I/O and VOSA helper functions
=============================

Reading photometric data
------------------------

From VOSA files
~~~~~~~~~~~~~~~

.. code:: ipython3

    import src.binary_sed_fitting as bsf
    file_name = 'data/vosa_results/WOCS2002.bfit.phot.dat'
    data = bsf.load_data(file_name, mode='vosa')
    data


.. parsed-literal::

    /Users/vikrantjadhav/Documents/work/Binary_SED_Fitting/src/binary_sed_fitting.py:158: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\s+'`` instead
      data = pd.read_csv(file_name, engine='python', comment='#',
    



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From a CSV file
~~~~~~~~~~~~~~~

-  The flux has to be extinction corrected
-  units of flux = erg/s/A/cm2
-  units of wavelength = A

.. code:: ipython3

    import src.binary_sed_fitting as bsf
    file_name = 'data/extinction_corrected_flux_files/WOCS2002.csv'
    data = bsf.load_data(file_name, mode='csv')
    data




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Reading model files
-------------------

.. code:: ipython3

    import xarray as xr
    import src.binary_sed_fitting as bsf
    
    da = xr.open_dataarray(bsf.DIR_MODELS+'koester_synthetic_photometry.nc')
    da




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    </style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (FilterID: 8162, Te: 82, logg: 13)&gt; Size: 70MB
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        Wavelengths:  [1.28470318e+03 1.35336435e+03 1.35623969e+03 ... 3.8683349...
        unit:         erg/s/cm2/A
        long_name:    Flux</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>FilterID</span>: 8162</li><li><span class='xr-has-index'>Te</span>: 82</li><li><span class='xr-has-index'>logg</span>: 13</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6111a7e5-74ab-4ad6-9089-e905f1cc3b79' class='xr-array-in' type='checkbox' checked><label for='section-6111a7e5-74ab-4ad6-9089-e905f1cc3b79' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[8700692 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-f2377c58-a64b-4787-adaf-79383591a718' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f2377c58-a64b-4787-adaf-79383591a718' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>FilterID</span></div><div class='xr-var-dims'>(FilterID)</div><div class='xr-var-dtype'>&lt;U38</div><div class='xr-var-preview xr-preview'>&#x27;IUE/IUE.1250-1300&#x27; ... &#x27;QUIJOTE...</div><input id='attrs-abbf4265-7f70-4c7d-aa21-33cd780d0708' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-abbf4265-7f70-4c7d-aa21-33cd780d0708' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eee8901-357a-418a-8754-339bde241baa' class='xr-var-data-in' type='checkbox'><label for='data-5eee8901-357a-418a-8754-339bde241baa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;IUE/IUE.1250-1300&#x27;, &#x27;HST/STIS_FUV.F25LYA_G140M&#x27;,
           &#x27;HST/STIS_FUV.F25LYA_G140L&#x27;, ..., &#x27;QUIJOTE/MFI.13GHz_H1&#x27;,
           &#x27;QUIJOTE/MFI.11GHz_H1&#x27;, &#x27;QUIJOTE/MFI.11GHz_H3&#x27;], dtype=&#x27;&lt;U38&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Te</span></div><div class='xr-var-dims'>(Te)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>5000 5250 5500 ... 70000 80000</div><input id='attrs-d86d195e-6fdf-4c86-bdb8-7e7c728b1ca3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d86d195e-6fdf-4c86-bdb8-7e7c728b1ca3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a745387-89b8-4754-9c17-d340c7da3724' class='xr-var-data-in' type='checkbox'><label for='data-9a745387-89b8-4754-9c17-d340c7da3724' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 5000,  5250,  5500,  5750,  6000,  6250,  6500,  6750,  7000,  7250,
            7500,  7750,  8000,  8250,  8500,  8750,  9000,  9250,  9500,  9750,
           10000, 10250, 10500, 10750, 11000, 11250, 11500, 11750, 12000, 12250,
           12500, 12750, 13000, 13250, 13500, 13750, 14000, 14250, 14500, 14750,
           15000, 15250, 15500, 15750, 16000, 16250, 16500, 16750, 17000, 17250,
           17500, 17750, 18000, 18250, 18500, 18750, 19000, 19250, 19500, 19750,
           20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000, 29000,
           30000, 32000, 34000, 35000, 36000, 38000, 40000, 45000, 50000, 60000,
           70000, 80000], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>logg</span></div><div class='xr-var-dims'>(logg)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.5 6.75 7.0 7.25 ... 9.0 9.25 9.5</div><input id='attrs-5d7b45e3-6cf5-4882-9d87-f2eb72def909' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d7b45e3-6cf5-4882-9d87-f2eb72def909' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a67ad7a5-436d-4034-ba0d-d1323861d505' class='xr-var-data-in' type='checkbox'><label for='data-a67ad7a5-436d-4034-ba0d-d1323861d505' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6.5 , 6.75, 7.  , 7.25, 7.5 , 7.75, 8.  , 8.25, 8.5 , 8.75, 9.  , 9.25,
           9.5 ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da38729d-fe91-44f4-879a-8ca830d3a4c8' class='xr-section-summary-in' type='checkbox'  ><label for='section-da38729d-fe91-44f4-879a-8ca830d3a4c8' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>FilterID</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ca5fa581-4386-4089-9f5b-0288c3a1f146' class='xr-index-data-in' type='checkbox'/><label for='index-ca5fa581-4386-4089-9f5b-0288c3a1f146' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;IUE/IUE.1250-1300&#x27;, &#x27;HST/STIS_FUV.F25LYA_G140M&#x27;,
           &#x27;HST/STIS_FUV.F25LYA_G140L&#x27;, &#x27;TD1/SPEC.F1360&#x27;, &#x27;HST/STIS_FUV.F25LYA&#x27;,
           &#x27;TD1/SPEC.F1380&#x27;, &#x27;TD1/SPEC.F1400&#x27;, &#x27;TD1/TD1.140&#x27;, &#x27;TD1/NARROW1.1400&#x27;,
           &#x27;HST/ACS_SBC.F122M&#x27;,
           ...
           &#x27;COSMOSOMAS/COSMO15.17GHz&#x27;, &#x27;COSMOSOMAS/COSMO15.15GHz&#x27;,
           &#x27;QUIJOTE/MFI.17GHz_H2&#x27;, &#x27;COSMOSOMAS/COSMO15.13GHz&#x27;,
           &#x27;COSMOSOMAS/COSMO11.Ch1&#x27;, &#x27;COSMOSOMAS/COSMO11.Ch2&#x27;,
           &#x27;QUIJOTE/MFI.13GHz_H3&#x27;, &#x27;QUIJOTE/MFI.13GHz_H1&#x27;, &#x27;QUIJOTE/MFI.11GHz_H1&#x27;,
           &#x27;QUIJOTE/MFI.11GHz_H3&#x27;],
          dtype=&#x27;object&#x27;, name=&#x27;FilterID&#x27;, length=8162))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Te</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-81225a49-8f62-4741-97d3-114e3ac73c91' class='xr-index-data-in' type='checkbox'/><label for='index-81225a49-8f62-4741-97d3-114e3ac73c91' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 5000,  5250,  5500,  5750,  6000,  6250,  6500,  6750,  7000,  7250,
            7500,  7750,  8000,  8250,  8500,  8750,  9000,  9250,  9500,  9750,
           10000, 10250, 10500, 10750, 11000, 11250, 11500, 11750, 12000, 12250,
           12500, 12750, 13000, 13250, 13500, 13750, 14000, 14250, 14500, 14750,
           15000, 15250, 15500, 15750, 16000, 16250, 16500, 16750, 17000, 17250,
           17500, 17750, 18000, 18250, 18500, 18750, 19000, 19250, 19500, 19750,
           20000, 21000, 22000, 23000, 24000, 25000, 26000, 27000, 28000, 29000,
           30000, 32000, 34000, 35000, 36000, 38000, 40000, 45000, 50000, 60000,
           70000, 80000],
          dtype=&#x27;int32&#x27;, name=&#x27;Te&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>logg</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-67925933-2a58-4ad3-9a27-3123704ea1d0' class='xr-index-data-in' type='checkbox'/><label for='index-67925933-2a58-4ad3-9a27-3123704ea1d0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([6.5, 6.75, 7.0, 7.25, 7.5, 7.75, 8.0, 8.25, 8.5, 8.75, 9.0, 9.25, 9.5], dtype=&#x27;float64&#x27;, name=&#x27;logg&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-25978b29-a554-4ffb-a8cd-b48f65e9e40b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-25978b29-a554-4ffb-a8cd-b48f65e9e40b' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Wavelengths :</span></dt><dd>[1.28470318e+03 1.35336435e+03 1.35623969e+03 ... 3.86833493e+08
     5.62983439e+08 5.99076004e+08]</dd><dt><span>unit :</span></dt><dd>erg/s/cm2/A</dd><dt><span>long_name :</span></dt><dd>Flux</dd></dl></div></li></ul></div></div>



Creating VOSA input files
-------------------------

VOSA input format
~~~~~~~~~~~~~~~~~

VOSA requires following format for uploading the photometric information
files

+------+------+------+------+------+------+------+------+------+------+
| ob   | RA   | DEC  | dis  | Av   | fi   | flux | e    | pnt  | obj  |
| ject |      |      |      |      | lter |      | rror | opts | opts |
+======+======+======+======+======+======+======+======+======+======+
| —    | —    | —    | —    | —    | —    | —    | —    | —    | —    |
+------+------+------+------+------+------+------+------+------+------+

Identify the filters in the files you are uploading (create the
“filter_list” accordingly)

Create a file with “name, ra, dec, magnitudes and magnitude errors”.
This “photomety_file” be converted to VOSA format

-  Note: This code is for a cluster, hence distance and extinction is
   kept constant

The VOSA_input.txt file has magnitudes in the “flux” column. So while
uploading to VOSA, keep “file type” as “magnitudes”

.. code:: ipython3

    import pandas as pd
    import numpy as np
    
    df_photometry = pd.read_csv('data/example_photomety_file.csv', engine='python')
    df_photometry.set_index('name', inplace=True)
    
    # Adding distance and Av values
    distance      = '831.76+-11'       # [pc]
    Av            = '0.1736+-0.017'    # 3.1 * E(B-V) [mag]
    df_photometry['distance'] = distance
    df_photometry['Av'] = Av
    
    print((df_photometry.columns))
    df_photometry.head()


.. parsed-literal::

    Index(['ra', 'dec', 'B', 'e_B', 'V', 'e_V', 'I', 'e_I', 'U', 'e_U', 'R', 'e_R',
           'F148W', 'e_F148W', 'F154W', 'e_F154W', 'F169M', 'e_F169M', 'distance',
           'Av'],
          dtype='object')
    



.. 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>ra</th>
          <th>dec</th>
          <th>B</th>
          <th>e_B</th>
          <th>V</th>
          <th>e_V</th>
          <th>I</th>
          <th>e_I</th>
          <th>U</th>
          <th>e_U</th>
          <th>R</th>
          <th>e_R</th>
          <th>F148W</th>
          <th>e_F148W</th>
          <th>F154W</th>
          <th>e_F154W</th>
          <th>F169M</th>
          <th>e_F169M</th>
          <th>distance</th>
          <th>Av</th>
        </tr>
        <tr>
          <th>name</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>WOCS2002</th>
          <td>132.8492</td>
          <td>11.83040</td>
          <td>12.300</td>
          <td>NaN</td>
          <td>11.540</td>
          <td>NaN</td>
          <td>11.060</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>18.66000</td>
          <td>0.058</td>
          <td>18.69300</td>
          <td>0.065</td>
          <td>18.58200</td>
          <td>0.058</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
        </tr>
        <tr>
          <th>WOCS1007</th>
          <td>132.8931</td>
          <td>11.85297</td>
          <td>11.070</td>
          <td>NaN</td>
          <td>10.960</td>
          <td>NaN</td>
          <td>11.072</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>17.03341</td>
          <td>0.018</td>
          <td>16.87171</td>
          <td>0.016</td>
          <td>16.63688</td>
          <td>0.019</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
        </tr>
        <tr>
          <th>Y1168</th>
          <td>132.8331</td>
          <td>11.81147</td>
          <td>18.409</td>
          <td>NaN</td>
          <td>18.632</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>18.393</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>17.02925</td>
          <td>0.039</td>
          <td>17.06582</td>
          <td>0.033</td>
          <td>17.17563</td>
          <td>0.033</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
        </tr>
      </tbody>
    </table>
    </div>



Renaming the columns with SVO Filter Profile Service names
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

-  http://svo2.cab.inta-csic.es/theory/fps/index.php?mode=browse

.. code:: ipython3

    filter_list = ['KPNO/Mosaic.B', 'KPNO/Mosaic.V','KPNO/Mosaic.I', 'KPNO/Mosaic.U','KPNO/Mosaic.R',
                   'Astrosat/UVIT.F148W','Astrosat/UVIT.F154W', 'Astrosat/UVIT.F169M']
    
    df_photometry.rename(columns={'ra'      : 'ra', 
                                  'dec'     : 'dec',
                                  'B'       : 'KPNO/Mosaic.B',
                                  'e_B'     : 'e_KPNO/Mosaic.B',
                                  'V'       : 'KPNO/Mosaic.V',
                                  'e_V'     : 'e_KPNO/Mosaic.V',
                                  'I'       : 'KPNO/Mosaic.I',
                                  'e_I'     : 'e_KPNO/Mosaic.I',
                                  'U'       : 'KPNO/Mosaic.U',
                                  'e_U'     : 'e_KPNO/Mosaic.U',
                                  'R'       : 'KPNO/Mosaic.R',
                                  'e_R'     : 'e_KPNO/Mosaic.R',
                                  'F148W'   : 'Astrosat/UVIT.F148W',
                                  'e_F148W' : 'e_Astrosat/UVIT.F148W',
                                  'F154W'   : 'Astrosat/UVIT.F154W',
                                  'e_F154W' : 'e_Astrosat/UVIT.F154W',
                                  'F169M'   : 'Astrosat/UVIT.F169M',
                                  'e_F169M' : 'e_Astrosat/UVIT.F169M',
                                  'distance': 'distance',
                                  'Av'      : 'Av'}, 
                         inplace=True)
    df_photometry.head()




.. 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>ra</th>
          <th>dec</th>
          <th>KPNO/Mosaic.B</th>
          <th>e_KPNO/Mosaic.B</th>
          <th>KPNO/Mosaic.V</th>
          <th>e_KPNO/Mosaic.V</th>
          <th>KPNO/Mosaic.I</th>
          <th>e_KPNO/Mosaic.I</th>
          <th>KPNO/Mosaic.U</th>
          <th>e_KPNO/Mosaic.U</th>
          <th>KPNO/Mosaic.R</th>
          <th>e_KPNO/Mosaic.R</th>
          <th>Astrosat/UVIT.F148W</th>
          <th>e_Astrosat/UVIT.F148W</th>
          <th>Astrosat/UVIT.F154W</th>
          <th>e_Astrosat/UVIT.F154W</th>
          <th>Astrosat/UVIT.F169M</th>
          <th>e_Astrosat/UVIT.F169M</th>
          <th>distance</th>
          <th>Av</th>
        </tr>
        <tr>
          <th>name</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>WOCS2002</th>
          <td>132.8492</td>
          <td>11.83040</td>
          <td>12.300</td>
          <td>NaN</td>
          <td>11.540</td>
          <td>NaN</td>
          <td>11.060</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>18.66000</td>
          <td>0.058</td>
          <td>18.69300</td>
          <td>0.065</td>
          <td>18.58200</td>
          <td>0.058</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
        </tr>
        <tr>
          <th>WOCS1007</th>
          <td>132.8931</td>
          <td>11.85297</td>
          <td>11.070</td>
          <td>NaN</td>
          <td>10.960</td>
          <td>NaN</td>
          <td>11.072</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>17.03341</td>
          <td>0.018</td>
          <td>16.87171</td>
          <td>0.016</td>
          <td>16.63688</td>
          <td>0.019</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
        </tr>
        <tr>
          <th>Y1168</th>
          <td>132.8331</td>
          <td>11.81147</td>
          <td>18.409</td>
          <td>NaN</td>
          <td>18.632</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>18.393</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>NaN</td>
          <td>17.02925</td>
          <td>0.039</td>
          <td>17.06582</td>
          <td>0.033</td>
          <td>17.17563</td>
          <td>0.033</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
        </tr>
      </tbody>
    </table>
    </div>



Creating the VOSA compliant input file from the dataframe
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code:: ipython3

    def create_VOSA_input_file(df_photometry, filter_list, output_name=None):
        # combining data from all stars to make the VOSA upload file 
        df_VOSA_input = pd.DataFrame(columns = ['object', 'ra', 'dec','distance','Av','filter','flux','error','pntopts','objopts']) 
        counter=0
        for name in df_photometry.index:
            for filter_name in filter_list:
                df_VOSA_input.loc[counter, 'object']   = name
                df_VOSA_input.loc[counter, 'ra']       = df_photometry['ra'][name]
                df_VOSA_input.loc[counter, 'dec']      = df_photometry['dec'][name]
                df_VOSA_input.loc[counter, 'distance'] = df_photometry['distance'][name]
                df_VOSA_input.loc[counter, 'Av']       = df_photometry['Av'][name]
                df_VOSA_input.loc[counter, 'filter']   = filter_name
                df_VOSA_input.loc[counter, 'flux']     = df_photometry[filter_name][name]
                df_VOSA_input.loc[counter, 'error']    = df_photometry['e_'+filter_name][name]
                df_VOSA_input.loc[counter, 'pntopts']  = '---'
                df_VOSA_input.loc[counter, 'objopts']  = '---'
    
                # Rewriting the row if flux is not available for this star-filter combination
                if ~np.isnan(df_photometry[filter_name][name]):
                    counter=counter+1
        df_VOSA_input.fillna('---', inplace=True)
        if output_name !=None:
            df_VOSA_input.to_csv(output_name, header=None, index=None, sep=' ')
        return df_VOSA_input
    
    create_VOSA_input_file(df_photometry, filter_list, output_name='data/example_VOSA_input_file.txt')


.. parsed-literal::

    /var/folders/2v/ztlgg9951mdc3j88_z3gbscc0000gn/T/ipykernel_5393/3182199717.py:21: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
      df_VOSA_input.fillna('---', inplace=True)
    



.. 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>object</th>
          <th>ra</th>
          <th>dec</th>
          <th>distance</th>
          <th>Av</th>
          <th>filter</th>
          <th>flux</th>
          <th>error</th>
          <th>pntopts</th>
          <th>objopts</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>WOCS2002</td>
          <td>132.8492</td>
          <td>11.83040</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>KPNO/Mosaic.B</td>
          <td>12.30000</td>
          <td>---</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>1</th>
          <td>WOCS2002</td>
          <td>132.8492</td>
          <td>11.83040</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>KPNO/Mosaic.V</td>
          <td>11.54000</td>
          <td>---</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>2</th>
          <td>WOCS2002</td>
          <td>132.8492</td>
          <td>11.83040</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>KPNO/Mosaic.I</td>
          <td>11.06000</td>
          <td>---</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>3</th>
          <td>WOCS2002</td>
          <td>132.8492</td>
          <td>11.83040</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>Astrosat/UVIT.F148W</td>
          <td>18.66000</td>
          <td>0.058</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>4</th>
          <td>WOCS2002</td>
          <td>132.8492</td>
          <td>11.83040</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>Astrosat/UVIT.F154W</td>
          <td>18.69300</td>
          <td>0.065</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>5</th>
          <td>WOCS2002</td>
          <td>132.8492</td>
          <td>11.83040</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>Astrosat/UVIT.F169M</td>
          <td>18.58200</td>
          <td>0.058</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>6</th>
          <td>WOCS1007</td>
          <td>132.8931</td>
          <td>11.85297</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>KPNO/Mosaic.B</td>
          <td>11.07000</td>
          <td>---</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>7</th>
          <td>WOCS1007</td>
          <td>132.8931</td>
          <td>11.85297</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>KPNO/Mosaic.V</td>
          <td>10.96000</td>
          <td>---</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>8</th>
          <td>WOCS1007</td>
          <td>132.8931</td>
          <td>11.85297</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>KPNO/Mosaic.I</td>
          <td>11.07200</td>
          <td>---</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>9</th>
          <td>WOCS1007</td>
          <td>132.8931</td>
          <td>11.85297</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>Astrosat/UVIT.F148W</td>
          <td>17.03341</td>
          <td>0.018</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>10</th>
          <td>WOCS1007</td>
          <td>132.8931</td>
          <td>11.85297</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>Astrosat/UVIT.F154W</td>
          <td>16.87171</td>
          <td>0.016</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>11</th>
          <td>WOCS1007</td>
          <td>132.8931</td>
          <td>11.85297</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>Astrosat/UVIT.F169M</td>
          <td>16.63688</td>
          <td>0.019</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>12</th>
          <td>Y1168</td>
          <td>132.8331</td>
          <td>11.81147</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>KPNO/Mosaic.B</td>
          <td>18.40900</td>
          <td>---</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>13</th>
          <td>Y1168</td>
          <td>132.8331</td>
          <td>11.81147</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>KPNO/Mosaic.V</td>
          <td>18.63200</td>
          <td>---</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>14</th>
          <td>Y1168</td>
          <td>132.8331</td>
          <td>11.81147</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>KPNO/Mosaic.U</td>
          <td>18.39300</td>
          <td>---</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>15</th>
          <td>Y1168</td>
          <td>132.8331</td>
          <td>11.81147</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>Astrosat/UVIT.F148W</td>
          <td>17.02925</td>
          <td>0.039</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>16</th>
          <td>Y1168</td>
          <td>132.8331</td>
          <td>11.81147</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>Astrosat/UVIT.F154W</td>
          <td>17.06582</td>
          <td>0.033</td>
          <td>---</td>
          <td>---</td>
        </tr>
        <tr>
          <th>17</th>
          <td>Y1168</td>
          <td>132.8331</td>
          <td>11.81147</td>
          <td>831.76+-11</td>
          <td>0.1736+-0.017</td>
          <td>Astrosat/UVIT.F169M</td>
          <td>17.17563</td>
          <td>0.033</td>
          <td>---</td>
          <td>---</td>
        </tr>
      </tbody>
    </table>
    </div>



Now upload the file at
http://svo2.cab.inta-csic.es/theory/vosa/index.php?action=myfiles&otype=star&seeall=

Make sure to change the File type to \`magnitude`\`

Keep SED Type: Flux vs Lambda

Select the file and search through VO for all possible detections Look
at the SEDs,

Possibly remove some telescopes (e.g. SDSS photometry is problematic for
bright M67 stars, Gaia DR3 synthetic photometry may or may not be
usable)

Create flux corrected files from VOSA
-------------------------------------

-  DEPRECATED

   -  NO LONGER REQUIRED AS ``bsf.load_data()`` CAN NOW USE VOSA FILES
      DIRECTLY

.. code:: ipython3

    import pandas as pd
    def create_extinction_corrected_files_from_VOSA(name, file_name, output_name, verbose=True):
        # Reading parameters derived by VOSA
        flux         = pd.read_csv(file_name, engine='python', comment='#', delim_whitespace= True, skipinitialspace=True, header=None)
        flux.columns = ['FilterID','wavelength','Obs.Flux','Obs.Error','flux','error','model_flux_A','Fitted','Excess','FitExc','UpLim']
    
        # Removing filters noted as "upper limit"
        if verbose: print('WARNING: %s removed due to upper limit'%(flux[flux['UpLim']=='1']['FilterID'].values))
        flux         = flux[flux['UpLim'] == '---']
        flux         = flux.drop(columns=['Obs.Flux','Obs.Error','Fitted','Excess','FitExc','UpLim','model_flux_A'])
        flux.to_csv(output_name, index=False)
        return flux
    
    DIR_OBS = 'data/'
    
    for idx, name in enumerate(['WOCS2002','WOCS1007','Y1168']):    
        print(name)
        # file_name = DIR_OBS + name +'/bestfitp/'+ name +'.bfit.phot.dat'
        file_name = 'data/vosa_results/%s.bfit.phot.dat'%name
        flux = create_extinction_corrected_files_from_VOSA(name,file_name, output_name='data/extinction_corrected_flux_files/'+name+'.csv')


.. parsed-literal::

    WOCS2002
    WARNING: ['WISE/WISE.W4'] removed due to upper limit
    WOCS1007
    WARNING: ['WISE/WISE.W4'] removed due to upper limit
    Y1168
    WARNING: ['WISE/WISE.W3' 'WISE/WISE.W4'] removed due to upper limit
    

.. parsed-literal::

    /var/folders/2v/ztlgg9951mdc3j88_z3gbscc0000gn/T/ipykernel_5393/1800319444.py:4: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\s+'`` instead
      flux         = pd.read_csv(file_name, engine='python', comment='#', delim_whitespace= True, skipinitialspace=True, header=None)
    /var/folders/2v/ztlgg9951mdc3j88_z3gbscc0000gn/T/ipykernel_5393/1800319444.py:4: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\s+'`` instead
      flux         = pd.read_csv(file_name, engine='python', comment='#', delim_whitespace= True, skipinitialspace=True, header=None)
    /var/folders/2v/ztlgg9951mdc3j88_z3gbscc0000gn/T/ipykernel_5393/1800319444.py:4: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use ``sep='\s+'`` instead
      flux         = pd.read_csv(file_name, engine='python', comment='#', delim_whitespace= True, skipinitialspace=True, header=None)
    

Logo
----

.. code:: ipython3

    import xarray as xr
    import src.binary_sed_fitting as bsf
    import matplotlib.pyplot as plt
    
    da = xr.open_dataarray(bsf.DIR_MODELS+'koester_spectral_library.nc')
    y1 = da.sel(Te=6000).sel(logg=7.0)*4e2
    y2 = da.sel(Te=15000).sel(logg=7.0)*1.5
    
    plt.style.use('dark_background')
    fig, ax = plt.subplots(figsize=(2,1))
    
    ax.fill_between(da.Wavelength, y1, alpha=0.6, color='#FF6961')
    ax.fill_between(da.Wavelength, y2, alpha=0.6, color='#6198ff')
    
    ax.loglog()
    ax.set_xlim(969,8000)
    ax.set_ylim(3000000)
    ax.axis('off')
    ax.text(0.5, 0.5, 'Binary     \n  SED      \n    Fitting', fontsize=14, transform=ax.transAxes,
            va='center', ha='center', color='white', family='serif', variant='small-caps', weight='bold')
    plt.savefig('plots/logo.png', dpi=200, bbox_inches='tight')



.. image:: examples_io/output_19_0.png