dpnp.ravel

dpnp.ravel(a, order='C')

Return a contiguous flattened array.

For full documentation refer to numpy.ravel.

Parameters:

• x ({dpnp.ndarray, usm_ndarray}) -- Input array. The elements in a are read in the order specified by order, and packed as a 1-D array.

• order ({None, "C", "F", "A"}, optional) -- The elements of a are read using this index order. "C" means to index the elements in row-major, C-style order, with the last axis index changing fastest, back to the first axis index changing slowest. "F" means to index the elements in column-major, Fortran-style order, with the first index changing fastest, and the last index changing slowest. Note that the "C" and "F" options take no account of the memory layout of the underlying array, and only refer to the order of axis indexing. "A" means to read the elements in Fortran-like index order if a is Fortran contiguous in memory, C-like order otherwise. order=None is an alias for order="C". Default: "C".

Returns:

out -- A contiguous 1-D array of the same subtype as a, with shape (a.size,).

Return type:

dpnp.ndarray

Limitations

order="K" is not supported and the function raises NotImplementedError exception.

See also

dpnp.ndarray.flat

1-D iterator over an array.

dpnp.ndarray.flatten

1-D array copy of the elements of an array in row-major order.

dpnp.ndarray.reshape

Change the shape of an array without changing its data.

dpnp.reshape

The same as dpnp.ndarray.reshape.

Notes

In row-major, C-style order, in two dimensions, the row index varies the slowest, and the column index the quickest. This can be generalized to multiple dimensions, where row-major order implies that the index along the first axis varies slowest, and the index along the last quickest. The opposite holds for column-major, Fortran-style index ordering.

When a view is desired in as many cases as possible, arr.reshape(-1) may be preferable.

Examples

>>> import dpnp as np
>>> x = np.array([[1, 2, 3], [4, 5, 6]])
>>> np.ravel(x)
array([1, 2, 3, 4, 5, 6])

>>> x.reshape(-1)
array([1, 2, 3, 4, 5, 6])

>>> np.ravel(x, order='F')
array([1, 4, 2, 5, 3, 6])

When order is "A", it will preserve the array's "C" or "F" ordering:

>>> np.ravel(x.T)
array([1, 4, 2, 5, 3, 6])
>>> np.ravel(x.T, order='A')
array([1, 2, 3, 4, 5, 6])