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lazy python 0.2.1

I will write this later…

What is lazy?
lazy is a module for making python lazily
evaluated (kinda).
lazy runs under python 3.5 and 3.4.

Why lazy?
Why not lazy?
I think lazy computation is pretty cool, I also think python is pretty
cool; combining them is double cool.

How to lazy?
There are 3 means of using lazy code:

lazy_function
run_lazy
IPython cell and line magics

lazy_function
lazy_function takes a python function and returns a new function that is
the lazy version. This can be used as a decorator.
Example:
@lazy_function
def f(a, b):
return a + b
Calling f(1, 2) will return a thunk that will add 1 and 2 when it
needs to be strict. Doing anything with the returned thunk will keep
chaining on more computations until it must be strictly evaluated.
Lazy functions allow for lexical closures also:
@lazy_function
def f(a):
def g(b):
return a + b
return g
When we call f(1) we will get back a thunk like we would expect;
however, this thunk is wrapping the function g. Because g was created
in a lazy context, it will also be a lazy_function implicitly. This means
that type(f(1)(2)) is thunk; but, f(1)(2) == 3.
We can use strict to strictly evaluate parts of a lazy function, for example:
>>> @lazy_function
... def no_strict():
... print('test')
...
>>> strict(no_strict())
In this example, we never forced print, so we never saw the result of the call.
Consider this function though:
>>> @lazy_function
... def with_strict():
... strict(print('test'))
...
>>> strict(with_strict())
test
>>> result = with_strict()
>>> strict(result)
test
Here we can see how strict works inside of a lazy function. strict causes
the argument to be strictly evaluated, forcing the call to print. We can also
see that just calling with_strict is not enough to evaluate the function,
we need to force a dependency on the result.
This is implemented at the bytecode level to frontload a large part of the cost
of using the lazy machinery. There is very little overhead at function call
time as most of the overhead was spent at function creation (definiton) time.

run_lazy
We can convert normal python into lazy python with the run_lazy function
which takes a string, the ‘name’, globals, and locals. This is like exec or
eval for lazy python. This will mutate the provided globals and locals so
that we can access the lazily evaluated code.
Example:
>>> code = """
print('not lazy')
strict(print('lazy'))
"""
>>> run_lazy(code)
lazy
This also uses the same bytecode manipulation as lazy_function so they will
give the same results.

IPython cell and line magics
If you have IPython installed, you may use the cell and line magic machinery to
write and evaluate lazy code. For example:
In [1]: from lazy import strict

In [2]: %lazy 2 + 2 # line magic acts as an expression
Out[2]: 4

In [3]: type(_2)
Out[3]: lazy._thunk.thunk

In [4]: %%lazy # cell magic is treated as a statement
...: print('lazy')
...: strict(print('strict'))
...:
strict

thunk
At its core, lazy is just a way of converting expressions into a tree
of deferred computation objects called thunks. thunks wrap normal
functions by not evaluating them until the value is needed. A thunk
wrapped function can accept thunks as arguments; this is how the
tree is built. Some computations cannot be deferred because there is some state
that is needed to construct the thunk, or the python standard defines the
return of some method to be a specific type. These are refered to as ‘strict
points’. Examples of strict points are str and bool because the python
standard says that these functions must return an instance of their own
type. Most of these converters are strict; however, some other things are
strict because it solves recursion issues in the interpreter, like accessing
__class__ on a thunk.

Custom Strictness Properties
strict is actually a type that cannot be put into a thunk. For
example:
>>> type(thunk(strict, 2))
int
Notice that this is not a thunk, and has been strictly evaluated.
To create custom strict objects, you can subclass strict. This
prevents the object from getting wrapped in thunks allowing you to
create strict data structures.
Objects may also define a __strict__ method that defines how to
strictly evalueate the object. For example, an object could be defined
as:
class StrictFive(object):
def __strict__(self):
return 5
This would make strict(StrictFive()) return 5 instead of an instance
of StrictFive.

undefined
undefined is a value that cannot be strictly evaluated. It is useful as a
placeholder for computations.
We can imagine undefined in python as:
@thunk.fromexpr
@Exception.__new__
class undefined(Exception):
def __strict__(self):
raise self
This object will raise an instance of itself when it is evaluated.
This is presented as an equivalent definition, though it is actually in c to
make nicer stack traces.

Known Issues
Currently, the following things are known to not work:

Recursively defined thunks
A recursively defined thunk is a thunk that appears in its own graph twice.
For example:
>>> a = thunk(lambda: a)
>>> strict(a)
This will cause an infinite loop because in order to strictly evaluate a,
we will call the function which returns a which we will try to strictly
evaluate.
Status: Bug, might fix.
This is basically correct, for example:
>>> a = lambda: a()
>>> a()
...
RuntimeError: maximum recursion depth exceeded
The difference in the thunk example is that we will drop into c code to preform
the recursion so it will not terminate in a reasonable amount of time.
The potential fix could be to try to detect these cycles and raise some
Exception; however, this might be a very expensive check in the good case
making thunk evaluation much slower.

Gotchas

I opened it up in the repl, everything is strict!
Because the python spec says the __repr__ of an object must return a
str, a call to repr must strictly evaluate the contents so that
we can see what it is. The repl will implicitly call repr on things
to display them. We can see that this is a thunk by doing:
>>> a = thunk(operator.add, 2, 3)
>>> type(a)
lazy.thunk.thunk
>>> a
5
Again, because we need to compute something to represent it, the repl is
a bad use case for this, and might make it appear at first like this is
always strict.

print didn’t do anything!
Um, what did you think it would do?
If we write:
@lazy_function
def f(a, b):
print('printing the sum of %s and %s' % (a, b))
return a + b
Then there is no reason that the print call should be executed. No
computation depends on the results, so it is casually skipped.
The solution is to force a dependency:
@lazy_function
def f(a, b):
strict(print('printing the sum of %s and %s' % (a, b)))
return a + b
strict is a function that is used to strictly evaluate things.
Because the body of the function is interpreted as lazy python, the
function call is converted into a thunk, and therefore we can
strict it.
This is true for any side-effectful function call.

x is being evaluated strictly when I think it should be lazy
There are some cases where things MUST be strict based on the python
language spec. Because this is not really a new language, just an
automated way of writing really inefficient python, python’s rules must
be followed.
For example, __bool__, __int__, and other converters expect that
the return type must be a the proper type. This counts as a place where
strictness is needed1.
This might not be the case though, instead, I might have missed
something and you are correct, it should be lazy. If you think I missed
something, open an issue and I will try to address it as soon as
possible.

Some stateful thing is broken
Sorry, you are using unmanaged state and lazy evaluation, you deserve
this. thunks cache the normal form so that calling strict the second
time will refer to the cached value. If this depended on some stateful
function, then it will not work as intended.

I tried to do x with a thunk and it broke!
The library is probably broken. This was written on a whim and I barely
thought through the use cases.
Please open an issue and I will try to get back to you as soon as
possible.

Notes

The function call for the constructor will be made lazy in the
LazyTransformer (like thunk(int, your_thunk)), so while this
is a place where strictness is needed, it can still be ‘optimized’
away.