Simple Usage
============

1. Voigt profile
----------------

.. code:: ipython3

    import numpy
    import matplotlib.pyplot as plt
    from exojax.spec import voigt

ExoJAX has both opacity calculators and radiative transfer algorithms.
The basis of the former capacility is `the Voigt
function <https://en.wikipedia.org/wiki/Voigt_profile>`__, which is the
convolution of a Gaussian and a Lorentian.

.. code:: ipython3

    x=numpy.linspace(-10,10,100) 
    y=voigt(x,1.0,2.0) #sigma_D=1.0, gamma_L=2.0
    plt.plot(x,y)
    plt.show()



.. image:: simple_usage_files/simple_usage_4_0.png


2. Cross section
----------------

This software has many features, but the laziest use is to use
spec.autospec. Let’s try to compute the cross section of carbon
monooxide.

.. code:: ipython3

    from exojax.spec.autospec import AutoXS

ExoJAX automatically downloads a molecular database, convert it to the
feather format and save it in .database directory.

.. code:: ipython3

    nus=numpy.linspace(1900.0,2300.0,40000,dtype=numpy.float64) #wavenumber (cm-1)
    autoxs=AutoXS(nus,"ExoMol","CO") #using ExoMol CO (12C-16O). HITRAN and HITEMP are also supported.  
    xsv=autoxs.xsection(1000.0,1.0) #cross section for 1000K, 1bar (cm2)


.. parsed-literal::

    Warning: CO is interpreted as 12C-16O
    Recommendated database by ExoMol:  Li2015
    CO/12C-16O/Li2015
    broadf= True
    Background atmosphere:  H2
    Reading .database/CO/12C-16O/Li2015/12C-16O__Li2015.trans.bz2
    .broad is used.
    Broadening code level= a0
    default broadening parameters are used for  71  J lower states in  152  states


.. parsed-literal::

      0%|          | 0/19 [00:00<?, ?it/s]

.. parsed-literal::

    # of lines= 3636
    LPF selected


.. parsed-literal::

    100%|██████████| 19/19 [00:01<00:00, 10.62it/s]


.. code:: ipython3

    plt.plot(nus,xsv) #cm2
    plt.xlabel("wavenumber (cm-1)")
    plt.ylabel("cross section (cm2)")
    plt.show()



.. image:: simple_usage_files/simple_usage_10_0.png


You can plot the line strengths.

.. code:: ipython3

    ls=autoxs.linest(1000.0) #line strength for 1000K
    plt.plot(autoxs.mdb.nu_lines,ls,".")
    plt.xlabel("wavenumber (cm-1)")
    plt.ylabel("line strength (cm)")
    plt.show()



.. image:: simple_usage_files/simple_usage_12_0.png


ExoJAX can solve the radiative transfer and derive the emission
spectrum. Let’s try it using spec.autospec.AutRT

.. code:: ipython3

    from exojax.utils.grids import wavenumber_grid
    from exojax.spec.autospec import AutoRT

We need to assume a temperature-Pressure profile (T-P profile) to
compute the emission spectrum. Here, we assume a power-law type T-P
profile. In addition to CO, we assume H2-H2 and H2-He `collisional
induced
absorption <https://en.wikipedia.org/wiki/Collision-induced_absorption_and_emission>`__
(CIA) as a continuum opacity.

.. code:: ipython3

    nus,wav,res=wavenumber_grid(1900.0,2300.0,150000,"cm-1")
    Parr=numpy.logspace(-8,2,100)
    Tarr = 500.*(Parr/Parr[-1])**0.02    
    autort=AutoRT(nus,1.e5,2.33,Tarr,Parr) #g=1.e5 cm/s2, mmw=2.33
    autort.addcia("H2-H2",0.74,0.74)       #CIA, mmr(H)=0.74
    autort.addcia("H2-He",0.74,0.25)       #CIA, mmr(He)=0.25
    autort.addmol("ExoMol","CO",0.01)      #CO line, mmr(CO)=0.01


.. parsed-literal::

    xsmode assumes ESLOG in wavenumber space: mode=lpf
    The wavenumber grid is evenly spaced in log space (ESLOG).
    xsmode= auto
    H2-H2
    H2-He
    Warning: CO is interpreted as 12C-16O
    Recommendated database by ExoMol:  Li2015
    CO/12C-16O/Li2015
    broadf= True
    Background atmosphere:  H2
    Reading .database/CO/12C-16O/Li2015/12C-16O__Li2015.trans.bz2
    .broad is used.
    Broadening code level= a0
    default broadening parameters are used for  71  J lower states in  152  states
    # of lines 3636
    # of lines= 3636
    MODIT selected
    xsmode= MODIT


.. parsed-literal::

    /home/kawahara/exojax/src/exojax/spec/autospec.py:313: UserWarning: negative cross section detected #=3822040 fraction=0.2548026666666667
      warnings.warn(msg, UserWarning)


Let’s run the radiative transfer.

.. code:: ipython3

    F=autort.rtrun()

.. code:: ipython3

    fig=plt.figure(figsize=(15,4))
    plt.plot(nus,F)
    plt.xlabel("wavenumber (cm-1)")
    plt.ylabel("flux (erg/s/cm2/cm-1)")
    plt.show()



.. image:: simple_usage_files/simple_usage_19_0.png


You may wanna take a planet spin rotation, RV shift, and observational
sampling? Yes, we can.

.. code:: ipython3

    nusobs=numpy.linspace(1900.0,2300.0,3000,dtype=numpy.float64) #observation wavenumber bin (cm-1)
    Fobs=autort.spectrum(nusobs,100000.0,20.0,0.0) #R=100000, vsini=10km/s, RV=0km/s

This is just a convolution. So, you should ignore the edges.

.. code:: ipython3

    fig=plt.figure(figsize=(15,4))
    plt.plot(nus,F)
    plt.plot(nusobs,Fobs)
    plt.xlabel("wavenumber (cm-1)")
    plt.ylabel("flux (erg/s/cm2/cm-1)")
    plt.show()



.. image:: simple_usage_files/simple_usage_23_0.png


.. code:: ipython3

    fig=plt.figure(figsize=(15,4))
    plt.plot(nus,F)
    plt.plot(nusobs,Fobs,"+")
    plt.xlim(2100,2110)
    plt.ylim(0,450)
    plt.xlabel("wavenumber (cm-1)")
    plt.ylabel("flux (erg/s/cm2/cm-1)")
    plt.show()



.. image:: simple_usage_files/simple_usage_24_0.png


Visualize the contribution function to see where the emission comes
from.

.. code:: ipython3

    from exojax.plot.atmplot import plotcf

.. code:: ipython3

    cf=plotcf(nus,autort.dtau,Tarr,Parr,autort.dParr)



.. image:: simple_usage_files/simple_usage_27_0.png


That’s it.