A wrapper for RADEX (www.sron.rug.nl/~vdtak/radex/) in python.
As of v0.2, created October 26, 2013, this package includes both a python wrapper of the command-line program and a direct wrapper of the fortran code created with f2py.
You need to have gfortran and f2py on your path. If you've successfully built numpy from source, you should have both.
You need to clone this repository first with --recursive enabled so that myRadex is downloaded:
git clone --recursive https://github.com/keflavich/pyradex.gitThen cd to the source directory and run:
$ python setup.py install_radex install_myradex installThis will call a procedure install_radex that downloads the latest version of RADEX from the radex homepage, patches the source, and builds a file radex.so, which is a python shared object that can be imported.
See the install page for more details.
If you want pyradex to look in a specific directory for the molecular data
files, you can specify an environmental variable RADEX_DATAPATH prior to
starting python. It can also be specified interactively with the datapath
keyword. If you do not specify one of these two variables, the code
will not work and may return strange errors.
The direct wrapper of the fortran code uses a class Radex as its underlying structure. This class is useful for direct manipulation of RADEX inputs and direct access to its outputs.
Example (assuming RADEX_DATAPATH has been specified as an environmental variable):
import pyradex
import numpy as np
R = pyradex.Radex(collider_densities={'oH2':900,'pH2':100}, column=1e16, species='co', temperature=20)
Tlvg = R(escapeProbGeom='lvg')
Tslab = R(escapeProbGeom='slab')
Tsphere = R(escapeProbGeom='sphere')
Tlvg[:3].pprint()
Tslab[:3].pprint()
Tsphere[:3].pprint()Result:
Tex tau frequency upperstateenergy upperlevel lowerlevel upperlevelpop lowerlevelpop flux
------------- -------------- ----------- ---------------- ---------- ---------- ---------------- --------------- -----------------
15.2747101724 0.937692338925 115.2712018 5.53 2 1 0.273140336953 0.453621905471 2.93964536078e-14
10.8673211326 2.74275175782 230.538 16.6 3 2 0.0518618367484 0.273140336953 9.26125039465e-14
8.30670325364 2.01021823976 345.7959899 33.19 4 3 0.00379591658449 0.0518618367484 8.16324298598e-14
Tex tau frequency upperstateenergy upperlevel lowerlevel upperlevelpop lowerlevelpop flux
------------- -------------- ----------- ---------------- ---------- ---------- ---------------- -------------- -----------------
17.8076937528 0.681341951256 115.2712018 5.53 2 1 0.312979158313 0.394862780876 2.89304678735e-14
14.8865118666 1.96024230849 230.538 16.6 3 2 0.102821702575 0.312979158313 1.38012283784e-13
11.448407058 2.03949857132 345.7959899 33.19 4 3 0.00920322307626 0.102821702575 1.6139902821e-13
Tex tau frequency upperstateenergy upperlevel lowerlevel upperlevelpop lowerlevelpop flux
------------- ------------- ----------- ---------------- ---------- ---------- ---------------- -------------- -----------------
14.38256087 1.06765591906 115.2712018 5.53 2 1 0.243400727834 0.480559204909 2.93394133644e-14
9.28920337666 3.1666639484 230.538 16.6 3 2 0.037299201561 0.243400727834 7.24810556601e-14
7.50189023571 1.84556901411 345.7959899 33.19 4 3 0.00307839203073 0.037299201561 6.19215196139e-14
Note that because of how RADEX was written, i.e. with common blocks, the values stored in each of these objects is identical! You cannot have two independent copies of the RADEX class ever.
There is a rich examples gallery. We have a few notebooks:
http://nbviewer.ipython.org/github/keflavich/pyradex/blob/master/examples/pH2CO_interactive.ipynb http://nbviewer.ipython.org/github/keflavich/pyradex/blob/master/examples/FittingTheGrid.ipynb http://nbviewer.ipython.org/github/keflavich/pyradex/blob/master/examples/Interactive.ipynb http://nbviewer.ipython.org/github/keflavich/pyradex/blob/master/examples/oH2CO-interactive.ipynb http://nbviewer.ipython.org/github/keflavich/pyradex/blob/master/examples/pH2CO_interactive.ipynb http://nbviewer.ipython.org/github/keflavich/pyradex/blob/master/examples/ph2co_interactive_mm.ipynb
and a series of more involved examples:
- examples/ch3cn_110_synthspec.py
- examples/h2co_grids.py
- examples/h2cs_thermometer.py
- examples/interactive_setup_mm.py
- examples/oh2co_density_grid.py
- examples/oh2co_distributions.py
- examples/oh2co_grids_2.py
- examples/ph2co_grid_computation.py
- examples/ph2co_grid_computation_mm.py
- examples/ph2co_grids.py
- examples/ph2co_grids_2.py
- examples/ph2co_required_sn.py
- examples/simple_co.py
- examples/simple_co_column.py
- examples/synthspec_ch3cn.py
- examples/timing.py
Most of these were written to make sensitivity estimates for observing proposals.
make radex as normal, but create two executables: radex_sphere, radex_lvg, and radex_slab by building with one of these three lines commented out each time:
c parameter (method = 1) ! uniform sphere parameter (method = 2) ! expanding sphere (LVG) c parameter (method = 3) ! plane parallel slab (shock)Copy these to your system path
python setup.py install to install pyradex
Using some trivial defaults:
In [1]: import pyradex
In [2]: T = pyradex.radex(collider_densities={'H2':1000})
WARNING: Assumed thermal o/p ratio since only H2 was given but collider file has o- and p- H2 [pyradex.core]
In [3]: T.pprint(show_units=True)
J_up J_low E_UP FREQ WAVE T_EX TAU T_R POP_UP POP_LOW FLUX_Kkms FLUX_Inu
K GHz um K K K km / s erg / (cm2 s)
---- ----- ---- -------- --------- ----- --------- ------- ------ ------- --------- -------------
1 0 5.5 115.2712 2600.7576 5.044 0.0004447 0.00086 0.4709 0.47 0.0009155 1.806e-11
In [4]: T.meta
Out[4]:
{'Column density [cm-2]': '1.000E+12',
'Density of H2 [cm-3]': '1.000E+03',
'Density of oH2 [cm-3]': '3.509E-04',
'Density of pH2 [cm-3]': '1.000E+03',
'Geometry': 'Uniform sphere',
'Line width [km/s]': '1.000',
'Molecular data file': '/Users/adam/repos/Radex/data/co.dat',
'Radex version': '20nov08',
'T(background) [K]': '2.730',
'T(kin) [K]': '10.000'}
i.e., how fast is it?:
%timeit T = pyradex.pyradex(collider_densities={'H2':1000})
10 loops, best of 3: 31.8 ms per loop
for n in 10**np.arange(6):
%timeit T = pyradex.pyradex(collider_densities={'H2':n})
10 loops, best of 3: 32.1 ms per loop
10 loops, best of 3: 32.5 ms per loop
10 loops, best of 3: 32 ms per loop
10 loops, best of 3: 32.1 ms per loop
10 loops, best of 3: 32.4 ms per loop
10 loops, best of 3: 31.9 ms per loop
for n in 10**np.arange(12,18):
%timeit T = pyradex.pyradex(collider_densities={'H2':1000}, column=n)
10 loops, best of 3: 31.8 ms per loop
10 loops, best of 3: 32.2 ms per loop
10 loops, best of 3: 32.5 ms per loop
10 loops, best of 3: 32.2 ms per loop
10 loops, best of 3: 32.7 ms per loop
10 loops, best of 3: 33.1 ms per loop
If you redo these tests comparing the fortran wrapper to the "naive" version, the difference can be enormous. The following tests can be seen in timing.py:
Python external call: 0.0323288917542 Fortran-wrapped: 0.0183672904968 Fortran-wrapped, no reload: 0.000818204879761 Fortran-wrapped, no reload, reuse: 0.000756096839905 Fortran (call method): 0.0270668029785 py/fortran: 1.76013395986 py/fortran, __call__ method: 1.1944111678 py/fortran, no reload: 39.5119762224 py/fortran, no reload, reuse: 42.7576072904 Python external call: 0.0332223176956 Fortran-wrapped: 0.0169018030167 Fortran-wrapped, no reload: 0.000811815261841 Fortran-wrapped, no reload, reuse: 0.000753211975098 Fortran (call method): 0.0275466918945 py/fortran: 1.96560790957 py/fortran, __call__ method: 1.20603656594 py/fortran, no reload: 40.9234948605 py/fortran, no reload, reuse: 44.1075272221 Python external call: 0.0312483787537 Fortran-wrapped: 0.0216565847397 Fortran-wrapped, no reload: 0.00535380840302 Fortran-wrapped, no reload, reuse: 0.000751805305481 Fortran (call method): 0.031253194809 py/fortran: 1.44290427735 py/fortran, __call__ method: 0.999845901985 py/fortran, no reload: 5.83666362361 py/fortran, no reload, reuse: 41.5644562839 Python external call: 0.0316061973572 Fortran-wrapped: 0.0228497028351 Fortran-wrapped, no reload: 0.00549430847168 Fortran-wrapped, no reload, reuse: 0.000753903388977 Fortran (call method): 0.031331205368 py/fortran: 1.38322137427 py/fortran, __call__ method: 1.00877693615 py/fortran, no reload: 5.75253419427 py/fortran, no reload, reuse: 41.9234053319 Python external call: 0.0318208932877 Fortran-wrapped: 0.0216773033142 Fortran-wrapped, no reload: 0.00544350147247 Fortran-wrapped, no reload, reuse: 0.000751280784607 Fortran (call method): 0.0315539121628 py/fortran: 1.46793597093 py/fortran, __call__ method: 1.0084611101 py/fortran, no reload: 5.84566633234 py/fortran, no reload, reuse: 42.3555266415 Python external call: 0.0322543859482 Fortran-wrapped: 0.0225975990295 Fortran-wrapped, no reload: 0.00569999217987 Fortran-wrapped, no reload, reuse: 0.00075900554657 Fortran (call method): 0.0314954996109 py/fortran: 1.42733685583 py/fortran, __call__ method: 1.02409507221 py/fortran, no reload: 5.65867196486 py/fortran, no reload, reuse: 42.4955866185 [ 0.006951 0.006911 0.006956] [ 0.006951 0.006911 0.006956] [ 0.006951 0.006911 0.006956] pyradex.pyradex timing for a 3^4 grid: [2.6063590049743652, 2.598068952560425, 2.592205047607422] [ 0.00694859 0.00690934 0.00695345] [ 0.00694859 0.00690934 0.00695345] [ 0.00694859 0.00690934 0.00695345] pyradex.Radex() timing for a 3^4 grid: [3.8620870113372803, 3.838628053665161, 3.805685043334961] [ 0.00694859 0.00690934 0.00695345] [ 0.00694859 0.00690934 0.00695345] [ 0.00694859 0.00690934 0.00695345] pyradex.Radex() class-based timing for a 3^4 grid: [3.1014058589935303, 3.2805678844451904, 3.160888195037842] [ 0.00694859 0.00690934 0.00695345] [ 0.00694859 0.00690934 0.00695345] [ 0.00694859 0.00690934 0.00695345] pyradex.Radex() class-based timing for a 3^4 grid, using optimal parameter-setting order: [0.9963750839233398, 1.0024840831756592, 0.9699358940124512]
Is more efficient with scripts, but you can still do it...
R = pyradex.Radex(species='co', collider_densities={'H2':1000}, column=1e15)
for n in 10**np.arange(12,18):
T = R(collider_densities={'H2':1000}, column=n)
T[:1].pprint()
Tex tau frequency upperstateenergy upperlevel lowerlevel upperlevelpop lowerlevelpop brightness T_B
K GHz K erg / (cm2 Hz s sr) K
------------- ----------------- ----------- ---------------- ---------- ---------- -------------- -------------- ------------------- ----------------
11.0274813968 0.000166783361591 115.2712018 5.53 1 0 0.540537331305 0.297561763825 5.20877418593e-18 0.00127591598469
Tex tau frequency upperstateenergy upperlevel lowerlevel upperlevelpop lowerlevelpop brightness T_B
K GHz K erg / (cm2 Hz s sr) K
------------- ---------------- ----------- ---------------- ---------- ---------- -------------- -------------- ------------------- ---------------
11.0274813968 0.00166783361591 115.2712018 5.53 1 0 0.540537331305 0.297561763825 5.2048669339e-17 0.0127495888324
Tex tau frequency upperstateenergy upperlevel lowerlevel upperlevelpop lowerlevelpop brightness T_B
K GHz K erg / (cm2 Hz s sr) K
------------- --------------- ----------- ---------------- ---------- ---------- -------------- -------------- ------------------- --------------
10.9980972475 0.0166790919823 115.2712018 5.53 1 0 0.538730147174 0.296964688622 5.14681095066e-16 0.126073777202
Tex tau frequency upperstateenergy upperlevel lowerlevel upperlevelpop lowerlevelpop brightness T_B
K GHz K erg / (cm2 Hz s sr) K
------------- -------------- ----------- ---------------- ---------- ---------- -------------- -------------- ------------------- -------------
11.7797140751 0.150601068675 115.2712018 5.53 1 0 0.530489509066 0.282823341198 4.78772386104e-15 1.17277754545
Tex tau frequency upperstateenergy upperlevel lowerlevel upperlevelpop lowerlevelpop brightness T_B
K GHz K erg / (cm2 Hz s sr) K
------------- -------------- ----------- ---------------- ---------- ---------- -------------- -------------- ------------------- -------------
15.0692631019 0.955344506002 115.2712018 5.53 1 0 0.454752879863 0.218821739485 2.92170292028e-14 7.15686133711
Tex tau frequency upperstateenergy upperlevel lowerlevel upperlevelpop lowerlevelpop brightness T_B
K GHz K erg / (cm2 Hz s sr) K
------------- ------------- ----------- ---------------- ---------- ---------- -------------- -------------- ------------------- -------------
22.6356250741 4.17742617995 115.2712018 5.53 1 0 0.318586709967 0.135596426565 7.69430015071e-14 18.8475833332
If you want to create a grid with the directly wrapped version, do loops with constant temperature: every time you load a new temperature, RADEX must read in the molecular data file and interpolate across the collision rate values, which may be a substantial overhead.
If you want to build a grid, do not make an astropy table each time! That appears to dominate the overhead at each iteration.
LVG computations have weird units. The opacity of a line only depends on the velocity-coherent column along the line of sight, i.e. the column per km/s.
The key assumption in the LVG Sobolev approximation is that each "cell" can be treated independently such that there are no nonlocal radiative effects.
This independence implies that there is a separation between the local volume density and the total line-of-sight column density.
However, the quantities reported by typical codes - RADEX, DESPOTIC - are integrated line-of-sight values. The column density, abundance, and local volume density are not independent, then.
In order to have a self-consistent cloud (or line of sight), you must assume some length scale. Usually, one specifies a velocity gradient per length scale rather than an absolute length scale, but the length scale is important.
If a total column density of hydrogen N(H) is specified along with a density n(H), the length scale is trivial: N(H)/n(H) = L. If you increase the density, this length scale decreases - so far all is fine.
Within RADEX, the standard free variable is the column of the molecule of interest. If you change the column of the molecule, which is possible to do explicitly, and hold everything else fixed in RADEX (n(H), dV), the change can be interpreted as a change in the size scale or the column.
One could consider the alternative possibility of treating the length scale as a free parameter, but this approach contains a danger of changing the interpretation of the processes involved: if the length scale is decreased for a fixed delta-V, the velocity gradient dv/dl must be larger. This interpretation should be avoided as it bears the risk of breaking the LVG assumption. The velocity gradient is also often an imposed constraint via the observed linewidth, while the length scale is only weakly constrained in most situations.
In DESPOTIC, the free variables are the total column density, the density, the abundance, and the velocity gradient. Length is therefore left as the dependent variable, consistent with the above.
The Classes (Despotic & Radex) are constructed such that length is a dependent variable and all the others can be changed. Since abundance is not an explicit input into RADEX, this is done with some property machinery behind the scenes. In v0.3, the length in Radex has been fixed to 1 pc.
