updated product registry tests

backend/core/test/test_product_registry.py

@@ -9,7 +9,7 @@
 from unittest import mock
 
 import pandas as pd
-from core.models import Product, ProductFile, ProductType, Release
+from core.models import Product, ProductContent, ProductFile, ProductType, Release
 from core.product_handle import ProductHandle
 from core.product_steps import RegistryProduct
 from core.table_data_collector import MainTableDataCollector
@@ -134,7 +134,7 @@ def create_multi_file_archive(self, extension="zip"):
             archive.add(file_b, arcname=file_b.name)
         return archive_path
 
-    def create_fake_hats_archive(self, properties_filename="hats.properties"):
+    def create_fake_hats_archive(self, properties_filename="hats.properties", data=None):
         temp_dir = Path(tempfile.mkdtemp(prefix="pz_hats_archive_"))
         hats_root = temp_dir / "mock_hats"
         dataset_dir = hats_root / "dataset" / "Norder=0" / "Npix=0"
@@ -144,7 +144,14 @@ def create_fake_hats_archive(self, properties_filename="hats.properties"):
             "catalog_name=mock_hats\ndataproduct_type=object\n",
             encoding="utf-8",
         )
-        (dataset_dir / "part-0.parquet").write_text("", encoding="utf-8")
+        if data is None:
+            data = pd.DataFrame(
+                [
+                    {"ra": 0.0, "dec": 0.0, "z": 0.0},
+                    {"ra": 1.0, "dec": -1.0, "z": 0.1},
+                ]
+            )
+        data.to_parquet(dataset_dir / "part-0.parquet", index=False)
 
         archive_path = temp_dir / "mock_hats.zip"
         with zipfile.ZipFile(archive_path, "w") as archive:
@@ -176,8 +183,15 @@ def create_fake_hats_collection_archive(self):
             "catalog_name=mock_margin\ndataproduct_type=margin\n",
             encoding="utf-8",
         )
-        (catalog_dataset_dir / "part-0.parquet").write_text("", encoding="utf-8")
-        (margin_dataset_dir / "part-0.parquet").write_text("", encoding="utf-8")
+        pd.DataFrame(
+            [
+                {"ra": 0.0, "dec": 0.0, "z": 0.0},
+                {"ra": 1.0, "dec": -1.0, "z": 0.1},
+            ]
+        ).to_parquet(catalog_dataset_dir / "part-0.parquet", index=False)
+        pd.DataFrame([{"margin_id": 1}]).to_parquet(
+            margin_dataset_dir / "part-0.parquet", index=False
+        )
 
         archive_path = temp_dir / "mock_collection.zip"
         with zipfile.ZipFile(archive_path, "w") as archive:
@@ -187,19 +201,43 @@ def create_fake_hats_collection_archive(self):
                     archive.write(file_path, arcname=arcname)
         return archive_path
 
-    def create_fake_lsdb_module(self, n_rows=12, columns_as_index=False):
+    def create_fake_lsdb_module(
+        self,
+        n_rows=12,
+        columns_as_index=False,
+        columns=None,
+        all_columns=None,
+        preview_columns=None,
+    ):
+        catalog_columns = columns or ["ra", "dec", "z"]
+        catalog_all_columns = (
+            all_columns if all_columns is not None else catalog_columns
+        )
+        catalog_preview_columns = preview_columns or catalog_columns
+
         class FakeCatalog:
-            columns = pd.Index(["ra", "dec", "z"]) if columns_as_index else ["ra", "dec", "z"]
-            all_columns = ["ra", "dec", "z"]
+            columns = (
+                pd.Index(catalog_columns) if columns_as_index else catalog_columns
+            )
+            all_columns = catalog_all_columns
 
             def __len__(self):
                 return n_rows
 
             def head(self, n=5):
-                rows = [
-                    {"ra": float(i), "dec": float(i) * -1.0, "z": float(i) * 0.1}
-                    for i in range(n_rows)
-                ]
+                rows = []
+                for i in range(n_rows):
+                    row = {}
+                    for column in catalog_preview_columns:
+                        if column == "ra":
+                            row[column] = float(i)
+                        elif column == "dec":
+                            row[column] = float(i) * -1.0
+                        elif column == "z":
+                            row[column] = float(i) * 0.1
+                        else:
+                            row[column] = f"{column}_{i}"
+                    rows.append(row)
                 return pd.DataFrame(rows).head(n)
 
         fake_module = types.ModuleType("lsdb")
@@ -428,6 +466,36 @@ def test_registry_handles_lsdb_columns_as_pandas_index(self):
 
         self.assertEqual(response.status_code, 200)
 
+    def test_registry_uses_lsdb_columns_for_hats_content_columns(self):
+        product = self.create_product(product_type_name="validation_results")
+        expected_columns = ["object_id", "ra", "dec", "z", "quality_flag"]
+        archive_path = self.create_fake_hats_archive()
+        self.upload_main_file_from_path(product, archive_path)
+        url = reverse("products-registry", kwargs={"pk": product.pk})
+        fake_lsdb = self.create_fake_lsdb_module(
+            n_rows=1,
+            columns=expected_columns,
+            all_columns=expected_columns,
+            preview_columns=expected_columns,
+        )
+
+        with mock.patch.dict(sys.modules, {"lsdb": fake_lsdb}):
+            response = self.client.post(url)
+
+        self.assertEqual(response.status_code, 200)
+        registered_columns = list(
+            ProductContent.objects.filter(product=product)
+            .order_by("order")
+            .values_list("column_name", flat=True)
+        )
+        self.assertEqual(registered_columns, expected_columns)
+
+        preview_path = Path(
+            settings.MEDIA_ROOT, product.path, RegistryProduct.TABLE_PREVIEW_FILENAME
+        )
+        payload = json.loads(preview_path.read_text(encoding="utf-8"))
+        self.assertEqual(payload["columns"], expected_columns)
+
     def test_registry_without_columns(self):
 
         # Cria um novo produto.