# jax.numpy.left_shift¶

jax.numpy.left_shift(x1, x2)

Shift the bits of an integer to the left.

LAX-backend implementation of left_shift(). Original docstring below.

left_shift(x1, x2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj])

Bits are shifted to the left by appending x2 0s at the right of x1. Since the internal representation of numbers is in binary format, this operation is equivalent to multiplying x1 by 2**x2.

Parameters
• x1 (array_like of integer type) – Input values.

• x2 (array_like of integer type) – Number of zeros to append to x1. Has to be non-negative. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

Returns

out – Return x1 with bits shifted x2 times to the left. This is a scalar if both x1 and x2 are scalars.

Return type

array of integer type

right_shift()

Shift the bits of an integer to the right.

binary_repr()

Return the binary representation of the input number as a string.

Examples

>>> np.binary_repr(5)
'101'
>>> np.left_shift(5, 2)
20
>>> np.binary_repr(20)
'10100'

>>> np.left_shift(5, [1,2,3])
array([10, 20, 40])


Note that the dtype of the second argument may change the dtype of the result and can lead to unexpected results in some cases (see Casting Rules):

>>> a = np.left_shift(np.uint8(255), 1) # Expect 254
>>> print(a, type(a)) # Unexpected result due to upcasting
510 <class 'numpy.int64'>
>>> b = np.left_shift(np.uint8(255), np.uint8(1))
>>> print(b, type(b))
254 <class 'numpy.uint8'>