.. _example_wvt:

WVT Example
===========

One the new feature added to the fitting procedure of Luci is a weighted Voronoï tessellation. We will describe here an example to showcase this method for your own data analysis.


.. code-block:: python

    # Imports
    import sys
    sys.path.insert(0, '/home/carterrhea/Documents/LUCI/')
    from LuciBase import Luci
    import matplotlib.pyplot as plt
    import numpy as np
    import LUCI.LuciPlotting as lplt
    from astropy.io import fits
    from astropy.wcs import WCS
    from matplotlib import cm
    from matplotlib.colors import LogNorm


The next step is to load/read the HDF5 data cube. To do this we invoke LUCI by initiating an instance of her along with the proper parameters. First we define the correct parameters:


.. code-block:: python

  #Set Parameters
  # Using Machine Learning Algorithm for Initial Guess
    Luci_path = '/home/carterrhea/Documents/LUCI/'
    cube_dir = '/mnt/carterrhea/carterrhea/M33'  # Path to data cube
    cube_name = 'M33_Field7_SN3.merged.cm1.1.0'  # don't add .hdf5 extension
    object_name = 'M33_Field7'
    redshift = -0.00006  # Redshift of M33
    resolution = 5000

From there we will load the HDF5 cube following this command as usual.


.. code-block:: python

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


And extract a background region.


.. code-block:: python

 bkg_axis, bkg_sky = cube.extract_spectrum_region(Luci_path+'Examples/regions/bkg_M33.reg', mean=True)  # We use mean=True to take the mean of the emission in the region instead of the sum

Now we can call the wvt_fit_region function that will create the weighted Voronoï region and fit the bins to produce the maps we need.


.. code-block:: python

  cube.wvt_fit_region(
                1200, 1350,    # x bounds
                1700, 1950,    # y bounds
                ['NII6548', 'Halpha', 'NII6583'], 
                'sincgauss',
                [1,1,1],
                [1,1,1],
                stn_target = 20,
                bkg=bkg_sky,
                bayes_bool=False, 
                uncertainty_bool=False, 
                n_threads=20)


As we can see there are many arguments in this function. Let's go through them one by one to make sure we use them correctly.

The first four arguments correspond to the position of the region we want to fit in the cube.

The fifth argument refers to the emission lines we want to fit.

'sincgauss' is the fitting function to be used.

The next two arguments describes the relational constraints between the lines. For example, if we are fitting three lines and we want the velocities of the second and third lines to be tied to one another, we would simply set vel_rel=[1,2,2]. If we wanted all the lines tied, then we would put [1,1,1]. The sigma_rel parameter functions in the exact same way except it affects the broadening (velocity dispersion) of the lines.


The stn_target parameter determines the signal to noise value that will act as a threshold to create the Voronoï tessels.


We then pass the background we want to subtract, as well as the Boolean parameters to determine whether or not to run Bayesian and uncertainty analysis.

The n_threads argument determines the number of threads used for the paralelization of the function, which accelerates the whole process.

The console will output something like this:

.. code-block:: python

    #----------------WVT Algorithm----------------#
    #----------------Creating SNR Map--------------#
    100%|█████████████████████████████████████████| 250/250 [03:37<00:00,  1.15it/s]
    #----------------Algorithm Part 1----------------#
    /home/carterrhea/Documents/LUCI/Examples
    We have 37500 Pixels! :)
    Running Nearest Neighbor Algorithm
    Finished Nearest Neighbor Algorithm
    Starting Bin Accretion Algorithm
    Completed Bin Accretion Algorithm
    There are a total of 16790 bins!
    The first part of the algorithm took 00:08:37.
    #----------------Algorithm Part 2----------------#
    Beginning WVT
    We are on step 1
    Completed WVT in 1 step(s)!
    There are a total of 16790 bins!
    #----------------Algorithm Complete--------------#
    #----------------Bin Mapping--------------#
    #----------------Numpy Bin Mapping--------------#
    100%|████████████████████████████████████| 16789/16789 [01:02<00:00, 268.26it/s]
    #----------------WVT Fitting--------------#
    100%|███████████████████████████████████| 16789/16789 [9:29:06<00:00,  2.03s/it]



.. code-block:: python

    plt.rcdefaults()

    flux_map = fits.open('/export/home/carterrhea/M33/Luci_outputs/Fluxes/M33_wvt_20_1_Halpha_Flux.fits')[0].data.T
    header = fits.open('/export/home/carterrhea/M33/Luci_outputs/M33_deep.fits')[0].header
    wcs = WCS(header)
    cmap = cm.CMRmap
    cmap.set_bad('black',1.)

    fig = plt.figure(figsize=(8, 8))
    ax = plt.subplot(projection=wcs)
    plt.imshow(flux_map[1200: 1350, 1700: 1950].T, norm = LogNorm(vmin=1e-17, vmax=2e-15), origin='lower', cmap=cmap)
    plt.rcParams["font.weight"] = "bold"
    plt.rcParams["axes.labelweight"] = "bold"
    plt.xlabel(r'RA', fontsize=16)
    plt.ylabel(r'Dec', fontsize=16)
    cbar = plt.colorbar()
    cbar.set_label(r'Flux [ergs s$^{-1}$ cm$^{-2}$ $\AA^{-1}$]', fontsize=16)
    plt.savefig('/home/carterrhea/Downloads/WVT_Example.png')


.. image:: WVT_Example.png
    :alt: WVT Example
    
    
Since the SNR in around the nebula is low we have very large bins. Comparitively, the bins in the central regions are quite small.