Delucchi/radius

src/lsdb/catalog/catalog.py

@@ -210,7 +210,7 @@ def crossmatch(
         hc_catalog = hc.catalog.Catalog(new_catalog_info, alignment.pixel_tree)
         return Catalog(ddf, ddf_map, hc_catalog)
 
-    def cone_search(self, ra: float, dec: float, radius: float):
+    def cone_search(self, ra: float, dec: float, radius_arcsec: float):
         """Perform a cone search to filter the catalog
 
         Filters to points within radius great circle distance to the point specified by ra and dec in degrees.
@@ -219,13 +219,13 @@ def cone_search(self, ra: float, dec: float, radius: float):
         Args:
             ra (float): Right Ascension of the center of the cone in degrees
             dec (float): Declination of the center of the cone in degrees
-            radius (float): Radius of the cone in degrees
+            radius (float): Radius of the cone in arcseconds
 
         Returns:
             A new Catalog containing the points filtered to those within the cone, and the partitions that
             overlap the cone.
         """
-        return self._search(ConeSearch(ra, dec, radius, self.hc_structure))
+        return self._search(ConeSearch(ra, dec, radius_arcsec, self.hc_structure))
 
     def box(self, ra: Tuple[float, float] | None = None, dec: Tuple[float, float] | None = None) -> Catalog:
         """Performs filtering according to right ascension and declination ranges.

src/lsdb/core/search/cone_search.py

@@ -19,34 +19,34 @@ class ConeSearch(AbstractSearch):
     Filters partitions in the catalog to those that have some overlap with the cone.
     """
 
-    def __init__(self, ra, dec, radius, metadata):
-        validate_radius(radius)
+    def __init__(self, ra, dec, radius_arcsec, metadata):
+        validate_radius(radius_arcsec)
         validate_declination_values(dec)
 
         self.ra = ra
         self.dec = dec
-        self.radius = radius
+        self.radius_arcsec = radius_arcsec
         self.metadata = metadata
 
     def search_partitions(self, pixels: List[HealpixPixel]) -> List[HealpixPixel]:
         """Determine the target partitions for further filtering."""
         pixel_tree = PixelTreeBuilder.from_healpix(pixels)
-        return filter_pixels_by_cone(pixel_tree, self.ra, self.dec, self.radius)
+        return filter_pixels_by_cone(pixel_tree, self.ra, self.dec, self.radius_arcsec)
 
     def search_points(self, frame: pd.DataFrame) -> pd.DataFrame:
         """Determine the search results within a data frame"""
-        return cone_filter(frame, self.ra, self.dec, self.radius, self.metadata)
+        return cone_filter(frame, self.ra, self.dec, self.radius_arcsec, self.metadata)
 
 
 @dask.delayed
-def cone_filter(data_frame: pd.DataFrame, ra, dec, radius, metadata: hc.catalog.Catalog):
+def cone_filter(data_frame: pd.DataFrame, ra, dec, radius_arcsec, metadata: hc.catalog.Catalog):
     """Filters a dataframe to only include points within the specified cone
 
     Args:
         data_frame (pd.DataFrame): DataFrame containing points in the sky
         ra (float): Right Ascension of the center of the cone in degrees
         dec (float): Declination of the center of the cone in degrees
-        radius (float): Radius of the cone in degrees
+        radius_arcsec (float): Radius of the cone in arcseconds
         metadata (hipscat.Catalog): hipscat `Catalog` with catalog_info that matches `data_frame`
 
     Returns:
@@ -56,6 +56,7 @@ def cone_filter(data_frame: pd.DataFrame, ra, dec, radius, metadata: hc.catalog.
     df_decs = data_frame[metadata.catalog_info.dec_column].values
     df_coords = SkyCoord(df_ras, df_decs, unit="deg")
     center_coord = SkyCoord(ra, dec, unit="deg")
-    df_separations = df_coords.separation(center_coord).value
-    data_frame = data_frame.iloc[df_separations < radius]
+    df_separations_deg = df_coords.separation(center_coord).value
+    radius_degrees = radius_arcsec / 3600
+    data_frame = data_frame.iloc[df_separations_deg < radius_degrees]
     return data_frame

tests/data/small_sky_to_xmatch/partition_info.csv

@@ -0,0 +1,2 @@
+Norder,Npix,Dir
+0,11,0

tests/data/small_sky_to_xmatch/partition_join_info.csv

@@ -0,0 +1,4 @@
+Norder,Npix,join_Norder,join_Npix
+0,11,1,44
+0,11,1,45
+0,11,1,46

tests/data/small_sky_xmatch/partition_info.csv

@@ -1,4 +1,4 @@
-Norder,Dir,Npix,num_rows
-1,0,44,41
-1,0,45,29
-1,0,46,41
+Norder,Npix,Dir
+1,44,0
+1,45,0
+1,46,0

tests/data/small_sky_xmatch_margin/partition_info.csv

@@ -0,0 +1,6 @@
+Norder,Npix,Dir
+0,4,0
+1,44,0
+1,45,0
+1,46,0
+1,47,0

tests/lsdb/catalog/test_catalog.py

@@ -199,7 +199,7 @@ def test_save_catalog_with_some_empty_partitions(small_sky_order1_catalog, tmp_p
     base_catalog_path = os.path.join(tmp_path, "small_sky")
 
     # The result of this cone search is known to have one empty partition
-    cone_search_catalog = small_sky_order1_catalog.cone_search(0, -80, 15)
+    cone_search_catalog = small_sky_order1_catalog.cone_search(0, -80, 15 * 3600)
     assert cone_search_catalog._ddf.npartitions == 2
 
     non_empty_pixels = []

tests/lsdb/catalog/test_cone_search.py

@@ -6,15 +6,16 @@
 def test_cone_search_filters_correct_points(small_sky_order1_catalog, assert_divisions_are_correct):
     ra = 0
     dec = -80
-    radius = 20
+    radius_degrees = 20
+    radius = radius_degrees * 3600
     center_coord = SkyCoord(ra, dec, unit="deg")
     cone_search_catalog = small_sky_order1_catalog.cone_search(ra, dec, radius)
     cone_search_df = cone_search_catalog.compute()
     for _, row in small_sky_order1_catalog.compute().iterrows():
         row_ra = row[small_sky_order1_catalog.hc_structure.catalog_info.ra_column]
         row_dec = row[small_sky_order1_catalog.hc_structure.catalog_info.dec_column]
         sep = SkyCoord(row_ra, row_dec, unit="deg").separation(center_coord)
-        if sep.degree <= radius:
+        if sep.degree <= radius_degrees:
             assert len(cone_search_df.loc[cone_search_df["id"] == row["id"]]) == 1
         else:
             assert len(cone_search_df.loc[cone_search_df["id"] == row["id"]]) == 0
@@ -24,7 +25,7 @@ def test_cone_search_filters_correct_points(small_sky_order1_catalog, assert_div
 def test_cone_search_filters_partitions(small_sky_order1_catalog):
     ra = 0
     dec = -80
-    radius = 20
+    radius = 20 * 3600
     hc_conesearch = small_sky_order1_catalog.hc_structure.filter_by_cone(ra, dec, radius)
     consearch_catalog = small_sky_order1_catalog.cone_search(ra, dec, radius)
     assert len(hc_conesearch.get_healpix_pixels()) == len(consearch_catalog.get_healpix_pixels())
@@ -36,7 +37,7 @@ def test_cone_search_filters_partitions(small_sky_order1_catalog):
 def test_cone_search_filters_no_matching_points(small_sky_order1_catalog, assert_divisions_are_correct):
     ra = 0
     dec = -80
-    radius = 0.2
+    radius = 0.2 * 3600
     cone_search_catalog = small_sky_order1_catalog.cone_search(ra, dec, radius)
     cone_search_df = cone_search_catalog.compute()
     assert len(cone_search_df) == 0
@@ -46,7 +47,7 @@ def test_cone_search_filters_no_matching_points(small_sky_order1_catalog, assert
 def test_cone_search_filters_no_matching_partitions(small_sky_order1_catalog, assert_divisions_are_correct):
     ra = 20
     dec = 80
-    radius = 20
+    radius = 20 * 3600
     cone_search_catalog = small_sky_order1_catalog.cone_search(ra, dec, radius)
     cone_search_df = cone_search_catalog.compute()
     assert len(cone_search_df) == 0

tests/lsdb/catalog/test_crossmatch.py

@@ -12,9 +12,10 @@
 class TestCrossmatch:
     @staticmethod
     def test_kdtree_crossmatch(algo, small_sky_catalog, small_sky_xmatch_catalog, xmatch_correct):
-        xmatched = small_sky_catalog.crossmatch(
-            small_sky_xmatch_catalog, algorithm=algo, radius_arcsec=0.01 * 3600
-        ).compute()
+        with pytest.warns(RuntimeWarning, match="Results may be inaccurate"):
+            xmatched = small_sky_catalog.crossmatch(
+                small_sky_xmatch_catalog, algorithm=algo, radius_arcsec=0.01 * 3600
+            ).compute()
         assert len(xmatched) == len(xmatch_correct)
         for _, correct_row in xmatch_correct.iterrows():
             assert correct_row["ss_id"] in xmatched["id_small_sky"].values
@@ -24,9 +25,10 @@ def test_kdtree_crossmatch(algo, small_sky_catalog, small_sky_xmatch_catalog, xm
 
     @staticmethod
     def test_kdtree_crossmatch_thresh(algo, small_sky_catalog, small_sky_xmatch_catalog, xmatch_correct_005):
-        xmatched = small_sky_catalog.crossmatch(
-            small_sky_xmatch_catalog, radius_arcsec=0.005 * 3600, algorithm=algo
-        ).compute()
+        with pytest.warns(RuntimeWarning, match="Results may be inaccurate"):
+            xmatched = small_sky_catalog.crossmatch(
+                small_sky_xmatch_catalog, radius_arcsec=0.005 * 3600, algorithm=algo
+            ).compute()
         assert len(xmatched) == len(xmatch_correct_005)
         for _, correct_row in xmatch_correct_005.iterrows():
             assert correct_row["ss_id"] in xmatched["id_small_sky"].values
@@ -38,9 +40,10 @@ def test_kdtree_crossmatch_thresh(algo, small_sky_catalog, small_sky_xmatch_cata
     def test_kdtree_crossmatch_multiple_neighbors(
         algo, small_sky_catalog, small_sky_xmatch_catalog, xmatch_correct_3n_2t_no_margin
     ):
-        xmatched = small_sky_catalog.crossmatch(
-            small_sky_xmatch_catalog, n_neighbors=3, radius_arcsec=2 * 3600, algorithm=algo
-        ).compute()
+        with pytest.warns(RuntimeWarning, match="Results may be inaccurate"):
+            xmatched = small_sky_catalog.crossmatch(
+                small_sky_xmatch_catalog, n_neighbors=3, radius_arcsec=2 * 3600, algorithm=algo
+            ).compute()
         assert len(xmatched) == len(xmatch_correct_3n_2t_no_margin)
         for _, correct_row in xmatch_correct_3n_2t_no_margin.iterrows():
             assert correct_row["ss_id"] in xmatched["id_small_sky"].values
@@ -102,9 +105,10 @@ def test_wrong_suffixes(algo, small_sky_catalog, small_sky_xmatch_catalog):
 
 
 def test_custom_crossmatch_algorithm(small_sky_catalog, small_sky_xmatch_catalog, xmatch_mock):
-    xmatched = small_sky_catalog.crossmatch(
-        small_sky_xmatch_catalog, algorithm=MockCrossmatchAlgorithm, mock_results=xmatch_mock
-    ).compute()
+    with pytest.warns(RuntimeWarning, match="Results may be inaccurate"):
+        xmatched = small_sky_catalog.crossmatch(
+            small_sky_xmatch_catalog, algorithm=MockCrossmatchAlgorithm, mock_results=xmatch_mock
+        ).compute()
     assert len(xmatched) == len(xmatch_mock)
     for _, correct_row in xmatch_mock.iterrows():
         assert correct_row["ss_id"] in xmatched["id_small_sky"].values