Python umath.add方法代碼示例

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def test_ufunc_override_not_implemented(self):

        class A(object):
            def __array_ufunc__(self, *args, **kwargs):
                return NotImplemented

        msg = ("operand type(s) all returned NotImplemented from "
               "__array_ufunc__(<ufunc 'negative'>, '__call__', <*>): 'A'")
        with assert_raises_regex(TypeError, fnmatch.translate(msg)):
            np.negative(A())

        msg = ("operand type(s) all returned NotImplemented from "
               "__array_ufunc__(<ufunc 'add'>, '__call__', <*>, <object *>, "
               "out=(1,)): 'A', 'object', 'int'")
        with assert_raises_regex(TypeError, fnmatch.translate(msg)):
            np.add(A(), object(), out=1) 

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def test_prepare(self):

        class with_prepare(np.ndarray):
            __array_priority__ = 10

            def __array_prepare__(self, arr, context):
                # make sure we can return a new
                return np.array(arr).view(type=with_prepare)

        a = np.array(1).view(type=with_prepare)
        x = np.add(a, a)
        assert_equal(x, np.array(2))
        assert_equal(type(x), with_prepare) 

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def test_prepare_out(self):

        class with_prepare(np.ndarray):
            __array_priority__ = 10

            def __array_prepare__(self, arr, context):
                return np.array(arr).view(type=with_prepare)

        a = np.array([1]).view(type=with_prepare)
        x = np.add(a, a, a)
        # Returned array is new, because of the strange
        # __array_prepare__ above
        assert_(not np.shares_memory(x, a))
        assert_equal(x, np.array([2]))
        assert_equal(type(x), with_prepare) 

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def test_attributes(self):
        add = ncu.add
        assert_equal(add.__name__, 'add')
        assert_(add.ntypes >= 18)  # don't fail if types added
        assert_('ii->i' in add.types)
        assert_equal(add.nin, 2)
        assert_equal(add.nout, 1)
        assert_equal(add.identity, 0) 

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def test_doc(self):
        # don't bother checking the long list of kwargs, which are likely to
        # change
        assert_(ncu.add.__doc__.startswith(
            "add(x1, x2, /, out=None, *, where=True"))
        assert_(ncu.frexp.__doc__.startswith(
            "frexp(x[, out1, out2], / [, out=(None, None)], *, where=True")) 

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def test_reduceat():
    """Test bug in reduceat when structured arrays are not copied."""
    db = np.dtype([('name', 'S11'), ('time', np.int64), ('value', np.float32)])
    a = np.empty([100], dtype=db)
    a['name'] = 'Simple'
    a['time'] = 10
    a['value'] = 100
    indx = [0, 7, 15, 25]

    h2 = []
    val1 = indx[0]
    for val2 in indx[1:]:
        h2.append(np.add.reduce(a['value'][val1:val2]))
        val1 = val2
    h2.append(np.add.reduce(a['value'][val1:]))
    h2 = np.array(h2)

    # test buffered -- this should work
    h1 = np.add.reduceat(a['value'], indx)
    assert_array_almost_equal(h1, h2)

    # This is when the error occurs.
    # test no buffer
    np.setbufsize(32)
    h1 = np.add.reduceat(a['value'], indx)
    np.setbufsize(np.UFUNC_BUFSIZE_DEFAULT)
    assert_array_almost_equal(h1, h2) 

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def test_reduceat_empty():
    """Reduceat should work with empty arrays"""
    indices = np.array([], 'i4')
    x = np.array([], 'f8')
    result = np.add.reduceat(x, indices)
    assert_equal(result.dtype, x.dtype)
    assert_equal(result.shape, (0,))
    # Another case with a slightly different zero-sized shape
    x = np.ones((5, 2))
    result = np.add.reduceat(x, [], axis=0)
    assert_equal(result.dtype, x.dtype)
    assert_equal(result.shape, (0, 2))
    result = np.add.reduceat(x, [], axis=1)
    assert_equal(result.dtype, x.dtype)
    assert_equal(result.shape, (5, 0)) 

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def test_ufunc_override_disabled(self):
        # 2016-01-29: NUMPY_UFUNC_DISABLED
        # This test should be removed when __numpy_ufunc__ is re-enabled.

        class MyArray(object):
            def __numpy_ufunc__(self, *args, **kwargs):
                self._numpy_ufunc_called = True

        my_array = MyArray()
        real_array = np.ones(10)
        assert_raises(TypeError, lambda: real_array + my_array)
        assert_raises(TypeError, np.add, real_array, my_array)
        assert not hasattr(my_array, "_numpy_ufunc_called") 

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def test_attributes(self):
        add = ncu.add
        assert_equal(add.__name__, 'add')
        assert_(add.__doc__.startswith('add(x1, x2[, out])\n\n'))
        self.assertTrue(add.ntypes >= 18)  # don't fail if types added
        self.assertTrue('ii->i' in add.types)
        assert_equal(add.nin, 2)
        assert_equal(add.nout, 1)
        assert_equal(add.identity, 0) 

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def test_ufunc_override_disabled(self):

        class OptOut(object):
            __array_ufunc__ = None

        opt_out = OptOut()

        # ufuncs always raise
        msg = "operand 'OptOut' does not support ufuncs"
        with assert_raises_regex(TypeError, msg):
            np.add(opt_out, 1)
        with assert_raises_regex(TypeError, msg):
            np.add(1, opt_out)
        with assert_raises_regex(TypeError, msg):
            np.negative(opt_out)

        # opt-outs still hold even when other arguments have pathological
        # __array_ufunc__ implementations

        class GreedyArray(object):
            def __array_ufunc__(self, *args, **kwargs):
                return self

        greedy = GreedyArray()
        assert_(np.negative(greedy) is greedy)
        with assert_raises_regex(TypeError, msg):
            np.add(greedy, opt_out)
        with assert_raises_regex(TypeError, msg):
            np.add(greedy, 1, out=opt_out) 

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def getmaskarray (a):
    """Mask of values in a; an array of zeros if mask is nomask
     or not a masked array, and is a byte-sized integer.
     Do not try to add up entries, for example.
    """
    m = getmask(a)
    if m is nomask:
        return make_mask_none(shape(a))
    else:
        return m 

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def __add__(self, other):
        "Return add(self, other)"
        return add(self, other) 

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def count (self, axis = None):
        "Count of the non-masked elements in a, or along a certain axis."
        m = self._mask
        s = self._data.shape
        ls = len(s)
        if m is nomask:
            if ls == 0:
                return 1
            if ls == 1:
                return s[0]
            if axis is None:
                return reduce(lambda x, y:x*y, s)
            else:
                n = s[axis]
                t = list(s)
                del t[axis]
                return ones(t) * n
        if axis is None:
            w = fromnumeric.ravel(m).astype(int)
            n1 = size(w)
            if n1 == 1:
                n2 = w[0]
            else:
                n2 = umath.add.reduce(w)
            return n1 - n2
        else:
            n1 = size(m, axis)
            n2 = sum(m.astype(int), axis)
            return n1 - n2 

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def sum (target, axis=None, dtype=None):
    if axis is None:
        target = ravel(target)
        axis = 0
    return add.reduce(target, axis, dtype) 

# 需要導入模塊: from numpy.core import umath [as 別名]
# 或者: from numpy.core.umath import add [as 別名]
def test_attributes(self):
        add = ncu.add
        assert_equal(add.__name__, 'add')
        assert_(add.__doc__.startswith('add(x1, x2[, out])\n\n'))
        self.assertTrue(add.ntypes >= 18) # don't fail if types added
        self.assertTrue('ii->i' in add.types)
        assert_equal(add.nin, 2)
        assert_equal(add.nout, 1)
        assert_equal(add.identity, 0)