Upstream spencermountain/compromise state of the touched files before a9042f8c5f

src/1-one/output/api/wrap.js

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+const toText = function (term) {
+  const pre = term.pre || ''
+  const post = term.post || ''
+  return pre + term.text + post
+}
+
+const findStarts = function (doc, obj) {
+  const starts = {}
+  Object.keys(obj).forEach(reg => {
+    const m = doc.match(reg)
+    m.fullPointer.forEach(a => {
+      starts[a[3]] = { fn: obj[reg], end: a[2] }
+    })
+  })
+  return starts
+}
+
+const wrap = function (doc, obj) {
+  // index ids to highlight
+  const starts = findStarts(doc, obj)
+  let text = ''
+  doc.docs.forEach((terms, n) => {
+    for (let i = 0; i < terms.length; i += 1) {
+      const t = terms[i]
+      // do a span tag
+      if (Object.hasOwn(starts, t.id)) {
+        const { fn, end } = starts[t.id]
+        const m = doc.update([[n, i, end]])
+        text += terms[i].pre || ''
+        text += fn(m)
+        i = end - 1
+        text += terms[i].post || ''
+      } else {
+        text += toText(t)
+      }
+    }
+  })
+  return text
+}
+export default wrap

tests/02-two/output/out.test.js

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+import test from 'tape'
+import nlp from '../../lib/two.js'
+const here = '[two/output/out] '
+
+test('out-tags', function (t) {
+  const out = nlp(`he's cool.`).out('tags')
+  t.equal(out.length, 1, 'one sentence')
+  t.equal(out[0].he.indexOf('Pronoun') !== -1, true, here + 'has he:Pronoun')
+  t.equal(out[0].is.indexOf('Copula') !== -1, true, here + 'has is:Copula')
+  t.equal(out[0].cool.indexOf('Adjective') !== -1, true, here + 'has cool:Adjective')
+  t.end()
+})
+
+test('out-topk', function (t) {
+  const doc = nlp(`What'd I say? Monorail. and it called? Monorail.`)
+  const out = doc.out('freq')
+  // t.equal(out.length, 3, here + 'three results')
+  t.equal(out[0].normal, 'monorail.', here + 'top-result is most-frequent')
+  t.equal(out[0].count, 2, here + 'got count')
+  t.end()
+})
+
+test('out-array', function (t) {
+  const arr = nlp('1-2').terms().out('array')
+  t.equal(arr.length, 1, here + 'one result')
+  t.equal(arr[0], '1-2', here + 'got contraction text')
+  t.end()
+})
+
+test('out-wrap', function (t) {
+  let doc = nlp("soft and yielding like a nerf ball")
+  let out = doc.out({
+    '#Adjective': (m) => `[${m.text()}]`
+  })
+  t.equal(out, `[soft] and [yielding] like a nerf ball`, here + 'two matches')
+
+  // pre-post
+  doc = nlp("before (match) after")
+  out = doc.wrap({
+    'match': () => `few more words`,
+  })
+  t.equal(out, `before (few more words) after`, here + 'pre+post')
+  t.end()
+})