%global _empty_manifest_terminate_build 0
Name: python-pampy
Version: 0.3.0
Release: 1
Summary: The Pattern Matching for Python you always dreamed of
License: MIT License
URL: https://github.com/santinic/pampy
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4c/07/576e0f0ed7c7e8488930a0506fd54926c58b79e45eec400914a8e5edb242/pampy-0.3.0.tar.gz
BuildArch: noarch
%description
# Pampy: Pattern Matching for Python
[]()
[](https://travis-ci.org/santinic/pampy)
[](https://coveralls.io/github/santinic/pampy?branch=master)
[](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).
## You can write many patterns
Patterns are evaluated in the order they appear.
## 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, _
```
%package -n python3-pampy
Summary: The Pattern Matching for Python you always dreamed of
Provides: python-pampy
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-pampy
# Pampy: Pattern Matching for Python
[]()
[](https://travis-ci.org/santinic/pampy)
[](https://coveralls.io/github/santinic/pampy?branch=master)
[](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).
## You can write many patterns
Patterns are evaluated in the order they appear.
## 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, _
```
%package help
Summary: Development documents and examples for pampy
Provides: python3-pampy-doc
%description help
# Pampy: Pattern Matching for Python
[]()
[](https://travis-ci.org/santinic/pampy)
[](https://coveralls.io/github/santinic/pampy?branch=master)
[](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).
## You can write many patterns
Patterns are evaluated in the order they appear.
## 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, _
```
%prep
%autosetup -n pampy-0.3.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-pampy -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot - 0.3.0-1
- Package Spec generated