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

.. only:: html

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

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

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

.. _sphx_glr_examples_galaxies_plot_photometry.py:


Optical Photometry
==================

This example demonstrates how to model galaxy photometric magnitudes using the
kcorrect spectral energy distribution templates as implemented in SkyPy.

.. GENERATED FROM PYTHON SOURCE LINES 11-57

kcorrect Spectral Templates
---------------------------

In SkyPy, the rest-frame spectral energy distributions (SEDs) of galaxies can
be modelled as a linear combination of the five kcorrect basis templates
[1]_. One possible model for the coefficients is a redshift-dependent
Dirichlet distribution [2]_ which can be sampled from using the
:func:`dirichlet_coefficients <skypy.galaxies.spectrum.dirichlet_coefficients>`
function. The coefficients are then taken by the :meth:`kcorrect.absolute_magnitudes
<skypy.utils.photometry.SpectrumTemplates.absolute_magnitudes>` and
:meth:`kcorrect.apparent_magnitudes
<skypy.utils.photometry.SpectrumTemplates.apparent_magnitudes>`
methods to calculate the relevant photometric quantities using the
:doc:`speclite <speclite:overview>` package. Note that since the kcorrect
templates are defined per unit stellar mass, the total stellar mass of each
galaxy must either be given or calculated from its absolute magnitude in
another band using :meth:`kcorrect.stellar_mass
<skypy.galaxies.spectrum.KCorrectTemplates.stellar_mass>`.
An example simulation for the SDSS u- and r-band apparent magnitudes of "red"
and "blue" galaxy populations is given by the following config file:

.. literalinclude:: ../../../examples/galaxies/sdss_photometry.yml
  :language: YAML
  :caption: examples/galaxies/sdss_photometry.yml

The config file can be downloaded
:download:`here <../../../examples/galaxies/sdss_photometry.yml>`
and the simulation can be run either from the command line and saved to FITS
files:

.. code-block:: bash

   $ skypy examples/galaxies/sdss_photometry.yml sdss_photometry.fits

or in a python script using the :class:`Pipeline <skypy.pipeline.Pipeline>`
class as demonstrated in the `SDSS Photometry`_ section below. For more
details on writing config files see the :doc:`Pipeline Documentation </pipeline/index>`.

SDSS Photometry
---------------

Here we compare the apparent magnitude distributions of our simulated
galaxies with data from a :math:`10 \, \mathrm{deg^2}` region of the Sloan
Digital Sky Survey [3]_. The binned SDSS magnitude distributions were
generated from a query of the DR7 data release and can be downloaded
:download:`here <../../../examples/galaxies/sdss_dered_10deg2.ecsv>`.

.. GENERATED FROM PYTHON SOURCE LINES 57-84

.. code-block:: python3


    from astropy.table import Table, vstack
    from matplotlib import pyplot as plt
    import numpy as np
    from skypy.pipeline import Pipeline

    # Execute SkyPy galaxy photometry simulation pipeline
    pipeline = Pipeline.read("sdss_photometry.yml")
    pipeline.execute()
    skypy_galaxies = vstack([pipeline['blue_galaxies'], pipeline['red_galaxies']])

    # SDSS magnitude distributions for a 10 degree^2 region
    sdss_data = Table.read("sdss_dered_10deg2.ecsv", format='ascii.ecsv')

    # Plot magnitude distributions for SkyPy simulation and SDSS data
    bins = np.linspace(14.95, 25.05, 102)
    plt.hist(skypy_galaxies['mag_r'], bins=bins, alpha=0.5, color='r', label='SkyPy-r')
    plt.hist(skypy_galaxies['mag_u'], bins=bins, alpha=0.5, color='b', label='SkyPy-u')
    plt.plot(sdss_data['magnitude'], sdss_data['dered_r'], color='r', label='SDSS-r')
    plt.plot(sdss_data['magnitude'], sdss_data['dered_u'], color='b', label='SDSS-u')
    plt.xlim(16, 24)
    plt.yscale('log')
    plt.xlabel(r'$\mathrm{Apparent\,Magnitude}$')
    plt.ylabel(r'$\mathrm{N} \, [\mathrm{deg}^{-2} \, \mathrm{mag}^{-1}]$')
    plt.legend()
    plt.show()




.. image-sg:: /examples/galaxies/images/sphx_glr_plot_photometry_001.png
   :alt: plot photometry
   :srcset: /examples/galaxies/images/sphx_glr_plot_photometry_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 85-93

References
----------

.. [1] M. R. Blanton and S. Roweis, 2007, AJ, 125, 2348
.. [2] J. Herbel, T. Kacprzak, A. Amara, A. Refregier, C.Bruderer and
   A. Nicola 2017, JCAP, 1708, 035
.. [3] K. N. Abazajian et al. 2009, ApJS, 182, 543



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

   **Total running time of the script:** (0 minutes 23.738 seconds)


.. _sphx_glr_download_examples_galaxies_plot_photometry.py:

.. only:: html

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




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

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

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

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


.. only:: html

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

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_