#Declare data for the analisis
AbundancesFileExtension = '_' + catalogue_dict['Datatype'] + '_linesLog_emission.txt'
cHbeta_type = 'cHbeta_reduc'
#Atoms for the abundances
MC_length = 500
dz.load_elements()
oxygen_emision = ['O2_3726A', 'O3_4363A', 'O3_4959A', 'O3_5007A', 'O2_7330A']
nitrogen_emision = ['N2_6548A', 'N2_6584A']
sulfur_emision = ['S2_6716A', 'S3_6312A', 'S3_9069A', 'S3_9531A']
Te = random.normal(10000, 2000, size = MC_length)
ne = random.normal(100, 20, size = MC_length)
dz.create_pdfDoc('/home/vital/Desktop/example_line_abundances')
#Loop through the objects
for i in range(len(catalogue_df.index)):
#Object
objName = catalogue_df.iloc[i].name
print 'Treating object: ', objName
#Locate the files
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
fits_reduc = catalogue_df.iloc[i].reduction_fits
fits_emission = catalogue_df.iloc[i].stellar_fits
fits_stellar = ouput_folder + objName + '_StellarContinuum.fits'
lineslog_address = '{objfolder}{codeName}{lineslog_extension}'.format(objfolder = ouput_folder, codeName=objName, lineslog_extension=AbundancesFileExtension)
header=0,
delim_whitespace=True)
linformat_df.lambda_theo = round(linformat_df.lambda_theo.values, 2)
for objName in catalogue_df.loc[dz.idx_include].index:
if objName in ['8', 'SHOC579']:
local_reference = objName.replace('_', '-')
quick_reference = catalogue_df.loc[objName].quick_index
pdf_address = '/home/vital/Dropbox/Astrophysics/Thesis/images/{}_absEffect.png'.format(
quick_reference)
dz.create_pdfDoc(pdf_address, pdf_type='table')
dz.pdf_insert_table(table_format='l' + 'ccc')
group_dict = OrderedDict()
# Make dict with all the objects lines dataframes
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
linelog_reducAddress = '{objfolder}{codeName}_WHT_linesLog_reduc.txt'.format(
objfolder=ouput_folder, codeName=objName)
linelog_emisAddress = '{objfolder}{codeName}_WHT_linesLog_emission_2nd.txt'.format(
objfolder=ouput_folder, codeName=objName)
reduc_linedf = dz.load_lineslog_frame(linelog_reducAddress)
emission_linedf = dz.load_lineslog_frame(linelog_emisAddress)
Hbeta_F = reduc_linedf.loc['H1_4861A'].line_Flux
#Declare data for the analisis
AbundancesFileExtension = '_' + catalogue_dict[
'Datatype'] + '_linesLog_reduc.txt'
cHbeta_type = 'cHbeta_reduc'
#Atoms for the abundances
MC_length = 500
dz.load_elements()
oxygen_emision = ['O2_3726A', 'O3_4363A', 'O3_4959A', 'O3_5007A', 'O2_7330A']
nitrogen_emision = ['N2_6548A', 'N2_6584A']
sulfur_emision = ['S2_6716A', 'S3_6312A', 'S3_9069A', 'S3_9531A']
Te = random.normal(10000, 2000, size=MC_length)
ne = random.normal(100, 20, size=MC_length)
dz.create_pdfDoc('/home/vital/Desktop/example_line_abundances')
#Loop through the objects
for i in range(len(catalogue_df.index)):
#Object
objName = catalogue_df.iloc[i].name
print 'Treating object: ', objName
#Locate the files
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
fits_reduc = catalogue_df.iloc[i].reduction_fits
fits_emission = catalogue_df.iloc[i].stellar_fits
fits_stellar = ouput_folder + objName + '_StellarContinuum.fits'
lineslog_address = '{objfolder}{codeName}{lineslog_extension}'.format(
