.. _example_fit_snr:

Example SNR Calculation
=======================



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.

.. 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 = '/media/carterrhea/carterrhea/SIGNALS/LUCI/'
    cube_dir = '/media/carterrhea/carterrhea/CFHT/Analysis-Paper3/NGC2207'  # Path to data cube
    cube_name = 'IC2163_SN3.merged.cm1.1.0'  # don't add .hdf5 extension
    object_name = 'NGC2207'
    redshift = 0.009176  # Redshift of NGC 1275
    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, ML_bool)

The output will look something like this:

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



Create SNR Map
^^^^^^^^^^^^^^

We have two options to make the SNR map:
    1. method=1 --> Calculate the signal as the peak flux in the spectrum
    2. method=2 --> Calculate the signal as the total region under the spectrum taking into account only the regions around the NII doublet and Halpha emission lines

I strongly suggest using method 2 since it is more robust!

These SNR maps are perfect for future masking :)

.. code-block:: python

    cube.create_snr_map(x_min=450, x_max=1700, y_min=550, y_max=1500, method=2)


.. image:: SNR-example.png
    :alt: SNR-example