In [1]:

    

import numpy as np


    

In [2]:

    

a = np.arange(12).reshape(3, 4)
print(a)


    

    




[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]

In [3]:

    

b = np.arange(-5, 7).reshape(3, 4)
print(b)


    

    




[[-5 -4 -3 -2]
 [-1  0  1  2]
 [ 3  4  5  6]]

In [4]:

    

a_copysign = np.copysign(a, b)
print(a_copysign)


    

    




[[-0. -1. -2. -3.]
 [-4.  5.  6.  7.]
 [ 8.  9. 10. 11.]]

In [5]:

    

print(a_copysign.dtype)


    

    




float64

In [6]:

    

print(np.copysign(10, -5))


    

    




-10.0

In [7]:

    

print(type(np.copysign(10, -5)))


    

    




<class 'numpy.float64'>

In [8]:

    

print(a)


    

    




[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]

In [9]:

    

b_small = np.array([-100, -100, 100, 100])
print(b_small)


    

    




[-100 -100  100  100]

In [10]:

    

print(a + b_small)


    

    




[[-100  -99  102  103]
 [ -96  -95  106  107]
 [ -92  -91  110  111]]

In [11]:

    

print(np.copysign(a, b_small))


    

    




[[-0. -1.  2.  3.]
 [-4. -5.  6.  7.]
 [-8. -9. 10. 11.]]

In [12]:

    

b_mismatch = np.array([-100, -100, 100])
print(b_mismatch)


    

    




[-100 -100  100]

In [13]:

    

# print(np.copysign(a, b_mismatch))
# ValueError: operands could not be broadcast together with shapes (3,4) (3,)


    

In [14]:

    

print(np.copysign(b, -10))


    

    




[[-5. -4. -3. -2.]
 [-1. -0. -1. -2.]
 [-3. -4. -5. -6.]]

In [15]:

    

print(np.abs(b) * -1)


    

    




[[-5 -4 -3 -2]
 [-1  0 -1 -2]
 [-3 -4 -5 -6]]

In [16]:

    

print(np.abs(b) * -1.0)


    

    




[[-5. -4. -3. -2.]
 [-1. -0. -1. -2.]
 [-3. -4. -5. -6.]]

In [17]:

    

a_special = np.array([0.0, -0.0, np.inf, -np.inf, np.nan])
print(a_special)


    

    




[  0.  -0.  inf -inf  nan]

In [18]:

    

print(np.copysign(a_special, 1))


    

    




[ 0.  0. inf inf nan]

In [19]:

    

print(np.copysign(a_special, -1))


    

    




[ -0.  -0. -inf -inf  nan]

In [20]:

    

print(np.copysign([10, 10, 10, 10, 10], a_special))


    

    




[ 10. -10.  10. -10.  10.]

In [21]:

    

print(np.copysign([-10, -10, -10, -10, -10], a_special))


    

    




[ 10. -10.  10. -10.  10.]

In [22]:

    

print(np.copysign(10, 0))


    

    




10.0

In [23]:

    

print(np.copysign(0, 10))


    

    




0.0

In [24]:

    

print(np.copysign(0, -10))


    

    




-0.0

In [25]:

    

a_complex = np.array([10 + 10j, -10 + 10j])
print(a_complex)


    

    




[ 10.+10.j -10.+10.j]

In [26]:

    

# print(np.copysign(a_complex, 1))
# TypeError: ufunc 'copysign' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''


    

In [27]:

    

# print(np.copysign([1, 1], a_complex))
# TypeError: ufunc 'copysign' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''