Remove margin fine filtering, and healpy dependency.

docs/index.rst

@@ -28,7 +28,8 @@ need to install or upgrade versions of dependencies to work with hats-import.
 .. tip::
     Installing on Mac
 
-    ``healpy`` is a very necessary dependency for hats libraries at this time, but
+    ``healpy`` is an optional dependency for hats-import (included in the ``full`` extra)
+    to support converting from older HiPSCat catalogs, but
     native prebuilt binaries for healpy on Apple Silicon Macs 
     `do not yet exist <https://healpy.readthedocs.io/en/latest/install.html#binary-installation-with-pip-recommended-for-most-other-python-users>`_, 
     so it's recommended to install via conda before proceeding to hats-import.

pyproject.toml

@@ -42,6 +42,7 @@ full = [
     "fsspec[full]", # complete file system specs.
     "ipykernel", # Support for Jupyter notebooks
     "ipywidgets", # useful for tqdm in notebooks.
+    "healpy", # used only in hipscat conversion
 ]
 
 [build-system]

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

@@ -4,7 +4,7 @@
 import tempfile
 from typing import no_type_check
 
-import hats.pixel_math.healpix_shim as hp
+import hats.pixel_math.healpix_shim as healpix
 import numpy as np
 import pyarrow.parquet as pq
 from dask.distributed import as_completed, get_worker
@@ -128,12 +128,10 @@ 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,
                     )
                 ],
             )
@@ -155,7 +153,11 @@ def _convert_partition_file(pixel, args, schema, ra_column, dec_column):
         raise exception
 
 
+# pylint: disable=import-outside-toplevel
 def _write_nested_fits_map(input_dir, output_dir):
+    # Healpy is an optional dependency, used only for reads of legacy fits files.
+    import healpy as hp
+
     input_file = input_dir / "point_map.fits"
     if not input_file.exists():
         return
@@ -169,11 +171,6 @@ def _write_nested_fits_map(input_dir, output_dir):
                 map_fits_image = hp.read_map(_tmp_file.name)
             else:
                 map_fits_image = map_fits_image[0]
+    map_fits_image = map_fits_image.astype(np.int32)
 
-    output_file = output_dir / "point_map.fits"
-    with tempfile.NamedTemporaryFile() as _tmp_file:
-        with output_file.open("wb") as _map_file:
-            hp.write_map(
-                _tmp_file.name, map_fits_image, overwrite=True, dtype=np.int32, nest=True, coord="CEL"
-            )
-            _map_file.write(_tmp_file.read())
+    file_io.write_fits_image(map_fits_image, output_dir / "point_map.fits")

src/hats_import/margin_cache/margin_cache_arguments.py

@@ -28,7 +28,7 @@
     order of healpix partitioning in the source catalog. if `margin_order` is left
     default or set to -1, then the `margin_order` will be set dynamically to the
     highest partition order plus 1."""
-    fine_filtering: bool = True
+    fine_filtering: bool = False
     """should we perform the precise boundary checking? if false, some results may be
     greater than `margin_threshold` away from the border (but within `margin_order`)."""
 
@@ -54,6 +54,9 @@
             if len(self.catalog.get_healpix_pixels()) == 0:
                 raise ValueError("debug_filter_pixel_list has created empty catalog")
 
+        if self.fine_filtering:
+            raise NotImplementedError("Fine filtering temporarily removed.")
+
         highest_order = int(self.catalog.partition_info.get_highest_order())
 
         if self.margin_order < 0:
@@ -64,9 +67,7 @@
                 "margin_order must be of a higher order than the highest order catalog partition pixel."
             )
 
-        margin_pixel_nside = hp.order2nside(self.margin_order)
-        margin_pixel_avgsize = hp.nside2resol(margin_pixel_nside, arcmin=True)
-        margin_pixel_mindist = hp.avgsize2mindist(margin_pixel_avgsize)
+        margin_pixel_mindist = hp.order2mindist(self.margin_order)
         if margin_pixel_mindist * 60.0 < self.margin_threshold:
             raise ValueError("margin pixels must be larger than margin_threshold")
 

src/hats_import/margin_cache/margin_cache_map_reduce.py

@@ -3,15 +3,14 @@
 import pandas as pd
 import pyarrow as pa
 import pyarrow.dataset as ds
-from hats import pixel_math
 from hats.io import file_io, paths
 from hats.pixel_math.healpix_pixel import HealpixPixel
 
 from hats_import.margin_cache.margin_cache_resume_plan import MarginCachePlan
 from hats_import.pipeline_resume_plan import get_pixel_cache_directory, print_task_failure
 
 
-# pylint: disable=too-many-arguments
+# pylint: disable=too-many-arguments, unused-argument
 def map_pixel_shards(
     partition_file,
     mapping_key,
@@ -26,6 +25,9 @@ def map_pixel_shards(
 ):
     """Creates margin cache shards from a source partition file."""
     try:
+        if fine_filtering:
+            raise NotImplementedError("Fine filtering temporarily removed.")
+
         schema = file_io.read_parquet_metadata(original_catalog_metadata).schema.to_arrow_schema()
         data = file_io.read_parquet_file_to_pandas(partition_file, schema=schema)
         source_pixel = HealpixPixel(data["Norder"].iloc[0], data["Npix"].iloc[0])
@@ -41,12 +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))}
@@ -78,6 +78,7 @@ def map_pixel_shards(
         raise exception
 
 
+# pylint: disable=too-many-arguments, unused-argument
 def _to_pixel_shard(
     filtered_data,
     pixel,
@@ -89,18 +90,7 @@ def _to_pixel_shard(
     fine_filtering,
 ):
     """Do boundary checking for the cached partition and then output remaining data."""
-    if fine_filtering:
-        margin_check = pixel_math.check_margin_bounds(
-            filtered_data[ra_column].values,
-            filtered_data[dec_column].values,
-            pixel.order,
-            pixel.pixel,
-            margin_threshold,
-        )
-
-        margin_data = filtered_data.iloc[margin_check]
-    else:
-        margin_data = filtered_data
+    margin_data = filtered_data
 
     num_rows = len(margin_data)
     if num_rows:

src/hats_import/soap/resume_plan.py

@@ -7,7 +7,7 @@
 
 import hats.pixel_math.healpix_shim as hp
 import numpy as np
-from hats import read_hats
+from hats import pixel_math, read_hats
 from hats.catalog import Catalog
 from hats.io import file_io
 from hats.pixel_math.healpix_pixel import HealpixPixel
@@ -193,8 +193,7 @@ def source_to_object_map(object_catalog, source_catalog):
 
     for source, objects in source_to_object.items():
         # get all neighboring pixels
-        nside = hp.order2nside(source.order)
-        neighbors = hp.get_all_neighbours(nside, source.pixel, nest=True)
+        neighbors = pixel_math.get_margin(source.order, source.pixel, 0)
 
         ## get rid of -1s and normalize to max order
         explosion_factor = 4 ** (max_order - source.order)

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/hipscat_conversion/test_run_conversion.py

@@ -24,6 +24,16 @@ def test_bad_args():
         runner.run(args, None)
 
 
+# pylint: disable=unused-import
+try:
+    import healpy as hp
+
+    HAVE_HEALPY = True
+except ImportError:
+    HAVE_HEALPY = False
+
+
+@pytest.mark.skipif(not HAVE_HEALPY, reason="healpy is not installed")
 @pytest.mark.dask
 def test_run_conversion_object(
     test_data_dir,
@@ -88,6 +98,7 @@ def test_run_conversion_object(
     assert data.index.name is None
 
 
+@pytest.mark.skipif(not HAVE_HEALPY, reason="healpy is not installed")
 @pytest.mark.dask
 def test_run_conversion_source(
     test_data_dir,

tests/hats_import/margin_cache/test_margin_cache.py

@@ -35,7 +35,7 @@ def test_margin_cache_gen(small_sky_source_catalog, tmp_path, dask_client):
 
     data = pd.read_parquet(test_file)
 
-    assert len(data) == 13
+    assert len(data) == 88
 
     assert all(data[paths.PARTITION_ORDER] == norder)
     assert all(data[paths.PARTITION_PIXEL] == npix)

tests/hats_import/margin_cache/test_margin_cache_map_reduce.py

@@ -53,7 +53,7 @@ def test_to_pixel_shard_equator(tmp_path, basic_data_shard_df):
 
     assert os.path.exists(path)
 
-    validate_result_dataframe(path, 2)
+    validate_result_dataframe(path, 360)
 
 
 @pytest.mark.timeout(5)
@@ -79,7 +79,7 @@ def test_to_pixel_shard_polar(tmp_path, polar_data_shard_df):
 def test_map_pixel_shards_error(tmp_path, capsys):
     """Test error behavior on reduce stage. e.g. by not creating the original
     catalog parquet files."""
-    with pytest.raises(FileNotFoundError):
+    with pytest.raises(NotImplementedError):
         margin_cache_map_reduce.map_pixel_shards(
             paths.pixel_catalog_file(tmp_path, HealpixPixel(1, 0)),
             mapping_key="1_21",
@@ -94,9 +94,10 @@ def test_map_pixel_shards_error(tmp_path, capsys):
         )
 
     captured = capsys.readouterr()
-    assert "Parquet file does not exist" in captured.out
+    assert "Fine filtering temporarily removed" in captured.out
 
 
+@pytest.mark.skip()
 @pytest.mark.timeout(30)
 def test_map_pixel_shards_fine(tmp_path, test_data_dir, small_sky_source_catalog):
     """Test basic mapping behavior, with fine filtering enabled."""
@@ -210,7 +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),

tests/hats_import/margin_cache/test_margin_round_trip.py

@@ -72,7 +72,7 @@ def test_margin_import_gaia_minimum(
 
     data = pd.read_parquet(test_file)
 
-    assert len(data) == 1
+    assert len(data) == 4
 
 
 @pytest.mark.dask(timeout=180)
@@ -117,7 +117,7 @@ def test_margin_import_mixed_schema_csv(
     catalog = read_hats(args.catalog_path)
     assert catalog.on_disk
     assert catalog.catalog_path == args.catalog_path
-    assert len(catalog.get_healpix_pixels()) == 5
+    assert len(catalog.get_healpix_pixels()) == 19
 
     norder = 2
     npix = 187