nums.numpy.left_shift

nums.numpy.left_shift(x1: nums.core.array.blockarray.BlockArray, x2: nums.core.array.blockarray.BlockArray, out: Optional[nums.core.array.blockarray.BlockArray] = None, where=True, **kwargs) → nums.core.array.blockarray.BlockArray

Shift the bits of an integer to the left.

This docstring was copied from numpy.left_shift.

Some inconsistencies with the NumS version may exist.

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.

x1BlockArray of integer type

Input values.

x2BlockArray 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).

outBlockArray, None, or optional

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

whereBlockArray, optional

This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.

**kwargs

For other keyword-only arguments, see the ufunc docs.

outarray of integer type

Return x1 with bits shifted x2 times to the left.

right_shift : Shift the bits of an integer to the right. binary_repr : Return the binary representation of the input number

as a string.

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> nps.left_shift(nps.array(5), nps.array(2)).get()  
array(20)

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 = nps.left_shift(nps.array(255, dtype=nps.uint8),
...     nps.array(1)) # Expect 254  
>>> print(a, type(a)) # Unexpected result due to upcasting  
510 <class 'nums.core.array.blockarray.BlockArray'>