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)
#Sulfur lines to plot
lines_interest = ['S3_9069A','S3_9531A', 'H1_9015A', 'H1_9229A', 'H1_9546A']
for i in range(len(catalogue_df.index)):
print '\n-- Treating {} @ {}'.format(catalogue_df.iloc[i].name, catalogue_df.iloc[i].Red_file)
codeName = catalogue_df.iloc[i].name
fits_file = catalogue_df.iloc[i].Red_file
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], codeName)
if codeName == '8':
#Get object
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
from dazer_methods import Dazer
from DZ_observation_reduction import spectra_reduction
from numpy import isnan
import os.path
dz = Dazer()
dz_reduc = spectra_reduction()
script_code = dz.get_script_code()
catalogue_dict = dz.import_catalogue()
catalogue_df = dz.load_excel_DF(
'/home/vital/Dropbox/Astrophysics/Data/WHT_observations/WHT_Galaxies_properties.xlsx'
)
dz.FigConf(Figtype='Grid_size', n_columns=1, n_rows=2)
for i in range(len(catalogue_df.index)):
#print '\n-- Treating {} @ {}'.format(catalogue_df.iloc[i].name, catalogue_df.iloc[i].Red_file)
codeName = catalogue_df.iloc[i].name
fits_file = catalogue_df.iloc[i].Red_file
ouput_folder = '{}{}/'.format(catalogue_dict['Obj_Folder'], codeName)
redshift_factor = 1 + catalogue_df.iloc[i].z_Red
star = catalogue_df.iloc[i].telluric_star
calibration_star = catalogue_df.iloc[i].calibration_star.split(';')[0]
if os.path.isfile(
'{reduc_folder}objects/{calibStar}_Red_slit5.0_n.fits'.format(
reduc_folder=catalogue_df.iloc[i].obsfolder,
)
cHbeta_type = 'cHbeta_reduc'
nebular_exten = '_NebularContinuum.fits'
Stellar_ext = '_StellarContinuum.fits'
emitting_ext = '_Emission.fits'
#Define plot frame and colors
size_dict = {
'axes.labelsize': 20,
'legend.fontsize': 18,
'font.family': 'Times New Roman',
'mathtext.default': 'regular',
'xtick.labelsize': 18,
'ytick.labelsize': 18
}
dz.FigConf(plotSize=size_dict)
#Reddening properties
R_v = 3.4
red_curve = 'G03_average'
cHbeta_type = 'cHbeta_reduc'
obj_lines = {}
obj_lines['SHOC579'] = OrderedDict()
obj_lines['SHOC579'][r'Balmer jump'] = (3646.0, 2.1e-16)
obj_lines['SHOC579'][r'$HI_{11}\lambda3770\AA$'] = (3771.0, 2.1e-16)
obj_lines['SHOC579'][r'$HI_{20}\lambda3683\AA$ '] = (3676.0, 1.85e-16)
ak = lineid_plot.initial_annotate_kwargs()
ak['arrowprops']['relpos'] = (0.5, 0.0)
ak['rotation'] = 90
# from ManageFlow import DataToTreat
# from CodeTools.PlottingManager import myPickle
# from Plotting_Libraries.dazer_plotter import Plot_Conf
# from astropy.coordinates import EarthLocation
# from astropy.coordinates import SkyCoord
# from astropy.time import Time
# favoured_objects = ['72', '60', '61', '12', '29', '36', 'Mar1987', 'Mar2232', 'Mar2474', 'Mar88', 'Mar1315', 'Mar1390', 'Mar652', 'Mar2018', 'Mar1715', 'Mar2260', 'Mar1342', 'Mar87']
favoured_objects = ['02', '03', '05', '09', 'Mar2232']
#Generate dazer object
dz = Dazer()
#Define figure format
dz.FigConf(n_colors=2)
cmap = dz.cmap_pallete()
#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)):
'''
Created on Mar 15, 2017
@author: vital
'''
import numpy as np
from dazer_methods import Dazer
dz = Dazer()
#Set figure format
sizing_dict = {'figure.figsize': (8, 8)}
dz.FigConf(sizing_dict)
te_SIII = np.linspace(8000, 20000, 50)
te_SII = 0.88 * te_SIII + 1560
line_unity = te_SIII
dz.data_plot(te_SIII, te_SII, label=r'$T_e[SII]=0.88T_e[SIII]+1560$')
dz.data_plot(te_SIII, te_SIII, label='', color='grey', linestyle='--')
dz.Axis.set_xlim(8000, 20000)
dz.Axis.set_ylim(8000, 20000)
dz.Axis.grid(True)
ticklines = dz.Axis.get_xticklines() + dz.Axis.get_yticklines()
gridlines = dz.Axis.get_xgridlines() + dz.Axis.get_ygridlines()
ticklabels = dz.Axis.get_xticklabels() + dz.Axis.get_yticklabels()
for line in ticklines:
#Read table data
df = pd.read_excel(
'/home/vital/Dropbox/Astrophysics/Thesis/notes/table_yp_literature.xlsx',
sheetname='Sheet1')
#Define plot frame and colors
size_dict = {
'figure.figsize': (18, 8),
'axes.labelsize': 28,
'legend.fontsize': 35,
'font.family': 'Times New Roman',
'mathtext.default': 'regular',
'xtick.labelsize': 28,
'ytick.labelsize': 28
}
dz.FigConf(plotStyle='colorblind', plotSize=size_dict)
# Generate the color map
dz.gen_colorList(0, df.index.size)
marker_dict = {'Peimbert': 's', 'Skillman': '^', 'Izotov': 'o'}
#Loop through the lines
for i in range(df.index.size):
author, value, error, year, comments, upper_limit, group = df.iloc[
i].values
marker_type = '_' if group not in marker_dict else marker_dict[group]
if error < 0.236:
dz.load_input_data(config_dict)
#Perform SSP synthesis
start = timer()
fit_products = dz.fit_ssp(config_dict['input_z'], config_dict['input_sigma'],
config_dict['input_Av'])
end = timer()
print 'ssp', ' time ', (end - start)
# start = timer()
# Gamma_FF_HI = neb.FreeFreeContinuum("HI")
# end = timer()
# print 'FF', ' time ', (end - start)
#Plot the results
dzp.FigConf()
dzp.data_plot(dz.sspFit_dict['obs_wave'],
dz.sspFit_dict['obs_flux'],
label='obs_flux')
dzp.data_plot(dz.sspFit_dict['obs_wave'],
dz.sspFit_dict['zero_mask'],
label='my mask')
# dzp.data_plot(dz.fit_conf['obs_wave'], mis_cosas[1], label='Hector mask')
# dzp.data_plot(dz.fit_conf['obs_wave'], mis_cosas[2], label='Hector fit')
dzp.data_plot(dz.sspFit_dict['obs_wave'],
fit_products['flux_sspFit'],
label='my fit')
dzp.FigWording('Wave', 'Flux', 'Input spectra')
dzp.display_fig()
return PartialWavelength, PartialIntensity, LineHeight, LineExpLoc
#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'
)
#Set figure format
dz.FigConf('night')
#Not sure what this is
dz.force_WD = True
#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)
print '-- Treating {} @ {}'.format(objName, fits_file)
#Spectrum data
import numpy as np
from dazer_methods import Dazer
from lib.Astro_Libraries.Nebular_Continuum import NebularContinuumCalculator
from timeit import default_timer as timer
import timeit
dz = Dazer()
neb = NebularContinuumCalculator()
for_dic = {'figure.figsize': (14, 20)}
dz.FigConf(for_dic)
Te = 10000.0
wave = np.arange(2000, 7000)
HeI, HeII = 0.01, 0.001
Flux_recomb = 1.5e-13
wave_ryd = 3
neb.PropertiesfromUser(Te, Flux_recomb, HeI, HeII, wave)
neb.load_neb_constants()
wave_ryd = (neb.const['h'] * neb.const['c_Angs']) / (neb.const['Ryd2erg'] *
wave)
Gamma_FF_HI_fast = neb.free_free_gCont(wave, Te)
Gamma_2q_HI_fast = neb.two_photon_gCont(wave, Te)
Gamma_FB_HI_fast = neb.free_bound_gCont(wave_ryd, Te, neb.HI_fb_dict)
Gamma_FB_HeI_fast = neb.free_bound_gCont(wave_ryd, Te, neb.HeI_fb_dict)
Gamma_FB_HeII_fast = neb.free_bound_gCont(wave_ryd, Te, neb.HeII_fb_dict)
Gamma_FB_HI = neb.FreeBoundContinuum_EP("HeII")
Gamma_Total, Gamma_lambda, Gamma_FB_HI, Gamma_FB_HeI, Gamma_FB_HeII, Gamma_2q, Gamma_FF = neb.Calculate_Nebular_gamma(
Hbeta_dist = random.normal(
reduc_lineslog_df.loc['H1_4861A'].line_Int.nominal_value,
reduc_lineslog_df.loc['H1_4861A'].line_Int.std_dev, MC_length)
#Insert new section in pdf
with dz.pdfDoc.create(Section('HII Galaxy: {}'.format(objName))):
dz.add_page()
#------Plot Oxygen lines
element_lines = reduc_lineslog_df.loc[(
reduc_lineslog_df.index.isin(oxygen_emision))].index.values
if len(element_lines) > 0:
dz.FigConf(plotStyle='seaborn-colorblind',
Figtype='grid',
plotSize=sizing_dict,
n_columns=int(len(element_lines)),
n_rows=int(np_ceil(len(element_lines) / n_columns)))
for j in range(len(element_lines)):
#Define plotting regions
regions_wavelengths = reduc_lineslog_df.loc[
element_lines[j],
['Wave1', 'Wave2', 'Wave3', 'Wave4', 'Wave5', 'Wave6'
]].values
idcs_obj, idcs_stellar = searchsorted(
wave_reduc, regions_wavelengths), searchsorted(
wave_stellar, regions_wavelengths)
subwave_solar, subFlux_solar = wave_stellar[
idcs_stellar[0]:idcs_stellar[5]], flux_stellar[
