CIA coefficient

from exojax.utils.grids import wavenumber_grid

nus, wav, res = wavenumber_grid(5000, 50000, 1000, unit="AA", xsmode="lpf")
from exojax.spec import contdb

cdbH2H2 = contdb.CdbCIA(".database/H2-H2_2011.cia", nus)

xsmode =  lpf
xsmode assumes ESLOG in wavenumber space: xsmode=lpf
======================================================================
The wavenumber grid should be in ascending order.
The users can specify the order of the wavelength grid by themselves.
Your wavelength grid is in *  descending  * order
======================================================================
H2-H2
/home/kawahara/exojax/src/exojax/spec/unitconvert.py:63: UserWarning: Both input wavelength and output wavenumber are in ascending order.
  warnings.warn(
/home/kawahara/exojax/src/exojax/utils/grids.py:144: UserWarning: Resolution may be too small. R=433.86018742134854
  warnings.warn("Resolution may be too small. R=" + str(resolution), UserWarning)

logacia can provide an absorption coeffcient as a function of temperature

from exojax.spec.hitrancia import interp_logacia_vector
import jax.numpy as jnp

Tfix = jnp.array([1000.0, 1300.0, 1600.0])
lc = interp_logacia_vector(Tfix, nus, cdbH2H2.nucia, cdbH2H2.tcia, cdbH2H2.logac)

plotting…

import matplotlib.pyplot as plt

plt.style.use("bmh")
for i in range(0, len(Tfix)):
    plt.plot(wav, lc[:, i], lw=1, label=str(int(Tfix[i])) + " K")
plt.axvspan(22876.0, 23010.0, alpha=0.3, color="green")
plt.xlabel("wavelength ($\\AA$)")
plt.ylabel("absorption coefficient ($cm^5$)")
plt.legend()
plt.savefig("cia.png")
../_images/CIA_opacity_5_0.png 
#max value
import numpy as np
1.e8 / nus[np.argmax(lc[1, :])]

23858.80474469375