Best JavaScript code snippets using builtins.Array.data(Showing top 10 results out of 315)

option.title.text = title;
if (res.errno == 0) {
 for (let i = 0; i < res.data.length; i++) {
  let data = res.data[i];
  if (stateOption == 'envOption') {
   if (data.type == 1) {
    option.xAxis[0].data.push('正式');
   } else if (data.type == 2) {
    option.xAxis[0].data.push('预发布');
   } else if (data.type == 3) {
    option.xAxis[0].data.push('测试');
   option.xAxis[0].data.push(data.name);
  option.series[0].data.push(data.pub);

for (const commentItem of comments.data) {
 const comment = {};
 comment.content = Buffer.from(commentItem.content, 'base64').toString();

test('linear regression', function(t) {
  test('correctly generates a line for a 0, 0 to 1, 1 dataset', function(t) {
    var l = ss.linear_regression().data([[0, 0], [1, 1]]);
    t.equal(l.line()(0), 0);
    t.equal(l.line()(0.5), 0.5);
    var l = ss.linear_regression().data([[0, 0], [1, 0]]);
    t.equal(l.line()(0), 0);
    t.equal(l.line()(0.5), 0);
    var l = ss.linear_regression().data([[0, 0], [1, 0]]);
    t.deepEqual(l.data(), [[0, 0], [1, 0]]);
    t.end();
  });
    var l = ss.linear_regression().data([[0, 0]]).line();
    t.deepEqual(l(10), 0);
    t.end();
    var l = ss.linear_regression().data([[0, 0], [1, 0]]);
    t.equal(l.m(), 0);
    t.equal(l.b(), 0);
    var l = ss.linear_regression().data([[0, 0], [1, 0.5]]);
    t.equal(l.m(), 0.5);
    t.equal(l.b(), 0);
    var l = ss.linear_regression().data([[0, 20], [1, 10]]);
    t.equal(l.m(), -10);
    t.equal(l.b(), 20);

if (pathItem.data) {
    pathItem.data[1] = 0;
    pathItem.data[1] = pathItem.data[0];
    pathItem.data[0] = 0;
    (matrix.data[4] !== 0 ||
    matrix.data[5] !== 0)
  ) {
    newPoint = transformPoint(matrix.data, pathItem.data[0], pathItem.data[1]);
    set(pathItem.data, newPoint);
    set(pathItem.coords, newPoint);
    matrix.data[4] = 0;
    matrix.data[5] = 0;
      transformArc(pathItem.data, matrix.data);
      if (Math.abs(pathItem.data[2]) > 80) {
        var a = pathItem.data[0],
          rotation = pathItem.data[2];
        pathItem.data[0] = pathItem.data[1];
        pathItem.data[1] = a;
        pathItem.data[2] = rotation + (rotation > 0 ? -90 : 90);
      newPoint = transformPoint(matrix.data, pathItem.data[5], pathItem.data[6]);
      pathItem.data[5] = newPoint[0];

if (data.data) {
 data = data.data;
} else if (data.error) {
 complete = true;

for(let o1 of obj.parameterList)
  arguments.callee(o1,o.data,obj.dataType=="array"?1:null)
for(let o1 of obj.parameterList)
  arguments.callee(o1,o.data[0].data)

option.title.text = title;
if (res.errno == 0) {
 for (let i = 0; i < res.data.length; i++) {
  let data = res.data[i];
  if (stateOption == 'envOption') {
   if (data.type == 1) {
    option.xAxis[0].data.push('正式');
   } else if (data.type == 2) {
    option.xAxis[0].data.push('预发布');
   } else if (data.type == 3) {
    option.xAxis[0].data.push('测试');
   option.xAxis[0].data.push(data.name);
  option.series[0].data.push(data.pub);

option.title.text = title;
if (res.errno == 0) {
 for (let i = 0; i < res.data.length; i++) {
  let data = res.data[i];
  if (stateOption == 'envOption') {
   if (data.type == 1) {
    option.xAxis[0].data.push('正式');
   } else if (data.type == 2) {
    option.xAxis[0].data.push('预发布');
   } else if (data.type == 3) {
    option.xAxis[0].data.push('测试');
   option.xAxis[0].data.push(data.name);
  option.series[0].data.push(data.pub);

option.title.text = title;
if (res.errno == 0) {
 for (let i = 0; i < res.data.length; i++) {
  let data = res.data[i];
  if (stateOption == 'envOption') {
   if (data.type == 1) {
    option.xAxis[0].data.push('正式');
   } else if (data.type == 2) {
    option.xAxis[0].data.push('预发布');
   } else if (data.type == 3) {
    option.xAxis[0].data.push('测试');
   option.xAxis[0].data.push(data.name);
  option.series[0].data.push(data.pub);

test('linear regression', function(t) {
  test('correctly generates a line for a 0, 0 to 1, 1 dataset', function(t) {
    var l = ss.linear_regression().data([[0, 0], [1, 1]]);
    t.equal(l.line()(0), 0);
    t.equal(l.line()(0.5), 0.5);
    var l = ss.linear_regression().data([[0, 0], [1, 0]]);
    t.equal(l.line()(0), 0);
    t.equal(l.line()(0.5), 0);
    var l = ss.linear_regression().data([[0, 0], [1, 0]]);
    t.deepEqual(l.data(), [[0, 0], [1, 0]]);
    t.end();
  });
    var l = ss.linear_regression().data([[0, 0]]).line();
    t.deepEqual(l(10), 0);
    t.end();
    var l = ss.linear_regression().data([[0, 0], [1, 0]]);
    t.equal(l.m(), 0);
    t.equal(l.b(), 0);
    var l = ss.linear_regression().data([[0, 0], [1, 0.5]]);
    t.equal(l.m(), 0.5);
    t.equal(l.b(), 0);
    var l = ss.linear_regression().data([[0, 20], [1, 10]]);
    t.equal(l.m(), -10);
    t.equal(l.b(), 20);