Semi-automated multi-COmponent Universal Spectral-line fitting Engine
Copyright (c) 2015 Jonathan D. Henshaw
SCOUSE is a spectral line fitting algorithm. The current version fits Gaussian files to spectral line emission. Please refer to Henshaw et al. 2016 for further information. Note that a Python version of SCOUSE now exists and can be found here: scousepy. While I can continue to assist with the IDL version on an individual basis I will no longer be maintaining this code.
The function files need to be located in the working directory, or in some other location specified in your IDL_PATH.
SCOUSE also uses the following (publicly available) libraries:
The IDL Astronomy User's Library:
http://idlastro.gsfc.nasa.gov/homepage.html
IDL Coyote:
http://www.idlcoyote.com/documents/programs.php#COYOTE_LIBRARY_DOWNLOAD
Markwardt IDL:
Note that SCOUSE was developed in IDL 8.3. It is possible that there may be some issues for IDL 7 users.
If you use SCOUSE please cite the paper in which it is presented: Henshaw et al. 2016, MNRAS, 457, 2675
JUNE 2016
Important: Work around for issue with coordinates - added keyword optional "/PIXELS" to file_read.pro - this will output pixel values instead of absolute coordinates or offset coordinates. This keyword is available in release v0.1.1.
Users are strongly advised to make use of either the /OFFSETS or /PIXELS keywords in 'file_read' and to double-check the output. Although this issue does not affect the fitting process it can affect the output coordinates (see issues).
DECEMBER 2016
Important: XQuartz update causing SCOUSE to crash...problem (and fix) outlined here http://blogs.qub.ac.uk/screenshotsfromtheedge/2016/10/25/xquartz-2-7-10-and-libxt-motif-idl/
The method is broken down into seven stages. The codes used to implement each stage of the process can be found in the directory SCOUSE/SCOUSE_main. Each stage is summarised below.
Here SCOUSE identifies the spatial area over which to fit the data. It
generates a grid of spectral averaging areas (SAAs). The user is required to
provide several input values. Please refer to stage_1.pro for more details on
these.
User-interactive fitting of the spatially averaged spectra output from
stage 1.
Non user-interactive fitting of individual spectra contained within all SAAs.
The user is required to input several tolerance levels to SCOUSE. Please refer
to Henshaw+ 2015 for more details on each of these.
Here SCOUSE selects the best-fits that are output in stage 3.
Unfortunately there is no one-size-fits-all method to selecting a best-fitting solution when multiple choices are available (stage 4). SCOUSE uses the Akaike Information Criterion, which weights the chisq of a best-fitting solution according to the number of free-parameters.
While AIC does a good job of returning the best-fitting solutions, there are areas where the best-fitting solutions can be improved. As such the following stages are optional but highly recommended.
Given the level of user interaction, this is the most time-consuming part of the routine. However, changing the tolerance levels in stage 3 can help. A quick run through of stage 5 is recommended to see whether or not the tolerance levels should be changed. Once the user is satisfied with the tolerance levels of stage 3, a more detailed inspection of the spectra should take place.
Depending on the data a user may wish to perform a few iterations of Stages 5-7.
Checking the fits. Here the user is required to check the best-fitting
solutions to the spectra. The user enters the indices of spectra that they
would like to revisit. One can typically expect to re-analyse (stage 6) around
5-10% of the fits. However, this is dependent on the complexity of the
spectral line profiles.
Re-analysing the identified spectra. In this stage the user is required to
either select an alternative solution or re-fit completely the spectra
identified in stage 5.
SCOUSE then integrates these new solutions into the solution file.
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