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""" Utilities that manipulate strides to achieve desirable effects.
An explanation of strides can be found in the "ndarray.rst" file in the NumPy reference guide.
"""
"""Dummy object that just exists to hang __array_interface__ dictionaries and possibly keep alive a reference to a base array. """
# if input was an ndarray subclass and subclasses were OK, # then view the result as that subclass. # Since we have done something akin to a view from original_array, we # should let the subclass finalize (if it has it implemented, i.e., is # not None).
""" Create a view into the array with the given shape and strides.
.. warning:: This function has to be used with extreme care, see notes.
Parameters ---------- x : ndarray Array to create a new. shape : sequence of int, optional The shape of the new array. Defaults to ``x.shape``. strides : sequence of int, optional The strides of the new array. Defaults to ``x.strides``. subok : bool, optional .. versionadded:: 1.10
If True, subclasses are preserved. writeable : bool, optional .. versionadded:: 1.12
If set to False, the returned array will always be readonly. Otherwise it will be writable if the original array was. It is advisable to set this to False if possible (see Notes).
Returns ------- view : ndarray
See also -------- broadcast_to: broadcast an array to a given shape. reshape : reshape an array.
Notes ----- ``as_strided`` creates a view into the array given the exact strides and shape. This means it manipulates the internal data structure of ndarray and, if done incorrectly, the array elements can point to invalid memory and can corrupt results or crash your program. It is advisable to always use the original ``x.strides`` when calculating new strides to avoid reliance on a contiguous memory layout.
Furthermore, arrays created with this function often contain self overlapping memory, so that two elements are identical. Vectorized write operations on such arrays will typically be unpredictable. They may even give different results for small, large, or transposed arrays. Since writing to these arrays has to be tested and done with great care, you may want to use ``writeable=False`` to avoid accidental write operations.
For these reasons it is advisable to avoid ``as_strided`` when possible. """ # first convert input to array, possibly keeping subclass
# The route via `__interface__` does not preserve structured # dtypes. Since dtype should remain unchanged, we set it explicitly.
view.flags.writeable = False
raise ValueError('cannot broadcast a non-scalar to a scalar array') raise ValueError('all elements of broadcast shape must be non-' 'negative') (array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras, op_flags=[op_flag], itershape=shape, order='C') # never really has writebackifcopy semantics result.flags.writeable = True
return (array,)
"""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]]) """
"""Returns the shape of the arrays that would result from broadcasting the supplied arrays against each other. """ return () # use the old-iterator because np.nditer does not handle size 0 arrays # consistently # unfortunately, it cannot handle 32 or more arguments directly # 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 args
def broadcast_arrays(*args, **kwargs): """ Broadcast any number of arrays against each other.
Parameters ---------- `*args` : array_likes The arrays to broadcast.
subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned arrays will be forced to be a base-class array (default).
Returns ------- broadcasted : list of arrays These arrays are views on the original arrays. They are typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location. If you need to write to the arrays, make copies first.
Examples -------- >>> x = np.array([[1,2,3]]) >>> y = np.array([[4],[5]]) >>> np.broadcast_arrays(x, y) [array([[1, 2, 3], [1, 2, 3]]), array([[4, 4, 4], [5, 5, 5]])]
Here is a useful idiom for getting contiguous copies instead of non-contiguous views.
>>> [np.array(a) for a in np.broadcast_arrays(x, y)] [array([[1, 2, 3], [1, 2, 3]]), array([[4, 4, 4], [5, 5, 5]])]
""" # nditer is not used here to avoid the limit of 32 arrays. # Otherwise, something like the following one-liner would suffice: # return np.nditer(args, flags=['multi_index', 'zerosize_ok'], # order='C').itviews
raise TypeError('broadcast_arrays() got an unexpected keyword ' 'argument {!r}'.format(list(kwargs.keys())[0]))
# Common case where nothing needs to be broadcasted.
# TODO: consider making the results of broadcast_arrays readonly to match # broadcast_to. This will require a deprecation cycle. for array in args] |