uplim/management/commands/set_contaminated.py

# uplim/management/commands/set_contaminated.py
# add custom flux-radius mapping?
# add specifying the columns?
# do contamination setting per survey?
# include nside for surveys?

from django.core.management.base import BaseCommand

import pandas as pd
import healpy as hp
import numpy as np
from astropy.coordinates import SkyCoord

from uplim.models import Pixel

class Command(BaseCommand):
    help = "Set the 'contaminated' flag for all pixels based on the fluxes in the provided catalog."
    
    # COMMAND LINE ARGUMENTS
    # **********************
    
    def add_arguments(self, parser):
        
        parser.add_argument(
            '--catalog',
            type=str,
            required=False,
            help='Path to the catalog.dat file'
        )
        
        # parser.add_argument(
        #     '--survey',
        #     type=int,
        #     required=False,
        #     help='integer number of the survey to set the flag for'
        # )
        
        parser.add_argument(
            '--reset',
            action='store_true',
            default=False,
            help='Reset the contamination flag across all pixels back to False.'
        )
        
    def handle(self, *args, **options):
        
        # RESET BEHAVIOR: SET CONTAMINATION FLAG TO FALSE FOR ALL PIXELS
        # **************************************************************
        
        if options['reset']:
            
            self.stdout.write("Resetting the contamination flag...")
            
            Pixel.objects.update(contaminated = False)
            
            self.stdout.write("Done")
            return
        
        if not options['catalog']:
            self.stdout.write("No catalog file provided, exiting")
            return
        
        catalog_file = options['catalog']
        
        self.stdout.write(f"Catalog file:\t{catalog_file}")
        
        # READ THE CATALOG FILE USING PANDAS READ_FWF
        # *******************************************
        
        # Define column positions based on the byte ranges in your table
        colspecs = [
            (0, 4),    # SrcID (1-4)
            (5, 26),   # Name (6-26)
            (27, 37),  # RAdeg (28-37)
            (38, 48),  # DEdeg (39-48)
            (49, 55),  # ePos (50-55)
            (56, 63),  # Signi (57-63)
            (64, 76),  # Flux (65-76)
            (77, 89),  # e_Flux (78-89)
            (90, 118), # CName (91-118)
            (119, 120),# NewXray (120)
            (121, 134) # Type (122-134)
        ]

        # Define column names
        colnames = [
            "SrcID", "Name", "RAdeg", "DEdeg", "ePos", "Signi", "Flux",
            "e_Flux", "CName", "NewXray", "Type"
        ]

        # Read the file using the fixed-width format
        catalog = pd.read_fwf(catalog_file, colspecs=colspecs, names=colnames)
        
        self.stdout.write(str(catalog.head()))
                
        # hard coded nside and flux-radius mapping
        # maybe change that
        
        nside = 4096
        npix = hp.nside2npix(nside)
        
        flux_bins =      [0,  125,  250, 2000, 20000, np.inf]          # define bin edges
        mask_radii_deg = [ 0.06, 0.15, 0.5,  0.9,   2.5     ]           # corresponding mask radii in degrees
        
        # Convert mask radii from degrees to radians (required by query_disc)
        mask_radii = [np.radians(r) for r in mask_radii_deg]

        # Use pandas.cut to assign each source a bin index (0, 1, or 2)
        catalog['flux_bin'] = pd.cut(catalog['Flux'], bins=flux_bins, labels=False)

        # manually add and change some sources
        manual_additions = pd.DataFrame(
            [
                {'RAdeg' : 279.9804336, 'DEdeg' : 5.0669542,   'flux_bin' : 3},
                {'RAdeg' : 266.5173685, 'DEdeg' : -29.1252321, 'flux_bin' : 3},
            ]
        )

        catalog = pd.concat([catalog, manual_additions], ignore_index=True)

        catalog.loc[catalog['SrcID'] == 1101, 'flux_bin'] = 2  



        mask_array = np.ones(npix, dtype=bool)

        masked_pixels_set = set()

        self.stdout.write("\nCreating a list of contaminated pixels...")

        # process each source in the catalog
        for _, row in catalog.iterrows():
            
            ra = row['RAdeg']
            dec = row['DEdeg']
            
            src_coord = SkyCoord(
                ra, dec, unit = 'deg', frame = 'icrs'
            )
            gal = src_coord.galactic
            
            ra, dec = gal.l.deg, gal.b.deg
            
            flux_bin = row['flux_bin']  # 0, 1, or 2
            # Get the corresponding mask radius (in radians) for this flux bin
            radius = mask_radii[flux_bin]
            
            # Convert (ra, dec) to HEALPix spherical coordinates
            theta = np.radians(90.0 - dec)
            phi = np.radians(ra)
            vec = hp.ang2vec(theta, phi)
            
            # Query all pixels within the given radius
            # 'inclusive=True' makes sure pixels on the edge are included
            pix_indices = hp.query_disc(nside, vec, radius, inclusive=True)
            
            # Mark these pixels as bad (False) in our mask
            mask_array[pix_indices] = False
            # Add the pixel indices to our set of masked pixels
            masked_pixels_set.update(pix_indices)



        # Convert the set of masked pixels to a sorted list.
        masked_pixels_list = sorted(list(masked_pixels_set))

        # print("Number of masked pixels:", len(masked_pixels_list))

        self.stdout.write("\nList ready, updating the database...")
        
        
        if masked_pixels_list:
            Pixel.objects.filter(hpid__in=masked_pixels_list).update(contaminated=True)
            self.stdout.write(f"\nMarked {len(masked_pixels_list)} pixels as contaminated.")
        else:
            self.stdout.write("No pixels marked as contaminated, exiting.")