Python numpy.broadcast() 使用实例

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Example 1

def _broadcast_to(array, shape, subok, readonly):
    shape = tuple(shape) if np.iterable(shape) else (shape,)
    array = np.array(array, copy=False, subok=subok)
    if not shape and array.shape:
        raise ValueError('cannot broadcast a non-scalar to a scalar array')
    if any(size < 0 for size in shape):
        raise ValueError('all elements of broadcast shape must be non-'
                         'negative')
    needs_writeable = not readonly and array.flags.writeable
    extras = ['reduce_ok'] if needs_writeable else []
    op_flag = 'readwrite' if needs_writeable else 'readonly'
    broadcast = np.nditer(
        (array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras,
        op_flags=[op_flag], itershape=shape, order='C').itviews[0]
    result = _maybe_view_as_subclass(array, broadcast)
    if needs_writeable and not result.flags.writeable:
        result.flags.writeable = True
    return result 

Example 2

def _broadcast_shape(*args):
    """Returns the shape of the ararys that would result from broadcasting the
    supplied arrays against each other.
    """
    if not args:
        raise ValueError('must provide at least one argument')
    # use the old-iterator because np.nditer does not handle size 0 arrays
    # consistently
    b = np.broadcast(*args[:32])
    # unfortunately, it cannot handle 32 or more arguments directly
    for pos in range(32, len(args), 31):
        # ironically, np.broadcast does not properly handle np.broadcast
        # objects (it treats them as scalars)
        # use broadcasting to avoid allocating the full array
        b = broadcast_to(0, b.shape)
        b = np.broadcast(b, *args[pos:(pos + 31)])
    return b.shape 

Example 3

def _broadcast_to(array, shape, subok, readonly):
    shape = tuple(shape) if np.iterable(shape) else (shape,)
    array = np.array(array, copy=False, subok=subok)
    if not shape and array.shape:
        raise ValueError('cannot broadcast a non-scalar to a scalar array')
    if any(size < 0 for size in shape):
        raise ValueError('all elements of broadcast shape must be non-'
                         'negative')
    needs_writeable = not readonly and array.flags.writeable
    extras = ['reduce_ok'] if needs_writeable else []
    op_flag = 'readwrite' if needs_writeable else 'readonly'
    broadcast = np.nditer(
        (array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras,
        op_flags=[op_flag], itershape=shape, order='C').itviews[0]
    result = _maybe_view_as_subclass(array, broadcast)
    if needs_writeable and not result.flags.writeable:
        result.flags.writeable = True
    return result 

Example 4

def _broadcast_shape(*args):
    """Returns the shape of the ararys that would result from broadcasting the
    supplied arrays against each other.
    """
    if not args:
        raise ValueError('must provide at least one argument')
    # use the old-iterator because np.nditer does not handle size 0 arrays
    # consistently
    b = np.broadcast(*args[:32])
    # unfortunately, it cannot handle 32 or more arguments directly
    for pos in range(32, len(args), 31):
        # ironically, np.broadcast does not properly handle np.broadcast
        # objects (it treats them as scalars)
        # use broadcasting to avoid allocating the full array
        b = broadcast_to(0, b.shape)
        b = np.broadcast(b, *args[pos:(pos + 31)])
    return b.shape 

Example 5

def _broadcast_shape(*args):
    """Returns the shape of the ararys that would result from broadcasting the
    supplied arrays against each other.
    """
    if not args:
        raise ValueError('must provide at least one argument')
    if len(args) == 1:
        # a single argument does not work with np.broadcast
        return np.asarray(args[0]).shape
    # use the old-iterator because np.nditer does not handle size 0 arrays
    # consistently
    b = np.broadcast(*args[:32])
    # unfortunately, it cannot handle 32 or more arguments directly
    for pos in range(32, len(args), 31):
        # ironically, np.broadcast does not properly handle np.broadcast
        # objects (it treats them as scalars)
        # use broadcasting to avoid allocating the full array
        b = broadcast_to(0, b.shape)
        b = np.broadcast(b, *args[pos:(pos + 31)])
    return b.shape 

Example 6

def broadcast_shape(shp1, shp2):
    """Broadcast the shape of those arrays

    Parameters
    ----------
    shp1 : tuple
        shape of array 1
    shp2 : tuple
        shape of array 2

    Returns
    -------
    tuple
        shape resulting from broadcasting two arrays using numpy rules

    Raises
    ------
    ValueError
        Arrays cannot be broadcasted
    """
    try:
        return np.broadcast(np.empty(shp1), np.empty(shp2)).shape
    except ValueError:
        raise ValueError("Arrays cannot be broadcasted - %s and %s " % (str(shp1), str(shp2))) 

Example 7

def _broadcast_to(array, shape, subok, readonly):
    shape = tuple(shape) if np.iterable(shape) else (shape,)
    array = np.array(array, copy=False, subok=subok)
    if not shape and array.shape:
        raise ValueError('cannot broadcast a non-scalar to a scalar array')
    if any(size < 0 for size in shape):
        raise ValueError('all elements of broadcast shape must be non-'
                         'negative')
    needs_writeable = not readonly and array.flags.writeable
    extras = ['reduce_ok'] if needs_writeable else []
    op_flag = 'readwrite' if needs_writeable else 'readonly'
    broadcast = np.nditer(
        (array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras,
        op_flags=[op_flag], itershape=shape, order='C').itviews[0]
    result = _maybe_view_as_subclass(array, broadcast)
    if needs_writeable and not result.flags.writeable:
        result.flags.writeable = True
    return result 

Example 8

def _broadcast_shape(*args):
    """Returns the shape of the ararys that would result from broadcasting the
    supplied arrays against each other.
    """
    if not args:
        raise ValueError('must provide at least one argument')
    # use the old-iterator because np.nditer does not handle size 0 arrays
    # consistently
    b = np.broadcast(*args[:32])
    # unfortunately, it cannot handle 32 or more arguments directly
    for pos in range(32, len(args), 31):
        # ironically, np.broadcast does not properly handle np.broadcast
        # objects (it treats them as scalars)
        # use broadcasting to avoid allocating the full array
        b = broadcast_to(0, b.shape)
        b = np.broadcast(b, *args[pos:(pos + 31)])
    return b.shape 

Example 9

def _broadcast_to(array, shape, subok, readonly):
    shape = tuple(shape) if np.iterable(shape) else (shape,)
    array = np.array(array, copy=False, subok=subok)
    if not shape and array.shape:
        raise ValueError('cannot broadcast a non-scalar to a scalar array')
    if any(size < 0 for size in shape):
        raise ValueError('all elements of broadcast shape must be non-'
                         'negative')
    needs_writeable = not readonly and array.flags.writeable
    extras = ['reduce_ok'] if needs_writeable else []
    op_flag = 'readwrite' if needs_writeable else 'readonly'
    broadcast = np.nditer(
        (array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras,
        op_flags=[op_flag], itershape=shape, order='C').itviews[0]
    result = _maybe_view_as_subclass(array, broadcast)
    if needs_writeable and not result.flags.writeable:
        result.flags.writeable = True
    return result 

Example 10

def _broadcast_shape(*args):
    """Returns the shape of the arrays that would result from broadcasting the
    supplied arrays against each other.
    """
    if not args:
        raise ValueError('must provide at least one argument')
    # use the old-iterator because np.nditer does not handle size 0 arrays
    # consistently
    b = np.broadcast(*args[:32])
    # unfortunately, it cannot handle 32 or more arguments directly
    for pos in range(32, len(args), 31):
        # ironically, np.broadcast does not properly handle np.broadcast
        # objects (it treats them as scalars)
        # use broadcasting to avoid allocating the full array
        b = broadcast_to(0, b.shape)
        b = np.broadcast(b, *args[pos:(pos + 31)])
    return b.shape 

Example 11

def _broadcast_to(array, shape, subok, readonly):
    shape = tuple(shape) if np.iterable(shape) else (shape,)
    array = np.array(array, copy=False, subok=subok)
    if not shape and array.shape:
        raise ValueError('cannot broadcast a non-scalar to a scalar array')
    if any(size < 0 for size in shape):
        raise ValueError('all elements of broadcast shape must be non-'
                         'negative')
    needs_writeable = not readonly and array.flags.writeable
    extras = ['reduce_ok'] if needs_writeable else []
    op_flag = 'readwrite' if needs_writeable else 'readonly'
    broadcast = np.nditer(
        (array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras,
        op_flags=[op_flag], itershape=shape, order='C').itviews[0]
    result = _maybe_view_as_subclass(array, broadcast)
    if needs_writeable and not result.flags.writeable:
        result.flags.writeable = True
    return result 

Example 12

def _broadcast_shape(*args):
    """Returns the shape of the ararys that would result from broadcasting the
    supplied arrays against each other.
    """
    if not args:
        raise ValueError('must provide at least one argument')
    # use the old-iterator because np.nditer does not handle size 0 arrays
    # consistently
    b = np.broadcast(*args[:32])
    # unfortunately, it cannot handle 32 or more arguments directly
    for pos in range(32, len(args), 31):
        # ironically, np.broadcast does not properly handle np.broadcast
        # objects (it treats them as scalars)
        # use broadcasting to avoid allocating the full array
        b = broadcast_to(0, b.shape)
        b = np.broadcast(b, *args[pos:(pos + 31)])
    return b.shape 

Example 13

def test_broadcast_in_args(self):
        # gh-5881
        arrs = [np.empty((6, 7)), np.empty((5, 6, 1)), np.empty((7,)),
                np.empty((5, 1, 7))]
        mits = [np.broadcast(*arrs),
                np.broadcast(np.broadcast(*arrs[:2]), np.broadcast(*arrs[2:])),
                np.broadcast(arrs[0], np.broadcast(*arrs[1:-1]), arrs[-1])]
        for mit in mits:
            assert_equal(mit.shape, (5, 6, 7))
            assert_equal(mit.nd, 3)
            assert_equal(mit.numiter, 4)
            for a, ia in zip(arrs, mit.iters):
                assert_(a is ia.base) 

Example 14

def test_broadcast_single_arg(self):
        # gh-6899
        arrs = [np.empty((5, 6, 7))]
        mit = np.broadcast(*arrs)
        assert_equal(mit.shape, (5, 6, 7))
        assert_equal(mit.nd, 3)
        assert_equal(mit.numiter, 1)
        assert_(arrs[0] is mit.iters[0].base) 

Example 15

def test_number_of_arguments(self):
        arr = np.empty((5,))
        for j in range(35):
            arrs = [arr] * j
            if j < 1 or j > 32:
                assert_raises(ValueError, np.broadcast, *arrs)
            else:
                mit = np.broadcast(*arrs)
                assert_equal(mit.numiter, j) 

Example 16

def test_boolean_assignment_value_mismatch(self):
        # A boolean assignment should fail when the shape of the values
        # cannot be broadcast to the subscription. (see also gh-3458)
        a = np.arange(4)

        def f(a, v):
            a[a > -1] = v

        assert_raises(ValueError, f, a, [])
        assert_raises(ValueError, f, a, [1, 2, 3])
        assert_raises(ValueError, f, a[:1], [1, 2, 3]) 

Example 17

def setUp(self):
        self.a = np.arange(np.prod([3, 1, 5, 6])).reshape(3, 1, 5, 6)
        self.b = np.empty((3, 0, 5, 6))
        self.complex_indices = ['skip', Ellipsis,
            0,
            # Boolean indices, up to 3-d for some special cases of eating up
            # dimensions, also need to test all False
            np.array(False),
            np.array([True, False, False]),
            np.array([[True, False], [False, True]]),
            np.array([[[False, False], [False, False]]]),
            # Some slices:
            slice(-5, 5, 2),
            slice(1, 1, 100),
            slice(4, -1, -2),
            slice(None, None, -3),
            # Some Fancy indexes:
            np.empty((0, 1, 1), dtype=np.intp),  # empty and can be broadcast
            np.array([0, 1, -2]),
            np.array([[2], [0], [1]]),
            np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()),
            np.array([2, -1], dtype=np.int8),
            np.zeros([1]*31, dtype=int),  # trigger too large array.
            np.array([0., 1.])]  # invalid datatype
        # Some simpler indices that still cover a bit more
        self.simple_indices = [Ellipsis, None, -1, [1], np.array([True]), 'skip']
        # Very simple ones to fill the rest:
        self.fill_indices = [slice(None, None), 0] 

Example 18

def broadcast_to(array, shape, subok=False):
    """Broadcast an array to a new shape.

    Parameters
    ----------
    array : array_like
        The array to broadcast.
    shape : tuple
        The shape of the desired array.
    subok : bool, optional
        If True, then sub-classes will be passed-through, otherwise
        the returned array will be forced to be a base-class array (default).

    Returns
    -------
    broadcast : array
        A readonly view on the original array with the given shape. It is
        typically not contiguous. Furthermore, more than one element of a
        broadcasted array may refer to a single memory location.

    Raises
    ------
    ValueError
        If the array is not compatible with the new shape according to NumPy's
        broadcasting rules.

    Notes
    -----
    .. versionadded:: 1.10.0

    Examples
    --------
    >>> x = np.array([1, 2, 3])
    >>> np.broadcast_to(x, (3, 3))
    array([[1, 2, 3],
           [1, 2, 3],
           [1, 2, 3]])
    """
    return _broadcast_to(array, shape, subok=subok, readonly=True) 

Example 19

def test_broadcast_in_args(self):
        # gh-5881
        arrs = [np.empty((6, 7)), np.empty((5, 6, 1)), np.empty((7,)),
                np.empty((5, 1, 7))]
        mits = [np.broadcast(*arrs),
                np.broadcast(np.broadcast(*arrs[:2]), np.broadcast(*arrs[2:])),
                np.broadcast(arrs[0], np.broadcast(*arrs[1:-1]), arrs[-1])]
        for mit in mits:
            assert_equal(mit.shape, (5, 6, 7))
            assert_equal(mit.nd, 3)
            assert_equal(mit.numiter, 4)
            for a, ia in zip(arrs, mit.iters):
                assert_(a is ia.base) 

Example 20

def test_broadcast_single_arg(self):
        # gh-6899
        arrs = [np.empty((5, 6, 7))]
        mit = np.broadcast(*arrs)
        assert_equal(mit.shape, (5, 6, 7))
        assert_equal(mit.nd, 3)
        assert_equal(mit.numiter, 1)
        assert_(arrs[0] is mit.iters[0].base) 

Example 21

def test_number_of_arguments(self):
        arr = np.empty((5,))
        for j in range(35):
            arrs = [arr] * j
            if j < 1 or j > 32:
                assert_raises(ValueError, np.broadcast, *arrs)
            else:
                mit = np.broadcast(*arrs)
                assert_equal(mit.numiter, j) 

Example 22

def test_boolean_assignment_value_mismatch(self):
        # A boolean assignment should fail when the shape of the values
        # cannot be broadcast to the subscription. (see also gh-3458)
        a = np.arange(4)

        def f(a, v):
            a[a > -1] = v

        assert_raises(ValueError, f, a, [])
        assert_raises(ValueError, f, a, [1, 2, 3])
        assert_raises(ValueError, f, a[:1], [1, 2, 3]) 

Example 23

def setUp(self):
        self.a = np.arange(np.prod([3, 1, 5, 6])).reshape(3, 1, 5, 6)
        self.b = np.empty((3, 0, 5, 6))
        self.complex_indices = ['skip', Ellipsis,
            0,
            # Boolean indices, up to 3-d for some special cases of eating up
            # dimensions, also need to test all False
            np.array(False),
            np.array([True, False, False]),
            np.array([[True, False], [False, True]]),
            np.array([[[False, False], [False, False]]]),
            # Some slices:
            slice(-5, 5, 2),
            slice(1, 1, 100),
            slice(4, -1, -2),
            slice(None, None, -3),
            # Some Fancy indexes:
            np.empty((0, 1, 1), dtype=np.intp),  # empty and can be broadcast
            np.array([0, 1, -2]),
            np.array([[2], [0], [1]]),
            np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()),
            np.array([2, -1], dtype=np.int8),
            np.zeros([1]*31, dtype=int),  # trigger too large array.
            np.array([0., 1.])]  # invalid datatype
        # Some simpler indices that still cover a bit more
        self.simple_indices = [Ellipsis, None, -1, [1], np.array([True]), 'skip']
        # Very simple ones to fill the rest:
        self.fill_indices = [slice(None, None), 0] 

Example 24

def broadcast_to(array, shape, subok=False):
    """Broadcast an array to a new shape.

    Parameters
    ----------
    array : array_like
        The array to broadcast.
    shape : tuple
        The shape of the desired array.
    subok : bool, optional
        If True, then sub-classes will be passed-through, otherwise
        the returned array will be forced to be a base-class array (default).

    Returns
    -------
    broadcast : array
        A readonly view on the original array with the given shape. It is
        typically not contiguous. Furthermore, more than one element of a
        broadcasted array may refer to a single memory location.

    Raises
    ------
    ValueError
        If the array is not compatible with the new shape according to NumPy's
        broadcasting rules.

    Notes
    -----
    .. versionadded:: 1.10.0

    Examples
    --------
    >>> x = np.array([1, 2, 3])
    >>> np.broadcast_to(x, (3, 3))
    array([[1, 2, 3],
           [1, 2, 3],
           [1, 2, 3]])
    """
    return _broadcast_to(array, shape, subok=subok, readonly=True) 

Example 25

def iterkeys(self,index):
        #import pdb; pdb.set_trace()
        if not isinstance(index,tuple) and self.shape[0] == 1:
            index = (1,index)
        if isinstance(index, int):
            key = np.unravel_index(index-1, self.shape, order='F')
            yield tuple(k+1 for k in key)
        elif isinstance(index,slice):
            index = range((index.start or 1)-1,
                          index.stop or np.prod(self.shape),
                          index.step or 1)
            for key in np.transpose(np.unravel_index(index, self.shape, order='F')): # 0-based
                yield tuple(k+1 for k in key)
        elif isinstance(index,(list,np.ndarray)):
            index = np.asarray(index)-1
            for key in np.transpose(np.unravel_index(index, self.shape, order='F')):
                yield tuple(k+1 for k in key)
        else:
            assert isinstance(index,tuple),index.__class__
            indices = []  # 1-based
            for i,ix in enumerate(index):
                if isinstance(ix,slice):
                    indices.append(np.arange((ix.start or 1),
                                             (ix.stop  or self.shape[i]) + 1,
                                             ix.step   or 1,
                                             dtype=int))
                else:
                    indices.append(np.asarray(ix))
            assert len(index) == 2
            indices[0].shape = (-1,1)
            for key in np.broadcast(*indices):
                yield tuple(map(int,key)) 

Example 26

def test_broadcast_in_args(self):
        # gh-5881
        arrs = [np.empty((6, 7)), np.empty((5, 6, 1)), np.empty((7,)),
                np.empty((5, 1, 7))]
        mits = [np.broadcast(*arrs),
                np.broadcast(np.broadcast(*arrs[:2]), np.broadcast(*arrs[2:])),
                np.broadcast(arrs[0], np.broadcast(*arrs[1:-1]), arrs[-1])]
        for mit in mits:
            assert_equal(mit.shape, (5, 6, 7))
            assert_equal(mit.nd, 3)
            assert_equal(mit.numiter, 4)
            for a, ia in zip(arrs, mit.iters):
                assert_(a is ia.base) 

Example 27

def test_number_of_arguments(self):
        arr = np.empty((5,))
        for j in range(35):
            arrs = [arr] * j
            if j < 2 or j > 32:
                assert_raises(ValueError, np.broadcast, *arrs)
            else:
                mit = np.broadcast(*arrs)
                assert_equal(mit.numiter, j) 

Example 28

def test_boolean_assignment_value_mismatch(self):
        # A boolean assignment should fail when the shape of the values
        # cannot be broadcast to the subscription. (see also gh-3458)
        a = np.arange(4)

        def f(a, v):
            a[a > -1] = v

        assert_raises(ValueError, f, a, [])
        assert_raises(ValueError, f, a, [1, 2, 3])
        assert_raises(ValueError, f, a[:1], [1, 2, 3]) 

Example 29

def setUp(self):
        self.a = np.arange(np.prod([3, 1, 5, 6])).reshape(3, 1, 5, 6)
        self.b = np.empty((3, 0, 5, 6))
        self.complex_indices = ['skip', Ellipsis,
            0,
            # Boolean indices, up to 3-d for some special cases of eating up
            # dimensions, also need to test all False
            np.array(False),
            np.array([True, False, False]),
            np.array([[True, False], [False, True]]),
            np.array([[[False, False], [False, False]]]),
            # Some slices:
            slice(-5, 5, 2),
            slice(1, 1, 100),
            slice(4, -1, -2),
            slice(None, None, -3),
            # Some Fancy indexes:
            np.empty((0, 1, 1), dtype=np.intp),  # empty and can be broadcast
            np.array([0, 1, -2]),
            np.array([[2], [0], [1]]),
            np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()),
            np.array([2, -1], dtype=np.int8),
            np.zeros([1]*31, dtype=int),  # trigger too large array.
            np.array([0., 1.])]  # invalid datatype
        # Some simpler indices that still cover a bit more
        self.simple_indices = [Ellipsis, None, -1, [1], np.array([True]), 'skip']
        # Very simple ones to fill the rest:
        self.fill_indices = [slice(None, None), 0] 

Example 30

def _compare_index_result(self, arr, index, mimic_get, no_copy):
        """Compare mimicked result to indexing result.
        """
        arr = arr.copy()
        indexed_arr = arr[index]
        assert_array_equal(indexed_arr, mimic_get)
        # Check if we got a view, unless its a 0-sized or 0-d array.
        # (then its not a view, and that does not matter)
        if indexed_arr.size != 0 and indexed_arr.ndim != 0:
            assert_(np.may_share_memory(indexed_arr, arr) == no_copy)
            # Check reference count of the original array
            if no_copy:
                # refcount increases by one:
                assert_equal(sys.getrefcount(arr), 3)
            else:
                assert_equal(sys.getrefcount(arr), 2)

        # Test non-broadcast setitem:
        b = arr.copy()
        b[index] = mimic_get + 1000
        if b.size == 0:
            return  # nothing to compare here...
        if no_copy and indexed_arr.ndim != 0:
            # change indexed_arr in-place to manipulate original:
            indexed_arr += 1000
            assert_array_equal(arr, b)
            return
        # Use the fact that the array is originally an arange:
        arr.flat[indexed_arr.ravel()] += 1000
        assert_array_equal(arr, b) 

Example 31

def shape_from_elems(*elems):
    if len(elems) == 0:
        return 1,
    return np.broadcast(*[np.ones(elem.shape) for elem in elems]).shape 

Example 32

def Softmax(x):
    # TODO make this numerically stable by shifting by max?
    exp = Exp(x)
    if len(x.shape) == 1:  # workaround because numpy einsum can't broadcast? https://github.com/numpy/numpy/issues/9984
        return exp / Einsum("i->", exp)
    elif len(x.shape) == 2:
        return exp / Einsum("bi,o->bo", exp, np.array([1]))
    elif len(x.shape) == 3:
        return exp / Einsum("abi,o->abo", exp, np.array([1]))
    elif len(x.shape) == 4:
        return exp / Einsum("abci,o->abco", exp, np.array([1]))
    else:
        raise ValueError("5D tensors not yet supported") 

Example 33

def test_broadcast_in_args(self):
        # gh-5881
        arrs = [np.empty((6, 7)), np.empty((5, 6, 1)), np.empty((7,)),
                np.empty((5, 1, 7))]
        mits = [np.broadcast(*arrs),
                np.broadcast(np.broadcast(*arrs[:2]), np.broadcast(*arrs[2:])),
                np.broadcast(arrs[0], np.broadcast(*arrs[1:-1]), arrs[-1])]
        for mit in mits:
            assert_equal(mit.shape, (5, 6, 7))
            assert_equal(mit.nd, 3)
            assert_equal(mit.numiter, 4)
            for a, ia in zip(arrs, mit.iters):
                assert_(a is ia.base) 

Example 34

def test_number_of_arguments(self):
        arr = np.empty((5,))
        for j in range(35):
            arrs = [arr] * j
            if j < 2 or j > 32:
                assert_raises(ValueError, np.broadcast, *arrs)
            else:
                mit = np.broadcast(*arrs)
                assert_equal(mit.numiter, j) 

Example 35

def test_boolean_assignment_value_mismatch(self):
        # A boolean assignment should fail when the shape of the values
        # cannot be broadcast to the subscription. (see also gh-3458)
        a = np.arange(4)

        def f(a, v):
            a[a > -1] = v

        assert_raises(ValueError, f, a, [])
        assert_raises(ValueError, f, a, [1, 2, 3])
        assert_raises(ValueError, f, a[:1], [1, 2, 3]) 

Example 36

def setUp(self):
        self.a = np.arange(np.prod([3, 1, 5, 6])).reshape(3, 1, 5, 6)
        self.b = np.empty((3, 0, 5, 6))
        self.complex_indices = ['skip', Ellipsis,
            0,
            # Boolean indices, up to 3-d for some special cases of eating up
            # dimensions, also need to test all False
            np.array(False),
            np.array([True, False, False]),
            np.array([[True, False], [False, True]]),
            np.array([[[False, False], [False, False]]]),
            # Some slices:
            slice(-5, 5, 2),
            slice(1, 1, 100),
            slice(4, -1, -2),
            slice(None, None, -3),
            # Some Fancy indexes:
            np.empty((0, 1, 1), dtype=np.intp),  # empty and can be broadcast
            np.array([0, 1, -2]),
            np.array([[2], [0], [1]]),
            np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()),
            np.array([2, -1], dtype=np.int8),
            np.zeros([1]*31, dtype=int),  # trigger too large array.
            np.array([0., 1.])]  # invalid datatype
        # Some simpler indices that still cover a bit more
        self.simple_indices = [Ellipsis, None, -1, [1], np.array([True]), 'skip']
        # Very simple ones to fill the rest:
        self.fill_indices = [slice(None, None), 0] 

Example 37

def _compare_index_result(self, arr, index, mimic_get, no_copy):
        """Compare mimicked result to indexing result.
        """
        arr = arr.copy()
        indexed_arr = arr[index]
        assert_array_equal(indexed_arr, mimic_get)
        # Check if we got a view, unless its a 0-sized or 0-d array.
        # (then its not a view, and that does not matter)
        if indexed_arr.size != 0 and indexed_arr.ndim != 0:
            assert_(np.may_share_memory(indexed_arr, arr) == no_copy)
            # Check reference count of the original array
            if no_copy:
                # refcount increases by one:
                assert_equal(sys.getrefcount(arr), 3)
            else:
                assert_equal(sys.getrefcount(arr), 2)

        # Test non-broadcast setitem:
        b = arr.copy()
        b[index] = mimic_get + 1000
        if b.size == 0:
            return  # nothing to compare here...
        if no_copy and indexed_arr.ndim != 0:
            # change indexed_arr in-place to manipulate original:
            indexed_arr += 1000
            assert_array_equal(arr, b)
            return
        # Use the fact that the array is originally an arange:
        arr.flat[indexed_arr.ravel()] += 1000
        assert_array_equal(arr, b) 

Example 38

def broadcast_to(array, shape, subok=False):
    """Broadcast an array to a new shape.

    Parameters
    ----------
    array : array_like
        The array to broadcast.
    shape : tuple
        The shape of the desired array.
    subok : bool, optional
        If True, then sub-classes will be passed-through, otherwise
        the returned array will be forced to be a base-class array (default).

    Returns
    -------
    broadcast : array
        A readonly view on the original array with the given shape. It is
        typically not contiguous. Furthermore, more than one element of a
        broadcasted array may refer to a single memory location.

    Raises
    ------
    ValueError
        If the array is not compatible with the new shape according to NumPy's
        broadcasting rules.

    Notes
    -----
    .. versionadded:: 1.10.0

    Examples
    --------
    >>> x = np.array([1, 2, 3])
    >>> np.broadcast_to(x, (3, 3))
    array([[1, 2, 3],
           [1, 2, 3],
           [1, 2, 3]])
    """
    return _broadcast_to(array, shape, subok=subok, readonly=True) 

Example 39

def test_broadcast_in_args(self):
        # gh-5881
        arrs = [np.empty((6, 7)), np.empty((5, 6, 1)), np.empty((7,)),
                np.empty((5, 1, 7))]
        mits = [np.broadcast(*arrs),
                np.broadcast(np.broadcast(*arrs[:2]), np.broadcast(*arrs[2:])),
                np.broadcast(arrs[0], np.broadcast(*arrs[1:-1]), arrs[-1])]
        for mit in mits:
            assert_equal(mit.shape, (5, 6, 7))
            assert_equal(mit.nd, 3)
            assert_equal(mit.numiter, 4)
            for a, ia in zip(arrs, mit.iters):
                assert_(a is ia.base) 

Example 40

def test_broadcast_single_arg(self):
        # gh-6899
        arrs = [np.empty((5, 6, 7))]
        mit = np.broadcast(*arrs)
        assert_equal(mit.shape, (5, 6, 7))
        assert_equal(mit.nd, 3)
        assert_equal(mit.numiter, 1)
        assert_(arrs[0] is mit.iters[0].base) 

Example 41

def test_number_of_arguments(self):
        arr = np.empty((5,))
        for j in range(35):
            arrs = [arr] * j
            if j < 1 or j > 32:
                assert_raises(ValueError, np.broadcast, *arrs)
            else:
                mit = np.broadcast(*arrs)
                assert_equal(mit.numiter, j) 

Example 42

def test_boolean_assignment_value_mismatch(self):
        # A boolean assignment should fail when the shape of the values
        # cannot be broadcast to the subscription. (see also gh-3458)
        a = np.arange(4)

        def f(a, v):
            a[a > -1] = v

        assert_raises(ValueError, f, a, [])
        assert_raises(ValueError, f, a, [1, 2, 3])
        assert_raises(ValueError, f, a[:1], [1, 2, 3]) 

Example 43

def setUp(self):
        self.a = np.arange(np.prod([3, 1, 5, 6])).reshape(3, 1, 5, 6)
        self.b = np.empty((3, 0, 5, 6))
        self.complex_indices = ['skip', Ellipsis,
            0,
            # Boolean indices, up to 3-d for some special cases of eating up
            # dimensions, also need to test all False
            np.array(False),
            np.array([True, False, False]),
            np.array([[True, False], [False, True]]),
            np.array([[[False, False], [False, False]]]),
            # Some slices:
            slice(-5, 5, 2),
            slice(1, 1, 100),
            slice(4, -1, -2),
            slice(None, None, -3),
            # Some Fancy indexes:
            np.empty((0, 1, 1), dtype=np.intp),  # empty and can be broadcast
            np.array([0, 1, -2]),
            np.array([[2], [0], [1]]),
            np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()),
            np.array([2, -1], dtype=np.int8),
            np.zeros([1]*31, dtype=int),  # trigger too large array.
            np.array([0., 1.])]  # invalid datatype
        # Some simpler indices that still cover a bit more
        self.simple_indices = [Ellipsis, None, -1, [1], np.array([True]), 'skip']
        # Very simple ones to fill the rest:
        self.fill_indices = [slice(None, None), 0] 

Example 44

def broadcast_to(array, shape, subok=False):
    """Broadcast an array to a new shape.

    Parameters
    ----------
    array : array_like
        The array to broadcast.
    shape : tuple
        The shape of the desired array.
    subok : bool, optional
        If True, then sub-classes will be passed-through, otherwise
        the returned array will be forced to be a base-class array (default).

    Returns
    -------
    broadcast : array
        A readonly view on the original array with the given shape. It is
        typically not contiguous. Furthermore, more than one element of a
        broadcasted array may refer to a single memory location.

    Raises
    ------
    ValueError
        If the array is not compatible with the new shape according to NumPy's
        broadcasting rules.

    Notes
    -----
    .. versionadded:: 1.10.0

    Examples
    --------
    >>> x = np.array([1, 2, 3])
    >>> np.broadcast_to(x, (3, 3))
    array([[1, 2, 3],
           [1, 2, 3],
           [1, 2, 3]])
    """
    return _broadcast_to(array, shape, subok=subok, readonly=True) 

Example 45

def test_broadcast_in_args(self):
        # gh-5881
        arrs = [np.empty((6, 7)), np.empty((5, 6, 1)), np.empty((7,)),
                np.empty((5, 1, 7))]
        mits = [np.broadcast(*arrs),
                np.broadcast(np.broadcast(*arrs[:2]), np.broadcast(*arrs[2:])),
                np.broadcast(arrs[0], np.broadcast(*arrs[1:-1]), arrs[-1])]
        for mit in mits:
            assert_equal(mit.shape, (5, 6, 7))
            assert_equal(mit.ndim, 3)
            assert_equal(mit.nd, 3)
            assert_equal(mit.numiter, 4)
            for a, ia in zip(arrs, mit.iters):
                assert_(a is ia.base) 

Example 46

def test_broadcast_single_arg(self):
        # gh-6899
        arrs = [np.empty((5, 6, 7))]
        mit = np.broadcast(*arrs)
        assert_equal(mit.shape, (5, 6, 7))
        assert_equal(mit.ndim, 3)
        assert_equal(mit.nd, 3)
        assert_equal(mit.numiter, 1)
        assert_(arrs[0] is mit.iters[0].base) 

Example 47

def test_number_of_arguments(self):
        arr = np.empty((5,))
        for j in range(35):
            arrs = [arr] * j
            if j < 1 or j > 32:
                assert_raises(ValueError, np.broadcast, *arrs)
            else:
                mit = np.broadcast(*arrs)
                assert_equal(mit.numiter, j) 

Example 48

def test_boolean_assignment_value_mismatch(self):
        # A boolean assignment should fail when the shape of the values
        # cannot be broadcast to the subscription. (see also gh-3458)
        a = np.arange(4)

        def f(a, v):
            a[a > -1] = v

        assert_raises(ValueError, f, a, [])
        assert_raises(ValueError, f, a, [1, 2, 3])
        assert_raises(ValueError, f, a[:1], [1, 2, 3]) 

Example 49

def setUp(self):
        self.a = np.arange(np.prod([3, 1, 5, 6])).reshape(3, 1, 5, 6)
        self.b = np.empty((3, 0, 5, 6))
        self.complex_indices = ['skip', Ellipsis,
            0,
            # Boolean indices, up to 3-d for some special cases of eating up
            # dimensions, also need to test all False
            np.array(False),
            np.array([True, False, False]),
            np.array([[True, False], [False, True]]),
            np.array([[[False, False], [False, False]]]),
            # Some slices:
            slice(-5, 5, 2),
            slice(1, 1, 100),
            slice(4, -1, -2),
            slice(None, None, -3),
            # Some Fancy indexes:
            np.empty((0, 1, 1), dtype=np.intp),  # empty and can be broadcast
            np.array([0, 1, -2]),
            np.array([[2], [0], [1]]),
            np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()),
            np.array([2, -1], dtype=np.int8),
            np.zeros([1]*31, dtype=int),  # trigger too large array.
            np.array([0., 1.])]  # invalid datatype
        # Some simpler indices that still cover a bit more
        self.simple_indices = [Ellipsis, None, -1, [1], np.array([True]),
                               'skip']
        # Very simple ones to fill the rest:
        self.fill_indices = [slice(None, None), 0] 

Example 50

def broadcast_to(array, shape, subok=False):
    """Broadcast an array to a new shape.

    Parameters
    ----------
    array : array_like
        The array to broadcast.
    shape : tuple
        The shape of the desired array.
    subok : bool, optional
        If True, then sub-classes will be passed-through, otherwise
        the returned array will be forced to be a base-class array (default).

    Returns
    -------
    broadcast : array
        A readonly view on the original array with the given shape. It is
        typically not contiguous. Furthermore, more than one element of a
        broadcasted array may refer to a single memory location.

    Raises
    ------
    ValueError
        If the array is not compatible with the new shape according to NumPy's
        broadcasting rules.

    Notes
    -----
    .. versionadded:: 1.10.0

    Examples
    --------
    >>> x = np.array([1, 2, 3])
    >>> np.broadcast_to(x, (3, 3))
    array([[1, 2, 3],
           [1, 2, 3],
           [1, 2, 3]])
    """
    return _broadcast_to(array, shape, subok=subok, readonly=True) 
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