Guide to create ``FilterSet``
=============================

- Can be initialised using 3 approches:

  1. ``list_pivot_wavelengths``: A list of pivot wavelengths with
     appropriate units
  2. ``list_filter_names``: A list of standard filter IDs
  3. ``file_list``: A list of local files to read the filter
     transmission

.. code:: ipython3

    from astropy import units as u
    import numpy as np
    import sed_analysis_tools as st

1. Creating infinitesimally narrow bands - RECOMMENDED
------------------------------------------------------

- This is 1000 times faster than providing filter transmission cirves,
  which leads to slower processing due to on-the-fly synthetic
  photometric calculations

.. code:: ipython3

    filter_set_1 = st.FilterSet(list_pivot_wavelengths=np.linspace(1000, 10000, 10) * u.AA)
    filter_set_1.plot_all_filters()
    print(filter_set_1.list_pivot_wavelengths)


.. parsed-literal::

    [ 1000.  2000.  3000.  4000.  5000.  6000.  7000.  8000.  9000. 10000.] Angstrom



.. image:: guide_FilterSet/output_3_1.png


.. code:: ipython3

    list_pivot_wavelengths = np.array([1481,1541,1607,2303,4357,5035,5366,
                                       5822,7619,8101,8578,12350,16620,21589,
                                       33525,46027,115608])* u.AA
    filter_set_2 = st.FilterSet(list_pivot_wavelengths=list_pivot_wavelengths)
    filter_set_2.plot_all_filters()
    print(filter_set_2.list_pivot_wavelengths)


.. parsed-literal::

    [  1481.   1541.   1607.   2303.   4357.   5035.   5366.   5822.   7619.
       8101.   8578.  12350.  16620.  21589.  33525.  46027. 115608.] Angstrom



.. image:: guide_FilterSet/output_4_1.png


2. Creating ``FilterSet`` based on filter names
-----------------------------------------------

- Names as given in SVO filter profile survice:
  http://svo2.cab.inta-csic.es/theory/fps/
- The required transmission curves will be downloaded automatically
- If ``dir_filter_transmission`` is provided, it will first look in the
  folder before downloading files

.. code:: ipython3

    list_filter_names = [
        "GALEX/GALEX.FUV",
        "GALEX/GALEX.NUV",
        "Generic/Bessell.U",
        "Generic/Bessell.V",
        "Generic/Bessell.B",
        "Generic/Bessell.R",
        "Generic/Bessell.I",
        "2MASS/2MASS.H",
        "2MASS/2MASS.J",
        "2MASS/2MASS.Ks",
    ]
    filter_set_3 = st.FilterSet(list_filter_names=list_filter_names)
    filter_set_3.plot_all_filters()
    print(filter_set_2.list_pivot_wavelengths)


.. parsed-literal::

    [  1481.   1541.   1607.   2303.   4357.   5035.   5366.   5822.   7619.
       8101.   8578.  12350.  16620.  21589.  33525.  46027. 115608.] Angstrom



.. image:: guide_FilterSet/output_6_1.png


3. Reading transmission curves from ascii files
-----------------------------------------------

- Provide \`file_list’ with two columns without any header
- wavelength in Angstrom | Filter transmission (cm2 or normalized
  response both work)

.. code:: ipython3

    file_list = [
        "data/filter_transmission/2MASS_2MASS.H.dat",
        "data/filter_transmission/2MASS_2MASS.J.dat",
        "data/filter_transmission/2MASS_2MASS.Ks.dat",
        "data/filter_transmission/GALEX_GALEX.FUV.dat",
        "data/filter_transmission/GALEX_GALEX.NUV.dat",
        "data/filter_transmission/Generic_Bessell.B.dat",
        "data/filter_transmission/Generic_Bessell.I.dat",
        "data/filter_transmission/Generic_Bessell.R.dat",
        "data/filter_transmission/Generic_Bessell.U.dat",
        "data/filter_transmission/Generic_Bessell.V.dat",
    ]
    
    filter_set_4 = st.FilterSet(list_files=file_list)
    filter_set_4.plot_all_filters()
    filter_set_4.list_pivot_wavelengths




.. math::

    [1528.081,~2271.0709,~3584.777,~4371.0712,~5477.6973,~6498.0883,~8020.1419,~12358.089,~16457.504,~21603.095] \; \mathrm{\mathring{A}}




.. image:: guide_FilterSet/output_8_1.png