abhinayar

/**
 * FUNCTION = (inputData) => string
 * Creates the lesson slug based on the data passed in
 * --
 * Takes in all the inputData we are working with and uses the title, composer name,
 * artist name and other information to construct the slug which we use in the lesson
 * URL, i.e. https://tonebase.co/lessons/preview/__________ (ex: leo-brouwer-teaches-estudios-sencillos-by-leo-brouwer)
 */
function constructSlug(inputData) {
  // == EXTRACT DATA FROM INPUT DATA

abhinayar

# Your init script
#
# Atom will evaluate this file each time a new window is opened. It is run
# after packages are loaded/activated and after the previous editor state
# has been restored.
#
# An example hack to log to the console when each text editor is saved.
#
# atom.workspace.observeTextEditors (editor) ->
#   editor.onDidSave ->

abhinayar

// NOTE:
// To omit any section, simply set it to be null
// Like so... blogPosts: null
module.exports = {
  general: {
    // List the issue #
    // Check last weeks newsletter if you need a refresher on the number
    issue: 55,
    // Breifly describe the content of this newsletter
    // - note, you might need to load the page once before you do this...

abhinayar

axios.get('https://medium.com/@tonebaseteam/latest?format=json').then(function (html) {
    // First we get the HTML version of the json results
    let unParsedData = html.data.toString();
    //console.log(unParsedData)

    // Now we need to strip the CORS security buffer used by Medium
    // I.E. they add "])}while(1);</x>" to the beginning of each response
    // so you cant just simply parse the JSON data
    unParsedData = unParsedData.slice(16, unParsedData.length);
    //console.log(unParsedData);

abhinayar

stopBodyScrolling(bool) {
    if (bool === true) {
      document.body.addEventListener("touchmove", (e) => {
        e.preventDefault()
      }, false);
    } else {
      document.body.removeEventListener("touchmove", (e) => {
        e.preventDefault()
      }, false);
    }

abhinayar

function [components variances] = pca(X, m)
  close all

  % We start yby calcing the mean
  % This gives us a 1 x i matrix with the average of all i rows of an image
  meanX = mean(X);

  % Now iterate through X and center the values
  for i = 1:size(X,1)
      for j = 1:size(X,2)