz.ColorVector[1], fontsize=11)
Title = r'Sample declination versus equivalent width'
Title_X = r'$Eqw$ $(H\beta)$'
