radexgrid is a python wrapper for the RADEX molecular radiative
transfer code (http://strw.leidenuniv.nl/~moldata/radex.html). Model grids
with multiprocessing support can be run over kinetic temperature, spatial
density, column density, background temperature, linewidth, and collision
partner. The computed results are returned in high-performance and flexible
pandas.DataFrame objects.
radexgrid benefits from code incorporated from the pyradex
(http://github.com/keflavich/pyradex) wrapper authored by Adam Ginsburg.
Installation has only been tested on Linux so far. If you find any bugs, please open an issue or file a pull request, and I'll patch the code as quickly as possible.
If you would like to use all of the supported RADEX escape probability
methods or geometries, first comment/uncomment the appropriate method
in the src/radex.inc (near lineno 34) fortran source file, compile
RADEX, and assign the binaries unique names.
c parameter (method = 1) ! uniform sphere
parameter (method = 2) ! expanding sphere (LVG)
c parameter (method = 3) ! plane parralel slab (shock)Download the code from GitHub:
$ git clone git://github.com/autocorr/radexgrid $ cd radexgrid
Copy the radexgrid.cfg configuration file in the cloned directory to
~/.radexgrid.cfg in your home directory with the names or paths to the
RADEX binaries and the directory where the molecular datafiles are stored.
[radex-paths] radex_sphere = radex_sphere radex_lvg = radex_lvg radex_slab = radex_slab data_path = /path/to/moldata/
The default configuration file above assumes that you have three separate
RADEX binaries in your shell's path. Finally, install the package:
$ python setup.py install
However, if you do need a system wide install, you can add the radexgrid
directory to your shell's PYTHONPATH.
radexgrid requires the following python modules, all are available in PyPI via pip:
numpy pandas astropy
The RadexGrid class is the primary interface to run RADEX models.
Custom classes for running RADEX and parsing the output can be passed via
the available methods. To use package builtins, simply run the
mygrid.run_model() method and a python pandas.DataFrame will be
returned with attributes for the grid properties.
Here is an example use-case in an interactive IPython session for a 10x10x10
model grid over kinetic temperature, spatial density, and column density for
HCO+. All transitions within the frequency interval are returned, ie J=4-3 and
J=3-2. Multi-processing is supported through the nprocs keyword for the
number of processes to spawn. Please see the documentation for description of
the specific calling calling sequence.
In [1]: import radexgrid
In [2]: rg = radexgrid.RadexGrid(molecule='hco+', freq=(200,400),
....: tkin=(10,20,10,'lin'), dens=(1e3,1e4,10,'lin'),
....: column_density=(1e12,1e14,10,'log'), colliders='H2', nprocs=4)
In [3]: df = rg.run_model()
In [4]: ls
radex.log radex_model.inp radex_model.rdx
In [5]: df
Out [5]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2000 entries, 0 to 1999
Data columns (total 18 columns):
J_up 2000 non-null values
J_low 2000 non-null values
E_up 2000 non-null values
freq 2000 non-null values
wave 2000 non-null values
T_ex 2000 non-null values
tau 2000 non-null values
T_R 2000 non-null values
pop_up 2000 non-null values
pop_low 2000 non-null values
flux_Kkms 2000 non-null values
flux_Inu 2000 non-null values
T_K 2000 non-null values
n_coll 2000 non-null values
T_bg 2000 non-null values
N_mol 2000 non-null values
dv 2000 non-null values
Coll 2000 non-null values
dtypes: float64(15), object(3)
In [6]: df.head()
Out [6]:
J_up J_low E_up freq wave T_ex tau T_R pop_up \
0 3 2 25.7 267.5573 1120.4795 2.809 0.015110 0.000250 0.000407
1 4 3 42.8 356.7338 840.3814 3.462 0.000209 0.000019 0.000004
2 3 2 25.7 267.5573 1120.4795 2.809 0.025280 0.000417 0.000408
3 4 3 42.8 356.7338 840.3814 3.461 0.000349 0.000031 0.000004
4 3 2 25.7 267.5573 1120.4795 2.809 0.042340 0.000695 0.000410
pop_low flux_Kkms flux_Inu T_K n_coll T_bg N_mol dv Coll
0 0.028110 0.000533 1.315000e-10 10 1000 2.73 1.000000e+12 2 H2
1 0.000407 0.000040 2.347000e-11 10 1000 2.73 1.000000e+12 2 H2
2 0.028180 0.000889 2.192000e-10 10 1000 2.73 1.668101e+12 2 H2
3 0.000408 0.000067 3.917000e-11 10 1000 2.73 1.668101e+12 2 H2
4 0.028300 0.001480 3.650000e-10 10 1000 2.73 2.782559e+12 2 H2
In [7]: df.meta
Out [7]:
{'colliders': ('H2',),
'column_density': (1000000000000.0, 100000000000000.0, 10, 'log'),
'dens': (1000.0, 10000.0, 10, 'lin'),
'freq': (200, 400),
'geometry': 'sphere',
'linewidth': (2, 2, 1, 'lin'),
'molecule': 'hco+',
'tbg': (2.73, 2.73, 1, 'lin'),
'tkin': (10, 20, 10, 'lin'),
'units': [('J_up', None),
('J_low', None),
('E_up', Unit("K")),
('freq', Unit("GHz")),
('wave', Unit("um")),
('T_ex', Unit("K")),
('tau', None),
('T_R', Unit("K")),
('pop_up', None),
('pop_low', None),
('flux_Kkms', Unit("K km / s")),
('flux_Inu', Unit("erg / (cm2 s)")),
('T_K', Unit("K")),
('n_coll', Unit("1 / cm3")),
('T_bg', Unit("K")),
('N_mol', Unit("1 / cm2")),
('dv', Unit("km / s")),
('Coll', None)]}
In [8]: df.to_csv('hcop_grid.csv', index=False)
In [9]: df.to_hdf('hcop_grid.hdf', 'table', append=True)Copyright 2013 Brian Svoboda
Radexgrid is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License (v3) as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
Radexgrid is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with Radexgrid. If not, see http://www.gnu.org/licenses/.
| Author: | Brian Svoboda |
|---|---|
| Email: | svobodb@email.arizona.edu |
| Source: | https://github.com/autocorr/besl |
| Version: | 0.1 |