Mochi

Mochi is a dynamically typed programming language for functional programming and actor-style programming.

Its interpreter is written in Python3. The interpreter translates a program written in Mochi to Python3's AST / bytecode.

Features

Python-like syntax
Tail recursion optimization (self tail recursion only), and no loop syntax
Re-assignments are not allowed in function definition.
Basic collection type is a persistent data structure. (using Pyrsistent)
Pattern matching / Data types, like algebraic data types
Pipeline operator
Syntax sugar of anonymous function definition
Actor, like the actor of Erlang (using Eventlet)
Macro, like the traditional macro of Lisp
Builtin functions includes functions exported by itertools module, recipes, functools module and operator module

Examples

Factorial

def factorial(n, m):
    if n == 1:
        m
    else:
        factorial(n - 1, n * m)


factorial(10000, 1)
# => 28462596809170545189064132121198688...

# Or

def factorial:
    n: factorial(n, 1)
    0, acc: acc
    n, acc: factorial(n - 1, acc * n)

factorial(10000)
# => 28462596809170545189064132121198688...

FizzBuzz

def fizzbuzz(n):
    match [n % 3, n % 5]:
        [0, 0]: "fizzbuzz"
        [0, _]: "fizz"
        [_, 0]: "buzz"
        _: n

range(1, 31)
|> map(fizzbuzz)
|> pvector()
|> print()

Actor

def show():
    receive:
        message:
            print(message)
            show()

actor = spawn(show)

send('foo', actor)
actor ! 'bar' # send('bar', actor)

sleep(1)
# -> foo
# -> bar


'foo' !> spawn(show)

sleep(1)
# -> foo

['foo', 'bar'] !&> spawn(show)
# The meaning of the above is the same as the meaning of the following.
# spawn(show) ! 'foo'
# spawn(show) ! 'bar'

sleep(1)
# -> foo
# -> bar

def show_loop():
    receive:
        [tag, value]:
            print(tag, value)
            show_loop()

actor2 = spawn(show_loop)

actor2 ! ["bar", 2000]
sleep(1)
# -> bar 2000

['foo', 1000] !> spawn(show_loop)
sleep(1)
# -> foo 1000

[['foo', 1000],['bar', 2000]] !&> spawn(show_loop)
sleep(1)
# -> foo 1000
# -> bar 2000

Distributed Computing

# comsumer.mochi
from mochi.actor.mailbox import KombuMailbox, ZmqInbox

def consumer():
    receive:
        'exit':
            print('exit!')
        other:
            print(other)
            consumer()

mailbox = KombuMailbox('sqs://<access_key_id>@<secret_access_key>:80//',
                       '<queue_name>',
                       dict(region='<region>'))
spawn_with_mailbox(consumer, mailbox)

mailbox = ZmqInbox('tcp://*:9999')
spawn_with_mailbox(consumer, mailbox)

wait_all()

# producer.mochi
from mochi.actor.mailbox import KombuMailbox, ZmqOutbox

mailbox = KombuMailbox('sqs://<access_key_id>@<secret_access_key>:80//',
                       '<queue_name>',
                       dict(region='<region>'))
mailbox ! [1, 2, 3]
mailbox ! 'exit'

mailbox = ZmqOutbox('tcp://localhost:9999')
mailbox ! [4, 5, 6]
mailbox ! 'exit'

Flask

from flask import Flask

app = Flask('demo')

@app.route('/')
def hello():
    'Hello World!'

app.run()

RxPY

# usage: mochi -no-mp timer.mochi
# original:
# https://github.com/ReactiveX/RxPY/blob/master/examples/parallel/timer.py

import rx
import concurrent.futures
import time

seconds = [5, 1, 2, 4, 3]


def sleep(t):
    time.sleep(t)
    return t


def output(result):
    print('%d seconds' % result)


with concurrent.futures.ProcessPoolExecutor(5) as executor:
    rx.Observable.from_(seconds)
                 .flat_map((s) -> executor.submit(sleep, s))
                 .subscribe(output)

# 1 seconds
# 2 seconds
# 3 seconds
# 4 seconds
# 5 seconds

aif (Anaphoric macro)

macro aif(test, true_expr, false_expr):
    quasi_quote:
        it = unquote(test)
        if it:
            unquote(true_expr)
        else:
            unquote(false_expr)

aif([], first(it), "empty")
# => "empty"
aif([10, 20], first(it), "empty")
# => 10

Requirements

CPython >= 3.2 or PyPy >= 3.2.1
rply >= 0.7.2
pyrsistent >= 0.10.1
pathlib >= 1.0.1
eventlet >= 0.17.1
msgpack-python >= 0.4.6
typeannotations >= 0.1.0
astunparse>=1.2.2

Installation

$ pip3 install mochi

Optional Installation

$ pip3 install flask Flask-RESTful Pillow RxPY  # to run the examples
$ pip3 install pyzmq # to use ZmqInbox and ZmqOutbox
$ pip3 install kombu # to use KombuMailbox
$ pip3 install boto # to use SQS as transport of KombuMailbox

The following may occur when you run Mochi on PyPy.

ImportError: Importing zmq.backend.cffi failed with version mismatch, 0.8.2 != 0.9.2

In this case, please change the version of cffi to 0.8.2 using pip on PyPy.

$ pip3 uninstall cffi
$ pip3 install cffi==0.8.2

Usage

REPL

$ mochi
>>>

loading and running a file

$ cat kinako.mochi
print('kinako')
$ mochi kinako.mochi
kinako
$ mochi -no-mp kinako.mochi  # not apply eventlet's monkey patching
kinako

byte compilation

$ mochi -c kinako.mochi > kinako.mochic

running a byte-compiled file

$ mochi -e kinako.mochic
kinako
$ mochi -e -no-mp kinako.mochic  # not apply eventlet's monkey patching
kinako

generating .pyc

$ ls
kagami.mochi
$ cat kagami.mochi
print('kagami')
$ mochi
>>> import kagami
kagami
>>> exit()
$ ls
kagami.mochi kagami.pyc
$ python3 kagami.pyc
kagami

Or

$ mochi -pyc kagami.mochi > kagami.pyc
$ python3 kagami.pyc
kagami
$ mochi -pyc -no-mp kagami.mochi > kagami.pyc  # not apply eventlet's monkey patching
$ python3 kagami.pyc
kagami

Examples for each feature

Persistent data structures

[1, 2, 3]
# => pvector([1, 2, 3])

v(1, 2, 3)
# => pvector([1, 2, 3])

vec = [1, 2, 3]
vec2 = vec.set(0, 8)
# => pvector([8, 2, 3]
vec
# => pvector([1, 2, 3])
[x, y, z] = vec
x # => 1
y # => 2
z # => 3

get(vec, 0) # => 1
get(vec, 0, 2) # => [1, 2]

vec[0] # => 1
vec[0:2] # => [1, 2]

{'x': 100, 'y': 200}
# => pmap({'y': 200, 'x': 100})

ma = {'x': 100, 'y': 200}
ma.get('x') # => 100
ma.x # => 100
ma['x'] # => 100
ma2 = ma.set('x', 10000)
# => pmap({'y': 200, 'x': 10000})
ma # => pmap({'y': 200, 'x': 100})
get(ma, 'y') # => 200
ma['y'] # => 200

m(x=100, y=200)
# => pmap({'y': 200, 'x': 100})

s(1, 2, 3)
# => pset([1, 2, 3])

b(1, 2, 3)
# => pbag([1, 2, 3])

Function definitions

def hoge(x):
    'hoge' + str(x)

hoge(3)
# => hoge3

Pattern matching

lis = [1, 2, 3]

# Sequence pattern
match lis:
    [1, 2, x]: x
    _: None
# => 3

match lis:
    [1, &rest]: rest
    _: None

# => pvector([2, 3])


foo_map = {'foo' : 'bar'}

# Mapping pattern
match foo_map:
    {'foo' : value}: value
    _: None
# => 'bar'


# Type pattern
# <name of variable refers to type> <pattern>: <action>
match 10:
    int x: 'int'
    float x: 'float'
    str x: 'str'
    bool x: 'bool'
    _: 'other'
# => 'int'

match [1, 2, 3]:
    [1, str x, 3]: 'str'
    [1, int x, 3]: 'int'
    _: 'other'
# => 'int'

num = union(int, float)
vector nums[num]
vector strs[str]

match nums([1, 2, 3]):
    nums[x, y, z]: z
    strs[x, y, z]: x
# => 3

Positive = predicate(-> $1 > 0)
Even = predicate(-> $1 % 2 == 0)
EvenAndPositive = predicate(-> ($1 % 2 == 0) and ($1 >= 0))

match 10:
    EvenAndPositive n: str(n) + ':Even and Positive'
    Even n: str(n) + ':Even'
    Positive n: str(n) + ':Positive'

# => 10:Even and Positive


# Or pattern
match ['foo', 100]:
    ['foo' or 'bar', value]: value
    _: 10000
# => 100

match ['foo', 100]:
    [str x or int x, value]: value
    _: 10000
# => 100


# Record pattern
record Person(name, age)

foo = Person('foo', 32)

match foo:
    Person('bar', age):
        'bar:' + str(age)
    Person('foo', age):
        'foo:' + str(age)
    _: None
# => 'foo:32'

Records

record Mochi
record AnkoMochi(anko) < Mochi
record KinakoMochi(kinako) < Mochi

anko_mochi = AnkoMochi(anko=3)

isinstance(anko_mochi, Mochi)
# => True
isinstance(anko_mochi, AnkoMochi)
# => True
isinstance(anko_mochi, KinakoMochi)
# => False

match anko_mochi:
    KinakoMochi(kinako): 'kinako ' * kinako + ' mochi'
    AnkoMochi(anko): 'anko ' * anko + 'mochi'
    Mochi(_): 'mochi'
# => 'anko anko anko mochi'


record Person(name, age):
    def show(self):
        print(self.name + ': ' + self.age)

foo = Person('foo', '32')
foo.show()
# -> foo: 32

# runtime type checking
record Point(x:int, y:int, z:optional(int))
Point(1, 2, None)
# => Point(x=1, y=2, z=None)
Point(1, 2, 3)
# => Point(x=1, y=2, z=3)
Point(1, None, 3)
# => TypeError

Bindings

x = 3000
# => 3000

[a, b] = [1, 2]
a
# => 1
b
# => 2

[c, &d] = [1, 2, 3]
c
# => 1
d
# => pvector([2, 3])

Data types, like algebraic data types

data Point:
    Point2D(x, y)
    Point3D(x, y, z)

# The meaning of the above is the same as the meaning of the following.
# record Point
# record Point2D(x, y) < Point
# record Point3D(x, y, z) < Point

p1 = Point2D(x=1, y=2)
# => Point2D(x=1, y=2)

p2 = Point2D(3, 4)
# => Point2D(x=3, y=4)

p1.x
# => 1

Pattern-matching function definitions

data Point:
    Point2D(x, y)
    Point3D(x, y, z)

def offset:
    Point2D(x1, y1), Point2D(x2, y2):
        Point2D(x1 + x2, y1 + y2)
    Point3D(x1, y1, z1), Point3D(x2, y2, z2):
        Point3D(x1 + x2, y1 + y2, z1 + z2)
    _: None

offset(Point2D(1, 2), Point2D(3, 4))
# => Point2D(x=4, y=6)
offset(Point3D(1, 2, 3), Point3D(4, 5, 6))
# => Point3D(x=5, y=7, z=9)

def show:
    int x, message: print('int', x, message)
    float x, message: print('float', x, message)
    _: None

show(1.0, 'msg')
# -> float 1.0 msg
# => None

FileMode = options('r', 'w', 'a', 'r+', 'w+', 'a+')

def open_file:
    str path, FileMode mode:
        open(path, mode)
    str path:
        open(path, 'r')

Anonymous function

# Arrow expression.
add = (x, y) -> x + y
add(1, 2)
# => 3

add = -> $1 + $2
add(1, 2)
# => 3

foo = (x, y) ->
    if x == 0:
        y
    else:
        x

foo(1, 2)
# => 1

foo(0, 2)
# => 2

pvector(map(-> $1 * 2, [1, 2, 3]))
# => pvector([2, 4, 6])

Pipeline operator

add = -> $1 + $2
2 |> add(10) |> add(12)
# => 24
None |>? add(10) |>? add(12)
# => None

Lazy sequences

def fizzbuzz(n):
    match [n % 3, n % 5]:
        [0, 0]: "fizzbuzz"
        [0, _]: "fizz"
        [_, 0]: "buzz"
        _: n


result = range(1, 31) |> map(fizzbuzz)
pvector(result)
# => pvector([1, 2, fizz, 4, 'buzz', 'fizz', 7, 8, 'fizz', 'buzz', 11, 'fizz', 13, 14, 'fizzbuzz', 16, 17, 'fizz', 19, 'buzz', 'fizz', 22, 23, 'fizz', 'buzz', 26, 'fizz', 28, 29, 'fizzbuzz'])
pvector(result)
# => pvector([])
pvector(result)
# => pvector([])


# Iterator -> lazyseq
lazy_result = range(1, 31) |> map(fizzbuzz) |> lazyseq()
pvector(lazy_result)
# => pvector([1, 2, fizz, 4, 'buzz', 'fizz', 7, 8, 'fizz', 'buzz', 11, 'fizz', 13, 14, 'fizzbuzz', 16, 17, 'fizz', 19, 'buzz', 'fizz', 22, 23, 'fizz', 'buzz', 26, 'fizz', 28, 29, 'fizzbuzz'])
pvector(lazy_result)
# => pvector([1, 2, fizz, 4, 'buzz', 'fizz', 7, 8, 'fizz', 'buzz', 11, 'fizz', 13, 14, 'fizzbuzz', 16, 17, 'fizz', 19, 'buzz', 'fizz', 22, 23, 'fizz', 'buzz', 26, 'fizz', 28, 29, 'fizzbuzz'])
pvector(lazy_result)
# => pvector([1, 2, fizz, 4, 'buzz', 'fizz', 7, 8, 'fizz', 'buzz', 11, 'fizz', 13, 14, 'fizzbuzz', 16, 17, 'fizz', 19, 'buzz', 'fizz', 22, 23, 'fizz', 'buzz', 26, 'fizz', 28, 29, 'fizzbuzz'])

Trailing closures

# The following trailing closure expression is passed to a function as the function’s first argument.
result = map([1, 2, 3]) ->
    print($1)
    $1 * 2

print(doall(result))

# -> 1
# -> 2
# -> 3
# => pvector([2, 4, 6])


def foreach(closure, seq):
    doall(filter(closure, seq))

# The following trailing closure expression is passed to a function as the function’s first argument.
foreach([1, 2, 3]) (item) ->
    new_item = item * 100
    print(new_item)

# -> 100
# -> 200
# -> 300
# => pvector([])

# Or

def foreach(seq, closure):
    doall(filter(closure, seq))

# The following trailing closure expression is passed to a function as the function’s final argument.
foreach([1, 2, 3]) @ (item) ->
    new_item = item * 100
    print(new_item)

# -> 100
# -> 200
# -> 300
# => pvector([])

Short form for keyword arguments and dict keys

def foo(a, b, c):
    a + b + c

a = 1
b = 2
c = 3

# This is the same as foo(a=a, b=b, c=c)
foo(=a, =b, =c))
# => 6

# This is the same as {'a': a, 'b': b}
{=a, =b}
# => pmap({'a': 1, 'b': 2})

Macros

macro rest_if_first_is_true(first, &args):
     match first:
         quote(True): quasi_quote(v(unquote_splicing(args)))
         _: quote(False)

rest_if_first_is_true(True, 1, 2, 3)
# => pvector([1, 2, 3])
rest_if_first_is_true("foo", 1, 2, 3)
# => False

macro pipeline(&args):
    [Symbol('|>')] + args

pipeline([1, 2, 3],
         map(-> $1 * 2),
         filter(-> $1 != 2),
         pvector())
# => pvector([4, 6])

Including a file at compile time

$ cat anko.mochi
x = 10000
y = 20000

require 'anko.mochi'
x
# => 10000

x = 30000

require 'anko.mochi' # include once at compile time
x
# => 30000

Module

module Math:
    export add, sub

    def add(x, y):
        x + y

    def sub(x, y):
        x - y

Math.add(1, 2)
# => 3

$ cat foobar.mochi
foo = 'foo'
bar = 'bar'

require 'foobar.mochi'
[foo, bar]
# => pvector(['foo', 'bar'])

foo = 'foofoofoo'

module X:
    export foobar
    require 'foobar.mochi'
    def foobar:
        [foo, bar]

X.foobar()
# => pvector(['foo', 'bar'])

[foo, bar]
# => pvector(['foofoofoo', 'bar'])

TODO

Improve documentation
Improve parsing
Support type annotation

License

MIT License

Contributors

https://github.com/i2y/mochi/graphs/contributors

Name		Name	Last commit message	Last commit date
Latest commit History 381 Commits
bin		bin
doc		doc
examples		examples
mochi		mochi
tests		tests
.gitignore		.gitignore
CHANGES.txt		CHANGES.txt
CONTRIBUTING.md		CONTRIBUTING.md
Docker.md		Docker.md
Dockerfile		Dockerfile
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.en.md		README.en.md
README.ja.md		README.ja.md
README.md		README.md
README.rst		README.rst
requirements.txt		requirements.txt
setup.cfg		setup.cfg
setup.py		setup.py
tasks.py		tasks.py

License

pya/mochi

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Mochi

Features

Examples

Factorial

FizzBuzz

Actor

Distributed Computing

Flask

RxPY

aif (Anaphoric macro)

Requirements

Installation

Optional Installation

Usage

REPL

loading and running a file

byte compilation

running a byte-compiled file

generating .pyc

Examples for each feature

Persistent data structures

Function definitions

Pattern matching

Records

Bindings

Data types, like algebraic data types

Pattern-matching function definitions

Anonymous function

Pipeline operator

Lazy sequences

Trailing closures

Short form for keyword arguments and dict keys

Macros

Including a file at compile time

Module

TODO

License

Contributors

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