看看Rx, net的响应式扩展。他们指出，使用IEnumerable，你基本上是从流中“拉”出来的。IQueryable/IEnumerable上的Linq查询是集合操作，从集合中“吸”出结果。但是在IObservable上使用相同的操作符，你可以编写“反应”的Linq查询。

例如，您可以编写这样的Linq查询 (from MyObservableSetOfMouseMovements中的m m.X<100 m.Y<100 选择新的点(m.X,m.Y))。

有了Rx扩展，就是这样:你有UI代码，它会对传入的鼠标移动流做出反应，并在你处于100,100框时进行绘制……

伙计，这主意太棒了!为什么1998年的时候我没有发现?总之，这是我对Fran教程的理解。建议是最受欢迎的，我正在考虑开始一个基于此游戏引擎。

import pygame
from pygame.surface import Surface
from pygame.sprite import Sprite, Group
from pygame.locals import *
from time import time as epoch_delta
from math import sin, pi
from copy import copy

pygame.init()
screen = pygame.display.set_mode((600,400))
pygame.display.set_caption('Functional Reactive System Demo')

class Time:
    def __float__(self):
        return epoch_delta()
time = Time()

class Function:
    def __init__(self, var, func, phase = 0., scale = 1., offset = 0.):
        self.var = var
        self.func = func
        self.phase = phase
        self.scale = scale
        self.offset = offset
    def copy(self):
        return copy(self)
    def __float__(self):
        return self.func(float(self.var) + float(self.phase)) * float(self.scale) + float(self.offset)
    def __int__(self):
        return int(float(self))
    def __add__(self, n):
        result = self.copy()
        result.offset += n
        return result
    def __mul__(self, n):
        result = self.copy()
        result.scale += n
        return result
    def __inv__(self):
        result = self.copy()
        result.scale *= -1.
        return result
    def __abs__(self):
        return Function(self, abs)

def FuncTime(func, phase = 0., scale = 1., offset = 0.):
    global time
    return Function(time, func, phase, scale, offset)

def SinTime(phase = 0., scale = 1., offset = 0.):
    return FuncTime(sin, phase, scale, offset)
sin_time = SinTime()

def CosTime(phase = 0., scale = 1., offset = 0.):
    phase += pi / 2.
    return SinTime(phase, scale, offset)
cos_time = CosTime()

class Circle:
    def __init__(self, x, y, radius):
        self.x = x
        self.y = y
        self.radius = radius
    @property
    def size(self):
        return [self.radius * 2] * 2
circle = Circle(
        x = cos_time * 200 + 250,
        y = abs(sin_time) * 200 + 50,
        radius = 50)

class CircleView(Sprite):
    def __init__(self, model, color = (255, 0, 0)):
        Sprite.__init__(self)
        self.color = color
        self.model = model
        self.image = Surface([model.radius * 2] * 2).convert_alpha()
        self.rect = self.image.get_rect()
        pygame.draw.ellipse(self.image, self.color, self.rect)
    def update(self):
        self.rect[:] = int(self.model.x), int(self.model.y), self.model.radius * 2, self.model.radius * 2
circle_view = CircleView(circle)

sprites = Group(circle_view)
running = True
while running:
    for event in pygame.event.get():
        if event.type == QUIT:
            running = False
        if event.type == KEYDOWN and event.key == K_ESCAPE:
            running = False
    screen.fill((0, 0, 0))
    sprites.update()
    sprites.draw(screen)
    pygame.display.flip()
pygame.quit()

简而言之:如果每个组成部分都可以被视为一个数字，那么整个系统就可以被视为一个数学方程，对吗?

如果你想感受一下FRP，你可以从1998年的Fran教程开始，它有动画插图。对于论文，从函数反应动画开始，然后在我的主页上的出版物链接和Haskell wiki上的FRP链接上跟踪链接。

就我个人而言，我喜欢在讨论如何实施FRP之前思考它意味着什么。 (没有规范的代码是没有问题的答案，因此“甚至没有错”。) 因此，我没有像Thomas K在另一个答案(图、节点、边、触发、执行等)中那样用表示/实现术语描述FRP。 有许多可能的实现风格，但没有一种实现说明FRP是什么。

I do resonate with Laurence G's simple description that FRP is about "datatypes that represent a value 'over time' ". Conventional imperative programming captures these dynamic values only indirectly, through state and mutations. The complete history (past, present, future) has no first class representation. Moreover, only discretely evolving values can be (indirectly) captured, since the imperative paradigm is temporally discrete. In contrast, FRP captures these evolving values directly and has no difficulty with continuously evolving values.

FRP is also unusual in that it is concurrent without running afoul of the theoretical & pragmatic rats' nest that plagues imperative concurrency. Semantically, FRP's concurrency is fine-grained, determinate, and continuous. (I'm talking about meaning, not implementation. An implementation may or may not involve concurrency or parallelism.) Semantic determinacy is very important for reasoning, both rigorous and informal. While concurrency adds enormous complexity to imperative programming (due to nondeterministic interleaving), it is effortless in FRP.

那么，什么是FRP? 你可以自己发明的。 从这些想法开始:

Dynamic/evolving values (i.e., values "over time") are first class values in themselves. You can define them and combine them, pass them into & out of functions. I called these things "behaviors". Behaviors are built up out of a few primitives, like constant (static) behaviors and time (like a clock), and then with sequential and parallel combination. n behaviors are combined by applying an n-ary function (on static values), "point-wise", i.e., continuously over time. To account for discrete phenomena, have another type (family) of "events", each of which has a stream (finite or infinite) of occurrences. Each occurrence has an associated time and value. To come up with the compositional vocabulary out of which all behaviors and events can be built, play with some examples. Keep deconstructing into pieces that are more general/simple. So that you know you're on solid ground, give the whole model a compositional foundation, using the technique of denotational semantics, which just means that (a) each type has a corresponding simple & precise mathematical type of "meanings", and (b) each primitive and operator has a simple & precise meaning as a function of the meanings of the constituents. Never, ever mix implementation considerations into your exploration process. If this description is gibberish to you, consult (a) Denotational design with type class morphisms, (b) Push-pull functional reactive programming (ignoring the implementation bits), and (c) the Denotational Semantics Haskell wikibooks page. Beware that denotational semantics has two parts, from its two founders Christopher Strachey and Dana Scott: the easier & more useful Strachey part and the harder and less useful (for software design) Scott part.

如果你坚持这些原则，我希望你能得到或多或少符合FRP精神的东西。

Where did I get these principles? In software design, I always ask the same question: "what does it mean?". Denotational semantics gave me a precise framework for this question, and one that fits my aesthetics (unlike operational or axiomatic semantics, both of which leave me unsatisfied). So I asked myself what is behavior? I soon realized that the temporally discrete nature of imperative computation is an accommodation to a particular style of machine, rather than a natural description of behavior itself. The simplest precise description of behavior I can think of is simply "function of (continuous) time", so that's my model. Delightfully, this model handles continuous, deterministic concurrency with ease and grace.

正确有效地实现这个模型是一个相当大的挑战，但那是另一个故事了。

好的，从背景知识和阅读你所指向的维基百科页面来看，响应式编程似乎有点像数据流计算，但有特定的外部“刺激”触发一组节点来触发并执行它们的计算。

这非常适合UI设计，例如，触摸用户界面控件(例如，音乐播放应用程序上的音量控制)可能需要更新各种显示项和音频输出的实际音量。当您修改体积(比如一个滑块)时，这将对应于修改有向图中与节点相关的值。

具有“体积值”节点边缘的各种节点将自动被触发，任何必要的计算和更新将自然地贯穿整个应用程序。应用程序对用户刺激“做出反应”。函数式响应式编程只是在函数式语言中实现这一思想，或者通常在函数式编程范式中实现。

有关“数据流计算”的更多信息，请在维基百科或使用您喜欢的搜索引擎上搜索这两个词。总体思想是这样的:程序是一个节点的有向图，每个节点执行一些简单的计算。这些节点通过图链接相互连接，图链接将一些节点的输出提供给其他节点的输入。

当节点触发或执行其计算时，连接到其输出的节点将“触发”或“标记”相应的输入。任何触发/标记/可用所有输入的节点都会自动触发。图可以是隐式的，也可以是显式的，具体取决于响应式编程是如何实现的。

Nodes can be looked at as firing in parallel, but often they are executed serially or with limited parallelism (for example, there may be a few threads executing them). A famous example was the Manchester Dataflow Machine, which (IIRC) used a tagged data architecture to schedule execution of nodes in the graph through one or more execution units. Dataflow computing is fairly well suited to situations in which triggering computations asynchronously giving rise to cascades of computations works better than trying to have execution be governed by a clock (or clocks).

响应式编程引入了这种“执行级联”的思想，似乎以一种类似数据流的方式来考虑程序，但有一个附带条件，即一些节点与“外部世界”挂钩，当这些类似感知的节点发生变化时，执行级联就会被触发。程序的执行看起来就像一个复杂的反射弧。程序在两个刺激之间可能是基本固定的，也可能不是，也可能在两个刺激之间稳定在基本固定的状态。

"non-reactive" programming would be programming with a very different view of the flow of execution and relationship to external inputs. It's likely to be somewhat subjective, since people will likely be tempted to say anything that responds to external inputs "reacts" to them. But looking at the spirit of the thing, a program that polls an event queue at a fixed interval and dispatches any events found to functions (or threads) is less reactive (because it only attends to user input at a fixed interval). Again, it's the spirit of the thing here: one can imagine putting a polling implementation with a fast polling interval into a system at a very low level and program in a reactive fashion on top of it.

Conal Elliott的论文《Simply efficient functional reactivity》(直接PDF, 233 KB)是一个相当好的介绍。相应的库也可以工作。

这篇论文现在被另一篇论文取代，推拉函数式反应性编程(直接PDF, 286 KB)。

关于响应式编程的简短而清晰的解释出现在Cyclejs -响应式编程中，它使用了简单和可视化的示例。

一个[模块/组件/对象]是反应性的意味着它是完全负责的 通过对外部事件的反应来管理自己的状态。 这种方法的好处是什么?这就是控制反转， 主要是因为[module/Component/object]对自己负责，使用私有方法来改进封装。

这是一个很好的起点，而不是一个完整的知识来源。从那里你可以跳到更复杂和更深入的文件。