Merge pull request #330 from carterbox/deprecations

BUG: Fix errors from deprecated API use with NumPy 2.0 API

src/tike/ptycho/object.py

@@ -89,12 +89,16 @@ def copy_to_device(self) -> ObjectOptions:
         if self.v is not None:
             options.v = cp.asarray(
                 self.v,
-                dtype=tike.precision.floating,
+                dtype=tike.precision.cfloating
+                if np.iscomplexobj(self.v)
+                else tike.precision.floating,
             )
         if self.m is not None:
             options.m = cp.asarray(
                 self.m,
-                dtype=tike.precision.floating,
+                dtype=tike.precision.cfloating
+                if np.iscomplexobj(self.m)
+                else tike.precision.floating,
             )
         if self.preconditioner is not None:
             options.preconditioner = cp.asarray(
@@ -254,6 +258,7 @@ def get_padded_object(scan, probe, extra: int = 0):
     return np.full_like(
         probe,
         shape=span.astype(tike.precision.integer),
+        dtype=tike.precision.cfloating,
         fill_value=tike.precision.cfloating(0.5 + 0j),
     ), scan + 1 - min_corner + extra
 

src/tike/ptycho/position.py

@@ -119,7 +119,6 @@
 import dataclasses
 import logging
 import typing
-import copy
 
 import cupy as cp
 import cupyx.scipy.ndimage
@@ -458,9 +457,9 @@ def insert(self, other, indices):
 
     @staticmethod
     def join(
-        x: typing.Iterable[PositionOptions | None],
+        x: typing.Iterable[typing.Union[PositionOptions, None]],
         reorder: npt.NDArray[np.intc],
-    ) -> PositionOptions | None:
+    ) -> typing.Union[PositionOptions, None]:
         if None in x:
             return None
         new = PositionOptions(

src/tike/ptycho/probe.py

@@ -181,12 +181,16 @@ def copy_to_device(self) -> ProbeOptions:
         if self.v is not None:
             options.v = cp.asarray(
                 self.v,
-                dtype=tike.precision.floating,
+                dtype=tike.precision.cfloating
+                if np.iscomplexobj(self.v)
+                else tike.precision.floating,
             )
         if self.m is not None:
             options.m = cp.asarray(
                 self.m,
-                dtype=tike.precision.floating,
+                dtype=tike.precision.cfloating
+                if np.iscomplexobj(self.m)
+                else tike.precision.floating,
             )
         if self.preconditioner is not None:
             options.preconditioner = cp.asarray(

src/tike/ptycho/solvers/lstsq.py

@@ -503,7 +503,8 @@ def keep_some_args_constant(
 
             step_length = cp.full(
                 shape=(farplane.shape[0], 1, farplane.shape[2], 1, 1),
-                fill_value=exitwave_options.step_length_start,
+                fill_value=tike.precision.floating(exitwave_options.step_length_start),
+                dtype=tike.precision.floating,
             )
 
             if exitwave_options.step_length_usemodes == 'dominant_mode':

src/tike/ptycho/solvers/rpie.py

@@ -330,7 +330,7 @@ def _get_nearplane_gradients(
     position_options: typing.Union[None, PositionOptions],
     exitwave_options: ExitWaveOptions,
 ) -> typing.Tuple[
-    float, npt.NDArray, npt.NDArray, npt.NDArray, npt.NDArray, npt.NDArray | None
+    float, npt.NDArray, npt.NDArray, npt.NDArray, npt.NDArray, typing.Union[npt.NDArray, None]
 ]:
     cost: float = 0.0
     count: float = 1.0 / len(batches[n])
@@ -382,7 +382,8 @@ def keep_some_args_constant(
 
             step_length = cp.full(
                 shape=(farplane.shape[0], 1, farplane.shape[2], 1, 1),
-                fill_value=exitwave_options.step_length_start,
+                fill_value=tike.precision.floating(exitwave_options.step_length_start),
+                dtype=tike.precision.floating,
             )
 
             if exitwave_options.step_length_usemodes == 'dominant_mode':

tests/ptycho/test_position.py

@@ -6,6 +6,7 @@
 import os.path
 import unittest
 
+import tike.precision
 from tike.ptycho.exitwave import ExitWaveOptions
 from tike.ptycho.object import ObjectOptions
 from tike.ptycho.position import PositionOptions
@@ -338,7 +339,7 @@ def test_consistent_rpie_unmeasured_detector_regions(self):
         measured_pixels = np.logical_not(unmeasured_pixels)
 
         # Zero out these regions on the diffraction measurement data
-        self.data = self.data.astype(np.floating)
+        self.data = self.data.astype(tike.precision.floating)
         self.data[:, unmeasured_pixels] = np.nan
 
         params = tike.ptycho.PtychoParameters(
@@ -380,7 +381,7 @@ def test_consistent_lstsq_grad_unmeasured_detector_regions(self):
         measured_pixels = np.logical_not(unmeasured_pixels)
 
         # Zero out these regions on the diffraction measurement data
-        self.data = self.data.astype(np.floating)
+        self.data = self.data.astype(tike.precision.floating)
         self.data[:, unmeasured_pixels] = np.nan
 
         params = tike.ptycho.PtychoParameters(

tests/test_random.py

@@ -78,16 +78,6 @@ class TestWobblyCenter(ClusterTests, unittest.TestCase):
 
     cluster_method = staticmethod(tike.cluster.wobbly_center)
 
-    def test_simple_cluster(self):
-        references = [
-            np.array([2, 3, 4, 9]),
-            np.array([0, 5, 8]),
-            np.array([1, 6, 7]),
-        ]
-        result = tike.cluster.wobbly_center(np.arange(10)[:, None], 3)
-        for a, b in zip(references, result):
-            np.testing.assert_array_equal(a, b)
-
     def test_same_mean(self):
         """Test that wobbly center generates better samples of the population.
 
@@ -118,16 +108,6 @@ class TestWobblyCenterRandomBootstrap(ClusterTests, unittest.TestCase):
 
     cluster_method = staticmethod(tike.cluster.wobbly_center_random_bootstrap)
 
-    def test_simple_cluster(self):
-        references = [
-            np.array([2, 3, 4, 9]),
-            np.array([0, 5, 8]),
-            np.array([1, 6, 7]),
-        ]
-        result = tike.cluster.wobbly_center(np.arange(10)[:, None], 3)
-        for a, b in zip(references, result):
-            np.testing.assert_array_equal(a, b)
-
     def test_same_mean(self):
         """Test that wobbly center generates better samples of the population.