from collections import OrderedDict
from dazer_methods import Dazer
from lib.inferenceModel import SpectraSynthesizer
from lib.Astro_Libraries.spectrum_fitting.import_functions import make_folder
# Import functions
dz = Dazer()
specS = SpectraSynthesizer()
# Declare data location
root_folder = 'E:\\Dropbox\\Astrophysics\\Data\\WHT_observations\\bayesianModel\\'
whtSpreadSheet = 'E:\\Dropbox\\Astrophysics\\Data\\WHT_observations\\WHT_Galaxies_properties.xlsx'
# Import data
catalogue_dict = dz.import_catalogue()
catalogue_df = dz.load_excel_DF(whtSpreadSheet)
default_lines = [
'H1_4341A', 'H1_6563A', 'He1_4471A', 'He1_5876A', 'He1_6678A', 'He2_4686A',
'O2_7319A', 'O2_7319A_b', 'O2_7330A', 'O3_4363A', 'O3_4959A', 'O3_5007A',
'N2_6548A', 'N2_6584A', 'S2_6716A', 'S2_6731A', 'S3_6312A', 'S3_9069A',
'S3_9531A', 'Ar3_7136A', 'Ar4_4740A'
]
# Quick indexing
dz.quick_indexing(catalogue_df)
# Sample objects
excludeObjects = [
'SHOC579', 'SHOC575_n2', '11', 'SHOC588', 'SDSS3', 'SDSS1', 'SHOC36'
]
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
#Load spectrum data
import numpy as np
from uncertainties import ufloat, unumpy
from dazer_methods import Dazer
#Generate dazer object
dz = Dazer()
#Set figure format
dz.FigConf()
file_tradional_reduc = '/home/vital/Dropbox/Astrophysics/Data/Fabian_Catalogue/data/Traditional_Abundances.xlsx'
file_starlight = '/home/vital/Dropbox/Astrophysics/Data/Fabian_Catalogue/data/Starlight_Abundances.xlsx'
df_dict = {}
df_dict['traditional'] = dz.load_excel_DF(file_tradional_reduc)
df_dict['starlight'] = dz.load_excel_DF(file_starlight)
type = 'traditional'
element = 'Oxygen'
conf_dict = {}
conf_dict['Oxygen_xlabel'] = r'y'
conf_dict['Nitrogen_xlabel'] = r'N/H $(10^{-6})$'
conf_dict['ylabel'] = r'$Y$'
conf_dict['title'] = 'Helium mass fraction versus {} abundance'.format(element)
conf_dict['legend_label'] = 'Data generated from {} treatment'.format(type)
conf_dict['Oxygen_color'] = 'green'
conf_dict['Nitrogen_color'] = 'blue'
x = df_dict[type][element]
y = df_dict[type].y
'SDSS_reference', 'SDSS_Web', 'z_Blue', 'RA', 'DEC', 'Dichroic'
]
headers = [
'Local reference', 'SDSS reference', 'z',
'RA (hh:{arcmin}:{arcsec})'.format(arcmin="'", arcsec='"'),
'DEC (deg:{arcmin}:{arcsec})'.format(arcmin="'",
arcsec='"'), 'ISIS configuration'
]
#Import library object
dz = Dazer()
#Load observational data
catalogue_df = dz.load_excel_DF(
'E:\\Dropbox\\Astrophysics\\Data\\WHT_observations\\WHT_Galaxies_properties.xlsx'
)
#Define data to load
pdf_address = 'E:\\Dropbox\\Astrophysics\\Papers\\Yp_BayesianMethodology\\source files\\tables\\reference_table_noPreamble'
#Generate pdf
#dz.create_pdfDoc(pdf_address, pdf_type='table')
dz.pdf_insert_table(headers)
#Quick indexing
dz.quick_indexing(catalogue_df)
# Sample objects
excludeObjects = ['SHOC579', 'SHOC575_n2', '11', 'SHOC588', 'SDSS1', 'SHOC36'
] # SHOC579, SHOC575, SHOC220, SHOC588, SHOC592, SHOC036
import pandas as pd
from dazer_methods import Dazer
from numpy import isnan, nan, sort, unique, zeros, core, full
from numpy.core.defchararray import add as add_str_array
from astropy import units as u
from astropy.coordinates import SkyCoord
dz = Dazer()
#Define data location
table_address_new = '/home/vital/Dropbox/Astrophysics/Papers/Determination 2photon continua/Master_table.xlsx'
#Load the tables
new_df = dz.load_excel_DF(table_address_new)
for i in range(len(new_df)):
if pd.notnull(new_df.iloc[i].Ra):
coordinate = r'{} {}'.format(new_df.iloc[i].Ra, new_df.iloc[i].Dec)
c = SkyCoord(coordinate,
unit=(u.hourangle, u.deg)).to_string('decimal').split()
pn_code = new_df.iloc[i].name
new_df.loc[pn_code, 'Ra_deg'] = c[0]
new_df.loc[pn_code, 'Dec_deg'] = c[1]
# Create a Pandas Excel writer using XlsxWriter as the engine.
writer = pd.ExcelWriter(table_address_new, engine='xlsxwriter')
import pandas as pd
from dazer_methods import Dazer
from numpy import isnan, nan, sort, unique, zeros
dz = Dazer()
table_address = '/home/vital/Desktop/NGC5457_CHAOSIII_regions.xlsx'
old_df = dz.load_excel_DF(table_address)
regions_list = pd.unique(old_df.index)
wavelength = sort(pd.unique(old_df.wavelength)[0:-1])
physical_param = ['C(Hbeta)', 'F(Hbeta)', 'EW(Hbeta)', 'EW(Halpha)']
#Generate the good columns
line_labels = zeros(len(wavelength)).astype(str)
for i in range(len(wavelength)):
wave = wavelength[i]
ions = unique(old_df.line_label[(old_df.wavelength == wave)].values)
line_labels[i] = str(wave) + '_'+ ions[0]
#Create dataframe
new_df = pd.DataFrame(columns = list(line_labels) + physical_param, index=regions_list)
#Add error column
for column in new_df.columns.values:
new_df.insert(new_df.columns.get_loc(column) + 1, column + '_error', nan)
#Transfer data from old df to the new
for i in range(len(old_df)):
region = old_df.iloc[i].name
wave = old_df.iloc[i].wavelength
check_err = old_df.iloc[i].flux_label
from numpy import object_
import pyneb as pn
import pandas as pd
from string import ascii_uppercase
from dazer_methods import Dazer
from collections import OrderedDict
from uncertainties import UFloat, unumpy
#Generate dazer object
dz = Dazer()
sciData_address = '/home/vital/Dropbox/Astrophysics/Data/WHT_observations_BackUp/WHT_Galaxies_properties.xlsx'
sciData_saving_test = '/home/vital/Dropbox/Astrophysics/Data/WHT_observations_BackUp/WHT_Galaxies_properties_saveTest.xlsx'
catalogue_sheetDF = dz.load_excel_DF(sciData_address)
# for coso in dz.ipExcel_sheetColumns:
# print coso, dz.ipExcel_sheetColumns[coso]
#
# print catalogue_sheetDF.columns.values
# print catalogue_sheetDF['Code1']
# catalogue_sheetDF['Code3'] = unumpy.uarray(catalogue_sheetDF.Code1.values, catalogue_sheetDF.Code2.values)
# catalogue_sheetDF['Code4'] = unumpy.uarray(catalogue_sheetDF.Code1.values, catalogue_sheetDF.Code2.values)
#
# dz.ipExcel_sheetColumns['Data_Properties'].append('Code3')
# dz.ipExcel_sheetColumns['Data_Properties'].append('Code4')
dz.save_excel_DF(catalogue_sheetDF, sciData_saving_test, df_sheet_format = 'catalogue_data')
lines_log_format_address = 'C:\\Users\\Vital\\PycharmProjects\\dazer\\format\\emlines_pyneb_optical_infrared.dz'
whtDataFile = 'E:\\Dropbox\\Astrophysics\\Data\\WHT_observations\\WHT_Galaxies_properties.xlsx'
output_folder = 'E:\\Dropbox\\Astrophysics\\Data\\WHT_observations\\objects\\'
saving_folder = 'E:\\Dropbox\\Astrophysics\\Papers\\Yp_BayesianMethodology\\source files\\images\\'
lines_log_format_headers = ['line_label', 'ion', 'lambda_theo', 'latex_format']
lines_df = read_csv(lines_log_format_address,
index_col=0,
names=lines_log_format_headers,
delim_whitespace=True)
# linformat_df = read_csv(lines_log_format_address, names=['line_label', 'ion', 'lambda_theo', 'latex_format'], delim_whitespace=True)
# Load catalogue dataframe
catalogue_dict = dz.import_catalogue()
catalogue_df = dz.load_excel_DF(whtDataFile)
norm_factor = 100
# Treatment add quick index
dz.quick_indexing(catalogue_df)
# Declare data for the analisis
AbundancesFileExtension = '_' + catalogue_dict[
'Datatype'] + '_linesLog_reduc.txt'
# Reddening properties
R_v = 3.4
red_curve = 'G03_average'
cHbeta_type = 'cHbeta_emis'
# Define table properties
from dazer_methods import Dazer
from numpy import nanmean, nanstd, mean, nan as np_nan, zeros
from uncertainties import ufloat, unumpy
import pandas as pd
#Generate dazer object
dz = Dazer()
#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'
)
ICF_IR_df = dz.load_excel_DF(
'/home/vital/Dropbox/Astrophysics/Data/WHT_observations/data/SIV_ICF_literature.xlsx'
)
dz.quick_indexing(catalogue_df)
idcs = (pd.notnull(catalogue_df.TeSIII_emis2nd)) & (pd.notnull(
catalogue_df.ICF_SIV_emis2nd)) & (catalogue_df.quick_index.notnull())
idcs_oxygen = (pd.notnull(catalogue_df.OI_HI_emis2nd)) & (pd.notnull(
catalogue_df.OII_HII_emis2nd)) & (catalogue_df.quick_index.notnull())
for index in catalogue_df.index:
print 'Troleo', index, catalogue_df.loc[index, 'ICF_SIV_emis2nd']
#Define plot frame and colors
size_dict = {
'axes.labelsize': 35,
'legend.fontsize': 22,
