.. _example_fit_ngc6888:

Example of fitting regions in NGC6888
=====================================

In this notebook, we go through fitting two regions of NGC 6888. This notebook exists for the sole purpose of demonstrating LUCI's prowess at fitting regions :)

This notebook thus assumes that you have already gone through the tutorials.



.. 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.

.. code-block:: python

    #Set Parameters
    # Using Machine Learning Algorithm for Initial Guess
    Luci_path = '/home/carterrhea/Documents/LUCI/'
    cube_dir = '/home/carterrhea/Documents/NGC6888'  # Path to data cube
    cube_name = 'NGC6888_SN3'  # don't add .hdf5 extension
    object_name = 'NGC6888'
    redshift = 0.0
    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)

Picking the regions
-------------------

Now let's take a quick look at the deep image to decide the regions we will fit.

.. image:: NGC6888-deep.png
    :alt: NGC 6888 Deep image
    
    
I've highlighted the background red in green and the two fit regions in magenta. Region 1 is near the right edge of the image and reigon 2 is at the center3.

Let's take a look at the background
-----------------------------------

.. code-block:: python

  bkg_axis, bkg_sky = cube.extract_spectrum_region(cube_dir+'/bkg.reg', mean=True)  # We use mean=True to take the mean of the emission in the region instead of the sum
  lplt.plot_spectrum(bkg_axis, bkg_sky)
  
.. image:: NGC6888_bkg.png
    :alt: NGC 6888 Background


Now we can define our fit region and fit it!

.. code-block:: python

    axis, sky, fit_dict = cube.fit_spectrum_region(
                                ['NII6548', 'Halpha', 'NII6583', 'SII6716', 'SII6731'],
                                'sincgauss',
                                [1,1,1,1,1], [1,1,1,1,1],
                                region=cube_dir+'/region1.reg', bkg=bkg_sky,
                                bayes_bool=False)
    lplt.plot_fit(axis, sky, fit_dict['fit_vector'])



And let's see how this looks

.. image:: NGC6888_reg1.png
    :alt: NGC 6888 Region 1
    
    
.. code-block:: python

    axis, sky, fit_dict = cube.fit_spectrum_region(
                                ['NII6548', 'Halpha', 'NII6583', 'SII6716', 'SII6731'],
                                'sincgauss',
                                [1,1,1,1,1], [1,1,1,1,1],
                                region=cube_dir+'/region2.reg', bkg=bkg_sky,
                                bayes_bool=False)
    lplt.plot_fit(axis, sky, fit_dict['fit_vector'])
    
    

And let's see how this looks

.. image:: NGC6888-reg2.png
    :alt: NGC 6888 Region 2
    
    
    
Overall, these fits look pretty great :D