/*  EDIT THESE  */

let csv_file = 'NVDA_data.csv';

let column_num = 4; //starts at zero, so 3rd column = 2

let truncation_value = 0.5;

let download_images = true;

let show_text = true; //this will only work if download_images = true

let header = "NVDA_data";

let height = 1024;

let width = 1024;

let min_dist = 0.0;

let max_dist = 0.1;

let start_truncation = 0.0;

let min_trunc = 0.0;

let max_trunc = -0.25;

let start_vector = [0.1321460263303403,-0.017658108045331522,-0.017009001976869453,0.09694461755682256,0.07883845283356423,0.001142680848173927,0.009564564544465661,-0.017096572507426397,-0.00995297534101465,0.11024770752233481,-0.04822396079400155,0.08410709805496011,-0.042740235158478725,-0.05467609206292965,-0.0542955084710398,-0.06914951467655893,0.005621568931098796,0.0998405548648956,0.0167810526322867,0.001620556699093386,0.020533307925018212,-0.060524104555574304,-0.019634843426189097,-0.09799968041348153,-0.10599032053343632,-0.10748248132780291,0.12389557057201173,-0.12456946949299594,0.08745654522988651,-0.20433648912677166,-0.02484375542180768,0.03892085762427472,-0.012274755432136686,-0.020800100202646898,0.009087727136829893,0.16981952384183316,0.006469123910696234,-0.006035264284048915,0.10933246554046848,-0.029777627602015286,-0.0573459981725349,0.05175483262155303,-0.016792895046488843,0.09089344424731964,-0.1253902250118213,0.06690130595799387,-0.02608434802532985,0.07051404439029477,-0.017050834572082847,0.06172028617047573,0.0917978707395693,-0.08890673629979798,0.07351556294438441,-0.15062265960816712,-0.10941339216076015,0.15007115119319187,0.013539376780007953,-0.0017772366207779253,-0.010462988411940856,-0.008768193208769653,-0.002932430678519735,0.0131194499380242,0.02709357272432804,0.0503147380289756,-0.03835211656435743,-0.06400789029592201,-0.04180341771999227,0.10791477167480251,-0.11676610245596745,-0.020503914028491728,-0.05797738223602018,-0.031222808927923056,-0.0290481044890038,-0.07040130604516529,-0.0326806962615911,-0.03968711465679445,-0.10082288577018697,-0.1394530665440186,0.0564542748891605,0.09234862096020596,0.16185046041924186,-0.010984096683154978,0.025062818364582087,-0.07682779127861422,0.02286469667537864,-0.06353020035536372,-0.008250074298171888,0.04896658926594687,0.05083786016305235,0.06097650978101597,0.027259622273587334,0.03440728416876087,-0.059239440994470205,0.12681445246049905,0.016556808080754038,0.01478005997218117,-0.05516184532940101,0.00652909803308983,0.12607078956353882,-0.008354363582956485,0.008000662642322125,0.05740263440588973,0.006320731725038537,-0.17071661354094197,0.10243486822177174,-0.12499137416103484,0.1808054872884099,-0.0006948065729600779,-0.03568790105589375,0.030714143063366502,0.021116172251404716,-0.03246541642444826,-0.046339996436283504,-0.0447640824671707,-0.07190061526837431,0.0909298162537466,0.031558594422432196,-0.013385947036795083,-0.10278438246465757,0.044015942833301264,-0.0079482895757944,-0.08482549915874366,0.05156056916785532,0.09328675063339856,-0.02422837627968986,0.060898376105118654,0.004528015837912007,-0.16683499613797115,-0.0686875380975933,-0.002345181080371614,-0.055055011817161735,-0.11366779207305668,-0.091520488649868,-0.010531710578934698,0.06895541838972498,0.12322723233285479,0.14007537962829744,0.07741145369598357,-0.06426375534801912,0.05837046039745465,0.11927023603368936,0.05742023459027651,-0.12424806166247862,0.011746292375414864,-0.14338611710272053,0.14835920412303136,0.09731600335277067,-0.08433288211401331,0.016431969349277445,0.12782238994271808,-0.016029685170114084,-0.059104969582568945,0.06114973185905122,-0.13899181209741954,-0.01666510764077027,-0.02476360813749576,0.1951769739928652,-0.017820139596294055,0.04411000180828964,-0.014502583877882051,0.06447782977959265,0.08010061464386582,0.0645848733343316,0.06363587035015517,-0.028501887339346676,0.02006003426095271,-0.15125849756181942,0.0656077791598017,0.10617353761696559,0.21326011621609053,0.031239875748799106,0.005975235750546729,-0.03169691545018729,-0.0020384105449226114,0.17160692839906244,-0.05287388741738139,0.02844670245780684,0.1177596412032093,0.043166763185731635,0.03361252709910132,-0.006928581649746295,0.20595687317045075,-0.011171220011375809,-0.0009015541029425908,0.02774373539662621,0.017726661761329726,0.12159719582593498,-0.10907645313597031,0.054145391342194246,-0.09560541713243105,-0.09632005600385182,-0.046107204248790334,0.08217511830908047,-0.027236143169418338,0.0057847771888899155,0.12518307067016365,-0.013766634798506216,0.1240332711892673,-0.05195683712929211,0.20320216145366668,0.07332376434683609,0.08209753394408334,-0.08359892974100062,-0.04664588764802863,-0.1191322952123495,0.021914505044194996,-0.04204659437663635,-0.08557745606332699,0.09844456572277786,-0.08270856777760821,0.04661016152496312,0.00007917569212563882,-0.005997446517673505,0.10583341520088826,0.12321029033646184,-0.18237087483211142,-0.0034614506472894976,-0.016184779346748644,0.018254801365062093,-0.10655901144304862,0.11748175207982617,-0.017328388907489105,-0.06346277068638581,0.14197059774582355,-0.044673023945496944,0.11324843299955012,-0.01327774150903294,0.12831874511264318,0.031142584009928434,-0.031086195919535473,0.03407163184152462,-0.019373201982158722,-0.0645009890535985,0.05278100379573058,0.04819237870562673,-0.05402254564310509,0.013779119005905506,-0.056919607164086276,0.024033971669786044,-0.05143866130020408,0.043818431987718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​

​

​

​

/*----------------------------*/

/* No need to edit below here */

​

let table;

let stock_data;

let data_min, data_max;

let vectors_out = [];

let trunc_out = [];

let total_frame_count;

let v1;

function preload() {

  table = loadTable(csv_file, 'csv', 'header');

}

​

function setup() {

  pixelDensity(1);

  createCanvas(width, height);

  textAlign(LEFT, TOP);

  textSize(40);

  // debug_csv() //uncomment this if you want to look at the data

  stock_data = get_csv_column_diff_data(column_num);

  stock_data_abs = get_abs(stock_data)

  total_frame_count = stock_data.length;

  data_min_abs = Math.min(...stock_data_abs);

  data_max_abs = Math.max(...stock_data_abs);

  data_min = Math.min(...stock_data);

  data_max = Math.max(...stock_data);

  // print(nj_uniform_random(1).tolist())

//   print(data_min)

//   print(data_max)

//   print(data_min_abs)

//   print(data_max_abs)

  //convert vectors to numjs format

  v1 = nj.array(start_vector);

  t1 = start_truncation;

  vectors_out.push(v1);

  trunc_out.push(t1);

  for(let d = 0; d < stock_data.length; d++) {

    //get start vector

    v = vectors_out[d];

    t = trunc_out[d];

    //calculate new vector (change in absolute)

    let diff = stock_data_abs[d]

    //convert price difference to distance change

    let vmap = map(diff,data_min_abs,data_max_abs,min_dist,max_dist)

    vectors_out.push(v.add(nj_uniform_random(vmap)))

    //calculate truncation

    let tdiff = stock_data[d]

    if(tdiff <=0){

      let tmap = map(tdiff,0,data_max,min_trunc,max_trunc)

      trunc_out.push(t+tmap)

    } else {

      let tmap = map(tdiff,data_min,0,-max_trunc,-min_trunc)

      trunc_out.push(t+tmap)

    }

  }

  generateInterpolation();

}

​

function get_csv_column_diff_data(column_counter){

  //cycle through the table, get closing price

  data = [];

  for (let r = 1; r < table.getRowCount(); r++) {

    let diff = (table.getString(r-1, column_counter)) - (table.getString(r, column_counter))

    data.push(diff);

  }

  return data

}

​

function get_abs(data_in) {

  data_out = [];

  for(let d = 0; d < data_in.length; d++){

    data_out.push(Math.abs(data_in[d]))

  }

  return data_out;

}

​

function debug_csv(){

  //count the columns

  print(table.getRowCount() + ' total rows in table');

  print(table.getColumnCount() + ' total columns in table');

  //cycle through the table

  for (let r = 0; r < table.getRowCount(); r++) {

    for (let c = 0; c < table.getColumnCount(); c++) {

      print(table.getString(r, c));

    }

  }

}

​

​

function generateInterpolation() {

  a = vectors_out[count].tolist();

  t = trunc_out[count];

  // if(count > 0) {

  //   print('movement: ' + stock_data[count-1])

  //   print('current truncation: ' + t)

  // }

  const path = "http://localhost:8001/query";

  const data = {

    z: a,

    truncation: t

  };

  httpPost(path, 'json', data, gotImage, gotError);

}

​

function gotError(error) {

  console.error(error);

}

​

function gotImage(result) {

  i = createImg(result.image, "", 'anonymous', imageReady);

  i.hide();

}

​

let count = 0;

​

function imageReady() {

  image(i, 0, 0);

  fill(0);

  text(header+".csv", 40 ,40);

  text(table.getString(count, 0),40,80)

  text("Price: $" + table.getString(count, 4),40,120)

  // if (count > 0){

  //   text("change since "+ table.getString(count-1, 0) + ": ",40,120)

  //   text("     " + stock_data[count-1],40,160)

  // }

  if (download_images) save(`step${nf(count, 4)}`);

  count++;

  if (count <= total_frame_count) {

    print('generating image ' + (count) +'/'+ total_frame_count)

    setTimeout(generateInterpolation, 100);

  }

}

​

function nj_uniform_random(dist) {

  return nj.clip(nj.random(512).subtract(.5).multiply(2*dist),-1.0,1.0)

}

​

function njlerp(v0, v1, t) {

  return v0.multiply(1-t).add(v1.multiply(t));

}