Merge pull request #37 from jlvdb/numpy_v2

Numpy 2.0 support

CHANGELOG.rst

@@ -1,6 +1,11 @@
 Change log
 ==========
 
+Version 2.5.8
+-------------
+
+Updates to be compatible with numpy version 2.0.
+
 
 Version 2.5.7
 -------------

src/yaw/__init__.py

@@ -18,7 +18,6 @@
         (Ruhr-Universität Bochum, Astronomisches Institut)
 """
 
-
 import logging as _logging
 
 _logging.getLogger(__name__).addHandler(_logging.NullHandler())  # noqa
@@ -55,4 +54,4 @@
     "HistogramData",
     "PairCountResult",
 ]
-__version__ = "2.5.7"
+__version__ = "2.5.8"

src/yaw/catalogs/catalog.py

@@ -219,31 +219,31 @@ def pos(self) -> CoordSky:
 
     @property
     @abstractmethod
-    def ra(self) -> NDArray[np.float_]:
+    def ra(self) -> NDArray[np.float64]:
         """Get an array of the right ascension values in radians."""
         pass
 
     @property
     @abstractmethod
-    def dec(self) -> NDArray[np.float_]:
+    def dec(self) -> NDArray[np.float64]:
         """Get an array of the declination values in radians."""
         pass
 
     @property
     @abstractmethod
-    def redshifts(self) -> NDArray[np.float_] | None:
+    def redshifts(self) -> NDArray[np.float64] | None:
         """Get the redshifts as array or ``None`` if not available."""
         pass
 
     @property
     @abstractmethod
-    def weights(self) -> NDArray[np.float_]:
+    def weights(self) -> NDArray[np.float64]:
         """Get the object weights as array or ``None`` if not available."""
         pass
 
     @property
     @abstractmethod
-    def patch(self) -> NDArray[np.int_]:
+    def patch(self) -> NDArray[np.int64]:
         """Get the patch indices of each object as array."""
         pass
 
@@ -264,7 +264,7 @@ def total(self) -> float:
         available."""
 
     @abstractmethod
-    def get_totals(self) -> NDArray[np.float_]:
+    def get_totals(self) -> NDArray[np.float64]:
         """Get an array of the sum of weights or number of objects in each
         patch."""
 

src/yaw/catalogs/scipy/catalog.py

@@ -41,8 +41,8 @@ def _worker_correlate(
 
 
 def _worker_true_redshifts(
-    args: tuple[PatchCatalog, NDArray[np.float_]]
-) -> NDArray[np.float_]:
+    args: tuple[PatchCatalog, NDArray[np.float64]]
+) -> NDArray[np.float64]:
     return utils.count_histogram_patch(*args)
 
 
@@ -136,7 +136,7 @@ def __init__(
         with TimedLog(self._logger.info, f"processed {n_obj_str} records"):
             limits = LimitTracker()
             patches: dict[int, PatchCatalog] = {}
-            patch_iter = data.groupby(patch_name)
+            patch_iter = data.groupby(patch_name, observed=True)
             if progress:
                 patch_iter = job_progress_bar(patch_iter, total=n_patches)
             for patch_id, patch_data in patch_iter:
@@ -260,22 +260,22 @@ def has_weights(self) -> bool:
         return all(patch.has_weights() for patch in self._patches.values())
 
     @property
-    def ra(self) -> NDArray[np.float_]:
+    def ra(self) -> NDArray[np.float64]:
         return np.concatenate([patch.ra for patch in iter(self)])
 
     @property
-    def dec(self) -> NDArray[np.float_]:
+    def dec(self) -> NDArray[np.float64]:
         return np.concatenate([patch.dec for patch in iter(self)])
 
     @property
-    def redshifts(self) -> NDArray[np.float_] | None:
+    def redshifts(self) -> NDArray[np.float64] | None:
         if self.has_redshifts():
             return np.concatenate([patch.redshifts for patch in iter(self)])
         else:
             return None
 
     @property
-    def weights(self) -> NDArray[np.float_]:
+    def weights(self) -> NDArray[np.float64]:
         weights = []
         for patch in iter(self):
             if patch.has_weights():
@@ -285,7 +285,7 @@ def weights(self) -> NDArray[np.float_]:
         return np.concatenate(weights)
 
     @property
-    def patch(self) -> NDArray[np.int_]:
+    def patch(self) -> NDArray[np.int64]:
         return np.concatenate([np.full(len(patch), patch.id) for patch in iter(self)])
 
     def get_min_redshift(self) -> float:
@@ -298,7 +298,7 @@ def get_max_redshift(self) -> float:
     def total(self) -> float:
         return self.get_totals().sum()
 
-    def get_totals(self) -> NDArray[np.float_]:
+    def get_totals(self) -> NDArray[np.float64]:
         return np.array([patch.total for patch in self._patches.values()])
 
     @property

src/yaw/catalogs/scipy/kdtree.py

@@ -31,7 +31,7 @@ class SphericalKDTree:
     def __init__(
         self,
         position: Coordinate,
-        weights: NDArray[np.float_] | None = None,
+        weights: NDArray[np.float64] | None = None,
         leafsize: int = 16,
     ) -> None:
         """Build a new tree from a set of coordinates.
@@ -67,7 +67,7 @@ def total(self) -> float:
     def count(
         self,
         other: SphericalKDTree,
-        scales: NDArray[np.float_],
+        scales: NDArray[np.float64],
         dist_weight_scale: float | None = None,
         weight_res: int = 50,
     ) -> NDArray:

src/yaw/catalogs/scipy/patches.py

@@ -232,15 +232,15 @@ def data(self) -> DataFrame:
         return self._data
 
     @property
-    def ra(self) -> NDArray[np.float_]:
+    def ra(self) -> NDArray[np.float64]:
         """Get an array of the right ascension values in radians.
 
         Raises a :obj:`CachingError` if data is not loaded."""
         self.require_loaded()
         return self._data["ra"].to_numpy()
 
     @property
-    def dec(self) -> NDArray[np.float_]:
+    def dec(self) -> NDArray[np.float64]:
         """Get an array of the declination values in radians.
 
         Raises a :obj:`CachingError` if data is not loaded."""
@@ -260,7 +260,7 @@ def pos(self) -> CoordSky:
         return CoordSky(self.ra, self.dec)
 
     @property
-    def redshifts(self) -> NDArray[np.float_]:
+    def redshifts(self) -> NDArray[np.float64]:
         """Get the redshifts as array or ``None`` if not available.
 
         Raises a :obj:`CachingError` if data is not loaded."""
@@ -271,7 +271,7 @@ def redshifts(self) -> NDArray[np.float_]:
             return None
 
     @property
-    def weights(self) -> NDArray[np.float_]:
+    def weights(self) -> NDArray[np.float64]:
         """Get the object weights as array or ``None`` if not available.
 
         Raises a :obj:`CachingError` if data is not loaded."""
@@ -312,7 +312,7 @@ def radius(self) -> DistSky:
         return self._radius
 
     def iter_bins(
-        self, z_bins: NDArray[np.float_], allow_no_redshift: bool = False
+        self, z_bins: NDArray[np.float64], allow_no_redshift: bool = False
     ) -> Iterator[tuple[Interval, PatchCatalog]]:
         """Iterate the patch in bins of redshift.
 
@@ -335,7 +335,9 @@ def iter_bins(
             for intv in pd.IntervalIndex.from_breaks(z_bins, closed="left"):
                 yield intv, self
         else:
-            for intv, bin_data in self._data.groupby(pd.cut(self.redshifts, z_bins)):
+            for intv, bin_data in self._data.groupby(
+                pd.cut(self.redshifts, z_bins), observed=True
+            ):
                 yield intv, PatchCatalog(
                     self.id,
                     bin_data,
@@ -356,7 +358,7 @@ def get_tree(self, **kwargs) -> SphericalKDTree:
 # the scipy.cluster module.
 
 
-def assign_patches(centers: Coordinate, position: Coordinate) -> NDArray[np.int_]:
+def assign_patches(centers: Coordinate, position: Coordinate) -> NDArray[np.int64]:
     """Assign objects based on their coordinate to a list of points based on
     proximit."""
     patches, dist = vq.vq(position.to_3d().values, centers.to_3d().values)
@@ -368,7 +370,7 @@ def assign_patches(centers: Coordinate, position: Coordinate) -> NDArray[np.int_
 
     def treecorr_patches(
         position: Coordinate, n_patches: int, **kwargs
-    ) -> tuple[Coord3D, NDArray[np.int_]]:
+    ) -> tuple[Coord3D, NDArray[np.int64]]:
         """Use the *k*-means clustering algorithm of :obj:`treecorr.Catalog` to
         generate spatial patches and assigning objects to those patches.
         """
@@ -383,7 +385,7 @@ def treecorr_patches(
         xyz = np.atleast_2d(cat.patch_centers)
         centers = Coord3D.from_array(xyz)
         if n_patches == 1:
-            patches = np.zeros(len(position), dtype=np.int_)
+            patches = np.zeros(len(position), dtype=np.int64)
         else:
             patches = assign_patches(centers=centers, position=position)
         del cat  # might not be necessary
@@ -395,7 +397,7 @@ def treecorr_patches(
 
     def scipy_patches(
         position: Coordinate, n_patches: int, n_max: int = 500_000
-    ) -> tuple[Coord3D, NDArray[np.int_]]:
+    ) -> tuple[Coord3D, NDArray[np.int64]]:
         """Use the *k*-means clustering algorithm of :obj:`scipy.cluster` to
         generate spatial patches and assigning objects to those patches.
         """

src/yaw/catalogs/scipy/utils.py

@@ -188,18 +188,18 @@ def merge_pairs_patches(
 
 
 def count_histogram_patch(
-    patch: PatchCatalog, z_bins: NDArray[np.float_]
-) -> NDArray[np.float_]:
+    patch: PatchCatalog, z_bins: NDArray[np.float64]
+) -> NDArray[np.float64]:
     """Compute a histogram of redshifts in a single patch.
 
     Args:
         patch (:obj:`yaw.catalogs.scipy.PatchCatalog`):
             The input patch catalogue.
-        z_bins (:obj:`NDArray[np.float_]`):
+        z_bins (:obj:`NDArray[np.float64]`):
             The bin edges including the right-most edge.
 
     Returns:
-        :obj:`NDArray[np.float_]`:
+        :obj:`NDArray[np.float64]`:
             Counts in the provided redshift bins.
     """
     is_loaded = patch.is_loaded()
@@ -211,17 +211,17 @@ def count_histogram_patch(
 
 
 def merge_histogram_patches(
-    hist_counts: NDArray[np.float_],
-    z_bins: NDArray[np.float_],
+    hist_counts: NDArray[np.float64],
+    z_bins: NDArray[np.float64],
     sampling_config: ResamplingConfig | None = None,
 ) -> HistData:
     """Merge redshift histogram from patches into a histogram data container.
 
     Args:
-        hist_counts (:obj:`NDArray[np.float_]`):
+        hist_counts (:obj:`NDArray[np.float64]`):
             A two-dimensional array with histogram counts with shape
             `(n_patches, n_bins)`.
-        z_bins (:obj:`NDArray[np.float_]`):
+        z_bins (:obj:`NDArray[np.float64]`):
             The bin edges including the right-most edge.
         sampling_config: (:obj:`yaw.config.ResamplingConfig`, optional):
             Specify the resampling method and its configuration.

src/yaw/catalogs/treecorr/catalog.py

@@ -53,7 +53,7 @@ def _iter_bin_masks(
 
 
 def take_subset(
-    cat: TreecorrCatalog, items: NDArray[np.bool_] | NDArray[np.int_] | slice
+    cat: TreecorrCatalog, items: NDArray[np.bool_] | NDArray[np.int64] | slice
 ) -> TreecorrCatalog | EmptyCatalog:
     """Construct a new TreecorrCatalog with a subset of its entries."""
     ra = cat.ra[items]
@@ -85,7 +85,7 @@ def __len__(self) -> int:
     def total(self) -> float:
         return 0.0
 
-    def get_totals(self) -> NDArray[np.float_]:
+    def get_totals(self) -> NDArray[np.float64]:
         return np.zeros(self.n_patches)
 
 
@@ -234,23 +234,23 @@ def has_weights(self) -> bool:
         return self.weights is not None
 
     @property
-    def ra(self) -> NDArray[np.float_]:
+    def ra(self) -> NDArray[np.float64]:
         return self._catalog.ra
 
     @property
-    def dec(self) -> NDArray[np.float_]:
+    def dec(self) -> NDArray[np.float64]:
         return self._catalog.dec
 
     @property
-    def redshifts(self) -> NDArray[np.float_] | None:
+    def redshifts(self) -> NDArray[np.float64] | None:
         return self._catalog.r
 
     @property
-    def weights(self) -> NDArray[np.float_]:
+    def weights(self) -> NDArray[np.float64]:
         return self._catalog.w
 
     @property
-    def patch(self) -> NDArray[np.int_]:
+    def patch(self) -> NDArray[np.int64]:
         return self._catalog.patch
 
     def get_min_redshift(self) -> float:
@@ -273,7 +273,7 @@ def get_max_redshift(self) -> float:
     def total(self) -> float:
         return self._catalog.sumw
 
-    def get_totals(self) -> NDArray[np.float_]:
+    def get_totals(self) -> NDArray[np.float64]:
         return np.array([patch.sumw for patch in iter(self)])
 
     @property
@@ -296,7 +296,7 @@ def radii(self) -> DistSky:
         return DistSky.from_dists(radii)
 
     def iter_bins(
-        self, z_bins: NDArray[np.float_], allow_no_redshift: bool = False
+        self, z_bins: NDArray[np.float64], allow_no_redshift: bool = False
     ) -> Iterator[tuple[Interval, TreecorrCatalog | EmptyCatalog]]:
         """Iterate the catalogue in bins of redshift.
 

src/yaw/config/binning.py

@@ -23,7 +23,7 @@
 class BinningConfig(BaseConfig):
     """TODO"""
 
-    zbins: NDArray[np.float_] = field(
+    zbins: NDArray[np.float64] = field(
         metadata=Parameter(
             type=float,
             nargs="*",
@@ -103,7 +103,7 @@ def is_manual(self) -> bool:
     def create(
         cls,
         *,
-        zbins: NDArray[np.float_] | None = None,
+        zbins: NDArray[np.float64] | None = None,
         zmin: float | None = None,
         zmax: float | None = None,
         zbin_num: int = DEFAULT.Binning.zbin_num,
@@ -124,7 +124,7 @@ def create(
             that require cosmological distance computations.
 
         Args:
-            zbins (:obj:`NDArray[np.float_]`):
+            zbins (:obj:`NDArray[np.float64]`):
                 Monotonically increasing redshift bin edges, including the upper
                 edge (ignored if ``zmin`` and ``zmax`` are provided).
             zmin (:obj:`float`):
@@ -165,7 +165,7 @@ def create(
 
     def modify(
         self,
-        zbins: NDArray[np.float_] | None = DEFAULT.NotSet,
+        zbins: NDArray[np.float64] | None = DEFAULT.NotSet,
         zmin: float | None = DEFAULT.NotSet,
         zmax: float | None = DEFAULT.NotSet,
         zbin_num: int = DEFAULT.NotSet,