Simple check for numpy 2.0 compatbility (#570)

Enable compatibility with numpy 2.0

* Fixed some copy keyword arguments to adhere to new numpy standard
* Fixed np.ptp, which does no longer exist in numpy 2
* Remove restriction to numpy<2
* Replace `as_strided`, which is not available in numpy 2.0
* Fixed type issues because we support numpy 1 and 2

pde/fields/collection.py

@@ -122,7 +122,7 @@ def __init__(
             dof += len(this_data)
 
         # initialize the data from the individual fields
-        data_arr = number_array(fields_data, dtype=dtype, copy=False)
+        data_arr = number_array(fields_data, dtype=dtype, copy=None)
 
         # initialize the class
         super().__init__(grid, data_arr, label=label)

pde/fields/datafield_base.py

@@ -76,7 +76,7 @@ def __init__(
             # use full data without copying (unless necessary)
             if data is None or isinstance(data, str):
                 raise ValueError("`data` must be supplied if with_ghost_cells==True")
-            data_arr = number_array(data, dtype=dtype, copy=False)
+            data_arr = number_array(data, dtype=dtype, copy=None)
             super().__init__(grid, data=data_arr, label=label)
 
         else:
@@ -108,7 +108,7 @@ def __init__(
 
             else:
                 # initialize empty data and set the valid data
-                data_arr = number_array(data, dtype=dtype, copy=False)
+                data_arr = number_array(data, dtype=dtype, copy=None)
                 empty_data = np.empty(full_shape, dtype=data_arr.dtype)
                 super().__init__(grid, data=empty_data, label=label)
                 self.data = data_arr

pde/pdes/cahn_hilliard.py

@@ -95,10 +95,9 @@ def _make_pde_rhs_numba(  # type: ignore
                 An example for the state defining the grid and data types
 
         Returns:
-            A function with signature `(state_data, t)`, which can be called
-            with an instance of :class:`~numpy.ndarray` of the state data and
-            the time to obtained an instance of :class:`~numpy.ndarray` giving
-            the evolution rate.
+            A function with signature `(state_data, t)`, which can be called with an
+            instance of :class:`~numpy.ndarray` of the state data and the time to
+            obtained an instance of :class:`~numpy.ndarray` giving the evolution rate.
         """
         arr_type = nb.typeof(state.data)
         signature = arr_type(arr_type, nb.double)

pde/tools/math.py

@@ -50,7 +50,7 @@ def __init__(self, x, y, sigma: float | None = None):
             self.y = self.y[idx]
 
         if sigma is None:
-            self.sigma = float(self.sigma_auto_scale * self.x.ptp() / len(self.x))
+            self.sigma = float(self.sigma_auto_scale * np.ptp(self.x) / len(self.x))
         else:
             self.sigma = sigma
 

pde/tools/misc.py

@@ -90,7 +90,7 @@ def preserve_scalars(method: TFunc) -> TFunc:
 
     @functools.wraps(method)
     def wrapper(self, *args):
-        args = [number_array(arg, copy=False) for arg in args]
+        args = [number_array(arg, copy=None) for arg in args]
         if args[0].ndim == 0:
             args = [arg[None] for arg in args]
             return method(self, *args)[0]
@@ -362,37 +362,42 @@ def get_common_dtype(*args):
 
 
 def number_array(
-    data: ArrayLike, dtype: DTypeLike = None, copy: bool = True
+    data: ArrayLike, dtype: DTypeLike = None, copy: bool | None = None
 ) -> np.ndarray:
-    """convert an array with arbitrary dtype either to np.double or np.cdouble
+    """convert data into an array, assuming float numbers if no dtype is given
 
     Args:
         data (:class:`~numpy.ndarray`):
-            The data that needs to be converted to a float array. This can also be any
+            The data that needs to be converted to a number array. This can also be any
             iterable of numbers.
         dtype (numpy dtype):
             The data type of the field. All the numpy dtypes are supported. If omitted,
-            it will be determined from `data` automatically.
+            it will be :class:`~numpy.double` unless `data` contains complex numbers in
+            which case it will be :class:`~numpy.cdouble`.
         copy (bool):
             Whether the data must be copied (in which case the original array is left
-            untouched). Note that data will always be copied when changing the dtype.
+            untouched). The default `None` implies that data is only copied if
+            necessary, e.g., when changing the dtype.
 
     Returns:
         :class:`~numpy.ndarray`: An array with the correct dtype
     """
+    if np.__version__.startswith("1") and copy is None:
+        copy = False  # fall-back for numpy 1
+
     if dtype is None:
         # dtype needs to be determined automatically
         try:
             # convert the result to a numpy array with the given dtype
-            result = np.array(data, dtype=get_common_dtype(data), copy=copy)
+            result = np.array(data, dtype=get_common_dtype(data), copy=copy)  # type: ignore
         except TypeError:
             # Conversion can fail when `data` contains a complex sympy number, i.e.,
             # sympy.I. In this case, we simply try to convert the expression using a
             # complex dtype
-            result = np.array(data, dtype=np.cdouble, copy=copy)
+            result = np.array(data, dtype=np.cdouble, copy=copy)  # type: ignore
 
     else:
         # a specific dtype is requested
-        result = np.array(data, dtype=np.dtype(dtype), copy=copy)
+        result = np.array(data, dtype=np.dtype(dtype), copy=copy)  # type: ignore
 
     return result

pde/tools/numba.py

@@ -274,7 +274,7 @@ def array_constructor() -> np.ndarray:
         """helper that reconstructs the array from the pointer and structural info"""
         data: np.ndarray = nb.carray(address_as_void_pointer(data_addr), shape, dtype)
         if strides is not None:
-            data = np.lib.index_tricks.as_strided(data, shape, strides)  # type: ignore
+            data = np.lib.stride_tricks.as_strided(data, shape, strides)
         return data
 
     return array_constructor  # type: ignore

pde/tools/resources/requirements_basic.txt

@@ -1,7 +1,7 @@
 # These are the basic requirements for the package
 matplotlib>=3.1
 numba>=0.59
-numpy>=1.22,<2
+numpy>=1.22
 scipy>=1.10
 sympy>=1.9
 tqdm>=4.66

pde/tools/resources/requirements_full.txt

@@ -3,7 +3,7 @@ ffmpeg-python>=0.2
 h5py>=2.10
 matplotlib>=3.1
 numba>=0.59
-numpy>=1.22,<2
+numpy>=1.22
 pandas>=2
 py-modelrunner>=0.18
 rocket-fft>=0.2.4

pde/tools/resources/requirements_mpi.txt

@@ -4,7 +4,7 @@ matplotlib>=3.1
 mpi4py>=3
 numba>=0.59
 numba-mpi>=0.22
-numpy>=1.22,<2
+numpy>=1.22
 pandas>=2
 scipy>=1.10
 sympy>=1.9

pyproject.toml

@@ -27,7 +27,7 @@ classifiers = [
 ]
 
 # Requirements for setuptools
-dependencies = ["matplotlib>=3.1", "numba>=0.59", "numpy>=1.22,<2", "scipy>=1.10", "sympy>=1.9", "tqdm>=4.66"]
+dependencies = ["matplotlib>=3.1", "numba>=0.59", "numpy>=1.22", "scipy>=1.10", "sympy>=1.9", "tqdm>=4.66"]
 
 [project.optional-dependencies]
 io = ["h5py>=2.10", "pandas>=2", "ffmpeg-python>=0.2"]

requirements.txt

@@ -1,6 +1,6 @@
 matplotlib>=3.1
 numba>=0.59
-numpy>=1.22,<2
+numpy>=1.22
 scipy>=1.10
 sympy>=1.9
 tqdm>=4.66

scripts/create_requirements.py

@@ -72,7 +72,7 @@ def line(self, relation: str = ">=") -> str:
     ),
     Requirement(
         name="numpy",
-        version_min="1.22,<2",
+        version_min="1.22",
         usage="Handling numerical data",
         essential=True,
     ),

tests/requirements_full.txt

@@ -3,7 +3,7 @@ ffmpeg-python>=0.2
 h5py>=2.10
 matplotlib>=3.1
 numba>=0.59
-numpy>=1.22,<2
+numpy>=1.22
 pandas>=2
 py-modelrunner>=0.18
 rocket-fft>=0.2.4

tests/requirements_min.txt

@@ -1,7 +1,7 @@
 # These are the minimal requirements used to test compatibility
 matplotlib~=3.1
 numba~=0.59
-numpy~=1.22,<2
+numpy~=1.22
 scipy~=1.10
 sympy~=1.9
 tqdm~=4.66

tests/requirements_mpi.txt

@@ -4,7 +4,7 @@ matplotlib>=3.1
 mpi4py>=3
 numba>=0.59
 numba-mpi>=0.22
-numpy>=1.22,<2
+numpy>=1.22
 pandas>=2
 scipy>=1.10
 sympy>=1.9

tests/tools/test_numba.py

@@ -3,8 +3,15 @@
 """
 
 import numpy as np
+import pytest
 
-from pde.tools.numba import Counter, flat_idx, jit, numba_environment
+from pde.tools.numba import (
+    Counter,
+    flat_idx,
+    jit,
+    make_array_constructor,
+    numba_environment,
+)
 
 
 def test_environment():
@@ -45,3 +52,14 @@ def test_counter():
     c2 = Counter(3)
     assert c1 is not c2
     assert c1 == c2
+
+
+@pytest.mark.parametrize(
+    "arr", [np.arange(5), np.linspace(0, 1, 3), np.arange(12).reshape(3, 4)[1:, 2:]]
+)
+def test_make_array_constructor(arr):
+    """test implementation to create array"""
+    constructor = jit(make_array_constructor(arr))
+    arr2 = constructor()
+    np.testing.assert_equal(arr, arr2)
+    assert np.shares_memory(arr, arr2)