Remove typing imports for List, Tuple, Union (#544)

src/lsdb/catalog/catalog.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Callable, List, Tuple, Type
+from typing import Callable, Type
 
 import hats as hc
 import nested_dask as nd
@@ -130,7 +130,7 @@ def assign(self, **kwargs) -> Catalog:
     def crossmatch(
         self,
         other: Catalog,
-        suffixes: Tuple[str, str] | None = None,
+        suffixes: tuple[str, str] | None = None,
         algorithm: (
             Type[AbstractCrossmatchAlgorithm] | BuiltInCrossmatchAlgorithm
         ) = BuiltInCrossmatchAlgorithm.KD_TREE,
@@ -298,7 +298,7 @@ def cone_search(self, ra: float, dec: float, radius_arcsec: float, fine: bool =
         """
         return self.search(ConeSearch(ra, dec, radius_arcsec, fine))
 
-    def box_search(self, ra: Tuple[float, float], dec: Tuple[float, float], fine: bool = True) -> Catalog:
+    def box_search(self, ra: tuple[float, float], dec: tuple[float, float], fine: bool = True) -> Catalog:
         """Performs filtering according to right ascension and declination ranges. The right ascension
         edges follow great arc circles and the declination edges follow small arc circles.
 
@@ -363,7 +363,7 @@ def order_search(self, min_order: int = 0, max_order: int | None = None) -> Cata
         """
         return self.search(OrderSearch(min_order, max_order))
 
-    def pixel_search(self, pixels: List[Tuple[int, int]]) -> Catalog:
+    def pixel_search(self, pixels: list[tuple[int, int]]) -> Catalog:
         """Finds all catalog pixels that overlap with the requested pixel set.
 
         Args:
@@ -407,12 +407,12 @@ def merge(
         self,
         other: Catalog,
         how: str = "inner",
-        on: str | List | None = None,
-        left_on: str | List | None = None,
-        right_on: str | List | None = None,
+        on: str | list | None = None,
+        left_on: str | list | None = None,
+        right_on: str | list | None = None,
         left_index: bool = False,
         right_index: bool = False,
-        suffixes: Tuple[str, str] | None = None,
+        suffixes: tuple[str, str] | None = None,
     ) -> nd.NestedFrame:
         """Performs the merge of two catalog Dataframes
 
@@ -461,7 +461,7 @@ def merge_asof(
         self,
         other: Catalog,
         direction: str = "backward",
-        suffixes: Tuple[str, str] | None = None,
+        suffixes: tuple[str, str] | None = None,
         output_catalog_name: str | None = None,
     ):
         """Uses the pandas `merge_asof` function to merge two catalogs on their indices by distance of keys
@@ -512,7 +512,7 @@ def join(
         left_on: str | None = None,
         right_on: str | None = None,
         through: AssociationCatalog | None = None,
-        suffixes: Tuple[str, str] | None = None,
+        suffixes: tuple[str, str] | None = None,
         output_catalog_name: str | None = None,
     ) -> Catalog:
         """Perform a spatial join to another catalog

src/lsdb/catalog/dataset/dataset.py

@@ -1,5 +1,3 @@
-from typing import List
-
 import hats as hc
 import nested_dask as nd
 import nested_pandas as npd
@@ -46,7 +44,7 @@ def compute(self) -> npd.NestedFrame:
         """Compute dask distributed dataframe to pandas dataframe"""
         return self._ddf.compute()
 
-    def to_delayed(self, optimize_graph: bool = True) -> List[Delayed]:
+    def to_delayed(self, optimize_graph: bool = True) -> list[Delayed]:
         """Get a list of Dask Delayed objects for each partition in the dataset
 
         Used for more advanced custom operations, but to use again with LSDB, the delayed objects

src/lsdb/catalog/dataset/healpix_dataset.py

@@ -2,7 +2,7 @@
 
 import warnings
 from pathlib import Path
-from typing import Any, Callable, Dict, Iterable, List, Tuple, Type, cast
+from typing import Any, Callable, Iterable, Type, cast
 
 import astropy
 import dask
@@ -118,15 +118,15 @@ def _create_modified_hc_structure(self, **kwargs) -> HCHealpixDataset:
             moc=self.hc_structure.moc,
         )
 
-    def get_healpix_pixels(self) -> List[HealpixPixel]:
+    def get_healpix_pixels(self) -> list[HealpixPixel]:
         """Get all HEALPix pixels that are contained in the catalog
 
         Returns:
             List of all Healpix pixels in the catalog
         """
         return self.hc_structure.get_healpix_pixels()
 
-    def get_ordered_healpix_pixels(self) -> List[HealpixPixel]:
+    def get_ordered_healpix_pixels(self) -> list[HealpixPixel]:
         """Get all HEALPix pixels that are contained in the catalog,
         ordered by breadth-first nested ordering.
 
@@ -189,7 +189,7 @@ def _perform_search(
         self,
         metadata: hc.catalog.Catalog | hc.catalog.MarginCatalog,
         search: AbstractSearch,
-    ) -> Tuple[DaskDFPixelMap, nd.NestedFrame]:
+    ) -> tuple[DaskDFPixelMap, nd.NestedFrame]:
         """Performs a search on the catalog from a list of pixels to search in
 
         Args:
@@ -237,7 +237,7 @@ def map_partitions(
         self,
         func: Callable[..., npd.NestedFrame],
         *args,
-        meta: pd.DataFrame | pd.Series | Dict | Iterable | Tuple | None = None,
+        meta: pd.DataFrame | pd.Series | dict | Iterable | tuple | None = None,
         include_pixel: bool = False,
         **kwargs,
     ) -> Self | dd.Series:
@@ -329,7 +329,7 @@ def prune_empty_partitions(self, persist: bool = False) -> Self:
         filtered_hc_structure = self.hc_structure.filter_from_pixel_list(non_empty_pixels)
         return self.__class__(search_ddf, ddf_partition_map, filtered_hc_structure)
 
-    def _get_non_empty_partitions(self) -> Tuple[List[HealpixPixel], np.ndarray]:
+    def _get_non_empty_partitions(self) -> tuple[list[HealpixPixel], np.ndarray]:
         """Determines which pixels and partitions of a catalog are not empty
 
         Returns:
@@ -356,7 +356,7 @@ def skymap_data(
         order: int | None = None,
         default_value: Any = 0.0,
         **kwargs,
-    ) -> Dict[HealpixPixel, Delayed]:
+    ) -> dict[HealpixPixel, Delayed]:
         """Perform a function on each partition of the catalog, returning a dict of values for each pixel.
 
         Args:
@@ -438,7 +438,7 @@ def skymap(
         order: int | None = None,
         default_value: Any = 0,
         projection="MOL",
-        plotting_args: Dict | None = None,
+        plotting_args: dict | None = None,
         **kwargs,
     ) -> tuple[Figure, WCSAxes]:
         """Plot a skymap of an aggregate function applied over each partition
@@ -722,7 +722,7 @@ def plot_points(
         color_col: str | None = None,
         projection: str = "MOL",
         title: str | None = None,
-        fov: Quantity | Tuple[Quantity, Quantity] | None = None,
+        fov: Quantity | tuple[Quantity, Quantity] | None = None,
         center: SkyCoord | None = None,
         wcs: astropy.wcs.WCS | None = None,
         frame_class: Type[BaseFrame] | None = None,

src/lsdb/core/crossmatch/abstract_crossmatch_algorithm.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from abc import ABC
-from typing import TYPE_CHECKING, Tuple
+from typing import TYPE_CHECKING
 
 import nested_pandas as npd
 import numpy as np
@@ -66,7 +66,7 @@ def __init__(
         left_catalog_info: TableProperties,
         right_catalog_info: TableProperties,
         right_margin_catalog_info: TableProperties | None,
-        suffixes: Tuple[str, str],
+        suffixes: tuple[str, str],
     ):
         """Initializes a crossmatch algorithm
 
@@ -107,7 +107,7 @@ def crossmatch(self, **kwargs) -> npd.NestedFrame:
             )
         return self._create_crossmatch_df(l_inds, r_inds, extra_cols)
 
-    def perform_crossmatch(self) -> Tuple[np.ndarray, np.ndarray, pd.DataFrame]:
+    def perform_crossmatch(self) -> tuple[np.ndarray, np.ndarray, pd.DataFrame]:
         """Performs a crossmatch to get the indices of the matching rows and any extra columns
 
         Any additional keyword arguments needed can be added to this method in the subclass, and the user

src/lsdb/core/crossmatch/bounded_kdtree_match.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Tuple
+from typing import TYPE_CHECKING
 
 import numpy as np
 import pandas as pd
@@ -36,7 +36,7 @@ def perform_crossmatch(
         n_neighbors: int = 1,
         radius_arcsec: float = 1,
         min_radius_arcsec: float = 0,
-    ) -> Tuple[np.ndarray, np.ndarray, pd.DataFrame]:
+    ) -> tuple[np.ndarray, np.ndarray, pd.DataFrame]:
         """Perform a cross-match between the data from two HEALPix pixels
 
         Finds the n closest neighbors in the right catalog for each point in the left catalog that

src/lsdb/core/crossmatch/kdtree_match.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, Tuple
+from typing import TYPE_CHECKING
 
 import numpy as np
 import numpy.typing as npt
@@ -44,7 +44,7 @@ def perform_crossmatch(
         self,
         n_neighbors: int = 1,
         radius_arcsec: float = 1,
-    ) -> Tuple[np.ndarray, np.ndarray, pd.DataFrame]:
+    ) -> tuple[np.ndarray, np.ndarray, pd.DataFrame]:
         """Perform a cross-match between the data from two HEALPix pixels
 
         Finds the n closest neighbors in the right catalog for each point in the left catalog that
@@ -72,7 +72,7 @@ def perform_crossmatch(
         )
         return left_idx, right_idx, extra_columns
 
-    def _get_point_coordinates(self) -> Tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]:
+    def _get_point_coordinates(self) -> tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]:
         left_xyz = _lon_lat_to_xyz(
             lon=self.left[self.left_catalog_info.ra_column].to_numpy(),
             lat=self.left[self.left_catalog_info.dec_column].to_numpy(),

src/lsdb/core/crossmatch/kdtree_utils.py

@@ -1,5 +1,4 @@
 import math
-from typing import Tuple
 
 import numpy as np
 import numpy.typing as npt
@@ -12,7 +11,7 @@ def _find_crossmatch_indices(
     n_neighbors: int,
     max_distance: float,
     min_distance: float = 0,
-) -> Tuple[npt.NDArray[np.float64], npt.NDArray[np.int64], npt.NDArray[np.int64]]:
+) -> tuple[npt.NDArray[np.float64], npt.NDArray[np.int64], npt.NDArray[np.int64]]:
     # If right catalog is empty, tree.query will raise an exception
     # Left catalog cannot be empty, as it checked in perform_crossmatch()
     if len(right_xyz) == 0:
@@ -52,7 +51,7 @@ def _query_min_max_neighbors(
     n_neighbors: int,
     min_distance: float,
     max_distance: float,
-) -> Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     """Finds `n_neighbors` within a distance range for all points in a pair of partitions"""
     left_tree = KDTree(left_xyz, compact_nodes=True, balanced_tree=True, copy_data=False)
 

src/lsdb/core/plotting/plot_points.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Tuple, Type
+from typing import Type
 
 import astropy
 import matplotlib.pyplot as plt
@@ -22,7 +22,7 @@ def plot_points(
     color_col: str | None = None,
     projection: str = "MOL",
     title: str = "",
-    fov: Quantity | Tuple[Quantity, Quantity] | None = None,
+    fov: Quantity | tuple[Quantity, Quantity] | None = None,
     center: SkyCoord | None = None,
     wcs: astropy.wcs.WCS | None = None,
     frame_class: Type[BaseFrame] | None = None,

src/lsdb/core/plotting/skymap.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Any, Callable, Dict
+from typing import Any, Callable
 
 import hats.pixel_math.healpix_shim as hp
 import nested_pandas as npd
@@ -40,7 +40,7 @@ def apply_func(df):
 
 
 def compute_skymap(
-    pixel_map: Dict[HealpixPixel, Any], order: int | None = None, default_value: Any = 0.0
+    pixel_map: dict[HealpixPixel, Any], order: int | None = None, default_value: Any = 0.0
 ) -> np.ndarray:
     """Returns a histogram map of healpix_pixels to values.
 

src/lsdb/core/search/abstract_search.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from abc import ABC, abstractmethod
-from typing import TYPE_CHECKING, Tuple, Type
+from typing import TYPE_CHECKING, Type
 
 import astropy
 import nested_pandas as npd
@@ -45,7 +45,7 @@ def plot(
         self,
         projection: str = "MOL",
         title: str = "",
-        fov: Quantity | Tuple[Quantity, Quantity] | None = None,
+        fov: Quantity | tuple[Quantity, Quantity] | None = None,
         center: SkyCoord | None = None,
         wcs: astropy.wcs.WCS | None = None,
         frame_class: Type[BaseFrame] | None = None,

src/lsdb/core/search/pixel_search.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING, List, Tuple
+from typing import TYPE_CHECKING
 
 import nested_pandas as npd
 from hats.pixel_math import HealpixPixel
@@ -18,7 +18,7 @@ class PixelSearch(AbstractSearch):
     Does not filter points inside those partitions.
     """
 
-    def __init__(self, pixels: List[Tuple[int, int]]):
+    def __init__(self, pixels: list[tuple[int, int]]):
         super().__init__(fine=False)
         self.pixels = [HealpixPixel(o, p) for o, p in set(pixels)]
 

src/lsdb/dask/crossmatch_catalog_data.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import TYPE_CHECKING, Tuple, Type
+from typing import TYPE_CHECKING, Type
 
 import dask
 import nested_dask as nd
@@ -76,12 +76,12 @@ def perform_crossmatch(
 def crossmatch_catalog_data(
     left: Catalog,
     right: Catalog,
-    suffixes: Tuple[str, str],
+    suffixes: tuple[str, str],
     algorithm: (
         Type[AbstractCrossmatchAlgorithm] | BuiltInCrossmatchAlgorithm
     ) = BuiltInCrossmatchAlgorithm.KD_TREE,
     **kwargs,
-) -> Tuple[nd.NestedFrame, DaskDFPixelMap, PixelAlignment]:
+) -> tuple[nd.NestedFrame, DaskDFPixelMap, PixelAlignment]:
     """Cross-matches the data from two catalogs
 
     Args:

src/lsdb/dask/divisions.py

@@ -1,14 +1,12 @@
 from __future__ import annotations
 
-from typing import List, Tuple
-
 import numpy as np
 from hats.pixel_math import HealpixPixel
 from hats.pixel_math.healpix_pixel_function import get_pixel_argsort
 from hats.pixel_math.spatial_index import healpix_to_spatial_index
 
 
-def get_pixels_divisions(healpix_pixels: List[HealpixPixel]) -> Tuple[int, ...] | None:
+def get_pixels_divisions(healpix_pixels: list[HealpixPixel]) -> tuple[int, ...] | None:
     """Calculates the Dask Dataframe divisions for a list of HEALPix pixels.
 
     Divisions include the minimum value of every HEALPix pixel spatial_index