cHbeta_type = 'cHBeta_red'
emission_log = '_' + Catalogue_Dic['Datatype'] + '_LinesLog_v3.txt'
# emission_log_st = '_' + Catalogue_Dic['Datatype'] + '_emission_LinesLog_v3.txt'
#Get file list
FilesList = dz.Folder_Explorer(emission_log,
Catalogue_Dic['Obj_Folder'],
CheckComputer=False)
#Get the dictionary with the headers format and the data
dz.EmissionLinesLog_header()
#Generate list of objects (Dazer should have a method for this)
for i in range(len(FilesList)):
CodeName, FileName, FileFolder = dz.Analyze_Address(FilesList[i])
#load object data
cHbeta = dz.GetParameter_ObjLog(CodeName,
FileFolder,
cHbeta_type,
Assumption='float')
obj_lines_frame = dz.load_object_frame(FileFolder,
CodeName,
emission_log,
chbeta_coef=cHbeta_type)
# obj_lines_frame_star = dz.load_object_frame(FileFolder, CodeName, emission_log_st, chbeta_coef = cHbeta_type)
Hbeta_Flux = obj_lines_frame['line_Flux']['H1_4861A']
Hbeta_Int = obj_lines_frame['line_Int']['H1_4861A']
#Generate object row of data
for i in range(len(catalogue_df.index)):
#Object
objName = catalogue_df.iloc[i].name
#Joining pointing
joining_wavelength = catalogue_df.iloc[i].join_wavelength
#Treat each arm file
for color in ['Blue', 'Red']:
fits_file = catalogue_df.iloc[i]['z{}_file'.format(color)]
print fits_file
CodeName, FileName_Blue, FileFolder = dz.Analyze_Address(fits_file)
wave, flux, ExtraData = dz.get_spectra_data(fits_file)
dz.data_plot(wave,
flux,
label='{} {} arm'.format(CodeName, color),
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),
#Define operation
Catalogue_Dic = dz.import_catalogue()
Pattern = '_log'
FilesList = dz.Folder_Explorer(Pattern,
Catalogue_Dic['Obj_Folder'],
CheckComputer=False)
Hbeta_values, Flux_values, names, sn_values, z_values = [], [], [], [], []
g_mags, r_mags = [], []
Declination_values = []
for i in range(len(FilesList)):
#Get the frame row
CodeName, FileName, FileFolder = dz.Analyze_Address(FilesList[i],
verbose=False)
#Load the observational data
Hbeta_Flux = dz.GetParameter_ObjLog(CodeName,
FileFolder,
'SDSS_Flux_Hbeta',
Assumption='float')
Hbeta_Ew = dz.GetParameter_ObjLog(CodeName,
FileFolder,
'SDSS_Eqw_Hbeta',
Assumption='float')
SN_median = dz.GetParameter_ObjLog(CodeName,
FileFolder,
'SDSS_SNmedian',
Assumption='float')
z_SDSS = dz.GetParameter_ObjLog(CodeName,
#Create class object
dz = Dazer()
FilesList = dz.Folder_Explorer(
'',
'/home/vital/Dropbox/Astrophysics/Data/WHT_observations/',
CheckComputer=False,
verbose=False,
Sort_Output='alphabetically')
files_to_keep = []
files_to_delete = []
for file_address in FilesList:
CodeName, FileName, FolderName = dz.Analyze_Address(file_address,
verbose=False)
if (FileName in [
'WHT_Galaxies_properties.txt'
]) or ('run' in FileName) or ('_lick_indeces.txt' in FileName):
files_to_keep.append(FolderName + FileName)
else:
files_to_delete.append(FolderName + FileName)
print '\n--These files will be deleted:'
for file_address in files_to_delete:
print file_address, ' -> X'
print '\n--These files will be preserved:'
for file_address in files_to_keep:
print file_address, ' -> V'
#Loop through the objects
for i in range(len(catalogue_df.index)):
#Object
objName = catalogue_df.iloc[i].name
if objName == 'SHOC579':
#Joining pointing
joining_wavelength = catalogue_df.iloc[i].join_wavelength
#Treat each arm file
blue_fits_file = catalogue_df.iloc[i]['zBlue_file']
red_fits_file = catalogue_df.iloc[i]['zRed_file']
CodeName_Blue, FileName_Blue, FileFolder_Blue = dz.Analyze_Address(
blue_fits_file)
CodeName_Red, FileName_Red, FileFolder_Red = dz.Analyze_Address(
red_fits_file)
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')
]
# flux_calibrated = ['/home/vital/Astrodata/WHT_2016_04/Night1/objects/MRK36_Blue_cr_f_t_w_e_fglobal.fits',
# '/home/vital/Astrodata/WHT_2016_04/Night1/objects/MRK36_Red_cr_f_t_w_bg_e_fglobal.fits']
flux_calibrated = [
'/home/vital/Dropbox/Astrophysics/Data/WHT_observations/objects/MRK36_A1/MRK36_A1_Blue_fglobal.fits',
'/home/vital/Dropbox/Astrophysics/Data/WHT_observations/objects/MRK36_A2/MRK36_A2_Blue_fglobal.fits',
'/home/vital/Dropbox/Astrophysics/Data/WHT_observations/objects/MRK36_A1/MRK36_A1_Red_fglobal.fits',
'/home/vital/Dropbox/Astrophysics/Data/WHT_observations/objects/MRK36_A2/MRK36_A2_Red_fglobal.fits',
]
for i in range(len(flux_calibrated)):
# color = 'Blue' if 'Blue' in extracted_files[i] else 'Red'
CodeName, FileName, FileFolder = dz.Analyze_Address(flux_calibrated[i])
wavelength, Flux_array, Header_0 = dz.get_spectra_data(flux_calibrated[i])
# for j in range(Header_0['NAXIS2']):
#
# color_plot = 'orangish' if j == 0 else 'dark blue'
# dz.data_plot(wavelength, Flux_array[j], label = 'Apperture {} {}'.format(j, color), color=dz.colorVector[color_plot])
dz.data_plot(wavelength, Flux_array, label=FileName)
dz.FigWording(r'Wavelength $(\AA)$',
'Flux ' + r'$(erg\,cm^{-2} s^{-1} \AA^{-1})$', 'MRK36')
dz.display_fig()
