.. _example_sn1_sn2:

Example SN1 & SN2
=================

This example is the much condensed version of our basic example for those of us
already familiar with the parameters and how `LUCI` works. Let's get started!


You can find the data used in this tutorial at the CADC database ([http://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/en/search](http://www.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/en/search)) searching
for M33_FIELD7 SN1 and SN2.

.. code-block:: python

    # Imports
    import sys
    sys.path.insert(0, '/media/carterrhea/carterrhea/SIGNALS/LUCI/')  # Location of Luci
    from LuciBase import Luci
    import LUCI.LuciPlotting as lplt

We now will set the required parameters. We are also going to be using our machine learning algorithm to get the initial guesses for SN1 only.

.. code-block:: python

    #Set Parameters
    # Using Machine Learning Algorithm for Initial Guess
    Luci_path = '/media/carterrhea/carterrhea/SIGNALS/LUCI/'
    cube_dir = '/media/carterrhea/carterrhea/M33'  # Path to data cube
    cube_name = 'M33_Field7_SN1.merged.cm1.1.0'  # don't add .hdf5 extension
    object_name = 'M33_Field7_SN1'
    redshift = -0.0006  # Redshift of M33
    resolution = 5000


We intialize our LUCI object

.. code-block:: python

    # Create Luci object
    cube = Luci(Luci_path, cube_dir+'/'+cube_name, cube_dir, object_name, redshift, resolution)

The output will look something like this:

.. image:: ReadingIn.png
    :alt: Luci Initialization Output


Let's quickly create a deep frame

.. code-block:: python

    # Create Deep Image
    cube.create_deep_image()


We now fit part of our cube defined by the bounding box 1000<x<1500 and 250<y<750 with a Gaussian on the Halpha line, the NII-doublet, and the SII-doublet with a binning of 4.
We are also going to constrain our velocities and sigmas.

.. code-block:: python

    # Fit!
    vel_map, broad_map, flux_map, ampls_map = cube.fit_cube(['OII3726', 'OII3729'], 'gaussian', [1,1], [1,1], 500, 1100, 700, 1300, bkg=bkg_sky, binning=2)

The output should look something like this:

.. image:: Fitting.png
    :alt: Fit Output

Let's take a look at the velocity map. We can play with the colorbar limits with the `clims` argument.

.. code-block:: python

    lplt.plot_map(flux_map[:,:,0], 'flux', output_dir=cube_dir, header=cube.header, clims=[1e-17, 3e-16])

And let's see what this looks like!

.. image:: M33_Field7_SN1.png
    :alt: M33_SN1_Flux


Let's do the same for SN2 but this time set the machine learning boolean to False. Please note that the machine learning algorithm DOES work for SN2.

.. code-block:: python

    #Set Parameters
    # Using Machine Learning Algorithm for Initial Guess
    Luci_path = '/media/carterrhea/carterrhea/SIGNALS/LUCI/'
    cube_dir = '/media/carterrhea/carterrhea/M33'  # Path to data cube
    cube_name = 'M33_Field7_SN2.merged.cm1.1.0'  # don't add .hdf5 extension
    object_name = 'M33_Field7_SN2'
    redshift = -0.0006  # Redshift of M33
    resolution = 5000
    ML_bool = False


We intialize our LUCI object

.. code-block:: python

    # Create Luci object
    cube = Luci(Luci_path, cube_dir+'/'+cube_name, cube_dir, object_name, redshift, resolution, ML_bool)

The output will look something like this:

.. image:: ReadingIn.png
    :alt: Luci Initialization Output


Let's quickly create a deep frame

.. code-block:: python

    # Create Deep Image
    cube.create_deep_image()


We now fit part of our cube defined by the bounding box 1000<x<1500 and 250<y<750 with a Gaussian on the Halpha line, the NII-doublet, and the SII-doublet with a binning of 4.
We are also going to constrain our velocities and sigmas.

.. code-block:: python

    # Fit!
    vel_map, broad_map, flux_map, ampls_map = cube.fit_cube(['OIII4959', 'OIII5007', 'Hbeta'], 'gaussian', [1,1,1], [1,1,1], 500, 1100, 700, 1300, bkg=bkg_sky, binning=2)

The output should look something like this:

.. image:: Fitting.png
    :alt: Fit Output

Let's take a look at the velocity map. We can play with the colorbar limits with the `clims` argument.

.. code-block:: python

    lplt.plot_map(flux_map[:,:,0], 'flux', output_dir=cube_dir, header=cube.header, clims=[1e-17, 3e-16])

And let's see what this looks like!

.. image:: M33_Field7_SN2.png
    :alt: M33_SN2_Flux