sklam · Mar 6, 2023 · Nov 24, 2022 · Dec 22, 2022 · Nov 24, 2022 · Nov 24, 2022
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
@@ -12,14 +12,14 @@ jobs:
     name: macOS
     vmImage: macos-11
     matrix:
-      py38_np118:
+      py38_np121:
         PYTHON: '3.8'
-        NUMPY: '1.18'
+        NUMPY: '1.21'
         CONDA_ENV: 'azure_ci'
         TEST_START_INDEX: 0
-      py310_np123:
+      py310_np124:
         PYTHON: '3.10'
-        NUMPY: '1.23'
+        NUMPY: '1.24'
         CONDA_ENV: 'azure_ci'
         TEST_THREADING: 'tbb'
         TEST_START_INDEX: 1
@@ -29,89 +29,89 @@ jobs:
     name: Linux
     vmImage: ubuntu-20.04
     matrix:
-      py38_np118_vanilla:
+      py38_np121_vanilla:
         PYTHON: '3.8'
-        NUMPY: '1.18'
+        NUMPY: '1.21'
         CONDA_ENV: azure_ci
         VANILLA_INSTALL: yes
         TEST_START_INDEX: 2
-      py38_np118_cov:
+      py38_np121_cov:
         PYTHON: '3.8'
-        NUMPY: '1.18'
+        NUMPY: '1.21'
         CONDA_ENV: azure_ci
         RUN_COVERAGE: yes
         RUN_FLAKE8: yes
         RUN_MYPY: yes
         TEST_START_INDEX: 3
-      py38_np119_tbb:
+      py38_np122_tbb:
         PYTHON: '3.8'
-        NUMPY: '1.19.2=*_0'
+        NUMPY: '1.22'
         CONDA_ENV: azure_ci
         TEST_THREADING: 'tbb'
         TEST_START_INDEX: 4
-      py38_np119_omp:
+      py38_np122_omp:
         PYTHON: '3.8'
-        NUMPY: '1.19.2=*_0'
+        NUMPY: '1.22'
         CONDA_ENV: azure_ci
         TEST_THREADING: omp
         TEST_START_INDEX: 5
-      py38_np119_workqueue:
+      py38_np122_workqueue:
         PYTHON: '3.8'
-        NUMPY: '1.19.2=*_0'
+        NUMPY: '1.22'
         CONDA_ENV: azure_ci
         TEST_THREADING: workqueue
         TEST_START_INDEX: 6
-      py38_np120_doc:
+      py38_np123_doc:
         PYTHON: '3.8'
-        NUMPY: '1.20'
+        NUMPY: '1.23'
         CONDA_ENV: azure_ci
         BUILD_DOC: yes
         TEST_START_INDEX: 7
-      py38_np120:
+      py38_np122:
         PYTHON: '3.8'
-        NUMPY: '1.20'
+        NUMPY: '1.22'
         CONDA_ENV: azure_ci
         TEST_START_INDEX: 8
-      py38_np120_svml:
+      py38_np123_svml:
         PYTHON: '3.8'
-        NUMPY: '1.20'
+        NUMPY: '1.23'
         CONDA_ENV: azure_ci
         TEST_SVML: yes
         TEST_START_INDEX: 9
-      py38_np122:
+      py38_np124:
         PYTHON: '3.8'
-        NUMPY: '1.22'
+        NUMPY: '1.24'
         CONDA_ENV: azure_ci
         TEST_START_INDEX: 10
-      py39_np119:
+      py39_np123:
         PYTHON: '3.9'
-        NUMPY: '1.19.2=*_0'
+        NUMPY: '1.23'
         CONDA_ENV: azure_ci
         TEST_START_INDEX: 11
-      py39_np120_typeguard:
+      py39_np123_typeguard:
         PYTHON: '3.9'
-        NUMPY: '1.20'
+        NUMPY: '1.23'
         CONDA_ENV: azure_ci
         RUN_TYPEGUARD: yes
         TEST_START_INDEX: 12
-      py39_np121:
+      py39_np122:
         PYTHON: '3.9'
-        NUMPY: '1.21'
+        NUMPY: '1.22'
         CONDA_ENV: azure_ci
         TEST_START_INDEX: 13
-      py39_np123:
+      py39_np124:
         PYTHON: '3.9'
-        NUMPY: '1.23'
+        NUMPY: '1.24'
         CONDA_ENV: azure_ci
         TEST_START_INDEX: 14
-      py310_np121:
+      py310_np123:
         PYTHON: '3.10'
-        NUMPY: '1.21'
+        NUMPY: '1.23'
         CONDA_ENV: azure_ci
         TEST_START_INDEX: 15
-      py310_np123:
+      py310_np124:
         PYTHON: '3.10'
-        NUMPY: '1.23'
+        NUMPY: '1.24'
         CONDA_ENV: azure_ci
         TEST_START_INDEX: 16
 

diff --git a/buildscripts/azure/azure-windows.yml b/buildscripts/azure/azure-windows.yml
@@ -8,14 +8,14 @@ jobs:
     vmImage: ${{ parameters.vmImage }}
   strategy:
     matrix:
-      py310_np123:
+      py310_np124:
         PYTHON: '3.10'
-        NUMPY: '1.23'
+        NUMPY: '1.24'
         CONDA_ENV: 'testenv'
         TEST_START_INDEX: 17
-      py38_np118:
+      py38_np121:
         PYTHON: '3.8'
-        NUMPY: '1.18'
+        NUMPY: '1.21'
         CONDA_ENV: 'testenv'
         TEST_START_INDEX: 18
 

diff --git a/buildscripts/gpuci/build.sh b/buildscripts/gpuci/build.sh
@@ -27,7 +27,7 @@ fi;
 # Test with different NumPy versions with each toolkit (it's not worth testing
 # the Cartesian product of versions here, we just need to test with different
 # CUDA and NumPy versions).
-declare -A CTK_NUMPY_VMAP=( ["11.0"]="1.19" ["11.1"]="1.21" ["11.2"]="1.22" ["11.5"]="1.23")
+declare -A CTK_NUMPY_VMAP=( ["11.0"]="1.21" ["11.1"]="1.22" ["11.2"]="1.23" ["11.5"]="1.24")
 NUMPY_VER="${CTK_NUMPY_VMAP[$CUDA_TOOLKIT_VER]}"
 
 

diff --git a/docs/source/user/installing.rst b/docs/source/user/installing.rst
@@ -5,7 +5,7 @@ Installation
 Compatibility
 -------------
 
-Numba is compatible with Python 3.8--3.10, and NumPy versions 1.18 or later.
+Numba is compatible with Python 3.8--3.10, and NumPy versions 1.21--1.24.
 
 Our supported platforms are:
 
@@ -257,7 +257,7 @@ information.
 +----------++--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
 | Numba     | Release date | Python                    | NumPy                      | llvmlite                     | LLVM              | TBB                         |
 +===========+==============+===========================+============================+==============================+===================+=============================+
-| 0.57.x    | TBC          | 3.8.x <= version < 3.12   | 1.19 <= version < 1.24     | 0.40.x                       | 11.x              | 2021.x                      |
+| 0.57.0    | TBC          | 3.8.x <= version < 3.12   | 1.21 <= version < 1.24     | 0.40.x                       | 11.x              | 2021.x                      |
 +-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+
 | 0.56.4    | 2022-11-03   | 3.7.x <= version < 3.11   | 1.18 <= version < 1.24     | 0.39.x                       | 11.x              | 2021.x                      |
 +-----------+--------------+---------------------------+----------------------------+------------------------------+-------------------+-----------------------------+

@@ -833,7 +833,7 @@ def test_hmax(self):
         arg2 = np.float16(4.)
         compiled[1, 1](ary, arg1, arg2)
         np.testing.assert_allclose(ary[0], arg2)
-        arg1 = np.float(5.)
+        arg1 = np.float16(5.)
         compiled[1, 1](ary, arg1, arg2)
         np.testing.assert_allclose(ary[0], arg1)
 
@@ -845,7 +845,7 @@ def test_hmin(self):
         arg2 = np.float16(4.)
         compiled[1, 1](ary, arg1, arg2)
         np.testing.assert_allclose(ary[0], arg1)
-        arg1 = np.float(5.)
+        arg1 = np.float16(5.)
         compiled[1, 1](ary, arg1, arg2)
         np.testing.assert_allclose(ary[0], arg2)
 

diff --git a/numba/np/arraymath.py b/numba/np/arraymath.py
@@ -3964,6 +3964,10 @@ def histogram_impl(a, bins=10, range=None):
 # This module is imported under the compiler lock which should deal with the
 # lack of thread safety in the warning filter.
 def _gen_np_machar():
+    # NumPy 1.24 removed np.MachAr
+    if numpy_version >= (1, 24):
+        return
+
     np122plus = numpy_version >= (1, 22)
     w = None
     with warnings.catch_warnings(record=True) as w:

diff --git a/numba/np/ufunc/_internal.c b/numba/np/ufunc/_internal.c
@@ -306,9 +306,7 @@ static struct _ufunc_dispatch {
     PyCFunctionWithKeywords ufunc_accumulate;
     PyCFunctionWithKeywords ufunc_reduceat;
     PyCFunctionWithKeywords ufunc_outer;
-#if NPY_API_VERSION >= 0x00000008
     PyCFunction ufunc_at;
-#endif
 } ufunc_dispatch;
 
 static int
@@ -324,10 +322,8 @@ init_ufunc_dispatch(int *numpy_uses_fastcall)
             if (strncmp(crnt_name, "accumulate", 11) == 0) {
                 ufunc_dispatch.ufunc_accumulate =
                     (PyCFunctionWithKeywords)crnt->ml_meth;
-#if NPY_API_VERSION >= 0x00000008
             } else if (strncmp(crnt_name, "at", 3) == 0) {
                 ufunc_dispatch.ufunc_at = crnt->ml_meth;
-#endif
             } else {
                 result = -1;
             }
@@ -347,10 +343,15 @@ init_ufunc_dispatch(int *numpy_uses_fastcall)
             } else if (strncmp(crnt_name, "reduceat", 9) == 0) {
                 ufunc_dispatch.ufunc_reduceat =
                     (PyCFunctionWithKeywords)crnt->ml_meth;
+            } else if (strncmp(crnt_name, "resolve_dtypes", 15) == 0) {
+              /* Ignored */
             } else {
                 result = -1;
             }
             break;
+        case '_':
+            // We ignore private methods
+            break;
         default:
             result = -1; /* Unknown method */
         }
@@ -362,6 +363,8 @@ init_ufunc_dispatch(int *numpy_uses_fastcall)
                 *numpy_uses_fastcall = crnt->ml_flags & METH_FASTCALL;
             }
             else if (*numpy_uses_fastcall != (crnt->ml_flags & METH_FASTCALL)) {
+                PyErr_SetString(PyExc_RuntimeError,
+                    "ufunc.at() flags do not match numpy_uses_fastcall");
                 return -1;
             }
         }
@@ -372,11 +375,13 @@ init_ufunc_dispatch(int *numpy_uses_fastcall)
                   && (ufunc_dispatch.ufunc_accumulate != NULL)
                   && (ufunc_dispatch.ufunc_reduceat != NULL)
                   && (ufunc_dispatch.ufunc_outer != NULL)
-#if NPY_API_VERSION >= 0x00000008
                   && (ufunc_dispatch.ufunc_at != NULL)
-#endif
                   );
+    } else {
+        char const * const fmt = "Unexpected ufunc method %s()";
+        PyErr_Format(PyExc_RuntimeError, fmt, crnt_name);
     }
+
     return result;
 }
 
@@ -446,13 +451,11 @@ dufunc_outer_fast(PyDUFuncObject * self,
 }
 
 
-#if NPY_API_VERSION >= 0x00000008
 static PyObject *
 dufunc_at(PyDUFuncObject * self, PyObject * args)
 {
     return ufunc_dispatch.ufunc_at((PyObject*)self->ufunc, args);
 }
-#endif
 
 static PyObject *
 dufunc__compile_for_args(PyDUFuncObject * self, PyObject * args,
@@ -630,11 +633,9 @@ static struct PyMethodDef dufunc_methods[] = {
     {"outer",
         (PyCFunction)dufunc_outer,
         METH_VARARGS | METH_KEYWORDS, NULL},
-#if NPY_API_VERSION >= 0x00000008
     {"at",
         (PyCFunction)dufunc_at,
         METH_VARARGS, NULL},
-#endif
     {"_compile_for_args",
         (PyCFunction)dufunc__compile_for_args,
         METH_VARARGS | METH_KEYWORDS,
@@ -664,11 +665,9 @@ static struct PyMethodDef dufunc_methods_fast[] = {
     {"outer",
         (PyCFunction)dufunc_outer_fast,
         METH_FASTCALL | METH_KEYWORDS, NULL},
-#if NPY_API_VERSION >= 0x00000008
     {"at",
         (PyCFunction)dufunc_at,
         METH_VARARGS, NULL},
-#endif
     {"_compile_for_args",
         (PyCFunction)dufunc__compile_for_args,
         METH_VARARGS | METH_KEYWORDS,
@@ -837,9 +836,7 @@ MOD_INIT(_internal)
     if (PyModule_AddIntMacro(m, PyUFunc_One)
         || PyModule_AddIntMacro(m, PyUFunc_Zero)
         || PyModule_AddIntMacro(m, PyUFunc_None)
-#if NPY_API_VERSION >= 0x00000007
         || PyModule_AddIntMacro(m, PyUFunc_ReorderableNone)
-#endif
         )
         return MOD_ERROR_VAL;
 

@@ -21,6 +21,7 @@
                             find_callname, require, find_const, GuardException)
 from numba.core.errors import NumbaValueError
 from numba.core.utils import OPERATORS_TO_BUILTINS
+from numba.np import numpy_support
 
 
 def _compute_last_ind(dim_size, index_const):
@@ -264,7 +265,11 @@ def _mk_stencil_parfor(self, label, in_args, out_arr, stencil_ir,
             dtype_g_np_assign = ir.Assign(dtype_g_np, dtype_g_np_var, loc)
             init_block.body.append(dtype_g_np_assign)
 
-            dtype_np_attr_call = ir.Expr.getattr(dtype_g_np_var, return_type.dtype.name, loc)
+            return_type_name = numpy_support.as_dtype(
+                               return_type.dtype).type.__name__
+            if return_type_name == 'bool':
+                return_type_name = 'bool_'
+            dtype_np_attr_call = ir.Expr.getattr(dtype_g_np_var, return_type_name, loc)
             dtype_attr_var = ir.Var(scope, mk_unique_var("$np_attr_attr"), loc)
             self.typemap[dtype_attr_var.name] = types.functions.NumberClass(return_type.dtype)
             dtype_attr_assign = ir.Assign(dtype_np_attr_call, dtype_attr_var, loc)

diff --git a/numba/tests/test_array_methods.py b/numba/tests/test_array_methods.py
@@ -1337,7 +1337,7 @@ def test_sum_dtype_kws(self):
         pyfunc = array_sum_dtype_kws
         cfunc = jit(nopython=True)(pyfunc)
         all_dtypes = [np.float64, np.float32, np.int64, np.int32, np.uint32,
-                      np.uint64, np.complex64, np.complex128, TIMEDELTA_M]
+                      np.uint64, np.complex64, np.complex128]
         all_test_arrays = [
             [np.ones((7, 6, 5, 4, 3), arr_dtype),
              np.ones(1, arr_dtype),
@@ -1351,24 +1351,23 @@ def test_sum_dtype_kws(self):
                       np.dtype('uint32'): [np.float64, np.int64, np.float32],
                       np.dtype('uint64'): [np.float64, np.int64],
                       np.dtype('complex64'): [np.complex64, np.complex128],
-                      np.dtype('complex128'): [np.complex128],
-                      np.dtype(TIMEDELTA_M): [np.dtype(TIMEDELTA_M)]}
+                      np.dtype('complex128'): [np.complex128]}
 
         for arr_list in all_test_arrays:
             for arr in arr_list:
                 for out_dtype in out_dtypes[arr.dtype]:
                     subtest_str = ("Testing np.sum with {} input and {} output"
                                    .format(arr.dtype, out_dtype))
                     with self.subTest(subtest_str):
-                        self.assertPreciseEqual(pyfunc(arr, dtype=out_dtype),
-                                                cfunc(arr, dtype=out_dtype))
+                            self.assertPreciseEqual(pyfunc(arr, dtype=out_dtype),
+                                                    cfunc(arr, dtype=out_dtype))
 
     def test_sum_axis_dtype_kws(self):
         """ test sum with axis and dtype parameters over a whole range of dtypes """
         pyfunc = array_sum_axis_dtype_kws
         cfunc = jit(nopython=True)(pyfunc)
         all_dtypes = [np.float64, np.float32, np.int64, np.int32, np.uint32,
-                      np.uint64, np.complex64, np.complex128, TIMEDELTA_M]
+                      np.uint64, np.complex64, np.complex128]
         all_test_arrays = [
             [np.ones((7, 6, 5, 4, 3), arr_dtype),
              np.ones(1, arr_dtype),
@@ -1382,9 +1381,7 @@ def test_sum_axis_dtype_kws(self):
                       np.dtype('uint32'): [np.float64, np.int64, np.float32],
                       np.dtype('uint64'): [np.float64, np.uint64],
                       np.dtype('complex64'): [np.complex64, np.complex128],
-                      np.dtype('complex128'): [np.complex128],
-                      np.dtype(TIMEDELTA_M): [np.dtype(TIMEDELTA_M)],
-                      np.dtype(TIMEDELTA_Y): [np.dtype(TIMEDELTA_Y)]}
+                      np.dtype('complex128'): [np.complex128]}
 
         for arr_list in all_test_arrays:
             for arr in arr_list: