from numpy import empty
from DZ_observation_reduction import spectra_reduction
from shutil import copy as shu_copy
#Load iraf pypeline object
dz = spectra_reduction()
#Entries for new files
data_dict = {'reduc_tag': 'biascorr'}
#Load reduction data frame
dz.declare_catalogue(dz.Catalogue_folder)
#Search for objects we want to treat for bias
list_for_bias = dz.observation_dict['Standard_stars'] + dz.observation_dict[
'objects'] + ['flat', 'arc', 'sky']
#Loop through the arms
colors = ['Blue', 'Red']
for arm_color in colors:
#Get the files to bias correct
indeces_arm = (dz.reducDf.ISIARM == '{color} arm'.format(
color=arm_color)) & (dz.reducDf.file_location.str.contains('raw_fits')
) & dz.reducDf.frame_tag.isin(list_for_bias) & (
dz.reducDf.valid_file)
frames_type = dz.reducDf.loc[indeces_arm, 'frame_tag'].values
files_folders = dz.reducDf.loc[indeces_arm, 'file_location'].values
files_names = dz.reducDf.loc[indeces_arm, 'file_name'].values
#Get the correction files
indmin, indmax = searchsorted(PartialWavelength, (Bot, Top))
LineHeight = max(PartialIntensity[indmin:indmax])
LineExpLoc = median(PartialWavelength[where(PartialIntensity == LineHeight)])
return PartialWavelength, PartialIntensity, LineHeight, LineExpLoc
def region_indeces(wave_min, wave_max, wavenlength_range):
low_trim, up_trim = searchsorted(wavenlength_range, [wave_min, wave_max])
indeces_array = array(range(low_trim, up_trim))
return indeces_array
dz = Dazer()
dz_reduc = spectra_reduction()
script_code = dz.get_script_code()
lickIndcs_extension = '_lick_indeces.txt'
#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')
image_address = '/home/vital/Dropbox/Astrophysics/Papers/Yp_AlternativeMethods/images/telluric_correction_detail'
SIII_theo = 2.469
H7_H8_ratio_theo = 1.98
#Set figure format
size_dict = {'figure.figsize': (16, 10), 'axes.labelsize':20, 'legend.fontsize':20, 'font.family':'Times New Roman', 'mathtext.default':'regular', 'xtick.labelsize':20, 'ytick.labelsize':20}
dz.FigConf(plotStyle='seaborn-colorblind', plotSize = size_dict, Figtype = 'Grid_size', n_columns = 1, n_rows = 2)
import os
import sys
sys.path.append('/home/vital/git/Dazer/Dazer/dazer/')
os.environ['TCL_LIBRARY'] = '/home/vital/anaconda/python27/lib/tcl8.5'
os.environ['TK_LIBRARY'] = '/home/vital/anaconda/python27/lib/tk8.5'
from DZ_observation_reduction import spectra_reduction
#Load iraf pypeline object
dz = spectra_reduction()
#Load reduction data frame
dz.declare_catalogue(dz.Catalogue_folder)
#Output tag
data_dict = {'reduc_tag': 'flat_combine_trim'}
#Loop through the arms
colors = ['Blue', 'Red']
for arm_color in colors:
#Get object and global indeces
idx_arc = (dz.reducDf.reduc_tag == 'nflatcombine') & (dz.reducDf.ISIARM == '{color} arm'.format(color = arm_color)) & (dz.reducDf.valid_file)
File_Folder = dz.reducDf.loc[idx_arc, 'file_location'].values[0]
File_Name = dz.reducDf.loc[idx_arc, 'file_name'].values[0]
File_Name_trim = File_Name[0:File_Name.find('.')] + '_t.fits'
#Define cropping region
cropping = dz.observation_dict[arm_color + '_cropping']
cropping_region = '[{rawA}:{rawB},{columnA}:{columnB}]'.format(rawA=cropping[0], rawB=cropping[1],columnA=cropping[2], columnB=cropping[3])
GridAxis.plot(x_values_ver, y_values_ver, label = files_name[j], linestyle = line_dict[str(i)], linewidth = 4)
GridAxis.set_xlabel('Pixel', fontsize = font_size)
GridAxis.set_ylabel('Mean pixel value', fontsize = font_size)
GridAxis.set_title('Extracted spectrum', fontsize = font_size)
GridAxis.legend(fontsize = font_size)
plt.show()
#Load the catalogues
dz_vit = spectra_reduction()
dz_vit.declare_catalogue(catalogue_address = '/home/vital/Astrodata/WHT_2011_11/Night1/')
dz_ele = spectra_reduction()
dz_ele.declare_catalogue(catalogue_address = '/home/vital/Astrodata/WHT_2011_11/Night1_Elena/')
# #Plot
# list_files1, list_files2 = ['master_bias_Blue.fits'], ['Zero-blue.fits']
# idx1 = dz_vit.reducDf.file_name.isin(list_files1) & (dz_vit.reducDf.ISIARM == 'Blue arm')
# idx2 = dz_ele.reducDf.file_name.isin(list_files2) & (dz_ele.reducDf.ISIARM == 'Blue arm')
# plotter_4_grid([idx1, idx2], [dz_vit, dz_ele])
#Loop through the files
list_files1, list_files2 = ['r01725595_b.fits', 'flat_combine_Blue.fits' ], ['ccdblue1725595.fits', 'Flat.fits']
list_files1, list_files2 = ['master_bias_Blue_b.fits'], ['Zero-blue.fits']
list_files1, list_files2 = ['flat_combine_Blue.fits'], ['Flat.fits']
linestyle=line_dict[str(i)],
linewidth=4)
GridAxis.set_xlabel('Pixel', fontsize=font_size)
GridAxis.set_ylabel('Mean pixel value', fontsize=font_size)
GridAxis.set_title('Extracted spectrum', fontsize=font_size)
GridAxis.legend(fontsize=font_size)
plt.show()
#Load the catalogues
dz_vit = spectra_reduction()
dz_vit.declare_catalogue(
catalogue_address='/home/vital/Astrodata/WHT_2011_11/Night1/')
dz_ele = spectra_reduction()
dz_ele.declare_catalogue(
catalogue_address='/home/vital/Astrodata/WHT_2011_11/Night1_Elena/')
# #Plot
# list_files1, list_files2 = ['master_bias_Blue.fits'], ['Zero-blue.fits']
# idx1 = dz_vit.reducDf.file_name.isin(list_files1) & (dz_vit.reducDf.ISIARM == 'Blue arm')
# idx2 = dz_ele.reducDf.file_name.isin(list_files2) & (dz_ele.reducDf.ISIARM == 'Blue arm')
# plotter_4_grid([idx1, idx2], [dz_vit, dz_ele])
#Loop through the files
list_files1, list_files2 = ['r01725595_b.fits', 'flat_combine_Blue.fits'
], ['ccdblue1725595.fits', 'Flat.fits']
from pandas import read_csv
from dazer_methods import Dazer
from uncertainties import ufloat
from DZ_observation_reduction import spectra_reduction
#Define main class
dz = Dazer()
dz_sr = spectra_reduction()
#Making the plot:
dz.FigConf()
#Set object and line to measure
objName = 'IZW18_A1'
extension = 1
S3_lines = ['S3_9069A', 'S3_9531A']
#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')
lickIndcs_ext = '_lick_indeces.txt'
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
lick_idcs_df = read_csv(ouput_folder + objName + lickIndcs_ext, delim_whitespace = True, header = 0, index_col = 0, comment='L') #Dirty trick to avoid the Line_label row
#Define fits file:
ratios_dict = {}
for extension in [0, 1]:
fits_file = '/home/vital/Astrodata/WHT_2016_04/Night1/objects/IZW18_Red_cr_f_t_w_e_{testing_extension}_fglobal.fits'.format(testing_extension=dz_sr.testing_extension)
#fits_file = '/home/vital/Astrodata/WHT_2016_04/Night1/objects/IZW18_Red_cr_f_t_w_bg_e_fglobal.fits'
redshift_factor = 1 + catalogue_df.loc[objName].z_Red