updated

.gitignore

@@ -98,9 +98,6 @@ dmypy.json
 # PyCharm
 .idea/
 
-# Local test data
-test_data/
-
 # Temporary files
 *.tmp
 *.temp
@@ -115,8 +112,5 @@ test_data/
 # Backup files
 *.bak
 
-# Output files
-*.fits
-
 # HDF5 files
 *.h5

README.md

@@ -5,16 +5,12 @@ qsoabsfind
 
 .. image:: https://img.shields.io/badge/GitHub-abhi0395%2Fqsoabsfind-blue.svg?style=flat
     :target: https://github.com/abhi0395/qsoabsfind
-.. image:: https://github.com/abhi0395/qsoabsfind/workflows/Tests/badge.svg
-    :target: https://github.com/abhi0395/qsoabsfind/actions?query=workflow%3ATests
+.. image:: https://github.com/abhi0395/qsoabsfind/actions/workflows/tests.yml/badge.svg
+    :target: https://github.com/abhi0395/qsoabsfind/actions?query=workflow%3Atests
 .. image:: http://img.shields.io/badge/license-MIT-blue.svg?style=flat
     :target: https://github.com/abhi0395/qsoabsfind/blob/main/LICENSE
 .. image:: http://img.shields.io/badge/arXiv-2103.15842-orange.svg?style=flat
     :target: https://arxiv.org/abs/2103.15842
-.. image:: https://coveralls.io/repos/github/abhi0395/qsoabsfind/badge.svg?branch=main&style=flat&v=2
-    :target: https://coveralls.io/github/abhi0395/qsoabsfind?branch=main
-.. image:: https://readthedocs.org/projects/qsoabsfind/badge/?version=latest
-    :target: http://qsoabsfind.readthedocs.io/en/latest/?badge=latest
 
 
 `qsoabsfind` is a Python module designed to detect absorbers with doublet properties in SDSS/DESI quasar spectra. This tool identifies potential absorbers using vonvolution-based adaptive S/N approach, applies Gaussian fitting and extensive checks to reject false positives, and computes equivalent widths (EWs) of the lines using a simple double gaussian.
@@ -75,8 +71,8 @@ Contributions are welcome! Please submit a pull request or open an issue to disc
 Citation
 --------
 
-Please cite `Anand, Nelson & Kauffmann 2021
-<https://arxiv.org/abs/2103.15842>`_ if you find this code useful in your  
+Please cite `[Anand, Nelson & Kauffmann 2021]
+(https://arxiv.org/abs/2103.15842)` if you find this code useful in your  
 research. The BibTeX entry for the paper is::
 
     @ARTICLE{2021MNRAS.504...65A,
@@ -103,7 +99,7 @@ License
 
 Copyright (c) 2021-2025 Abhijeet Anand.  
 
-`qsoabsfind` is free software made available under the MIT License. For details see  
+**qsoabsfind** is free software made available under the MIT License. For details see  
 the LICENSE file.
 
 

qsoabsfind/parallel_convolution.py

@@ -7,6 +7,7 @@
 from .absfinder import convolution_method_absorber_finder_in_QSO_spectra
 from .io import save_results_to_fits
 import re
+from .constants import search_parameters
 
 def run_convolution_method_absorber_finder_QSO_spectra(fits_file, spec_index, absorber, ker_width_pix, coeff_sigma, mult_resi, d_pix, pm_pixel, sn_line1, sn_line2, use_covariance):
     return convolution_method_absorber_finder_in_QSO_spectra(fits_file, spec_index, absorber, ker_width_pix, coeff_sigma, mult_resi, d_pix, pm_pixel, sn_line1, sn_line2, use_covariance)
@@ -128,13 +129,13 @@ def main():
     # Run the convolution method in parallel
     results = parallel_convolution_method_absorber_finder_QSO_spectra(
         args.input_fits_file, spec_indices, absorber=args.absorber,
-        ker_width_pix=constants.search_parameters[args.absorber]["ker_width_pixels"],
-        coeff_sigma=constants.search_parameters[args.absorber]["coeff_sigma"],
-        mult_resi=constants.search_parameters[args.absorber]["mult_resi"],
-        d_pix=constants.search_parameters[args.absorber]["d_pix"],
-        pm_pixel=constants.search_parameters[args.absorber]["pm_pixel"],
-        sn_line1=constants.search_parameters[args.absorber]["sn1_thresh"],
-        sn_line2=constants.search_parameters[args.absorber]["sn2_thresh"],
+        ker_width_pix=search_parameters[args.absorber]["ker_width_pixels"],
+        coeff_sigma=search_parameters[args.absorber]["coeff_sigma"],
+        mult_resi=search_parameters[args.absorber]["mult_resi"],
+        d_pix=search_parameters[args.absorber]["d_pix"],
+        pm_pixel=search_parameters[args.absorber]["pm_pixel"],
+        sn_line1=search_parameters[args.absorber]["sn1_thresh"],
+        sn_line2=search_parameters[args.absorber]["sn2_thresh"],
         use_covariance=False, n_jobs=args.n_tasks * args.ncpus
     )