#!/usr/bin/env python import pymc import numpy as np from dazer_methods import Dazer from scipy.interpolate import interp1d from libraries.Astro_Libraries.Nebular_Continuum import Nebular_Bayesian from lmfit.models import LinearModel #Declare code classes dz = Dazer() nb = Nebular_Bayesian() #Declare data to treat Catalogue_Dic = dz.import_catalogue() nebular_exten = '_NebularContinuum.fits' Stellar_ext = '_StellarContinuum.fits' emitting_ext = '_Emission.fits' cHbeta_type = 'cHBeta_red' AbundancesFileExtension = '_' + Catalogue_Dic['Datatype'] + '_emission_LinesLog_v3.txt' #First data log for reduced spectra #Find and organize files from terminal command or .py file FilesList = dz.Folder_Explorer(Stellar_ext, Catalogue_Dic['Obj_Folder'], CheckComputer=False) catalogue_frame = dz.load_catalogue_frame(FilesList) # # print catalogue_frame # # #Define plot frame and colors dz.FigConf(n_colors=5, fontsize=30) # # lineal_mod = LinearModel(prefix='lineal_')
from dazer_methods import Dazer
#Create class object
dz = Dazer()
script_code = dz.get_script_code()
#Define operation
catalogue_dict = dz.import_catalogue()
#Load catalogue dataframe
catalogue_df = dz.load_dataframe(catalogue_dict['dataframe'])
#Set figure format
dz.FigConf()
#Loop through the objects
for i in range(len(catalogue_df.index)):
#Treat each arm file
for color in ['Blue', 'Red']:
if (color == 'Red') and (catalogue_df.iloc[i].tell_correction != 'None'):
fits_file = catalogue_df.iloc[i].tellRed_file
else:
fits_file = catalogue_df.iloc[i]['{}_file'.format(color)]
#Read the data
redshift = catalogue_df.iloc[i]['z_{}'.format(color)]
z_fits_file = fits_file.replace('.fits', '_z.fits')
from pandas import read_csv
from dazer_methods import Dazer
from timeit import default_timer as timer
from DZ_LineMesurer import LineMesurer_v2
#Define main class
dz = Dazer()
lm = LineMesurer_v2('/home/vital/workspace/dazer/format/',
'DZT_LineLog_Headers.dz')
#Making the plot:
dz.FigConf()
#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_extension = '_lick_indeces.txt'
#Declare object to treat
objName = 'SHOC575_n2'
#Load line regions
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
lick_idcs_df = read_csv(ouput_folder + objName + lickIndcs_extension,
delim_whitespace=True,
header=0,
index_col=0,
comment='L') #Dirty trick to avoid the Line_label row
#!/usr/bin/python
from dazer_methods import Dazer
#Create class object
dz = Dazer()
script_code = dz.get_script_code()
#Load catalogue dataframe
catalogue_dict = dz.import_catalogue()
catalogue_df = dz.load_dataframe(catalogue_dict['dataframe'])
log_exension = '_' + catalogue_dict['Datatype'] + '_linesLog_reduc.txt'
#Define operation
Catalogue_Dic = dz.import_catalogue()
Pattern = '_' + Catalogue_Dic['Datatype'] + '_linesLog_reduc.txt'
#Define table shape
Width = "%" + str(50+2) + "s"
HeaderSize = 2
LickIndexesHeader = ['Ion', 'lambda_theo', 'group_label','Wave1', 'Wave2', 'Wave3', 'Wave4', 'Wave5', 'Wave6', 'add_wide_component']
columns_format = ['%11.6f', '%11.6f', '%11.6f','%11.6f', '%11.6f', '%11.6f', '%11.6f', '%11.6f', '%11.6f', '%11.6f']
#Loop through the objects
for i in range(len(catalogue_df.index)):
#Object
objName = catalogue_df.iloc[i].name
fits_file = catalogue_df.iloc[i].reduction_fits
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], objName)
lineslog_address = '{objfolder}{codeName}_WHT_linesLog_reduc.txt'.format(objfolder = ouput_folder, codeName=objName)
from dazer_methods import Dazer import matplotlib # Generate dazer object dz = Dazer() # Choose plots configuration dz.FigConf() # Import catalogue Catalogue = dz.import_catalogue() # Perform operations x = [1, 2, 3, 4, 5, 6] y = [1, 2, 3, 4, 5, 6] # Plot the data dz.data_plot(x, y, markerstyle="o") # Generate the figure dz.display_fig() # from DZ_DataExplorer import Plots_Manager # # # We declare the folder and log file to drop the lines data # pv = Plots_Manager() # # # Forcing the remake of new files # pv.RemakeFiles = True
from numpy import where
from uncertainties import ufloat
from dazer_methods import Dazer
#Declare objects
dz = Dazer()
#Define data type and location
Catalogue_Dic = dz.import_catalogue()
Table_Name = '_lineslog'
log_extension = '_log.txt'
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,
#!/usr/bin/python
from dazer_methods import Dazer
#Create class object
dz = Dazer()
script_code = dz.get_script_code()
#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'
)
log_exension = '_' + catalogue_dict['Datatype'] + '_linesLog_reduc.txt'
#Define operation
Catalogue_Dic = dz.import_catalogue()
Pattern = '_' + Catalogue_Dic['Datatype'] + '_linesLog_reduc.txt'
#Define table shape
Width = "%" + str(50 + 2) + "s"
HeaderSize = 2
LickIndexesHeader = [
'Ion', 'lambda_theo', 'group_label', 'Wave1', 'Wave2', 'Wave3', 'Wave4',
'Wave5', 'Wave6', 'add_wide_component'
]
columns_format = [
'%11.6f', '%11.6f', '%11.6f', '%11.6f', '%11.6f', '%11.6f', '%11.6f',
'%11.6f', '%11.6f', '%11.6f'
]
from pymc import deterministic, stochastic, Normal, Uniform, MCMC, Bernoulli, stochastic_from_dist from dazer_methods import Dazer #Generate dazer object dz = Dazer() #Choose plots configuration dz.FigConf() #Import catalogue Catalogue = dz.import_catalogue() #Perform operations x = [1,2,3,4,5,6] y = [1,2,3,4,5,6] #Plot the data dz.data_plot(x, y, markerstyle = 'o') #Generate the figure dz.display_fig()
