#!/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()