Metadata-Version: 2.1
Name: pampy
Version: 0.3.0
Summary: The Pattern Matching for Python you always dreamed of
Home-page: https://github.com/santinic/pampy
Author: Claudio Santini
Author-email: hireclaudio@gmail.com
License: UNKNOWN
Description: ![Pampy in Star Wars](https://raw.githubusercontent.com/santinic/pampy/master/imgs/pampy.png "Pampy in Star Wars")
        
        # Pampy: Pattern Matching for Python
        [![License MIT](https://go-shields.herokuapp.com/license-MIT-blue.png)]()
        [![Travis-CI Status](https://api.travis-ci.org/santinic/pampy.svg?branch=master)](https://travis-ci.org/santinic/pampy)
        [![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 `patter.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, _
        ```
        
        
Platform: any
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >3.6
Description-Content-Type: text/markdown
