.. _example_basic_lite:

Basic Example Lite
==================

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!

Let's start with importing `LUCI`. Just as descsribed on the *readthedocs* page, we need to point our system path to our install location of the **LUCI** folder.
We will additionally import the Luci plotting functions :)

You can download the example data using the following command:


.. code-block:: bash

    wget -O NGC6946_SN3.hdf5 https://ws.cadc-ccda.hia-iha.nrc-cnrc.gc.ca/data/pub/CFHT/2307000z.hdf5?RUNID=xc9le6u8llecp7fp

This will download the hdf5 file for SN3 (R~400) NGC 6946. The file is just under 900 Mb,
so the download may take a while.
Note you may need to change the name of the HDF5 file to `NGC6946_SN3.merged.cm1.1.0`.

The region files used in the examples can be
found in the 'Examples/regions' folder. To run the examples, place these region files in the same
directory as the hdf5 file.


.. 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/LUCI_test'  # Path to data cube
    cube_name = 'NGC6946_SN3'  # don't add .hdf5 extension
    object_name = 'NGC6946'
    redshift = 0.000133
    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()

Let's extract a background region and take a look at it. The background region is defined in a ds9 region file called `bkg.reg`.

.. 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)

We now fit part of our cube defined by the bounding box 500<x<1100 and 700<y<1300 with a Gaussian on the Halpha line, the NII-doublet, and the SII-doublet with a binning of 2.
We are also going to constrain our velocities and sigmas. Furthermore, we will calculate uncertainties using the Hessian method. We can also run multiple threads with the `n_threads` argument.

.. code-block:: python

    # Fit!
    vel_map, broad_map, flux_map, ampls_map = cube.fit_cube(['Halpha', 'NII6548', 'NII6583', 'SII6716', 'SII6731'], 'gaussian', [1,1,1,1,1], [1,1,1,1,1], 500, 1100, 700, 1300, bkg=bkg_sky, binning=2, uncertainty_bool=True, n_threads=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. Please note that the
flux plot is automatically scaled by log10. However, the velocity and broadening maps are not scaled automatically.

.. code-block:: python

    lplt.plot_map(flux_map[:,:,0], 'flux', object_name=object_name, filter_name=filter_name, output_dir=cube_dir, header=cube.header, clims=[-19, -15])

And let's see what this looks like!

.. image:: NGC6946_Flux.png
    :alt: NGC6946_Flux

We can also plot the velocity and broadening.

.. code-block:: python

    lplt.plot_map(vel_map[:,:,0], 'velocity', object_name=object_name, filter_name=filter_name, output_dir=cube_dir, header=cube.header, clims=[-200, 200])


.. code-block:: python

    lplt.plot_map(broad_map[:,:,0], 'broadening', object_name=object_name, filter_name=filter_name, output_dir=cube_dir, header=cube.header, clims=[10, 50])

The resulting data maps will be placed in a folder called *Luci_outputs*. Inside there, you
will find additional folders containing the Flux, Amplitude, Velocity, and Broadening maps
for each line and their uncertainties.