Use radec2pix for healpix pixel calculation.

src/hats_import/catalog/map_reduce.py

@@ -60,20 +60,16 @@ def _iterate_input_file(
         else:
             # Set up the pixel data
             if isinstance(data, pd.DataFrame):
-                mapped_pixels = hp.ang2pix(
-                    2**highest_order,
+                mapped_pixels = hp.radec2pix(
+                    highest_order,
                     data[ra_column].to_numpy(copy=False, dtype=float),
                     data[dec_column].to_numpy(copy=False, dtype=float),
-                    lonlat=True,
-                    nest=True,
                 )
             else:
-                mapped_pixels = hp.ang2pix(
-                    2**highest_order,
+                mapped_pixels = hp.radec2pix(
+                    highest_order,
                     data[ra_column].to_numpy(),
                     data[dec_column].to_numpy(),
-                    lonlat=True,
-                    nest=True,
                 )
         yield chunk_number, data, mapped_pixels
 
@@ -299,8 +295,8 @@ def reduce_pixel_shards(
                 SPATIAL_INDEX_COLUMN,
                 [
                     pixel_math.compute_spatial_index(
-                        merged_table[ra_column].to_numpy(),
-                        merged_table[dec_column].to_numpy(),
+                        merged_table[ra_column].to_numpy().astype(np.float64),
+                        merged_table[dec_column].to_numpy().astype(np.float64),
                     )
                 ],
             ).sort_by(SPATIAL_INDEX_COLUMN)

src/hats_import/hipscat_conversion/run_conversion.py

@@ -4,6 +4,7 @@
 import tempfile
 from typing import no_type_check
 
+import hats.pixel_math.healpix_shim as healpix
 import healpy as hp
 import numpy as np
 import pyarrow.parquet as pq
@@ -128,12 +129,10 @@ def _convert_partition_file(pixel, args, schema, ra_column, dec_column):
                 0,
                 "_healpix_29",
                 [
-                    hp.ang2pix(
-                        2**29,
+                    healpix.radec2pix(
+                        29,
                         table[ra_column].to_numpy(),
                         table[dec_column].to_numpy(),
-                        nest=True,
-                        lonlat=True,
                     )
                 ],
             )

src/hats_import/margin_cache/margin_cache_map_reduce.py

@@ -43,12 +43,10 @@ def map_pixel_shards(
             f"margin_pixel >= {margin_pixel_range_start} and margin_pixel < {margin_pixel_range_end}"
         )
 
-        margin_pixel_list = hp.ang2pix(
-            2**margin_order,
+        margin_pixel_list = hp.radec2pix(
+            margin_order,
             data[ra_column].values,
             data[dec_column].values,
-            lonlat=True,
-            nest=True,
         )
         margin_pixel_filter = pd.DataFrame(
             {"margin_pixel": margin_pixel_list, "filter_value": np.arange(0, len(margin_pixel_list))}

tests/hats_import/catalog/test_map_reduce.py

@@ -436,12 +436,10 @@ def test_reduce_with_sorting_complex(assert_parquet_file_ids, tmp_path):
     file2_data.to_parquet(shard_dir / "file_2_shard_1.parquet")
 
     combined_data = pd.concat([file1_data, file2_data])
-    combined_data["norder19_healpix"] = hp.ang2pix(
-        2**19,
+    combined_data["norder19_healpix"] = hp.radec2pix(
+        19,
         combined_data["ra"].values,
         combined_data["dec"].values,
-        lonlat=True,
-        nest=True,
     )
     ## Use this to prune generated columns like Norder, Npix, and _healpix_29
     comparison_columns = ["source_id", "object_id", "time", "ra", "dec"]

tests/hats_import/margin_cache/test_margin_cache_map_reduce.py

@@ -211,7 +211,7 @@ def test_reduce_margin_shards(tmp_path):
     hats_indexes = pixel_math.compute_spatial_index(ras, dec)
     margin_order = np.full(360, 0)
     margin_dir = np.full(360, 0)
-    margin_pixels = hp.ang2pix(2**3, ras, dec, lonlat=True, nest=True)
+    margin_pixels = hp.radec2pix(3, ras, dec)
 
     test_df = pd.DataFrame(
         data=zip(hats_indexes, ras, dec, norder, ndir, npix, margin_order, margin_dir, margin_pixels),