changed the contamination handling logic to also exclude contaminated pixels from the source region

management/commands/load_survey.py

@@ -11,6 +11,9 @@ from django.db import transaction
 from uplim.models import Pixel
 from django.db.models import Max
 
+from tqdm import tqdm
+import sys
+
 # DEFINE BATCH SIZE AND BATCH
 # **************************************************************
 
@@ -121,6 +124,11 @@ class Command(BaseCommand):
         )
         start_index = last_hpid + 1
 
+        pixels_to_insert = total_pixels - start_index
+
+        if pixels_to_insert <= 0:
+            self.stdout.write("All pixels have already been inserted. Exiting.")
+
         pixel_generator = (
             Pixel(
                 hpid=i,
@@ -133,13 +141,23 @@ class Command(BaseCommand):
         )
 
         total_inserted = start_index
+
+        pbar = tqdm(
+            total=pixels_to_insert,
+            unit="pix",
+            desc=f"Survey {survey_number}",
+            # file=sys.stdout,
+        )
+
         # 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)
+            # total_inserted += len(pixel_batch)
-            percentage = total_inserted / total_pixels * 100
+            # percentage = total_inserted / total_pixels * 100
-            timestamp = datetime.now().strftime("%H:%M:%S")
+            # timestamp = datetime.now().strftime("%H:%M:%S")
-            self.stdout.write(f"[{timestamp}] {percentage:.2f}% inserted")
+            # self.stdout.write(f"[{timestamp}] {percentage:.2f}% inserted")
+            pbar.update(BATCH_SIZE)
 
-        self.stdout.write(f"Inserted a total of {total_inserted} pixels.")
+        pbar.close()
+        self.stdout.write(f"Done: Inserted a total of {total_inserted} pixels.")

management/commands/set_contaminated.py

@@ -161,42 +161,79 @@ class Command(BaseCommand):
         nside = 4096
         npix = hp.nside2npix(nside)
 
-        flux_bins = [0, 125, 250, 2000, 20000, np.inf]  # define bin edges
+        # flux_bins = [0, 125, 250, 2000, 20000, np.inf]  # define bin edges
-        mask_radii_deg = [
+        # mask_radii_deg = [
-            0.06,
+        #     0.06,
-            0.15,
+        #     0.15,
-            0.5,
+        #     0.5,
-            0.9,
+        #     0.9,
-            2.5,
+        #     2.5,
-        ]  # corresponding mask radii in degrees
+        # ]  # corresponding mask radii in degrees
 
-        # Convert mask radii from degrees to radians (required by query_disc)
+        # # Convert mask radii from degrees to radians (required by query_disc)
-        mask_radii = [np.radians(r) for r in mask_radii_deg]
+        # 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)
+        # # 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)
+
+        flux_bins = [0, 125, 250, 2000, 20000, np.inf]
         catalog["flux_bin"] = pd.cut(catalog["Flux"], bins=flux_bins, labels=False)
 
+        bin_to_radius_deg = {
+            0: 0.06,
+            1: 0.15,
+            2: 0.5,
+            3: 0.9,
+            4: 2.5,
+        }
+
+        catalog["mask_radius_deg"] = catalog["flux_bin"].map(bin_to_radius_deg)
+
         # 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},
+        #         {
+        #             "RAdeg": 194.9350000,
+        #             "DEdeg": 27.9124722,
+        #             "flux_bin": 4,
+        #         },  # Coma Cluster, 2.5 degrees
+        #         {
+        #             "RAdeg": 187.6991667,
+        #             "DEdeg": 12.3852778,
+        #             "flux_bin": 4,
+        #         },  # Virgo cluster, 2.5 degrees
+        #     ]
+        # )
+
+        # catalog = pd.concat([catalog, manual_additions], ignore_index=True)
+
+        # catalog.loc[catalog["SrcID"] == 1101, "flux_bin"] = 2
+
         manual_additions = pd.DataFrame(
             [
-                {"RAdeg": 279.9804336, "DEdeg": 5.0669542, "flux_bin": 3},
+                {
-                {"RAdeg": 266.5173685, "DEdeg": -29.1252321, "flux_bin": 3},
+                    "RAdeg": 279.9804336,
+                    "DEdeg": 5.0669542,
+                    "mask_radius_deg": 0.9,
+                },
+                {"RAdeg": 266.5173685, "DEdeg": -29.1252321, "mask_radius_deg": 0.9},
                 {
                     "RAdeg": 194.9350000,
                     "DEdeg": 27.9124722,
-                    "flux_bin": 4,
+                    "mask_radius_deg": 1.17,
-                },  # Coma Cluster, 2.5 degrees
+                },  # Coma, 70 arcmin radius
                 {
                     "RAdeg": 187.6991667,
                     "DEdeg": 12.3852778,
-                    "flux_bin": 4,
+                    "mask_radius_deg": 2.5,
-                },  # Virgo cluster, 2.5 degrees
+                },  # Virgo, 2.5 deg radius
             ]
         )
-
         catalog = pd.concat([catalog, manual_additions], ignore_index=True)
 
-        catalog.loc[catalog["SrcID"] == 1101, "flux_bin"] = 2
+        catalog.loc[catalog["SrcID"] == 1101, "mask_radius_deg"] = 0.5  # e.g. override
 
         mask_array = np.ones(npix, dtype=bool)
 
@@ -215,17 +252,24 @@ class Command(BaseCommand):
 
             ra, dec = gal.l.deg, gal.b.deg
 
-            flux_bin = row["flux_bin"]  # 0, 1, or 2
+            # flux_bin = row["flux_bin"]  # 0, 1, or 2
-            # Get the corresponding mask radius (in radians) for this flux bin
+            # # Get the corresponding mask radius (in radians) for this flux bin
-            radius = mask_radii[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
+            # # Query all pixels within the given radius
-            # 'inclusive=True' makes sure pixels on the edge are included
+            # # 'inclusive=True' makes sure pixels on the edge are included
+            # pix_indices = hp.query_disc(nside, vec, radius, inclusive=True)
+
+            # read the explicit per-source radius in degrees, convert to radians:
+            radius_deg = row["mask_radius_deg"]
+            radius = np.radians(radius_deg)
+
+            # now query:
             pix_indices = hp.query_disc(nside, vec, radius, inclusive=True)
 
             # Mark these pixels as bad (False) in our mask

views.py

@@ -229,8 +229,9 @@ class UpperLimitView(APIView):
             or annulus_pixels.filter(contaminated=True).exists()
         )
 
-        # exclude contaminated pixels from the background calculations
+        # exclude contaminated pixels from the background and source regions
         annulus_pixels = annulus_pixels.exclude(contaminated=True)
+        source_pixels = source_pixels.exclude(contaminated=True)
 
         status_int = 0
         error_message = ""