ExoMol, HITEMP, HITRAN¶
November 4th (2022) Hajime Kawahara
Since version 1.2, the standard molecular database I/O for ExoMol, HITEMP, and HITRAN was shared with the radis team. We moved the I/O for these database to exojax.spec.api.
ExoMol¶
How to load ExoMol CO database¶
>>> from exojax.spec.api import MdbExomol
>>> mdb = MdbExomol(".database/CO/12C-16O/Li2015", nurange=[4200.0, 4300.0])
We can check which line infomation is included in mdb by
>>> mdb.__slots__
['Sij0', 'logsij0', 'nu_lines', 'A', 'elower', 'eupper', 'gupper', 'jlower', 'jupper']
Some opacity calculator (currently only PreMODIT) does not use some arrays on a GPU device. Switch gpu_transfer off in this case. Then, we can save the use of the device memory.
>>> mdb = MdbExomol(".database/CO/12C-16O/Li2015", nurange=[4200.0, 4300.0], gpu_transfer=False)
>>> mdb.logsij0
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: logsij0
This table is a short summary of the line information. “on” means gpu_transfer = True, off corresponds to False.
quantity |
instance |
unit |
off/on |
line center |
nu_lines |
cm-1 |
np/np |
lower state energy |
elower |
cm-1 |
np/jnp |
Einstein coefficient |
A |
s-1 |
np/jnp |
reference line strength |
Sij0 |
cm |
np/jnp |
log_e Sij0 |
logsij0 |
-/jnp |
|
statistical weight |
gupper |
np/jnp |
|
J_lower |
jlower |
np/jnp |
|
J_upper |
jupper |
np/jnp |
|
temperature exponent |
n_Tref |
np/jnp |
|
alpha_ref (gamma0) |
alpha_ref |
np/jnp |
|
natural broadening |
gamma_natural |
cm-1 |
np/jnp |
line center |
dev_nu_lines |
cm-1 |
-/jnp |
HITEMP¶
How to load HITEMP CO database¶
Here are examples for loading CO from HITEMP.
>>> from exojax.spec.api import MdbHitemp
>>> MdbHitemp("CO", nurange=[4200.0, 4300.0])
>>> MdbHitemp(".database/CO/", nurange=[4200.0, 4300.0])
>>> MdbHitemp(".database/05/", nurange=[4200.0, 4300.0])
The style used in ExoJAX 1 is also acceptable (not recommended):
>>> MdbHitemp(".database/CO/05_HITEMP2019/05_HITEMP2019.par.bz2", nurange=[4200.0, 4300.0])
If you have the error like,
Please fix/delete the radis.json entry, change the `databank_name`, or change the default local databases path entry 'DEFAULT_DOWNLOAD_PATH' in `radis.config` or ~/radis.json
remove radis.json and retry it.
quantity |
instance |
unit |
off/on |
line center |
nu_lines |
cm-1 |
np/np |
line center |
dev_nu_lines |
cm-1 |
-/jnp |
lower state energy |
elower |
cm-1 |
np/jnp |
natural broadening |
gamma_natural |
cm-1 |
np/jnp |
air pressure broadening |
gamma_air |
cm-1 |
np/jnp |
self broadning |
gamma_self |
cm-1 |
np/jnp |
Einstein coefficient |
A |
s-1 |
np/jnp |
reference line strength |
Sij0 |
cm |
np/jnp |
log_e Sij0 |
logsij0 |
-/jnp |
|
statistical weight |
gpp |
np/jnp |
|
temperature exponent |
n_air |
np/jnp |
Isotope¶
HITEMP includes all of the isotopes. To know which isotopes are included in mdb, use uniqiso instance.
>>> mdb = MdbHitemp(".database/CO/", nurange=[4200.0, 4210.0], crit=1.e-30)
>>> mdb.uniqiso #-> [1,2,3,4,6]
You can know what isotope name “isotope=1” corresponds to
>>> mdb.exact_isotope_name(1) #-> (12C)(16O)
Loading HITEMP for Each Isotope¶
Sometimes it’s useful to take it out for each isotope. To load C12 O16 (isotope = 1), use the isotope option. “isotope” is the isotope number used in HITRAN/HITEMP, which starts from 1.
>>> mdb = MdbHitemp(".database/CO/", nurange=[4200.0, 4300.0], isotope = 1)
Parition Function (Ratio) for Each Isotope¶
In MdbHitemp, QT_interp and qr_interp have the isotope option. Here is an example of specifying an isotope for the partition function computation.
>>> T = 1000 #K
>>> isotope = 1
>>> QT = mdb.QT_interp(isotope, T) # partition function Q(T) for isotope=1
>>> q_ratio = mdb.qr_interp(isotope, T) # partition function ratio Q(T)/Q(Tref)
Direct Load of the HITRAN parameter file (.par)¶
We can directly use the HITRAN parameter file (.par file). The following is an example of reading .par directly:
>>> from exojax.spec.api import MdbHitemp
>>> from exojax.utils.grids import wavenumber_grid
>>> nus, wav, res = wavenumber_grid(22920.0,23100.0,20000,unit="AA",xsmode="modit")
xsmode = modit
xsmode assumes ESLOG in wavenumber space: mode=modit
>>> mdb = MdbHitemp("CO",nus,parfile="05_HITEMP_SAMPLE.par")
Optional Quantum States¶
As in the case of MdbExomol, we can use vibrational quantum numbers and electronic states for filtering See ” qstates ” for the use of the optional quantum states.
HITRAN¶
The mdb for HITRAN is currently functioning much almost the same as MdbHITEMP. However, due to the possibility of implementing different functions in the future, separate classes are provided.
How to load HITRAN CO database¶
>>> from exojax.spec.api import Mdbhitran
>>> Mdbhitran(".database/CO/", nurange=[4200.0, 4300.0])
>>> Mdbhitran(".database/05/", nurange=[4200.0, 4300.0])
The style used in ExoJAX 1 is also acceptable (not recommended):
>>> Mdbhitran(".database/CO/05_hit12.par", nurange=[4200.0, 4300.0])
DataFrames¶
ExoJAX mdb class inherits DataFrame of the common API when calling “inherit_dataframe=True”, in “df” instance as. This DataFrame is not masked by “nurange” and/or “crit” options and has the format of Vaex lazy I/O.
>>> mdb = MdbExomol(".database/CO/12C-16O/Li2015", nurange=[4200.0, 4300.0], inherit_dataframe=True)
>>> mdb.df
# i_upper i_lower A nu_lines gup jlower jupper elower Sij0
0 84 42 1.155e-06 2.405586 3 0 1 66960.7124 3.811968898414225e-164
1 83 41 1.161e-06 2.441775 3 0 1 65819.903 9.663028103692631e-162
2 82 40 1.162e-06 2.477774 3 0 1 64654.9206 2.7438392479197905e-159
3 81 39 1.159e-06 2.513606 3 0 1 63465.8042 8.73322833971394e-157
4 80 38 1.152e-06 2.549292 3 0 1 62252.5793 3.115220404216648e-154
... ... ... ... ... ... ... ... ... ...
125,491 306 253 7.164e-10 22147.135424 15 6 7 80.7354 1.8282485593637477e-31
125,492 474 421 9.852e-10 22147.86595 23 10 11 211.4041 2.0425455665383687e-31
125,493 348 295 7.72e-10 22147.897299 17 7 8 107.6424 1.9589545250222689e-31
125,494 432 379 9.056e-10 22148.262711 21 9 10 172.978 2.0662209116961706e-31
125,495 390 337 8.348e-10 22148.273111 19 8 9 138.3903 2.0387827253771594e-31
For instance, if you want to call “i_upper”, use “values” like:
>>> i_upper = mdb.df.i_upper.values
>>> i_upper
array([ 84, 83, 82, ..., 348, 432, 390])
Notice the above array is not masked. So, the length is different from for instance “mdb.nu_lines”.
>>> len(i_upper)
125496
>>> len(mdb.nu_lines)
771
Quantum States Filtering (ExoMol/HITEMP)¶
The only quantum state needed to calculate the cross section is the rotational quantity index. However, some databases also describe vibrational quantum numbers and electronic states. We can use this information for filtering.
When we would like to filter the lines based on vibration states (v), we can mask the lines using Data Frame.
To do so, we do not activate mdb when initialization. Also, we need to load the optional quantum states. Here is an example for the initialization.
>>> from exojax.utils.grids import wavenumber_grid
>>> from exojax.spec import api
>>> nus, wav, res = wavenumber_grid(24000.0, 26000.0, 1000, unit="AA")
>>> mdb = api.MdbExomol(""CO/12C-16O/Li2015/"", nus, optional_quantum_states=True, activation=False)
Then, let’s check DataFrame.
>>> print(mdb.df)
You find the following fields are available for Li2015:
i_upper i_lower A nu_lines gup jlower jupper elower v_l v_u kp_l kp_u Sij0
For instance, v_l means the rotational quantum number (nu) for the lower state, v_u the upper state. We would use the lines with the condition delta v = 3. Make the mask using DataFrame.
>>> mask = (mdb.df["v_u"] - mdb.df["v_l"] == 3)
Activate the mdb with the mask we made. The activation includes making the instances (such as mdb.nu_lines … ), computing broadening parameters etc.
>>> mdb.activate(mdb.df, mask)
Then, we can use mdb as usual. This is a plot of the activated lines and all of the lines in DataFrame.
See also ” Quatum states of Carbon Monoxide and Fortrat Diagram “