dz.data_plot(
unumpy.nominal_values(ratios_dict['in_x']),
unumpy.nominal_values(trendline_in),
r'First regression $c(H\beta) = {Value} \pm {Error}$'.format(
Value=round(cHbeta_all_MagEr.nominal_value, 3),
Error=round(cHbeta_all_MagEr.std_dev, 3)),
linestyle='--',
color='#0072B2')
dz.data_plot(
unumpy.nominal_values(ratios_dict2['in_x']),
unumpy.nominal_values(trendline_in2),
r'Second regression $c(H\beta) = {Value} \pm {Error}$'.format(
Value=round(cHbeta_all_MagEr2.nominal_value, 3),
Error=round(cHbeta_all_MagEr2.std_dev, 3)),
linestyle='--',
color='#D55E00')
#Figure format
dz.Axis.set_ylim(-0.5, 0.4)
dz.Axis.set_xlim(-0.65, 0.4)
Title = r'Object {Codename} reddening coefficient calculation'.format(
Codename=catalogue_df.loc[objName].quick_index)
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, loc=4)
output_pickle = '/home/vital/Dropbox/Astrophysics/Papers/Yp_AlternativeMethods/Images/reddening_evolution.png'
dz.save_manager(output_pickle, save_pickle=False)
print 'All data treated', dz.display_errors()
idx_sort = np.argsort(Te_vector)
print Te_vector[idx_sort]
He1 = pn.RecAtom('He', 1)
HeI_5875_emis = He1.getEmissivity(Te_vector[idx_sort], 100.2, label='5876.0')
print HeI_5875_emis
print emis_dict['HE 1 5875.64A'][idx_sort]
dz.data_plot(Te_vector[idx_sort], emis_dict['HE 1 5875.64A'][idx_sort],
'Cloudy emissivities')
dz.data_plot(Te_vector[idx_sort], HeI_5875_emis, 'Pyneb emissivities')
dz.FigWording(r'Temperature', 'Emissivity', 'Emissivities comparison')
dz.display_fig()
# # Defining the object that will manage the input file for Cloudy
# c_input = pc.CloudyInput(full_model_name)
# c_input.set_other(('init file "hheonly.ini"'))
# c_input.set_other(('element helium abundance -3'))
# c_input.set_BB(Teff=Teff, lumi_unit='q(H)', lumi_value=qH)
# c_input.set_other(('constant temperature 4'))
# c_input.set_cste_density(dens)
# c_input.set_radius(r_in=dist)
# c_input.set_other(('set dr 0'))
# c_input.set_stop('zone 1')
# c_input.set_other(('stop zone 1'))
# c_input.set_emis_tab(emis_tab) # better use read_emis_file(file) for long list of lines, where file is an external file
axins.set_ylim(-0.2e-16, 2.0e-16)
mark_inset(dz.ax1, axins, loc1=3, loc2=4, fc="none", ec="0.5")
axins2 = zoomed_inset_axes(dz.ax1, zoom=6, loc=7)
axins2.step(wave_obs[region_indeces(wave_join, wave_max, wave_obs)], flux_obs[region_indeces(wave_join, wave_max, wave_obs)], label='Observed spectrum', linestyle='--', color=colorVector['dark blue'], where = 'mid')
axins2.step(wave_tell, flux_tell, color=colorVector['orangish'], where = 'mid', linewidth=0.75)
axins2.set_xlim(9540, 9620)
axins2.set_ylim(-0.2e-16, 3.0e-16)
mark_inset(dz.ax1, axins2, loc1=2, loc2=4, fc="none", ec="0.5")
axins.get_xaxis().set_visible(False)
axins.get_yaxis().set_visible(False)
axins2.get_xaxis().set_visible(False)
axins2.get_yaxis().set_visible(False)
dz.FigWording('', 'Flux ' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$', '', loc='upper left', graph_axis=dz.ax1, sort_legend=True)
dz.FigWording(r'Wavelength $(\AA)$', 'Normalized flux', '', loc='lower center', graph_axis=dz.ax2, ncols_leg=4)
dz.ax2.set_ylim(0.2,1.25)
if 'S3_9531A_continuum' in obj_dict:
dz.ax1.set_ylim(-2 * obj_dict['S3_9531A_continuum'], 1.1 * obj_dict['S3_9531A_Peak'])
else:
dz.ax1.set_ylim(0.005 * mean_flux, 20 * mean_flux)
dz.insert_image('/home/vital/Downloads/obj08_SDSS_image_color.png', Image_Coordinates = [0.50,0.80], Zoom=0.25, Image_xyCoords = 'axes fraction', axis_plot=dz.ax1)
dz.savefig(image_address, extension='.png')
#dz.display_fig()
# output_pickle = '{objFolder}{stepCode}_{objCode}_{ext}'.format(objFolder=ouput_folder, stepCode=script_code, objCode=objName, ext='Telluric correction')
# dz.save_manager(output_pickle, save_pickle = True)
'Valid points regression',
linestyle='--',
color='black')
#Store reddening coefficient
catalogue_df.loc[objName, 'cHbeta_reduc'] = cHbeta_in_MagEr
#Figure format
dz.Axis.set_ylim(-0.4, 0.4)
Title = r'Object {Codename} reddening: $c(H\beta) = {Value} \pm {Error}$'.format(
Codename=objName,
Value=round(cHbeta_all_MagEr.nominal_value, 3),
Error=round(cHbeta_all_MagEr.std_dev, 3))
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) #, XLabelPad = 20
output_pickle = '{objFolder}{stepCode}_{objCode}_{ext}'.format(
objFolder=ouput_folder,
stepCode=script_code,
objCode=objName,
ext='CHbeta_Calculation')
dz.save_manager(output_pickle, save_pickle=True)
dz.save_excel_DF(
catalogue_df,
'/home/vital/Dropbox/Astrophysics/Data/WHT_observations/WHT_Galaxies_properties.xlsx',
df_sheet_format='catalogue_data')
print 'All data treated', dz.display_errors()
objCodes = np.arange(1, len(df_dict[type].index) + 1).astype(str)
NO = (unumpy.uarray(df_dict[type]['Nitrogen'].values,
df_dict[type]['Nitrogen_error'].values) *
1e-6) / (unumpy.uarray(df_dict[type]['Oxygen'].values,
df_dict[type]['Oxygen_error'].values) * 1e-5)
dz.data_plot(y,
unumpy.nominal_values(NO),
label=conf_dict['legend_label'],
color=conf_dict[element + '_color'],
markerstyle='o',
x_error=y_err,
y_error=unumpy.std_devs(NO))
dz.plot_text(y, unumpy.nominal_values(NO), objCodes)
dz.FigWording(conf_dict[element + '_xlabel'], 'N/O', conf_dict['title'])
dz.display_fig()
# import numpy as np
# from uncertainties import ufloat, unumpy
# from dazer_methods import Dazer
#
# #Generate dazer object
# dz = Dazer()
#
# #Set figure format
# dz.FigConf()
#
# file_tradional_reduc = '/home/vital/Dropbox/Astrophysics/Data/Fabian_Catalogue/data/Traditional_Abundances.xlsx'
# file_starlight = '/home/vital/Dropbox/Astrophysics/Data/Fabian_Catalogue/data/Starlight_Abundances.xlsx'
idcs = (pd.notnull(catalogue_df.OI_HI_emis2nd)) & (pd.notnull(
catalogue_df.NI_HI_emis2nd)) & (
~catalogue_df.index.isin(['0564', '3', '70', '51991-224']))
#Prepare data
O_values = catalogue_df.loc[idcs].OI_HI_emis2nd.values
N_values = catalogue_df.loc[idcs].NI_HI_emis2nd.values
objects = catalogue_df.loc[idcs].index.values
print objects
N_O_ratio = N_values / O_values
for i in range(len(N_O_ratio)):
print objects[i], O_values[i], N_values[i], N_O_ratio[i]
dz.data_plot(unumpy.nominal_values(O_values) * 1e5,
unumpy.nominal_values(N_O_ratio),
label='Abundances from our sample',
markerstyle='o',
x_error=unumpy.std_devs(O_values) * 1e5,
y_error=unumpy.std_devs(N_O_ratio))
dz.plot_text(unumpy.nominal_values(O_values) * 1e5,
unumpy.nominal_values(N_O_ratio),
text=objects)
dz.FigWording(r'$O/H$ $(10^5)$', r'$N/O$',
r'N/O relation for HII galaxy sample')
dz.display_fig()
#Set figure format
sizing_dict = {'figure.figsize': (8, 8)}
dz.FigConf(sizing_dict)
te_SIII = np.linspace(8000, 20000, 50)
te_SII = 0.88 * te_SIII + 1560
line_unity = te_SIII
dz.data_plot(te_SIII, te_SII, label=r'$T_e[SII]=0.88T_e[SIII]+1560$')
dz.data_plot(te_SIII, te_SIII, label='', color='grey', linestyle='--')
dz.Axis.set_xlim(8000, 20000)
dz.Axis.set_ylim(8000, 20000)
dz.Axis.grid(True)
ticklines = dz.Axis.get_xticklines() + dz.Axis.get_yticklines()
gridlines = dz.Axis.get_xgridlines() + dz.Axis.get_ygridlines()
ticklabels = dz.Axis.get_xticklabels() + dz.Axis.get_yticklabels()
for line in ticklines:
line.set_linewidth(3)
for line in gridlines:
line.set_linestyle('-.')
dz.FigWording(xlabel=r'$T_e[SIII]$ $(K)$',
ylabel=r'$T_e[SII]$ $(K)$',
title='Relation between $S^{+1}$ and $S^{+2}$ temperatures')
dz.display_fig()
# Linear data
elemt_scale = Regresions_dict['factor'][i]
x_regression_range = np.linspace(0.0, np.max(x_nat) * 1.10, 20)
y_regression_range = m_Median * x_regression_range + n_Median
# Plotting the data,
label_regression = r'Plank prediction: $Y = 0.24709\pm0.00025$'
dz.data_plot(x_regression_range * elemt_scale, y_regression_range, label=label_regr, linestyle='--', color=Regresions_dict['color'][i])
dz.data_plot(x_nat * elemt_scale, y, label='HII galaxies included', markerstyle='o', x_error=x_er_nat * elemt_scale, y_error=y_er, color=Regresions_dict['color'][i])
#dz.plot_text(x_nat, y, objRegress)
# Plot WMAP prediction
dz.data_plot(WMAP_coordinates[0].nominal_value, WMAP_coordinates[1].nominal_value, color=dz.colorVector['pink'],
label='Planck prediction', markerstyle='o', x_error=WMAP_coordinates[0].std_dev, y_error=WMAP_coordinates[1].std_dev)
dz.FigWording(Regresions_dict['x label'][i], Regresions_dict['y label'][i], '', loc='lower center', ncols_leg=2)
dz.Axis.set_ylim(0.1, 0.4)
output_pickle = '{objFolder}{element}_BayesDataRegression'.format(objFolder=article_folder, element=element)
dz.save_manager(output_pickle, save_pickle=False)
# Perform the triple regression
idcs_3Regres = bayes_catalogue_df['O_abund_nom'].notnull() &\
bayes_catalogue_df['N_abund_nom'].notnull() &\
bayes_catalogue_df['S_abund_nom'].notnull() &\
bayes_catalogue_df['Ymass_S_nom'].notnull() &\
~bayes_catalogue_df.quick_reference.isin(Regresions_dict['excluded'][0] + Regresions_dict['excluded'][1] + Regresions_dict['excluded'][2])
objRegress = bayes_catalogue_df[idcs_3Regres].quick_reference.values
O, O_er = bayes_catalogue_df.loc[idcs_3Regres, 'O_abund_nom'].values, bayes_catalogue_df.loc[idcs_3Regres, 'O_abund_er'].values
neb_gCont = neb.calculate_neb_gCont(wave, Te, HeI, HeII)
neb_fCont = neb.gCont_calibration(wave, Te, Flux_recomb, neb_gCont)
dz.data_plot(wave, neb_fCont, label='Total new')
# dz.data_plot(wave, Gamma_FF_HI_fast, label = 'H Free-Free')
# dz.data_plot(wave, 4 * Gamma_FF_HI_fast,label = 'He Free-Free')
# dz.data_plot(wave, Gamma_2q_HI_fast, label = 'H Two-photon')
# dz.data_plot(wave, Gamma_FB_HI_fast, label = 'H Free-Bound')
# dz.data_plot(wave, Gamma_FB_HeI_fast, label = r'$He$ Free-Bound')
# dz.data_plot(wave, Gamma_FB_HeII_fast, label = r'$He^{+}$ Free-Bound')
# dz.data_plot(wave, Gamma_FB_HI, label = 'H Free-Bound EP')
dz.Axis.set_yscale('log')
dz.FigWording(r'Wavelength $(\AA)$',
r'$\gamma$ $\left(erg\,cm^{3}\,s^{-1}\,Hz^{-1}\right)$',
title='Hydrogen and helium nebular components')
# dz.savefig('/home/vital/Dropbox/Astrophysics/Seminars/Cloudy School 2017/nebular_components')
dz.display_fig()
# start = timer()
# Gamma_FF_HI = neb.FreeFreeContinuum("HI")
# end = timer()
# print 'FF', ' time ', (end - start)
#
# start = timer()
# Gamma_2q = neb.TwoPhotonContinuum()
# end = timer()
# print '2q', ' time ', (end - start)
#
# start = timer()
# Gamma_FB_HI = neb.FreeBoundContinuum_EP("HI")
wave_Blue, flux_Blue, ExtraData_Blue = dz.get_spectra_data(
blue_fits_file)
wave_Red, flux_Red, ExtraData_Red = dz.get_spectra_data(red_fits_file)
idx_blue = searchsorted(wave_Blue, joining_wavelength)
idx_red = searchsorted(wave_Red, joining_wavelength)
wave_comb = concatenate([wave_Blue[0:idx_blue], wave_Red[idx_red:-1]])
flux_comb = concatenate([flux_Blue[0:idx_blue], flux_Red[idx_red:-1]])
dz.data_plot(wave_Blue, flux_Blue, label='Blue arm')
dz.data_plot(wave_Red, flux_Red, label='Red arm')
dz.FigWording(r'Wavelength $(\AA)$',
'Flux ' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$',
'',
ncols_leg=2)
dz.savefig(
'/home/vital/Dropbox/Astrophysics/Thesis/images/SHOC579_z.png')
# dz.display_fig()
# #Store the figure
# output_pickle = FileFolder_Blue + script_code + '_' + CodeName_Blue + '_combined_emission'
# dz.save_manager(output_pickle, save_pickle = True)
# dz.Data_2_Fits(FileFolder_Blue, CodeName_Blue + '_WHT.fits', ExtraData_Blue, wave_comb, flux_comb, NewKeyWord = ['WHTJOINW', str(joining_wavelength)])
#
# #Store file address in dataframe
# combined_fits_name = FileFolder_Blue + CodeName_Blue + '_WHT.fits'
# catalogue_df.loc[objName, 'reduction_fits'] = combined_fits_name
1.20 * np_max(unumpy.nominal_values(x_data)), 10)
regr_dict = bces_regression(unumpy.nominal_values(x_data),
unumpy.nominal_values(y_data),
unumpy.std_devs(x_data), unumpy.std_devs(y_data))
y_fit = regr_dict['m'][reg_code] * x_regression + regr_dict['n'][reg_code]
dz.data_plot(x_regression, y_fit, 'BCES linear fit from data', linestyle='-')
dz.data_plot(x_regression,
m_lmfit * x_regression + n_lmfit,
'lmfit Montecarlo',
linestyle=':')
print regr_dict['m'][reg_code]
print regr_dict['n'][reg_code]
Title = r'Sulfur versus Oxygen temperature comparison'
y_Title = r'$T_{e}[SIII]\,(K)$'
x_Title = r'$T_{e}[OIII]\,(K)$'
dz.FigWording(x_Title, y_Title, Title)
dz.display_fig()
#dz.savefig('/home/vital/Dropbox/Astrophysics/Papers/Yp_AlternativeMethods/Images/temperatures_comparison')
# #Run MCMC with MAP
# MAP_Model = pm.MAP(bayes_model(unumpy.nominal_values(x_data), unumpy.std_devs(x_data), unumpy.nominal_values(y_data), unumpy.std_devs(y_data)))
# MAP_Model.fit(method = 'fmin_powell')
# MAP_Model.revert_to_max()
#
# print MAP_Model.variables
#
# MDL = pm.MCMC(MAP_Model.variables)
# MDL.sample(iter=20000, burn=1000, thin=2) #Optimun test
dtype=types_list,
unpack=True)
objects = loadtxt(table_address, dtype=str, usecols=(0), unpack=True)
O_mean = O_mean * 1e-4
O_err = O_err * 1e-4
print objects
print Y_mean
print Y_sys
print Y_random
print O_mean
print O_err
dz = Dazer()
dz.FigConf()
dz.data_plot(O_mean,
Y_mean,
label='Peimbert et al (2016)',
markerstyle='o',
x_error=O_err,
y_error=Y_sys)
dz.plot_text(O_mean, Y_mean, text=objects)
dz.FigWording(xlabel='Oxygen abundance',
ylabel='Helium mass fraction',
title=r'$Y_{P}$ oxygen regression, Peimbert et al (2016)')
dz.display_fig()
colors_list = ['#CC79A7', '#D55E00', '#bcbd22']
metals_list = [0.0001, 0.004, 0.02]
for i in range(len(metals_list)):
Z = metals_list[i]
indeces = (Frame_MetalsEmission["Z"] == Z)
x = np_log10((Frame_MetalsEmission.loc[indeces, '[SII]6716'] + Frame_MetalsEmission.loc[indeces, '[SII]6731']) / (Frame_MetalsEmission.loc[indeces, '[SIII]9069'] + Frame_MetalsEmission.loc[indeces, '[SIII]9532']))
y = Frame_MetalsEmission.loc[indeces, 'logU']
dz.data_plot(x, y, label='z = {Z}'.format(Z = Z), markerstyle=markers[i], color=colors_list[i])
x_array = hstack([x_array, x])
y_array = hstack([y_array, y])
#Lineal model
lineal_mod = LinearModel(prefix='lineal_')
Lineal_parameters = lineal_mod.guess(y_array, x=x_array)
x_lineal = linspace(np_min(x_array), np_max(x_array), 100)
y_lineal = Lineal_parameters['lineal_slope'].value * x_lineal + Lineal_parameters['lineal_intercept'].value
dz.data_plot(x_lineal, y_lineal, label='', color = 'black', linestyle='--')
obj_u = Lineal_parameters['lineal_slope'].value * obj_SIIbySIII + Lineal_parameters['lineal_intercept'].value
dz.data_plot(obj_SIIbySIII, obj_u, label='Sample galaxies in linear fitting', color = 'black', markerstyle='x')
#Displaying the figure
dz.FigWording(r'$log(I([SII]\lambda\lambda6716+6731\AA)/I([SIII]\lambda\lambda9069+9531\AA))$', r'$log(U)$', '')
dz.Axis.set_xlim(-1.55, 2)
# dz.display_fig()
dz.savefig(output_address = '/home/vital/Dropbox/Astrophysics/Papers/Yp_AlternativeMethods/images/logU_vs_Sratio')
#Prepare data
O_values = catalogue_df.loc[idcs].OI_HI_emis2nd.values
N_values = catalogue_df.loc[idcs].NI_HI_emis2nd.values
HeII_HI = catalogue_df.loc[idcs].HeII_HII_from_O_emis2nd.values
HeIII_HI = catalogue_df.loc[idcs].HeIII_HII_from_O_emis2nd.values
objects = catalogue_df.loc[idcs].index.values
He_ratio = HeII_HI / HeIII_HI
print objects
N_O_ratio = N_values / O_values
for i in range(len(N_O_ratio)):
print objects[i], O_values[i], N_values[i], N_O_ratio[i]
dz.data_plot(unumpy.nominal_values(HeII_HI),
unumpy.nominal_values(N_O_ratio),
label='Abundances from our sample',
markerstyle='o',
x_error=unumpy.std_devs(HeII_HI),
y_error=unumpy.std_devs(N_O_ratio))
dz.plot_text(unumpy.nominal_values(HeII_HI),
unumpy.nominal_values(N_O_ratio),
text=objects)
dz.FigWording(r'$HeII/HeIII$', r'$N/O$', r'N/O relation for HII galaxy sample')
dz.display_fig()
# dz.data_plot(Wave_N, Int_N, '', color='tab:orange')
# dz.data_plot(Wave_S, Int_S, '', color='tab:green')
recombZones = [[4330, 4350], [4464, 4479], [4707, 4720], [4850, 4870],
[4913, 4931], [5013, 5021]]
colorLineZones(Wave_O, IntObs_dered, recombZones, 'tab:blue')
recombZones = [[4356, 4373], [4947, 4969], [4992, 5013]]
colorLineZones(Wave_O, IntObs_dered, recombZones, 'tab:pink')
#Set titles and legend
PlotTitle = r'{} continua comparison'.format(objName)
dz.FigWording(r'Wavelength $(\AA)$',
'Flux' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$',
'',
loc='upper right')
mean_flux = Int_O.mean()
#dz.Axis.set_yscale('log')
dz.Axis.set_ylim(-0.05 * mean_flux, 4 * mean_flux)
dz.Axis.set_xlim(4580, 4830)
# colorLineZones(Wave_O, IntObs_dered, [[4750, 4780]], 'tab:red')
# colorLineZones(Wave_O, IntObs_dered, [[4615, 4636]], 'tab:red')
adjacentRegions = [[4750, 4780], [4615, 4636]]
LineZones(Wave_O, IntObs_dered, adjacentRegions, 'tab:red')
# colorLineZones(Wave_O, IntObs_dered, recombZones, 'tab:pink')
LineZones(Wave_O, IntObs_dered, [[4707, 4720]], 'tab:blue')
linewidth=2.0,
color=dz.colorVector['yellow'])
#Plot individual components
for i in range(line_data['line_number']):
dz.data_plot(line_data['x_resample'],
line_data['y_comps'][i],
'Components',
linestyle='--',
color=dz.colorVector['orangish'],
linewidth=1.0)
#Plot peaks
#dz.data_plot(line_data['peak_waves'], line_data['peak_Maxima'], 'Input values', markerstyle='o', color = dz.colorVector['pink'])
dz.Axis.set_xlim([subwave[0], subwave[-1]])
dz.Axis.set_ylim(median(subflux) / 10, lineHeight * 2)
dz.FigWording(r'Wavelength $(\AA)$',
'Flux' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$',
r'Object {} $H\alpha$ wide component'.format(objName))
#Plot output figures
output_pickle = '{objFolder}{stepCode}_{objCode}_{ext}'.format(
objFolder=ouput_folder,
stepCode=dz.ScriptCode,
objCode=objName,
ext='_Halpha_region')
dz.save_manager(output_pickle, save_pickle=True)
print 'Data treated'
dz = Dazer()
dz.FigConf()
data_folder = '/home/vital/Dropbox/Astrophysics/Seminars/Cloudy School 2017/popstar_kroupa_004/'
for file_name in sorted(os.listdir(data_folder)):
print file_name
file_address = os.path.abspath(os.path.join(data_folder, file_name))
wave, flux = np.loadtxt(file_address, usecols=(0, 3), unpack=True)
age_float = 10**float(file_name[file_name.find('_t') + 2:])
print age_float / 1000000
age = 'age = {} Myr'.format(round(age_float / 1000000, 2))
dz.data_plot(wave, flux, label=age)
dz.Axis.set_xlim(0, 10000)
dz.Axis.set_ylim(1e-9, 1)
dz.Axis.set_yscale('log')
dz.FigWording(
r'Wavelength $(\AA)$', r'Normalized flux',
'Popstar grid for Kroupa IMF, $Z_{gas}=0.004$\n (including nebular continua)'
)
dz.display_fig()
#Plot the results
dzp.FigConf()
dzp.data_plot(dz.sspFit_dict['obs_wave'],
dz.sspFit_dict['obs_flux'],
label='obs_flux')
dzp.data_plot(dz.sspFit_dict['obs_wave'],
dz.sspFit_dict['zero_mask'],
label='my mask')
# dzp.data_plot(dz.fit_conf['obs_wave'], mis_cosas[1], label='Hector mask')
# dzp.data_plot(dz.fit_conf['obs_wave'], mis_cosas[2], label='Hector fit')
dzp.data_plot(dz.sspFit_dict['obs_wave'],
fit_products['flux_sspFit'],
label='my fit')
dzp.FigWording('Wave', 'Flux', 'Input spectra')
dzp.display_fig()
# #Plot input spectra and regions
# dzp.FigConf()
# dzp.data_plot(dz.fit_conf['obs_wave'], dz.fit_conf['obs_flux'], label='obs_flux')
# dzp.data_plot(dz.fit_conf['obs_wave'], dz.fit_conf['obs_flux_mask'], label='obs_flux_mask')
# # dzp.data_plot(dz.fit_conf['obs_wave'], dz.fit_conf['Av_mask'], label='Av mask')
# # dzp.data_plot(dz.fit_conf['obs_wave'], dz.fit_conf['obs_flux_err'], label='obs_flux_err')
# # dz.data_plot(wave_unc, flux_unc_input, label='flux_unc_input')
# # dz.data_plot(wave_unc, e_flux_unc, label='e_flux_unc')
# # dz.data_plot(wave_unc, color_unc, label='color_unc')
# # dz.data_plot(wave_unc, masked, label='masked')
# # dz.data_plot(wave_unc, masked2, label='masked2')
# # dz.data_plot(wave_unc, masked_Av, label='masked_Av')
# # dz.data_plot(wave_unc, flux_masked, label='flux_masked')
unpack=True)
dz.data_plot(He1_5875,
He2_4685,
markerstyle='o',
color=colors_list[idx_color],
label=r'log(age) = {}'.format(age))
dz.data_plot(9.82 / 100,
15.23 / 100,
markerstyle='o',
color='#bcbd22',
label='Observational point')
dz.FigWording(r'$F(HeI5876\AA/H\beta$)',
r'$F(HeII4685\AA/H\beta$)',
r'Relation He lines',
loc='best')
dz.Axis.set_yscale('log')
dz.Axis.set_xscale('log')
# dz.display_fig()
dz.savefig(
'/home/vital/Dropbox/Astrophysics/Seminars/Cloudy School 2017/teporingos/fluxes_grid_obs_age_effect',
extension='.jpg')
# from collections import OrderedDict
# from dazer_methods import Dazer
# import numpy as np
#
dz.Axis.axhspan(value - error,
value + error,
alpha=0.5,
label=label,
color='saddlebrown')
else:
dz.data_plot(year,
value,
label='',
markerstyle=marker_type,
markersize=50,
y_error=error,
color='black')
dz.Axis.set_ylim(0.18, 0.28)
dz.FigWording(r'Year', r'$Y_{P}$', '', loc=8)
#dz.savefig('/home/vital/Dropbox/Astrophysics/Thesis/images/Yp_references_evolution')
dz.display_fig()
# import pandas as pd
# from dazer_methods import Dazer
#
#
# #Import library object
# dz = Dazer()
#
# #Read table data
# df = pd.read_excel('/home/vital/Dropbox/Astrophysics/Thesis/notes/table_yp_literature.xlsx', sheetname='Sheet1')
#
# #Define plot frame and colors
# size_dict = {'axes.labelsize':28, 'legend.fontsize':18, 'font.family':'Times New Roman', 'mathtext.default':'regular', 'xtick.labelsize':28, 'ytick.labelsize':28}
#Plot data
dz.data_plot(wave_obs,
flux_obs,
label='Observed spectrum',
linestyle='step',
graph_axis=dz.ax1)
dz.data_plot(wave_star,
flux_star,
label='Calibration star spectrum',
linestyle='step',
graph_axis=dz.ax2)
dz.ax1.set_ylim(bottom=0)
dz.FigWording(r'Wavelength $(\AA)$',
'Flux' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$',
r'Object {} Star {}'.format(codeName, calibration_star),
loc='best',
graph_axis=dz.ax1)
dz.FigWording(r'Wavelength $(\AA)$',
'Normalized flux',
'',
loc='lower center',
graph_axis=dz.ax2)
output_pickle = '{objFolder}{stepCode}_{objCode}_{ext}'.format(
objFolder=ouput_folder,
stepCode=script_code,
objCode=codeName,
ext='check sky emission')
dz.save_manager(output_pickle, save_pickle=True, 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(x_values, y_values, names, 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.display_fig()
dz.savefig(output_address=Catalogue_Dic['Folder'] + 'dec_eqw_M',
reset_fig=True)
# print 'donde va esto', Catalogue_Dic['Folder'] + 'dec_eqw_M'
#------Plot Flux vs Eqw
x_values = array(Hbeta_values)
y_values = array(Flux_values)
sn_values = array(sn_values)
z_values = array(z_values)
dz.data_plot(x_values,
y_values,
dz.area_fill(metals_regions - 10,
metals_regions + 10,
'Collisional excitation lines',
color=dz.colorVector['olive'],
alpha=0.5)
dz.area_fill(recombination_regions - 10,
recombination_regions + 10,
'Recombination lines',
color=dz.colorVector['pink'],
alpha=0.5)
#Set titles and legend
PlotTitle = ''
dz.FigWording(r'Wavelength $(\AA)$',
'Flux' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$',
PlotTitle,
loc='upper right',
ncols_leg=2)
dz.Axis.set_xlim(3550, 10000)
axins2 = zoomed_inset_axes(dz.Axis, zoom=16, loc=7)
axins2.step(
Wave_O, Int_O, label='Observed spectrum'
) #, linestyle='--', color=colorVector['dark blue'], where = 'mid')
axins2.step(
Wave_N, Int_N, label='Nebular continuum model', linestyle='-'
) #,color=colorVector['orangish'], where = 'mid', linewidth=0.75)
axins2.step(
Wave_S, Int_S, label='Stellar continuum model', linestyle='-'
) #,color=colorVector['orangish'], where = 'mid', linewidth=0.75)
color=color_dict[color])
idx_point = searchsorted(wave, joining_wavelength)
if (wave[0] < joining_wavelength) and (joining_wavelength < wave[-1]):
#dz.data_plot(wave[idx_point], flux[idx_point], label = 'Joining lambda {} $\AA$'.format(joining_wavelength), color=dz.colorVector['green'], markerstyle = 'o')
dz.Axis.axvline(
joining_wavelength,
label='Joining lambda {} $\AA$'.format(joining_wavelength),
color=dz.colorVector['green'])
else:
mean_continuum_flux = median(flux)
#dz.data_plot(joining_wavelength, mean_continuum_flux, label = 'Joining lambda {} $\AA$'.format(joining_wavelength), color=dz.colorVector['green'], markerstyle = 'o')
dz.Axis.axvline(
joining_wavelength,
label='Joining lambda {} $\AA$'.format(joining_wavelength),
color=dz.colorVector['green'])
mean_flux = median(flux)
dz.Axis.set_ylim(mean_flux / 15, mean_flux * 10)
dz.Axis.set_xlim(joining_wavelength - 300, joining_wavelength + 300)
dz.FigWording(r'Wavelength $(\AA)$',
'Flux ' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$',
'Redshift correction')
#Store the figure
output_address = FileFolder + script_code + '_' + CodeName + '_joining_point'
dz.save_manager(output_address, save_pickle=True)
print "\nAll data treated generated"
radious = elemIon_df['#depth'].values
ion_frac = elemIon_df[ion].values
label = r'{0:1.1e} $M_\odot$'.format(float(z_list[i]))
dz.data_plot(radious / 1e19,
ion_frac,
color=ions_colors_O[j],
linestyle=line_type[i],
label=r'Cluster mass {}'.format(label))
dz.plot_text(labels_coords_O[j][i][0] / 1e19,
labels_coords_O[j][i][1],
text=ions_labels_O[j],
color=ions_colors_O[j],
fontsize=20,
axis_plot=None)
dz.FigWording(r'$R_{19}$ $(10^{19}cm)$', r'$X(A^{+i})$', '', ncols_leg=1)
leg = dz.Axis.get_legend()
leg.legendHandles[0].set_color('black')
leg.legendHandles[1].set_color('black')
#dz.display_fig()
dz.savefig(
'/home/vital/Dropbox/Astrophysics/Papers/Yp_AlternativeMethods/images/SulfurIonization_fraction_vs_cloudThickness'
)
# dz.savefig('/home/vital/Dropbox/Astrophysics/Seminars/Stasinska conference/SulfurIonization_fraction_vs_cloudThickness')
# #Load catalogue dataframe
# catalogue_dict = dz.import_catalogue()
# catalogue_df = dz.load_excel_DF('/home/vital/Dropbox/Astrophysics/Data/WHT_observations/WHT_Galaxies_properties.xlsx')
#
arcsec = lambda cm: conv_arc(dist=dist_small, dist_proj=cm)
mask_cirleB = make_mask_circle(arcsec, smallCloud_sphere) #ap_size=[50, 1.5]
mask_small = ~np.logical_not(mask_cirleB)
#Calculate abundance
small_lines_dict = extract_fluxes_combSlits(bigCloud_sphere, smallCloud_sphere, mask_big, mask_small)
small_abund_dict = calculate_abundances(small_lines_dict, extensions=['comb'])
print 'Small abund rad {}'.format(radius), small_abund_dict['S_comb']
dz.data_plot(float(radius), small_abund_dict['S_comb'], label = 'Secondary clouds abundance', color='red', markerstyle = 'o')
# arcsec = lambda cm: conv_arc(dist=dist_small, dist_proj=cm)
# mask_cirleB = make_mask_circle(arcsec, smallCloud_sphere) #ap_size=[50, 1.5]
# mask_small = ~np.logical_not(mask_cirleB)
#
# #Calculate abundance
# small_lines_dict = extract_fluxes_pure_slits(smallCloud_sphere)
# small_abund_dict = calculate_abundances(small_lines_dict)
# print 'Small abund rad {}'.format(radius), small_abund_dict['S_tot'], small_abund_dict['S_slit']
dz.FigWording(r'log(radious) (cm)', r'log(S/H)', 'Sulfur abundance evolution with high density clumps')
dz.display_fig()
print 'Data generated'
stellar_cont_fits = objName + '_StellarContinuum.fits'
dz.Data_2_Fits(ouput_folder,
stellar_cont_fits,
header_0,
Input_Wavelength,
Output_Flux,
NewKeyWord=['STALIGHT', 'Basic Treatment'])
#Plot the data
dz.data_plot(wave, flux_dered, "obs de-red")
dz.data_plot(Input_Wavelength, Input_Flux, "Input Spectra")
dz.data_plot(Input_Wavelength, Output_Flux, "Stellar absorption")
#Set titles and legend
PlotTitle = 'Object ' + objName + ' emission and stellar and spectra'
dz.FigWording(r'Wavelength $(\AA)$',
'Flux' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$', PlotTitle)
mean_flux = Input_Flux.mean()
dz.Axis.set_ylim(-0.05 * mean_flux, 15 * mean_flux)
dz.Axis.set_xlim(3500, 5250)
#Save plot
output_pickle = '{objFolder}{stepCode}_{objCode}_{ext}'.format(
objFolder=ouput_folder,
stepCode=dz.ScriptCode,
objCode=objName,
ext='StellarContinuum')
dz.save_manager(output_pickle, save_pickle=True)
except:
dz.log_error(objName)
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(0.30, 0.15, formula, transform=dz.Axis.transAxes, fontsize=28)
#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/Papers/Yp_AlternativeMethods/images/sulfur_argon_ionicAbundances',
extension='.png')
print 'Data treated otro'
lm.fit_dict.zerolev_mean,
flux[lm.fit_dict.idx0:lm.fit_dict.idx1],
facecolor=dz.colorVector['green'],
step='mid',
alpha=0.5)
dz.Axis.fill_between(wave[lm.fit_dict.idx2:lm.fit_dict.idx3],
lm.fit_dict.zerolev_mean,
flux[lm.fit_dict.idx2:lm.fit_dict.idx3],
facecolor=dz.colorVector['dark blue'],
step='mid',
alpha=0.5)
dz.Axis.fill_between(wave[lm.fit_dict.idx4:lm.fit_dict.idx5],
lm.fit_dict.zerolev_mean,
flux[lm.fit_dict.idx4:lm.fit_dict.idx5],
facecolor=dz.colorVector['green'],
step='mid',
alpha=0.5)
dz.Axis.plot(lm.fit_dict.x_resample, lm.fit_dict.y_resample, linestyle='--')
dz.Axis.plot(lm.fit_dict.x_resample,
lm.fit_dict.zerolev_resample,
linestyle='--')
dz.Axis.scatter(lm.Current_TheoLoc, lm.fit_dict.zerolev_mean)
dz.FigWording(r'Wavelength $(\AA)$',
'Flux' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$',
r'Object {} emission line: {}'.format(objName, line),
loc='upper right')
dz.display_fig()
print 'DOne'
print 'New methods'
print m[0], m[1], m[2], m[3]
print m_err[0], m_err[1], m_err[2], m_err[3]
print n[0], n[1], n[2], n[3]
print n_err[0], n_err[1], n_err[2], n_err[3]
#Get fit mean values
n_Median, n_16th, n_84th = median(n_vector), percentile(n_vector,16), percentile(n_vector,84)
m_Median, m_16th, m_84th = median(m_vector), percentile(m_vector,16), percentile(m_vector,84)
print 'Classical'
print n_Median, round_sig(n_Median-n_16th,2, scien_notation=False), round_sig(n_84th-n_Median,2, scien_notation=False)
#Linear data
x_regression_range = linspace(0.0, max(nominal_values(x)) * 1.10, 20)
y_regression_range = m_Median * x_regression_range + n_Median
#Plotting the data,
dz.data_plot(nominal_values(x), nominal_values(y), color = Regresions_dict['Colors'][i], label=regression, markerstyle='o', x_error=std_devs(x), y_error=std_devs(y))
dz.data_plot(WMAP_coordinates[0].nominal_value, WMAP_coordinates[1].nominal_value, color = dz.colorVector['pink'], label=r'WMAP prediction: $Y = 0.24709\pm0.00025$', markerstyle='o', x_error=WMAP_coordinates[0].std_dev, y_error=WMAP_coordinates[1].std_dev)
dz.data_plot(x_regression_range, y_regression_range, label = 'Linear regression', color = Regresions_dict['Colors'][i], linestyle = '--')
dz.plot_text(nominal_values(x), nominal_values(y), objects)
plotTitle = r'{title}: $Y_{{P}} = {n}_{{-{lowerlimit}}}^{{+{upperlimit}}}$'.format(title = Regresions_dict['title'][i], n = round_sig(n_Median,4, scien_notation=False), lowerlimit = round_sig(n_Median-n_16th,2, scien_notation=False), upperlimit = round_sig(n_84th-n_Median,2, scien_notation=False))
dz.FigWording(Regresions_dict['x label'][i], Regresions_dict['y label'][i], plotTitle, loc='lower right')
output_pickle = '{objFolder}{element}_regression'.format(objFolder=catalogue_dict['Data_Folder'], element = element)
dz.save_manager(output_pickle, save_pickle = True)
