wave, flux, header = sr.import_fits_data(spec_file,
instrument='xshooter',
frame_idx=0)
wave_sigma, sigma, header_sigma = sr.import_fits_data(
sigm_file, instrument='xshooter', frame_idx=0)
lm = sr.LineMesurer(wave,
flux,
crop_waves=[wmin_array[i], wmax_array[i]],
input_err=sigma,
normFlux=norm_flux,
redshift=z_obj)
# lm.plot_spectrum(obsLinesTable=obsLinesTable, matchedLinesDF=maskLinesDF, specLabel=f'{objName}')
mask_local = data_folder / f'{objName}_mask'
mask_local_df = sr.lineslogFile_to_DF(mask_local)
obsLines = mask_local_df.index.values
for j, lineLabel in enumerate(obsLines):
wave_regions = mask_local_df.loc[lineLabel, 'w1':'w6'].values
if lineLabel in ['O3_5007A_b']:
# wave_regions[2] = 8400.0/(1+lm.redshift)
# wave_regions[3] = 8445.0/(1+lm.redshift)
lm.fit_from_wavelengths(lineLabel,
wave_regions,
user_conf=profile_conf)
lm.print_results(show_plot=True,
show_fit_report=False,
log_scale=False,
frame='rest')
objMask = dataFolder / f'{obj}_{ext}_mask.txt'
results_file = objFolder / f'{obj}_{ext}_measurements.txt'
lineLog_file = objFolder / f'{obj}_{ext}_linesLog_it3.txt'
# output spectrum
spectrum_ssp = objFolder / f'{obj}_starlight_spectrum.txt'
# Load the data
wave, flux_array, header = sr.import_fits_data(fits_file,
instrument='OSIRIS')
flux = flux_array[idx_band][0] if ext in ('_B', '_R') else flux_array
lm = sr.LineMesurer(wave,
flux,
redshift=z_array[i],
crop_waves=(wmin_array[i], wmax_array[i]))
linesDF = sr.lineslogFile_to_DF(lineLog_file)
# Add new masks
linesDF = sr.lineslogFile_to_DF(lineLog_file)
ini_mask, end_points = obsData[obj]['ini_mask_array'], obsData[obj][
'end_mask_array']
labels = ['cont_mask_' + str(int(x)) for x in ini_mask]
for j, label_mask in enumerate(labels):
linesDF.loc[labels[j], ['w3', 'w4']] = ini_mask[j], end_points[j]
# Compute the mask array
mask_array = np.zeros(lm.wave.size)
for mask_label in linesDF.index:
w_blue, w_red = linesDF.loc[mask_label, ['w3', 'w4']].values
idcs_mask = (lm.wave > w_blue) & (lm.wave < w_red)
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,
for i, obj in enumerate(objList):
if i == 0:
# Data location
objFolder = resultsFolder
cube_address_i = fitsFolder / fileList[i]
mask_address = dataFolder / obsConf['data_location']['mask_global']
db_address = objFolder / f'{obj}_database.fits'
# Load the data
wave, data, header = sr.import_fits_data(cube_address_i,
instrument='fits-cube',
frame_idx=0)
mask_global_DF = sr.lineslogFile_to_DF(mask_address)
# Declare voxels to analyse
flux5007_image = fits.getdata(db_address,
f'{ref_flux_line}_flux',
ver=1)
flux5007_levels = np.nanpercentile(flux5007_image, percentil_array)
ion, wavelength, latexLabel = sr.label_decomposition(
ref_flux_line, scalar_output=True)
for idx_level, flux_level in enumerate(flux5007_levels):
if idx_level + 1 < len(flux5007_levels):
if idx_level == 0:
maFlux_image = np.ma.masked_where(
import pathlib
import numpy as np
import pandas as pd
import src.specsiser as sr
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 = data_folder/f'AVO_dataframe.txt'
pertil_array = obsData['sample_data']['percentil_array']
# Load the list of objects
logDF = sr.lineslogFile_to_DF(objList_file)
arrayFluxLevel = np.percentile(logDF['He2_4685A'].values, pertil_array)
# Recover the group of objects
for i, flux_level in enumerate(arrayFluxLevel):
if i == 0:
idcs_spec = logDF['He2_4685A'] >= flux_level
else:
idcs_spec = (logDF['He2_4685A'] < arrayFluxLevel[i-1]) & (logDF['He2_4685A'] >= flux_level)
logDF.loc[idcs_spec, 'intensity_Group'] = i
df_file = data_folder/f'AVO_catalogue_dataframe.txt'
with open(df_file, 'wb') as output_file:
normFlux=normFlux,
crop_waves=(1 + z_i) *
np.array([4685 - 100, 5100]))
yield idx_obj, obj_name, lm.wave, lm.flux
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'])
catalogueDataframe = data_folder / f'AVO_catalogue_dataframe.txt'
normFlux = obsData['sample_data']['norm_flux']
# Load the list of objects
logDF = sr.lineslogFile_to_DF(catalogueDataframe)
# logDF['valid'] = False
# if 'valid' not in logDF:
# logDF['valid'] = False
print('empezamos', (logDF['valid'] == True).sum())
# for obj in ['1734-53034-56', '859-52317-170', '616-52442-101', '525-52295-193', '2956-54525-19', '2173-53874-491']:
# logDF.loc[obj, 'valid'] = True
#
# with open(catalogueDataframe, 'wb') as output_file:
# string_DF = logDF.to_string()
# output_file.write(string_DF.encode('UTF-8'))
first_obj = 0
list_groups = np.arange(8)
for idx_group in list_groups:
conf_file_address = '../../../papers/gtc_greenpeas/gtc_greenpeas_data.ini'
obsData = sr.loadConfData(conf_file_address, group_variables=False)
dataFolder = Path(obsData['file_information']['data_folder'])
resultsFolder = Path(obsData['file_information']['results_folder'])
fileList = obsData['file_information']['files_list']
color_dict = {'_SDSS': 'tab:purple', '_B': 'tab:blue', '_R': 'tab:red'}
label_dict = {'_SDSS': 'SDSS dr16', '_B': 'Blue arm - Osiris', '_R': 'Red arm - Osiris'}
counter = 0
for i, obj in enumerate(objList):
objFolder = resultsFolder/f'{obj}'
mainLinesLog = objFolder/f'{obj}_BR_linesLog.txt'
mainLogDF = sr.lineslogFile_to_DF(mainLinesLog)
flux_Hbeta, err_Hbeta = mainLogDF.loc['H1_4861A', 'intg_flux'], mainLogDF.loc['H1_4861A', 'intg_err']
flux_norm = ufloat(flux_Hbeta, err_Hbeta)
idcs_obsLines = ~mainLogDF.index.str.contains('_b')
obsLines = mainLogDF.loc[idcs_obsLines].index.values
linesFlux = mainLogDF.loc[idcs_obsLines, 'intg_flux'].values
LinesErr = mainLogDF.loc[idcs_obsLines, 'intg_err'].values
lineFluxArray = unumpy.uarray(linesFlux, LinesErr)
lineNormArray = lineFluxArray/flux_norm
ion_array, wave_array, latexLabel_array = sr.label_decomposition(obsLines, combined_dict=obsData['default_line_fitting'])
# Plot Configuration
wave = wave * 10 if objName != 'j131037' else wave
wave_sigma = wave_sigma * 10 if objName != 'j131037' else wave_sigma
trim_limits = [wmin_array[j], wmax_array[j]]
lm = sr.LineMesurer(wave,
flux,
crop_waves=trim_limits,
input_err=sigma,
normFlux=norm_flux,
redshift=z_obj[i])
if verbose:
lm.plot_spectrum(continuumFlux=lm.errFlux)
# Find lines
global_mask = data_folder / f'global_mask.txt'
global_mask_df = sr.lineslogFile_to_DF(global_mask)
lineLabel = 'H1_6563A_b'
wave_regions = global_mask_df.loc[lineLabel, 'w1':'w6'].values
lm.fit_from_wavelengths(lineLabel,
wave_regions,
user_conf=profile_conf)
w80 = lm.plot_line_velocity(plot_title=objName)
print(f'{objName} {w80} {lineLabel}')
# norm_spec = lm.continuum_remover(noiseRegionLims=norm_region)
# obsLinesTable = lm.line_finder(norm_spec, noiseWaveLim=norm_region, intLineThreshold=3)
# matchedDF = lm.match_lines(obsLinesTable, global_mask_df, find_line_borders=False)
# if verbose:
# lm.plot_spectrum(obsLinesTable=obsLinesTable, matchedLinesDF=matchedDF, specLabel=f'Emission line detection')
fluxImage_6312 = lineFlux_dict['S3_6312A']
fluxLevels_6312 = levelFlux_dict['S3_6312A']
int_level = fluxLevels_6312[-4]#[-4]
array_idcs_voxels = np.argwhere(fluxImage_6312 > int_level)
for idx_voxel, idx_pair in enumerate(array_idcs_voxels):
idx_j, idx_i = idx_pair
# Data location
outputDb = voxelFolder/f'{idx_j}-{idx_i}_fitting.db'
local_lineslog = voxelFolder/f'{idx_j}-{idx_i}_lineslog_{obj}.txt'
inputLinesLog = voxelFolder/f'{idx_j}-{idx_i}_inputLinesLog.txt'
# Asociate the corresponding flux to the appropiate line
linesDF = sr.lineslogFile_to_DF(local_lineslog)
if 'H1_4861A' in linesDF.index:
# Sampler object
obj1_model = sr.SpectraSynthesizer()
# Adjust the lines log for the fitting
flux_Hbeta = linesDF.loc['H1_4861A', 'intg_flux']
linesDF.insert(loc=1, column='obsFlux', value=np.nan)
linesDF.insert(loc=2, column='obsFluxErr', value=np.nan)
linesDF['obsFlux'] = linesDF['intg_flux']/flux_Hbeta
linesDF['obsFluxErr'] = linesDF['intg_err']/flux_Hbeta
linesDF.rename(columns={'wavelength': 'obsWave'}, inplace=True)
for ion in ['H1', 'He1', 'He2']:
idcs_ions = linesDF.ion == ion
from matplotlib import pyplot as plt, rcParams, cm, colors
from astropy.wcs import WCS
import time
conf_file_address = '../sampleHeII.ini'
obsData = sr.loadConfData(conf_file_address)
fits_folder = pathlib.Path(obsData['data_location']['fits_folder'])
treatment_folder = pathlib.Path(obsData['data_location']['treatment_folder'])
results_folder = pathlib.Path(obsData['data_location']['results_folder'])
catalogueDataframe = treatment_folder / f'AVO_catalogue_dataframe.txt'
normFlux = obsData['sample_data']['norm_flux']
noise_region = obsData['sample_data']['noiseRegion_array']
logDF = sr.lineslogFile_to_DF(catalogueDataframe)
idcs_obj = logDF.valid == True
objList = logDF.loc[idcs_obj].index.values
mask_global = treatment_folder / 'AVO_global_mask.txt'
maskDF = pd.read_csv(mask_global, delim_whitespace=True, header=0, index_col=0)
linesForced = [
'He2_4686A', 'O3_4959A', 'O3_5007A', 'H1_4861A', 'H1_6563A', 'N2_6584A',
'S2_6716A', 'S2_6731A'
]
columns = ['Eqw_Halpha', 'Eqw_Halpha_err']
properties_df = pd.DataFrame(index=objList, columns=columns)
properties_df_addresss = treatment_folder / 'AVO_properties_log_gaussian_flux.txt'
for line in linesForced:
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']
ef = EmissionFitting()
percentil_array = obsConf['sample_data']['percentil_array']
for i, obj in enumerate(objList):
if i == 0:
# Input data
objFolder = resultsFolder
cube_address = fitsFolder / fileList[i]
mask_global_DF = sr.lineslogFile_to_DF(
dataFolder / obsConf['data_location']['mask_global'])
# Output data
db_addresss = objFolder / f'{obj}_database.fits'
hdul_lineslog = fits.HDUList()
# Load the data
print('\n', db_addresss)
wave, data, header = import_fits_data(cube_address,
crval3=obsConf[obj]['CRVAL3'],
frame_idx=0)
for lineLabel in ['O3_5007A_b']:
lineWaves = mask_global_DF.loc[lineLabel, 'w1':'w6']
idcsLineRegion, idcsContRegion = ef.define_masks(
wave / (1 + z_list[i]), data, lineWaves)
