AtomNeb-py is a library written in Python for reading atomic data from AtomNeb, which is a database containing atomic data stored in the Flexible Image Transport System (FITS) file format for collisionally excited lines and recombination lines typically observed in spectra of ionized gaseous nebulae. The AtomNeb database were generated for use in pyEQUIB, proEQUIB, and other nebular spectral analysis tools.
AtomNeb for collisionally excited lines contains sets of atomic datasets, which include energy levels (Ej), collision strengths (Ωij), and transition probabilities (Aij) of the most ions commonly observed in ionized nebulae.
The atomic datasets for collisionally excited lines are as follows:
- Collection from the National Institute of Standards and Technology (NIST) Atomic Spectra Database, the CHIANTI atomic database, and some improved atomic data from Cloudy v13.04 and pyNeb v1.0. This collection was compiled according to the atomic data used in pyNeb v1.0.
- Chianti52 from the CHIANTI atomic database version 5.2. This dataset was compiled according to the atomic data used in MOCASSIN.
- Chianti60 from the CHIANTI atomic database version 6.0. This dataset was compiled according to the atomic data used in MOCASSIN.
- Chianti70 from the CHIANTI atomic database version 7.0. This dataset was compiled according to the atomic data used in MOCASSIN.
- Chianti90 from the CHIANTI atomic database version 9.0. This dataset was compiled according to the atomic data used in NEAT.
Each dataset contains the following atomic data FITS files: AtomElj.fits for Energy Levels (Ej), AtomOmij.fits for Collision Strengths (Ωij), and AtomAij.fits for Transition Probabilities (Aij).
AtomNeb for recombination lines contains sets of effective recombination coefficients (αeff) of recombination lines of H I, He I, He II, C I, C II, C III, C VI, N II, N III, N IV, N V, N VI, N VII, O II, O III, O IV, O V, O VI, O VIII, and Ne II ions typically observed in ionized nebulae, as well as Branching ratios (Br) of O II and N II lines.
The atomic datasets for recombination lines are as follows:
- RC Collection, effective recombination coefficients for C II (Davey et al. 2000), N II (Escalante and Victor 1990), O II (Storey 1994; Liu et al. 1995), and Ne II ions (Kisielius et al. 1998), including Branching ratios (Br) for O II and N II ions. This collection was compiled according to the atomic data used in MOCASSIN.
- SH95 Collection, hydrogenic ions for Z=1 to 8, namely H I, He II, Li III, Be IV, B V, C VI, N VII, and O VIII ions from Storey and Hummer (1995).
- PPB91 Collection, effective recombination coefficients for H, He, C, N, O, Ne ions from Pequignot, Petitjean and Boisson (1991).
- PFSD12 He I data, effective He I recombination coefficients from Porter et al (2012) and (2013a).
- FSL13 N II data, effective N II recombination coefficients (corrigendum) from Fang, Storey and Liu (2011) and (2013b).
- SSB17 O II data, effective O II recombination coefficients of 8889 recombination lines for Cases A, B, and C, and 2433 optical (3500-9000Å) recombination lines for Case B from Storey, Sochi and Bastin (2017).
This package requires the following packages:
The previous version relied on pandas, but all the data structures were changed from pandas.DataFrame to those defined by NumPy that speed up the computations and reduce the memory usage.
- To get this package with all the FITS file, you can simply use
gitcommand as follows:
git clone https://github.com/atomneb/AtomNeb-py
- If you plan to use the recent O II recombination coefficients (Storey, Sochi and Bastin 2017), you need to unpack them:
cd AtomNeb-py/atomic-data-rc/ tar -xvf *.fits.tar.gz
To install the last version, all you should need to do is
$ python setup.py install
To install the stable version, you can use the preferred installer program (pip):
$ pip install atomneb
or you can install it from the cross-platform package manager conda:
$ conda install -c conda-forge atomneb
The Documentation of the functions provides in detail in the API Documentation (atomneb.github.io/AtomNeb-py/doc). There are two main categories: collisionally excited lines (CEL) and recombination lines (RC).
See Jupyter Notebooks: Notebooks.ipynb
Run Jupyter Notebooks on Binder:
The atomic data for collisionally excited lines (CEL) contain Energy Levels (Ej), Collision Strengths (Ωij), and Transition Probabilities (Aij). We have four atomic datasets for them: collection, chianti52, chianti60, and chianti70.
You need to load the atomneb library as follows:
import atomneb import numpy as np import os base_dir = '.' data_dir = os.path.join('atomic-data', 'chianti70') atom_elj_file = os.path.join(base_dir,data_dir, 'AtomElj.fits') atom_omij_file = os.path.join(base_dir,data_dir, 'AtomOmij.fits') atom_aij_file = os.path.join(base_dir,data_dir, 'AtomAij.fits') elj_data_list = atomneb.read_elj_list(atom_elj_file) omij_data_list = atomneb.read_omij_list(atom_omij_file) aij_data_list = atomneb.read_aij_list(atom_aij_file)
Now you have access to:
Energy Levels (Ej):
atom='o' ion='iii' oiii_elj_data = atomneb.read_elj(atom_elj_file, atom, ion, level_num=6) print(oiii_elj_data['j_v']) print(oiii_elj_data['ej'])
which gives:
0.00000 1.00000 2.00000 2.00000 0.00000 2.00000 0.00000 113.200 306.200 20273.30 43185.69 60324.80
Collision Strengths (Ωij):
atom='o' ion='iii' oiii_omij_data = atomneb.read_omij(atom_omij_file, atom, ion) print(oiii_omij_data['level1']) print(oiii_omij_data['level2']) print(oiii_omij_data['strength'][0])
which gives:
0 1 1 1 1 ... 0 2 3 4 5 ... 100.0 158.50 251.20 398.10 631.0 ...
Transition Probabilities (Aij):
atom='o' ion='iii' oiii_aij_data = atomneb.read_aij(atom_aij_file, atom, ion) print(oiii_aij_data['aij'][0])
which gives:
0.0000 2.5969e-05 0.0000 2.3220e-06 ...
The atomic data for recombination lines (RC) contain effective recombination coefficients (αeff) of emission lines from different collections: RC Collection, SH95 Collection, PPB91 Collection, PFSD12 He I data, FSL13 N II data, and SSB17 O II data.
You need to load the atomneb libary:
import atomneb import numpy as np import os base_dir = '.' data_rc_dir = os.path.join('atomic-data-rc')
Now you have access to effective recombination coefficients (αeff) of the following collections:
RC Collection:
atom_rc_file = os.path.join(base_dir,data_rc_dir, 'rc_collection.fits') atom='c' ion='iii' cii_rc_data = atomneb.read_aeff_collection(atom_rc_file, atom, ion) n_line = len(cii_rc_data['wavelength']) for i in range(0, n_line): print(cii_rc_data['wavelength'][i], cii_rc_data['a'][i], cii_rc_data['b'][i], cii_rc_data['c'][i], cii_rc_data['d'][i], cii_rc_data['f'][i])
which gives:
914.00000 0.69280000 0.021400000 -0.016300000 -0.24310000 -0.88000000 962.00000 1.0998000 -0.0042000000 -0.027900000 -0.22940000 -0.96560000 997.00000 0.78210000 -0.36840000 0.00030000000 -0.12170000 -0.78740000 ...
SH95 Collection:
atom_rc_file = os.path.join(base_dir,data_rc_dir, 'rc_SH95.fits') atom='h' ion='ii' hi_rc_data = atomneb.read_aeff_sh95(atom_rc_file, atom, ion) print(hi_rc_data['aeff'][0])
which gives:
100.00000 500.00000 0.0000000 4.2140000e-27 1.7560000e-27 1.0350000e-27 ...
PPB91 Collection:
atom_rc_file = os.path.join(base_dir,data_rc_dir, 'rc_PPB91.fits') atom='c' ion='iii' cii_rc_data = atomneb.read_aeff_ppb91(atom_rc_file, atom, ion) n_line = len(cii_rc_data['wavelength']) for i in range(0, n_line): print(cii_rc_data['ion'][i], cii_rc_data['case1'][i], cii_rc_data['wavelength'][i], cii_rc_data['a'][i], cii_rc_data['b'][i], cii_rc_data['c'][i], cii_rc_data['d'][i], cii_rc_data['br'][i], cii_rc_data['q'][i], cii_rc_data['y'][i])
which gives:
C2+A 9903.4600 0.69700000 -0.78400000 4.2050000 0.72000000 1.0000000 1.6210000 C2+A 4267.1500 1.0110000 -0.75400000 2.5870000 0.71900000 0.95000000 2.7950000 ...
PFSD12 He I data:
atom_rc_file = os.path.join(base_dir,data_rc_dir, 'rc_he_ii_PFSD12.fits') atom='he' ion='ii' hei_rc_data = atomneb.read_aeff_he_i_pfsd12(atom_rc_file, atom, ion) hei_rc_data_wave = atomneb.read_aeff_he_i_pfsd12(atom_rc_file, atom, ion, wavelength=True) print(hei_rc_data['aeff'][0])
which gives:
5000.0000 10.000000 -25.379540 -25.058970 -25.948440 -24.651820 -25.637660 ...
FSL13 N II data:
atom_rc_file = os.path.join(base_dir,data_rc_dir, 'rc_n_iii_FSL13.fits') atom='n' ion='iii' wavelength_range=[4400.0, 7100.0] nii_rc_data = atomneb.read_aeff_n_ii_fsl13(atom_rc_file, atom, ion, wavelength_range) nii_rc_data_wave = atomneb.read_aeff_n_ii_fsl13(atom_rc_file, atom, ion, wavelength_range, wavelength=True) print(nii_rc_data['aeff'][0]) n_line = len(hei_rc_data_wave['wavelength']) for i in range(0, n_line): print(nii_rc_data_wave['wavelength'][i], nii_rc_data_wave['tr'][i], nii_rc_data_wave['trans'][i])
which gives:
255.000 79.5000 47.3000 12.5000 6.20000 4.00000 2.86000 258.000 54.4000 29.7000 7.92000 4.11000 2.72000 2.00000 310.000 48.1000 23.7000 5.19000 2.55000 1.65000 1.21000 434.000 50.3000 23.2000 4.71000 2.26000 1.45000 1.05000 6413.23 6g - 4f2p6g G[9/2]o4 - 2p4f F[7/2]e3 6556.32 6g - 4f2p6g G[9/2]o5 - 2p4f G[7/2]e4 6456.97 6g - 4f2p6g G[9/2]o5 - 2p4f F[7/2]e4 6446.53 6g - 4f2p6g F[7/2]o3 - 2p4f D[5/2]e2 6445.34 6g - 4f2p6g F[7/2]o4 - 2p4f D[5/2]e3 ...
SSB17 O II data: You first need to unpack rc_o_iii_SSB17_orl_case_b.fits.tar.gz, e.g.:
tar -xvf rc_o_iii_SSB17_orl_case_b.fits.tar.gz
If you need to have access to the full dataset (entire wavelengths, case A and B):
tar -xvf rc_o_iii_SSB17.fits.tar.gz
Please note that using the entire atomic data will make your program very slow and you may need to have a higher memory on your system. Without the above comment, as default, the cose uses rc_o_iii_SSB17_orl_case_b.fits. You can also unpack them using tarfile shown below:
import atomneb import numpy as np import os import tarfile base_dir = '.' data_rc_dir = os.path.join('atomic-data-rc') atom_rc_file = os.path.join(base_dir,data_rc_dir, 'rc_o_iii_SSB17_orl_case_b.fits') atom_rc_file_tar_gz = os.path.join(base_dir,data_rc_dir, 'rc_o_iii_SSB17_orl_case_b.fits.tar.gz') atom_rc_path = os.path.join(base_dir,data_rc_dir) tar = tarfile.open(atom_rc_file_tar_gz, "r:gz") tar.extractall(path=atom_rc_path) tar.close() atom = 'o' ion = 'iii' # O II case1 = 'B' wavelength_range=[5320.0, 5330.0] oii_rc_data = atomneb.read_aeff_o_ii_ssb17(atom_rc_file, atom, ion, case1, wavelength_range) oii_rc_data_wave = atomneb.read_aeff_o_ii_ssb17(atom_rc_file, atom, ion, case1, wavelength_range, wavelength=True) print(oii_rc_data['aeff'][0]) n_line = len(oii_rc_data_wave['wavelength']) for i in range(0, n_line): print(oii_rc_data_wave['wavelength'][i], oii_rc_data_wave['lower_term'][i], oii_rc_data_wave['upper_term'][i])
which gives:
1.64100e-30 1.60000e-30 1.56400e-30 1.54100e-30 1.52100e-30 1.50900e-30 ... 5327.17 2s22p2(1S)3p 2Po 5325.42 2s22p2(1S)3p 2Po 5327.18 2s22p2(1D)3d 2Ge 5326.84 2s22p2(1D)3d 2Ge ...
For more information on how to use the API functions from the AtomNeb Python package, please read the API Documentation published on atomneb.github.io/AtomNeb-py.
- Danehkar, A. (2020). AtomNeb Python Package, an addendum to AtomNeb: IDL Library for Atomic Data of Ionized Nebulae. J. Open Source Softw., 5, 2797. doi: 10.21105/joss.02797 ads: 2020JOSS....5.2797D.
- Danehkar, A. (2019). AtomNeb: IDL Library for Atomic Data of Ionized Nebulae. J. Open Source Softw., 4, 898. doi: 10.21105/joss.00898 ads: 2019JOSS....4..898D.
Using the AtomNeb Python package in a scholarly publication? Please cite these papers:
@article{Danehkar2020,
author = {{Danehkar}, Ashkbiz},
title = {AtomNeb Python Package, an addendum to AtomNeb: IDL Library
for Atomic Data of Ionized Nebulae},
journal = {Journal of Open Source Software},
volume = {5},
number = {55},
pages = {2797},
year = {2020},
doi = {10.21105/joss.02797}
}and if you use the AtomNeb IDL library:
@article{Danehkar2019,
author = {{Danehkar}, Ashkbiz},
title = {AtomNeb: IDL Library for Atomic Data of Ionized Nebulae},
journal = {Journal of Open Source Software},
volume = {4},
number = {35},
pages = {898},
year = {2019},
doi = {10.21105/joss.00898}
}| Documentation | https://atomneb-py.readthedocs.io/ |
| Repository | https://github.com/atomneb/AtomNeb-py |
| Issues & Ideas | https://github.com/atomneb/AtomNeb-py/issues |
| Conda-Forge | https://anaconda.org/conda-forge/atomneb |
| PyPI | https://pypi.org/project/atomneb/ |
| DOI | 10.21105/joss.02797 |
| Archive | 10.5281/zenodo.4287565 |