A function is a named body of commands that do significant work, converting input arguments into a returned answer. When we define a function, we should summarize its knowledge-production capabilities. This is critical when we assemble a program from a collection of functions, because we connect functions together based on what they are capable of doing.

A function is a “mini-program”: it has inputs, namely the arguments assigned to its parameters, and it has outputs, namely the information returned as the function’s answer. It acts like a logic gate or chip, with “input wires” and “output wires”.

Here is an example that shows the format we use for writing a function:

```
def recip(n) : # HEADER LINE : LISTS NAME AND PARAMETERS
"""recip computes the reciprocal for n"""
"""
{ pre n != 0 # PRECONDITION : REQUIRED PROPERTY OF ARGUMENTS
post ans == 1.0 / n # POSTCONDITION : THE GENERATED KNOWLEDGE
return ans # VARIABLE USED TO RETURN THE RESULT }
"""
ans = 1.0 / n # BODY (no assignment to parameter n allowed)
return ans
```

The precondition states the situation under which the function operates
correctly, and the postcondition states what the function has accomplished when
it terminates.
For simplicity, we require that *no assignment is made to a parameter inside the
body of the function*.
In a call-by-value language like Python and Java, this is never a handicap.

The functions’s *specification* consists of its header line and its `pre`

-,
`post`

-condition, and `return`

lines;
it tells us how to use the function correctly:
(The comment with the informal description is also nice to have.)
Here is the specification for the above function:

```
def recip(n) :
"""recip computes the reciprocal for n"""
"""
{ pre n != 0
post ans == 1.0 / n
return ans }
"""
```

The specification tells us what we need to know to use the function correctly.
*The person/program who calls the function is not supposed to read the
function’s code to know how to use the function*.
This is especially crucial when you use a function from a library that was
written by someone else – ideally, you should not read the library’s code to
learn how to call the library’s functions!

Back to the example: Since we do not allow assignments to parameter `n`

within
the body of `recip`

, we know that the value of `n`

in the postcondition is
the same as the value of `n`

in the precondition –
this makes the postcondition meaningful.

To call the function, we supply an argument that binds to (is assigned to) its parameter, and we supply a variable that is the target for the returned answer. Once again, for this specification:

```
def recip(n) :
"""recip computes the reciprocal for n"""
"""
{ pre n != 0
post ans == 1.0 / n
return ans }
"""
```

we correctly call the function like this:

```
a = readInt()
if a > 0 :
"""
{ 1. a > 0 premise
2. a != 0 algebra 1 } # this proves recip's precondition
"""
b = recip(a)
"""{ 1. b == 1.0 / a premise }""" # in return, we presume its postcondition
print "the reciprocal of", a, "is", b
else :
. . .
```

To call the function, *we must prove the function’s precondition at the point of
call for its argument*.
As our reward for establishing the precondition, we receive in return the
postcondition, stated in terms of `a`

and `b`

: `b == 1.0 / a`

.

For simplicity, we require that *the target variable that receives the
function’s answer does not appear in the function’s arguments*, e.g., the call,
`b = recip(b+2)`

, is not allowed, because it confuses the `b`

in the
argument to `recip`

with the `b`

in the answer.

In contrast, we cannot presume `recip`

’s postcondition in this situation:

```
x = readInt()
# We do not know if x is nonzero....
y = recip(x)
"""{ ??? }"""
```

Since we did not prove `x != 0`

as a precondition for applying `recip`

,
we cannot assert the postcondition after the call.
Literally, we have no knowledge of what the function produces in this situation
– perhaps the call will “crash” (generate an exception)!

Here is a summary of what we have developed so far.
Given a function, `f`

, with a pre-post specification, we must show that the
presumption of the precondition at the beginning of the function’s body lets us
prove the postcondition at the end of the body:

```
def f(x):
"""
{ pre Q (where assertion Q can mention x) Call this, f_pre.
post R (where assertion R can mention ans and x) Call this, f_post.
return ans (the name of the answer variable) }
"""
"""{ 1. Q premise }"""
BODY # does not assign to x
"""{ ... prove R here, that is, ans and x have property R }"""
```

Outside of the function, `f`

, we write `f_pre`

for `Q`

and `f_post`

for
`R`

.
Here is the schematic for a correct call of the function.
It is a variation of the assignment law:

```
"""{ [e/x]f_pre }""" # prove the precondition, where e binds to parameter x
y = f(e) # y does not appear in e
"""
{ 1. [y/ans][e/x]f_post premise # presume the postcondition,
# where y gets ans's value
2. [y_old/y][e/x]f_pre premise # knowledge before the call is revised
. . . }
"""
```

Recall that `[e/x]f_pre`

defines a substitution of `e`

for all occurrences
of `x`

within formula `f_pre`

.

Here is another example, an absolute-value function:

```
def absValue(x) :
"""
{ pre x != 0
post ans > 0 and (ans == x or ans == 0 - x)
return ans }
"""
if x < 0 :
ans = 0 - x
else :
ans = x
return ans
```

The precondition is crucial to the success of the postcondition (that the answer is positive). We construct a proof that converts the knowledge in the precondition into the knowledge in the postcondition:

```
def absValue(x) :
"""
{ pre x != 0
post ans > 0 and (ans == x or ans == 0 - x)
return ans }
"""
if x < 0 :
ans = 0 - x
"""
{ 1. x < 0 premise
2. ans == 0 - x premise
3. ans > 0 algebra 1 2
4. ans == x or ans == 0 - x algebra 2
5. return 3 4 } # lets us collect two facts at once
"""
else :
"""
{ 1. x != 0 premise
2. not(x < 0) premise
3. x > 0 algebra 1 2 }
"""
ans = x
"""
{ 1. ans == x premise
2. x > 0 premise
3. ans > 0 subst 1 2
4. ans == x or ans == 0 - x algebra 1
5. return 3 4 }
"""
# in each arm, we proved ans == x or ans == 0 - x and also ans > 0
"""
{ 1. ans == x or ans == 0 - x premise
2. ans > 0 premise
3. ans > 0 and (ans == x or ans == 0 - x) andi 2 1 }
"""
return ans
```

Now that the function is certified to satisfy its specification, we can call it:

```
n = readInt()
if n != 0 :
"""{ 1. n != 0 premise }"""
m = absValue(n)
"""
{ 1. ans > 0 and (ans == x or ans == 0 - x) premise
2. m > 0 ande 1 }
"""
```

Function specifications are critical to software libraries: Every library component simply must has a specification that describes how to connect the component into an application. Often the specifications are written in English, but underlying the English descriptions are the pre-postconditions illustrated in this section.

When you call a function that someone else wrote, you rely on to tell you how
the function behaves.
(In many cases, you can’t read the code, e.g., .NET components)
If you the person *writing* the function for others to use, you supply the
function’s specification.
You can calculate the specification using the laws we studied in the previous
chapter.
But better still, you should *start with the specification and write the
function so that it matches the specification*.

You start by stating, in English or otherwise, the function’s goal. You study the goal and consider how to meet it; you write the code. You take note of the requirements your function needs on its entry parameters and global variables to reach the goal. Finally, you apply the programming-logic laws to show that the coding matches the specification.

Here is a simplistic example.
Say you must write a function that receives two numbers as inputs and selects
the maximum, or larger, of the two as the function’s output.
The specification of the function, `max`

, might look like this:

```
def max(x, y)
"""max selects the larger of x and y and returns it as its answer"""
"""
{ pre ???
post (ans == x v ans == y) ^ (ans >= x ^ ans >= y)
return ... }
"""
```

You must write the code for function max so that it meets the postcondition.
Along the way, you might require some restrictions on `x`

and `y`

for your
solution to work.
These would be listed in the precondition.

The logical operators in the postcondition sometimes give us hints how to code
the function.
Here, we see that the function’s answer will be either `x`

or `y`

,
suggesting that the function will use assignment commands of the form,
`ans = x`

and `ans = y`

.
We also see that there is an “or” operator in the specification.
This suggests that we will require an if-command to choose which of the two
assignments to use.
Although it is not an exact science, we can move from the postcondition to this
possible coding:

```
def max(x, y)
"""
{ pre ???
post (ans == x v ans == y) ^ (ans >= x ^ ans >= y)
return ans }
"""
if x > y :
ans = x
else :
ans = y
return ans
```

Now, we use the laws for assignments and conditionals to compute whether a
precondition is needed that places restrictions on `x`

and `y`

so that the
function behaves properly.
What we see here is that our coding works correctly with numbers
(and with strings, too!).
But it might not work correctly, say, if `x`

or `y`

are arrays or pointers.
For this reason, it seems safe to use this precondition:

```
def max(x, y)
"""max selects the larger of x and y and returns it as its answer"""
"""
{ pre x and y are both numbers or both strings
post (ans == x v ans == y) ^ (ans >= x ^ ans >= y)
return ans }
"""
if x > y :
ans = x
else :
ans = y
return ans
```

Now, we have a function — a component — that others can insert into their programs.

Here is a precise statement of how to reason backwards from a goal across a function call. First, given the specification,

```
def f(x):
"""
{ pre Q
post R
return ans
}
"""
```

If a call to `f`

is meant to achieve a goal, `G`

, we reason backwards from
`G`

to its subgoal like this:

```
"""{ subgoal: [e/x]f_pre ^ ([e/x][y/ans]f_post --> G) }"""
y = f(e) # y does not appear in argument e
"""{ goal: G }"""
```

That is, for the call, `f(e)`

, to accomplish `G`

, it must be the case that
`f`

’s postcondition implies `G`

and that we can prove `f`

’s precondition
holds true (so that we can call `f`

).
An example:

```
def recip(n) :
"""
{ pre n != 0
post ans == 1.0 / n
return ans
}
"""
```

and the program

```
a = readInt()
"""{ subgoal: ??? }"""
b = recip(a)
"""{ goal: b < 1.0/3 }"""
```

we compute that the subgoal is `a != 0 ^ (b = 1.0/a --> b < 1.0/3)`

.
This simplifies to `a != 0 ^ (1.0/a < 1.0/3))`

, which simplifies to
`a != 0 ^ a > 3`

, which simplifies to `a > 3`

.
This tells us what `assert`

or `if`

command to add to our program:

```
a = readInt()
assert a > 3
"""{ subgoal: a > 3 }"""
b = recip(a)
"""{ goal: b < 1.0/3 }"""
```

That is, if we expect the computed reciprocal to be less than one-third, then we must supply an input int that is 4 or more.

The term, “function”, suggests that the named body of commands will accept one
or more arguments and return an answer.
But we can have functions that return no answers and even receive no arguments,
because they can read and update the values of non-local, *global* variables.
We call a function that updates a global variable, a *procedure*.

Say that a variable, `v`

, is “global” to a function `f`

if `v`

exists
prior to a call to `f`

.
(More precisely stated, `v`

’s cell does not live in `f`

’s
namespace/activation-record.)
For example, `pi`

is global to `circumference`

here:

```
pi = 3.14159
def circumference(diameter) :
answer = pi * diameter
return answer
. . .
r = readFloat("Type radius of a circle: ")
c = circumference(2 * r) # compute circumference of circle
area = r * r * pi # compute circle's area
```

A global variable “lives” before a function is called and lives after the
function finishes.
In the above example, `pi`

is defined before the function is called, and
it exists after the function finishes.
In contrast, `pi`

is local here:

```
def circ(d) :
pi = 3.14159 # pi is created each time circ is called
answer = pi * diameter
return answer
. . .
r = readFloat("Type radius of a circle: ")
c = circumference(2 * r)
# pi does not exist here; it cannot be referenced:
area = r * r * pi # ???!
```

A global variable that is read (but *not updated*) by a function body can be
safely used in the function’s pre- and post-conditions;
it acts just like an extra parameter to the function:

```
pi = 3.14159
def circumference(diameter) :
"""
{ pre diameter >= 0 ^ pi > 3
post answer = pi * diameter
return answer }
"""
answer = pi * diameter
return answer
```

We must restrict all calls to the function so that the call does *not* use the
global variable as the target of the function’s answer.
That is, `c = circumference(5)`

is ok, and so is `c = circumference(pi)`

,
but `pi = circumference(5)`

*is not*.

We use the name, *procedure*, for a function that updates a global variable.
A global variable that is updated by a function acts like an extra answer
variable for the function.
In the Python language, every global variable that is updated by a function must
be listed in the `global`

line that immediately follows the function’s header
line.
This is a good practice that we will follow.

Here is a simple but important example: a timer that counts down to zero.
The `init`

and `tick`

methods maintain the global variable `time`

.

```
time = 0
"""
{ 1. time == 0 premise
2. time >= 0 algebra 1 } # time shouldn't be negative
"""
def tick() :
"""
{ pre time >= 0
post time >= 0
return ans }
"""
global time # this line says the function can update time
"""{ 1. time >= 0 premise }"""
if time != 0 :
time = time - 1
"""
{ 1. time_old != 0 premise
2. time_old >= 0 premise
3. time_old > 0 algebra 1 2
4. time == time_old - 1 premise
5. time >= 0 algebra 3 4 }
"""
else :
print "RING RING RING"
"""{ 1. time >= 0 premise }"""
"""{ 1. time >= 0 premise }"""
ans = time
return ans
def init(starttime) :
"""
{ pre starttime > 0
post time >= 0 }
"""
global time
time = starttime
"""
{ 1. starttime > 0 premise
2. time == starttime premise
3. time >= 0 algebra 1 2 }
"""
# Driver code starts here: run the timer
"""{ 1. time >= 0 premise }"""
novar = init(10)
"""{ 1. time >= 0 premise }"""
a = tick()
"""{ 1. time >= 0 premise }"""
a = tick()
"""{ 1. time >= 0 premise }"""
a = tick()
"""{ 1. time >= 0 premise }"""
```

Each of the functions can change variable `time`

, and each time a function
finished, there is a postcondition that `time >= 0`

.
We will learn that a property that repeats over and over is *invariant*.
Here, it is invariant that `time`

stays nonnegative.
This next example is similar and important:
The procedure that withdraws money from a bank account must never let the
balance go negative:

```
balance = 100; # global variable in the module for the bank account
def withdraw(howmuch) :
"""withdraw removes howmuch from balance"""
"""
{ pre howmuch >= 0 ^ balance >= 0
post balance == balance_in + cash ^ balance >= 0
return cash }
"""
global balance
if howmuch <= balance :
cash = howmuch
else :
cash = balance
balance = balance - cash
return cash
```

`withdraw`

’s pre- and post-conditions are carefully written to ensure that
the procedure is used correctly.
Recall that `balance_in`

means the value of global variable `balance`

on
procedure entry.
We will return to both these examples shortly.

Here is a precise statement of the law we use for procedures:

```
g = ... # the global variable
def f(x):
"""
{ pre Q (where assertion Q mentions x and g) This is f_pre.
post R (where R mentions ans, x, g, and g_in) This is f_post.
return ans (this line is optional) }
"""
global g # notes that g can be updated by f
BODY # does not assign to x but may assign to g
return ans # this line is optional
```

To invoke the function, we prove the precondition:

```
"""{ [e/x]f_pre }"""
y = f(e) # y and g do not appear in e, and y and g are distinct
"""
{ 1. [y/ans][e/x][g_old/g_in]f_post premise
2. [y_old/y][g_old/g][e/x]f_pre premise
... }
"""
```

Since global variable `g`

acts as a second answer variable, `g`

cannot
appear in argument `e`

nor can it be the same as `y`

.

A global variable is often shared by more than one procedure, and the procedures
must cooperate and keep the global variable in proper shape for shared use.
For this reason, a global variable often has attached to it a
*global-variable invariant* that asserts a property of the variable’s value that
must be preserved by every procedure that updates the variable.

Earlier, we saw a timer module that maintained a nonnegative value for `time`

.
It is best to assert that `time >= 0`

is a global invariant.
Then, each of the two functions can assume the invariant when they execute.
(And, each function must ensure the invariant is still holding true when they
exit!)

Here’s the timer example, reworked:

```
### simple example of a global variable invariant
time = 0
# we maintain this invariant property:
"""{ globalinv time >= 0 }"""
# the function can use the invariant on entry, and the function
# must ensure that the invariant holds on exit:
def tick() :
"""
{ pre True
post ans == time
return ans }
"""
global time
# as needed, we introduce the globalinv in a proof:
"""{ 1. time >= 0 premise }"""
if time != 0 :
time = time - 1
"""{ 1. time_old != 0 premise
2. time_old >= 0 premise
3. time_old > 0 algebra 1 2
4. time == time_old - 1 premise
5. time >= 0 algebra 3 4
}"""
else :
print "RING RING RING"
"""{ 1. time >= 0 premise }"""
ans = time
return ans
def init(starttime) :
"""
{ pre starttime > 0
post True }
"""
global time
time = starttime
### Driver code starts here: run the timer
# global invariant holds true
novar = init(10)
# invariant holds true:
"""{ 1. time >= 0 globalinv }"""
a = tick()
# invariant holds true:
"""{ 1. time >= 0 globalinv }"""
a = tick()
# invariant holds true:
"""{ 1. time >= 0 globalinv }"""
a = tick()
# and so on ...
```

In an object-oriented language, when a global variable and its procedures
(“methods”) are defined within a class, the global-variable invariant is called
the *class invariant*.

Here’s an example, a class that is embedded in a toy bank into which a child inserts ten-cent coins. (The bank is a “reactive system” that processes “coin-insertion events”!) The class enforces its invariant – that’s its mission.

```
class DimesBank { # MAINTAINS A TOY BANK
# fields in the class:
dimes = 0 # how many coins inserted so far into the bank
money = 0 # how much the coins are worth
# the class maintains this invariant property:
"""{ globalinv money == dimes * 10 }"""
# the handler method below can assume the invariant on entry,
# and the method must ensure that the invariant holds on exit:
def handleCoinInsertion(howmany) :
"""called when a child inserts howmany dimes into the bank"""
"""
{ pre howmany >= 0
post True } # the method enforces the invariant, that's it.
"""
global dimes, money
# we introduce the invariant as a premise when needed:
"""{ 1. money == dimes * 10 premise }"""
dimes = dimes + howmany
"""
{ 1. money == dimes_old * 10 premise
2. dimes == dimesold + howmany premise
3. money + (howmany * 10) == dimes * 10 algebra 1 2
}"""
# the invariant is broken here, but the next command restores it:
money = money + (howmany * 10)
"""
{ 1. money_old + (howmany * 10) == dimes * 10 premise
2. money == moneyold + (howmany * 10) premise
3. money == dimes * 10 subst 2 1 }
"""
# } END CLASS
# the following code might be in a controller component that
# accepts input events and calls event handlers:
coins = readInt("insert your coins and press the button!")
assert coins > 0
novar = handleCoinInsertion(coins) # see the method above
print money # on the toy bank's GUI
```

*When you code a class, first decide on the class’s fields (its “attributes”)
and write down the class invariant*.
Then, define and code the methods so that they maintain the invariant.

Here is a second example, a class that models a bank account. The account’s balance should always be kept nonnegative:

```
# A bank-account "class" and its maintenance methods.
# The balance balance must be kept nonnegative.
class BankAccount {
# The global variable, the money in a bank balance:
balance = 0
"""
{ 1. balance == 0 premise
2. balance >= 0 algebra 1 }
"""
# the global invariant:
"""{ globalinv balance >= 0 }""" # this property is currently true, and
# we want the methods to preserve it
def deposit(howmuch) :
"""deposit adds howmuch to balance"""
"""
{ pre howmuch >= 0
post balance == balance_in + howmuch }
"""
# balance_in is the value of balance when the method was entered
global balance
"""
{ 1. balance >= 0 premise # the globalinv holds on entry
2. howmuch >= 0 premise # the precondition
3. balance == balance_in premise # the value of balance on entry
4. return 1 2 3 } # remember all three facts for later
"""
balance = balance + howmuch
"""{ 1. balance == balance_old + howmuch premise
2. balance_old >= 0 premise
3. howmuch >= 0 premise
4. balance_old == balance_in premise
5. balance == balance_in + howmuch subst 4 1
6. balance >= 0 algebra 1 2 3
7. return 5 6 }
"""
# The last line asserts that the global invariant is preserved
# at the exit, and the postcondition is proved, too.
def withdraw(howmuch) :
"""withdraw removes howmuch from balance"""
"""
{ pre howmuch >= 0
post balance == balance_in + cash
return cash }
"""
global balance
"""
{ 1. balance >= 0 premise # the globalinv holds on entry
2. howmuch >= 0 premise # the precondition
3. balance == balance_in premise # the value of balance on entry
4. return 1 2 3 } # remember all three facts for later
"""
if howmuch <= balance :
balance = balance - howmuch
cash = howmuch
"""{ ... # prove here balance == balance_in + cash and balance >= 0 }"""
else :
cash = 0
"""{ ... # prove here balance == balance_in + cash and balance >= 0 }"""
"""{ 1. balance == balance_in + cash and balance >= 0 premise }"""
return cash
def getBalance() :
"""getBalance returns the current balance"""
"""
{ pre True
post ans == balance
return ans }
"""
ans = balance
"""{ 1. ans == balance premise }"""
return ans
} # END CLASS
```

All the class’s methods pledge to keep `balance`

nonnegative.
Assuming that no other program commands update the `balance`

, the proofs
ensure that `balance`

’s global invariant holds always.

Whenever a module or class holds a data structure, there
*always should be a global invariant that states the critical properties of the
structure that must be maintained*.
The procedures that maintain the data structure pledge to preserve its invariant.

This is the technique used in modular and object-oriented programming, where
a data structure, its global invariant, and the data structure’s maintenance
procedures/methods are placed together.
Since only the maintenance procedures update the global variable and preserve
the invariant, the rest of the program can always rely on the global invariant
to hold true
*Without this convention, it is impossible to do component-based programming.*

Here is the law used in the above example:

```
g = ... # the global variable
"""{ globalinv I_g }""" # must be proved true here
def f(x):
"""
{ pre Q (where assertion Q mentions x and g) This is f_pre.
post R (where R mentions ans, x, g, and g_in) This is f_post.
return ans }
"""
global g # g can be updated by f
"""
{ 1. Q premise
2. I_g premise
... }
"""
BODY # does not assign to x but may assign to g
"""{ R ^ I_g }""" # we must prove both R and I_g on exit
return ans
```

For the function’s invocation, we deduce `[e/x]pre_f`

to get the result.
Since global variable `g`

acts as a second answer variable, `g`

cannot
appear in argument `e`

nor can it be the same as `y`

.

```
"""{ [e/x]pre_f, that is, Q_e,g }"""
y = f(e) # y and g do not appear in e, and y and g are distinct names
"""
{ 1. [y/ans][e/x][g_old/g_in]f_post premise
2. [y_old/y][g_old/g][e/x]f_pre premise
3. I_g globalinv
... }
"""
```

Further, provided that all assignments to global variable `g`

occur only
within functions that preserve its `I_g`

, we can always assert `I_g`

as
needed in the main program.

There are many other examples of modules/classes that use global invariants. Here are three obvious ones:

- A spreadsheet program: a module holds a grid that models a spreadsheet, and the key invariant property is that the last cell in each row of the spreadsheet holds the sum of the numbers in that row. So, the functions that insert or change numbers in the cells of the spreadsheet must compute new totals for the rows so that the invariant is preserved.
- A board game, like chess: a module holds a grid that models the chess board and the pieces placed on it. The global invariant is that the pieces correctly show the history of the moves made so far during the game. The functions that make moves and captures must preserve the invariant.
- An animation: a module holds a three-dimensional space inhabited by one or more sprites (moving objects). The invariant is that no sprite can travel outside the dimensions of the space. The functions that insert, move, and delete sprites must preserve the invariant.

*A key job when doing object-oriented programming is writing class invariants*,
that is, global invariants for the private fields in each class.
When you design a class, define the fields (data structures) and their
invariants before you write the methods that maintain the data structures and
preserve the invariants.

All the techniques apply when the code inside a procedure body calls another procedure: as long as we establish the precondition for the called procedure, we can call it, and we obtain the postcondition as new knowledge in return. In this way, procedures can build on each others’ postconditions.

It is interesting that a procedure can call *itself*, and we can reason about
this in the same way that we use for any other procedure call.
Here is a detailed example.

For integer `n > 0`

, the *factorial* of `n`

, written `n!`

is defined as
`1 * 2 * 3 * ...up to... * n`

.
It is the tradition to define `0! = 1`

, but factorials for negative integers
do not exist.

Factorial lists the number of permutations (combinations or “shuffles”), e.g.,
`fact(3) = 6`

notes there six permutations for arranging three items,
`a`

, `b`

, and `c`

:

```
abc
bac
bca
acb
cab
cba
```

There is a profound recursive definition of factorial:

```
0! == 1
n! == (n-1)! * n, for n > 0
```

For example, we calculate `4!`

like this:

```
4! == 3! * 4
where 3! == 2! * 3
where 2! == 1! * 2
where 1! == 0! * 1
where 0! == 1
So...
0! == 1
1! == 0! * 1 = 1
2! == 1! * 2 = 2
3! == 2! * 3 = 6
and finally,
4! == 3! * 4 = 24
```

The process of counting downwards from `4!`

to `3!`

to `2!`

to `1!`

to
`0!`

and assembling the answer as `1 * 1 * 2 * 3 * 4`

can be programmed as a
function that repeatedly calls *itself* for the answers to `3!`

, `2!`

,
`1!`

, etc.:

```
def fact(n) : # returns n!
"""
{ pre n >= 0
post (to come)
return ans }
"""
if n == 0 :
ans = 1 # this computes 0! = 1
else :
a = fact(n-1) # this computes (n-1)!
ans = a * n # this computes n! = (n-1)! * n
return ans
```

The easiest way to understand the computation of, say, `fact(3)`

, is to draw
out the function calls, making a new copy of the called function each time it is
restarted, like this:

```
fact(3) => n = 3
if n == 0 :
ans = 1
else :
a = fact(n-1)
ans = a * n
return ans
```

Notice how the *binding* of argument `3`

to parameter `n`

is enacted with
the assignment, `n = 3`

.
(This is how it is implemented within a computer, too.)
The code for `fact(3)`

activates a fresh copy of `fact`

with argument 2:

```
fact(3) => n = 3
a = fact(2) => n = 2
if n == 0 :
ans = a * n ans = 1
return ans else :
a = fact(n-1)
ans = a * n
return ans
```

This generates another call (fresh copy of) fact:

```
fact(3) => n = 3
a = fact(2) => n = 2
ans = a * n a = fact(1) => n = 1
if n == 0 :
return ans ans = a * 2 ans = 1
return ans else :
a = fact(n-1)
ans = a * n
return ans
```

This expands to:

```
fact(3) => n = 3
a = fact(2) => n = 2
ans = a * n a = fact(1) => n = 1
return ans ans = a * n a = fact(0) => n = 0
if n == 0 :
return ans ans = a * n ans = 1
return ans else :
. . .
return ans
```

We see a sequence, or “stack”, of activations of `fact`

, one per call.
Within a computer, an *activation-record stack* remembers the activations.
The call to `fact(0)`

returns an answer – 1:

```
fact(3) => n = 3
a = fact(2) => n = 2
ans = a * n a = fact(1) => n = 1
return ans ans = a * n a = fact(0) => return 1
return ans ans = a * n
return ans
```

This makes the most recent activation (copy) of `fact`

disappear, and the
returned answer is assigned to `ans`

in the previous call:

```
fact(3) => n = 3
a = fact(2) => n = 2
ans = a * 3 a = fact(1) => n = 1
return ans ans = a * 2 a = 1
return ans ans = a * n
return ans
```

allowing the previous call to return *its* answer to *its* caller:

```
fact(3) => n = 3
a = fact(2) => n = 2
ans = a * 3 a = fact(1) => return 1
return ans ans = a * 2
return ans
```

You see the pattern – the calls are finishing in reverse order, returning the
partial answers, `fact(0) = 1`

, `fact(1) = 1`

, `fact(2) = 2`

, and so on:

```
fact(3) => n = 3
a = fact(2) => n = 2
ans = a * n a = 1
return ans ans = a * n
return ans
```

and then:

```
fact(3) => n = 3
a = fact(2) => return 2
ans = a * n
return ans
```

and:

```
fact(3) => n = 3
a = 2
ans = a * n
return ans
```

and finally:

```
fact(3) => return 6
```

Within the computer, the code for `fact`

is not copied at each call –
instead, a new *namespace* (activation record) is generated for each call to
`fact`

.
When a call finishes, its answer is returned and its namespace is erased
(popped from the activation stack).

Every function has its own pre- and post- conditions.
So, if a function calls itself, it can use *its own pre- and postconditions to
deduce the properties of the answer computed by the self-call*.
This is remarkable and exactly correct.

Here is the desired specification of fact:

```
def fact(n)
"""
{ pre n >= 0
post ans == n!
return ans }
"""
```

When `fact`

calls itself, we use the above pre- and post-conditions to deduce
what happens.
In the process, we deduce that the completed coding of `fact`

possesses
exactly these same pre- and post-conditions!

Recall again the “official definition” of `n!`

:

```
0! == 1
n! == (n-1)! * n, for n > 0
```

Here is the deduction that `fact`

meets its stated pre- and post-conditions:

```
def fact(n) :
"""
{ pre n >= 0
post ans == n!
return ans }
"""
if n == 0 :
ans = 1
"""
{ 1. ans == 1 premise
2. n == 0 premise
3. ans == 0! definition of 0! == 1
4. ans == n! subst 2 3 }
"""
else :
"""
{ 1. ~(n == 0) premise
2. n > 0 premise
3. n - 1 >= 0 algebra 1 2 } # this proves pre_fac
"""
sub = fact(n-1)
"""{ 1. sub == (n-1)! premise }""" # post_fac
ans = sub * n
"""
{ 1. ans == sub * n premise
2. sub == (n-1)! premise
3. ans == (n-1)! * n subst 2 1
4. ans == n! definition of n! == (n-1)! * n }
"""
"""{ 1. ans == n! premise }"""
return ans
```

We did it!
The proof is not magic or trickery – notice that the call, `fact(n - 1)`

,
uses an argument value that is different — one smaller than — argument `n`

used with `fact(n)`

.
This style of recursion by counting-downward-by-ones-until-zero will be exposed
in the next chapter as an instance of *mathematical induction*.

Here is a sample call of the end result:

```
"""{ 200 >= 0, that is [200/n]pre_fact }"""
x = fact(200)
"""{ [x/ans][200/n]post_fact, that is, x == 200! }"""
```

In the next chapter, we will see a strong connection between a function’s self-call and a loop – in both cases, the construct reuses its very own “pre-post-condition” when repeating itself.

We used a variety of laws for function definitions and calls.

Here is the simplest version, a forwards law where there is no updating to global variables:

def f(x): """ { pre Q (where assertion Q can mention x) Call this, f_pre. post R (where assertion R can mention ans and x) Call this, f_post. return ans (the name of the answer variable) } """ """{ 1. Q premise }""" BODY # does not assign to x """{ ... prove R here, that is, ans and x have property R }"""

Outside of the function,

`f`

, we write`f_pre`

for`Q`

and`f_post`

for`R`

. Here is the schematic for a correct call of the function:"""{ [e/x]f_pre }""" # prove the precondition, where e binds to parameter x y = f(e) # y does not appear in e """ { 1. [y/ans][e/x]f_post premise # presume the postcondition where # y receives the ans value 2. [y_old/y][e/x]f_pre premise . . . } """

Recall that

`[e/x]f_pre`

defines a substitution of`e`

for all occurrences of`x`

within formula`f_pre`

. The other two substitions explain how`f_post`

is stated in terms of the receiving variable,`y`

, and how the entry premise is propagated after the call.When we use a procedure, that is, a function that updates a global variable, the laws become more complex, because the global variable acts like a second answer from the function. Every global variable should have an invariant property, and every function that uses the global variable should pledge to preserve that invariant property:

g = ... # the global variable """{ globalinv I_g }""" # must be proved true here def f(x): """ { pre Q (where assertion Q mentions x and g) This is f_pre. post R (where R mentions ans, x, g, and g_in) This is f_post. return ans }""" global g # g can be updated by f """ { 1. Q premise 2. I_g premise ... } """ BODY # does not assign to x but may assign to g """{ R ^ I_g }""" # we must prove both R and I_g on exit return ans

For the function’s invocation, we deduce

`[e/x]pre_f`

to get the result. Since global variable`g`

acts as a second answer variable,`g`

cannot appear in argument`e`

nor can it be the same as`y`

."""{ [e/x]pre_f, that is, Q_e,g }""" y = f(e) # y and g do not appear in e, and y and g are distinct names """ { 1. [y/ans][e/x][g_old/g_in]f_post premise 2. [y_old/y][g_old/g][e/x]f_pre premise 3. I_g globalinv ... } """

Further, provided that all assignments to global variable

`g`

occur only within functions that preserve its`I_g`

, we can always assert`I_g`

as needed in the main program.If we wish to reason in a backwards fashion about function calls, we can use a different law. To keep it simple, assume no global variables are updated by the function. For the specification,

def f(x): """ { pre Q_x post R_ans,x return ans } """

if a call to

`f`

is meant to achieve a goal,`G`

, we reason backwards from`G`

to its subgoal like this:"""{ subgoal: [e/x]pre_f ^ ([e/x][y/ans]post_f --> G) }""" y = f(e) # y does not appear in argument e """{ goal: G }"""

It is a good exercise to extend this backwards law to procedures. Try it.

*
This note was adapted from David Schmidt's CIS 301, 2008,
Chapter 3
course note.
*