A wide range transmission spectrum using ExoMolOP CKD opacity
=============================================================

`ExoMolOP <https://arxiv.org/abs/2009.00687>`__ provides the CKD h5 data
through `ExoMol website <https://www.exomol.com>`__. The precomputing
opacity is very handy to compute wide low resolution spectra. This
tutorial shows how to compute a transmission spectrum using ``OpaCKD``
from the external opacity source, ExoMolOP.

.. code:: ipython3

    import numpy as np
    import matplotlib.pyplot as plt
    from jax import config
    
    # ExoJAX imports
    from exojax.opacity import OpaCKD
    from exojax.rt import ArtTransPure
    
    # Enable 64-bit precision for accurate calculations
    config.update("jax_enable_x64", True)
    
    art = ArtTransPure(
        pressure_top=1.0e-8,
        pressure_btm=1.0e2,
        nlayer=50,  # Fewer layers for transmission calculations
        integration="simpson"  # Simpson integration for better accuracy
    )
    
    # Create atmospheric profiles
    Tarr = np.linspace(1000.0, 1500.0, 50)  # Temperature profile
    mmr_arr = np.full(50, 0.1)  # Constant H2O mixing ratio
    mean_molecular_weight = np.full(50, 2.33)  # Mean molecular weight (H2-dominated)
    
    # Planetary parameters (Jupiter-like)
    radius_btm = 6.9e9  # Planet radius at bottom of atmosphere (cm)
    gravity = 2478.57   # Surface gravity (cm/s²)



.. parsed-literal::

    integration:  simpson
    Simpson integration, uses the chord optical depth at the lower boundary and midppoint of the layers.


.. parsed-literal::

    /home/kawahara/exojax/src/exojax/rt/common.py:40: UserWarning: nu_grid is not given. specify nu_grid when using 'run' 
      warnings.warn(


Loading ExoMolOP Opacity Data
-----------------------------

When calling ``OpaCKD`` as shown below, it automatically downloads
ExoMolOP data and configures the opacity object. If you already have the
data file, you can specify the file path directly instead.

.. code:: ipython3

    nurange = [1000.0, 10000.0]
    opa = OpaCKD.from_external("exomolop", ".database/CO/12C-16O/Li2015/", nurange=nurange)
    
    #opa = OpaCKD.from_external("exomolop","12C-16O__Li2015.R1000_0.3-50mu.ktable.petitRADTRANS.h5")
    #opa = OpaCKD.from_external("exomolop", ".database/CO/12C-16O/Li2015/12C-16O__Li2015.R1000_0.3-50mu.ktable.petitRADTRANS.h5")


.. parsed-literal::

    /home/kawahara/exojax/src/exojax/utils/molname.py:197: FutureWarning: e2s will be replaced to exact_molname_exomol_to_simple_molname.
      warnings.warn(
    /home/kawahara/exojax/src/exojax/utils/molname.py:91: FutureWarning: exojax.utils.molname.exact_molname_exomol_to_simple_molname will be replaced to radis.api.exomolapi.exact_molname_exomol_to_simple_molname.
      warnings.warn(


.. parsed-literal::

    Downloading from https://www.exomol.com/db/CO/12C-16O/Li2015/12C-16O__Li2015.R1000_0.3-50mu.ktable.petitRADTRANS.h5
    .database/CO/12C-16O/Li2015/12C-16O__Li2015.R1000_0.3-50mu.ktable.petitRADTRANS.h5 already exists. Skip downloading.


Computing a Transmission Spectra using CKD
------------------------------------------

One can compute a transmission spectrum with the following code.

.. code:: ipython3

    xs_ckd = opa.xstensor_ckd(Tarr, art.pressure)
    dtau_ckd = art.opacity_profile_xs_ckd(xs_ckd, mmr_arr, opa.molmass, gravity)
    transit_ckd = art.run_ckd(dtau_ckd, Tarr, mean_molecular_weight, radius_btm, gravity, opa.ckd_info.weights)


Visualization
-------------

.. code:: ipython3

    # Create plot
    plt.figure(figsize=(10, 4))
    
    # Plot CKD spectrum
    plt.plot(opa.nu_bands, transit_ckd,
             'o-', label="CKD Method",
             markersize=4, linewidth=2, color='C0')
    plt.xlabel("Wavenumber [cm$^{-1}$]", fontsize=14)
    plt.xscale('log')



.. image:: transmission_ckd_exomolop_files/transmission_ckd_exomolop_8_0.png