2d:null 2e:["$","title",null,{"children":"Normal Table | Grégoire Geis | Observable"}] 33:T4a0,viewof example = { let from_mean = false; const lower = html`${tex`-\infty`}`, upper = html``, result = html``, wrapper = html`${tex`P(`}${lower}${tex` < N < `}${upper}${tex`) = `} ${result}`; lower.addEventListener("click", () => { from_mean = !from_mean; lower.innerHTML = ""; lower.appendChild(from_mean ? tex`\ \ \ 0\ \ ` : tex`-\infty`); update(true); }); upper.addEventListener("change", (e) => e.stopPropagation() || update(false)); upper.addEventListener("input", (e) => e.stopPropagation() || update(false)); function update(dispatch) { const upperValue = +upper.value, resultValue = (from_mean ? 0 : .5) + normal_(upperValue); result.innerHTML = ""; result.appendChild(tex`${d3.format("0.5f")(resultValue)}`); if (dispatch) { wrapper.value = { from_mean }; wrapper.dispatchEvent(new CustomEvent("input")); } } update(true); return wrapper; }34:T835,normal_table = { let div = d3.create('div'); let table = div .append('table') .attr('border', '0') .attr('align', 'center') .attr('cellspacing', '2') .attr('cellpadding', 3); let first_row = table.append('tr'); first_row .append('th') .attr('class', 'tex') .html('Z'); for (let j = 0; j < 0.09999; j = j + 0.01) { first_row .append('th') .attr('col', `${Math.round(100 * j)}`) .text(d3.format("0.2f")(j)); } for (let i = 0; i <= 3.4001; i = i + 0.1) { let row = d3.create('tr'); row .append('th') .text(d3.format("0.1f")(i)) .attr('row', `${Math.round(10 * i)}`); for (let j = 0; j < 0.0999; j = j + 0.01) { let td = row .append('td') .attr('row', `${Math.round(10 * i)}`) .attr('col', `${Math.round(100 * j)}`) .attr('title', `Z = ${d3.format('0.2f')(i + j)}`) .text(normal(i + j)); const tip = tippy(td.node(), { // Not sure why I can't dynamically set the content of the tip // content: function() { // return 'ad'; // }, delay: [50, 0], duration: [200, 50], placement: 'top', arrow: true }); // Again, not sure why I can't dynamically set the content of the tip //tip.setContent('ad'); } table.append(() => row.node()); } div .selectAll('td') .on('mouseover', function() { let row = d3.select(this).attr('row'); let col = d3.select(this).attr('col'); d3.selectAll(`[row="${row}"]`).attr('bgcolor', '#ddd'); d3.selectAll(`[col="${col}"]`).attr('bgcolor', '#ddd'); }) .on('mouseleave', function() { let row = d3.select(this).attr('row'); let col = d3.select(this).attr('col'); d3.selectAll(`[row="${row}"]`).attr('bgcolor', '#fff'); d3.selectAll(`[col="${col}"]`).attr('bgcolor', '#fff'); }); return div.node(); // Might typeset with KaTeX another day. // div.selectAll('.tex').each(function(d) { // katex.render(d3.select(this).text(), d3.select(this).node()); // }); }35:[] 31:{"id":"b363209280b5d167","slug":"normal-table","title":"Normal Table","thumbnail":"83c6a50c19076ee06ff439ca732a8f3cd615d436243885272fb9e2ab0e954f82","access_level":"public","create_time":"$D2021-04-07T10:08:32.418Z","update_time":"$D2021-04-07T11:00:56.800Z","view_time":null,"first_public_time":"$D2021-04-07T11:01:30.708Z","has_unpublished_edits":null,"fork":{"id":"da2eef67cf09430b","slug":"normal-table","title":"Normal Table","owner":{"id":"d771c8ae812d5130","login":"mcmcclur","name":"Mark McClure"}},"stars":0,"starred":false,"owner":{"type":"team","id":"6817f8df4b71afcf","login":"71","name":"Grégoire Geis","avatar_url":"https://avatars-next.observableusercontent.com/046456a6fcb599cae062b5a172a9e2c85acef403c376bdb60a4cc6d135a60aad","tier":"starter_2024"},"version":425,"publish_version":425,"role":null,"authored":false,"authors":[{"type":"individual","id":"3881cfc7f3117323","login":"71","name":"Grégoire Geis","avatar_url":"https://avatars-next.observableusercontent.com/046456a6fcb599cae062b5a172a9e2c85acef403c376bdb60a4cc6d135a60aad","tier":null}],"members":[{"type":"individual","id":"3881cfc7f3117323","login":"71","name":"Grégoire Geis","avatar_url":"https://avatars-next.observableusercontent.com/046456a6fcb599cae062b5a172a9e2c85acef403c376bdb60a4cc6d135a60aad","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Normal Table","cells":[{"id":0,"value":"md`# Normal Table`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":296,"value":"md`## Example`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":332,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":298,"value":"md`## Table`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":81,"value":"md`Here's a table to compute ${tex`P(${lower_bound} < N < Z)`}, where ${tex`N`} has a standard normal distribution. It's computed by numerically evaluating\n${tex.block`\\frac{1}{\\sqrt{2\\pi}}\\int_${lower_bound}^Z e^{-x^2/2}dx`}\nusing [Ricky Reusser](https://observablehq.com/@rreusser)'s implementation of an [adaptive Simpson's rule](https://observablehq.com/@rreusser/integration#adaptiveSimpson). There's a little interactivity as well.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":22,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":304,"value":"md`## Appendix`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":358,"value":"function normal(x) {\n return d3.format(\"0.4f\")((example.from_mean ? 0 : .5) + normal_(x));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"function normal_(x) {\n return adaptiveSimpson(\n t => Math.exp((-t * t) / 2) / Math.sqrt(2 * Math.PI),\n 0,\n x,\n 1e-120,\n 8\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":311,"value":"lower_bound = example.from_mean ? \"0\" : \"{-\\\\infty}\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":203,"value":"tippy_style = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":230,"value":"html`