Styjun

I'm trying to implement numpy's ufunc to work with a class, using the __array_ufunc__ method introduced in numpy v1.13.

To simplify, here's what the class could look like :

class toto():
 def __init__(self, value, name):
 self.value = value
 self.name = name
 def __add__(self, other):
 """add values and concatenate names"""
 return toto(self.value + other.value, self.name + other.name)
 def __sub__(self, other):
 """sub values and concatenate names"""
 return toto(self.value - other.value, self.name + other.name) 

tata = toto(5, "first") 
titi = toto(1, "second")

Now if I try to apply np.add between these two, I get the expected result, as np.add relies on add. But if I call say np.exp, I get an error as expected :

>>> np.exp(tata)
AttributeError: 'toto' object has no attribute 'exp'

Now what I would like to do is to "override" all numpy ufuncs to work smoothly with this class without having to redefine every methods (exp(self), log(self), ...) in the class.

I was planning to use numpy ufunc's [__array_ufunc__]1 to do this, but I don't really understand the doc as it doesn't provide a simple exemple of implementation.

If anyone has had any experience with this new functionnality that looks promising, could you provide a simple example ?

If I extend your class with a __array_ufunc__ method (and __repr__):

class toto():
 def __init__(self, value, name):
 self.value = value
 self.name = name
 def __add__(self, other):
 """add values and concatenate names"""
 return toto(self.value + other.value, self.name + other.name)
 def __sub__(self, other):
 """sub values and concatenate names"""
 return toto(self.value - other.value, self.name + other.name)

 def __repr__(self):
 return f"toto: self.value, self.name"
 def __array_ufunc__(self, *args, **kwargs):
 print(args)
 print(kwargs)

And try some ufunc calls:

In [458]: np.exp(tata) 
(<ufunc 'exp'>, '__call__', toto: 5, first)

In [459]: np.exp.reduce(tata) 
(<ufunc 'exp'>, 'reduce', toto: 5, first)

In [460]: np.multiply.reduce(tata) 
(<ufunc 'multiply'>, 'reduce', toto: 5, first)

In [461]: np.exp.reduce(tata,axes=(1,2)) 
(<ufunc 'exp'>, 'reduce', toto: 5, first)
'axes': (1, 2)
In [463]: np.exp.reduce(tata,axes=(1,2),out=np.arange(3)) 
(<ufunc 'exp'>, 'reduce', toto: 5, first)
'axes': (1, 2), 'out': (array([0, 1, 2]),)

That shows the information that your class receives. Evidently you can do what you want that. It can return NotImplemented. I suppose in your case it could apply the first argument to your self.value, or do some custom calculation.

For example if I add

 val = args[0].__call__(self.value) 
 return toto(val, self.name) 

I get:

In [468]: np.exp(tata) 
(<ufunc 'exp'>, '__call__', toto: 5, first)

Out[468]: toto: 148.4131591025766, first
In [469]: np.sin(tata) 
(<ufunc 'sin'>, '__call__', toto: 5, first)

Out[469]: toto: -0.9589242746631385, first

However if I put the object in an array, I still get the method error

In [492]: np.exp(np.array(tata)) 
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-492-4dc37eb0ffe3> in <module>
----> 1 np.exp(np.array(tata))

AttributeError: 'toto' object has no attribute 'exp'

Apparently ufunc on an object dtype array iterates on the elements of the array, expecting to use a 'relevant' method. For np.add (+) it looks for the __add__ method. For np.exp it looks for an exp method. This __array_ufunc__ isn't called.

So it looks like it's intended more for a subclass of ndarray, or something equivalent. You, I think, are trying to implement a class that can work as elements of an object dtype array.