Quickstart with LT/SPRAT

Using the SPRAT instrument on the Liverpool Telescope as an example quickstart. We are only showing one science spectrum until the end where all the reduced science and standard spectra are shown.

0. Import all the required libraries:

   import sys
   import numpy as np
   from astropy.io import fits
   from aspired import image_reduction
   from aspired import spectral_reduction
   import plotly.io as pio
   
   # If using jupyter notebook
   pio.renderers.default = 'notebook'
   
   # If you want to display it in your default browser
   pio.renderers.default = 'browser'
   
   # If you want the image
   pio.renderers.default = 'png'
   

1. In order to perform image reduction, users have to provide a file list of the spectra to be reduced. Alternatively, a fits.hdu.image.PrimaryHDU object can be supplied For the science spectral image, the file list is contained in examples/sprat_LHS6328.list

   #flat, sprat_LHS6328_Hiltner102_raw/v_slit_red_1.fits.gz
   dark, sprat_LHS6328_Hiltner102_raw/v_dark_1.fits.gz
   arc, sprat_LHS6328_Hiltner102_raw/v_a_20180810_13_1_0_1.fits.gz
   light, sprat_LHS6328_Hiltner102_raw/v_e_20180810_12_1_0_0.fits.gz
   light, sprat_LHS6328_Hiltner102_raw/v_e_20180810_12_2_0_0.fits.gz
   light, sprat_LHS6328_Hiltner102_raw/v_e_20180810_12_3_0_0.fits.gz
   light, sprat_LHS6328_Hiltner102_raw/v_e_20180810_12_4_0_0.fits.gz
   light, sprat_LHS6328_Hiltner102_raw/v_e_20180810_12_5_0_0.fits.gz
   

   To reduce the image with the built-in reduction method ImageReduction, execute the following, the rederer options are those of plotly’s:

   science_frame = image_reduction.ImageReduction()
   science_frame.add_filelist('examples/sprat_LHS6328.list')
   science_frame.load_data()
   science_frame.reduce()
   science_frame.inspect()
   

   and for the standard spectral image, the file list is contained in examples/sprat_Hiltner102.list

   #flat, sprat_LHS6328_Hiltner102_raw/v_slit_red_1.fits.gz
   dark, sprat_LHS6328_Hiltner102_raw/v_dark_1.fits.gz
   arc, sprat_LHS6328_Hiltner102_raw/v_a_20180810_28_1_0_1.fits.gz
   light, sprat_LHS6328_Hiltner102_raw/v_s_20180810_27_1_0_0.fits.gz
   

   Similar to the science frame, execute the following:

   standard_frame = image_reduction.ImageReduction()
   standard_frame.add_filelist('examples/sprat_Hiltner102.list')
   standard_frame.load_data()
   standard_frame.reduce()
   standard_frame.inspect()
   

2. With the image reduced, we can start performing spectral reduction, starting from the 2D spectrum. The arc frames listed in the filelist were also loaded, and they are propagated to the TwoDSpec automatically:

   science2D = spectral_reduction.TwoDSpec(science_frame)
   standard2D = spectral_reduction.TwoDSpec(standard_frame)
   

3. To trace the respective brightest spectrum in the science and standard frames, run

   science2D.ap_trace()
   standard2D.ap_trace()
   

4. And then extract the spectra from the traces by using the ap_extract() method. The science spectrum is optimally extracted with an aperture with the default size of 7 pixel on each side of the trace, the sky is measured by fitting a, by default, first order polynomial to the sky region of 5 pixels on each side from the aperture by default. The aperture and the sky regions are separated by 3 pixels by default. After the extraction, display the results with the default renderer (plotly graph in a browser).

   science2D.ap_extract()
   standard2D.ap_extract()
   

   The two spectra from the science frame:

   and the spectrum of the standard frame:

5. The extract_arc_spec() automatrically apply the traces found above to extract the spectra of the arcs.

   science2D.extract_arc_spec()
   standard2D.extract_arc_spec()
   

6. Initialise the OneDSpec for wavelength and flux calibration; get the traces and the extracted spectra from the TwoDSpec objects,

   onedspec = spectral_reduction.OneDSpec()
   onedspec.from_twodspec(science2D, stype='science')
   onedspec.from_twodspec(standard2D, stype='standard')
   

7. Identify the arclines from the extracted spectrum of the arc.

   onedspec.find_arc_lines()
   

   Then, the position of the peaks of the arc lines, can be found for performing wavelength calibration for each trace.

8. Initialise a calibrator and add element lines to prepare for wavelength calibration, set the various calibrator, Hough transform and RANSAC properties before performing the Hough Transform that is used for the automated wavelength calibration. And finally fit for the solution and apply to the spectra.

   onedspec.initialise_calibrator()
   onedspec.add_atlas(
       ['Xe'],
       min_atlas_wavelength=3500.,
       max_atlas_wavelength=8500.)
   onedspec.set_hough_properties()
   onedspec.set_ransac_properties()
   onedspec.do_hough_transform()
   onedspec.fit()
   onedspec.apply_wavelength_calibration()
   

9. Next step is the perform the flux calibration, which requires comparing the spectrum of the standard to the literature values. To do this, first we need to load the literature template from the built-in library, which contains all the iraf and ESO standards.

   onedspec.load_standard(target='hiltner102')
   onedspec.inspect_standard()
   

   onedspec.get_sensitivity()
   onedspec.inspect_sensitivity()
   

10. Apply the fluxcalibration and inspect the reduced spectra.

onedspec.apply_flux_calibration(
onedspec.inspect_reduced_spectrum()

The two science spectra:

and the standard spectrum: