Metadata-Version: 2.1
Name: awesome-pattern-matching
Version: 0.9.0
Summary: Awesome Pattern Matching
Home-page: https://github.com/scravy/awesome-pattern-matching
Author: Julian Fleischer
Author-email: tirednesscankill@warhog.net
License: UNKNOWN
Description: # Awesome Pattern Matching (_apm_) for Python
        
        [![Github Actions](https://github.com/scravy/awesome-pattern-matching/workflows/Python%20application/badge.svg)](https://github.com/scravy/awesome-pattern-matching/actions) [![Downloads](https://static.pepy.tech/personalized-badge/awesome-pattern-matching?period=total&units=international_system&left_color=black&right_color=orange&left_text=Downloads)](https://pepy.tech/project/awesome-pattern-matching) [![PyPI version](https://badge.fury.io/py/awesome-pattern-matching.svg)](https://pypi.org/project/awesome-pattern-matching/)
        
        - Simple
        - Powerful
        - Extensible
        - Composable
        - Functional  
        - Python 3.8+
        - Typed (IDE friendly)
        - Offers different styles (expression, declarative, statement, ...)
        
        There's a ton of pattern matching libraries available for python, all with varying degrees of maintenance and usability;
        also [there's a PEP on it's way for a match construct](https://www.python.org/dev/peps/pep-0634/). However, I wanted
        something which works well and works now, so here we are.
        
        _`apm`_ defines patterns as objects which are _composable_ and _reusable_. Pieces can be matched and captured into
        variables, much like pattern matching in Haskell or Scala (a feature which most libraries actually lack, but which also
        makes pattern matching useful in the first place - the capability to easily extract data). Here is an example:
        
        ```python
        from apm import *
        
        if result := match([1, 2, 3, 4, 5], [1, '2nd' @ _, '3rd' @ _, 'tail' @ Remaining(...)]):
            print(result['2nd'])  # 2
            print(result['3rd'])  # 3
            print(result['tail'])  # [4, 5]
        
        # If you find it more readable, '>>' can be used instead of '@' to capture a variable
        match([1, 2, 3, 4, 5], [1, _ >> '2nd', _ >> '3rd', Remaining(...) >> 'tail'])
        ```
        
        Patterns can be composed using `&`, `|`, and `^`, or via their more explicit counterparts `AllOf`, `OneOf`, and `Either`
        . Since patterns are objects, they can be stored in variables and be reused.
        
        ```python
        positive_integer = InstanceOf(int) & Check(lambda x: x >= 0)
        ```
        
        Some fancy matching patterns are available out of the box:
        
        ```python
        from apm import *
        
        def f(x: int, y: float) -> int:
            pass
        
        if match(f, Arguments(int, float) & Returns(int)):
            print("Function satisfies required signature")
        ```
        
        For matching and selecting from multiple cases, choose your style:
        
        ```python
        from apm import *
        
        value = 7
        
        # The simple style
        if match(value, Between(1, 10)):
            print("It's between 1 and 10")
        elif match(value, Between(11, 20)):
            print("It's between 11 and 20")
        else:
            print("It's not between 1 and 20")
        
        # The expression style
        case(value) \
            .of(Between(1, 10), lambda: print("It's between 1 and 10")) \
            .of(Between(11, 20), lambda: print("It's between 11 and 20")) \
            .otherwise(lambda: print("It's not between 1 and 20"))
        
        # The statement style
        try:
            match(value)
        except Case(Between(1, 10)):
            print("It's between 1 and 10")
        except Case(Between(11, 20)):
            print("It's between 11 and 20")
        except Default:
            print("It's not between 1 and 20")
        
        # The declarative style
        @case_distinction
        def f(n: Match(Between(1, 10))):
            print("It's between 1 and 10")
        
        @case_distinction
        def f(n: Match(Between(11, 20))):
            print("It's between 11 and 20")
        
        @case_distinction
        def f(n):
            print("It's not between 1 and 20")
        
        f(value)
        
        # pampy style
        match(value,
              Between( 1, 10), lambda: print("It's between 1 and 10"),
              Between(11, 20), lambda: print("It's between 11 and 20"),
              _,               lambda: print("It's not between 1 and 20"))
        ```
        
        ## Installation
        
        ```bash
        pip install awesome-pattern-matching
        ```
        
        ## Nested pattern matches
        
        Patterns are applied recursively, such that nested structures can be matched arbitrarily deep.
        This is super useful for extracting data from complicated structures:
        
        ```python
        from apm import *
        
        sample_k8s_response = {
            "containers": [
                {
                    "args": [
                        "--cert-dir=/tmp",
                        "--secure-port=4443",
                        "--kubelet-preferred-address-types=InternalIP,ExternalIP,Hostname",
                        "--kubelet-use-node-status-port"
                    ],
                    "image": "k8s.gcr.io/metrics-server/metrics-server:v0.4.1",
                    "imagePullPolicy": "IfNotPresent",
                    "name": "metrics-server",
                    "ports": [
                        {
                            "containerPort": 4443,
                            "name": "https",
                            "protocol": "TCP"
                        }
                    ]
                }
            ]
        }
        
        if result := match(sample_k8s_response, {
                "containers": Each({
                    "image": 'image' @ _,
                    "name": 'name' @ _,
                    "ports": Each({
                        "containerPort": 'port' @ _
                    }),
                })
            }):
            print(f"Image: {result['image']}, Name: {result['name']}, Port: {result['port']}")
        ```
        
        The above will print
        
        ```
        Image: k8s.gcr.io/metrics-server/metrics-server:v0.4.1, Name: metrics-server, Port: 4443
        ```
        
        ## Multimatch
        
        By default `match` records all matches for captures. If for example `'item' @ InstanceOf(int)` matches multiple times,
        each match will be recorded in `result['item']`.
        
        ```python
        if result := match([{'foo': 5}, 3, {'foo': 7, 'bar': 9}], Each(OneOf({'foo': 'item' @ _}, ...))):
            print(result['item'])  # [5, 7]
        ```
        
        If the capture matches only once `result['item']` returns exactly that.
        
        ```python
        if result := match([{'foo': 5}, 3, {'quux': 7, 'bar': 9}], Each(OneOf({'foo': 'item' @ _}, ...))):
            print(result['item'])  # 5
        ```
        
        If the capture matched several items a list of these items will be returned. `match` accepts a `multimatch` keyword argument
        which can be set to `False` to avoid this (in that case the capture will be set to the last match).
        
        ```python
        if result := match([{'foo': 5}, 3, {'foo': 7, 'bar': 9}], Each(OneOf({'foo': 'item' @ _}, ...)), multimatch=False):
            print(result['item'])  # 7
        ```
        
        ## Strict vs non-strict matches
        
        Any value which occurs verbatim in a pattern is matched verbatim (`int`, `str`, `list`, ...), except Dictionaries (
        anything which has an `items()` actually).
        
        Thus:
        
        ```python
        some_very_complex_object = {
            "A": 1,
            "B": 2,
            "C": 3,
        }
        match(some_very_complex_object, {"C": 3})  # matches!
        ```
        
        If you do not want unknown keys to be ignored, wrap the pattern in a `Strict`:
        
        ```python
        # does not match, only matches exactly `{"C": 3}`
        match(some_very_complex_object, Strict({"C": 3}))
        ```
        
        Lists (anything iterable which does not have an `items()` actually) are also compared as they are, i.e.:
        
        ```python
        ls = [1, 2, 3]
        match(ls, [1, 2, 3])  # matches
        match(ls, [1, 2])  # does not match
        ```
        
        ## Match head and tail of a list
        
        It is possible to match the remainder of a list though:
        
        ```python
        match(ls, [1, 2, Remaining(InstanceOf(int))])
        ```
        
        And each item:
        
        ```python
        match(ls, Each(InstanceOf(int)))
        ```
        
        Patterns can be joined using `&`, `|`, and `^`:
        
        ```python
        match(ls, Each(InstanceOf(int) & Between(1, 3)))
        ```
        
        Wild-card matches are supported using Ellipsis (`...`):
        
        ```python
        match(ls, [1, Remaining(..., at_least=2)])
        ```
        
        The above example also showcases how `Remaining` can be made to match
        `at_least` _n_ number of items (`Each` also has an `at_least` keyword argument).
        
        ## Wildcard matches anything using `...` or `_`
        
        A wildcard pattern can be expressed using `...`, the ellipsis object. An alternate, to some people more familiar syntax,
        is `_`. There is actually a difference between `...` and `_`. The ellipsis (`...`) is a native python type, whereas `_`
        is defined as `Value(...)`. That is: `_` is an instance of `Pattern`, whereas `...` is not.
        
        ```python
        # These are equivalent
        match([1, 2, 3, 4], [1, _, 3, _])
        match([1, 2, 3, 4], [1, ..., 3, ...])
        ```
        
        ## The different styles in detail
        
        ### Simple style
        
        - 💚 has access to result captures
        - 💚 vanilla python
        - 💔 can not return values (since it's a statement, not an expression)
        
        ```python
        from apm import *
        
        value = {"a": 7, "b": "foo", "c": "bar"}
        
        if result := match(value, EachItem(_, 'value' @ InstanceOf(str) | ...)):
            print(result['value'])  # ["foo", "bar"]
            #     ^^^ access to capture
        ```
        
        ### Expression style
        
        - 💚 has access to result captures
        - 💚 vanilla python
        - 💚 can return values
        - 🧡 so terse that it is sometimes hard to read
        
        ```python
        from apm import *
        
        display_name = case({'user': 'some-user-id', 'first_name': "Jane", 'last_name': "Doe"}) \
            .of({'first_name': 'first' @ _, 'last_name': 'last' @ _}, lambda first, last: f"{first}, {last}") \
            .of({'user': 'user_id' @ _}, lambda user_id: f"#{user_id}") \
            .otherwise("anonymous")
        ```
        
        _Note: To return a value an `.otherwise(...)` case must always be present._
        
        ### Statement style
        
        This is arguable the most hacky style in _`apm`_, as it re-uses the `try .. except`
        mechanism. It is nevertheless quite readable.
        
        - 💚 has access to result captures
        - 💚 very readable
        - 💔 can not return values (since it's a statement, not an expression)
        - 🧡 misuse of the `try .. except` statement
        
        ```python
        from apm import *
        
        try:
            match({'user': 'some-user-id', 'first_name': "Jane", 'last_name': "Doe"})
        except Case({'first_name': 'first' @ _, 'last_name': 'last' @ _}) as result:
            user = f"{result['first']} {result['last']}"
        except Case({'user': 'user_id' @ _}) as result:
            user = f"#{result['user_id']}"
        except Default:
            user = "anonymous"
            
        print(user)  # "Jane Doe"
        ```
        
        ### Declarative style
        
        - 💔 does not have access to result captures
        - 💚 very readable
        - 💚 can return values
        - 🧡 the most bloated version of all styles
        
        ```python
        from apm import *
        
        @case_distinction
        def fib(n: Match(OneOf(0, 1))):
           return n
        
        @case_distinction
        def fib(n):
            return fib(n - 2) + fib(n - 1)
        
        for i in range(0, 6):
            print(fib(i))
        ```
        
        #### Nota bene: Overloading using `@case_distinction`
        
        If not for its pattern matching capabilities, `@case_distinction` can be used
        to implement overloading. In fact, it can be imported as `@overload`.
        The mechanism is aware of arity and argument types.
        
        ```python
        from apm.overload import overload
        @overload
        def add(a: str, b: str):
            return "".join([a, b])
        
        @overload
        def add(a: int, b: int):
            return a + b
        
        add("a", "b")
        add(1, 2)
        ```
        
        
        ## `Capture(pattern, name=<str>)`
        
        Captures a piece of the thing being matched by name.
        
        ```python
        if result := match([1, 2, 3, 4], [1, 2, Capture(Remaining(InstanceOf(int)), name='tail')]):
            print(result['tail'])  ## -> [3, 4]
        ```
        
        As this syntax is rather verbose, two short hand notations can be used:
        
        ```python
        # using the matrix multiplication operator '@' (syntax resembles that of Haskell and Scala)
        if result := match([1, 2, 3, 4], [1, 2, 'tail' @ Remaining(InstanceOf(int))]):
            print(result['tail'])  ## -> [3, 4]
        
        # using the right shift operator
        if result := match([1, 2, 3, 4], [1, 2, Remaining(InstanceOf(int)) >> 'tail']):
            print(result['tail'])  ## -> [3, 4]
        ```
        
        ## `Strict(pattern)`
        
        Performs a strict pattern match. A strict pattern match also compares the type of verbatim values. That is, while
        _`apm`_ would match `3` with `3.0` it would not do so when using `Strict`. Also _`apm`_ performs partial matches of
        dictionaries (that is: it ignores unknown keys). It will perform an exact match for dictionaries using `Strict`.
        
        ```python
        # The following will match
        match({"a": 3, "b": 7}, {"a": ...})
        match(3.0, 3)
        
        # These will not match
        match({"a": 3, "b": 7}, Strict({"a": ...}))
        match(3.0, Strict(3))
        ```
        
        ## `OneOf(pattern1, pattern2, ..)`
        
        Matches against any of the provided patterns. Equivalent to `p1 | p2 | p3 | ..`
        (but operator overloading does not work with values that do not inherit from `Pattern`)
        
        ```python
        match("quux", OneOf("bar", "baz", "quux"))
        ```
        
        ```python
        match(3, OneOf(InstanceOf(int), None))
        ```
        
        Patterns can also be joined using `|` to form a `OneOf` pattern:
        
        ```python
        match(3, InstanceOf(int) | InstanceOf(float))
        ```
        
        The above example is rather contrived, as `InstanceOf` already accepts multiple types natively:
        
        ```python
        match(3, InstanceOf(int, float))
        ```
        
        Since bare values do not inherit from `Pattern` they can be wrapped in `Value`:
        
        ```python
        match("quux", Value("foo") | Value("quux"))
        ```
        
        ## `AllOf(pattern1, pattern2, ..)`
        
        Checks whether the value matches all of the given pattern. Equivalent to `p1 & p2 & p3 & ..`
        (but operator overloading does not work with values that do not inherit from `Pattern`)
        
        ```python
        match("quux", AllOf(InstanceOf("str"), Regex("[a-z]+")))
        ```
        
        ## `Not(pattern)`
        
        Matches if the given pattern does not match.
        
        ```python
        match(3, Not(4))  # matches
        match(5, Not(4))  # matches
        match(4, Not(4))  # does not match
        ```
        
        The bitflip prefix operator (`~`) can be used to express the same thing. Note that it does not work on bare values,
        so they need to be wrapped in `Value`.
        
        ```python
        match(3, ~Value(4))  # matches
        match(5, ~Value(4))  # matches
        match(4, ~Value(4))  # does not match
        ```
        
        `Not` can be used do create a `NoneOf` kind of pattern:
        
        ```python
        match("string", ~OneOf("foo", "bar"))  # matches everything except "foo" and "bar"
        ```
        
        ## `Each(pattern [, at_least=]`
        
        Matches each item in an iterable.
        
        ```python
        match(range(1, 10), Each(Between(1, 9)))
        ```
        
        ## `EachItem(key_pattern, value_pattern)`
        
        Matches an object if each key satisfies `key_pattern` and each value satisfies `value_pattern`.
        
        ```python
        match({"a": 1, "b": 2}, EachItem(Regex("[a-z]+"), InstanceOf(int)))
        ```
        
        ## `Length(length)`
        
        Matches an object if it has the given length. Alternatively also accepts `at_least` and `at_most` keyword arguments.
        
        ```python
        match("abc", Length(3))
        match("abc", Length(at_least=2))
        match("abc", Length(at_most=4))
        match("abc", Length(at_least=2, at_most=4))
        ```
        
        ## `Check(predicate)`
        
        Matches an object if it satisfies the given predicate.
        
        ```python
        match(2, Check(lambda x: x % 2 == 0))
        ```
        
        ## `InstanceOf(type1 [, type2 [, ..]])`
        
        Matches an object if it is an instance of any of the given types.
        
        ```python
        match(1, InstanceOf(int, flaot))
        ```
        
        ## `Arguments(*types)`
        
        Matches a callable if it's type annotations correspond to the given types. Very useful for implementing rich APIs.
        
        ```python
        def f(x: int, y: float, z):
            ...
        
        
        match(f, Arguments(int, float, None))
        ```
        
        Arguments has an alternate form which can be used to match keyword arguments:
        
        ```python
        
        def f(x: int, y: float, z: str):
            ...
        
        match(f, Arguments(x=int, y=float))
        ```
        
        The strictness rules are the same as for dictionaries (which is why the above example works).
        
        ```python
        # given the f from above
        match(f, Strict(Arguments(x=int, y=float)))  # does not match
        match(f, Strict(Arguments(x=int, y=float, z=str)))  # matches
        ```
        
        
        ## `Returns(type)`
        
        Matches a callable if it's type annotations denote the given return type.
        
        ```python
        def g(x: int) -> str:
            ...
        
        
        match(g, Arguments(int) & Returns(str))
        ```
        
        ## `Transformed(function, pattern)`
        
        Transforms the currently looked at value by applying `function` on it and matches the result against `pattern`. In
        Haskell and other languages this is known as a [_view
        pattern_](https://gitlab.haskell.org/ghc/ghc/-/wikis/view-patterns).
        
        ```python
        def sha256(v: str) -> str:
            import hashlib
            return hashlib.new('sha256', v.encode('utf8')).hexdigest()
        
        
        match("hello", Transformed(sha256, "2cf24dba5fb0a30e26e83b2ac5b9e29e1b161e5c1fa7425e73043362938b9824"))
        ```
        
        ## `At(path, pattern)`
        
        Checks whether the nested object to be matched satisfied pattern at the given path. The match fails if the given path
        can not be resolved.
        
        ```python
        record = {
            "foo": {
                "bar": {
                    "quux": {
                        "value": "deeply nested"
                    }
                }
            }
        }
        
        result := match(record, At("foo.bar.quux", {"value": Capture(..., name="value")}))
        result['value']  # "deeply nested"
        
        # alternate form
        result := match(record, At(['foo', 'bar', 'quux'], {"value": Capture(..., name="value")}))
        ```
        
        ## Extensible
        
        New patterns can be added, just like the ones in `apm.patterns.*`. Simply extend the `apm.Pattern` class:
        
        ```python
        class Min(Pattern):
            def __init__(self, min):
                self.min = min
        
            def match(self, value, *, ctx: MatchContext, strict=False) -> MatchResult:
                return ctx.match_if(value >= self.min)
        
        match(3, Min(1))  # matches
        match(3, Min(5))  # does not match
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
