Wave_dict['Hbeta'] = '4_2'
Wave_dict['S3_10.5m'] = 105000
#Define physical conditions
tem = 10000
tem_range = linspace(5000, 25000, 1000)
den = 100
#Emissivities ranges
Hbeta_emis = HI.getEmissivity(tem = tem_range, den = den, label = Wave_dict['Hbeta'])
S4_105m_emis = S4.getEmissivity(tem = tem_range, den = den, wave = Wave_dict['S3_10.5m'])
t4_range = tem_range/10000
#Plotting Pyneb Emis vs Temp
S4_emis_vector_pyneb = log10((S4_105m_emis)/Hbeta_emis)
dz.data_plot(tem_range, S4_emis_vector_pyneb, label='PyNeb value: S4')
#Fitting characteristics
p_1, conv_curFit = Fitting_Emissivity(t4_range, S4_emis_vector_pyneb, A_0, B_0, C_0)
print 'Initial values', A_0, B_0, C_0
print 'Guessed values', p_1
#New fitting values
Vit_Emis = p_1[0] + p_1[1]/t4_range + p_1[2] * log10(t4_range)
dz.data_plot(tem_range, Vit_Emis, label='Vital fitting')
#Plot wording
xtitle = r'$T_{e}$ $(K)$'
ytitle = r'$log\left(\frac{E_{\left[SIII\right]9069}}{E_{H\beta}}\right)$'
title = r'Sulfur emissivity comparison: 9069$\AA$ line, $ n_e = $' + str(den)
'Eqw',
LinesLog_suffix='_txt_LinesLog_v3.txt')
if Eqw == None:
Age_dict[H_line][i] = Age
Eqw_dict[H_line][i] = None
else:
Age_dict[H_line][i] = Age
Eqw_dict[H_line][i] = Eqw * -1
#Generate plot
for k in range(len(H_Lines)):
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
print "longitudes", len(Objects), len(ArIII_HII_array), len(ArIV_HII_array), len(Temps), len(SIII_HII_array)
logArII_ArIII = umlog10(ArIII_HII_array / ArIV_HII_array)
for key in Model_dict.keys():
# Declare scripting name
ScriptName = ScriptPrefix + key + ".in"
# Generate lines dictionary with the output data
Line_dict = ct.load_predicted_lines(ScriptName, ScriptFolder)
x_values, y_values, TSIII, TOIII = Ar_S_model(Line_dict, diags)
# dz.data_plot(x_values, y_values, color=Colors_dict[key], label=Legends_dict[key], markerstyle='o')
dz.data_plot(TSIII, y_values, color=Colors_dict[key], label=Legends_dict[key], markerstyle="o")
list_xvalues = concatenate([list_xvalues, x_values])
list_yvalues = concatenate([list_yvalues, y_values])
list_TSIII = concatenate([list_TSIII, TSIII])
list_TOIII = concatenate([list_TOIII, TOIII])
Not_infinite = isfinite(list_yvalues)
list_xvalues_clean = list_xvalues[Not_infinite]
list_yvalues_clean = list_yvalues[Not_infinite]
list_TSIII_clean = list_TSIII[Not_infinite]
list_xvalues_Above = greater(list_xvalues_clean, 0)
list_xvalues_clean_greater = list_xvalues_clean[list_xvalues_Above]
logArII_ArIII = umlog10(ArIII_HII_array / ArIV_HII_array)
for key in Model_dict.keys():
#Declare scripting name
ScriptName = ScriptPrefix + key + '.in'
#Generate lines dictionary with the output data
Line_dict = ct.load_predicted_lines(ScriptName, ScriptFolder)
x_values, y_values, TSIII, TOIII = Ar_S_model(Line_dict, diags)
# dz.data_plot(x_values, y_values, color=Colors_dict[key], label=Legends_dict[key], markerstyle='o')
dz.data_plot(TSIII,
y_values,
color=Colors_dict[key],
label=Legends_dict[key],
markerstyle='o')
list_xvalues = concatenate([list_xvalues, x_values])
list_yvalues = concatenate([list_yvalues, y_values])
list_TSIII = concatenate([list_TSIII, TSIII])
list_TOIII = concatenate([list_TOIII, TOIII])
Not_infinite = isfinite(list_yvalues)
list_xvalues_clean = list_xvalues[Not_infinite]
list_yvalues_clean = list_yvalues[Not_infinite]
list_TSIII_clean = list_TSIII[Not_infinite]
list_xvalues_Above = greater(list_xvalues_clean, 0)
#Get gradients
cHbeta_all_MagEr, n_all_MagEr = LinfitLinearRegression(
x_BlueRed, y_BlueRed)
cHbeta_NoError, n_NoError = NumpyRegression(
x_BlueRed, unumpy.nominal_values(y_BlueRed))
#Trendline with all points and error
Trendline_all = cHbeta_all_MagEr * x_BlueRed + n_all_MagEr
#Trendline for he case we do not consider the error
y_NoError_trend = cHbeta_NoError * x_BlueRed + n_NoError
#Plot the data
dz.data_plot(x_Blue,
unumpy.nominal_values(y_Blue),
label='Blue arm emission lines',
markerstyle='^',
y_error=unumpy.std_devs(y_Blue))
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='--')
Wave_dict['O3_5007A'] = 5007
#Define physical conditions
tem = 10000
tem_range = linspace(5000, 25000, 1000)
den = 100
#Emissivities ranges
Hbeta_emis = HI.getEmissivity(tem = tem_range, den = den, label = Wave_dict['Hbeta'])
S3_9069A_emis = S3.getEmissivity(tem = tem_range, den = den, wave = Wave_dict['O3_4959A'])
S3_9531A_emis = S3.getEmissivity(tem = tem_range, den = den, wave = Wave_dict['O3_5007A'])
t4_range = tem_range/10000
#Plotting Pyneb Emis vs Temp
S3_emis_vector_pyneb = log10((S3_9069A_emis + S3_9531A_emis)/Hbeta_emis)
dz.data_plot(tem_range, S3_emis_vector_pyneb, label='PyNeb Emissivity')
#Plotting Hagele Emis vs temp
Ep_Emis = 12 - 6.1868 - 1.2491/t4_range + 0.5816 * log10(t4_range)
dz.data_plot(tem_range, Ep_Emis, label='Enrique Perez Emissivity')
#Plot wording
xtitle = r'$T_{e}$ $(K)$'
ytitle = r'$log(E_{O^{2+}}/E_{H^{+}})$'
title = r'Oxygen emissivity comparison, $ n_e = $' + str(den)
dz.FigWording(xtitle, ytitle, title, axis_Size = 20.0, title_Size = 20.0, legend_size=20.0)
#Display figure
dz.display_fig()
print 'Data treated'
#Coloring scheme
parameter_divider = zGas
color = Grid_Values['zGas'].index(parameter_divider)
label = r'$Z = {logage}$'.format(logage=Model_dict['zGas'])
#Calculate the grid point abundances
#x_values, y_values = Ar_S_model(Line_dict, threshold = 4, z = float(Model_dict['zGas']))
x_values, y_values = Ar_S_abundances_model(
Line_dict, diags, Ar3, Ar4, S3, S4, 3)
if (x_values != None) and (y_values != None):
#color=dz.ColorVector[2][color],
dz.data_plot(x_values,
y_values,
label=label,
markerstyle='o',
color=colors_list[color])
x_linealFitting = hstack([x_linealFitting, x_values])
y_linealFitting = hstack([y_linealFitting, y_values])
#Lineal model
lineal_mod = LinearModel(prefix='lineal_')
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,
Eqw = pv.GetParameter_LineLog(CodeName, FileFolder, H_line, 'Eqw', LinesLog_suffix='_txt_LinesLog_v3.txt')
if Eqw == None:
Age_dict[H_line][i] = Age
Eqw_dict[H_line][i] = None
else:
Age_dict[H_line][i] = Age
Eqw_dict[H_line][i] = Eqw * -1
#Generate plot
for k in range(len(H_Lines)):
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----------------------------------------
z_values.append(0)
g_mags.append(12)
r_mags.append(12)
#------Plot magnitudes
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)
den = 100
den_range = linspace(10, 300, 100)
# Comment the second if you want all the lines to be plotted
# S_Lines=[105100, 1404.81, 1423.84, 1398.04, 1416.89, 290100.0, 1387.46, 1406.02, 112300, 183200]
# S_Lines=[1404.81, 1423.84, 1416.89, 1406.02, 112300]
S_Lines=[105000]
#--------------------------Density case----------------------------------
#Plot the lines
for line in S_Lines:
y = S4.getEmissivity(tem, den_range, wave = line)
y_1000_100 = S4.getEmissivity(tem, den, wave = line)
dz.data_plot(den_range, y/y_1000_100, label=str(line) + r' $\AA$ line')
# 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')
#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)
Line_dict = ct.load_predicted_lines_individual(
ScriptName, ScriptFolder)
#Coloring scheme
parameter_divider = zGas
color = Grid_Values['zGas'].index(parameter_divider)
label = r'$Z = {logage}$'.format(
logage=round(float(parameter_divider) * 0.02, 3))
x_values, y_values = Ar_S_model(Line_dict, threshold=4)
if (x_values != None) and (y_values != None):
dz.data_plot(x_values,
y_values,
color=dz.ColorVector[2][color],
label=label,
markerstyle='o')
x_linealFitting = hstack([x_linealFitting, x_values])
y_linealFitting = hstack([y_linealFitting, y_values])
#Lineal model
lineal_mod = LinearModel(prefix='lineal_')
Lineal_parameters = lineal_mod.guess(y_linealFitting, x=x_linealFitting)
x_lineal = linspace(np_min(x_linealFitting), 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,
#Get stellar continuum
StellarContinuumFits = CodeName + '_StellarContinuum.fits'
Wave_Stellar, Int_Stellar, ExtraData = pv.File2Data(FileFolder, StellarContinuumFits)
Wave_StellarExtension = linspace(3000.0,3399.0,200)
Int_StellarExtension = zeros(len(Wave_StellarExtension))
Int_Stellar = hstack((Int_StellarExtension, Int_Stellar))
Wave_Stellar = hstack((Wave_StellarExtension, Wave_Stellar))
Interpolation = interp1d(Wave_Stellar, Int_Stellar, kind = 'slinear')
Int_Stellar_Resampled = Interpolation(Wave)
#Plotting the data:
dz.data_plot(Wave, Flux, label = 'Dereddened spectrum')
dz.data_plot(Wave, NebularFlux, label = 'Nebular spectrum')
dz.data_plot(Wave_Stellar, Int_Stellar, label = 'Stellar spectrum', linestyle='--')
dz.data_plot(Wave, Int_Stellar_Resampled + NebularFlux, label = 'Stellar + Nebular components')
# 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$'
Legend_Global = 'PopStar Models:\n z = 0.004, 0.008, 0.02, 0.05,\n log(age) = 5.0, 5.5, 6.0, 6.5, 7.0, 7.5'
#----Models data
for key in Model_dict.keys():
#Declare scripting name
ScriptName = ScriptPrefix + key + '.in'
#Generate the script
ct.select_script('Argon_Sulfur_LinesModel_Ages', ScriptName, ScriptFolder, key = key, data_dict=Model_dict)
#Generate lines dictionary with the output data
Line_dict = ct.load_predicted_lines(ScriptName, ScriptFolder)
x_values, y_values, TSIII, TOIII = Ar_S_model(Line_dict)
dz.data_plot(x_values, TSIII, color=Colors_dict[key], label=Legends_dict[key], markerstyle='o')
list_xvalues = concatenate([list_xvalues, x_values])
list_yvalues = concatenate([list_yvalues, y_values])
list_TSIII = concatenate([list_TSIII, TSIII])
list_TOIII = concatenate([list_TOIII, TOIII])
Not_infinite = isfinite(list_yvalues)
list_xvalues_clean = list_xvalues[Not_infinite]
list_yvalues_clean = list_yvalues[Not_infinite]
list_TSIII_clean = list_TSIII[Not_infinite]
list_xvalues_Above = greater(list_xvalues_clean, -3)
list_xvalues_clean_greater = list_xvalues_clean[list_xvalues_Above]
lambda_angs, incident, trans, diff_Out, net_trans, reflc, total = loadtxt(
FilesFolder + File_con,
skiprows=0,
usecols=(0, 1, 2, 3, 4, 5, 6),
unpack=True)
lambda_angs, incident, trans, diff_Out, net_trans, reflc, total = lambda_angs[::
-1], incident[::
-1], trans[::
-1], diff_Out[::
-1], net_trans[::
-1], reflc[::
-1], total[::
-1]
#Plotting .con continua
dz.data_plot(lambda_angs, incident, label='Incident continuum', linestyle='-')
# dz.data_plot(lambda_angs, trans, label='Transmitted continuum')
dz.data_plot(lambda_angs,
net_trans,
label='Net transmitted continuum',
linestyle='-')
# dz.data_plot(lambda_angs, reflc, label='Reflected continuum', linestyle=':')
# dz.data_plot(lambda_angs, total, label='Total continuum', linestyle='--')
#Importing the punch emission
# lambda_angs, Flux_trans = loadtxt(FilesFolder + File_trans_punch, skiprows = 9, usecols = (0, 1), unpack = True)
# lambda_angsI, Flux_inci = loadtxt(FilesFolder + File_inci_punch, skiprows = 9, usecols = (0, 1), unpack = True)
# dz.data_plot(lambda_angs, Flux_trans, label='Transmitted punch', linestyle='--')
# dz.data_plot(lambda_angsI, Flux_inci, label='Incident punch', linestyle='--')
#Declaring Halpha, Hbeta regions and continua
#Get stellar continuum
StellarContinuumFits = CodeName + '_StellarContinuum.fits'
Wave_Stellar, Int_Stellar, ExtraData = pv.File2Data(
FileFolder, StellarContinuumFits)
Wave_StellarExtension = linspace(3000.0, 3399.0, 200)
Int_StellarExtension = zeros(len(Wave_StellarExtension))
Int_Stellar = hstack((Int_StellarExtension, Int_Stellar))
Wave_Stellar = hstack((Wave_StellarExtension, Wave_Stellar))
Interpolation = interp1d(Wave_Stellar, Int_Stellar, kind='slinear')
Int_Stellar_Resampled = Interpolation(Wave)
#Plotting the data:
dz.data_plot(Wave, Flux, label='Dereddened spectrum')
dz.data_plot(Wave, NebularFlux, label='Nebular spectrum')
dz.data_plot(Wave_Stellar,
Int_Stellar,
label='Stellar spectrum',
linestyle='--')
dz.data_plot(Wave,
Int_Stellar_Resampled + NebularFlux,
label='Stellar + Nebular components')
# 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),
SIII_HII_array = Abundances_Matrix[:,4]
logArII_ArIII = umlog10(ArIII_HII_array/ArIV_HII_array)
for key in Model_dict.keys():
#Declare scripting name
ScriptName = ScriptPrefix + key + '.in'
#Generate lines dictionary with the output data
Line_dict = ct.load_predicted_lines(ScriptName, ScriptFolder)
print key, Colors_dict[key], Legends_dict[key]
x_values, y_values, TSIII, TOIII = Ar_S_model(Line_dict, diags)
dz.data_plot(x_values, y_values, color=Colors_dict[key], label=Legends_dict[key], markerstyle='o')
# dz.data_plot(TSIII, y_values, color=Colors_dict[key], label=Legends_dict[key], markerstyle='o')
list_xvalues = concatenate([list_xvalues, x_values])
list_yvalues = concatenate([list_yvalues, y_values])
list_TSIII = concatenate([list_TSIII, TSIII])
list_TOIII = concatenate([list_TOIII, TOIII])
Not_infinite = isfinite(list_yvalues)
list_xvalues_clean = list_xvalues[Not_infinite]
list_yvalues_clean = list_yvalues[Not_infinite]
list_TSIII_clean = list_TSIII[Not_infinite]
list_xvalues_Above = greater(list_xvalues_clean, 0)
ScriptName = Model_dict['Name'] + '.in'
Line_dict = ct.load_predicted_lines_individual(ScriptName, ScriptFolder)
#Coloring scheme
parameter_divider = zGas
color = Grid_Values['zGas'].index(parameter_divider)
label = r'$Z = {logage}$'.format(logage = Model_dict['zGas'])
#Calculate the grid point abundances
#x_values, y_values = Ar_S_model(Line_dict, threshold = 4, z = float(Model_dict['zGas']))
x_values, y_values = Ar_S_abundances_model(Line_dict, diags, Ar3, Ar4, S3, S4, 3)
if (x_values != None) and (y_values != None):
dz.data_plot(x_values, y_values, color=dz.ColorVector[2][color], label=label, markerstyle='o')
x_linealFitting = hstack([x_linealFitting, x_values])
y_linealFitting = hstack([y_linealFitting, y_values])
#Lineal model
lineal_mod = LinearModel(prefix='lineal_')
Lineal_parameters = lineal_mod.guess(y_linealFitting, x=x_linealFitting)
x_lineal = linspace(0, np_max(x_linealFitting), 100)
y_lineal = Lineal_parameters['lineal_slope'].value * x_lineal + Lineal_parameters['lineal_intercept'].value
dz.data_plot(x_lineal, y_lineal, label='Lineal fitting', color = 'black', linestyle='-')
# #Plot fitting formula
formula = r"$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(0.50, 0.15, formula, transform=dz.Axis.transAxes, fontsize=20)
#Create vector only with blue ones
x_BlueRed, y_BlueRed = x_Blue, y_Blue
#Get gradients
cHbeta_all_MagEr, n_all_MagEr = LinfitLinearRegression(x_BlueRed, y_BlueRed)
cHbeta_NoError, n_NoError = NumpyRegression(x_BlueRed, unumpy.nominal_values(y_BlueRed))
#Trendline with all points and error
Trendline_all = cHbeta_all_MagEr * x_BlueRed + n_all_MagEr
#Trendline for he case we do not consider the error
y_NoError_trend = cHbeta_NoError * x_BlueRed + n_NoError
#Plot the data
dz.data_plot(x_Blue, unumpy.nominal_values(y_Blue), label='Blue arm emission lines', markerstyle='^', y_error=unumpy.std_devs(y_Blue))
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
m_Median, m_16th, m_84th = median(m_dict[regression]), percentile(m_dict[regression],16), percentile(m_dict[regression],84)
pv.SaveParameter_ObjLog('WHT_Catalogue_properties.txt', Catalogue_Dic['Data_Folder'], Parameter = 'Yp_'+Element+'_Inf', Magnitude = n_Median, Error = '-', Log_extension='')
pv.SaveParameter_ObjLog('WHT_Catalogue_properties.txt', Catalogue_Dic['Data_Folder'], Parameter = 'Yp_'+Element+'_16th_p', Magnitude = n_16th, Error = '-', Log_extension='')
pv.SaveParameter_ObjLog('WHT_Catalogue_properties.txt', Catalogue_Dic['Data_Folder'], Parameter = 'Yp_'+Element+'_84th_p', Magnitude = n_84th, Error = '-', Log_extension='')
pv.SaveParameter_ObjLog('WHT_Catalogue_properties.txt', Catalogue_Dic['Data_Folder'], Parameter = 'Gradient_'+Element+'_Inf', Magnitude = m_Median, Error = '-', Log_extension='')
pv.SaveParameter_ObjLog('WHT_Catalogue_properties.txt', Catalogue_Dic['Data_Folder'], Parameter = 'Gradient_'+Element+'_16th_p', Magnitude = m_16th, Error = '-', Log_extension='')
pv.SaveParameter_ObjLog('WHT_Catalogue_properties.txt', Catalogue_Dic['Data_Folder'], Parameter = 'Gradient_'+Element+'_84th_p', Magnitude = m_84th, Error = '-', Log_extension='')
Obj_Elem, Metal_abun, Ymass_Metal = Abundances_dict[regression][0], Abundances_dict[regression][1], Abundances_dict[regression][2]
#Plotting the data
element_label = r'$Y_{{P}} = {n}_{{-{lowerlimit}}}^{{+{upperlimit}}}$'.format(n = round(n_Median,4), lowerlimit = round(n_Median-n_16th,4), upperlimit = round(n_84th-n_Median,4))
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'
ScriptName = ScriptPrefix + key + '.in'
#Generate the script
ct.select_script('Argon_Sulfur_LinesModel_Ages', ScriptName, ScriptFolder, key = key, data_dict=Model_dict)
#Run the cloudy script
# ct.run_script(ScriptName, ScriptFolder)
#
#Plot the .out cloudy file
# ct.open_output(ScriptName, ScriptFolder)
#Generate lines dictionary with the output data
Line_dict = ct.load_predicted_lines(ScriptName, ScriptFolder)
x_values, y_values, TSIII, TOIII = Ar_S_model(Line_dict)
dz.data_plot(x_values, y_values, color=Colors_dict[key], label=Legends_dict[key], markerstyle='o')
# dz.data_plot(x_values, TSIII, color=Colors_dict[key], label=Legends_dict[key], markerstyle='o')
list_xvalues = concatenate([list_xvalues, x_values])
list_yvalues = concatenate([list_yvalues, y_values])
list_TSIII = concatenate([list_TSIII, TSIII])
list_TOIII = concatenate([list_TOIII, TOIII])
Not_infinite = isfinite(list_yvalues)
list_xvalues_clean = list_xvalues[Not_infinite]
list_yvalues_clean = list_yvalues[Not_infinite]
list_TSIII_clean = list_TSIII[Not_infinite]
list_xvalues_Above = greater(list_xvalues_clean, 0)
den = 0
den_range = linspace(0, 300, 200)
# Comment the second if you want all the lines to be plotted
HeI_Lines=[3889.0, 4026.0, 4471.0, 5876.0, 6678.0, 7065.0, 10830.0]
print 'Emissivity', HeI.getEmissivity(tem, 1, wave=3889.0)
#--------------------------Density case----------------------------------
#Plot the lines
for line in HeI_Lines:
y_1000_100 = HeI.getEmissivity(tem, den_range, wave=line)
y = HeI.getEmissivity(tem, den, wave=line)
dz.data_plot(den_range, y_1000_100/y, label=str(line) + r' $\AA$ line')4
# 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')
#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)
tem_range = linspace(10000, 25000, 100)
den = 100
den_range = linspace(10, 300, 100)
# Comment the second if you want all the lines to be plotted
# S_Lines=[105100, 1404.81, 1423.84, 1398.04, 1416.89, 290100.0, 1387.46, 1406.02, 112300, 183200]
# S_Lines=[1404.81, 1423.84, 1416.89, 1406.02, 112300]
S_Lines = [105000]
#--------------------------Density case----------------------------------
#Plot the lines
for line in S_Lines:
y = S4.getEmissivity(tem, den_range, wave=line)
y_1000_100 = S4.getEmissivity(tem, den, wave=line)
dz.data_plot(den_range, y / y_1000_100, label=str(line) + r' $\AA$ line')
# 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')
#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,
else:
z_values.append(0)
g_mags.append(12)
r_mags.append(12)
#------Plot magnitudes
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
