.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_tutorials/io/export.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_tutorials_io_export.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_tutorials_io_export.py:


##############
Exporting Data
##############
If data is needed for external use, it may be helpful to export ATK data into a tabular format (i.e. a :class:`~pandas.DataFrame` or :class:`~astropy.table.Table`).

This can be done via the **IO methods** :meth:`~ATK.base.DataFrameIOMixin.to_dataframe` and :meth:`~ATK.base.TableIOMixin.to_table`.

|

.. note::

   The availability of IO methods depends on the kind of data that is being used. See the documentation of the specific data container for information (e.g. :class:`here <ATK.Models.Spectrum>` if working with :class:`~ATK.Models.Spectrum` objects).

|

In this tutorial, the aforementioned **IO methods** will be demonstrated on a :class:`~ATK.Models.Spectrum`:

.. GENERATED FROM PYTHON SOURCE LINES 19-26

.. code-block:: Python



    from ATK import query

    sdss_query = query("spectrum", targets=2802295461460467072, survey="sdss")
    sdss_query.show()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    <Spectrum DataSet>

    .kind:      Spectrum
    .targets:   2802295461460467072 | 12.352° 22.438° (icrs, 2016-01-01T00:00:00.000, 3.0″)
    .exception: False
    .data:      
          <sdss Spectrum>
                survey:     sdss
                correction: full
                search_pos: 12.352° 22.438° (icrs, 2017-01-01T00:00:00.000)
                separation: 0.084″
                exposure:   3603.38 s
                wavelength: [3600.80908203125, 3601.636962890625, ..., 10384.8408203125, 10387.2353515625] Å
                flux:       [12.829000473022461, 0.5849999785423279, ..., 46.59700012207031, 44.007999420166016] 1×10⁻¹⁷ erg Å⁻¹ s⁻¹ cm⁻²


    Available Methods: .add(), .apply(), .from_target(), .merge(), .open(), .plot(), .save(), .show(), .split(), .store()





.. GENERATED FROM PYTHON SOURCE LINES 31-34

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To convert the stored :class:`~ATK.Models.Spectrum` into a :class:`~pandas.DataFrame`, simply extract it from the :class:`~ATK.Models.DataSet` and call :meth:`~ATK.base.DataFrameIOMixin.to_dataframe`:

.. GENERATED FROM PYTHON SOURCE LINES 34-37

.. code-block:: Python


    df = sdss_query.data[0].to_dataframe()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <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>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>3600.808838</td>
          <td>12.829317</td>
        </tr>
        <tr>
          <th>1</th>
          <td>3601.637207</td>
          <td>0.585399</td>
        </tr>
        <tr>
          <th>2</th>
          <td>3602.467773</td>
          <td>9.223813</td>
        </tr>
        <tr>
          <th>3</th>
          <td>3603.296387</td>
          <td>6.716644</td>
        </tr>
        <tr>
          <th>4</th>
          <td>3604.125488</td>
          <td>1.041800</td>
        </tr>
        <tr>
          <th>...</th>
          <td>...</td>
          <td>...</td>
        </tr>
        <tr>
          <th>4597</th>
          <td>10377.670898</td>
          <td>5.930060</td>
        </tr>
        <tr>
          <th>4598</th>
          <td>10380.064453</td>
          <td>-22.424702</td>
        </tr>
        <tr>
          <th>4599</th>
          <td>10382.458008</td>
          <td>9.461142</td>
        </tr>
        <tr>
          <th>4600</th>
          <td>10384.840820</td>
          <td>46.597488</td>
        </tr>
        <tr>
          <th>4601</th>
          <td>10387.235352</td>
          <td>44.008152</td>
        </tr>
      </tbody>
    </table>
    <p>4602 rows × 2 columns</p>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 41-42

or to convert to a :class:`~astropy.table.Table`:

.. GENERATED FROM PYTHON SOURCE LINES 42-45

.. code-block:: Python


    table = sdss_query.data[0].to_table()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div><i>Table length=4602</i>
    <table id="table126624079905616" class="table-striped table-bordered table-condensed">
    <thead><tr><th>wavelength</th><th>flux</th></tr></thead>
    <thead><tr><th>Angstrom</th><th>1e-17 erg / (Angstrom s cm2)</th></tr></thead>
    <thead><tr><th>float32</th><th>float32</th></tr></thead>
    <tr><td>3600.8088</td><td>12.829317</td></tr>
    <tr><td>3601.6372</td><td>0.58539885</td></tr>
    <tr><td>3602.4678</td><td>9.223813</td></tr>
    <tr><td>3603.2964</td><td>6.716644</td></tr>
    <tr><td>3604.1255</td><td>1.0417998</td></tr>
    <tr><td>3604.9565</td><td>5.692421</td></tr>
    <tr><td>3605.786</td><td>11.1126375</td></tr>
    <tr><td>3606.6172</td><td>12.113641</td></tr>
    <tr><td>3607.447</td><td>9.009418</td></tr>
    <tr><td>...</td><td>...</td></tr>
    <tr><td>10368.116</td><td>30.32188</td></tr>
    <tr><td>10370.507</td><td>35.617973</td></tr>
    <tr><td>10372.898</td><td>62.638107</td></tr>
    <tr><td>10375.279</td><td>34.284332</td></tr>
    <tr><td>10377.671</td><td>5.93006</td></tr>
    <tr><td>10380.064</td><td>-22.424702</td></tr>
    <tr><td>10382.458</td><td>9.461142</td></tr>
    <tr><td>10384.841</td><td>46.59749</td></tr>
    <tr><td>10387.235</td><td>44.008152</td></tr>
    </table></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 49-56

|

.. note::

   In either case, non-array-like attributes are not included in the output :class:`~pandas.DataFrame` or :class:`~astropy.table.Table`. If required, these should be extracted from the Spectrum alongside the use of :meth:`~ATK.base.DataFrameIOMixin.to_dataframe` or :meth:`~ATK.base.TableIOMixin.to_table`.

   Also note that :meth:`~ATK.base.TableIOMixin.to_table` offers a key advantage over :meth:`~ATK.base.DataFrameIOMixin.to_dataframe`, in that units are retained and stored in the output :class:`~astropy.table.Table`.

.. GENERATED FROM PYTHON SOURCE LINES 58-63

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.. rubric:: Download this Tutorial


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 2.812 seconds)


.. _sphx_glr_download_auto_tutorials_io_export.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: export.ipynb <export.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: export.py <export.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: export.zip <export.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

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