Correct integer types in index catalog creation

src/hipscat_import/catalog/map_reduce.py

@@ -16,7 +16,7 @@
 # pylint: disable=too-many-locals,too-many-arguments
 
 
-def _get_pixel_directory(cache_path: FilePointer, order: np.int64, pixel: np.int64):
+def _get_pixel_directory(cache_path: FilePointer, order: np.int64, pixel: np.uint64):
     """Create a path for intermediate pixel data.
 
     This will take the form:
@@ -81,7 +81,7 @@ def _iterate_input_file(
                 )
             # Set up the pixel data
             mapped_pixels = hp.ang2pix(
-                2 ** highest_order,
+                2**highest_order,
                 data[ra_column].values,
                 data[dec_column].values,
                 lonlat=True,

src/hipscat_import/index/map_reduce.py

@@ -3,7 +3,8 @@
 import dask.dataframe as dd
 import numpy as np
 from dask.distributed import progress, wait
-from hipscat.io import file_io
+from hipscat.io import paths
+from hipscat.io.file_io import file_io
 from hipscat.pixel_math.hipscat_id import HIPSCAT_ID_COLUMN
 
 
@@ -16,13 +17,17 @@ def create_index(args):
     if args.include_order_pixel:
         include_columns.extend(["Norder", "Dir", "Npix"])
 
-    index_dir = file_io.append_paths_to_pointer(args.catalog_path, "index")
+    index_dir = paths.append_paths_to_pointer(args.catalog_path, "index")
 
+    metadata_file = paths.get_parquet_metadata_pointer(args.input_catalog_path)
+
+    metadata = file_io.read_parquet_metadata(metadata_file)
     data = dd.read_parquet(
         path=args.input_catalog_path,
         columns=include_columns,
         engine="pyarrow",
-        dataset={"partitioning": "hive"},
+        dataset={"partitioning": {"flavor": "hive", "schema": metadata.schema.to_arrow_schema()}},
+        filesystem="arrow",
     )
 
     if args.include_order_pixel:
@@ -33,6 +38,7 @@ def create_index(args):
     data = data.reset_index()
     if not args.include_hipscat_index:
         data = data.drop(columns=[HIPSCAT_ID_COLUMN])
+    data = data.drop_duplicates()
     data = data.repartition(partition_size=args.compute_partition_size)
     data = data.set_index(args.indexing_column)
     result = data.to_parquet(

src/hipscat_import/soap/map_reduce.py

@@ -46,12 +46,12 @@ def _count_joins_for_object(source_data, source_pixel, object_pixel, soap_args):
     joined_data = joined_data.reset_index()
 
     joined_data["Norder"] = np.full(rows_written, fill_value=object_pixel.order, dtype=np.uint8)
-    joined_data["Dir"] = np.full(rows_written, fill_value=object_pixel.dir, dtype=np.uint32)
-    joined_data["Npix"] = np.full(rows_written, fill_value=object_pixel.pixel, dtype=np.uint32)
+    joined_data["Dir"] = np.full(rows_written, fill_value=object_pixel.dir, dtype=np.uint64)
+    joined_data["Npix"] = np.full(rows_written, fill_value=object_pixel.pixel, dtype=np.uint64)
 
     joined_data["join_Norder"] = np.full(rows_written, fill_value=source_pixel.order, dtype=np.uint8)
-    joined_data["join_Dir"] = np.full(rows_written, fill_value=source_pixel.dir, dtype=np.uint32)
-    joined_data["join_Npix"] = np.full(rows_written, fill_value=source_pixel.pixel, dtype=np.uint32)
+    joined_data["join_Dir"] = np.full(rows_written, fill_value=source_pixel.dir, dtype=np.uint64)
+    joined_data["join_Npix"] = np.full(rows_written, fill_value=source_pixel.pixel, dtype=np.uint64)
 
     joined_data.to_parquet(output_file, index=True)
 
@@ -64,9 +64,9 @@ def _write_count_results(cache_path, source_healpix, results):
     dataframe = pd.DataFrame(results, columns=["Norder", "Npix", "num_rows"])
 
     dataframe["Dir"] = [int(order / 10_000) * 10_000 if order >= 0 else -1 for order, _, _ in results]
-    dataframe["join_Norder"] = np.full(num_results, fill_value=source_healpix.order, dtype=np.int32)
-    dataframe["join_Dir"] = [int(order / 10_000) * 10_000 for order in dataframe["join_Norder"]]
-    dataframe["join_Npix"] = np.full(num_results, fill_value=source_healpix.pixel, dtype=np.int32)
+    dataframe["join_Norder"] = np.full(num_results, fill_value=source_healpix.order, dtype=np.uint8)
+    dataframe["join_Dir"] = np.full(num_results, fill_value=source_healpix.dir, dtype=np.uint64)
+    dataframe["join_Npix"] = np.full(num_results, fill_value=source_healpix.pixel, dtype=np.uint64)
 
     ## Reorder columns.
     dataframe = dataframe[["Norder", "Dir", "Npix", "join_Norder", "join_Dir", "join_Npix", "num_rows"]]

tests/hipscat_import/data/small_sky_object_catalog/catalog_info.json

@@ -5,4 +5,4 @@
     "epoch": "J2000",
     "ra_column": "ra",
     "dec_column": "dec"
-}
+}

tests/hipscat_import/data/small_sky_object_catalog/provenance_info.json

@@ -1,43 +1,42 @@
 {
     "catalog_name": "small_sky_object_catalog",
     "catalog_type": "object",
-    "version": "0.0.10.dev7+g0a79f90.d20230418",
-    "generation_date": "2023.04.20",
-    "epoch": "J2000",
-    "ra_kw": "ra",
-    "dec_kw": "dec",
     "total_rows": 131,
+    "epoch": "J2000",
+    "ra_column": "ra",
+    "dec_column": "dec",
+    "version": "0.2.1",
+    "generation_date": "2024.01.09",
     "tool_args": {
         "tool_name": "hipscat_import",
-        "version": "0.0.4.dev28+g2e31821.d20230420",
+        "version": "0.2.1",
         "runtime_args": {
             "catalog_name": "small_sky_object_catalog",
-            "output_path": "/home/data",
+            "output_path": "/home/delucchi/git/hipscat-import/tests/hipscat_import/data/",
             "output_artifact_name": "small_sky_object_catalog",
             "tmp_dir": "",
             "overwrite": true,
-            "dask_tmp": "/tmp/pytest-of-delucchi/pytest-1261/test_dask_runner0",
+            "dask_tmp": "",
             "dask_n_workers": 1,
             "dask_threads_per_worker": 1,
-            "catalog_path": "/home/data/small_sky_object_catalog",
-            "tmp_path": "/tmp/pytest-of-delucchi/pytest-1261/test_dask_runner0/small_sky_object_catalog/intermediate",
+            "catalog_path": "/home/delucchi/git/hipscat-import/tests/hipscat_import/data/small_sky_object_catalog",
+            "tmp_path": "/home/delucchi/git/hipscat-import/tests/hipscat_import/data/small_sky_object_catalog/intermediate",
             "epoch": "J2000",
             "catalog_type": "object",
-            "input_path": "/home/data/small_sky_parts",
+            "input_path": "/home/delucchi/git/hipscat-import/tests/hipscat_import/data/small_sky",
             "input_paths": [
-                "/home/data/small_sky_parts/catalog_00_of_05.csv",
-                "/home/data/small_sky_parts/catalog_01_of_05.csv",
-                "/home/data/small_sky_parts/catalog_02_of_05.csv",
-                "/home/data/small_sky_parts/catalog_03_of_05.csv",
-                "/home/data/small_sky_parts/catalog_04_of_05.csv"
+                "file:///home/delucchi/git/hipscat-import/tests/hipscat_import/data/small_sky/catalog.csv"
             ],
             "input_format": "csv",
             "input_file_list": [],
             "ra_column": "ra",
             "dec_column": "dec",
-            "sort_columns": "id",
-            "highest_healpix_order": 1,
+            "use_hipscat_index": false,
+            "sort_columns": null,
+            "constant_healpix_order": -1,
+            "highest_healpix_order": 7,
             "pixel_threshold": 1000000,
+            "mapping_healpix_order": 7,
             "debug_stats_only": false,
             "file_reader_info": {
                 "input_reader_type": "CsvReader",
@@ -50,4 +49,4 @@
             }
         }
     }
-}
+}

tests/hipscat_import/index/test_run_index.py

@@ -2,6 +2,7 @@
 
 import os
 
+import numpy as np
 import pyarrow as pa
 import pyarrow.parquet as pq
 import pytest
@@ -108,3 +109,55 @@ def test_run_index_on_source(
 
     schema = pq.read_metadata(os.path.join(args.catalog_path, "_common_metadata")).schema.to_arrow_schema()
     assert schema.equals(basic_index_parquet_schema, check_metadata=False)
+
+
+@pytest.mark.dask
+def test_run_index_on_source_object_id(
+    small_sky_source_catalog,
+    dask_client, # pylint: disable=unused-argument
+    tmp_path,
+    assert_parquet_file_index,
+):
+    """Test appropriate metadata is written."""
+
+    args = IndexArguments(
+        input_catalog_path=small_sky_source_catalog,
+        indexing_column="object_id",
+        output_path=tmp_path,
+        output_artifact_name="small_sky_source_object_id_index",
+        overwrite=True,
+        include_hipscat_index=False,
+        progress_bar=False,
+    )
+    runner.run(args)
+
+    # Check that the catalog metadata file exists
+    catalog = Dataset.read_from_hipscat(args.catalog_path)
+    assert catalog.on_disk
+    assert catalog.catalog_path == args.catalog_path
+
+    basic_index_parquet_schema = pa.schema(
+        [
+            pa.field("Norder", pa.uint8()),
+            pa.field("Dir", pa.uint64()),
+            pa.field("Npix", pa.uint64()),
+            pa.field("object_id", pa.int64()),
+        ]
+    )
+
+    outfile = os.path.join(args.catalog_path, "index", "part.0.parquet")
+    schema = pq.read_metadata(outfile).schema.to_arrow_schema()
+    assert schema.equals(basic_index_parquet_schema, check_metadata=False)
+
+    id_range = np.arange(700, 831)
+    ## Some of the objects have sources that span two source partitions.
+    doubled_up = [706, 707, 716, 726, 730, 736, 740, 779, 780, 784, 787, 789, 790, 792, 797, 818, 820]
+    doubled_up.extend(id_range)
+
+    assert_parquet_file_index(outfile, doubled_up)
+
+    schema = pq.read_metadata(os.path.join(args.catalog_path, "_metadata")).schema.to_arrow_schema()
+    assert schema.equals(basic_index_parquet_schema, check_metadata=False)
+
+    schema = pq.read_metadata(os.path.join(args.catalog_path, "_common_metadata")).schema.to_arrow_schema()
+    assert schema.equals(basic_index_parquet_schema, check_metadata=False)