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pampy

(★ 3,524)

Pampy: The Pattern Matching for Python you always dreamed of.

파일 탐색기

최종 버전 다운로드 (.zip)

- pampy.png
- slide1.png
- slide2.png
- __init__.py
- helpers.py
- pampy.py
- __init__.py
- test_basic.py
- test_dataclass.py
- test_dict.py
- test_elaborate.py
- test_iterable.py
- test_typing.py
.coveralls.yml
.gitignore
.travis.yml
LICENSE
README.md
setup.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/santinic/pampy

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd pampy

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install .

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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![Pampy in Star Wars](https://raw.githubusercontent.com/santinic/pampy/master/imgs/pampy.png "Pampy in Star Wars")

# Pampy: Pattern Matching for Python
[![Coverage Status](https://coveralls.io/repos/github/santinic/pampy/badge.svg?branch=master)](https://coveralls.io/github/santinic/pampy?branch=master)
[![PyPI version](https://badge.fury.io/py/pampy.svg)](https://badge.fury.io/py/pampy)

Pampy is pretty small (150 lines), reasonably fast, and often makes your code more readable
and hence easier to reason about. [There is also a JavaScript version, called Pampy.js](https://github.com/santinic/pampy.js).

<kbd>
  <img src="https://raw.githubusercontent.com/santinic/pampy/master/imgs/slide1.png" width="700">
</kbd>

## You can write many patterns

Patterns are evaluated in the order they appear.

<kbd>
  <img src="https://raw.githubusercontent.com/santinic/pampy/master/imgs/slide2.png" width="700">
</kbd>

## You can write Fibonacci
The operator _ means "any other case I didn't think of".

```python
from pampy import match, _

def fibonacci(n):
    return match(n,
        1, 1,
        2, 1,
        _, lambda x: fibonacci(x-1) + fibonacci(x-2)
    )
```

## You can write a Lisp calculator in 5 lines

```python
from pampy import match, REST, _

def lisp(exp):
    return match(exp,
        int,                lambda x: x,
        callable,           lambda x: x,
        (callable, REST),   lambda f, rest: f(*map(lisp, rest)),
        tuple,              lambda t: list(map(lisp, t)),
    )

plus = lambda a, b: a + b
minus = lambda a, b: a - b
from functools import reduce

lisp((plus, 1, 2))                 	# => 3
lisp((plus, 1, (minus, 4, 2)))     	# => 3
lisp((reduce, plus, (range, 10)))       # => 45
```

## You can match so many things!

```python
match(x,
    3,              "this matches the number 3",

int,            "matches any integer",

(str, int),     lambda a, b: "a tuple (a, b) you can use in a function",

[1, 2, _],      "any list of 3 elements that begins with [1, 2]",

{'x': _},       "any dict with a key 'x' and any value associated",

_,              "anything else"
)
```

## You can match [HEAD, TAIL]

```python
from pampy import match, HEAD, TAIL, _

x = [1, 2, 3]

match(x, [1, TAIL],     lambda t: t)            # => [2, 3]

match(x, [HEAD, TAIL],  lambda h, t: (h, t))    # => (1, [2, 3])

```
`TAIL` and `REST` actually mean the same thing.

## You can nest lists and tuples

```python
from pampy import match, _

x = [1, [2, 3], 4]

match(x, [1, [_, 3], _], lambda a, b: [1, [a, 3], b])           # => [1, [2, 3], 4]
```

## You can nest dicts. And you can use _ as key!

```python

pet = { 'type': 'dog', 'details': { 'age': 3 } }

match(pet, { 'details': { 'age': _ } }, lambda age: age)        # => 3

match(pet, { _ : { 'age': _ } },        lambda a, b: (a, b))    # => ('details', 3)
```

It feels like putting multiple _ inside dicts shouldn't work. Isn't ordering in dicts not guaranteed ?
But it does because
[in Python 3.7, dict maintains insertion key order by default](https://mail.python.org/pipermail/python-dev/2017-December/151283.html)

## You can match class hierarchies

```python
class Pet:          pass
class Dog(Pet):     pass
class Cat(Pet):     pass
class Hamster(Pet): pass

def what_is(x):
    return match(x,
        Dog, 		'dog',
        Cat, 		'cat',
        Pet, 		'any other pet',
          _, 		'this is not a pet at all',
    )

what_is(Cat())      # => 'cat'
what_is(Dog())      # => 'dog'
what_is(Hamster())  # => 'any other pet'
what_is(Pet())      # => 'any other pet'
what_is(42)         # => 'this is not a pet at all'
```

## Using Dataclasses
Pampy supports Python 3.7 dataclasses. You can pass the operator `_` as arguments and it will match those fields.

```python
@dataclass
class Pet:
    name: str
    age: int

pet = Pet('rover', 7)

match(pet, Pet('rover', _), lambda age: age)                    # => 7
match(pet, Pet(_, 7), lambda name: name)                        # => 'rover'
match(pet, Pet(_, _), lambda name, age: (name, age))            # => ('rover', 7)
```

## Using typing
Pampy supports typing annotations.

```python

class Pet:          pass
class Dog(Pet):     pass
class Cat(Pet):     pass
class Hamster(Pet): pass

timestamp = NewType("year", Union[int, float])

def annotated(a: Tuple[int, float], b: str, c: E) -> timestamp:
    pass

match((1, 2), Tuple[int, int], lambda a, b: (a, b))             # => (1, 2)
match(1, Union[str, int], lambda x: x)                          # => 1
match('a', Union[str, int], lambda x: x)                        # => 'a'
match('a', Optional[str], lambda x: x)                          # => 'a'
match(None, Optional[str], lambda x: x)                         # => None
match(Pet, Type[Pet], lambda x: x)                              # => Pet
match(Cat, Type[Pet], lambda x: x)                              # => Cat
match(Dog, Any, lambda x: x)                                    # => Dog
match(Dog, Type[Any], lambda x: x)                              # => Dog
match(15, timestamp, lambda x: x)                               # => 15
match(10.0, timestamp, lambda x: x)                             # => 10.0
match([1, 2, 3], List[int], lambda x: x)                        # => [1, 2, 3]
match({'a': 1, 'b': 2}, Dict[str, int], lambda x: x)            # => {'a': 1, 'b': 2}
match(annotated, 
    Callable[[Tuple[int, float], str, Pet], timestamp], lambda x: x
)                                                               # => annotated
```
For iterable generics actual type of value is guessed based on the first element. 
```python
match([1, 2, 3], List[int], lambda x: x)                        # => [1, 2, 3]
match([1, "b", "a"], List[int], lambda x: x)                    # => [1, "b", "a"]
match(["a", "b", "c"], List[int], lambda x: x)                  # raises MatchError
match(["a", "b", "c"], List[Union[str, int]], lambda x: x)      # ["a", "b", "c"]

match({"a": 1, "b": 2}, Dict[str, int], lambda x: x)            # {"a": 1, "b": 2}
match({"a": 1, "b": "dog"}, Dict[str, int], lambda x: x)        # {"a": 1, "b": "dog"}
match({"a": 1, 1: 2}, Dict[str, int], lambda x: x)              # {"a": 1, 1: 2}
match({2: 1, 1: 2}, Dict[str, int], lambda x: x)                # raises MatchError
match({2: 1, 1: 2}, Dict[Union[str, int], int], lambda x: x)    # {2: 1, 1: 2}
```
Iterable generics also match with any of their subtypes.
```python
match([1, 2, 3], Iterable[int], lambda x: x)                     # => [1, 2, 3]
match({1, 2, 3}, Iterable[int], lambda x: x)                     # => {1, 2, 3}
match(range(10), Iterable[int], lambda x: x)                     # => range(10)

match([1, 2, 3], List[int], lambda x: x)                         # => [1, 2, 3]
match({1, 2, 3}, List[int], lambda x: x)                         # => raises MatchError
match(range(10), List[int], lambda x: x)                         # => raises MatchError

match([1, 2, 3], Set[int], lambda x: x)                          # => raises MatchError
match({1, 2, 3}, Set[int], lambda x: x)                          # => {1, 2, 3}
match(range(10), Set[int], lambda x: x)                          # => raises MatchError
```
For Callable any arg without annotation treated as Any. 
```python
def annotated(a: int, b: int) -> float:
    pass
    
def not_annotated(a, b):
    pass
    
def partially_annotated(a, b: float):
    pass

match(annotated, Callable[[int, int], float], lambda x: x)     # => annotated
match(not_annotated, Callable[[int, int], float], lambda x: x) # => raises MatchError
match(not_annotated, Callable[[Any, Any], Any], lambda x: x)   # => not_annotated
match(annotated, Callable[[Any, Any], Any], lambda x: x)       # => raises MatchError
match(partially_annotated, 
    Callable[[Any, float], Any], lambda x: x
)                                                              # => partially_annotated
```
TypeVar is not supported.

## All the things you can match

As Pattern you can use any Python type, any class, or any Python value.

The operator `_` and built-in types like `int` or `str`, extract variables that are passed to functions.

Types and Classes are matched via `instanceof(value, pattern)`.

`Iterable` Patterns match recursively through all their elements.  The same goes for dictionaries.

| Pattern Example | What it means | Matched Example |  Arguments Passed to function | NOT Matched Example |
| --------------- | --------------| --------------- | ----------------------------- | ------------------ |
| `"hello"` |  only the string `"hello"` matches | `"hello"` | nothing | any other value |
| `None` | only `None` | `None` | nothing | any other value |
| `int` | Any integer | `42` | `42` | any other value |
| `float` | Any float number | `2.35` | `2.35` | any other value |
| `str` | Any string | `"hello"` | `"hello"` | any other value |
| `tuple` | Any tuple | `(1, 2)` | `(1, 2)` | any other value |
| `list` | Any list | `[1, 2]` | `[1, 2]` | any other value |
| `MyClass` | Any instance of MyClass. **And any object that extends MyClass.** | `MyClass()` | that instance | any other object |
| `_` | Any object (even None) |  | that value | |
| `ANY` | The same as `_` | | that value | |
| `(int, int)` | A tuple made of any two integers | `(1, 2)` | `1` and `2` | (True, False) |
| `[1, 2, _]`  | A list that starts with 1, 2 and ends with any value | `[1, 2, 3]` | `3` | `[1, 2, 3, 4]` |
| `[1, 2, TAIL]` | A list that start with 1, 2 and ends with any sequence | `[1, 2, 3, 4]`| `[3, 4]` | `[1, 7, 7, 7]` |
| `{'type':'dog', age: _ }` | Any dict with `type: "dog"` and with an age | `{"type":"dog", "age": 3}` | `3` | `{"type":"cat", "age":2}` |
| `{'type':'dog', age: int }` | Any dict with `type: "dog"` and with an `int` age | `{"type":"dog", "age": 3}` | `3` | `{"type":"dog", "age":2.3}` |
| `re.compile('(\w+)-(\w+)-cat$')` | Any string that matches that regular expression expr | `"my-fuffy-cat"` | `"my"` and `"puffy"` | `"fuffy-dog"` | 
| `Pet(name=_, age=7)` | Any Pet dataclass with `age == 7` | `Pet('rover', 7)` | `['rover']` | `Pet('rover', 8)` |
| `Any` | The same as `_` | | that value | |
| `Union[int, float, None]` | Any integer or float number or None | `2.35` | `2.35` | any other value |
| `Optional[int]` | The same as `Union[int, None]` | `2` | `2` | any other value |
| `Type[MyClass]` | Any subclass of MyClass. **And any class that extends MyClass.** | `MyClass` | that class | any other object |
| `Callable[[int], float]` | Any callable with exactly that signature | `def a(q:int) -> float: ...` | that function | `def a(q) -> float: ...` |
| `Tuple[MyClass, int, float]` | The same as `(MyClass, int, float)` | | | |
| `Mapping[str, int]` Any subtype of `Mapping` acceptable too | any mapping or subtype of mapping with string keys and integer values | `{'a': 2, 'b': 3}` | that dict | `{'a': 'b', 'b': 'c'}` |
| `Iterable[int]` Any subtype of `Iterable` acceptable too | any iterable or subtype of iterable with integer values | `range(10)` and `[1, 2, 3]` | that iterable | `['a', 'b', 'v']` |

## Using default

By default `match()` is strict. If no pattern matches, it raises a `MatchError`.

You can instead provide a fallback value using `default` to be used when nothing matches.

```
>>> match([1, 2], [1, 2, 3], "whatever")
MatchError: '_' not provided. This case is not handled: [1, 2]

>>> match([1, 2], [1, 2, 3], "whatever", default=False)
False
```

## Using Regular Expressions
Pampy supports Python's Regex. You can pass a compiled regex as pattern, and Pampy is going to run `pattern.search()`, and then pass to the action function the result of `.groups()`.

```python 
def what_is(pet):
    return match(pet,
        re.compile('(\w+)-(\w+)-cat$'),     lambda name, my: 'cat '+name,
        re.compile('(\w+)-(\w+)-dog$'),     lambda name, my: 'dog '+name,
        _,                                  "something else"
    )

what_is('fuffy-my-dog')     # => 'dog fuffy'
what_is('puffy-her-dog')    # => 'dog puffy'
what_is('carla-your-cat')   # => 'cat carla'
what_is('roger-my-hamster') # => 'something else'
```

## Install for Python3

Pampy works in Python >= 3.6 [Because dict matching can work only in the latest Pythons](https://mail.python.org/pipermail/python-dev/2017-December/151283.html).

To install it:

```$ pip install pampy```

or
```$ pip3 install pampy```

## If you really must use Python2
Pampy is Python3-first, but you can use most of its features in Python2 via [this backport](https://pypi.org/project/backports.pampy/) by Manuel Barkhau:

```pip install backports.pampy```

```python
from backports.pampy import match, HEAD, TAIL, _
```