Redefine *= operator in numpy

Question

As mentioned here and here, this doesn't work anymore in numpy 1.7+ :

import numpy
A = numpy.array([1, 2, 3, 4], dtype=numpy.int16)
B = numpy.array([0.5, 2.1, 3, 4], dtype=numpy.float64)
A *= B

A workaround is to do:

def mult(a,b):
    numpy.multiply(a, b, out=a, casting="unsafe")

def add(a,b):
    numpy.add(a, b, out=a, casting="unsafe")

mult(A,B)

but that's way too long to write for each matrix operation!

How can override the numpy *= operator to do this by default?

Should I subclass something?

A *= B saves you only 3 characters over mult(A,B). Can you articulate why you need to have the shortest method possible? — SethMMorton
– SethMMorton, Commented Jul 30, 2016 at 18:55
@SethMMorton : better explicit than implicit : *= is standard among all major programming languages ; having to define a mult function is not natural — Basj
– Basj, Commented Jul 30, 2016 at 19:11
@Eric As A is an int array, and that we do inplace *=, I want A to stay an int at the end. — Basj
– Basj, Commented Jul 30, 2016 at 20:21

ali_m · Accepted Answer · 2016-07-30 13:22:53Z

6

You can use np.set_numeric_ops to override array arithmetic methods:

import numpy as np

def unsafe_multiply(a, b, out=None):
    return np.multiply(a, b, out=out, casting="unsafe")

np.set_numeric_ops(multiply=unsafe_multiply)

A = np.array([1, 2, 3, 4], dtype=np.int16)
B = np.array([0.5, 2.1, 3, 4], dtype=np.float64)
A *= B

print(repr(A))
# array([ 0,  4,  9, 16], dtype=int16)

answered Jul 30, 2016 at 13:22

ali_m

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Comments

Kasravnd · Accepted Answer · 2016-07-30 12:28:59Z

1

You can create a general function and pass the intended attribute to it:

def calX(a,b, attr):
    try:
        return getattr(numpy, attr)(a, b, out=a, casting="unsafe")
    except AttributeError:
        raise Exception("Please enter a valid attribute")

Demo:

>>> import numpy
>>> A = numpy.array([1, 2, 3, 4], dtype=numpy.int16)
>>> B = numpy.array([0.5, 2.1, 3, 4], dtype=numpy.float64)
>>> calX(A, B, 'multiply')
array([ 0,  4,  9, 16], dtype=int16)
>>> calX(A, B, 'subtract')
array([ 0,  1,  6, 12], dtype=int16)

Note that if you want to override the result you can just assign the function's return to the first matrix.

A = calX(A, B, 'multiply')

edited Jul 30, 2016 at 12:28

answered Jul 30, 2016 at 12:25

Kasravnd

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3 Comments

Basj Over a year ago

Nice but this is not shorter than mult(A,B) with def mult(a,b): numpy.multiply(a, b, out=a, casting="unsafe")... What I want is override A*=B

Kasravnd Over a year ago

@Basj This is a general way, which makes you don't define a function for all operations. Also you don't need to override anything.

Kasravnd Over a year ago

@Basj If you consist to use in-place operators you better to create a custom matrix object and define it's operators manually which still you'd need to do this for all the operators.

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