#setchanges

[deltarunebsd]

Check out SetChange or Mike will be sad

Ok but no seriously this is a great take made by a great person. It presents very interesting ideas and I wouldn't recommend it if it wasn't The Shit.

DELTARUNE: Set Change Masterpost

Chapter 1

Pages 1-2 Pages 3-4 Pages 5-6 Pages 7-9 Pages 10-13

[finediningwithrichard]

Thank you @sfopera tonight we saw #Puccini ‘s #ManonLescaut another great night @sfopera the ill fated Manon was sung beautifully by @liannaharoutounian brava diva👏👏👏👏🎼🎼🎼🎼🎼🎼🎼🌹🌹🌹🌹🌹🌹 her true love Chevalier des Grieux sung Handsomely by @brianjagdetenor we last saw him as Cavaradossi in Tosca; Calaf in Turandot It was wonderful seeing him again bravo 👏👏👏👏👏👏🎼🎼🎼🎼🎼🎼🎩🎩🎩🎩🎩 Manon’s older lover Geronte di Ravoir was also sung wonderfully by Philip Skinner Sing earlier this season in Romeo and Juliet and Billy Budd Bravo👏👏👏👏👏👏👏🎼🎼 Manon’s brother was sung by @anthony_clark_evans ⭐️⭐️⭐️⭐️⭐️⭐️ @philipgskinner⭐️⭐️⭐️⭐️⭐️⭐️🎩🎩🎩 #sfopera #sanfrancisco @sfopera #curtaincall #clasicalmusic #setchange #operasingersofinstagram #Puccini #Italianopera #bravo #brava #bravotuti #operalife #operalover #art #operalive #theatre #theatro #music #classicalmusic @sfoperaorch #gay #gaystagram #gaystyle #openingnight (at San Francisco Opera) https://www.instagram.com/p/B4p_cE2huV8/?igshid=zj4c0ipqe6yz

[copacabanalab]

• $3+_<|-|4|\|63 • #copacabanalab #art #photoart #setchange #stage #music #freakhouse #riomontreuxjazzfestival #portrait #bw #riodejaneiro #rj #brazil (at Rio de Janeiro) https://www.instagram.com/p/BydC9oBpGGW/?igshid=88jpfp5sb6su

[the-hypothetical-heart]

Coping

when I couldn’t set something up in time and got stuck on stage just holding it up for a scene but then the crew came to my rescue and helped me. Dear god I still don’t know why I couldn’t set it up fast enough. 

[emmbeth]

Stage management is all fun and games until a set change requires you to move a couch! #couch #moving #stagemanager #stagemanagement #setchanges #heavy #gotstuck #gotlaughedat #crushedfinger #shortandsweetsydney #nailedit @shortsweetfest (at The Depot Theatre)

[cvereport]

CVE-2021-42041

An issue was discovered in CentralAuth in MediaWiki through 1.36.2. The rightsnone MediaWiki message was not being properly sanitized and allowed for the injection and execution of HTML and JavaScript via the setchange log. source https://cve.report/CVE-2021-42041

[holytheoristtastemaker]

What is a neuron and a neural network? In biology a neuron is a cell that communicates with other cells via specialized connections called synapses. As we can see in the following picture, a neuron has a set of connections with different sizes and shapes.

In software, a neuron (artificial neuron) is a mathematical function conceived as a model of the biological neurons. Artificial neurons have a set of connections (x1, x2, x3) with a different set of weights (w1, w2, w3). The neuron itself generates an output (y) when it performs a function (o) with the values from the input connections (x1,x2,x3 … w1,w2,w3). Basically: a neuron is a function that gives a value depending on some input values.

A software artificial neural network is a collection of neurons connected to each other that represent a mathematical function that models something we want to accomplish.

Real-life can be decomposed into math. Imagine you want to write code to identify cats in pictures. This would require you a lot of time and complicated maths. Imagine: decomposing the images in groups of pixels, guessing which characteristics represent a cat, and analyzing if each set of pixels maps one of these characteristics. Sounds complicated. Here is where neural networks come in hand. Neural networks can be trained to learn how to solve a specific problem. At the beginning neural networks are a little "random". They are generated with random values and trained against a set of data (dataset). They adjust themselves over and over and learn how to give the expected results. Once a network has been trained to do something it will be able to give predictions with new data that it has never seen before. So, if you train a network with thousands of cat images it will be able to tell you when you are showing it a cat. But it will not be able to tell you when you are showing it a house. You have trained the network and now the network contains the function (or the code) that model cat characteristics, but nothing else. One of the best resources for learning about neural networks is the next video by 3blue1Brown

Writing our first neural network

What we are going to model is this:

We want to create the following entities:

Neuron: has input connection, output connections and a bias

Connection: Has a "origin" neuron a "destination" neuron, and a weight.

Layer: has neurons and an activation function

Network: has layers

With this simple neural network in JavaScript we will be able to magically auto-program simple logic gates (AND, OR, XOR, etc). This logic gates could be easily programmed with a normal function but we are going to show how a neural network can solve this problems automatically. With this knowledge you will be able to understand the basics of machine learning and escalate it to other needs. For more professional needs we advise you to use some solid frameworks like TensorFlow or Pytorch. Let's go to code our first neural network in JavaScript from the ground. In this example we will use object oriented programming with ES6 classes and unit tests. You can find all the code for this tutorial in the following repo: https://github.com/rafinskipg/neural-network-js

Neuron.js

As you can see, most of the code of the neuron is Boilerplate that you can omit (setters, print functions, etc), the only important things are:

bias

delta

output

error

connections

import uid from './uid' class Neuron { constructor() { this.inputConnections = [] this.outputConnections = [] this.bias = 0 // delta is used to store a percentage of change in the weight this.delta = 0 this.output = 0 this.error = 0 this.id = uid() } toJSON() { return { id: this.id, delta: this.delta, output: this.output, error: this.error, bias: this.bias, inputConnections: this.inputConnections.map(i => i.toJSON()), outputConnections: this.outputConnections.map(i => i.toJSON()) } } getRandomBias() { const min = -3; const max = 3 return Math.floor(Math.random() * (+max - +min)) +min; } addInputConnection(connection) { this.inputConnections.push(connection) } addOutputConnection(connection) { this.outputConnections.push(connection) } setBias(val) { this.bias = val } setOutput(val) { this.output = val } setDelta(val) { this.delta = val } setError(val) { this.error = val } } export default Neuron

Connection

Connections link from one neuron to another neuron. And have a weight. We will store also the change property to know how much the weight should change between iterations, in the backpropagation phase.

class Connection { constructor(from, to) { this.from = from this.to = to this.weight = Math.random() this.change = 0 } toJSON() { return { change: this.change, weight: this.weight, from: this.from.id, to: this.to.id } } setWeight(w) { this.weight = w } setChange(val) { this.change = val } } export default Connection

Layer

A layer is just a collection of neurons. When we do new Layer(5); we are creating a layer with a group of 5 neurons.

import Neuron from './neuron' class Layer { constructor(numberOfNeurons) { const neurons = [] for (var j = 0; j < numberOfNeurons; j++) { const neuron = new Neuron() neurons.push(neuron) } this.neurons = neurons } toJSON() { return this.neurons.map(n => { return n.toJSON() }) } } export default Layer

Simple for now. Right? Let's do a quick recap: we just have 3 different concepts or classes for now, that we can use in a simple way like this:

var myLayer = new Layer(5); // create a layer of 5 neurons // Create a connection var connection = new Connection(myLayer.neurons[0], myLayer.neurons[1]) // Store references to the connection in the neurons myLayer.neurons[0].addOutputConnection(connection) myLayer.neurons[1].addInputConnection(connection)

Basically, to create a network, we just need different layers, with different neurons each, and different connections with weights. To model this you can use another abstraction, you don't have to follow the one I did. For example, you could make just a matrix of objects and store all the data without using classes. I used OOP because it's easier for me to learn new concepts that I can model easily.

The network

There are some things we should understand before we create our network (group of layers). 1 - We need to create various layers

2 - The input layer neurons have no input connections, only output

3 - The output layer neurons have no output connections, only input

4 - All neurons are created with a random Bias value. Except the ones in the input layer which will have the Input values. Input values are the data we are going to use to give a prediction or a result. For example, in an image of 28x28 it would be 784 pixels of data. In a logic gate it will be 2 values (0 or 1). 5 - In each training step we will provide some values to the Input layer (the training data), then calculate the output and apply Backpropagation to recalculate the weights of the connections. 6 - Backpropagation is a way of adapting the weights of the connections based on the error difference of the desired output versus the real output. After executing it many times the network gives something more close to the expected result. This is training the network. Before we see all the network code we need to understand how a neuron calculates it's own value in each iteration.

const bias = this.layers[layer].neurons[neuron].bias // For each neuron in this layer we compute its output value, // the output value is obtained from all the connections comming to this neuron const connectionsValue = this.layers[layer].neurons[neuron].inputConnections.reduce((prev, conn) => { const val = conn.weight * conn.from.output return prev + val }, 0) this.layers[layer].neurons[neuron].setOutput(sigmoid(bias + connectionsValue))

We calculate the output of a neuron by adding all the products of weight and output from previous connections. It means, getting all the connections that come to this neuron, for each connection we will multiply the weight and output and add it to the total. Once we have the SUM of all products we will apply a Sigmoid function to normalize the output. What is a sigmoid function?

A sigmoid function is a mathematical function having a characteristic "S"-shaped curve or sigmoid curve. In neural networks sigmoid function is used to normalize the values of a neuron between 0 and 1. There are different kinds of functions neural networks use, these functions are called activation functions. Some of the most popular activation functions are Sigmoid, Tanh or ReLU. You can read a more in-depth explanation of activation functions here. For now we will just use sigmoid function written in JavaScript:

function sigmoid(z) { return 1 / (1 + Math.exp(-z)); } export default sigmoid

Let's take a look now at the full network code. There are many things going on in the network:

The network connects all neurons from one layer to the next one

When the network is training it runs the runInputSigmoid method, which uses the sigmoid function as an activation function.

Backpropagation is done by calculating the change needed in the weights (delta) and then applying it. The code for calculating weights and deltas is complex.

The run method just calls runInputSigmoid to give the results

import sigmoid from './sigmoid' import Connection from './connection' import Layer from './layer' class Network { constructor(numberOfLayers) { // Create a network with a number of layers. For layers different than the input layer we add a random Bias to each neuron this.layers = numberOfLayers.map((length, index) => { const layer = new Layer(length) if (index !== 0 ) { layer.neurons.forEach(neuron => { neuron.setBias(neuron.getRandomBias()) }) } return layer }) this.learningRate = 0.3 // multiply's against the input and the delta then adds to momentum this.momentum = 0.1 // multiply's against the specified "change" then adds to learning rate for change this.iterations = 0 // number of iterations in the training process this.connectLayers() } toJSON() { return { learningRate: this.learningRate, iterations: this.iterations, layers: this.layers.map(l => l.toJSON()) } } setLearningRate(value) { this.learningRate = value } setIterations(val) { this.iterations = val } connectLayers() { // Connects current layer with the previous one. This is for a fully connected network // (each neuron connects with all the neurons from the previous layer) for (var layer = 1; layer < this.layers.length; layer++) { const thisLayer = this.layers[layer] const prevLayer = this.layers[layer - 1] for (var neuron = 0; neuron < prevLayer.neurons.length; neuron++) { for(var neuronInThisLayer = 0; neuronInThisLayer < thisLayer.neurons.length; neuronInThisLayer++) { const connection = new Connection(prevLayer.neurons[neuron], thisLayer.neurons[neuronInThisLayer]) prevLayer.neurons[neuron].addOutputConnection(connection) thisLayer.neurons[neuronInThisLayer].addInputConnection(connection) } } } } // When training we will run this set of functions each time train(input, output) { // Set the input data on the first layer this.activate(input) // Forward propagate this.runInputSigmoid() // backpropagate this.calculateDeltasSigmoid(output) this.adjustWeights() // You can use as a debugger // console.log(this.layers.map(l => l.toJSON())) this.setIterations(this.iterations + 1) } activate(values) { this.layers[0].neurons.forEach((n, i) => { n.setOutput(values[i]) }) } run() { // For now we only use sigmoid function return this.runInputSigmoid() } runInputSigmoid() { for (var layer = 1; layer < this.layers.length; layer++) { for (var neuron = 0; neuron < this.layers[layer].neurons.length; neuron++) { const bias = this.layers[layer].neurons[neuron].bias // For each neuron in this layer we compute its output value, // the output value is obtained from all the connections comming to this neuron const connectionsValue = this.layers[layer].neurons[neuron].inputConnections.reduce((prev, conn) => { const val = conn.weight * conn.from.output return prev + val }, 0) this.layers[layer].neurons[neuron].setOutput(sigmoid(bias + connectionsValue)) } } return this.layers[this.layers.length - 1].neurons.map(n => n.output) } calculateDeltasSigmoid(target) { // calculates the needed change of weights for backpropagation, based on the error rate // It starts in the output layer and goes back to the first layer for (let layer = this.layers.length - 1; layer >= 0; layer--) { const currentLayer = this.layers[layer] for (let neuron = 0; neuron < currentLayer.neurons.length; neuron++) { const currentNeuron = currentLayer.neurons[neuron] let output = currentNeuron.output; let error = 0; if (layer === this.layers.length -1 ) { // Is output layer, // the error is the difference between the expected result and the current output of this neuron error = target[neuron] - output; // console.log('calculate delta, error, last layer', error) } else { // Other than output layer // the error is the sum of all the products of the output connection neurons * the connections weight for (let k = 0; k < currentNeuron.outputConnections.length; k++) { const currentConnection = currentNeuron.outputConnections[k] error += currentConnection.to.delta * currentConnection.weight // console.log('calculate delta, error, inner layer', error) } } currentNeuron.setError(error) currentNeuron.setDelta(error * output * (1 - output)) } } } adjustWeights() { // we start adjusting weights from the output layer back to the input layer for (let layer = 1; layer <= this.layers.length -1; layer++) { const prevLayer = this.layers[layer - 1] const currentLayer = this.layers[layer] for (let neuron = 0; neuron < currentLayer.neurons.length; neuron++) { const currentNeuron = currentLayer.neurons[neuron] let delta = currentNeuron.delta for (let i = 0; i < currentNeuron.inputConnections.length; i++) { const currentConnection = currentNeuron.inputConnections[i] let change = currentConnection.change // The change on the weight of this connection is: // the learningRate * the delta of the neuron * the output of the input neuron + (the connection change * momentum) change = (this.learningRate * delta * currentConnection.from.output) + (this.momentum * change); currentConnection.setChange(change) currentConnection.setWeight(currentConnection.weight + change) } currentNeuron.setBias(currentNeuron.bias + (this.learningRate * delta)) } } } } export default Network

I am not going to explain why the deltas and weights are calculated with that formula. Backpropagation is a complex topic that requires investigation from your side. Let me give you some resources for your investigation:

Backpropagation: https://en.wikipedia.org/wiki/Backpropagation

What is backpropagation really doing?

Multi-Layer Neural Networks with Sigmoid Function- Deep Learning for Rookies

Backpropagation is really simple who made it complicated?

With the code for the network you will be able to run backpropagation to train it. But it is important you take your time to elaborate on your thoughts on it.

Writing tests to train our network:

In the example repository you will find different tests that allow to train the network in different ways:

Here is our test for a XOR gate and it will serve as a full example on how to use this network for different purposes. You can try to train the network for different things and see what happens.

import Network from '../network' // Training data for a xor gate const trainingData = [{ input : [0,0], output: [0] }, { input : [0,1], output: [1] }, { input : [1,0], output: [1] }, { input : [1,1], output: [0] }] describe('XOR Gate', () => { let network beforeAll(done => { // Create the network network = new Network([2, 10, 10, 1]) // Set a learning rate const learningRate = 0.3 network.setLearningRate(learningRate) // Train the network for(var i = 0; i < 20000 ; i ++) { const trainingItem = trainingData[Math.floor((Math.random()*trainingData.length))] // Randomly train network.train(trainingItem.input, trainingItem.output); } done() }) it('should return 0 for a [0,0] input', () => { network.activate([0, 0]) const result = network.runInputSigmoid() expect(Math.round(result[0])).toEqual(0) }) it('should return 1 for a [0,1] input', () => { network.activate([0, 1]) const result = network.runInputSigmoid() expect(Math.round(result[0])).toEqual(1) }) it('should return 1 for a [1,0] input', () => { network.activate([1, 0]) const result = network.runInputSigmoid() expect(Math.round(result[0])).toEqual(1) }) it('should return 0 for a [1,1] input', () => { network.activate([1, 1]) const result = network.runInputSigmoid() expect(Math.round(result[0])).toEqual(0) }) })

If you want to do things that require GPU usage for training (more computational strength) or more complex layers you might need to use a more advanced library like:

[katiegaribaldi]

Scene 1 for my new music video "Unhappy Holiday" was an absolute success beyond my wildest dreams!!! 🎄🎄🎄 I'm so grateful. It's been nonstop...now back to @nissileebeauty HQ for a hair and makeup change transforming into a retro 60s Barbie doll look for my second look on the next scene tonight! 💁🏻 Running on adrenaline...trying to wrap my head around how blessed I am to work with this fantastic team, and blown away by everyone's performance today! 🤗❤️☃️❄️🎶 #nashville #hairandmakeup #makeupartist #hairstylist #nissileebeauty #glam #glamtime #singersongwriter #indieartist #musicvideo #HomeSweetChristmas #unhappyholiday #filming #setchange #lookchange #retro (at Nashville, Tennessee)

[mrjmichaelb]

Sooooon.... #setchange #comedy #standup #comedians #cabbagepatchsaloon

[knowledgewiki]

Applying Immer.js patches to an empty object

I am using Immer.js to change a multi-dimensional object that represents form data. However, I would also like to track, in a separate object with the same structure, what exact changes have been made to the object (so I can use that second object to send a PATCH request to an API).

In the onChange handler of my inputs, I do the following:

const setValue = (object, path, value) => { const lastKey = path.pop(); let target = object; path.forEach(key => { if (!(key in target)) { target[key] = {}; } target = target[key]; }); target[lastKey] = value; }; // ... onChange: e => { const value = e.target.value; const [nextState, patches] = produceWithPatches(data, draft => { setValue(draft, ['some', 'key', 'in_object'], value); }); setData(nextState); // Now to apply the patches from data on another object }

Now, ideally, I would just do setChanges(prevChanges => applyPatches(prevChanges, patches)) to track what’s been changed, however Immer will complain: Error: Cannot apply patch, path doesn't resolve: some/key/in_object.

So right now, I’m doing

setChanges(prevState => { patches.forEach(patch => { setValue(changes, patch.path, patch.value); }); return prevState; });

But that’s sort of wishy-washy as I’m really only supporting replace operations in the patches and not even explicitly checking for them.

What would be a better way to achieve my intended goal: tracking what exactly has been changed in the data object?

There’s a codepen with a full example: https://codepen.io/brammm/pen/WNNVewq

Archive from: https://stackoverflow.com/questions/59070409/applying-immer-js-patches-to-an-empty-object

from https://ift.tt/2DirnQ2

[jcole0809]

I'm super excited about @therejects, but I'm head over heels for this venue. 🙌😍🎶🎸 | #libertyhall #lawrenceks #allamericanrejects #friyay #livemusic #fangirl #setchange http://ift.tt/2uQOGyI

[finediningwithrichard]

Thank you to @sfopera for the wonderful new production of Hansel and Gretel by Engelbert Humperdinck in a joint new production with @royaloperahouse Hänsel was sung by the amazing mezzo Sasha Cooke @sashamezzomama 👏👏👏👏👏👏⭐️⭐️⭐️🎼🎼🎼🎼🎼🎼🥰🥰🥰🥰🥰🥰No stranger to @sfopera Audience’s most recently in the title role of Orlando with prior performances in The Gospel of Mary Magdalene; Anna in Les Troyens; Magdalene in Die Meistersinger von Nürnberg, It’s wonderful to see her return and especially in this new production that she marvels in! Gretel was sung by Heidi Stober ⭐️⭐️⭐️⭐️⭐️👏👏👏🎼🎼🎼another great performance also last Seen here in Orlando other past performances include Zdenka in Arabella; Johanna in Sweeney Todd; Norina in Don Pasquale; Magnolia Hawks in Show Boat; Robert Brubaker made for a terrifying scary witch 🦇👻🎼🎼⭐️; the production uses #Hitchcock a la #psycho and is very dark but not too terrifying! Rounding out the cast the mother was sung by Michaela Martens Adler alumni⭐️👏👏👏👏👏; Alfred Walker sang the father handsomely ⭐️⭐️👏👏👏; Adler fellow Ashley Dixon sang the sandman👏👏🎩⭐️🎉; Adler fellow Natalie Image sang the Dew Fairy beautifully @natalie.image 👏👏👏⭐️⭐️🎼🎼🎼; This production is great for serious opera fans and a great way to introduce young children to this amazing art form another wonderful new production under the marvelous leadership of @matthew_shilvock keep them coming! 👏👏👏👏👏👏👏🎩🎩🎩🎩🎩⭐️⭐️⭐️ #humperdinck #hanselandgretel #grimm #HeidiStober #Fairytale #sfopera #sanfrancisco @sfopera #curtaincall #clasicalmusic #setchange #operasingersofinstagram #Puccini #Germanopera #bravo #brava #bravotuti #operalife #operalover #art #operalive #theatre #theatro #music #classicalmusic @sfoperaorch #gay #gaystagram #gaystyle #openingnight (at San Francisco Opera) https://www.instagram.com/p/B468BdzhnGV/?igshid=12bb791frs1mb

[lupbiy]

用VUEJS做一个网易云音乐

const store = new Vuex.Store({

  state: {

    audio: {

      'id': 0,

      'name': '歌曲名称',

      'singer': '演唱者',

      'albumPic': '/static/player-bar.png',

      'location': '',

      'album': ''

    },

    lyric: '正在加载中。。',

    currentIndex: 0, // 当前播放的歌曲位置

    playing: false, // 是否正在播放

    loading: false, // 是否正在加载中

    showDetail: false,

    songList: [],    // 播放列表

    currentTime: 0,

    tmpCurrentTime: 0,

    durationTime: 0,

    bufferedTime: 0,

    change: false   // 判断是更改的时间还是播放的时间

  },

  getters: {

    audio: state => state.audio,

    playing: state => state.playing,

    loading: state => state.loading,

    showDetail: state => state.showDetail,

    durationTime: state => state.durationTime,

    currentIndex: state => state.currentIndex,

    bufferedTime: state => state.bufferedTime,

    tmpCurrentTime: state => state.tmpCurrentTime,

    songList: state => state.songList,

    change: state => state.change,

    currentTime: state => state.currentTime,

    prCurrentTime: state => {

      return state.currentTime / state.durationTime * 100

    },

    prBufferedTime: state => {

      return state.bufferedTime / state.durationTime * 100

    }

  },

  mutations: {

    play (state) {

      state.playing = true

    },

    pause (state) {

      state.playing = false

    },

    toggleDetail (state) {

      state.showDetail = !state.showDetail

    },

    setAudio (state) {

      state.audio = state.songList[state.currentIndex - 1]

    },

    setAudioIndex (state, index) {

      state.audio = state.songList[index]

      state.currentIndex = index + 1

    },

    removeAudio (state, index) {

      state.songList.splice(index, 1)

      state.audio = state.songList[index - 1]

      state.currentIndex = state.currentIndex - 1

      if (state.songList.length === 0) {

        state.audio = {

          'id': 0,

          'name': '歌曲名称',

          'singer': '演唱者',

          'albumPic': '/static/player-bar.png',

          'location': '',

          'album': ''

        }

        state.playing = false

      }

    },

    setChange (state, flag) {

      state.change = flag

    },

    setLocation (state, location) {

      state.audio.location = location

    },

    updateCurrentTime (state, time) {

      state.currentTime = time

    },

    updateDurationTime (state, time) {

      state.durationTime = time

    },

    updateBufferedTime (state, time) {

      state.bufferedTime = time

    },

    changeTime (state, time) {

      state.tmpCurrentTime = time

    },

    openLoading (state) {

      state.loading = true

    },

    closeLoading (state) {

      state.loading = false

    },

    resetAudio (state) {

      state.currentTime = 0

    },

    playNext (state) { // 播放下一曲

      state.currentIndex++

      if (state.currentIndex > state.songList.length) {

        state.currentIndex = 1

      }

      state.audio = state.songList[state.currentIndex - 1]

    },

    playPrev (state) { // 播放上一曲

      state.currentIndex--

      if (state.currentIndex < 1) {

        state.currentIndex = state.songList.length

      }

      state.audio = state.songList[state.currentIndex - 1]

    },

    addToList (state, item) {

      var flag = false

      state.songList.forEach(function (element, index) { // 检测歌曲重复

        if (element.id === item.id) {

          flag = true

          state.currentIndex = index + 1

        }

      })

      if (!flag) {

        state.songList.push(item)

        state.currentIndex = state.songList.length

      }

    },

    setLrc (state, lrc) {

      state.lyric = lrc

    }

  },

  // 异步的数据操作

  actions: {

    getSong ({commit, state}, id) {

      commit('openLoading')

      Axios.get(api.getSong(id)).then(res => {

        // 统一数据模型，方便后台接口的改变

        var url = res.data.data[0].url

        commit('setAudio')

        commit('setLocation', url)

      })

    },

    getLrc ({commit, state}, id) {

      commit('setLrc', '[txt](加载中。。。')

      Axios.get(api.getLrc(id)).then(res => {

        // 1、先判断是否有歌词

        if (res.data.nolyric) {

          commit('setLrc', '[txt](⊙０⊙) 暂无歌词')

        } else {

          console.log(res.data.lrc.lyric)

          commit('setLrc', res.data.lrc.lyric)

        }

      })

    }

  }

})

[cyndichamberssports]

#SouthernRoots Could ya give a girl a hand please... Shelby Mae Randle, with team The Fantastic 4, helps change an actor's costume during the Ocala Civc Theatre's competition between backstage hands. @cyndichamberssports on assignment • • • #backstage #costumechange #setchange #thearts #community #civictheatre #inmylittletown (at Ocala Civic Theatre)

[finediningwithrichard]

Thank you @sfopera tonight we saw #Puccini ‘s #ManonLescaut another great night @sfopera the ill fated Manon was sung beautifully by @liannaharoutounian brava diva👏👏👏👏🎼🎼🎼🎼🎼🎼🎼🌹🌹🌹🌹🌹🌹 her true love Chevalier des Grieux sung Handsomely by @brianjagdetenor we last saw him as Cavaradossi in Tosca; Calaf in Turandot It was wonderful seeing him again bravo 👏👏👏👏👏👏🎼🎼🎼🎼🎼🎼🎩🎩🎩🎩🎩 Manon’s older lover Geronte di Ravoir was also sung wonderfully by Philip Skinner Sing earlier this season in Romeo and Juliet and Billy Budd Bravo👏👏👏👏👏👏👏🎼🎼 Manon’s brother was sung by @anthony_clark_evans ⭐️⭐️⭐️⭐️⭐️⭐️ @philipgskinner⭐️⭐️⭐️⭐️⭐️⭐️🎩🎩🎩 #sfopera #sanfrancisco @sfopera #curtaincall #clasicalmusic #setchange #operasingersofinstagram #Puccini #Italianopera #bravo #brava #bravotuti #operalife #operalover #art #operalive #theatre #theatro #music #classicalmusic @sfoperaorch #gay #gaystagram #gaystyle #openingnight (at San Francisco Opera) https://www.instagram.com/p/B4osEP0BXc8/?igshid=1ofcbczshj5lj

[finediningwithrichard]

Thank you @sfopera tonight we saw #Puccini ‘s #ManonLescaut another great night @sfopera the ill fated Manon was sung beautifully by @liannaharoutounian brava diva👏👏👏👏🎼🎼🎼🎼🎼🎼🎼🌹🌹🌹🌹🌹🌹 her true love Chevalier des Grieux sung Handsomely by @brianjagdetenor we last saw him as Cavaradossi in Tosca; Calaf in Turandot It was wonderful seeing him again bravo 👏👏👏👏👏👏🎼🎼🎼🎼🎼🎼🎩🎩🎩🎩🎩 Manon’s older lover Geronte di Ravoir was also sung wonderfully by Philip Skinner Sing earlier this season in Romeo and Juliet and Billy Budd Bravo👏👏👏👏👏👏👏🎼🎼 Manon’s brother was sung by @anthony_clark_evans ⭐️⭐️⭐️⭐️⭐️⭐️ @philipgskinner⭐️⭐️⭐️⭐️⭐️⭐️🎩🎩🎩 #sfopera #sanfrancisco @sfopera #curtaincall #clasicalmusic #setchange #operasingersofinstagram #Puccini #Italianopera #bravo #brava #bravotuti #operalife #operalover #art #operalive #theatre #theatro #music #classicalmusic @sfoperaorch #gay #gaystagram #gaystyle #openingnight (at San Francisco Opera) https://www.instagram.com/p/B4oqw3Gh0wH/?igshid=n5c32xxw5hai