import src.specsiser as sr
from pathlib import Path
from astro.data.SDSS.shared_scripts import list_objName, obsConfaddress, obsFolder
# Declare data and files location
objList = list_objName(obsFolder, '.fits')
obsData = sr.loadConfData(obsConfaddress, objList, group_variables=False)
data_folder = Path(obsData['file_information']['data_folder'])
addressList = list(Path(f'{data_folder/objName}.fits') for objName in objList)
# Sample properties
norm_Flux = obsData['sample_data']['norm_flux']
wmin, wmax = obsData['sample_data']['wmin_array'], obsData['sample_data'][
'wmax_array']
# Analyse the spectrum
for i, file_address in enumerate(addressList):
# if i == 0:
# Open lineslog
objName = objList[i]
fitsFolder, fitsFile = file_address.parent, file_address.name
masksFolder, masksFile = fitsFolder, fitsFile.replace(
'.fits', '_masks.txt')
lineLogFolder, lineLogFile = fitsFolder / 'flux_analysis', fitsFile.replace(
'.fits', '_linesLog.txt')
plotFolder, plotGrid = fitsFolder / 'flux_analysis', fitsFile.replace(
'.fits', '_lineGrid.png')
print(f'\n- {i}: {objName}')
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import src.specsiser as sr
import pyneb as pn
c_KMpS = 299792.458
obsData = sr.loadConfData('./xshooter_LzLCS.ini')
data_folder = Path(obsData['data_location']['data_folder'])
results_folder = Path(obsData['data_location']['results_folder'])
objfile_list = obsData['data_location']['objfile_list']
sigmafile_list = obsData['data_location']['sigmafile_list']
objRef_list = obsData['data_location']['ref_list']
maskfile = obsData['data_location']['generalMask']
wmin_array = obsData['sample_data']['w_min_array']
wmax_array = obsData['sample_data']['w_max_array']
norm_flux = obsData['sample_data']['norm_flux']
z_obj = obsData['sample_data']['z_obj']
profile_conf = obsData['line_fitting']
# DF_list = [None, None]
# for i, objName in enumerate(objRef_list):
#
# # input data
# lineslog_file = results_folder/f'{objName}_linesLog.txt'
#
# # Load data
# linesDF = sr.lineslogFile_to_DF(lineslog_file)
def colorChooser(ObsRatio, TheRatio):
if (TheRatio * 0.95 < ObsRatio < TheRatio * 1.05):
color = 'ForestGreen' # 'green'#
elif (TheRatio * 0.90 < ObsRatio < TheRatio * 1.10):
color = 'YellowOrange' # 'yellow'#
else:
color = 'BrickRed'
return color
# Import the observation data
obsData = sr.loadConfData('../flux_comparison.ini', group_variables=False)
linesFile = Path(
'D:/Pycharm Projects/spectra-synthesizer/src/specsiser/literature_data/lines_data.xlsx'
)
linesDb = pd.read_excel(linesFile, sheet_name=0, header=0, index_col=0)
data_folder = Path(obsData['file_information']['data_folder'])
fileList = list_files(data_folder, '.fits')
addressList = list(data_folder / file for file in fileList)
fluxNorm = obsData['sample_data']['norm_flux']
tableHeaders = [
r'$\lambda(\AA)$', '$EW(\AA)$', '$F_{intg}(\AA)$', '$F_{gauss}(\AA)$'
]
# Analyse the spectrum
for i, file_address in enumerate(addressList):
from src.specsiser.data_printing import PdfPrinter, latex_labels, numberStringFormat
from pathlib import Path
import pandas as pd
def percent_func(current_value, ref_value):
return f' $({(1 - (current_value / ref_value)) * 100:.1f}\%)$'
def percent_func_txt(current_value, ref_value):
return f'{(1 - (current_value / ref_value)) * 100:.1f}'
conf_file_address = '../../../papers/gtc_greenpeas/gtc_greenpeas_data.ini'
obsData = sr.loadConfData(conf_file_address,
objList_check=True,
group_variables=False)
objList = obsData['file_information']['object_list']
resultsFolder = Path(obsData['file_information']['results_folder'])
tables_folder = Path(obsData['file_information']['tables_folder'])
ext = 'BR'
cycle = 'it3'
combined_line_dict = {
'O2_3726A_m': 'O2_3726A-O2_3729A',
'O2_7319A_m': 'O2_7319A-O2_7330A',
'S2_6716A_m': 'S2_6716A-S2_6731A'
}
for i, obj in enumerate(objList):
cHbeta_key = cfg_dict[objName]['cHbeta_label']
output_dict['cHbeta'] = measurements_dict['Extinction_it3'][cHbeta_key]
# Electron parameters
conversion_dict = {'n_e': 'ne', 'T_low': 'Te_low', 'T_high': 'Te_high'}
for fit_label, measure_label in conversion_dict.items():
if measure_label in measurements_dict['it3_electron_parameters']:
output_dict[fit_label] = measurements_dict[
'it3_electron_parameters'][measure_label]
return output_dict
conf_file_address = '../../../papers/gtc_greenpeas/gtc_greenpeas_data.ini'
obsData = sr.loadConfData(conf_file_address,
objList_check=True,
group_variables=False)
objList = obsData['file_information']['object_list']
dataFolder = Path(obsData['file_information']['data_folder'])
resultsFolder = Path(obsData['file_information']['results_folder'])
fileList = obsData['file_information']['files_list']
ext = 'BR'
cycle = 'it3'
combined_line_dict = {
'O2_3726A_m': 'O2_3726A-O2_3729A',
'O2_7319A_m': 'O2_7319A-O2_7330A'
}
# Analyse the spectrum
if os.name != 'nt':
conf_file_address = '../../../papers/gtc_greenpeas/gtc_greenpeas_data.ini'
dataFolder = Path(
'/home/vital/Dropbox/Astrophysics/Papers/gtc_greenpeas/data')
resultsFolder = Path(
'/home/vital/Dropbox/Astrophysics/Papers/gtc_greenpeas/treatment')
starlight_folder = Path('/home/vital/Dropbox/Astrophysics/Tools/Starlight')
else:
conf_file_address = '../../../papers/gtc_greenpeas/gtc_greenpeas_data.ini'
dataFolder = Path('D:/Dropbox/Astrophysics/Papers/gtc_greenpeas/data')
resultsFolder = Path(
'D:/Dropbox/Astrophysics/Papers/gtc_greenpeas/treatment')
starlight_folder = Path('D:/Dropbox/Astrophysics/Tools/Starlight')
obsData = sr.loadConfData(conf_file_address,
objList_check=True,
group_variables=False)
objList = obsData['file_information']['object_list']
fileList = obsData['file_information']['files_list']
idx_band = int(obsData['file_information']['band_flux'])
z_array = obsData['sample_data']['z_array']
wmin_array = obsData['sample_data']['wmin_array']
wmax_array = obsData['sample_data']['wmax_array']
arm_wave_boundary = obsData['sample_data']['w_div']
w_div_array = obsData['sample_data']['w_div']
red_law = obsData['sample_data']['red_law']
RV = obsData['sample_data']['RV']
import numpy as np
import src.specsiser as sr
from pathlib import Path
from astro.data.muse.common_methods import voxel_security_check, fits_db
from timeit import default_timer as timer
from astropy.table import Table
from astropy.io import fits
from matplotlib import pyplot as plt, rcParams, cm, colors
from astropy.wcs import WCS
import time
# Declare data and files location
obsData = sr.loadConfData('../muse_greenpeas.ini', group_variables=False)
objList = obsData['data_location']['object_list']
fileList = obsData['data_location']['file_list']
fitsFolder = Path(obsData['data_location']['fits_folder'])
dataFolder = Path(obsData['data_location']['data_folder'])
resultsFolder = Path(obsData['data_location']['results_folder'])
z_objs = obsData['sample_data']['z_array']
pertil_array = obsData['sample_data']['percentil_array']
noise_region = obsData['sample_data']['noiseRegion_array']
norm_flux = obsData['sample_data']['norm_flux']
verbose = True
for i, obj in enumerate(objList):
if i == 0:
# Data location
import numpy as np
from pathlib import Path
import src.specsiser as sr
from astro.papers.gtc_greenpeas.common_methods import check_previous_measurements
conf_file_address = '../../../papers/gtc_greenpeas/gtc_greenpeas_data.ini'
obsData = sr.loadConfData(conf_file_address, objList_check=True, group_variables=False)
objList = obsData['file_information']['object_list']
dataFolder = Path(obsData['file_information']['data_folder'])
resultsFolder = Path(obsData['file_information']['results_folder'])
fileList = obsData['file_information']['files_list']
ext = 'BR'
cycle = 'it3'
combined_line_dict = {'O2_3726A_m': 'O2_3726A-O2_3729A', 'O2_7319A_m': 'O2_7319A-O2_7330A'}
# Analyse the spectrum
for i, obj in enumerate(objList):
if i < 3:
# Declare input files
print(f'- Treating object ({i}): {obj}')
objFolder = resultsFolder / f'{obj}'
fits_file = dataFolder / f'{obj}_{ext}.fits'
objMask = dataFolder / f'{obj}_{ext}_mask.txt'
results_file = objFolder / f'{obj}_{ext}_measurements.txt'
lineLog_file = objFolder / f'{obj}_{ext}_linesLog_{cycle}.txt'
# Declare output files
from pathlib import Path
import numpy as np
import pandas as pd
import src.specsiser as sr
from astro.papers.gtc_greenpeas.common_methods import
from timeit import default_timer as timer
from physical_model.gasEmission_functions import gridInterpolatorFunction
conf_file_address = '../../../papers/gtc_greenpeas/gtc_greenpeas_data.ini'
obsData = sr.loadConfData(conf_file_address, objList_check=True, group_variables=False)
objList = obsData['file_information']['object_list']
dataFolder = Path(obsData['file_information']['data_folder'])
resultsFolder = Path(obsData['file_information']['results_folder'])
fileList = obsData['file_information']['files_list']
red_law = obsData['sample_data']['red_law']
RV = obsData['sample_data']['RV']
ext = 'BR'
cycle = 'it3'
HCm_folder = '/home/vital/Dropbox/Astrophysics/Tools/HCm-Teff_v5.01/'
# HCm_folder = 'D:/Dropbox/Astrophysics/Tools/HCm-Teff_v5.01/'
# grid_file = 'C17_bb_Teff_30-90_pp.dat'
conversion_dict = dict(O2_3726A_m='OII_3727',
Ne3_3869A='NeIII_3868',
O3_4363A='OIII_4363',
O3_4959A='OIII_4959',
import src.specsiser as sr
from pathlib import Path
from matplotlib import pyplot as plt, rcParams
from astropy.wcs import WCS
from src.specsiser.print.plot import STANDARD_PLOT
from astro.data.muse.common_methods import compute_line_flux_image, image_array_binning, VoxelPlotter
lineAreas = {
'H1_6563A': (6533.0, 6596.0),
'S3_6312A': (6310.0, 6319.0),
'O3_5007A': (4999.0, 5025.0),
'O3_4363A': (4355.0, 4374.0)
}
# Declare data and files location
obsData = sr.loadConfData('muse_J0925.ini', group_variables=False)
objList = np.array([obsData['sample_data']['object_list']])
fileList = np.array([obsData['sample_data']['file_list']])
dataFolder = Path(obsData['sample_data']['data_folder'])
resultsFolder = Path(obsData['sample_data']['results_folder'])
z_objs = np.array([obsData['sample_data']['z_array']])
pertil_array = obsData['sample_data']['percentil_array']
norm_flux = 1
for i, obj in enumerate(objList):
# Data location
cube_address_i = dataFolder / fileList[i]
objFolder = resultsFolder / obj
db_addresss = objFolder / f'{obj}_database.txt'
import numpy as np
import src.specsiser as sr
from src.specsiser.data_printing import latex_labels
from pathlib import Path
from astro.papers.muse_CGCG007.muse_CGCG007_methods import voxel_security_check
from timeit import default_timer as timer
from astropy.table import Table
from astropy.io import fits
from matplotlib import pyplot as plt, rcParams, cm, colors
from astropy.wcs import WCS
from src.specsiser.tools.line_measure import STANDARD_PLOT
# Declare data and files location
obsData = sr.loadConfData('../muse_CGCG007.ini')
objList = obsData['data_location']['object_list']
fileList = obsData['data_location']['file_list']
fitsFolder = Path(obsData['data_location']['fits_folder'])
dataFolder = Path(obsData['data_location']['data_folder'])
resultsFolder = Path(obsData['data_location']['results_folder'])
voxel_grid_size = obsData['sample_data']['grid_shape_array']
verbose = True
for i, obj in enumerate(objList):
# Data location
objFolder = resultsFolder / obj
voxelFolder = resultsFolder / obj / 'voxel_data'
db_address = objFolder / f'{obj}_database.fits'
fits_results_file = resultsFolder / obj / f'{obj}_chemical.fits'
import numpy as np
import src.specsiser as sr
from src.specsiser.data_printing import PdfPrinter, latex_labels, numberStringFormat
from pathlib import Path
def percent_func(current_value, ref_value):
return f' $({(1 - (current_value / ref_value)) * 100:.1f}\%)$'
conf_file_address = '../../../papers/gtc_greenpeas/gtc_greenpeas_data.ini'
obsData = sr.loadConfData(conf_file_address, objList_check=True, group_variables=False)
objList = obsData['file_information']['object_list']
resultsFolder = Path(obsData['file_information']['results_folder'])
tables_folder = Path(obsData['file_information']['tables_folder'])
ext = 'BR'
cycle = 'it3'
combined_line_dict = {'O2_3726A_m': 'O2_3726A-O2_3729A',
'O2_7319A_m': 'O2_7319A-O2_7330A',
'S2_6716A_m': 'S2_6716A-S2_6731A'}
# obj = 'gp121903'
for i, obj in enumerate(objList):
if i < 3:
objFolder = resultsFolder / f'{obj}'
results_file = objFolder / f'{obj}_{ext}_measurements.txt'
lineLog_file = objFolder / f'{obj}_{ext}_linesLog_{cycle}.txt'
from pathlib import Path
from astro.data.muse.common_methods import background_color, DARK_PLOT, label_Conver, latex_Conver, dinamicLines
from timeit import default_timer as timer
from astropy.table import Table
from astropy.io import fits
from matplotlib import pyplot as plt, rcParams, cm, colors
from astropy.wcs import WCS
# Declare data and files location
conf_file = Path(
'/home/vital/PycharmProjects/vital_tests/astro/data/muse/muse_greenpeas.ini'
)
seminar_folder = Path(
'/home/vital/Dropbox/Astrophysics/Seminars/UniVapo 2021/')
obsData = sr.loadConfData(conf_file, group_variables=False)
objList = obsData['data_location']['object_list']
fileList = obsData['data_location']['file_list']
fitsFolder = Path(obsData['data_location']['fits_folder'])
dataFolder = Path(obsData['data_location']['data_folder'])
resultsFolder = Path(obsData['data_location']['results_folder'])
voxel_grid_size = obsData['sample_data']['grid_shape_array']
verbose = True
for i, obj in enumerate(objList):
if i == 0:
# Data location
import lmfit
import numpy as np
import pandas as pd
import src.specsiser as sr
from matplotlib import pyplot as plt, rcParams
from pathlib import Path
# Import the observation data
obsData = sr.loadConfData(
'../../../papers/gtc_greenpeas/gtc_greenpeas_data.ini',
group_variables=False)
linesFile = Path(
'D:/Pycharm Projects/spectra-synthesizer/src/specsiser/literature_data/lines_data.xlsx'
)
data_folder = Path(obsData['file_information']['data_folder'])
file_list = obsData['file_information']['files_list']
addressList = list(data_folder / file for file in file_list)
# Analyse the spectrum
for i, file_address in enumerate(addressList):
# Get fits data
wave, flux, header = sr.import_fits_data(file_address, instrument='OSIRIS')
z_mean = obsData['sample_data']['z_array'][i]
wmin_array, wmax_array = obsData['sample_data']['wmin_array'], obsData[
'sample_data']['wmax_array']
# Define wave and flux ranges
wave_rest = wave / (1 + z_mean)
idx_wave = (wave_rest >= wmin_array[i]) & (wave_rest <= wmax_array[i])
from pathlib import Path
import numpy as np
import src.specsiser as sr
import pandas as pd
import astropy.coordinates as coord
import astropy.units as u
from matplotlib import pyplot as plt
import mplcursors
megara_library_stars = ['HR1544', 'HR5501', 'HR5501', 'HR5501', 'HR1544']
conf_file = Path('../obsConf.ini')
obsData = sr.loadConfData(conf_file)
data_folder = Path(obsData['data_location']['data_folder'])
results_folder = Path(obsData['data_location']['results_folder'])
targets_file = '/home/vital/Dropbox/Astrophysics/TelescopeTime/MEGARA_NOexcess_Gradients_2021/phase2/targets_coordinates.txt'
osirisStdStars_file = '/home/vital/Dropbox/Astrophysics/TelescopeTime/Standard_Stars/osirisStandardStars.csv'
esoStdStars_file = '/home/vital/Dropbox/Astrophysics/TelescopeTime/Standard_Stars/esoStandardStars.csv'
osirisStdStarsDF = pd.read_csv(osirisStdStars_file, header=0, index_col=0)
osirisStdStarsDF['type'] = 'Osiris Stars'
esoStdStarsDF = pd.read_csv(esoStdStars_file, header=0, index_col=0)
esoStdStarsDF['type'] = 'ESO Stars'
targetsDF = pd.read_csv(targets_file, delim_whitespace=True, header=0, index_col=0)
targetsDF['type'] = 'Targets'
def reconstruct_wavelength(header):
dw = header['CDELT3']
w_min = header['CRVAL3']
nPixels = header['NAXIS3']
w_max = w_min + dw * nPixels
return np.linspace(w_min, w_max, nPixels, endpoint=False)
# fits_folder = Path('/home/vital/Astro-data/Observations/MUSE - Amorin/FADO_analysis/Z4SalpP2000/')
# conf_file_address = '/home/vital/PycharmProjects/vital_tests/astro/papers/muse_CGCG007/muse_CGCG007.ini'
fits_folder = Path(
r'S:\Astro_data\Observations\MUSE - Amorin\FADO_analysis\Z4SalpP2000')
conf_file_address = r'D:\Pycharm Projects\vital_tests\astro\papers\muse_CGCG007\muse_CGCG007.ini'
obsData = sr.loadConfData(conf_file_address, group_variables=False)
fits_1 = 'cgcg007025_HBIN024_FDres2_2DnebSED.fits'
fits_2 = 'cgcg007025_HBIN024_FDres2_2DnoNEB.fits'
fits_3 = 'cgcg007025_HBIN024_FDres2_3DnebSED.fits'
fits_4 = 'cgcg007025_HBIN024_FDres2_3DnoNEB.fits'
fits_5 = 'cgcg007025_HBIN024_FDres2_3DOBS.fits'
fits_6 = 'cgcg007025_HBIN024_FDres2_3DstelFIT.fits'
fits_7 = 'cgcg007025_HBIN024_FDres2_2DnebSED.fits'
fits_8 = 'red_cgcg007025_3D_SLres_HBIN024_RY.fits'
fits_9 = 'red_cgcg007025_HBIN024_FDres2.fits'
coord = (170, 170)
file_path = Path(r'S:\Astro_data\Observations\MUSE - Amorin\CGCG007.fits')
wave, cube, header = sr.import_fits_data(file_path, instrument='MUSE')
z_obj = 0.004691
color = 'black'
elif (TheRatio < ObsRatio * 0.95):
color = 'ForestGreen' # 'green'#
elif (TheRatio < ObsRatio * 0.85 ):
color = 'YellowOrange' # 'yellow'#
else:
color = 'YellowOrange' # 'yellow'#
return color
# Import the observation data
obsData = sr.loadConfData('D:/Pycharm Projects/vital_tests/astro/data/SDSS/flux_comparison.ini', group_variables=False)
linesFile = Path('D:/Pycharm Projects/spectra-synthesizer/src/specsiser/literature_data/lines_data.xlsx')
linesDb = pd.read_excel(linesFile, sheet_name=0, header=0, index_col=0)
data_folder = Path(obsData['file_information']['data_folder'])
fileList = list_files(data_folder, '.fits')
addressList = list(data_folder / file for file in fileList)
flux_norm = obsData['sample_data']['norm_flux']
tableHeaders = [r'Object name', r'$EW(H\beta)$ $(\AA)$', r'$c(H\beta)$', r'$F(H\alpha)/F(H\beta)$']
scaleTable = 1000.0
fitsFolder, fitsFile = addressList[0].parent, addressList[0].name
sampleFolder, tableName = fitsFolder/'sample_data', 'sample_properties'
pdf = PdfPrinter()
pdf.create_pdfDoc(sampleFolder / tableName, pdf_type='table')
import numpy as np
import src.specsiser as sr
import time
import lime
from pathlib import Path
from astro.papers.muse_CGCG007.muse_CGCG007_methods import voxel_security_check
from astropy.io import fits
import os
# Declare data and files location
obsData = sr.loadConfData('../muse_CGCG007.ini')
objList = obsData['data_location']['object_list']
fileList = obsData['data_location']['file_list']
fitsFolder = Path(obsData['data_location']['fits_folder'])
dataFolder = Path(obsData['data_location']['data_folder'])
resultsFolder = Path(obsData['data_location']['results_folder'])
z_objs = obsData['sample_data']['z_array']
pertil_array = obsData['sample_data']['percentil_array']
noise_region = obsData['sample_data']['noiseRegion_array']
norm_flux = obsData['sample_data']['norm_flux']
merge_dict = {'O2_7319A_b': 'O2_7319A-O2_7330A'}
for i, obj in enumerate(objList):
# Data location
objFolder = resultsFolder / obj
db_addresss = objFolder / f'{obj}_database.fits'
fitsLog_addresss = objFolder / f'{obj}_linesLog.fits'
from pathlib import Path
import src.specsiser as sr
# Declare the data location
obsFitsFile = Path('./sample_data/gp121903_BR.fits')
lineMaskFile = Path('./sample_data/gp121903_BR_mask.txt')
cfgFile = Path('./sample_data/gtc_greenpeas_data.ini')
# Load the data
obsConf = sr.loadConfData(cfgFile, objList_check=True, group_variables=False)
maskDF = sr.lineslogFile_to_DF(lineMaskFile)
wave, flux, header = sr.import_fits_data(obsFitsFile, instrument='OSIRIS')
user_conf = obsConf['gp121903_line_fitting']
# Declare line measuring object
lm = sr.LineMesurer(wave,
flux,
redshift=obsConf['sample_data']['z_array'][2],
normFlux=obsConf['sample_data']['norm_flux'])
lm.plot_spectrum()
# Find lines
norm_spec = lm.continuum_remover(
noiseRegionLims=obsConf['sample_data']['noiseRegion_array'])
obsLinesTable = lm.line_finder(
norm_spec,
noiseWaveLim=obsConf['sample_data']['noiseRegion_array'],
intLineThreshold=3)
matchedDF = lm.match_lines(obsLinesTable, maskDF)
lm.plot_spectrum(obsLinesTable=obsLinesTable,
matchedLinesDF=matchedDF,
wget.download(url, out_file_address)
return
lineConversion = {
'He2_4685A': 'He_II 4685',
'H1_4861A': 'H_beta',
'H1_6563A': 'H_alpha',
'O3_4363A': '[O_III] 4363',
'O3_4959A': '[O_III] 4959',
'O3_4959A': '[O_III] 5007',
'S3_6312A': '[S_III] 6312'
}
conf_file_address = '../sampleHeII.ini'
obsData = sr.loadConfData(conf_file_address)
fits_folder = pathlib.Path(obsData['data_location']['fits_folder'])
data_folder = pathlib.Path(obsData['data_location']['treatment_folder'])
objList_file = obsData['data_location']['sample_file']
objDF = pd.read_csv(data_folder / objList_file,
delim_whitespace=True,
comment='#',
names=['PLATEID', 'MDJ_1', 'FIBERID_1'])
normFlux = obsData['sample_data']['norm_flux']
# Loop through the sample list
err_dict = {}
n_objs = objDF.index.values.size
from pathlib import Path
import src.specsiser as sr
from fitelp.read_spectra import read_spectra
data_folder = Path('D:/Google drive/Astrophysics/Datos/broad_multiComponent')
obsData = sr.loadConfData('broad_conf.ini', group_variables=False)
z_mean, z_err = sr.redshift_calculation(obsData['sample_data']['obs_waves'],
obsData['sample_data']['emis_waves'])
norm_flux = obsData['sample_data']['norm_flux']
obj_list = ['B6479s', 'R8731s']
for idx_obj, obj in enumerate(['R8731s']):
fits_address = data_folder / f'{obj}.fits'
wave_data, flux_data = read_spectra(fits_address, scaleFlux=1)
mask_address = data_folder / f'{obj}_mask.txt'
mask_DF = sr.lineslogFile_to_DF(mask_address)
# Individual line measurement
lm = sr.LineMesurer(wave_data[0],
flux_data[0],
normFlux=norm_flux,
redshift=z_mean)
fitConf = obsData[f'default_line_fitting']
# Loop through the lines
for lineLabel in ['H1_6563A_b']:
# Get line ranges
lineWaves = mask_DF.loc[lineLabel, 'w1':'w6'].values
from astro.data.muse.common_methods import compute_line_flux_image, lineAreas, red_corr_HalphaHbeta_ratio, default_linelog_types
from astropy.io import fits
from matplotlib import pyplot as plt, rcParams, cm, colors
from astropy.wcs import WCS
import time
from lmfit.models import PolynomialModel, Model
from src.specsiser.tools.line_fitting import gaussian_model, linear_model, c_KMpS
import fitelp.constants as constants
from fitelp.make_latex_tables import average_velocities_table_to_latex, halpha_regions_table_to_latex
from fitelp.bpt_plotting import bpt_plot
from fitelp.kinematics_calculations import RegionCalculations
from fitelp.fit_line_profiles import plot_profiles
from fitelp.line_profile_info import RegionParameters
# Declare data and files location
obsData = sr.loadConfData('../muse_greenpeas.ini', group_variables=False)
objList = obsData['data_location']['object_list']
fileList = obsData['data_location']['file_list']
fitsFolder = Path(obsData['data_location']['fits_folder'])
dataFolder = Path(obsData['data_location']['data_folder'])
resultsFolder = Path(obsData['data_location']['results_folder'])
z_objs = obsData['sample_data']['z_array']
pertil_array = obsData['sample_data']['percentil_array']
noise_region = obsData['sample_data']['noiseRegion_array']
norm_flux = obsData['sample_data']['norm_flux']
dict_errs = {}
dict_nan_values = {}
verbose = False
import numpy as np
from pathlib import Path
import src.specsiser as sr
# Import the observation data
obsData = sr.loadConfData(
'../../../papers/gtc_greenpeas/gtc_greenpeas_data.ini',
group_variables=False)
linesFile = Path(
'D:/Pycharm Projects/spectra-synthesizer/src/specsiser/literature_data/lines_data.xlsx'
)
data_folder = Path(obsData['file_information']['data_folder'])
file_list = obsData['file_information']['files_list']
addressList = list(data_folder / file for file in file_list)
# Analyse the spectrum
for i, file_address in enumerate(addressList):
if i == 2:
# Establish files location
objName = obsData['file_information']['object_list'][i]
fitsFolder, fitsFile = file_address.parent, file_address.name
lineLogFolder, lineLogFile = fitsFolder / 'flux_analysis', fitsFile.replace(
'.fits', '_linesLog.txt')
simFolder, simConf = fitsFolder / 'chemical_analysis', fitsFile.replace(
'_BR.fits', '_config.txt')
inputLinesLog = f'{objName}_inputLinesLog.txt'
outputDb = f'{objName}_fitting.db'
outputTxt = f'{objName}_fitting.txt'
print(f'- {objName}')
defaultConf = STANDARD_PLOT.copy()
defaultConf['axes.titlesize'] = 20
rcParams.update(defaultConf)
# # Declare data and files location
# obsData = sr.loadConfData('../muse_greenpeas.ini', group_variables=False)
# objList = obsData['data_location']['object_list']
# fileList = obsData['data_location']['file_list']
# fitsFolder = Path(obsData['data_location']['fits_folder'])
# dataFolder = Path(obsData['data_location']['data_folder'])
# resultsFolder = Path(obsData['data_location']['results_folder'])
# voxel_grid_size = obsData['sample_data']['grid_shape_array']
# coordinates_keys_list = obsData['data_location']['wcs_key_list']
# Declare data and files location
obsConf = sr.loadConfData('J0838_cubes.ini')
fitsFolder = Path(obsConf['data_location']['fits_folder'])
dataFolder = Path(obsConf['data_location']['data_folder'])
resultsFolder = Path(obsConf['data_location']['results_folder'])
fileList = obsConf['data_location']['file_list']
objList = obsConf['data_location']['object_list']
z_list = obsConf['sample_data']['z_array']
norm_flux = obsConf['sample_data']['norm_flux']
percentil_array = obsConf['sample_data']['percentil_array']
voxel_grid_size = obsConf['sample_data']['grid_shape_array']
# Store emissivity ratios at standard conditions
H1 = pn.RecAtom('H', 1)
temp, den = 10000.0, 100.0
import numpy as np
import pandas as pd
from pathlib import Path
import src.specsiser as sr
import scipy as spy
import exoplanet as xo
import time
from astropy.io import fits
from astropy.table import Table
from astro.data.muse.common_methods import grid_HII_CHI_mistry_conversion as labelConver, grid_columns
from astro.papers.gtc_greenpeas.common_methods import epm_HII_CHI_mistry
# Declare data and files location
file_conf = '/home/vital/PycharmProjects/vital_tests/astro/data/muse/muse_greenpeas.ini'
obsData = sr.loadConfData(file_conf, group_variables=False)
objList = obsData['data_location']['object_list']
fileList = obsData['data_location']['file_list']
fitsFolder = Path(obsData['data_location']['fits_folder'])
dataFolder = Path(obsData['data_location']['data_folder'])
resultsFolder = Path(obsData['data_location']['results_folder'])
z_objs = obsData['sample_data']['z_array']
pertil_array = obsData['sample_data']['percentil_array']
noise_region = obsData['sample_data']['noiseRegion_array']
norm_flux = obsData['sample_data']['norm_flux']
# Load the data
grid_file = Path(
'/home/vital/Dropbox/Astrophysics/Data/muse-Ricardo/Data/HII-CHI-mistry_1Myr_grid.csv'
)
#
# print(trace)
# print(pymc3.summary(trace))
# az.plot_trace(trace)
# plt.show()
# az.plot_posterior(trace)
# plt.show()
# -------------------------------------- Test 2 --------------------------------------------------
# Load synthetic observation
linesLogAddress = user_folder / f'Teff_LogU_epmGrids_linesLog.txt'
simulationData_file = user_folder / f'Teff_LogU_epmGrids_config.txt'
# Load simulation parameters
objParams = sr.loadConfData(simulationData_file, group_variables=False)
# Load emission lines
merged_lines = {'O2_3726A_m': 'O2_3726A-O2_3729A', 'O2_7319A_m': 'O2_7319A-O2_7330A'}
objLinesDF = sr.import_emission_line_data(linesLogAddress, include_lines=objParams['inference_model_configuration']['input_lines'], exclude_lines=['S3_9069A'])
# Establish lines used in photo-ionization model
lineLabels = objLinesDF.index.values
lines_Grid = np.array(list(gridLineDict.keys()))
idx_analysis_lines = np.in1d(lineLabels, lines_Grid)
lineFluxes = objLinesDF.obsFlux.values
lineErr = objLinesDF.obsFluxErr.values
lineWaves = objLinesDF.wavelength.values
# Extinction parameters
objRed = sr.ExtinctionModel(Rv=objParams['simulation_properties']['R_v'],
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import src.specsiser as sr
c_KMpS = 299792.458
obsData = sr.loadConfData('./xshooter_LzLCS.ini')
data_folder = Path(obsData['data_location']['data_folder'])
results_folder = Path(obsData['data_location']['results_folder'])
objfile_list = obsData['data_location']['objfile_list']
sigmafile_list = obsData['data_location']['sigmafile_list']
objRef_list = obsData['data_location']['ref_list']
maskfile = obsData['data_location']['generalMask']
wmin_array = obsData['sample_data']['w_min_array']
wmax_array = obsData['sample_data']['w_max_array']
norm_flux = obsData['sample_data']['norm_flux']
z_obj = obsData['sample_data']['z_obj']
profile_conf = obsData['line_fitting']
DF_list = [None, None]
for i, objName in enumerate(objRef_list):
# input data
lineslog_file = results_folder / f'{objName}_linesLog.txt'
# Load data
linesDF = sr.lineslogFile_to_DF(lineslog_file)
DF_list[i] = linesDF
