Question

What happens internally when I press Enter?

My motivation for asking, besides plain curiosity, is to figure out what happens when you

from sympy import *

and enter an expression. How does it go from Enter to calling

__sympifyit_wrapper(a,b)

in sympy.core.decorators? (That’s the first place winpdb took me when I tried inspecting an evaluation.) I would guess that there is some built-in eval function that gets called normally, and is overridden when you import sympy?

Answer 1

All right after playing around with it some more I think I’ve got it.. when I first asked the question I didn’t know about operator overloading.

So, what’s going on in this python session?

>>> from sympy import *
>>> x = Symbol(x)
>>> x + x
2*x

It turns out there’s nothing special about how the interpreter evaluates the expression; the important thing is that python translates

x + x

into

x.__add__(x)

and Symbol inherits from the Basic class, which defines __add__(self, other) to return Add(self, other). (These classes are found in sympy.core.symbol, sympy.core.basic, and sympy.core.add if you want to take a look.)

So as Jerub was saying, Symbol.__add__() has a decorator called _sympifyit which basically converts the second argument of a function into a sympy expression before evaluating the function, in the process returning a function called __sympifyit_wrapper which is what I saw before.

Using objects to define operations is a pretty slick concept; by defining your own operators and string representations you can implement a trivial symbolic algebra system quite easily:

symbolic.py —

class Symbol(object):
    def __init__(self, name):
        self.name = name
    def __add__(self, other):
        return Add(self, other)
    def __repr__(self):
        return self.name

class Add(object):
    def __init__(self, left, right):
        self.left = left
        self.right = right
    def __repr__(self):
        return self.left + '+' + self.right

Now we can do:

>>> from symbolic import *
>>> x = Symbol('x')
>>> x+x
x+x

With a bit of refactoring it can easily be extended to handle all basic arithmetic:

class Basic(object):
    def __add__(self, other):
        return Add(self, other)
    def __radd__(self, other): # if other hasn't implemented __add__() for Symbols
        return Add(other, self)
    def __mul__(self, other):
        return Mul(self, other)
    def __rmul__(self, other):
        return Mul(other, self)
    # ...

class Symbol(Basic):
    def __init__(self, name):
        self.name = name
    def __repr__(self):
        return self.name

class Operator(Basic):
    def __init__(self, symbol, left, right):
        self.symbol = symbol
        self.left = left
        self.right = right
    def __repr__(self):
        return '{0}{1}{2}'.format(self.left, self.symbol, self.right)

class Add(Operator):
    def __init__(self, left, right):
        self.left = left
        self.right = right
        Operator.__init__(self, '+', left, right)

class Mul(Operator):
    def __init__(self, left, right):
        self.left = left
        self.right = right
        Operator.__init__(self, '*', left, right)

# ...

With just a bit more tweaking we can get the same behavior as the sympy session from the beginning.. we’ll modify Add so it returns a Mul instance if its arguments are equal. This is a bit trickier since we have get to it before instance creation; we have to use __new__() instead of __init__():

class Add(Operator):
    def __new__(cls, left, right):
        if left == right:
            return Mul(2, left)
        return Operator.__new__(cls)
    ...

Don’t forget to implement the equality operator for Symbols:

class Symbol(Basic):
    ...
    def __eq__(self, other):
        if type(self) == type(other):
            return repr(self) == repr(other)
        else:
            return False
    ...

And voila. Anyway, you can think of all kinds of other things to implement, like operator precedence, evaluation with substitution, advanced simplification, differentiation, etc., but I think it’s pretty cool that the basics are so simple.