IDTKAP #4: __debug__ and -O

Wayyyyy back in June 2012, I posted the first Stupid Python Trick, showing a way to abuse the assert statement to write debug code that is completely stripped when you run Python using the -O flag to enable optimization.

The -O flag is documented as removing assert instructions as though you never wrote them. But that's not all it does. It also sets a constant, __debug__, which is normally True, to False. The value of __debug__ is known at compile time, so Python can use it to completely discard conditional blocks predicated on __debug__, just as it does with assert statements, when running with the -O flag. And in fact, this is exactly what Python does!

The upshot is that you can write debug code that is stripped by Python's -O flag without abusing assert. This is easily demonstrated using Python's bytecode dissambler, dis.

from dis import dis

def debug_func():
    if __debug__:
       print "debugging"
    return

def noop_func():
    return

print "debug_func:"
dis(debug_func)
print
print "noop_func:"
dis(noop_func)

Save this as debugtest.py, then execute it with python -O debugtest.py. You'll see that the disassembly of the two functions is identical aside from the line number offsets. It's just as if we never even wrote the if statement and its subordinate print statement! Literally zero performance impact to production code.

debug_func:
  4           0 LOAD_CONST               0 (None)
              3 RETURN_VALUE

noop_func:
  2           0 LOAD_CONST               0 (None)
              3 RETURN_VALUE

What's more, when we run this script without the -O flag, Python optimizes away the test. That is, Python knows __debug__ is true at compile time, and so it just compiles the code inside the if statement as if it weren't inside an if statement! Here's what the disassembly of debug_func looks like when __debug__ is True (i.e., no -O flag is used):

debug_func:
 3           0 LOAD_CONST               1 ('debugging')
             3 PRINT_ITEM
             4 PRINT_NEWLINE

 4           5 LOAD_CONST               0 (None)
             8 RETURN_VALUE

By comparison, here's what it would look like if we were using some other conditional (say, a global variable called DEBUG). You'll see that this is much more complicated, and if you time it, you'll find that executing the test at runtime actually adds significant overhead.

debug_func:
  2           0 LOAD_GLOBAL              0 (DEBUG)
              3 JUMP_IF_FALSE            9 (to 15)
              6 POP_TOP

  3           7 LOAD_CONST               1 ('debugging')
             10 PRINT_ITEM
             11 PRINT_NEWLINE
             12 JUMP_FORWARD             1 (to 16)
        >>   15 POP_TOP
        >>   16 LOAD_CONST               0 (None)
             19 RETURN_VALUE

So basically, Python will not only strip debugging code if it's conditionalized by testing __debug__, it will also slightly improve the performance of your debug code when running in debug mode compared to testing a runtime flag. And best of all, it does this magic using the same command line flag, -O, that strips assert statements!

But wait, there's more! If you use an else clause with your if __debug__ statement, Python is smart enough to strip whichever clause doesn't apply and "inline" the clause that does!

def get_run_mode():
    if __debug__:
        return "debug"
    else:
        return "production"

dis(get_run_mode) running without -O:
  3           0 LOAD_CONST               1 ('debug')
              3 RETURN_VALUE

dis(get_run_mode) running with -O:
  5           0 LOAD_CONST               1 ('production')
              3 RETURN_VALUE

Once again, for comparison, here's how the bytecode looks when the function is written to force runtime evaluation of the condition, by using a global variable DEBUG instead of __debug__:

 2           0 LOAD_GLOBAL              0 (DEBUG)
             3 JUMP_IF_FALSE            5 (to 11)
             6 POP_TOP

 3           7 LOAD_CONST               1 ('debug')
            10 RETURN_VALUE
       >>   11 POP_TOP

 5          12 LOAD_CONST               2 ('production')
            15 RETURN_VALUE
            16 LOAD_CONST               0 (None)
            19 RETURN_VALUE

So, is Python smart enough to optimize if not __debug__ in the same way? Sadly, no:

def not_debug_test():
    if not __debug__:
        print "production"

Bytecode:

>>> dis(not_debug_test)
  2           0 LOAD_GLOBAL              0 (__debug__)
              3 JUMP_IF_TRUE             9 (to 15)
              6 POP_TOP

  3           7 LOAD_CONST               1 ('production')
             10 PRINT_ITEM
             11 PRINT_NEWLINE
             12 JUMP_FORWARD             1 (to 16)
        >>   15 POP_TOP
        >>   16 LOAD_CONST               0 (None)
             19 RETURN_VALUE

So if you want to write code that's run only in production, don't use if not __debug__. Write it like this instead:

    if __debug__:
        pass
     else:
         print "production"

So what did we learn?

1. Use if __debug__ to write debug code (along with else if desired).
2. Don't make up your own flag for this, as it will prevent Python from being clever.
3. Don't  use if not __debug__ because this will also prevent Python from being clever.
4. Use assert to assert invariants, not to perform stupid Python tricks like I presented last June.
5. Use the -O command line flag to tell Python when it's running in production. If you don't, you may be executing debugging code you don't want, with the potential performance degradation that implies.

Thanks to user Reddit user "brucifer" who posted this informative comment, and to user "Rainfly_X" who brought it to my attention.

By the way, in Python 3.x, True and False are also constants whose value is known at compile-time, and Python optimizes if True and if False similarly. In Python 2.x, the values of True and False can be changed at run time (seriously, try it if you don't believe me!), so this optimization isn't possible. None can't be changed in Python 2.x, but is only a true compile-time constant in Python 3.x, with the upshot that code under if None is also subject to being stripped out in Python 3.x but not in Python 2.x.

Better Python APIs

Oz Katz has a good entry over at his blog, Eventual Consistency, about making better APIs for your Python libraries.

PP #1: The always-true condition

Python Pitfalls are quirks of the Python programming language that may trip up those new to the language or to programming. First in yet another series.

My experience with Python is that my code usually does what I expect it to—in fact, more so than with any other programming language I've used. But that doesn't mean it's impossible to write code that doesn't do what you expect! Far from it. I see code like this on Stack Overflow pretty much every day:

while True:
    command = raw_input("command> ").lower().strip()
    if command == "stop" or "exit":
         break
    elif command == "help":
        print "quit, exit: exit the program"
        print "help:       display this help message" 
    elif command:
        print "Unrecognized command."

Can you guess the question? It's some variation of "No matter what command I enter, it always exits."

And the reason is always the same: a Boolean expression using or that is always truthy. (See PF #1: Truth and Consequences.) In this case, it's this line:

    if command == "quit" or "exit":

The programmer who wrote that line intended it to match either "quit" or "exit." Instead, it matches any command. The reason for this becomes obvious if we look at how Python evaluates the condition.

As described in PF #2: And and/or or, the value of a Boolean or operation is truthy if either operand is truthy. The operands of or here are:

• command == "quit"
• "exit"

The latter operand, being a string with nonzero length, is truthy in Python, and therefore the result of the or is always truthy. Therefore, this test matches everything and the suite under the if is always executed!

This error is particularly pernicious because it reads exactly how you would phrase it in English, and expresses the programmer's intent clearly. It's just that the meaning of or is more specific in Python than in English.

One way to write this test so that it works as expected is to explicitly tell Python to test command against both alternatives:

    if command == "quit" or command == "exit":

Of course, there's a shorter and clearer way to write this in Python:

    if command in ("quit", "exit"):

If you will be testing for a lot of alternatives and performance is important, use a set defined outside the loop. Testing for membership in an existing set is a lot faster than testing for membership in a tuple that's created new each time the test is executed.

exit_synonyms = set(["quit", "halt", "stop", 
                     "end", "whoa", "exit"])

while True:
    command = raw_input("command> ").lower().strip()
    if command in exit_synonyms:
        break
    # and so on as above

PF #3: Non-short-circuiting Boolean operations

Third entry in the Python Foundations series, thoroughly exploring the basic building blocks of Python programs.

Wow, it's been months, hasn't it? Our last Python Foundations entry talked about how the Boolean operators and and or work in Python, including their short-circuting behavior. But what if you don't want short-circuiting? What if you want both operands of and or or to be fully evaluated even when the first operand's value is sufficient to determine the expression's truth value?

Here, we can take advantage of the fact that Booleans are a special kind of integer in Python (see IDKTAP #2: Booleans as fancy integers), and perform math on them.

If you create a table of the results of adding the various combinations of 0 and 1, which are the integer values of False and True in Python:

+  | 0  1
----------
0  | 0  1
1  | 1  2

It bears a striking resemblance to the results of the Boolean or operator: the result is truthy (see PF #1: Truth and Consequences) if either of the operands is truthy.

Similarly, the multiplication operator is a dead ringer for Boolean and:

*  | 0  1
----------
0  | 0  0
1  | 0  1

Zero times anything is zero, just as False and anything is False.

If you have studied digital electronics, you may recognize this as Boolean arithmetic: arithmetic using only the values 0 and 1, where and is in fact equivalent to multiplication and or is equivalent to addition. The result of addition is "clamped" to 1 (that is, the highest result an addition can be is 1), and the only valid operations are addition and multiplication. It may seem like a toy arithmetic, but it's the foundation of everything a computer does.

We can use this equivalence of logical and arithmetic operations to implement non-short-circuiting ("long-circuiting"?) and and or in Python. To support truthy and falsy values, which may not actually be integers, we convert the operands to proper Booleans first using the bool() constructor.

bool(x) + bool(y) + bool(z)    # x or y or z
bool(x) * bool(y) * bool(z)    # x and y and z

Here, x, y, and z are always fully evaluated. If one of them is, say, a function call, the function is always called. In the equivalent expression using Boolean operators, remember, the expression will short-circuit as soon as it can, and may not evaluate all arguments. We explored how to exploit this behavior for fun and profit in PF #2.

Conveniently, multiplication has precedence over (is performed before) addition, just as and has precedence over or, so an expression like the following works exactly as you would expect:

bool(a) * bool(b) + bool(x) * bool(y)    # a and b or x and y

In fact, this is an easy way to remember which Boolean operator has precedence: because and is equivalent to multiplication, it has precedence over or, which is equivalent to addition. This is a rule I had trouble remembering when I started programming.

So far, what we've discussed isn't really specific to Python; you can do the same non-short-circuiting Boolean math in virtually any language with only minor syntax changes. We can, however, use Python's any() and all() functions to help make our non-short-circuiting Boolean operators a little more readable. Both functions operate on a sequence.

• any() - Returns True if any element is truthy (equivalent to Boolean or)
• all() - Returns True if all elemens are truthy (equivalent to Boolean and)

Technically, any()and all() do in fact short-circuit: they will stop inspecting elements in the sequence as soon as they see a value that definitively determines the result. That is, all() returns False when it sees the first falsy element, because a single such element is enough to know that not all elements are truthy. And any() returns True when it sees the first truthy element, because that's enough to know that there is at least one such.

However, the short-circuiting behavior is apparent only with generators, which "lazily" calculates a sequence one element at a time. (There will surely be a Python Foundations entry on generators in the future!) If you pass a sequence (i.e., a list or a tuple) to any() or all(), all the elements of the sequence will be fully evaluated when the sequence is constructed, before any() or all() is even involved. The fact that any() and all() short-circuit is then merely a performance optimization. Useful, in other words, but not exploitable for side effects as with and and or.

And so we can use any() and all() as non-short-circuiting or and and, respectively. x, y, and z below may be expressions of arbitrary complexity, and will be evaluated fully.

any([x, y])      # x or y
all([x, y])      # x and y
any([x, y, z])   # x or y or z (any number of arguments)
all((x, y, z))   # you may use a tuple instead of a list


As for the last example, I personally prefer using the square brackets and passing a list since it stands out more and makes it clearer what is happening, but constructing a tuple is faster, a fact which may be useful in some circumstances.

any() and all() can be nested to implement compound non-short-circuiting Boolean operations:

any([all([a, b]), all([x, y]])    # a and b or x and y

However, this gets difficult to read pretty quickly (unless, perhaps, you have experience with Lisp, where all operations are prefix).. Fortunately, any() and all() always return True or False (not the deciding element from the sequence, which may be merely truthy or falsy), so you can easily combine their results with + and *.

all([a, b]) + all([x, y])         # a and b or x and y, as above

We're almost finished here; this has become rather more in-depth than I intended! But while were talking about any() and all(), I should mention that it is something of a shame that neither returns the value that made the decision (i.e., the first truthy value for any() or the first falsy value for all()). This keeps them from being used to actually find the first truthy or falsy value in a sequence, which is especially a pity when you're using them with a generator because that value, having been consumed within any()/all(), is forever lost.

You can write your own versions of these functions that do return the deciding value, but because they are implemented in Python rather than in C, they will be slower than the built-ins. Still, here are such implementations.

def all(iterable):
    for item in iterable: 
        if not item: return item
    return True

def any(iterable):
    for item in iterable: 
        if item: return item
    return False

IDKTAP #3: Backticks

Did you know that quoting an expression using backticks is an alias for repr() in Python 2.x? That is, it prints the "representation" of the result of the expression, which is generally what you'd have to paste into your Python source file to produce that same object.

print `"Think, it ain't illegal yet!"`

They took this out in Python 3.

IDKTAP #2: Booleans as fancy integers

I Didn't Know That About Python uncovers surprising tidbits about our favorite programming language. 

In Python, a Boolean (type bool) is a subtype of integers. True is equal to 1 and False is equal to 0.

True  == 1   # True
False == 0   # True

The special methods called __str__ and __repr__ define how an object is converted to a string and how it is represented in an interactive context, respectively. So bool is basically implemented like this:

class bool(int):
    def __str__(self):
        return "True" if self else "False"
    __repr__ = __str__


True, False = bool(1), bool(0)

(There are some other details I'm glossing over, such as the fact that True and False are singletons and only ever have one instance. That is, bool(1) is always the same object. The example above does not have this behavior. For a fun exercise, try adding it.)

The surprising thing is that in Python 2, True and False are simply built-in identifiers assigned to two specific bool singletons. This means that they can be reassigned:

True = 3

This will very likely cause quite odd behavior in your programs, which is why Python 3 reserves True and False as actual language keywords and doesn't let you reassign them. Still, issubclass(bool, int) remains True.

PF #2: And and/or or

Second in the Python Foundations series.

In the previous installment of Python Foundations, I described the informal nature of truth and falsity in Python (dubbed "truthy" and "falsy"). Now let's explore how the Boolean operators and and or interact with such values.

In many languages, you'd expect the result of a Boolean operation (an operation between two truth values) to be True or False. But since Python considers a wide range of values True and False in a Boolean context, it is useful to return one of the operands. That is, when you combine two values using and or or, you get one of the values you put in.

In Python, and and or are short-circuiting. They always evaluate their first operand, and if that result is sufficient to decide the truth value of the expression, they stop and don't evaluate their second operand. Say what?

OK, have a look at the truth table for and:

AND   | True   False
-----------------------
True  | True   False
False | False  False

As you can see, False and anything is always False. Therefore, when the first operand of and is falsy, Python need not evaluate the second operand, and in fact does not. This not only saves time, it can be exploited for useful side effects (more on that later).

OR    | True   False
-----------------------
True  | True   True
False | True   False

This truth table for or similarly reveals that when the first operand is True, the result is always True, so Python doesn't need to evaluate the second operand of or when the first operand is truthy. And once again, since it doesn't need to, it doesn't.

What does it mean to not evaluate the second operand of and or or?  Among other things it means that if the second operand is a function call, that second function is never called:

v = a() or b()

If a() returns something truthy, b() is never even called. But what is the value of v? Python assigns v the return value of the operand that decided the result: a() if a's return value is truthy, or b() if a's return value is falsy. It works similarly with and, except the result is a's return value if it is falsy and b's otherwise. In short, the actual operand values are used in place of the constants True and False.

Short-circuiting lets you use and and or as substitutes for if statements in certain circumstances. It's very easy to write unreadable code using short-circuiting and and or; there was an idiom that was commonly used in an attempt to make up for the fact that Python didn't have a proper ternary conditional operator. Then Python added a proper ternary conditional operator (v = a if b else c) and everybody forgot about the hackish workaround. But simpler uses of short-circuiting are pretty readable and useful.

For example, easily provide a default value for blank inputs:

name = raw_input("What is your name? ") or "Jude"
print "Hey,", name, "don't make it bad"

Or check to make sure something is callable before calling it:

callable(func) and func()

I find those generally more readable than the alternatives using if statements:

name = raw_input("What is your name? ")
if not name:
    name = "Jude"

if callable(func):
    func()

Short-circuiting is, by the way, the reason Python doesn't let you override and and or using special methods on your own objects the way you can with arithmetic operators. Both operands would be evaluated before the operation is performed, so short-circuiting simply isn't possible. (There is in fact a proposal to change that, but it hasn't been implemented yet.)