CIA coefficient

from exojax.utils.grids import wavenumber_grid

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

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

xsmode assumes ESLOG in wavenumber space: mode=lpf
H2-H2

/home/kawahara/exojax/src/exojax/utils/grids.py:123: UserWarning: Resolution may be too small. R=433.86018742134854
  warnings.warn('Resolution may be too small. R=' + str(resolution),

logacia can provide an absorption coeffcient as a function of temperature

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

Tfix = jnp.array([1000.0, 1300.0, 1600.0])
lc = logacia(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[::-1], 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, :])]

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