Elementwise operations and broadcasting

import sf

x = sf.array([0.1, -0.25, 0.5], dtype=sf.sf16)
x * x           # sfarray([0.00997925, 0.0625    , 0.25      ], dtype=sf16)
x + 0.25        # sfarray([0.3500061, 0.       , 0.75     ], dtype=sf16)

A = sf.array([[0.1, 0.2], [0.3, 0.4]], dtype=sf.sf16)
A + sf.array([0.1, 0.1], dtype=sf.sf16)       # the row broadcasts
# sfarray([[0.20001221, 0.30001831],
#          [0.3999939 , 0.5       ]], dtype=sf16)

x + x
# SFOverflowError: sf16 +: overflow in 1 of 3 element(s)

sf.array([0.5, 0.25], dtype=sf.sf16) / sf.array([0.0, 0.5], dtype=sf.sf16)
# SFZeroDivisionError: sf16 /: divide in 1 of 2 element(s)

with sf.errstate(over="ignore"):
    x + x       # sfarray([ 0.20001221, -0.5       ,  0.99996948], dtype=sf16)

b = sf.array([0.5, 0.0, 0.75], dtype=sf.sf16)
sf.div(x, b, where=b != 0)     # sfarray([0.20001221, 0.        , 0.66665649], dtype=sf16)

sf.sqrt(sf.array([0.25, 0.5], dtype=sf.sf16))    # sfarray([0.5       , 0.70709229], dtype=sf16)
sf.clip(x, -0.2, 0.2)                            # sfarray([ 0.1000061 , -0.20001221,  0.20001221], dtype=sf16)
sf.where(x > 0, x, 0)                            # sfarray([0.1000061, 0.       , 0.5      ], dtype=sf16)

x > 0           # array([ True, False,  True])
x == 0.1        # array([False, False, False]): exact, as for scalars

x.astype(sf.sf8)          # sfarray([ 0.1015625, -0.25     ,  0.5      ], dtype=sf8)
sf.cast(x, sf.sf8)        # the same