Example single fit
==================

Fitting method 

- Read data 

- Select and create model 

- Fit the data with model 

- Save plots and log

Quick fit
---------

.. code:: ipython3

    import src.binary_sed_fitting as bsf
    ################################################################################
    name        = 'WOCS2002'
    file_name   = 'data/extinction_corrected_flux_files/%s.csv'%name
    data        = bsf.load_data(file_name, mode='csv')
    distance    = 831. # pc
    e_distance  = 11.  # pc
    ################################################################################
    model_name = 'kurucz'
    limits = {'Te'   : [3500, 9000],
              'logg' : [   3,    5],
              'MH'   : [ 0.0,  0.0],
              'alpha': [ 0.0,  0.0]}
    model = bsf.Model(model_name, limits=limits)
    ################################################################################
    star = bsf.Star(name=name, 
                    distance=distance, 
                    e_distance=e_distance,
                    data=data, 
                    model=model)
    ################################################################################
    star.fit_chi2()
    star.plot()


.. parsed-literal::

    22:39:52 ----- WARNING  ----- estimate_runtime
    Calculating chi2: ETA ~ 0 s
    


.. image:: examples_single/output_3_1.png


Selecting model
---------------

-  Supported models: Kurucz, Koester and Kurucz_UVBLUE

.. code:: ipython3

    import src.binary_sed_fitting as bsf
    ################################################################################
    model_name = 'kurucz'
    limits = {'Te'   : [3500, 9000],
              'logg' : [   3,    5],
              'MH'   : [ 0.0,  0.0],
              'alpha': [ 0.0,  0.0]}
    model = bsf.Model(model_name, limits=limits)
    print(model_name, '\n\n\n' ,model.da)
    ################################################################################
    model_name = 'kurucz_uvblue'
    limits = {'Te'   : [3500, 9000],
              'logg' : [   3,    5],
              'MH'   : [ 0.0,  0.0]}
    model = bsf.Model(model_name, limits=limits)
    print(model_name, '\n\n\n' ,model.da)
    ################################################################################
    model_name = 'koester'
    limits = {'Te'   : [5000, 80000],
              'logg' : [ 6.5,   9.5]}
    model = bsf.Model(model_name, limits=limits)
    print(model_name, '\n\n\n' ,model.da)


.. parsed-literal::

    kurucz 
    
    
     <xarray.DataArray (FilterID: 8162, Te: 23, logg: 5, MH: 1, alpha: 1)> Size: 8MB
    [938630 values with dtype=float64]
    Coordinates:
      * FilterID  (FilterID) <U38 1MB 'Swift/UVOT.UVM2_fil' ... 'QUIJOTE/MFI.11GH...
      * Te        (Te) int32 92B 3500 3750 4000 4250 4500 ... 8250 8500 8750 9000
      * logg      (logg) float64 40B 3.0 3.5 4.0 4.5 5.0
      * MH        (MH) float64 8B 0.0
      * alpha     (alpha) float64 8B 0.0
    Attributes:
        Wavelengths:  [1.19128077e+00 1.24254209e+00 1.48134125e+02 ... 3.8683349...
        unit:         erg/s/cm2/A
        long_name:    Flux
    kurucz_uvblue 
    
    
     <xarray.DataArray (FilterID: 8162, Te: 12, logg: 5, MH: 1)> Size: 4MB
    [489720 values with dtype=float64]
    Coordinates:
      * FilterID  (FilterID) <U38 1MB 'Swift/UVOT.UVM2_fil' ... 'QUIJOTE/MFI.11GH...
      * Te        (Te) int32 48B 3500 4000 4500 5000 5500 ... 7500 8000 8500 9000
      * logg      (logg) float64 40B 3.0 3.5 4.0 4.5 5.0
      * MH        (MH) float64 8B 0.0
        alpha     float64 8B ...
    Attributes:
        Wavelengths:  [1.19128077e+00 1.24254209e+00 1.48134125e+02 ... 3.8683349...
        unit:         erg/s/cm2/A
        long_name:    Flux
    koester 
    
    
     <xarray.DataArray (FilterID: 8162, Te: 82, logg: 13)> Size: 70MB
    [8700692 values with dtype=float64]
    Coordinates:
      * FilterID  (FilterID) <U38 1MB 'IUE/IUE.1250-1300' ... 'QUIJOTE/MFI.11GHz_H3'
      * Te        (Te) int32 328B 5000 5250 5500 5750 ... 50000 60000 70000 80000
      * logg      (logg) float64 104B 6.5 6.75 7.0 7.25 7.5 ... 8.75 9.0 9.25 9.5
    Attributes:
        Wavelengths:  [1.28470318e+03 1.35336435e+03 1.35623969e+03 ... 3.8683349...
        unit:         erg/s/cm2/A
        long_name:    Flux
    

Recommended fitting routine
---------------------------

-  ``Star``

   -  ``name`` : Name of the star
   -  ``distance``, ``e_distance`` : Distance and it’s error in pc
   -  ``filters_to_drop`` : Filters to be removed from the fit (due to
      bad data, bad fit, saturation…)
   -  ``wavelength_range`` : Wavelength range to be fitted in Angstrom
   -  ``data`` : DataFrame with the photometric flux
   -  ``model`` : Model object of required model and parameter limits
   -  ``r_limits`` : 2 options

      -  ``blackbody`` : Radius range automatically calculated from
         blackbody fit
      -  ``[r_min,r_max]`` : Radius is varied between the r_min and
         r_max given in solar radii

   -  ``run_name`` : A string name for tracking different fits

-  ``fit_chi2``

   -  Calculates chi2 for the data and model.
   -  ``refit``

      -  if ``True`` : the fit is redone.
      -  If ``False``: If a previous fit is available, it is read.
         Otherwise a fresh fit is made.

-  ``fit_noisy_chi2``

   -  Fits chi2 by adding noise to the data. Useful for getting
      statistical errors.
   -  ``refit`` : if ``True`` the fit is redone. If ``False``, the
      previous fit is read.

-  ``plot``

   -  ``add_noisy_seds`` : Whether to plot the noisy SEDs.
   -  ``folder`` : Folder for saving the plot
   -  ``FR_cutoff`` : Fractional residual cutoff for indicating excess
      flux

-  ``plot_public``

   -  Plots a smaller plot with SED, FR and EWR

-  ``save_summary``

   -  Saves the fit parameters in a logfile

Example 1
~~~~~~~~~

.. code:: ipython3

    import src.binary_sed_fitting as bsf
    import warnings
    warnings.filterwarnings("ignore")
    bsf.console.setLevel(bsf.logging.INFO)
    ################################################################################
    name        = 'WOCS2002'
    file_name   = 'data/extinction_corrected_flux_files/%s.csv'%name
    data        = bsf.load_data(file_name, mode='csv')
    distance    = 831. # pc
    e_distance  = 11.  # pc
    
    refit    = False
    run_name = '0'
    ################################################################################
    model_name = 'kurucz'
    limits = {'Te'   : [3500, 9000],
              'logg' : [   3,    5],
              'MH'   : [ 0.0,  0.0],
              'alpha': [ 0.0,  0.0]}
    model = bsf.Model(model_name, limits=limits)
    ################################################################################
    star = bsf.Star(name=name, 
                    distance=distance, 
                    e_distance=e_distance,
                    filters_to_drop=['KPNO/Mosaic.I'], 
                    wavelength_range=[3000, 1_000_000_000],
                    data=data, 
                    model=model, 
                    r_limits='blackbody',     
                    run_name=run_name)
    ################################################################################
    star.fit_chi2(refit=refit)
    star.fit_noisy_chi2(refit=refit)
    ################################################################################
    star.plot(add_noisy_seds=False,
              folder='plots/',
              FR_cutoff=0.5)
    star.plot_public(add_noisy_seds=False,
                     folder='plots/',
                     FR_cutoff=0.5)
    ################################################################################
    star.save_summary()


.. parsed-literal::

    22:39:54 ----- INFO     ----- __init__
    ==========================================================
    ----------------------------------------------------------
    ------------            WOCS2002 A            ------------
    ----------------------------------------------------------
    ==========================================================
    22:39:54 ----- INFO     ----- drop_filters
                            Fitted           Not fitted
    wavelength                                         
    1481.000000                     Astrosat/UVIT.F148W
    1541.000000                     Astrosat/UVIT.F154W
    1608.000000                     Astrosat/UVIT.F169M
    2303.366368                         GALEX/GALEX.NUV
    4357.276538      KPNO/Mosaic.B                     
    5035.750275     GAIA/GAIA3.Gbp                     
    5366.240786      KPNO/Mosaic.V                     
    5822.388714       GAIA/GAIA3.G                     
    7619.959993     GAIA/GAIA3.Grp                     
    8101.609574                           KPNO/Mosaic.I
    8578.159519    GAIA/GAIA3.Grvs                     
    12350.000000     2MASS/2MASS.J                     
    16620.000000     2MASS/2MASS.H                     
    21590.000000    2MASS/2MASS.Ks                     
    33526.000000      WISE/WISE.W1                     
    46028.000000      WISE/WISE.W2                     
    115608.000000     WISE/WISE.W3                     
    22:39:54 ----- INFO     ----- drop_filters
    Filters: used/all = 12/17
    22:39:54 ----- INFO     ----- blackbody
    Fit parameters: T=5141 K, log_sf=-19.68
    22:39:54 ----- WARNING  ----- calculate_chi2
    Give "refit=True" if you want to rerun the fitting process.
    22:39:54 ----- INFO     ----- calculate_chi2
         Te  logg   MH  alpha            sf         chi2         R          L
    0  5250   4.5  0.0    0.0  2.092353e-20  1436.600470  5.331470  19.454716
    1  5250   4.0  0.0    0.0  2.092353e-20  1437.936819  5.331470  19.454716
    2  5250   5.0  0.0    0.0  2.092353e-20  1465.574398  5.331470  19.454716
    3  5250   3.5  0.0    0.0  2.092353e-20  1475.514792  5.331470  19.454716
    4  5250   4.5  0.0    0.0  2.141090e-20  1557.722256  5.393206  19.907874
    22:39:54 ----- INFO     ----- get_parameters_from_chi2_minimization
    Te	5250
    logg	4.5
    MH	0.0
    alpha	0.0
    sf	2.0923525610805822e-20
    chi2	1436.6004696876382
    R	5.331470347607279
    L	19.454715675310812
    vgf2	1409.3254983079803
    vgfb2	367.3732364927856
    22:39:54 ----- WARNING  ----- get_parameters_from_chi2_minimization
    Based on chi2, I recommend removal of following filters: ['GAIA/GAIA3.Grvs']; chi2=[19.2204403]
    22:39:54 ----- WARNING  ----- calculate_noisy_chi2
    Give "refit=True" if you want to rerun the fitting process.
    22:39:54 ----- INFO     ----- calculate_noisy_chi2
         Te  logg   MH  alpha            sf         chi2        R          L
    0  5250   4.5  0.0    0.0  2.092353e-20  1436.600470  5.33147  19.454716
    1  5250   4.5  0.0    0.0  2.092353e-20  1448.373499  5.33147  19.454716
    2  5250   4.0  0.0    0.0  2.092353e-20  1365.291238  5.33147  19.454716
    3  5250   4.5  0.0    0.0  2.092353e-20  1384.661354  5.33147  19.454716
    4  5250   4.5  0.0    0.0  2.092353e-20  1519.437073  5.33147  19.454716
    22:39:54 ----- INFO     ----- get_parameters_from_noisy_chi2_minimization
    Te	5250(-250,+250)
    logg	4.5(-0.5,+0.5)
    MH	0.0(-nan,+nan)
    alpha	0.0(-nan,+nan)
    sf	2.0923525610805822e-20(-4.762775903042835e-22,+4.8737152053873975e-22)
    R	5.331470347607279(-0.0933009594847496,+0.09376472807053667)
    L	19.454715675310812(-0.5150466243764595,+0.5150466243764595)
    22:39:55 ----- INFO     ----- save_summary
    Saving log in data/log_single_fitting.csv
    


.. image:: examples_single/output_9_1.png



.. image:: examples_single/output_9_2.png


Example 2
~~~~~~~~~

.. code:: ipython3

    import src.binary_sed_fitting as bsf
    import warnings
    warnings.filterwarnings("ignore")
    bsf.console.setLevel(bsf.logging.WARNING)
    ################################################################################
    name        = 'Y1168'
    file_name   = 'data/extinction_corrected_flux_files/%s.csv'%name
    data        = bsf.load_data(file_name, mode='csv')
    distance    = 831. # pc
    e_distance  = 11.  # pc
    
    refit    = False
    run_name = '0'
    ################################################################################
    model_name = 'koester'
    limits = {'Te'   : [5000, 80000],
              'logg' : [ 6.5,   9.5]}
    model = bsf.Model(model_name, limits=limits)
    ################################################################################
    star = bsf.Star(name=name, 
                    distance=distance, 
                    e_distance=e_distance,
                    filters_to_drop=[], 
                    wavelength_range=[0, 1_000_000_000],
                    data=data, 
                    model=model, 
                    r_limits='blackbody',     
                    run_name=run_name)
    ################################################################################
    star.fit_chi2(refit=refit)
    star.fit_noisy_chi2(refit=refit)
    ################################################################################
    star.plot(add_noisy_seds=False,
              folder='plots/',
              FR_cutoff=0.5)
    star.save_summary()


.. parsed-literal::

    22:39:56 ----- WARNING  ----- calculate_chi2
    Give "refit=True" if you want to rerun the fitting process.
    22:39:56 ----- WARNING  ----- get_parameters_from_chi2_minimization
    Based on chi2, I recommend removal of following filters: ['WISE/WISE.W1']; chi2=[1722.30668168]
    22:39:56 ----- WARNING  ----- calculate_noisy_chi2
    Give "refit=True" if you want to rerun the fitting process.
    22:39:56 ----- WARNING  ----- get_realistic_errors_from_iterations
    logg_A : The best fit value is at upper limit of the model.
    


.. image:: examples_single/output_11_1.png


Example 3
~~~~~~~~~

.. code:: ipython3

    import src.binary_sed_fitting as bsf
    import warnings
    warnings.filterwarnings("ignore")
    bsf.console.setLevel(bsf.logging.ERROR)
    ################################################################################
    name        = 'WOCS2002'
    file_name   = 'data/extinction_corrected_flux_files/%s.csv'%name
    data        = bsf.load_data(file_name, mode='csv')
    distance    = 831. # pc
    e_distance  = 11.  # pc
    
    refit    = False
    run_name = 'uvblue'
    ################################################################################
    model_name = 'kurucz_uvblue'
    limits = {'Te'   : [3500, 9000],
              'logg' : [   3,    5],
              'MH'   : [ 0.0,  0.0]}
    model = bsf.Model(model_name, limits=limits)
    ################################################################################
    star = bsf.Star(name=name, 
                    distance=distance, 
                    e_distance=e_distance,
                    filters_to_drop=[], 
                    wavelength_range=[3000, 1_000_000_000],
                    data=data, 
                    model=model, 
                    r_limits=[0.1, 10],     
                    run_name=run_name)
    ################################################################################
    star.fit_chi2(refit=refit)
    star.fit_noisy_chi2(refit=refit)
    ################################################################################
    star.plot(add_noisy_seds=False,
              folder='plots/',
              FR_cutoff=0.5)
    star.save_summary()



.. image:: examples_single/output_13_0.png


Advance options
---------------

-  Console logging options can be changes based on requirements

   -  For minimal logs: ``bsf.console.setLevel(bsf.logging.ERROR)``
   -  For typical logs: ``bsf.console.setLevel(bsf.logging.WARNING)``
   -  For detailed logs: ``bsf.console.setLevel(bsf.logging.INFO)``
   -  For debug logs: ``bsf.console.setLevel(bsf.logging.DEBUG)``

-  Editing the ``bsf`` file and re-importing the file
   ``python     import importlib     importlib.reload(bsf)``
-  ``Star``

   -  ``component`` : Name of the component. Useful for binary fits.

-  ``fit_blackbody``

   -  Fits a blackbody to given flux
   -  ``p0`` : Initial guess for the temperature and log(scaling
      factor). Default is [5000., -20]

-  ``fit_chi2``

   -  ``_trim`` : Trimming the number of chi2 fits to save memory

-  ``fit_noisy_chi2``

   -  ``total_iterations`` : Number of iterations for noisy fits

-  ``plot``

   -  ``show_plot`` : Showing/hiding the plots in the notebook

-  ``plot_public``

   -  Duplicate and modify the funcion to suit your need

-  ``create_sf_list``

   -  ``LOG_SF_STEPSIZE`` (=0.01) and ``LOG_SF_FLEXIBILITY`` (=2) are
      used to determine the stepsizes in scaling factor

-  ``data``

   -  Dataframe containing the observational data. It is cropped to the
      fitted filters based on ``filters_to_drop`` and
      ``wavelength_range``.

-  ``data_all``

   -  A copy of the original data with an added ‘fitted’ column
      indicating whether the filter is used. Mmodel flux, residuals, and
      statistical measures are added in
      ``Fitter.get_parameters_from_chi2_minimization`` and
      ``Fitter.get_parameters_from_noisy_chi2_minimization``.

-  ``data_not_fitted``

   -  DataFrame containing only removed filters based on
      ``filters_to_drop`` and ``wavelength_range``

-  ``save_fig``

   -  Edit the function to change the plot format (e.g. PDF, PNG or
      multiple formats)

- ``Star.df_chi2`` and ``Star.df_chi2_noisy``

   -  The chi2 dataframes are saved in ``outputs\``.

- log files

   -  The raw log files (with logging level DEBUG) are saved in ``data\log_<today's date>.txt``

   -  The summary logs are chi2 dataframe is saved in ``outputs\log_single_fitting.csv`` and ``outputs\log_starsystem_fitting.csv``.

.. code:: ipython3

    import src.binary_sed_fitting as bsf
    import warnings
    import importlib
    warnings.filterwarnings("ignore")
    importlib.reload(bsf)
    bsf.console.setLevel(bsf.logging.INFO)
    ################################################################################
    name        = 'WOCS2002'
    file_name   = 'data/extinction_corrected_flux_files/%s.csv'%name
    data        = bsf.load_data(file_name, mode='csv')
    distance    = 831. # pc
    e_distance  = 11.  # pc
    
    refit = False
    
    ################################################################################
    model_name = 'kurucz'
    limits = {'Te'   : [3500, 9000],
              'logg' : [   3,    5],
              'MH'   : [ 0.0,  0.0],
              'alpha': [ 0.0,  0.0]}
    model = bsf.Model(model_name, limits=limits)
    ################################################################################
    star = bsf.Star(name=name, 
                    distance=distance, 
                    e_distance=e_distance,
                    filters_to_drop=['KPNO/Mosaic.I'], 
                    wavelength_range=[3000, 1_000_000_000],
                    data=data, 
                    model=model, 
                    r_limits='blackbody',     
                    run_name='kur', 
                    component='A')
    ################################################################################
    star.fit_blackbody(p0=[5000., -20], 
                       plot=True, 
                       show_plot=True, 
                       folder=None)
    ################################################################################
    star.fit_chi2(refit=refit,
                  _trim=1000)
    star.fit_noisy_chi2(refit=refit,
                        total_iterations=100)
    ################################################################################
    star.plot(add_noisy_seds=False,
              show_plot=True, 
              folder=None,
              FR_cutoff=0.5)
    star.plot_public(add_noisy_seds=False,
                     show_plot=True, 
                     folder=None,
                     FR_cutoff=0.5)


.. parsed-literal::

    22:39:59 ----- INFO     ----- __init__
    ==========================================================
    ----------------------------------------------------------
    ------------            WOCS2002 A            ------------
    ----------------------------------------------------------
    ==========================================================
    22:39:59 ----- INFO     ----- drop_filters
                            Fitted           Not fitted
    wavelength                                         
    1481.000000                     Astrosat/UVIT.F148W
    1541.000000                     Astrosat/UVIT.F154W
    1608.000000                     Astrosat/UVIT.F169M
    2303.366368                         GALEX/GALEX.NUV
    4357.276538      KPNO/Mosaic.B                     
    5035.750275     GAIA/GAIA3.Gbp                     
    5366.240786      KPNO/Mosaic.V                     
    5822.388714       GAIA/GAIA3.G                     
    7619.959993     GAIA/GAIA3.Grp                     
    8101.609574                           KPNO/Mosaic.I
    8578.159519    GAIA/GAIA3.Grvs                     
    12350.000000     2MASS/2MASS.J                     
    16620.000000     2MASS/2MASS.H                     
    21590.000000    2MASS/2MASS.Ks                     
    33526.000000      WISE/WISE.W1                     
    46028.000000      WISE/WISE.W2                     
    115608.000000     WISE/WISE.W3                     
    22:39:59 ----- INFO     ----- drop_filters
    Filters: used/all = 12/17
    22:39:59 ----- INFO     ----- blackbody
    Fit parameters: T=5141 K, log_sf=-19.68
    22:39:59 ----- INFO     ----- blackbody
    Fit parameters: T=5141 K, log_sf=-19.68
    22:39:59 ----- WARNING  ----- calculate_chi2
    Give "refit=True" if you want to rerun the fitting process.
    22:39:59 ----- INFO     ----- calculate_chi2
         Te  logg   MH  alpha            sf       chi2         R          L
    0  5250   4.0  0.0    0.0  2.105389e-20  42.148650  5.348053  19.575928
    1  5250   4.5  0.0    0.0  2.105389e-20  43.750774  5.348053  19.575928
    2  5250   3.5  0.0    0.0  2.105389e-20  46.564792  5.348053  19.575928
    3  5250   5.0  0.0    0.0  2.105389e-20  51.654370  5.348053  19.575928
    4  5250   3.0  0.0    0.0  2.105389e-20  60.201435  5.348053  19.575928
    22:39:59 ----- INFO     ----- get_parameters_from_chi2_minimization
    Te	5250
    logg	4.0
    MH	0.0
    alpha	0.0
    sf	2.1053889442869984e-20
    chi2	42.148649603337915
    R	5.348053395835723
    L	19.57592810070829
    vgf2	11.474651537318044
    vgfb2	0.8009328529123378
    22:39:59 ----- WARNING  ----- calculate_noisy_chi2
    Give "refit=True" if you want to rerun the fitting process.
    22:39:59 ----- INFO     ----- calculate_noisy_chi2
         Te  logg   MH  alpha            sf       chi2         R          L
    0  5250   4.0  0.0    0.0  2.105389e-20  42.148650  5.348053  19.575928
    1  5250   4.0  0.0    0.0  2.105389e-20  48.786377  5.348053  19.575928
    2  5250   4.5  0.0    0.0  2.105389e-20  58.784976  5.348053  19.575928
    3  5250   4.0  0.0    0.0  2.105389e-20  83.279210  5.348053  19.575928
    4  5250   4.0  0.0    0.0  2.105389e-20  57.314788  5.348053  19.575928
    22:39:59 ----- INFO     ----- get_parameters_from_noisy_chi2_minimization
    Te	5250(-250,+250)
    logg	4.0(-0.5,+0.5)
    MH	0.0(-nan,+nan)
    alpha	0.0(-nan,+nan)
    sf	2.1053889442869984e-20(-4.792450334089126e-22,+4.904080842726991e-22)
    R	5.348053395835723(-0.09359116353916891,+0.0940563746344943)
    L	19.57592810070829(-0.518255617587945,+0.518255617587945)
    


.. image:: examples_single/output_16_1.png



.. image:: examples_single/output_16_2.png



.. image:: examples_single/output_16_3.png


.. code:: ipython3

    print(star.data.index)
    print(star.data_all.index)
    print(star.data_not_fitted.index)
    star.data.head()


.. parsed-literal::

    Index(['KPNO/Mosaic.B', 'GAIA/GAIA3.Gbp', 'KPNO/Mosaic.V', 'GAIA/GAIA3.G',
           'GAIA/GAIA3.Grp', 'GAIA/GAIA3.Grvs', '2MASS/2MASS.J', '2MASS/2MASS.H',
           '2MASS/2MASS.Ks', 'WISE/WISE.W1', 'WISE/WISE.W2', 'WISE/WISE.W3'],
          dtype='object', name='FilterID')
    Index(['Astrosat/UVIT.F148W', 'Astrosat/UVIT.F154W', 'Astrosat/UVIT.F169M',
           'GALEX/GALEX.NUV', 'KPNO/Mosaic.B', 'GAIA/GAIA3.Gbp', 'KPNO/Mosaic.V',
           'GAIA/GAIA3.G', 'GAIA/GAIA3.Grp', 'KPNO/Mosaic.I', 'GAIA/GAIA3.Grvs',
           '2MASS/2MASS.J', '2MASS/2MASS.H', '2MASS/2MASS.Ks', 'WISE/WISE.W1',
           'WISE/WISE.W2', 'WISE/WISE.W3'],
          dtype='object', name='FilterID')
    Index(['Astrosat/UVIT.F148W', 'Astrosat/UVIT.F154W', 'Astrosat/UVIT.F169M',
           'GALEX/GALEX.NUV', 'KPNO/Mosaic.I'],
          dtype='object', name='FilterID')
    



.. raw:: html

    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }
    
        .dataframe tbody tr th {
            vertical-align: top;
        }
    
        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>wavelength</th>
          <th>flux</th>
          <th>error</th>
          <th>error_fraction</th>
          <th>error_2percent</th>
          <th>error_10percent</th>
          <th>log_wavelength</th>
          <th>log_flux</th>
          <th>e_log_flux</th>
          <th>fitted</th>
          <th>...</th>
          <th>chi2_i</th>
          <th>vgf2_i</th>
          <th>vgfb2_i</th>
          <th>model_flux_median</th>
          <th>residual_flux_median</th>
          <th>fractional_residual_median</th>
          <th>ewr_median</th>
          <th>chi2_i_median</th>
          <th>vgf2_i_median</th>
          <th>vgfb2_i_median</th>
        </tr>
        <tr>
          <th>FilterID</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>KPNO/Mosaic.B</th>
          <td>4357.276538</td>
          <td>9.309407e-14</td>
          <td>6.130611e-15</td>
          <td>0.065854</td>
          <td>6.130611e-15</td>
          <td>9.309407e-15</td>
          <td>3.639215</td>
          <td>-13.031078</td>
          <td>0.028600</td>
          <td>1</td>
          <td>...</td>
          <td>0.545712</td>
          <td>0.545712</td>
          <td>2.366611e-01</td>
          <td>8.856525e-14</td>
          <td>4.528824e-15</td>
          <td>0.048648</td>
          <td>0.738723</td>
          <td>0.545712</td>
          <td>0.545712</td>
          <td>2.366611e-01</td>
        </tr>
        <tr>
          <th>GAIA/GAIA3.Gbp</th>
          <td>5035.750275</td>
          <td>1.028575e-13</td>
          <td>3.881303e-16</td>
          <td>0.003773</td>
          <td>2.057150e-15</td>
          <td>1.028575e-14</td>
          <td>3.702064</td>
          <td>-12.987764</td>
          <td>0.001639</td>
          <td>1</td>
          <td>...</td>
          <td>0.000081</td>
          <td>0.000003</td>
          <td>1.148069e-07</td>
          <td>1.028540e-13</td>
          <td>3.485137e-18</td>
          <td>0.000034</td>
          <td>0.008979</td>
          <td>0.000081</td>
          <td>0.000003</td>
          <td>1.148069e-07</td>
        </tr>
        <tr>
          <th>KPNO/Mosaic.V</th>
          <td>5366.240786</td>
          <td>1.079375e-13</td>
          <td>7.108112e-15</td>
          <td>0.065854</td>
          <td>7.108112e-15</td>
          <td>1.079375e-14</td>
          <td>3.729670</td>
          <td>-12.966827</td>
          <td>0.028600</td>
          <td>1</td>
          <td>...</td>
          <td>0.298089</td>
          <td>0.298089</td>
          <td>1.292734e-01</td>
          <td>1.118184e-13</td>
          <td>-3.880853e-15</td>
          <td>-0.035955</td>
          <td>-0.545975</td>
          <td>0.298089</td>
          <td>0.298089</td>
          <td>1.292734e-01</td>
        </tr>
        <tr>
          <th>GAIA/GAIA3.G</th>
          <td>5822.388714</td>
          <td>9.174216e-14</td>
          <td>2.539155e-16</td>
          <td>0.002768</td>
          <td>1.834843e-15</td>
          <td>9.174216e-15</td>
          <td>3.765101</td>
          <td>-13.037431</td>
          <td>0.001202</td>
          <td>1</td>
          <td>...</td>
          <td>4.897209</td>
          <td>0.093784</td>
          <td>3.751362e-03</td>
          <td>9.230407e-14</td>
          <td>-5.619057e-16</td>
          <td>-0.006125</td>
          <td>-2.212964</td>
          <td>4.897209</td>
          <td>0.093784</td>
          <td>3.751362e-03</td>
        </tr>
        <tr>
          <th>GAIA/GAIA3.Grp</th>
          <td>7619.959993</td>
          <td>7.878734e-14</td>
          <td>3.154436e-16</td>
          <td>0.004004</td>
          <td>1.575747e-15</td>
          <td>7.878734e-15</td>
          <td>3.881953</td>
          <td>-13.103544</td>
          <td>0.001739</td>
          <td>1</td>
          <td>...</td>
          <td>13.712759</td>
          <td>0.549535</td>
          <td>2.198141e-02</td>
          <td>7.995545e-14</td>
          <td>-1.168111e-15</td>
          <td>-0.014826</td>
          <td>-3.703074</td>
          <td>13.712759</td>
          <td>0.549535</td>
          <td>2.198141e-02</td>
        </tr>
      </tbody>
    </table>
    <p>5 rows × 24 columns</p>
    </div>