Fixed edge case where pyfunc has no attribute __name__

numpy/lib/function_base.py

@@ -24,7 +24,7 @@
 from numpy.core.function_base import add_newdoc
 from numpy.lib.twodim_base import diag
 from numpy.core.multiarray import (
-    _place, add_docstring, bincount, normalize_axis_index, _monotonicity,
+    _insert, 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
@@ -1311,8 +1311,6 @@ 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
 
@@ -1951,7 +1949,11 @@ def place(arr, mask, vals):
            [44, 55, 44]])
 
     """
-    return _place(arr, mask, vals)
+    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)
 
 
 def disp(mesg, device=None, linefeed=True):
@@ -2117,10 +2119,10 @@ def _create_arrays(broadcast_shape, dim_sizes, list_of_core_dims, dtypes,
 @set_module('numpy')
 class vectorize:
     """
-    vectorize(pyfunc, otypes=None, doc=None, excluded=None, cache=False,
-              signature=None)
+    vectorize(pyfunc=np._NoValue, otypes=None, doc=None, excluded=None,
+    cache=False, signature=None)
 
-    Generalized function class.
+    Returns an object that acts like pyfunc, but takes arrays as input.
 
     Define a vectorized function which takes a nested sequence of objects or
     numpy arrays as inputs and returns a single numpy array or a tuple of numpy
@@ -2134,8 +2136,9 @@ class vectorize:
 
     Parameters
     ----------
-    pyfunc : callable
+    pyfunc : callable, optional
         A python function or method.
+        Can be omitted to produce a decorator with keyword arguments.
     otypes : str or list of dtypes, optional
         The output data type. It must be specified as either a string of
         typecode characters or a list of data type specifiers. There should
@@ -2167,8 +2170,9 @@ class vectorize:
 
     Returns
     -------
-    vectorized : callable
-        Vectorized function.
+    out : callable
+        A vectorized function if ``pyfunc`` was provided,
+        a decorator otherwise.
 
     See Also
     --------
@@ -2265,18 +2269,44 @@ class vectorize:
            [0., 0., 1., 2., 1., 0.],
            [0., 0., 0., 1., 2., 1.]])
 
+    Decorator syntax is supported.  The decorator can be called as
+    a function to provide keyword arguments.
+    >>>@np.vectorize
+    ...def identity(x):
+    ...    return x
+    ...
+    >>>identity([0, 1, 2])
+    array([0, 1, 2])
+    >>>@np.vectorize(otypes=[float])
+    ...def as_float(x):
+    ...    return x
+    ...
+    >>>as_float([0, 1, 2])
+    array([0., 1., 2.])
     """
-    def __init__(self, pyfunc, otypes=None, doc=None, excluded=None,
-                 cache=False, signature=None):
+    def __init__(self, pyfunc=np._NoValue, otypes=None, doc=None,
+                 excluded=None, cache=False, signature=None):
+
+        if (pyfunc != np._NoValue) and (not callable(pyfunc)):
+            #Splitting the error message to keep
+            #the length below 79 characters.
+            part1 = "When used as a decorator, "
+            part2 = "only accepts keyword arguments."
+            raise TypeError(part1 + part2)
+
         self.pyfunc = pyfunc
         self.cache = cache
         self.signature = signature
-        self._ufunc = {}    # Caching to improve default performance
+        if pyfunc != np._NoValue and hasattr(pyfunc, '__name__'):
+            self.__name__ = pyfunc.__name__
 
+        self._ufunc = {}    # Caching to improve default performance
+        self._doc = None
+        self.__doc__ = doc
         if doc is None:
             self.__doc__ = pyfunc.__doc__
         else:
-            self.__doc__ = doc
+            self._doc = doc
 
         if isinstance(otypes, str):
             for char in otypes:
@@ -2298,7 +2328,15 @@ def __init__(self, pyfunc, otypes=None, doc=None, excluded=None,
         else:
             self._in_and_out_core_dims = None
 
-    def __call__(self, *args, **kwargs):
+    def _init_stage_2(self, pyfunc, *args, **kwargs):
+        self.__name__ = pyfunc.__name__
+        self.pyfunc = pyfunc
+        if self._doc is None:
+            self.__doc__ = pyfunc.__doc__
+        else:
+            self.__doc__ = self._doc
+
+    def _call_as_normal(self, *args, **kwargs):
         """
         Return arrays with the results of `pyfunc` broadcast (vectorized) over
         `args` and `kwargs` not in `excluded`.
@@ -2328,6 +2366,13 @@ def func(*vargs):
 
         return self._vectorize_call(func=func, args=vargs)
 
+    def __call__(self, *args, **kwargs):
+        if self.pyfunc is np._NoValue:
+            self._init_stage_2(*args, **kwargs)
+            return self
+
+        return self._call_as_normal(*args, **kwargs)
+
     def _get_ufunc_and_otypes(self, func, args):
         """Return (ufunc, otypes)."""
         # frompyfunc will fail if args is empty
@@ -2693,7 +2738,7 @@ 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=2)
+                      RuntimeWarning, stacklevel=3)
         fact = 0.0
 
     X -= avg[:, None]
@@ -2842,7 +2887,7 @@ 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=2)
+                      DeprecationWarning, stacklevel=3)
     c = cov(x, y, rowvar, dtype=dtype)
     try:
         d = diag(c)
@@ -3682,7 +3727,7 @@ def msort(a):
     warnings.warn(
         "msort is deprecated, use np.sort(a, axis=0) instead",
         DeprecationWarning,
-        stacklevel=2,
+        stacklevel=3,
     )
     b = array(a, subok=True, copy=True)
     b.sort(0)
@@ -4910,24 +4955,6 @@ 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
 
@@ -4936,7 +4963,7 @@ 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 a list of coordinate matrices from coordinate vectors.
+    Return coordinate matrices from coordinate vectors.
 
     Make N-D coordinate arrays for vectorized evaluations of
     N-D scalar/vector fields over N-D grids, given
@@ -4977,7 +5004,7 @@ def meshgrid(*xi, copy=True, sparse=False, indexing='xy'):
 
     Returns
     -------
-    X1, X2,..., XN : list of ndarrays
+    X1, X2,..., XN : ndarray
         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'
@@ -5414,7 +5441,7 @@ 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=2)
+                "integer", FutureWarning, stacklevel=3)
             indices = indices.astype(intp)
             # Code after warning period:
             #if obj.ndim != 1:

numpy/lib/tests/test_function_base.py

@@ -8,7 +8,7 @@
 import hypothesis
 from hypothesis.extra.numpy import arrays
 import hypothesis.strategies as st
-
+from functools import partial
 
 import numpy as np
 from numpy import ma
@@ -229,8 +229,8 @@ def test_basic(self):
     def test_nd(self):
         y1 = [[0, 0, 0], [0, 1, 0], [1, 1, 0]]
         assert_(np.any(y1))
-        assert_array_equal(np.any(y1, axis=0), [1, 1, 0])
-        assert_array_equal(np.any(y1, axis=1), [0, 1, 1])
+        assert_array_equal(np.sometrue(y1, axis=0), [1, 1, 0])
+        assert_array_equal(np.sometrue(y1, axis=1), [0, 1, 1])
 
 
 class TestAll:
@@ -247,8 +247,8 @@ def test_basic(self):
     def test_nd(self):
         y1 = [[0, 0, 1], [0, 1, 1], [1, 1, 1]]
         assert_(not np.all(y1))
-        assert_array_equal(np.all(y1, axis=0), [0, 0, 1])
-        assert_array_equal(np.all(y1, axis=1), [0, 0, 1])
+        assert_array_equal(np.alltrue(y1, axis=0), [0, 0, 1])
+        assert_array_equal(np.alltrue(y1, axis=1), [0, 0, 1])
 
 
 class TestCopy:
@@ -1217,13 +1217,6 @@ def test_x_signed_int_big_jump(self, x_dtype):
         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:
 
@@ -1787,6 +1780,69 @@ class subclass(np.ndarray):
         assert_equal(type(r), subclass)
         assert_equal(r, m * v)
 
+    def test_name(self):
+        #See gh-23021
+        @np.vectorize
+        def f2(a, b):
+            return a + b
+
+        assert f2.__name__ == 'f2'
+
+    def test_decorator(self):
+        @vectorize
+        def addsubtract(a, b):
+            if a > b:
+                return a - b
+            else:
+                return a + b
+
+        r = addsubtract([0, 3, 6, 9], [1, 3, 5, 7])
+        assert_array_equal(r, [1, 6, 1, 2])
+
+    def test_docstring(self):
+        @vectorize
+        def f(x):
+            """Docstring"""
+            return x
+
+        assert f.__doc__ == "Docstring"
+
+    def test_partial(self):
+        def foo(x, y):
+            return x + y
+
+        bar = partial(foo, 3)
+        vbar = np.vectorize(bar)
+        assert vbar(1) == 4
+
+    def test_signature_otypes_decorator(self):
+        @vectorize(signature='(n)->(n)', otypes=['float64'])
+        def f(x):
+            return x
+
+        r = f([1, 2, 3])
+        assert_equal(r.dtype, np.dtype('float64'))
+        assert_array_equal(r, [1, 2, 3])
+        assert f.__name__ == 'f'
+
+    def test_bad_input(self):
+        with assert_raises(TypeError):
+            A = np.vectorize(pyfunc = 3)
+
+    def test_no_keywords(self):
+        with assert_raises(TypeError):
+            @np.vectorize("string")
+            def foo():
+                return "bar"
+
+    def test_positional_regression_9477(self):
+        # This supplies the first keyword argument as a positional,
+        # to ensure that they are still properly forwarded after the
+        # enhancement for #9477
+        f = vectorize((lambda x: x), ['float64'])
+        r = f([2])
+        assert_equal(r.dtype, np.dtype('float64'))
+
 
 class TestLeaks:
     class A: