Merge branch 'main' into issue/312/subset

pyproject.toml

@@ -20,11 +20,12 @@ dependencies = [
     "healpy",
     "hipscat >=0.3.4",
     "ipykernel", # Support for Jupyter notebooks
+    "numpy",
     "pandas",
     "pyarrow",
     "pyyaml",
+    "scipy",
     "tqdm",
-    "numpy",
 ]
 
 # On a mac, install optional dependencies with `pip install '.[dev]'` (include the single quotes)

src/hipscat_import/catalog/file_readers.py

@@ -81,9 +81,16 @@ def read(self, input_file, read_columns=None):
     def provenance_info(self) -> dict:
         """Create dictionary of parameters for provenance tracking.
 
+        If any `storage_options` have been provided as kwargs, we will replace the
+        value with ``REDACTED`` for the purpose of writing to provenance info, as it
+        may contain user names or API keys.
+
         Returns:
             dictionary with all argument_name -> argument_value as key -> value pairs.
         """
+        all_args = vars(self)
+        if "kwargs" in all_args and "storage_options" in all_args["kwargs"]:
+            all_args["kwargs"]["storage_options"] = "REDACTED"
         return {"input_reader_type": type(self).__name__, **vars(self)}
 
     def regular_file_exists(self, input_file, storage_options: Union[Dict[Any, Any], None] = None, **_kwargs):

src/hipscat_import/catalog/map_reduce.py

@@ -14,7 +14,7 @@
 from hipscat_import.catalog.file_readers import InputReader
 from hipscat_import.catalog.resume_plan import ResumePlan
 from hipscat_import.catalog.sparse_histogram import SparseHistogram
-from hipscat_import.pipeline_resume_plan import get_pixel_cache_directory
+from hipscat_import.pipeline_resume_plan import get_pixel_cache_directory, print_task_failure
 
 # pylint: disable=too-many-locals,too-many-arguments
 
@@ -94,22 +94,28 @@ def map_to_pixels(
         ValueError: if the `ra_column` or `dec_column` cannot be found in the input file.
         FileNotFoundError: if the file does not exist, or is a directory
     """
-    histo = SparseHistogram.make_empty(highest_order)
+    try:
+        histo = SparseHistogram.make_empty(highest_order)
 
-    if use_hipscat_index:
-        read_columns = [HIPSCAT_ID_COLUMN]
-    else:
-        read_columns = [ra_column, dec_column]
+        if use_hipscat_index:
+            read_columns = [HIPSCAT_ID_COLUMN]
+        else:
+            read_columns = [ra_column, dec_column]
 
-    for _, _, mapped_pixels in _iterate_input_file(
-        input_file, file_reader, highest_order, ra_column, dec_column, use_hipscat_index, read_columns
-    ):
-        mapped_pixel, count_at_pixel = np.unique(mapped_pixels, return_counts=True)
+        for _, _, mapped_pixels in _iterate_input_file(
+            input_file, file_reader, highest_order, ra_column, dec_column, use_hipscat_index, read_columns
+        ):
+            mapped_pixel, count_at_pixel = np.unique(mapped_pixels, return_counts=True)
 
-        partial = SparseHistogram.make_from_counts(mapped_pixel, count_at_pixel, healpix_order=highest_order)
-        histo.add(partial)
+            partial = SparseHistogram.make_from_counts(
+                mapped_pixel, count_at_pixel, healpix_order=highest_order
+            )
+            histo.add(partial)
 
-    histo.to_file(ResumePlan.partial_histogram_file(tmp_path=resume_path, mapping_key=mapping_key))
+        histo.to_file(ResumePlan.partial_histogram_file(tmp_path=resume_path, mapping_key=mapping_key))
+    except Exception as exception:  # pylint: disable=broad-exception-caught
+        print_task_failure(f"Failed MAPPING stage with file {input_file}", exception)
+        raise exception
 
 
 def split_pixels(
@@ -142,30 +148,34 @@ def split_pixels(
         ValueError: if the `ra_column` or `dec_column` cannot be found in the input file.
         FileNotFoundError: if the file does not exist, or is a directory
     """
-    for chunk_number, data, mapped_pixels in _iterate_input_file(
-        input_file, file_reader, highest_order, ra_column, dec_column, use_hipscat_index
-    ):
-        aligned_pixels = alignment[mapped_pixels]
-        unique_pixels, unique_inverse = np.unique(aligned_pixels, return_inverse=True)
-
-        for unique_index, [order, pixel, _] in enumerate(unique_pixels):
-            mapped_indexes = np.where(unique_inverse == unique_index)
-            data_indexes = data.index[mapped_indexes[0].tolist()]
-
-            filtered_data = data.filter(items=data_indexes, axis=0)
-
-            pixel_dir = get_pixel_cache_directory(cache_shard_path, HealpixPixel(order, pixel))
-            file_io.make_directory(pixel_dir, exist_ok=True)
-            output_file = file_io.append_paths_to_pointer(
-                pixel_dir, f"shard_{splitting_key}_{chunk_number}.parquet"
-            )
-            if _has_named_index(filtered_data):
-                filtered_data.to_parquet(output_file, index=True)
-            else:
-                filtered_data.to_parquet(output_file, index=False)
-            del filtered_data, data_indexes
-
-    ResumePlan.splitting_key_done(tmp_path=resume_path, splitting_key=splitting_key)
+    try:
+        for chunk_number, data, mapped_pixels in _iterate_input_file(
+            input_file, file_reader, highest_order, ra_column, dec_column, use_hipscat_index
+        ):
+            aligned_pixels = alignment[mapped_pixels]
+            unique_pixels, unique_inverse = np.unique(aligned_pixels, return_inverse=True)
+
+            for unique_index, [order, pixel, _] in enumerate(unique_pixels):
+                mapped_indexes = np.where(unique_inverse == unique_index)
+                data_indexes = data.index[mapped_indexes[0].tolist()]
+
+                filtered_data = data.filter(items=data_indexes, axis=0)
+
+                pixel_dir = get_pixel_cache_directory(cache_shard_path, HealpixPixel(order, pixel))
+                file_io.make_directory(pixel_dir, exist_ok=True)
+                output_file = file_io.append_paths_to_pointer(
+                    pixel_dir, f"shard_{splitting_key}_{chunk_number}.parquet"
+                )
+                if _has_named_index(filtered_data):
+                    filtered_data.to_parquet(output_file, index=True)
+                else:
+                    filtered_data.to_parquet(output_file, index=False)
+                del filtered_data, data_indexes
+
+        ResumePlan.splitting_key_done(tmp_path=resume_path, splitting_key=splitting_key)
+    except Exception as exception:  # pylint: disable=broad-exception-caught
+        print_task_failure(f"Failed SPLITTING stage with file {input_file}", exception)
+        raise exception
 
 
 def reduce_pixel_shards(
@@ -227,84 +237,99 @@ def reduce_pixel_shards(
         ValueError: if the number of rows written doesn't equal provided
             `destination_pixel_size`
     """
-    destination_dir = paths.pixel_directory(output_path, destination_pixel_order, destination_pixel_number)
-    file_io.make_directory(destination_dir, exist_ok=True, storage_options=storage_options)
+    try:
+        destination_dir = paths.pixel_directory(
+            output_path, destination_pixel_order, destination_pixel_number
+        )
+        file_io.make_directory(destination_dir, exist_ok=True, storage_options=storage_options)
 
-    destination_file = paths.pixel_catalog_file(
-        output_path, destination_pixel_order, destination_pixel_number
-    )
+        destination_file = paths.pixel_catalog_file(
+            output_path, destination_pixel_order, destination_pixel_number
+        )
 
-    schema = None
-    if use_schema_file:
-        schema = file_io.read_parquet_metadata(
-            use_schema_file, storage_options=storage_options
-        ).schema.to_arrow_schema()
+        schema = None
+        if use_schema_file:
+            schema = file_io.read_parquet_metadata(
+                use_schema_file, storage_options=storage_options
+            ).schema.to_arrow_schema()
 
-    tables = []
-    healpix_pixel = HealpixPixel(destination_pixel_order, destination_pixel_number)
-    pixel_dir = get_pixel_cache_directory(cache_shard_path, healpix_pixel)
+        tables = []
+        healpix_pixel = HealpixPixel(destination_pixel_order, destination_pixel_number)
+        pixel_dir = get_pixel_cache_directory(cache_shard_path, healpix_pixel)
 
-    if schema:
-        tables.append(pq.read_table(pixel_dir, schema=schema))
-    else:
-        tables.append(pq.read_table(pixel_dir))
+        if schema:
+            tables.append(pq.read_table(pixel_dir, schema=schema))
+        else:
+            tables.append(pq.read_table(pixel_dir))
 
-    merged_table = pa.concat_tables(tables)
+        merged_table = pa.concat_tables(tables)
 
-    rows_written = len(merged_table)
+        rows_written = len(merged_table)
 
-    if rows_written != destination_pixel_size:
-        raise ValueError(
-            "Unexpected number of objects at pixel "
-            f"({healpix_pixel})."
-            f" Expected {destination_pixel_size}, wrote {rows_written}"
-        )
+        if rows_written != destination_pixel_size:
+            raise ValueError(
+                "Unexpected number of objects at pixel "
+                f"({healpix_pixel})."
+                f" Expected {destination_pixel_size}, wrote {rows_written}"
+            )
 
-    dataframe = merged_table.to_pandas()
-    if sort_columns:
-        dataframe = dataframe.sort_values(sort_columns.split(","))
-    if add_hipscat_index:
-        ## If we had a meaningful index before, preserve it as a column.
-        if _has_named_index(dataframe):
-            dataframe = dataframe.reset_index()
+        dataframe = merged_table.to_pandas()
+        if sort_columns:
+            dataframe = dataframe.sort_values(sort_columns.split(","))
+        if add_hipscat_index:
+            ## If we had a meaningful index before, preserve it as a column.
+            if _has_named_index(dataframe):
+                dataframe = dataframe.reset_index()
+
+            dataframe[HIPSCAT_ID_COLUMN] = pixel_math.compute_hipscat_id(
+                dataframe[ra_column].values,
+                dataframe[dec_column].values,
+            )
+            dataframe = dataframe.set_index(HIPSCAT_ID_COLUMN).sort_index()
 
-        dataframe[HIPSCAT_ID_COLUMN] = pixel_math.compute_hipscat_id(
-            dataframe[ra_column].values,
-            dataframe[dec_column].values,
-        )
-        dataframe = dataframe.set_index(HIPSCAT_ID_COLUMN).sort_index()
+            # Adjust the schema to make sure that the _hipscat_index will
+            # be saved as a uint64
+        elif use_hipscat_index:
+            if dataframe.index.name != HIPSCAT_ID_COLUMN:
+                dataframe = dataframe.set_index(HIPSCAT_ID_COLUMN)
+            dataframe = dataframe.sort_index()
+
+        dataframe["Norder"] = np.full(rows_written, fill_value=healpix_pixel.order, dtype=np.uint8)
+        dataframe["Dir"] = np.full(rows_written, fill_value=healpix_pixel.dir, dtype=np.uint64)
+        dataframe["Npix"] = np.full(rows_written, fill_value=healpix_pixel.pixel, dtype=np.uint64)
 
-        # Adjust the schema to make sure that the _hipscat_index will
-        # be saved as a uint64
         if schema:
-            pandas_index_column = schema.get_field_index("__index_level_0__")
-            if pandas_index_column != -1:
-                schema = schema.remove(pandas_index_column)
-            schema = schema.insert(0, pa.field(HIPSCAT_ID_COLUMN, pa.uint64()))
-    elif use_hipscat_index:
-        if dataframe.index.name != HIPSCAT_ID_COLUMN:
-            dataframe = dataframe.set_index(HIPSCAT_ID_COLUMN)
-        dataframe = dataframe.sort_index()
-
-    dataframe["Norder"] = np.full(rows_written, fill_value=healpix_pixel.order, dtype=np.uint8)
-    dataframe["Dir"] = np.full(rows_written, fill_value=healpix_pixel.dir, dtype=np.uint64)
-    dataframe["Npix"] = np.full(rows_written, fill_value=healpix_pixel.pixel, dtype=np.uint64)
-
-    if schema:
-        schema = (
-            schema.append(pa.field("Norder", pa.uint8()))
-            .append(pa.field("Dir", pa.uint64()))
-            .append(pa.field("Npix", pa.uint64()))
-        )
-        dataframe.to_parquet(destination_file, schema=schema, storage_options=storage_options)
-    else:
-        dataframe.to_parquet(destination_file, storage_options=storage_options)
+            schema = _modify_arrow_schema(schema, add_hipscat_index)
+            dataframe.to_parquet(destination_file, schema=schema, storage_options=storage_options)
+        else:
+            dataframe.to_parquet(destination_file, storage_options=storage_options)
 
-    del dataframe, merged_table, tables
+        del dataframe, merged_table, tables
 
-    if delete_input_files:
-        pixel_dir = get_pixel_cache_directory(cache_shard_path, healpix_pixel)
+        if delete_input_files:
+            pixel_dir = get_pixel_cache_directory(cache_shard_path, healpix_pixel)
 
-        file_io.remove_directory(pixel_dir, ignore_errors=True, storage_options=storage_options)
+            file_io.remove_directory(pixel_dir, ignore_errors=True, storage_options=storage_options)
+
+        ResumePlan.reducing_key_done(tmp_path=resume_path, reducing_key=reducing_key)
+    except Exception as exception:  # pylint: disable=broad-exception-caught
+        print_task_failure(
+            f"Failed REDUCING stage for shard: {destination_pixel_order} {destination_pixel_number}",
+            exception,
+        )
+        raise exception
+
+
+def _modify_arrow_schema(schema, add_hipscat_index):
+    if add_hipscat_index:
+        pandas_index_column = schema.get_field_index("__index_level_0__")
+        if pandas_index_column != -1:
+            schema = schema.remove(pandas_index_column)
+        schema = schema.insert(0, pa.field(HIPSCAT_ID_COLUMN, pa.uint64()))
+    schema = (
+        schema.append(pa.field("Norder", pa.uint8()))
+        .append(pa.field("Dir", pa.uint64()))
+        .append(pa.field("Npix", pa.uint64()))
+    )
 
-    ResumePlan.reducing_key_done(tmp_path=resume_path, reducing_key=reducing_key)
+    return schema