Python package for RADEX grid calculation
Project description
ndRADEX
Python package for RADEX grid calculation
TL;DR
ndRADEX is a Python package which can run RADEX, non-LTE molecular radiative transfer code, with parameters of multiple values (i.e., RADEX with grid parameters). The output will be multi-dimensional arrays, which may be useful for parameter search of physical conditions in comparison with observed values.
Features
- 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 during the package installation. 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 multi-dimensional 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).
Requirements
- Python 3.6, 3.7, or 3.8 (tested by the author)
- gfortran (necessary to build RADEX)
Installation
You can install ndradexhyperfine with pip:
$ pip install ndradexhyperfine
Please make sure that all requirements are met before the installation.
Usages
Within Python, import the package like:
>> > import ndradexhyperfine
Single RADEX run
The main funtion of ndRADEX is ndradex.run()
.
For example, to get RADEX results of CO(1-0) with kinetic temperature of 100 K, CO column density of 1e15 cm^-2, and H2 density of 1e3 cm^-3:
>>> ds = ndradexhyperfine.run('co.dat', '1-0', 100, 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 don't 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-08
You can access each result value like:
>>> flux = ds['F'].values
Grid RADEX run
As a natural extension, you can run grid RADEX calculation like:
>>> ds = ndradexhyperfine.run('co.dat', ['1-0', '2-1'], T_kin=[100, 200, 300],
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-08
For more information, run help(ndradexhyperfine.run)
to see the docstrings.
Save/load results
You can save and load the dataset like:
# save results to a netCDF file
>>> ndradexhyperfine.save_dataset(ds, 'results.nc')
# load results from a netCDF file
>>> ds = ndradexhyperfine.load_dataset('results.nc')
Customizations
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 file by setting an environment variable, NDRADEX_PATH
.
Changing default values
As mentioned above, you can change the default values of the run()
function like:
# config.toml
[grid]
T_bg = 10 # change default background temp to 10 K
geom = "lvg" # change default geometry to LVG
timeout = 30
n_procs = 2
You can also change the number of multiprocesses (n_procs
) and timeout (timeout
) here.
Setting datafile aliases
Sometimes datafile names are not intuitive (for example, name of CS datafile is cs@lique.dat
).
For convenience, you can define aliases of datafile names like:
# config.toml
[lamda]
CS = "cs@lique.dat"
CO = "~/your/local/co.dat"
H13CN = "https://home.strw.leidenuniv.nl/~moldata/datafiles/h13cn@xpol.dat"
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 = ndradexhyperfine.run('CS', '1-0', ...) # equiv to cs@lique.dat
References
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.