Multidimensional grid RADEX calculator
ndRADEX is a Python package which can run RADEX, non-LTE molecular radiative transfer code, with multiple grid parameters. The output will be multidimensional arrays provided by xarray, which would be useful for parameter search of physical conditions in comparison with observed values.
- Grid calculation: ndRADEX has a simple
run()function, where all parameters of RADEX can be griddable (i.e., they can be list-like with length of more than one). - Builtin RADEX: ndRADEX provides builtin RADEX binaries in the package, which are automatically downloaded and built on the first import. This also enables us to do RADEX calculations in the cloud such as Google Colaboratory.
- Multiprocessing: ndRADEX supports multiprocessing RADEX run by default. At least twice speedup is expected compared to single processing.
- Handy I/O: The output of ndRADEX is a xarray's Dataset, a standard multidimensional data structure as well as pandas. You can handle it in the same manner as NumPy and pandas (i.e., element-wise operation, save/load data, plotting, etc).
- Python 3.8-3.11 (tested by the author)
- gfortran (necessary to build RADEX)
You can install ndRADEX with pip:
$ pip install ndradexWithin Python, import the package like:
>>> import ndradexThe main function of ndRADEX is ndradex.run().
For example, to get RADEX results of CO(1-0) with kinetic temperature of 100.0 K, CO column density of 1e15 cm^-2, and H2 density of 1e3 cm^-3:
>>> ds = ndradex.run("co.dat", "1-0", 100.0, 1e15, 1e3)where "co.dat" is a name of LAMDA datafile and "1-0" is a name of transition.
The available values are listed in List of available LAMDA datafiles and transitions.
Note that you do not need to any download datafiles:
ndRADEX automatically manage this.
In this case, other parameters like line width, background temperature are default values defined in the function.
The geometry of escape probability is uniform ("uni") by default.
You can change these values with custom config (see customizations below).
The output is a xarray's Dataset with no dimension:
>>> print(ds)
<xarray.Dataset>
Dimensions: ()
Coordinates:
QN_ul <U3 '1-0'
T_kin int64 100
N_mol float64 1e+15
n_H2 float64 1e+03
T_bg float64 2.73
dv float64 1.0
geom <U3 'uni'
description <U9 'LAMDA(CO)'
Data variables:
E_u float64 5.5
freq float64 115.3
wavel float64 2.601e+03
T_ex float64 132.5
tau float64 0.009966
T_r float64 1.278
pop_u float64 0.4934
pop_l float64 0.1715
I float64 1.36
F float64 2.684e-08You can access each result value like:
>>> flux = ds["F"].valuesAs a natural extension, you can run grid RADEX calculation like:
>>> ds = ndradex.run("co.dat", ["1-0", "2-1"], T_kin=[100.0, 200.0, 300.0],
N_mol=1e15, n_H2=[1e3, 1e4, 1e5, 1e6, 1e7])There are 13 parameters which can be griddable:
QN_ul (transition name), T_kin (kinetic temperature), N_mol (column density), n_H2 (H2 density), n_pH2 (para-H2 density), n_oH2 (ortho-H2 density), n_e (electron density), n_H (atomic hydrogen density), n_He (Helium density), n_Hp (ionized hydrogen density), T_bg (background temperature), dv (line width), and geom (photon escape geometry).
The output of this example is a xarray's Dataset with three dimensions of (QN_ul, T_kin, n_H2):
>>> print(ds)
<xarray.Dataset>
Dimensions: (QN_ul: 2, T_kin: 3, n_H2: 5)
Coordinates:
* QN_ul (QN_ul) <U3 '1-0' '2-1'
* T_kin (T_kin) int64 100 200 300
N_mol float64 1e+15
* n_H2 (n_H2) float64 1e+03 1e+04 1e+05 1e+06 1e+07
T_bg float64 2.73
dv float64 1.0
geom <U3 'uni'
description <U9 'LAMDA(CO)'
Data variables:
E_u (QN_ul, T_kin, n_H2) float64 5.5 5.5 5.5 5.5 ... 16.6 16.6 16.6
freq (QN_ul, T_kin, n_H2) float64 115.3 115.3 115.3 ... 230.5 230.5
wavel (QN_ul, T_kin, n_H2) float64 2.601e+03 2.601e+03 ... 1.3e+03
T_ex (QN_ul, T_kin, n_H2) float64 132.5 -86.52 127.6 ... 316.6 301.6
tau (QN_ul, T_kin, n_H2) float64 0.009966 -0.005898 ... 0.0009394
T_r (QN_ul, T_kin, n_H2) float64 1.278 0.5333 ... 0.3121 0.2778
pop_u (QN_ul, T_kin, n_H2) float64 0.4934 0.201 ... 0.04972 0.04426
pop_l (QN_ul, T_kin, n_H2) float64 0.1715 0.06286 ... 0.03089 0.02755
I (QN_ul, T_kin, n_H2) float64 1.36 0.5677 ... 0.3322 0.2957
F (QN_ul, T_kin, n_H2) float64 2.684e-08 1.12e-08 ... 4.666e-08For more information, run help(ndradex.run) to see the docstrings.
You can save and load the dataset like:
# save results to a netCDF file
>>> ndradex.save_dataset(ds, "results.nc")
# load results from a netCDF file
>>> ds = ndradex.load_dataset("results.nc")For the first time you import ndRADEX, the custom configuration file is created as ~/.config/ndradex/config.toml.
By editing this, you can customize the following two settings of ndRADEX.
Note that you can change the path of configuration directory by setting an environment variable, NDRADEX_DIR.
As mentioned above, you can change the default values of the run() function like:
# config.toml
[defaults]
T_bg = 10.0 # change default background temp to 10.0 K
geom = "lvg" # change default geometry to LVG
timeout = 60.0
n_procs = 8You can also change the number of multiprocesses (n_procs) and timeout (timeout) here.
Sometimes datafile names are not intuitive (for example, name of CS datafile is [email protected]).
For convenience, you can define aliases of datafile names like:
# config.toml
[lamda.aliaes]
CS = "[email protected]"
CO = "~/your/local/co.dat"
H13CN = "https://home.strw.leidenuniv.nl/~moldata/datafiles/[email protected]"As shown in the second and third examples, you can also specify a local file path or a URL on the right hand.
After the customization, you can use these aliases in the run() function:
>>> ds = ndradex.run("CS", "1-0", ...) # equiv to [email protected]
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