update

README.md

@@ -1,3 +1,48 @@
 # axc_ul
 
 ART-XC upper limits django application
+
+## Management commands
+
+- load_survey
+
+Allows to populate the database with data for a survey in healpix format. Takes about 100 minutes for an nside=4096 map to load using sqlite. Supports resuming after interruption.
+
+Syntax: python manage.py load_survey --counts='*path_to_counts_map*' --exposure='*path_to_exposure_map*' --survey=*integer survey number*
+
+## Models (models.py)
+
+- Survey: 
+    - survey number
+
+- Pixel: 
+    - survey foreign key
+    - hpid (healpix pixel id)
+    - counts
+    - exposure
+    - contamination flag
+
+## Views (views.py)
+
+**Before public deployment views providing direct pixel data should be altered to not include it in the response json.**
+
+- PixelAggregateView:
+    For testing, outputs the aggregated data across specified surveys for a healpix pixel.
+
+    Returns:
+        Sum of counts and exposure over specified surveys for the specified pixel.
+    
+    URL: /api/pixel-aggregate/?pixel=123&survey=1
+         (/api/pixel-aggregate/?pixel=123&survey=1,3,5)
+         (/api/pixel-aggregate/?pixel=123&survey=1-5)
+
+- UpperLimitView:
+    The view providing upper limit calculation. 
+
+    Returns:
+        Upper and lower limits in units of counts, count rate, and physical flux for Bayesian and classic approaches, as well as a count rate estimate and aperture photometry data (counts in the aperture, background estimate, etc).
+
+    URL: /api/upper-limit/?ra=10.0&dec=10.0&cl=.95&survey=1 (or list 1,3,5 or hyphenated range 1-5).
+
+
+

management/commands/load_survey.py

@@ -1,12 +1,20 @@
+# axc_ul/management/commands/load_survey.py
+
 import numpy as np
 from astropy.io import fits
 
 from django.core.management.base import BaseCommand
 from django.db import transaction
-from axc_ul.models import Pixel
+from axc_ul.models import Pixel, Survey
+from django.db.models import Max
 
 from itertools import islice
 
+from datetime import datetime
+
+# DEFINE BATCH SIZE AND BATCH
+# **************************************************************
+
 BATCH_SIZE = 1000000
 
 def batch(iterable, size):
@@ -21,9 +29,14 @@ def batch(iterable, size):
         yield chunk
 
 
+
+
 class Command(BaseCommand):
     help = "Process FITS files and store the data in the database"
 
+    # COMMAND LINE ARGUMENTS
+    # **************************************************************
+
     def add_arguments(self, parser):
         parser.add_argument(
             '--counts', 
@@ -37,21 +50,29 @@ class Command(BaseCommand):
             required=True,
             help='Path of the exposure file'
         )
-        # parser.add_argument(
-        #     '--survey_number', 
-        #     type=int, 
-        #     required=True,
-        #     help='Integer ID of the survey being read'
-        # )
+        parser.add_argument(
+            '--survey_number', 
+            type=int, 
+            required=True,
+            help='Integer ID of the survey being read'
+        )
+        
+        
 
     def handle(self, *args, **options):
         
+        # GET FILENAMES FROM ARGUMENTS
+        # **************************************************************
+        
         counts_file = options['counts']
         exposure_file = options['exposure']
-        # survey_number = options['survey_number']
+        survey_number = options['survey_number']
 
-        self.stdout.write(f"Counts file:   {counts_file}")
-        self.stdout.write(f"Exposure file: {exposure_file}")
+        self.stdout.write(f"\nCounts file:\t{counts_file}")
+        self.stdout.write(f"Exposure file:\t{exposure_file}")
+
+        # OPEN BOTH FILES, RAVEL EACH
+        # **************************************************************
 
         with fits.open(counts_file) as hdul:
 
@@ -68,43 +89,62 @@ class Command(BaseCommand):
 
         exposure_data = exposure_map.ravel()
 
+        # COMPARE DATA SHAPES, ENSURE THEY'RE THE SAME
+        # **************************************************************
 
-
-        self.stdout.write(f"Counts Data Shape:   {counts_data.shape}")
-        self.stdout.write(f"Exposure Data Shape: {exposure_data.shape}")
+        self.stdout.write(f"\nCounts Data Shape:\t{counts_data.shape}")
+        self.stdout.write(f"Exposure Data Shape:\t{exposure_data.shape}")
+        
+        total_pixels = counts_data.shape[0]
+        self.stdout.write(f"\nTotal pixels to insert:\t{total_pixels}")
 
         assert counts_data.shape == exposure_data.shape, "Counts and exposure maps must have the same shape"
 
-        #rate_data = np.divide(counts_data, exposure_data)
+        # CREATE THE SURVEY IF IT DOES NOT EXIST
+        # **************************************************************
         
-        # with transaction.atomic():
+        with transaction.atomic():
             
-        #     survey,created = Survey.objects.get_or_create(number=survey_number)
+            survey,created = Survey.objects.get_or_create(number=survey_number)
             
-        #     if created:
-        #         self.stdout.write(f"Created a new survey instance with number: {survey.number}")
-        #     else:
-        #         self.stdout.write(f"Using existing survey instance with the number: {survey.number}")
+            if created:
+                self.stdout.write(f"Created a new survey instance with number: {survey.number}")
+            else:
+                self.stdout.write(f"Using existing survey instance with the number: {survey.number}")
+        
+        # FETCH THE LAST PROCESSED HPID AND CONTINUE FROM IT
+        # **************************************************************
+        
+        last_hpid = (
+            Pixel.objects
+                .filter(survey=survey)
+                .aggregate(max_hpid=Max('hpid'))['max_hpid']
+            or -1
+        )
+        start_index = last_hpid + 1
         
-
-        # Create a generator that yields Pixel objects one by one.
         pixel_generator = (
             Pixel(
                 hpid=i,
                 counts=int(count),
                 exposure=float(exposure),
-                #rate=float(rate),
-                #survey=survey
+                survey=survey
             )
             for i, (count, exposure) in enumerate(zip(counts_data, exposure_data))
+            if i >= start_index                     
         )
 
-        total_inserted = 0
-        # Process the generator in batches.
+
+        total_inserted = start_index
+        # Process in batches
         for pixel_batch in batch(pixel_generator, BATCH_SIZE):
             with transaction.atomic():
                 Pixel.objects.bulk_create(pixel_batch)
             total_inserted += len(pixel_batch)
-            self.stdout.write(f"Inserted {total_inserted} pixels")
+            percentage = total_inserted / total_pixels * 100
+            timestamp = datetime.now().strftime("%H:%M:%S")
+            self.stdout.write(
+                f"[{timestamp}] {percentage:.2f}% inserted"
+            )
 
         self.stdout.write(f"Inserted a total of {total_inserted} pixels.")

migrations/0007_survey_pixel_survey.py

@@ -0,0 +1,26 @@
+# Generated by Django 5.1.6 on 2025-05-07 08:58
+
+import django.db.models.deletion
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('axc_ul', '0006_pixel_contaminated'),
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name='Survey',
+            fields=[
+                ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('number', models.IntegerField(unique=True)),
+            ],
+        ),
+        migrations.AddField(
+            model_name='pixel',
+            name='survey',
+            field=models.ForeignKey(default=0, on_delete=django.db.models.deletion.CASCADE, related_name='pixels', to='axc_ul.survey'),
+        ),
+    ]

migrations/0008_survey_nside.py

@@ -0,0 +1,18 @@
+# Generated by Django 5.1.6 on 2025-05-08 11:23
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('axc_ul', '0007_survey_pixel_survey'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='survey',
+            name='nside',
+            field=models.IntegerField(default=4096),
+        ),
+    ]

migrations/0009_remove_survey_nside.py

@@ -0,0 +1,17 @@
+# Generated by Django 5.1.6 on 2025-05-08 11:26
+
+from django.db import migrations
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('axc_ul', '0008_survey_nside'),
+    ]
+
+    operations = [
+        migrations.RemoveField(
+            model_name='survey',
+            name='nside',
+        ),
+    ]

models.py

@@ -1,9 +1,37 @@
+# axc_ul/models.py
+
 from django.db import models
+from django.db.models import UniqueConstraint
+
+
+
+
+class Survey(models.Model):
+    
+    number = models.IntegerField(unique=True)
+    
+    # nside = models.IntegerField(
+    #     default=4096
+    # )
+    
+    def __str__(self):
+        return f"Survey {self.number} of NSIDE {self.nside}"
+
+
+
+
 
 class Pixel(models.Model):
     
     id = models.AutoField(primary_key=True)
     
+    survey = models.ForeignKey(
+        Survey,
+        on_delete=models.CASCADE,
+        related_name='pixels',
+        default=0
+    )
+    
     hpid = models.IntegerField(db_index=True)
     
     counts = models.IntegerField()
@@ -12,3 +40,10 @@ class Pixel(models.Model):
     
     contaminated = models.BooleanField(default=False)
     
+    class Meta:
+        constraints = [
+            UniqueConstraint(fields=['survey', 'hpid'], name='unique_hpid_per_survey'),
+        ]
+    
+    def __str__(self):
+        return f"Pixel {self.id} (Survey {self.survey.number})"

requirements.txt

@@ -0,0 +1,6 @@
+astropy
+numpy
+healpy
+scipy
+django
+djangorestframework

serializers.py

@@ -1,8 +1,8 @@
-# serializers.py
+# axc_ul/serializers.py
 from rest_framework import serializers
 from axc_ul.models import Pixel
 
-class PixelSerializer(serializers.ModelSerializer):
-    class Meta:
-        model = Pixel
-        fields = ('hpid', 'counts', 'exposure', 'contaminated')
+# class PixelSerializer(serializers.ModelSerializer):
+#     class Meta:
+#         model = Pixel
+#         fields = ('hpid', 'counts', 'exposure', 'contaminated')

urls.py

@@ -1,8 +1,10 @@
-# urls.py
+# axc_ul/urls.py
+
 from django.urls import path
-from .views import PixelDetailView, UpperLimitView
+from .views import PixelAggregateView, UpperLimitView #, PixelDetailView 
 
 urlpatterns = [
-    path('pixel/<int:hpid>/', PixelDetailView.as_view(), name='pixel-detail'),
-    path('upper-limit/', UpperLimitView.as_view(), name='upper-limit')
+    #path('pixel/<int:hpid>/', PixelDetailView.as_view(), name='pixel-detail'),
+    path('pixel-aggregate/', PixelAggregateView.as_view(), name='pixel-aggregate'),
+    path('upper-limit/', UpperLimitView.as_view(), name='upper-limit'),
 ]

views.py

@@ -7,7 +7,9 @@ import astropy.units as u
 from astropy.coordinates import SkyCoord
 
 import scipy.special as sp
+from astropy.stats import poisson_conf_interval
 
+from django.db.models import Sum
 from rest_framework.views import APIView
 from rest_framework.response import Response
 from rest_framework import status
@@ -15,41 +17,114 @@ from rest_framework import status
 from django.shortcuts import get_object_or_404
 
 from axc_ul.models import Pixel
+#from axc_ul_server.axc_ul.serializers import PixelSerializer
 
-from .serializers import PixelSerializer
 
-class PixelDetailView(APIView):
-    """
-    API endpoint that returns the pixel data (counts, exposure, rate)
-    for a given survey number and hpid.
-    """
-    def get(self, request, hpid):
-        # Get the survey using the survey_number field.
-        # survey = get_object_or_404(Survey, number=survey_number)
+# SURVEY PARAMETER PARSER
+# **************************************************************
+
+def parse_survey_param(raw: str) -> list[int]:
+    surveys = set()
+    for part in raw.split(","):
+        if "-" in part:
+            start, end = part.split("-", 1)
+            surveys.update(range(int(start), int(end) + 1))
+        else:
+            surveys.add(int(part))
+    return sorted(surveys)
+
+
+# PIXEL VIEW (MOSTLY FOR TESTING)
+# **************************************************************
+
+class PixelAggregateView(APIView):
+
+    def get(self, request):
+        # GET PARAMETERS FROM THE QUERY
+        # **************************************************************
+        raw_pixel  = request.query_params.get("pixel")
+        raw_survey = request.query_params.get("survey")
+
+        # 400 BADREQUEST
+        # **************************************************************
+        if raw_pixel is None or raw_survey is None:
+            return Response(
+                {"detail": "Both `pixel` and `survey` parameters are required."},
+                status=status.HTTP_400_BAD_REQUEST
+            )
+
+        # FILTER THE INPUTS
+        # **************************************************************
+        try:
+            hpid = int(raw_pixel)
+        except ValueError:
+            return Response(
+                {"detail": "`pixel` must be an integer."},
+                status=status.HTTP_400_BAD_REQUEST
+            )
+
+        try:
+            survey_numbers = parse_survey_param(raw_survey)
+        except ValueError:
+            return Response(
+                {"detail": "Malformed `survey`; use N, N,M or N-M formats."},
+                status=status.HTTP_400_BAD_REQUEST
+            )
+
+        # FILTER AND AGGREGATE
+        # **************************************************************
+        qs = Pixel.objects.filter(
+            hpid=hpid,
+            survey__number__in=survey_numbers
+        )
+        if not qs.exists():
+            # no matching pixel(s) → 404
+            get_object_or_404(Pixel, hpid=hpid, survey__number__in=survey_numbers)
+
+        result = qs.aggregate(
+            total_counts=Sum("counts"),
+            total_exposure=Sum("exposure")
+        )
+
+        # RETURN THE SUMS
+        # **************************************************************
+        return Response(result, status=status.HTTP_200_OK)
+
+
+
+
+
+# class PixelDetailView(APIView):
+#     """
+#     API endpoint that returns the pixel data (counts, exposure, rate)
+#     for a given hpid.
+#     """
+#     def get(self, request, hpid):
+#         # Get the survey using the survey_number field.
+#         # survey = get_object_or_404(Survey, number=survey_number)
         
-        # Get the pixel corresponding to the survey and hpid.
-        pixel = get_object_or_404(Pixel, hpid=hpid)
+#         # Get the pixel corresponding to the survey and hpid.
+#         pixel = get_object_or_404(Pixel, hpid=hpid)
         
-        # Serialize the pixel data to JSON.
-        serializer = PixelSerializer(pixel)
-        return Response(serializer.data, status=status.HTTP_200_OK)
+#         # Serialize the pixel data to JSON.
+#         serializer = PixelSerializer(pixel)
+#         return Response(serializer.data, status=status.HTTP_200_OK)
+
+
+
+# UPPER LIMIT COMPUTATION VIEW
+# **************************************************************
 
 
 class UpperLimitView(APIView):
     """
-    Calculate and return upper limits (UL, CRUL, FLUL) based on provided RA, DEC, and CL parameters.
-
-    Args:
-        request: HTTP GET request containing 'ra', 'dec', and 'cl' query parameters.
-
-    Returns:
-        Response object with JSON data containing calculated UL, CRUL, and FLUL values.
-
-    Raises:
-        ValueError if provided parameters are invalid.
+    Calculate confidence bounds based on aperture photometry using classic and bayesian methods
     """
     def get(self, request):
         
+        # GET PARAMETERS FROM THE REQUEST
+        # **************************************************************
+        
         try:
             ra = float(request.query_params.get('ra'))
             dec = float(request.query_params.get('dec'))
@@ -59,24 +134,48 @@ class UpperLimitView(APIView):
                 {"error": "Invalud parameters, provide RA, DEC, and CL"},
                 status = status.HTTP_400_BAD_REQUEST
             )
-        
+        #  ── NEW: pull & parse survey selection ──
+        raw_survey = request.query_params.get('survey')
+        if raw_survey is None:
+            return Response(
+                {"error": "Missing required `survey` parameter (e.g. ?survey=1,3-5)"},
+                status=status.HTTP_400_BAD_REQUEST
+            )
+        try:
+            survey_numbers = parse_survey_param(raw_survey)
+        except ValueError:
+            return Response(
+            {"error": "Malformed `survey`; use formats like 1, 2-5, or 1,3-4"},
+            status=status.HTTP_400_BAD_REQUEST
+        )
+
         # hp = HEALPix(
         #     nside = 4096,
         #     order = 'ring',
         #     frame = 'icrs'
         # )
         
+        # CREATE SKYCOORD, CONVERT TO GALACTIC BECAUSE THE HEALPIX
+        # MAP ITSELF WAS MADE IN GALACTIC COORDINATES
+        # **************************************************************
+        
         src_coord = SkyCoord(
             ra, dec, unit = 'deg', frame = 'icrs'
         )
         gal = src_coord.galactic
         src_vec = hp.ang2vec(gal.l.deg, gal.b.deg, lonlat = True)
 
-        aperture_radius = 50 # radius of the aperture in arc seconds
+        # DEFINE APERTURE AND ANNULUS RADII
+        # **************************************************************
+
+        aperture_radius = 71 # radius of the aperture in arc seconds
         # HPD ~48 arcseconds
         # 90% ~100 arcseconds
-        annulus_inner = 100 # 2 * aperture_radius
-        annulus_outer = 200 # 4 * aperture_radius
+        annulus_inner = 142 # 2 * aperture_radius
+        annulus_outer = 284 # 4 * aperture_radius
+        
+        # FETCH PIXEL DATA DEFINED VIA HP.QUERY_DISC (INCLUSIVE=FALSE)
+        # **************************************************************
         
         source_pixel_list = hp.query_disc(
             nside = 4096,
@@ -108,10 +207,26 @@ class UpperLimitView(APIView):
         ]
                 
                 
-        source_pixels = Pixel.objects.filter(hpid__in = source_pixel_list) 
+        source_pixels = Pixel.objects.filter(
+            hpid__in = source_pixel_list,
+            survey__number__in = survey_numbers
+        ) 
         
-        annulus_pixels = Pixel.objects.filter(hpid__in = annulus_pixel_list)
+        annulus_pixels = Pixel.objects.filter(
+            hpid__in = annulus_pixel_list,
+            survey__number__in = survey_numbers
+        )
         
+        if not source_pixels.exists() and not annulus_pixels.exists():
+            return Response(
+                {"detail": "No pixel data for the given survey selection."},
+                status=status.HTTP_404_NOT_FOUND
+        )
+
+        
+        
+        # COMPUTE COUNTS, BACKGROUND ESTIMATE, EXPOSURE
+        # **************************************************************
         
         N = sum(obj.counts for obj in source_pixels)
         
@@ -127,31 +242,84 @@ class UpperLimitView(APIView):
         
         t = tsum / Nnpix
         
+        # CONSTANTS
+        # **************************************************************
         
         EEF = .9      # eclosed energy fraction, .5 for hpd, .9 for w90
         ECF = 4e-11   # energy conversion factor
         
-        UL = (
-            sp.gammaincinv(
-                N + 1, 
-                confidence_level * sp.gammaincc(N + 1, B) + sp.gammainc(N + 1, B)
-            ) - B
-        )                   # count upper limit
+
                             
-         
-        CRUL = UL / t / EEF # count rate upper limit
+        # BAYESIAN IMPLEMENTATION VIA POISSON_CONF_INTERVAL
+        # **************************************************************
+                                    
+        low, high = poisson_conf_interval(
+            n = N, 
+            background = B, 
+            interval = 'kraft-burrows-nousek', 
+            confidence_level=confidence_level
+        )
+        
+        bayesian_count_ul = high
+        bayesian_count_ll = low
+         
+        bayesian_rate_ul = bayesian_count_ul / t / EEF # count rate limits
+        bayesian_rate_ll = bayesian_count_ll / t / EEF
+        
+        bayesian_flux_ul = bayesian_rate_ul * ECF   # flux limits
+        bayesian_flux_ll = bayesian_rate_ll * ECF
+        
+        # CLASSICAL IMPLEMENTATION VIA GAMMAINCCINV
+        # ****************************************************************
+        
+        classic_count_ul = sp.gammainccinv(N+1, 1 - confidence_level) - B
+        classic_count_ll = sp.gammainccinv(N, confidence_level) - B
+        
+        classic_count_ll = max(classic_count_ll, 0)        
+        
+        classic_rate_ul = classic_count_ul / t / EEF # count rate limits
+        classic_rate_ll = classic_count_ll / t / EEF 
+        
+        classic_flux_ul = classic_rate_ul * ECF   # flux limits
+        classic_flux_ll = classic_rate_ll * ECF
+        
+        # FLUX ESTIMATION
+        # ****************************************************************
+        
+        S = N - B            # counts as simply counts within aperture 
+                              # with the background estimate subtracted
+                              
+        CR = S / t / EEF     # count rate
+        
+        FL = CR * ECF         # conversion to flux
+        
+        Flux = max(FL, 0)     # flux cannot be lower than zero
         
-        FLUL = CRUL * ECF   # flux upper limit
         
         return Response({
-            'UpperLimit' : UL,
-            'CountRateUpperLimit' : CRUL,
-            'FluxUpperLimit' : FLUL,
-            'N' : N,
-            'Nnpix' : Nnpix,
-            'Bcounts' : Bcounts,
-            'Bnpix' : Bnpix,
-            'B' : B,
-            'tsum' : tsum,
-            't' : t
+            
+            'ClassicUpperLimit' : classic_count_ul,
+            'ClassicLowerLimit' : classic_count_ll,
+            'ClassicCountRateUpperLimit' : classic_rate_ul,
+            'ClassicCountRateLowerLimit' : classic_rate_ll,
+            'ClassicFluxUpperLimit' : classic_flux_ul,
+            'ClassicFluxLowerLimit' : classic_flux_ll,
+            
+            'BayesianUpperLimit' : bayesian_count_ul,
+            'BayesianLowerLimit' : bayesian_count_ll,
+            'BayesianCountRateUpperLimit' : bayesian_rate_ul,
+            'BayesianCountRateLowerLimit' : bayesian_rate_ll,
+            'BayesianFluxUpperLimit' : bayesian_flux_ul,
+            'BayesianFluxLowerLimit' : bayesian_flux_ll,
+            
+            'FluxEstimate' : Flux,
+            
+            # 'N' : N,
+            # 'Nnpix' : Nnpix,
+            # 'Bcounts' : Bcounts,
+            # 'Bnpix' : Bnpix,
+            # 'B' : B,
+            # 'tsum' : tsum,
+            # 't' : t
+            
         }, status=status.HTTP_200_OK)