def p3(obs, Tlow, Tmid, Thigh, mean_dens, verbose=True):
#we associate the Te with each ion
Te_dic = {
'N2': Tlow,
'O2': Tlow,
'S2': Tlow,
'S3': Tmid,
'Ar3': Tmid,
'O3': Thigh,
'Ne3': Thigh
}
all_atoms = pn.getAtomDict(atom_list=obs.getUniqueAtoms())
# This is the dictionnary which will contain the ionic abundances
ab_dic = {}
# we use the following lines to determine the ionic abundances
ab_labels = [
'N2_6584A', 'O2_3727A+', 'O3_5007A', 'S2_6716A', 'S3_9069A',
'Ar3_7136A', 'Ne3_3869A'
]
for line in obs.getSortedLines():
if line.label in ab_labels:
ab = all_atoms[line.atom].getIonAbundance(line.corrIntens,
Te_dic[line.atom],
mean_dens,
to_eval=line.to_eval,
Hbeta=100)
if verbose:
print '{0:9s}'.format(line.label) + ' '.join(
['{0:>8.2f}'.format(t) for t in 12 + np.log10(ab)])
ab_dic[line.atom] = ab
return ab_dic
def printAllSources(self, at_set=None, predef=None):
"""
Print bibliographic sources of the adopted data sets of a list of atoms.
Usage:
pn.atomicData.printAllSources(['O3', 'Ar4', 'S2'])
pn.atomicData.printAllSources([O, S])
pn.atomicData.printAllSources()
Parameters:
- at_set a list or tuple containing the atoms. If not set,
print bibliographic sources for all the atoms in PyNeb's default dictionary
"""
self.calling = 'printAllSources'
if (type(at_set) == list) or (type(at_set) == tuple) or at_set == '':
at_dict = {}
for item in at_set:
atom = parseAtom(item)[0]
spec = parseAtom(item)[1]
if spec is '':
for ispec in SPEC_LIST:
try:
at_dict[atom + ispec] = pn.Atom(atom, ispec)
except:
pass
else:
at_dict[item] = pn.Atom(atom, spec)
elif at_set is not None:
pn.log_.error('The argument at_set must be a list or a tuple',
calling=self.calling)
return None
elif (at_set is None) and (predef is None):
at_dict = pn.getAtomDict()
elif predef in pn.atomicData.getAllPredefinedDict():
current = pn.atomicData.predefined
pn.atomicData.setDataFileDict(predef)
at_dict = pn.getAtomDict()
pn.atomicData.setDataFileDict(current)
else:
pn.log_.error(
'The argument predef must be the label of a predefined dictionary',
calling=self.calling)
for item in sorted(at_dict):
at_dict[item].printSources()
def LM_GetPyNebAtoms():
PyNeb_Atoms = list(pn.getAtomDict())
PyNeb_Atoms.append("He1")
PyNeb_Atoms.append("He2")
PyNeb_Atoms.append("H1")
PyNeb_Atoms.sort()
return PyNeb_Atoms
def plot_all(save=False):
pn.log_.level = 1
AA = pn.getAtomDict(OmegaInterp='Linear')
for diag in pn.diags_dict:
atom, diag_eval, err = pn.diags_dict[diag]
if atom in AA:
plt.figure()
grid = pn.EmisGrid(atomObj=AA[atom])
grid.plotContours(to_eval=diag_eval)
if save:
plt.savefig('{0}_{1}.pdf'.format(atom,
diag_eval.replace('/', '_')))
def plot_all(save=False):
pn.log_.level = 1
AA = pn.getAtomDict(OmegaInterp='Linear')
# Loop over all the diags stored in pn.core.diags.diags_dict
for diag in diags_dict:
atom, diag_eval, err = diags_dict[diag]
# Skip Fe III as they are so many
if (atom in AA) and (atom != 'Fe3'):
print atom
plt.figure()
grid = pn.EmisGrid(atomObj=AA[atom])
grid.plotContours(to_eval=diag_eval)
if save:
plt.savefig('{0}_{1}.pdf'.format(atom,
diag_eval.replace('/', '_')))
def _print_LINE_LABEL_LIST(*args, **kwargs):
"""
The parameters are passed to getAtomDict. May be the fits directory.
"""
##
# @todo we have to check what are the differences in LINE_LABEL_LIST when changing the fits files
all_atoms = pn.getAtomDict(*args, **kwargs)
print("LINE_LABEL_LIST = {}")
for atom in np.sort(all_atoms.keys()):
wave = all_atoms[atom].lineList
opt = (wave < 10000.)
ir = (wave >= 10000.)
lines_opt = ["'%.0fA'" % line for line in np.sort(wave[opt])]
lines_ir = ["'%.1fm'" % line for line in np.sort(wave[ir]) / 1.e4]
lines = lines_opt + lines_ir
print("LINE_LABEL_LIST['" + atom + "'] = [" + ', '.join(lines) + "]")
def get_ions_dict(self,
ions_list,
recomb_atoms=('H1', 'He1', 'He2'),
atomic_references=pn.atomicData.defaultDict):
# Check if the atomic dataset is the default one
if atomic_references == pn.atomicData.defaultDict:
pn.atomicData.resetDataFileDict()
pn.atomicData.removeFitsPath()
else:
pn.atomicData.includeFitsPath()
pn.atomicData.setDataFileDict(atomic_references)
# Generate the dictionary with pyneb ions
ionDict = pn.getAtomDict(ions_list)
# Remove r extension from recom 'H1', 'He1', 'He2' emissions
for ion_r in recomb_atoms:
recomb_key = f'{ion_r}r'
if recomb_key in ionDict:
ionDict[ion_r] = ionDict.pop(recomb_key)
return ionDict
pn.atomicData.getAllDataFilePaths()
# Remove it if you gave the wrong dir
pn.atomicData.removeDataFilePath('./')
# Set 'o_iii_TEST.fits' to be the OIII atom file
pn.atomicData.setDataFile('o_iii_TEST.fits', 'O3', 'atom')
# Check that it is read from the right place
pn.atomicData.getDirForFile('o_ii_atom_TEST.fits')
# Summarize all data used
pn.atomicData.printDirForAllFiles()
# define an atom with the new data
O2test = pn.Atom("O", 2)
# define all atoms at once and put them in a dictionary
# (all of them defined with the latest dataset selected)
atoms = pn.getAtomDict(
) #this generates a lot of warnings as not all element-spectrum combination exist
# see what atoms have been built
atoms
# build only some atoms
atoms = pn.getAtomDict(atom_list=['O1', 'O2', 'O3', 'N2', 'N3'])
# explore some specific atom in the atoms collection
atoms['N2'].elem
# if you want to be able to access them directly rather than through a dictionary:
for key in atoms.keys():
vars()[key] = atoms[key]
# for example
O2.elem
def import_atomic_data(self,
atomic_data=None,
ftau_coefficients=None,
tempGrid=None,
denGrid=None):
# Load nebular continuum constants
self.loadNebCons(self.externalDataFolder)
# # Import default database with line labels if not provided # TODO not the right place for loading this file
# if atomic_data is not None:
# linesDataFile = atomic_data if atomic_data is not None else path.join(self.externalDataFolder, self.config['linesData_file'])
# self.linesDb = read_excel(linesDataFile, sheet_name=0, header=0, index_col=0)
# Additional code to adapt to old lines log labelling
self.linesDb['pyneb_code'] = self.linesDb['pyneb_code'].astype(str)
idx_numeric_pynebCode = ~self.linesDb['pyneb_code'].str.contains('A+')
self.linesDb.loc[idx_numeric_pynebCode,
'pyneb_code'] = self.linesDb.loc[
idx_numeric_pynebCode,
'pyneb_code'] #.astype(int)
self.linesDb['ion'].apply(str)
print('\n-- Loading atoms data with PyNeb')
# Load user atomic data references
# TODO is the atomic data really changing?
for reference in self.config['atomic_data_list']:
pn.atomicData.setDataFile(reference)
# Generate the dictionary with pyneb ions
self.ionDict = pn.getAtomDict(
atom_list=np.unique(self.linesDb.ion.values.astype(str)))
# Preparing Hbeta references
self.Hbeta_label = 'H1_4861A'
self.Hbeta_wave = 4862.683
self.Hbeta_pynebCode = '4_2'
# Import Optical depth function
ftauFile = ftau_coefficients if ftau_coefficients is not None else path.join(
self.externalDataFolder, self.config['ftau_file'])
self.ftau_coeffs = self.import_optical_depth_coeff_table(ftauFile)
# Declare diagnostic ratios
self.diagnosDict = {
'ROIII': {
'num': np.array([4363]),
'den': np.array([4959, 5007])
},
'RSIII': {
'num': np.array([6312]),
'den': np.array([9069, 9531])
},
'RSII': {
'num': np.array([6716]),
'den': np.array([6731])
}
}
# Defining temperature and density grids(These are already define at init)
if (tempGrid is not None) and (denGrid is not None):
self.tempRange = np.linspace(tempGrid[0], tempGrid[1], tempGrid[2])
self.denRange = np.linspace(denGrid[0], denGrid[1], denGrid[2])
X, Y = np.meshgrid(self.tempRange, self.denRange)
self.tempGridFlatten, self.denGridFlatten = X.flatten(), Y.flatten(
)
return
def getIonAb(obs_file, Ne_O2, Ne_Ar4, T_N2, T_O3, printIonAb = True):
### General settings
# define an Observation object and assign it to name 'obs'
obs = pn.Observation()
# fill obs with data read from file obs_data, with lines varying across rows and a default percent error on line intensities
obs.readData(obs_file, fileFormat='lines_in_rows', err_default=0.05, corrected=True)
if 'H1_4.1m' in obs.lineLabels:
temp = 14000
dens = 10**3.5
# Compute theoretical H5-4/Hbeta ratio from Hummer and Storey
# Instatiate the H1 recombination atom
H1 = pn.RecAtom('H', 1)
IR2Opt_theo = H1.getEmissivity(temp, dens, label='5_4') / H1.getEmissivity(temp, dens, label='4_2')
IR2Opt_obs = obs.getLine(label='H1_4.1m').corrIntens / obs.getLine(label='H1_4861A').corrIntens
for line in obs.lines:
if line.label[-1] == 'm':
line.corrIntens *= IR2Opt_theo/IR2Opt_obs
# instanciation of all the needed Atom objects
all_atoms = pn.getAtomDict(atom_list=obs.getUniqueAtoms())
# define a dictionnary for the ionic abundances
ab_dic = {}
# we use the following lines to determine the ionic abundances
ab_labels = ['C3_1907A', 'C3_1909A',
'N2_6583A', 'N3_57.4m', 'N4_1487A',
'O2_3726A', 'O2_3729A', 'O3_5007A', 'O4_25.9m',
'S2_6731A', 'S3_9069A', 'S4_10.5m',
'Ar3_7136A', 'Ar4_4740A', 'Ar5_7006A', 'Ar6_4.5m',
'Ne2_12.8m', 'Ne3_3869A', 'Ne5_3426A', 'Ne6_7.6m',
'Cl3_5518A', 'Cl3_5538A', 'Cl4_5323A',
'Mg4_4.5m', 'Mg5_5.6m']
# loop on the observed lines to determine the corresponding ionic abundances
for line in obs.getSortedLines():
# this is one way to define temp and dens in each zone
# must be adapted to each case
if (line.atom in all_atoms) and (line.label in ab_labels):
IP_cut = 30. #
if IP[line.atom] > IP_cut:
temp = T_O3
dens = Ne_Ar4
IP_used = 'H'
else:
temp = T_N2
dens = Ne_O2
IP_used = 'L'
ab = all_atoms[line.atom].getIonAbundance(line.corrIntens, temp, dens, to_eval=line.to_eval, Hbeta=100)
if printIonAb:
print('{0:13s} {1} '.format(line.label, IP_used) + ' '.join(['{0:>8.4f}'.format(t) for t in ab * 1e6]))
if line.atom not in ab_dic:
ab_dic[line.atom] = []
ab_dic[line.atom].append(ab)
He1 = pn.RecAtom('He', 1)
He2 = pn.RecAtom('He', 2)
ab_dic['He2']= He1.getIonAbundance(obs.getLine(label='He1_5876A').corrIntens,
T_N2, Ne_O2, wave=5876.0)
ab_dic['He3'] = He2.getIonAbundance(obs.getLine(label='He2_4686A').corrIntens,
T_O3, Ne_Ar4, lev_i= 4, lev_j= 3)
for atom in ab_dic:
mean = np.mean(np.asarray(ab_dic[atom]))
ab_dic[atom] = mean
for ion in np.sort(ab_dic.keys()):
if printIonAb:
print('{0} = {1:.3}'.format(ion, np.log10(ab_dic[ion])+12))
return ab_dic
if not os.path.exists(filepath):
os.mkdir(filepath)
### Plot
# Create the contour plot as the intersection of tem-den emission maps with dereddened line ratios
diags.plot(emisgrids, obs)
# Place the title
plt.title(title)
# Display the plot
plt.show()
# To save, click on the icon
plt.close()
# Define all atoms to make calculations (etAtomDict is overkill, but worth
# given the large number of ions required)
all_atoms = pn.getAtomDict()
# Simultaneously determine T(OIII) and N(SII)
try:
tem_O3, den_S2 = diags.getCrossTemDen('[OIII] 4363/5007', '[SII] 6731/6716', obs=obs)
except:
tem_O3 = 'NA'
den_S2 = 'NA'
pass
# Simultaneously determine T(NII) and N(SII)
try:
tem_N2, den_tmp = diags.getCrossTemDen('[NII] 5755/6548', '[SII] 6731/6716', obs=obs)
except:
tem_N2 = 'NA'
pass
"""
Sample PyNeb script
Plots the [O III] 4363/5007 ratio as a function of Te for several Ne values
"""
# Import relevant packages
import numpy as np
import matplotlib.pyplot as plt
import pyneb as pn
# Set high verbosity level to keep track of atom creation
pn.log_.level = 3
# Create a collection of atoms - a bit overkill if we just need O III
adict = pn.getAtomDict()
# Lower verbosity level
pn.log_.level = 2
# Function to compute line ratio
def line_ratio(atom, wave1, wave2, tem, den):
emis1 = adict[atom].getEmissivity(tem, den, wave = wave1)
emis2 = adict[atom].getEmissivity(tem, den, wave = wave2)
return emis1 / emis2
# Define array of Te
tem = np.arange(5000, 18000, 30)
# Plot
plt.figure(1)
for den in [1e2, 1e3, 1e4, 1e5]:
if not os.path.exists(filepath):
os.mkdir(filepath)
### Plot
# Create the contour plot as the intersection of tem-den emission maps with dereddened line ratios
diags.plot(emisgrids, obs)
# Place the title
plt.title(title)
# Display the plot
plt.show()
# To save, click on the icon
plt.close()
# Define all atoms to make calculations (etAtomDict is overkill, but worth
# given the large number of ions required)
all_atoms = pn.getAtomDict()
# Simultaneously determine T(OIII) and N(SII)
try:
tem_O3, den_S2 = diags.getCrossTemDen('[OIII] 4363/5007', '[SII] 6731/6716', obs=obs)
except:
tem_O3 = 'NA'
den_S2 = 'NA'
pass
# Simultaneously determine T(NII) and N(SII)
try:
tem_N2, den_tmp = diags.getCrossTemDen('[NII] 5755/6548', '[SII] 6731/6716', obs=obs)
except:
tem_N2 = 'NA'
pass
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from inspect import getargspec
from collections import OrderedDict
from scipy.optimize import curve_fit
from inferenceModel import SpectraSynthesizer
from Astro_Libraries.spectrum_fitting.gasEmission_functions import EmissionComponents
from lib.Astro_Libraries.spectrum_fitting.plot_tools import MCMC_printer
# Tools for printing
mcPrinter = MCMC_printer()
outputFolder = 'E:\\Research\\article_YpBayesian\\'
# Generate the dictionary with pyneb ions
ionDict = pn.getAtomDict(atom_list=['H1r', 'He1r'])
pynebcode_list = np.array(['4471', '5876', '6678'])
ions_list = np.array(['He1r', 'He1r', 'He1r'])
labels_list = np.array(['He1_4471A', 'He1_5876A', 'He1_6678A'])
epm2017_emisCoeffs = {
'He1_3889A': np.array([0.173, 0.00054, 0.904, 1e-5]),
'He1_4026A': np.array([-0.09, 0.0000063, 4.297, 1e-5]),
'He1_4471A': np.array([-0.1463, 0.0005, 2.0301, 1.5e-5]),
'He1_5876A': np.array([-0.226, 0.0011, 0.745, -5.1e-5]),
'He1_6678A': np.array([-0.2355, 0.0016, 2.612, 0.000146]),
'He1_7065A': np.array([0.368, 0.0017, 4.329, 0.0024]),
'He1_10830A': np.array([0.14, 0.00189, 0.337, -0.00027])
}
porter2007_emisCoeffs = {
'He1_4471A': np.array([-3.5209E+04, -4.5168E+02, 8.5367E+03, 9.1635E+03]),