HajimeKawahara/exojax

Reducing memory for HITEMP CH4
------------------------------

.. code:: ipython3

    from exojax.spec import moldb
    import numpy as np
    import seaborn as sns
    import matplotlib.pyplot as plt
    plt.style.use('bmh')

First of all, set a wavenumber bin in the unit of wavenumber (cm-1).
Here we set the wavenumber range as :math:`1000 \le \nu \le 10000`
(1/cm) with the resolution of 0.01 (1/cm).

We call moldb instance with the path of par file. If the par file does
not exist, moldb will try to download it from HITRAN website.

.. code:: ipython3

    # Setting wavenumber bins and loading HITEMP database
    wav=np.linspace(16370.0,16390.0,2000,dtype=np.float64) #AA
    nus=1.e8/wav[::-1] #cm-1

when initializing MdbHit, extract=True option does extract the opacity
in the wavenumber range we now considered. It reduces the use of DRAM.
(If you have very large DRAM, not necessarily)

.. code:: ipython3

    mdbCH4=moldb.MdbHit('~/exojax/data/CH4/06_HITEMP2020.par.bz2',nus,extract=True)


.. parsed-literal::

    Extract HITEMP: 31880412it [01:47, 296364.94it/s]


.. parsed-literal::

    self.path changed: ~/exojax/data/CH4/6101.281269066504_6108.7354917532075_1.0/06_HITEMP2020.par
    HAPI initializes all the par files in  ~/exojax/data/CH4/6101.281269066504_6108.7354917532075_1.0
    HAPI detected: 06_HITEMP2020.header
    HAPI detected: 06_HITEMP2020.par


.. code:: ipython3

    plt.plot(mdbCH4.nu_lines,mdbCH4.logsij0,".")
    plt.show()



.. image:: Reducing_memory_for_HITEMP_files/Reducing_memory_for_HITEMP_6_0.png