# Format the graphs
plotTitle = r"Object {} Nebular continuum calculation".format(objName)
dz.FigWording(r"Wavelength $(\AA)$", "Flux" + r"$(erg\,cm^{-2} s^{-1} \AA^{-1})$", plotTitle)
mean_flux = spectrum_dered.mean()
dz.Axis.set_ylim(-0.05 * mean_flux, 15 * mean_flux)
dz.Axis.set_xlim(3500, 5250)
output_pickle = "{objFolder}{stepCode}_{objCode}_{ext}".format(
objFolder=ouput_folder, stepCode=dz.ScriptCode, objCode=objName, ext="NebularContinuum_Comparison"
)
dz.save_manager(output_pickle, save_pickle=True)
except:
dz.log_error(objName)
print "All data treated", dz.display_errors()
# from dazer_methods import Dazer
# from libraries.Astro_Libraries.Nebular_Continuum import NebularContinuumCalculator
#
# def nebular_continua_calculation_plots():
#
# #Plotting the data
# dz.data_plot(Wave, Int, 'Reduced spectrum', dz.ColorVector[2][0])
# dz.data_plot(NebWave, NebularInt_Hbeta, 'Nebular continuum - Hbeta calibration', dz.ColorVector[2][1])
# dz.data_plot(NebWave, NebularInt_Halpha, 'Nebular continuum - Halfa calibration', dz.ColorVector[2][2])
# dz.data_plot(x_trendline, y_trendline, 'Balmer jump regression', dz.ColorVector[2][3], linestyle='--')
#
# #Format the graphs
# PlotTitle = r'Object '+ CodeName + ' Nebular continuum calculation'
# dz.FigWording(r'Wavelength $(\AA)$', 'Flux' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$', PlotTitle)
# Annotate the 2nd position with another image (a Grace Hopper portrait)
fn = get_sample_data('/home/vital/Dropbox/Astrophysics/Papers/Yp_AlternativeMethods/extra_files/{}.png'.format(quick_reference), asfileobj=False)
arr_img = plt.imread(fn, format='png')
imagebox = OffsetImage(arr_img, **global_offset)
imagebox.image.axes = ax
ab = AnnotationBbox(imagebox, **annotationBbox)
ax.add_artist(ab)
ax.update({'xlabel':r'Wavelength $(\AA)$', 'ylabel':'Flux ' + r'$(10^{-15} erg\,cm^{-2} s^{-1} \AA^{-1})$'})
saving_location = '/home/vital/Dropbox/Astrophysics/Papers/Yp_AlternativeMethods/supplementary material online/{}.png'.format(quick_reference)
plt.savefig(saving_location, dpi=150, bbox_inches='tight')
plt.cla()
#-----------------------------------------------------------------------------------------------------
print 'All data treated', dz.display_errors()
# # Loop through files
# for i in range(len(catalogue_df.index)):
#
# print '-- Treating {}'.format(catalogue_df.iloc[i].name)
#
# if catalogue_df.iloc[i].name not in ['IZW18_A2', 'AGC198691', '6', '70', '4_n2', '3', '27', '71', '0564',
# 'SHOC575_n1', 'SHOC593', 'J2225']:
# # Locate the objects
# codeName = catalogue_df.iloc[i].quick_index
# objName = catalogue_df.iloc[i].name
#
# ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
#Start to loop through recorded line
for i in range(len(lick_idcs_df.index)):
try:
print '-- Treating line:', lick_idcs_df.iloc[i].name
#Establish current line
dz.Current_Label = lick_idcs_df.iloc[i].name
dz.Current_Ion = lick_idcs_df.iloc[i].Ion
dz.Current_TheoLoc = lick_idcs_df.iloc[i].lambda_theo
selections = lick_idcs_df.iloc[i][3:9].values
#Proceed to measure
line_data = dz.measure_line(wave,
flux,
selections,
lineslog_df,
Measuring_Method='MC_lmfit',
store_data=True)
except:
dz.log_emLine_error(objName, dz.Current_Label)
dz.save_lineslog_dataframe(lineslog_df, lines_log_address)
print '-', lines_log_address, 'saving'
print "\nAll data treated", dz.display_errors('emLine measurement',
extra_verbose=True)
#Oxygen
dz.oxygen_abundance_scheme(T_low, T_high, ne)
#Nitrogen
dz.nitrogen_abundance_scheme(T_low, ne)
print '-Helium abundances'
if 'neSII' in dz.abunData:
dz.helium_abundance_elementalScheme(T_high,
ne,
lineslog_frame,
metal_ext='O')
dz.helium_abundance_elementalScheme(T_high,
ne,
lineslog_frame,
metal_ext='S')
#Store the abundances
dz.store_abundances_excel(objName,
catalogue_df,
extension=extensioin_table[i])
dz.save_excel_DF(
catalogue_df,
'/home/vital/Dropbox/Astrophysics/Data/WHT_observations/WHT_Galaxies_properties.xlsx',
df_sheet_format='catalogue_data')
print '\nAll data treated\n', dz.display_errors()
print '-- Treating {}'.format(catalogue_df.iloc[i].name)
try:
#Locate the objects
objName = catalogue_df.iloc[i].name
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
fits_file = catalogue_df.iloc[i].reduction_fits
#Declare the ouput file from starlight
Sl_OutputFile = objName + '_' + Catalogue_Dic['Datatype'] + ".slOutput"
LogMass = Calculate_Total_Mass(objName, ouput_folder, Sl_OutputFolder + Sl_OutputFile, catalogue_df.iloc[i].z_Blue)
#Converting starlight population output into a series of histograms
Light_fraction = dz.CumulativeHistogram_One(Sl_OutputFolder, Sl_OutputFile, PlotType = 'Light_fraction', Color_Vector = list_colors)
Mass_fraction = dz.CumulativeHistogram_One(Sl_OutputFolder, Sl_OutputFile, PlotType = 'Mass_fraction', Color_Vector = list_colors)
#Generating light fraction histogram
light_fraction_plot(Light_fraction, objName, ouput_folder, script_code)
#Generating light fraction histogram
mass_fraction_plot(Mass_fraction, objName, ouput_folder, dz.ScriptCode[0] + str(int(dz.ScriptCode[1]) + 1))
except:
dz.log_error(objName)
#-----------------------------------------------------------------------------------------------------
print 'All data treated', dz.display_errors()
print '-- Treating line:', lick_idcs_df.iloc[i].name
#Establish current line
dz.Current_Label = lick_idcs_df.iloc[i].name
dz.Current_Ion = lick_idcs_df.iloc[i].Ion
dz.Current_TheoLoc = lick_idcs_df.iloc[i].lambda_theo
selections = lick_idcs_df.iloc[i][3:9].values
#Proceed to measure
line_data = dz.measure_line(wave, flux, selections, lineslog_df, Measuring_Method = 'MC_lmfit', store_data = True)
except:
dz.log_emLine_error(objName, dz.Current_Label)
dz.save_lineslog_dataframe(lineslog_df, lines_log_address)
print '-', lines_log_address, 'saving'
print "\nAll data treated", dz.display_errors('emLine measurement', extra_verbose=True)
print 'Oxygen abundance'
dz.oxygen_abundance_scheme()
print 'Nitrogen abundance'
dz.nitrogen_abundance_scheme()
print 'Helium abundance'
dz.helium_abundance_scheme_Oxygen(lineslog_frame)
dz.helium_abundance_scheme_Sulfur(lineslog_frame)
#Store the abundances
frame_address = catalogue_dict['Obj_Folder'].replace('objects/', 'catalogue_df')
dz.store_abundances(objName, catalogue_df, frame_address)
print '\nAll data treated\n', dz.display_errors()
# from dazer_methods import Dazer
# from numpy import isnan
#
# #Generate dazer object
# dz = Dazer()
# dz.load_elements()
#
# #Load catalogue dataframe
# catalogue_dict = dz.import_catalogue()
# catalogue_df = dz.load_dataframe(catalogue_dict['dataframe'])
#
# #Declare data for the analisis
# AbundancesFileExtension = '_' + catalogue_dict['Datatype'] + '_LinesLog_MC.txt'
