Overview¶
Hask3 is a pure-Python, zero-dependencies library that mimics most of the core language tools from Haskell, including:
- Full Hindley-Milner type system (with typeclasses) that will typecheck any function decorated with a Hask type signature
- Easy creation of new algebraic data types and new typeclasses, with Haskell-like syntax
- Pattern matching with
case
expressions - Automagical function currying/partial application and function composition
- Efficient, immutable, lazily evaluated List type with Haskell-style list comprehensions
- All your favorite syntax and control flow tools, including operator sections, monadic error handling, guards, and more
- Python port of (some of) the standard libraries from Haskell’s base,
including:
- Algebraic datatypes from the Haskell Prelude, including Maybe and Either
- Typeclasses from the Haskell base libraries, including Functor, Applicative, Monad, Enum, Num, and all the rest
- Standard library functions from base, including all functions from Prelude, Data.List, Data.Maybe, and more.
Features¶
The List type and list comprehensions¶
Hask provides the List
type, a lazy and
statically-typed list, similar to Haskell’s standard list type.
To create a new List, just put the elements inside L[
and ]
brackets,
or wrap an existing iterable inside L[ ]
:
>>> from hask import L
>>> L[1, 2, 3]
L[1, 2, 3]
>>> my_list = ["a", "b", "c"]
>>> L[my_list]
L['a', 'b', 'c']
>>> L[(x**2 for x in range(1, 11))]
L[1 ...]
To add elements to the front of a List, use ^
, the cons operator. To
combine two lists, use +
, the concatenation operator:
>>> 1 ^ L[2, 3]
L[1, 2, 3]
>>> "goodnight" ^ ("sweet" ^ ("prince" ^ L[[]]))
L['goodnight', 'sweet', 'prince']
>>> "a" ^ L[1.0, 10.3] # doctests: +ELLIPSIS
Traceback (...)
...
TypeError: ...
>>> L[1, 2] + L[3, 4]
L[1, 2, 3, 4]
Lists are always evaluated lazily, and will only evaluate list elements as needed, so you can use infinite Lists or put never-ending generators inside of a List. (Of course, you can still blow up the interpreter if you try to evaluate the entirety of an infinite List, e.g. by trying to find the length of the List with len.)
One way to create infinite lists is via list comprehensions. As in Haskell, there are four basic type of list comprehensions:
>>> # list from 1 to infinity, counting by ones
>>> L[1 ...]
>>> # list from 1 to infinity, counting by twos
>>> L[1, 3, ...]
>>> # list from 1 to 20 (inclusive), counting by ones
>>> L[1, ..., 20]
>>> # list from 1 to 20 (inclusive), counting by fours
>>> L[1, 5, ..., 20]
List comprehensions can be used on ints, longs, floats, one-character strings,
or any other instance of the Enum
typeclass (more
on this later).
Hask provides all of the Haskell functions for List manipulation
(take()
, drop()
,
takeWhile()
, etc.), or you can also use Python-style
indexing:
>>> from hask import L
>>> L[1, ...]
L[1 ...]
>>> from hask.Data.List import take
>>> take(5, L["a", "b", ...])
L['a', 'b', 'c', 'd', 'e']
>>> L[1,...][5:10]
L[6, 7, 8, 9, 10]
>>> from hask.Data.List import map
>>> from hask.Data.Char import chr
>>> letters = map(chr, L[97, ...])
>>> letters[:9]
L['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i']
>>> # DON'T do this: len(L[1, 3, ...])
Otherwise, you can use List
just like you would
use a regular Python list:
>>> from hask import L
>>> for i in L[0, ..., 3]:
... print(i)
0
1
2
3
>>> 55 in L[1, 3, ...]
True
Algebraic Data Types¶
Hask allows you to define algebraic datatypes, which are immutable objects with a fixed number of typed, unnamed fields.
Here is the definition for the infamous Maybe
type:
>>> from hask import data, d, deriving
>>> from hask import Read, Show, Eq, Ord
>>> Maybe, Nothing, Just =\
... data.Maybe("a") == d.Nothing | d.Just("a") & \
... deriving(Read, Show, Eq, Ord)
Let’s break this down a bit. The syntax for defining a new type constructor is:
>>> data.TypeName("type param", "type param 2" ... "type param n")
This defines a new algebraic datatype with type parameters.
To define data constructors for this type,
use d
. The name of the data constructor goes first, followed by its
fields. Multiple data constructors should be separated by |
. If your data
constructor has no fields, you can omit the parens. For example:
>>> FooBar, Foo, Bar =\
... data.FooBar("a", "b") == d.Foo("a", "b", str) | d.Bar
To automagically derive typeclass instances for the type, add &
deriving(...typeclasses...)
after the data constructor declarations.
Currently, the only typeclasses that can be derived are
Eq
, Show
,
Read
, Ord
, and
Bounded
.
Putting it all together, here are the definitions of
Either
and Ordering
:
>>> from hask import Read, Show, Eq, Ord, Bounded
>>> Either, Left, Right =\
... data.Either("a", "b") == d.Left("a") | d.Right("b") & deriving(Read, Show, Eq)
>>> Ordering, LT, EQ, GT =\
... data.Ordering == d.LT | d.EQ | d.GT & deriving(Read, Show, Eq, Ord, Bounded)
You can now use the data constructors defined in a data statement to create instances of these new types. If the data constructor takes no arguments, you can use it just like a variable:
>>> Just(10)
Just(10)
>>> Nothing
Nothing
>>> Just(Just(10))
Just(Just(10))
>>> Left(1)
Left(1)
>>> Foo(1, 2, "hello")
Foo(1, 2, 'hello')
You can view the type of an object with _t()
(equivalent to :t in ghci).
>>> from hask import _t, L
>>> _t(1)
'int'
>>> _t(Just("soylent green"))
'(Maybe str)'
>>> _t(Right(("a", 1)))
'(Either a (str, int))'
>>> _t(Just)
'(a -> (Maybe a))'
>>> _t(L[1, 2, 3, 4])
'[int]'
The type system and typed functions¶
So what’s up with those types? Hask operates its own shadow Hindley-Milner
type system on top of Python’s type system; _t()
shows the Hask type of a particular object.
In Hask, typed functions take the form of
TypedFunc()
objects, which are typed wrappers
around Python functions. There are two ways to create TypedFunc objects:
Use the sig decorator to decorate the function with the type signature:
@sig(H/ "a" >> "b" >> "a") def const(x, y): return x
Use the
**
operator (similar to::
in Haskell) to provide the type.Useful for turning functions or lambdas into TypedFunc objects in the REPL, or wrapping already-defined Python functions:
def const(x, y): return x const = const ** (H/ "a" >> "b" >> "a")
TypedFunc objects have several special properties. First, they are type checked – when arguments are supplied, the type inference engine will check whether their types match the type signature, and raise a TypeError if there is a discrepancy.
>>> from hask import H
>>> f = (lambda x, y: x + y) ** (H/ int >> int >> int)
>>> f(2, 3)
5
>>> f(9, 1.0)
Traceback (...)
...
TypeError: ...
Second, TypedFunc objects can be partially applied:
>>> from hask import H
>>> g = (lambda a, b, c: a // (b + c)) ** (H/ int >> int >> int >> int)
>>> g(10, 2, 3)
2
>>> part_g = g(12)
>>> part_g(2, 2)
3
>>> g(20, 1)(4)
4
TypedFunc objects also have two special infix operators, the *
and %
operators. *
is the compose operator (equivalent to .
in Haskell), so
f * g
is equivalent to lambda x: f(g(x))
. %
is just the apply
operator, which applies a TypedFunc to one argument (equivalent to $
in
Haskell). The convinience of this notation (when combined with partial
application) cannot be overstated – you can get rid of a ton of nested
parenthesis this way:
>>> from hask.Prelude import flip
>>> h = (lambda x, y: x / y) ** (H/ float >> float >> float)
>>> h(3.0) * h(6.0) * flip(h, 2.0) % 36.0
9.0
The compose operation is also typed-checked, which makes it appealing to write programs in pointfree style, i.e. chaining together lots of functions with composition and relying on the type system to catch programming errors.
As you would expect, data constructors are also just TypedFunc objects:
>>> Just * Just * Just * Just % 77
Just(Just(Just(Just(77))))
The type signature syntax is very simple, and consists of a few basic primitives that can be combined to build any type signature:
Type literal for Python builtin type or user-defined class:
int
,float
,set
,list
Type variable:
"a"
,"b"
,"zz"
List
of some type:[int]
,["a"]
,[["a"]]
Tuple type:
(int, int)
,("a", "b", "c")
,(int, ("a", "b"))
ADT with type parameters:
t(Maybe, "a")
,t(Either, "a", str)
Unit type (
None
):None
Untyped Python function:
func
Typeclass constraint:
H[(Eq, "a"), (Show, "b")]/
,H[(Functor, "f"), (Show, "f")]/
Some examples:
# add two ints together
@sig(H/ int >> int >> int)
def add(x, y):
return x + y
# reverse order of arguments to a function
@sig(H/ (H/ "a" >> "b" >> "c") >> "b" >> "a" >> "c")
def flip(f, b, a):
return f(a, b)
# map a Python (untyped) function over a Python (untyped) set
@sig(H/ func >> set >> set)
def set_map(fn, lst):
return set((fn(x) for x in lst))
# map a typed function over a List
@sig(H/ (H/ "a" >> "b") >> ["a"] >> ["b"])
def map(f, xs):
return L[(f(x) for x in xs)]
# type signature with an Eq constraint
@sig(H[(Eq, "a")]/ "a" >> ["a"] >> bool)
def not_in(y, xs):
return not any((x == y for x in xs))
# type signature with a type constructor (Maybe) that has type arguments
@sig(H/ int >> int >> t(Maybe, int))
def safe_div(x, y):
return Nothing if y == 0 else Just(x/y)
# type signature for a function that returns nothing
@sig(H/ int >> None)
def launch_missiles(num_missiles):
print("Launching {0} missiles! Bombs away!" % num_missiles)
It is also possible to create type synonyms using
t()
. For example, check out the definition of
Rational
:
Ratio, R =\
data.Ratio("a") == d.R("a", "a") & deriving(Eq)
Rational = t(Ratio, int)
@sig(H/ Rational >> Rational >> Rational)
def addRational(rat1, rat2):
...
Pattern matching¶
Pattern matching is a more powerful control flow tool than the if
statement, and can be used to deconstruct iterables and ADTs and bind values
to local variables.
Pattern matching expressions follow this syntax:
~(caseof(value_to_match)
| m(pattern_1) >> return_value_1
| m(pattern_2) >> return_value_2
| m(pattern_3) >> return_value_3)
Here is a function that uses pattern matching to compute the fibonacci
sequence. Note that within a pattern match expression, m.*
is used to
bind variables, and p.*
is used to access them:
>>> from hask import caseof, m, p, sig, H
>>> @sig(H/ int >> int)
... def fib(x):
... return ~(caseof(x)
... | m(0) >> 1
... | m(1) >> 1
... | m(m.n) >> fib(p.n - 2) + fib(p.n - 1))
>>> fib(1)
1
>>> fib(6)
13
As the above example shows, you can combine pattern matching and recursive functions without a hitch.
You can also deconstruct an iterable using ^
(the cons operator). The
variable before the ^
is bound to the first element of the iterable, and
the variable after the ^
is bound to the rest of the iterable. Here is a
function that adds the first two elements of any iterable, returning
Nothing
if there are less than two elements:
>>> from hask import sig, t, caseof, m, p, H
>>> from hask import Num, Maybe, Just, Nothing
>>> @sig(H[(Num, "a")]/ ["a"] >> t(Maybe, "a"))
... def add_first_two(xs):
... return ~(caseof(xs)
... | m(m.x ^ (m.y ^ m.z)) >> Just(p.x + p.y)
... | m(m.x) >> Nothing)
>>> add_first_two(L[1, 2, 3, 4, 5])
Just(3)
>>> add_first_two(L[9.0])
Nothing
Pattern matching is also very useful for deconstructing ADTs and assigning their fields to temporary variables.
>>> from hask import caseof, m, p
>>> from hask import Num, Maybe, Just, Nothing
>>> def default_to_zero(x):
... return ~(caseof(x)
... | m(Just(m.x)) >> p.x
... | m(Nothing) >> 0)
>>> default_to_zero(Just(27))
27
>>> default_to_zero(Nothing)
0
If you find pattern matching on ADTs too cumbersome, you can also use numeric indexing on ADT fields. An IndexError will be thrown if you mess something up.
>>> Just(20.0)[0]
20.0
>>> Left("words words words words")[0]
'words words words words'
>>> Nothing[0] # IndexError
Typeclasses and typeclass instances¶
Typeclasses allow you to add additional functionality to your ADTs. Hask implements all of the major typeclasses from Haskell (see the Appendix for a full list) and provides syntax for creating new typeclass instances.
As an example, let’s add a Monad instance for the Maybe type. First, however, Maybe needs Functor and Applicative instances.
def maybe_fmap(fn, x):
"""Apply a function to the value inside of a (Maybe a) value"""
return ~(caseof(x)
| m(Nothing) >> Nothing
| m(Just(m.x)) >> Just(fn(p.x)))
instance(Functor, Maybe).where(
fmap = maybe_fmap
)
Maybe is now an instance of Functor. This allows us to call fmap
and map
any function of type a -> b
into a value of type Maybe a
.
>>> times2 = (lambda x: x * 2) ** (H/ int >> int)
>>> toFloat = float ** (H/ int >> float)
>>> fmap(toFloat, Just(10))
Just(10.0)
>>> fmap(toFloat, fmap(times2, Just(25)))
Just(50.0)
Lots of nested calls to fmap get unwieldy very fast. Fortunately, any instance
of Functor can be used with the infix fmap operator, *
. This is equivalent
to <$>
in Haskell. Rewriting our example from above:
>>> (toFloat * times2) * Just(25)
Just(50.0)
>>> (toFloat * times2) * Nothing
Nothing
Note that this example uses *
as both the function compose operator and as
fmap
, to lift functions into a Maybe value. If this seems confusing,
remember that fmap
for functions is just function composition!
Now that Maybe is an instance of Functor, we can make it an instance of
Applicative and then an instance of Monad by defining the appropriate function
implementations. To implement Applicative, we just need to provide
pure
. To implement Monad, we need to provide bind
.
>>> from hask import Applicative, Monad
>>> instance(Applicative, Maybe).where(
... pure = Just
... )
>>> instance(Monad, Maybe).where(
... bind = lambda x, f: ~(caseof(x)
... | m(Just(m.a)) >> f(p.a)
... | m(Nothing) >> Nothing)
... )
The bind
function also has an infix form, which is >>
in Hask.
>>> @sig(H/ int >> int >> t(Maybe, int))
... def safe_div(x, y):
... return Nothing if y == 0 else Just(x/y)
>>> from hask.Prelude import flip
>>> divBy = flip(safe_div)
>>> Just(9) >> divBy(3)
Just(3)
>>> Just(12) >> divBy(2) >> divBy(2) >> divBy(3)
Just(1)
>>> Just(12) >> divBy(0) >> divBy(6)
Nothing
As in Haskell, List is also a monad, and bind
for the List type is just
concatMap
.
>>> from hask.Data.List import replicate
>>> L[1, 2] >> replicate(2) >> replicate(2)
L[1, 1, 1, 1, 2, 2, 2, 2]
You can also define typeclass instances for classes that are not ADTs:
>>> class Person(object):
... def __init__(self, name, age):
... self.name = name
... self.age = age
>>> instance(Eq, Person).where(
... eq = lambda p1, p2: p1.name == p2.name and p1.age == p2.age
... )
>>> Person("Philip Wadler", 59) == Person("Simon Peyton Jones", 57)
False
If you want instances of the Show, Eq, Read, Ord, and Bounded typeclasses for
your ADTs, it is adviseable to use deriving
to
automagically generate instances rather than defining them manually.
Defining your own typeclasses is pretty easy–take a look at
Typeclass
and look at the typeclasses defined
in hask.Data.Functor
and hask.Data.Num
to see how it’s done.
Operator sections¶
Hask also supports operator sections (e.g. (1+)
in Haskell). Sections are
just TypedFunc
objects, so they are
automagically curried and typechecked.
>>> from hask import __
>>> f = (__ - 20) * (2 ** __) * (__ + 3)
>>> f(10)
8172
>>> ((90/__) * (10+__)) * Just(20)
Just(3)
>>> from hask.Data.List import takeWhile
>>> takeWhile(__<5, L[1, ...])
L[1, 2, 3, 4]
>>> (__+__)('Hello ', 'world')
'Hello world'
>>> (__**__)(2)(10)
1024
>>> from hask.Data.List import zipWith, take
>>> take(5) % zipWith(__ * __, L[1, ...], L[1, ...])
L[1, 4, 9, 16, 25]
As you can see, this much easier than using lambda and adding a type signature
with the (lambda x: ...) ** (H/ ...)
syntax.
In addition, the types of the TypedFuncs created by sections are always polymorphic, to allow for any operator overloading.
Note that if you are using IPython, Hask’s __
will conflict with IPython’s
special double underscore variable. To avoid conflicts, you can use from
hask import __ as _s
in IPython.
Guards¶
If you don’t need the full power of pattern matching and just want a neater switch statement, you can use guards. The syntax for guards is almost identical to the syntax for pattern matching.
- ::
- ~(guard(expr_to_test)
- c(test_1) >> return_value_1c(test_2) >> return_value_2otherwise >> return_value_3
)
As in Haskell, `~hask.lang.syntax.otherwise`:object: will always evaluate to True and can be used as a catch-all in guard expressions. If no match is found (and an otherwise clause is not present), a NoGuardMatchException will be raised.
Guards will also play nicely with sections:
>>> from hask import guard, c, otherwise
>>> porridge_tempurature = 80
>>> ~(guard(porridge_tempurature)
... | c(__ < 20) >> "Porridge is too cold!"
... | c(__ < 90) >> "Porridge is just right!"
... | c(__ < 150) >> "Porridge is too hot!"
... | otherwise >> "Porridge has gone thermonuclear"
... )
'Porridge is just right!'
If you need a more complex conditional, you can always use lambdas, regular Python functions, or any other callable in your guard condition.
>>> def examine_password_security(password):
... analysis = ~(guard(password)
... | c(lambda x: len(x) > 20) >> "Wow, that's one secure password"
... | c(lambda x: len(x) < 5) >> "You made Bruce Schneier cry"
... | c(__ == "12345") >> "Same combination as my luggage!"
... | otherwise >> "Hope it's not 'password'"
... )
... return analysis
>>> nuclear_launch_code = "12345"
>>> examine_password_security(nuclear_launch_code)
'Same combination as my luggage!'
Monadic error handling (of Python functions)¶
If you want to use Maybe
and
Either
to handle errors raised by Python functions
defined outside Hask, you can use the decorators in_maybe
and
in_either
to create functions that call the original function and return
the result wrapped inside a Maybe or Either value.
If a function wrapped in in_maybe
raises an exception, the wrapped
function will return Nothing. Otherwise, the result will be returned wrapped
in a Just.
>>> def eat_cheese(cheese):
... if cheese <= 0:
... raise ValueError("Out of cheese error")
... return cheese - 1
>>> maybe_eat = in_maybe(eat_cheese)
>>> maybe_eat(1)
Just(0)
>>> maybe_eat(0)
Nothing
Note that this is equivalent to lifting the original function into the Maybe
monad. That is, its type has changed from func to a -> Maybe b
. This
makes it easier to use the convineient monad error handling style commonly
seen in Haskell with existing Python functions.
Continuing with this silly example, let’s try to eat three pieces of cheese, returning Nothing if the attempt was unsuccessful:
>>> cheese = 10
>>> cheese_left = Just(cheese) >> maybe_eat >> maybe_eat >> maybe_eat
>>> cheese_left
Just(7)
>>> cheese = 1
>>> cheese_left = Just(cheese) >> maybe_eat >> maybe_eat >> maybe_eat
>>> cheese_left
Nothing
Notice that we have taken a regular Python function that throws Exceptions, and are now handling it in a type-safe, monadic way.
The in_either
function works just like in_maybe
. If an Exception is
thrown, the wrapped function will return the exception wrapped in
Left. Otherwise, the result will be returned wrapped in Right.
>>> either_eat = in_either(eat_cheese)
>>> either_eat(Right(10))
Right(9)
>>> either_eat(Right(0))
Left(ValueError('Out of cheese error',))
Chained cheese-eating in the Either monad is left as an exercise for the reader.
You can also use in_maybe
or in_either
as decorators:
@in_maybe
def some_function(x, y):
...