Update numpy dependency to v2

Signed-off-by: cwasicki <[email protected]>

Author: cwasicki

benchmarks/timeseries/periodic_feature_extractor.py

@@ -50,9 +50,9 @@ async def init_feature_extractor(
 
 def _calculate_avg_window(
     feature_extractor: PeriodicFeatureExtractor,
-    window: NDArray[np.float_],
+    window: NDArray[np.float64],
     window_size: int,
-) -> NDArray[np.float_]:
+) -> NDArray[np.float64]:
     """
     Reshapes the window and calculates the average.
 
@@ -77,10 +77,10 @@ def _calculate_avg_window(
 
 def _calculate_avg_window_py(
     feature_extractor: PeriodicFeatureExtractor,
-    window: NDArray[np.float_],
+    window: NDArray[np.float64],
     window_size: int,
     weights: list[float] | None = None,
-) -> NDArray[np.float_]:
+) -> NDArray[np.float64]:
     """
     Plain python version of the average calculator.
 
@@ -100,7 +100,7 @@ def _calculate_avg_window_py(
     """
 
     def _num_windows(
-        window: NDArray[np.float_] | MovingWindow, window_size: int, period: int
+        window: NDArray[np.float64] | MovingWindow, window_size: int, period: int
     ) -> int:
         """
         Get the number of windows that are fully contained in the MovingWindow.
@@ -118,7 +118,7 @@ def _num_windows(
             The number of windows that are fully contained in the MovingWindow.
         """
 
-        def length(window: NDArray[np.float_] | MovingWindow) -> int:
+        def length(window: NDArray[np.float64] | MovingWindow) -> int:
             return (
                 window.count_valid()
                 if isinstance(window, MovingWindow)
@@ -159,14 +159,14 @@ def length(window: NDArray[np.float_] | MovingWindow) -> int:
 
 
 def run_benchmark(
-    array: NDArray[np.float_],
+    array: NDArray[np.float64],
     window_size: int,
     feature_extractor: PeriodicFeatureExtractor,
 ) -> None:
     """Run the benchmark for the given ndarray and window size."""
 
     def run_avg_np(
-        array: NDArray[np.float_],
+        array: NDArray[np.float64],
         window_size: int,
         feature_extractor: PeriodicFeatureExtractor,
     ) -> None:
@@ -183,7 +183,7 @@ def run_avg_np(
         _calculate_avg_window(feature_extractor, array, window_size)
 
     def run_avg_py(
-        array: NDArray[np.float_],
+        array: NDArray[np.float64],
         window_size: int,
         feature_extractor: PeriodicFeatureExtractor,
     ) -> None:

pyproject.toml

@@ -33,7 +33,7 @@ dependencies = [
   "frequenz-channels >= 1.6.0, < 2.0.0",
   "frequenz-quantities[marshmallow] >= 1.0.0, < 2.0.0",
   "networkx >= 2.8, < 4",
-  "numpy >= 1.26.4, < 2",
+  "numpy >= 2, < 3",
   "typing_extensions >= 4.6.1, < 5",
   "marshmallow >= 3.19.0, < 4",
   "marshmallow_dataclass >= 8.7.1, < 9",

src/frequenz/sdk/timeseries/_periodic_feature_extractor.py

@@ -180,8 +180,8 @@ def _timestamp_to_rel_index(self, timestamp: datetime) -> int:
         return dist_to_oldest
 
     def _reshape_np_array(
-        self, array: NDArray[np.float_], window_size: int
-    ) -> NDArray[np.float_]:
+        self, array: NDArray[np.float64], window_size: int
+    ) -> NDArray[np.float64]:
         """
         Reshape a numpy array to a 2D array where each row represents a window.
 
@@ -361,7 +361,7 @@ def _get_buffer_bounds(
 
     def _get_reshaped_np_array(
         self, start: datetime, end: datetime
-    ) -> tuple[NDArray[np.float_], int]:
+    ) -> tuple[NDArray[np.float64], int]:
         """
         Create a reshaped numpy array from the MovingWindow.
 
@@ -392,7 +392,7 @@ def _get_reshaped_np_array(
 
     def avg(
         self, start: datetime, end: datetime, weights: list[float] | None = None
-    ) -> NDArray[np.float_]:
+    ) -> NDArray[np.float64]:
         """
         Create the average window out of the window defined by `start` and `end`.