Fit Pixel

In this notebook we will fit a single pixel in a data cube for M33 Field 7 SN3.

We should start by import the appropriate modules.

import os
import sys

import numpy as np
import matplotlib.pyplot as plt

# Get location of LUCI
path = os.path.abspath(os.path.pardir)
sys.path.insert(0, path)  # add LUCI to the available paths

from LuciBase import Luci
import LUCI.LuciPlotting as lplt
%config Completer.use_jedi=False  # enable autocompletion when typing in Jupyter notebooks

For example:

# Initialize paths and set parameters
Luci_path = '/home/carterrhea/Documents/LUCI/'
cube_dir = '/export/home/carterrhea/M33'  # Path to data cube
#cube_dir = '/mnt/carterrhea/carterrhea/NGC628'  # Full path to data cube (example 2)
cube_name = 'M33_SN3'  # don't add .hdf5 extension
object_name = 'M33'
filter_name = 'SN3'
redshift = -0.0006  # Redshift of object
resolution = 5000

With these parameters set, we can invoke LUCI with the following command:

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

Now we should get our background region.

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

We will now fit a single pixel and take a look at the fit. This fit commands has all the same options as all the other commands except for binning :)

axis, sky, fit_dict = cube.fit_pixel(
    ['Halpha', 'NII6548', 'NII6583'],  # lines
    'sincgauss',   # fit function
    [1,1,1],  # velocity relationship
    [1,1,1],  # sigma relationship
    1265, 1789,    # x & y coordinate
    binning=1,  # Set binnning around coordinate -- this will just fit the one pixel
    bkg=bkg_sky,  # Set background
)

We can plot the fit with the following:

lplt.plot_fit(axis, sky, fit_dict['fit_vector'], units='nm')
plt.xlim(650, 670)