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https://github.com/mysticfall/alleycat-reactive

A simple Python library to provide an API to implement the Reactive Object Pattern (ROP).
https://github.com/mysticfall/alleycat-reactive

fp library oop python rx

Last synced: 6 months ago
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A simple Python library to provide an API to implement the Reactive Object Pattern (ROP).

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# AlleyCat - Reactive



A part of the AlleyCat project which supports the _Reactive Object Pattern_.



## Introduction



AlleyCat Reactive is a project to explore the possibility of bridging the gap between the 

two most widely used programming paradigms, namely, the object-oriented programming (OOP), 

and functional programming (FP).



It aims to achieve its goal by proposing a new design pattern based on the 

[Reactive Extensions (Rx)](http://reactivex.io/).



Even though it is already available on [PyPI repository](https://pypi.org/project/alleycat-reactive/) 

as `alleycat-reactive` package, the project is currently at a proof-of-concept stage 

and highly experimental.



As such, there can be significant changes in the API at any time in future. Furthermore, the project 

may even be discontinued in case the idea it is based upon proves to be an infeasible one.



So, please use it at your discretion and consider opening an issue if you encounter a problem.



## Reactive Object Pattern



AlleyCat Reactive provides an API to implement what we term as _Reactive Object Pattern_ or 

_ROP_ for short. Despite its rather pretentious name, it merely means defining class properties 

which can also serve as an _Observable_ in Rx.



But why do we need such a thing?



We won't delve into this subject too much since you can learn about the concept from many 

other websites.



In short, it's much better to define data in a declarative manner, or as "data pipelines", 

especially when it's changing over time. And Rx is all about composing and manipulating such 

pipelines in a potentially asynchronous context.



But what if the data do not come from an asynchronous source, like Tweets or GUI events, 

but are just properties of an object? Of course, Rx can handle synchronous data as well, but the 

cost of using it may outweigh the benefits in such a scenario.



In a traditional OOP system, properties of an object are mere values which are often 

mutable, but not observable by default. To observe the change of a property over time, we 

must use an observer pattern, usually in the form of a separate event.



Some implementation of Rx allows converting such an event into an _Observable_. Still, it 

can be a tedious task to define an event for every such property, and it requires a lot of 

boilerplate code to convert them into Rx pipelines.



More importantly, the resulting pipelines and their handling code don't have any clear 

relationship with the original object or its properties from which they got derived. They 

are just a bunch of statements which you can put anywhere in the project, and that's not 

how you want to design your business logic in an OOP project.



In a well-designed OOP project, classes form a coherent whole by participating in 

inheritance hierarchies. They may reference, extend, or redefine properties of their 

parents or associated objects to express the behaviours and traits of the domain concepts 

they represent.



And there is another problem with using _Observables_ in such a manner. Once you build an Rx 

pipeline, you can't retrieve the value flowing inside unless you write still more boilerplate 

code to subscribe to the stream and store its value to an outside variable.



This practice is usually discouraged as an anti-pattern, and so is the use of _Subjects_. 

However, the object is not observing some outside data (e.g. Tweets) but owns it, which is 

one of the few cases where the use of a _Subject_ can be justified. In such a context, it's 

perfectly reasonable to assume that an object always has access to a snapshot of all the 

states it owns.



So, what if we can define such state data of an object as _Observables_ which can also 

behave like ordinary properties? Wouldn't it be nice if we can easily access them as in OOP 

while still being able to observe and compose them like in Rx?



And that is what this project is trying to achieve.



## Usage



To achieve the goal outlined in the previous section, we provide a way to define a property 

which can also turn into an _Observable_. And there are two different types of such 

property classes you can use for that purpose.



### Reactive Property



Firstly, there is a type of property that can manage its state, which is implemented by 

`ReactiveProperty[T]` class. To define such a property using an initial value, you can use 

a helper function `from_value` as follows:



```python

from alleycat.reactive import RP

from alleycat.reactive import functions as rv



class Toto:



    # Create an instance of ReactiveProperty[int] with an initial value of '99':

    value: RP[int] = rv.from_value(99) # You know the song, don't you? 

```



Note that `RP[T]` is just a convenient alias for `ReactiveProperty[T]` which you can use for 

type hinting. As with other type annotations in Python, it is not strictly necessary. But it 

can be particularly useful when you want to lazily initialize the property.

 

You can declare an 'empty' property using `new_property` and initialize it later as shown 

below. Because it may be difficult to see what type of data the property expects without an 

initial value, using an explicit type annotation can make the code more readable:  



```python

from alleycat.reactive import RP

from alleycat.reactive import functions as rv



class MyClass:



    # Declare an empty property first.

    value: RP[int] = rv.new_property()



    def __init__(self, init_value: int):



        # Then assign a value as you would do to an ordinary property.

        self.value = init_value

```



A `ReactiveProperty` can be can be read and modified like an ordinary class attribute. And 

also you can make it a read-only property by setting the `read_only` argument to `True` like the 

following example:



```python

from alleycat.reactive import RP

from alleycat.reactive import functions as rv



class ArcadiaBay:



    writeable: RP[str] = rv.from_value("life is strange")



    read_only: RP[str] = rv.from_value("the past", read_only=True)



place = ArcadiaBay()



print(place.writeable) # "life is strange"

print(place.read_only) # "the past"



place.writeable = "It's awesome"



print(place.writeable) # "It's awesome"



place.read_only = "Let me rewind." # Throws an AttributeError



# Of course, you can't change the past. But the game is hella cool!

```



But haven't we talked about Rx? Of course, we have! And that's the whole point of 

the library, after all.



To convert a reactive property into an _Observable_ of the same type, you can use observe 

method like this:



```python

from alleycat.reactive import RP

from alleycat.reactive import functions as rv



class Nena:



    ballons: RP[int] = rv.from_value(98)



nena = Nena()



luftballons = []



rv.observe(nena.ballons).subscribe(luftballons.append) # Returns a Disposable. See Rx API.



print(luftballons) # Returns [98].



nena.ballons = nena.ballons + 1



print(luftballons) # [98, 99] # Ok, I lied before. It's about Nena. not Toto :P

```



If you are familiar with Rx, you may notice the similarities between `ReactiveProperty` 

with _BehaviorSubject_. In fact, the former is a wrapper around the latter, and `observe` 

returns an _Observable_ instance backed by such a subject.



To learn about all the exciting things we can do with an _Observable_, you may want to 

read the official documentation of Rx. We will introduce a few examples later, but before 

that, we better learn about the other variant of the reactive value first.



### Reactive View



`ReactiveView[T]`(or `RV[T]` for short) is another derivative of `ReactiveValue`, from 

which `ReactiveProperty` is also derived (hence, the alias of `functions` module used 

above, _"rv"_).



The main difference is that while the latter owns a state value itself, a reactive view 

reflects it from an outside source specified as an _Observable_. To create a reactive 

view from an instance of _Observable_, you can use `from_observable` function like this:



```python

import rx

from alleycat.reactive import RV

from alleycat.reactive import functions as rv



class Joni:



    big: RV[str] = rv.from_observable(rx.of("Yellow", "Taxi"))

```



If you are familiar with Rx, you may see it as a wrapper around an _Observable_, while 

a reactive property can be seen as one around a _Subject_.



Like its counterpart, you can initialize a reactive view either eagerly or lazily. In order 

to create a lazy-initializing view, you can use `new_view`, and later provide an _Observable_ 

as shown below:



```python

import rx

from alleycat.reactive import RV

from alleycat.reactive import functions as rv



class BothSides:



    love: RV[str] = rv.new_view()



    def __init__(self):

        self.love = rx.of("Moons", "Junes", "Ferris wheels")

```



It also accepts `read_only` option from its constructor (default to `True`, in contrast to the case 

with `ReactiveProperty`) setting of which will make it 'writeable'.



It may sound unintuitive since a 'view' usually implies immutability. However, what changes 

when you set a value of a reactive view is the source _Observable_ that the view monitors, 

not the data itself, as is the case with a reactive property.



Lastly, you can convert a reactive property into a view by calling its `as_view` method. 

It's a convenient shortcut to call `observe` to obtain an _Observable_ of a reactive property 

so that it can be used to initialize an associated view.



The code below shows how you can derive a view from an existing reactive property:



```python

from alleycat.reactive import RP, RV

from alleycat.reactive import functions as rv



class Example:



    value: RP[str] = rv.from_value("Boring!") # I know. But it's not easy to make it interesting, alright?  



    view: RV[str] = value.as_view()

```



### Operators



As we know how to create reactive properties and values, now it's time to learn how to 

transform them. Both variants of `ReactiveValue` provides `map` method, with which you 

can map an arbitrary function or lambda expression over each value in the pipeline:



```python

from alleycat.reactive import RP, RV

from alleycat.reactive import functions as rv



class Counter:



    word: RP[str] = rv.new_property()



    count: RV[str] = word.as_view().map(len).map(lambda o, c: f"The word has {c} letter(s)")

    # The first argument 'o' is the current instance of Counter class.



counter = Counter()



counter.word = "Supercalifragilisticexpialidocious!"



print(counter.count) # Prints "The word has 35 letter(s)". Wait, did you actually count that?

```



You can also use `pipe` to chain arbitrary Rx operators to build a more complex pipeline like this: 



```python

from rx import operators as ops

from alleycat.reactive import RP, RV

from alleycat.reactive import functions as rv



class CrowsCounter:



    animal: RP[str] = rv.new_property()



    # The first argument 'o' is the current instance of Counter class.

    crows: RV[str] = animal.as_view().pipe(lambda o: (

        ops.map(str.lower),

        ops.filter(lambda v: v == "crow"),

        ops.map(lambda _: 1),

        ops.scan(lambda v1, v2: v1 + v2, 0)))



counting = CrowsCounter()



counting.animal = "cat"

counting.animal = "Crow"

counting.animal = "CROW"

counting.animal = "dog"



print(counting.crows) # Returns 2.

```



There are also convenient counterparts to `merge`, `combine_latest`, and `zip` from Rx API, 

which you can use to combine two or more reactive values in a more concise manner:



```python

from alleycat.reactive import RP, RV

from alleycat.reactive import functions as rv



class Rectangle:



    width: RP[int] = rv.from_value(100)



    height: RP[int] = rv.from_value(200)



    area: RV[int] = rv.combine_latest(width, height)(lambda v: v[0] * v[1])



rectangle = Rectangle()



print(rectangle.area) # Prints 20,000... you do the math!



rectangle.width = 150



print(rectangle.area) # Prints 30,000.



rectangle.height = 50



print(rectangle.area) # Prints 750.

```



## Install



The library can be installed using `pip` as follows:

```shell script

pip install alleycat-reactive

```



## License

This project is provided under the terms of _[MIT License](LICENSE)_.