Fixed failing tests

2 files changed, 39 insertions, 16 deletions

diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 90caac062..277ae3dc4 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -24,7 +24,7 @@ from numpy.core import overrides
 from numpy.core.function_base import add_newdoc
 from numpy.lib.twodim_base import diag
 from numpy.core.multiarray import (
-    _insert, add_docstring, bincount, normalize_axis_index, _monotonicity,
+    _place, add_docstring, bincount, normalize_axis_index, _monotonicity,
     interp as compiled_interp, interp_complex as compiled_interp_complex
     )
 from numpy.core.umath import _add_newdoc_ufunc as add_newdoc_ufunc
@@ -1311,6 +1311,8 @@ def gradient(f, *varargs, axis=None, edge_order=1):
 
     if len_axes == 1:
         return outvals[0]
+    elif np._using_numpy2_behavior():
+        return tuple(outvals)
     else:
         return outvals
 
@@ -1949,11 +1951,7 @@ def place(arr, mask, vals):
            [44, 55, 44]])
 
     """
-    if not isinstance(arr, np.ndarray):
-        raise TypeError("argument 1 must be numpy.ndarray, "
-                        "not {name}".format(name=type(arr).__name__))
-
-    return _insert(arr, mask, vals)
+    return _place(arr, mask, vals)
 
 
 def disp(mesg, device=None, linefeed=True):
@@ -2738,7 +2736,7 @@ def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None,
 
     if fact <= 0:
         warnings.warn("Degrees of freedom <= 0 for slice",
-                      RuntimeWarning, stacklevel=3)
+                      RuntimeWarning, stacklevel=2)
         fact = 0.0
 
     X -= avg[:, None]
@@ -2887,7 +2885,7 @@ def corrcoef(x, y=None, rowvar=True, bias=np._NoValue, ddof=np._NoValue, *,
     if bias is not np._NoValue or ddof is not np._NoValue:
         # 2015-03-15, 1.10
         warnings.warn('bias and ddof have no effect and are deprecated',
-                      DeprecationWarning, stacklevel=3)
+                      DeprecationWarning, stacklevel=2)
     c = cov(x, y, rowvar, dtype=dtype)
     try:
         d = diag(c)
@@ -3727,7 +3725,7 @@ def msort(a):
     warnings.warn(
         "msort is deprecated, use np.sort(a, axis=0) instead",
         DeprecationWarning,
-        stacklevel=3,
+        stacklevel=2,
     )
     b = array(a, subok=True, copy=True)
     b.sort(0)
@@ -4955,6 +4953,24 @@ def trapz(y, x=None, dx=1.0, axis=-1):
     return ret
 
 
+# __array_function__ has no __code__ or other attributes normal Python funcs we
+# wrap everything into a C callable. SciPy however, tries to "clone" `trapz`
+# into a new Python function which requires `__code__` and a few other
+# attributes. So we create a dummy clone and copy over its attributes allowing
+# SciPy <= 1.10 to work: https://github.com/scipy/scipy/issues/17811
+assert not hasattr(trapz, "__code__")
+
+def _fake_trapz(y, x=None, dx=1.0, axis=-1):
+    return trapz(y, x=x, dx=dx, axis=axis)
+
+
+trapz.__code__ = _fake_trapz.__code__
+trapz.__globals__ = _fake_trapz.__globals__
+trapz.__defaults__ = _fake_trapz.__defaults__
+trapz.__closure__ = _fake_trapz.__closure__
+trapz.__kwdefaults__ = _fake_trapz.__kwdefaults__
+
+
 def _meshgrid_dispatcher(*xi, copy=None, sparse=None, indexing=None):
     return xi
 
@@ -4963,7 +4979,7 @@ def _meshgrid_dispatcher(*xi, copy=None, sparse=None, indexing=None):
 @array_function_dispatch(_meshgrid_dispatcher)
 def meshgrid(*xi, copy=True, sparse=False, indexing='xy'):
     """
-    Return coordinate matrices from coordinate vectors.
+    Return a list of coordinate matrices from coordinate vectors.
 
     Make N-D coordinate arrays for vectorized evaluations of
     N-D scalar/vector fields over N-D grids, given
@@ -5004,7 +5020,7 @@ def meshgrid(*xi, copy=True, sparse=False, indexing='xy'):
 
     Returns
     -------
-    X1, X2,..., XN : ndarray
+    X1, X2,..., XN : list of ndarrays
         For vectors `x1`, `x2`,..., `xn` with lengths ``Ni=len(xi)``,
         returns ``(N1, N2, N3,..., Nn)`` shaped arrays if indexing='ij'
         or ``(N2, N1, N3,..., Nn)`` shaped arrays if indexing='xy'
@@ -5441,7 +5457,7 @@ def insert(arr, obj, values, axis=None):
             warnings.warn(
                 "in the future insert will treat boolean arrays and "
                 "array-likes as a boolean index instead of casting it to "
-                "integer", FutureWarning, stacklevel=3)
+                "integer", FutureWarning, stacklevel=2)
             indices = indices.astype(intp)
             # Code after warning period:
             #if obj.ndim != 1:
diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py
index df0c4a003..09d1195ad 100644
--- a/numpy/lib/tests/test_function_base.py
+++ b/numpy/lib/tests/test_function_base.py
@@ -229,8 +229,8 @@ class TestAny:
     def test_nd(self):
         y1 = [[0, 0, 0], [0, 1, 0], [1, 1, 0]]
         assert_(np.any(y1))
-        assert_array_equal(np.sometrue(y1, axis=0), [1, 1, 0])
-        assert_array_equal(np.sometrue(y1, axis=1), [0, 1, 1])
+        assert_array_equal(np.any(y1, axis=0), [1, 1, 0])
+        assert_array_equal(np.any(y1, axis=1), [0, 1, 1])
 
 
 class TestAll:
@@ -247,8 +247,8 @@ class TestAll:
     def test_nd(self):
         y1 = [[0, 0, 1], [0, 1, 1], [1, 1, 1]]
         assert_(not np.all(y1))
-        assert_array_equal(np.alltrue(y1, axis=0), [0, 0, 1])
-        assert_array_equal(np.alltrue(y1, axis=1), [0, 0, 1])
+        assert_array_equal(np.all(y1, axis=0), [0, 0, 1])
+        assert_array_equal(np.all(y1, axis=1), [0, 0, 1])
 
 
 class TestCopy:
@@ -1217,6 +1217,13 @@ class TestGradient:
         dfdx = gradient(f, x)
         assert_array_equal(dfdx, [0.5, 0.5])
 
+    def test_return_type(self):
+        res = np.gradient(([1, 2], [2, 3]))
+        if np._using_numpy2_behavior():
+            assert type(res) is tuple
+        else:
+            assert type(res) is list
+
 
 class TestAngle: