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Data Flow","contributors":[],"maintainers":[{"name":"hpcc-js","email":"gordon.smith@lexisnexisrisk.com"}],"readme":"# @hpcc-js/dataflow \n\n_A small functional library for processing \"data flows\" in JavaScript ([more examples on ObservableHQ](https://observablehq.com/@gordonsmith/hpcc-js-dataflow)).  Highlights:_\n\n* **Lazy Evaluation** - Implemented using modern JavaScript generators and iterators\n* **Memory Efficient** - Data \"streams\" from one \"activity\" to the next\n* **Functional** - Pure functional implementation.\n* **Fully Typed** - Written in typescript and supports typed chaining of functional activities\n* **UMD/ES6 Bundles** - Works in NodeJS / Browser and includes ES6 modules to ensure you only include what you use (when bundling with RollupJS / Webpack etc.)\n\n## Motivation\n\nThe underlying motivation for this library is to simplify the processing of data in an efficient way.  The analogy we use is one of a \"data\" pipe, which consists of:\n* Activities:  Functional components that modify data as it flows through the pipe.\n* Sensors:  Functional components that observe the data as it passes through the pipe.\n\nSome other properties of pipes are:\n* Can be defined, before being used.\n* A complex pipe is just another \"Activity\" and as such can be re-used inside other pipes.\n* Encourages the user to only iterate through the source data ONCE allowing for less memory use and better overall performance.\n\n## Terminology\n\n* **Activity** - A functional unit of work that is primary used to alter the data (`map`, `filter`, `sort`, ...). \n* **Sensor** - A function which \"observes\" the data without modifying it (`min`, `max`, `quartile`, ...). \n* **IterableActivity** - An \"Activity\" which produces an \"Iterable\" output (`map`, `filter`, `sort`, ...). \n* **ScalarActivity** - An \"Activity\" which produces a single value (`min`, `max`, `reduce`...).\n* **Process** or **Pipeline** - A series of \"Activities\" chained together, so that \"data\" \"flows\" through the process / pipeline.  \n\n## Quick Example \n_Simple example of data flowing through a `pipe` of activities:  _`filter`->`map`->`filter`->`first`_\n\n```javascript\nimport { count, filter, first, generate, map, max, pipe, sensor } from \"@hpcc-js/dataflow\";\n\nconst c1 = count();\nconst c2 = count();\nconst c3 = count();\nconst m1 = max(row => row.value);\n\nconst p1 = pipe(\n    sensor(c1),                         //  Keep running count of input\n    filter(n => n <= 0.5),              //  Filter out numbers > 0.5  \n    sensor(c2),                         //  Keep running count of filtered rows\n    map((n, idx) =>                     //  Convert to JSON Object \n        ({ index: idx, value: n })),\n    filter(row => row.index % 2 === 0), //  Filter even row indecies \n    sensor(c3),                         //  Keep running count of final rows\n    sensor(m1),                         //  Track largest value\n    first(3)                            //  Take first 3 rows\n);\n\nconsole.log(`Counts: ${c1.peek()}, ${c2.peek()}, ${c3.peek()}`);\n    // [1] => Counts: undefined, undefined, undefined\n\nconst outIterable = p1(generate(Math.random, 1000));\nconsole.log(`Counts: ${c1.peek()}, ${c2.peek()}, ${c3.peek()}`);\n    // [2] => Counts: undefined, undefined, undefined\n\nconsole.log(JSON.stringify([...outIterable]));\n    // [3] => [{\"index\":0,\"value\":0.19075931906641008},{\"index\":2,\"value\":0.4873469062925415},{\"index\":4,\"value\":0.4412516774100035}]\n\nconsole.log(`Counts: ${c1.peek()}, ${c2.peek()}, ${c3.peek()}, ${m1.peek()}`);\n    // [4] => Counts: 6, 5, 3, 0.4873469062925415\n\n\nconst outArray = [...p1([0.7, 0.5, 0.4, 0.8, 0.3, 1])];\nconsole.log(JSON.stringify(outArray));\n    // [5] => [{\"index\":0,\"value\":0.5},{\"index\":2,\"value\":0.3}]\n\nconsole.log(`Counts: ${c1.peek()}, ${c2.peek()}, ${c3.peek()}, ${m1.peek()}`);\n    // [6] => Counts:  6, 3, 2, 0.5\n\n```\n\nNotes:\n1. All sensors are undefined as expected\n2. All sensors are still undefined as `p1(generate(Math.random, 1000))` only returns an `IterableIterator`.  IOW no data has flown through the pipe yet.\n3. `[...outIterable]` Is a shorthand way to populate an array with data from an iterable.\n4. The sensors now have values we can peek at!\n5. The pipe `p1` can be reused with new data, this time the input is a simple array\n6. The same sensors will reflect the correct state from the second run\n\nFurther the sensors can be observed at any point during the process.\n\n```javascript\nfor (const row of p1(generate(Math.random, 1000000))) {\n    console.log(`${row.index}: ${c1.peek()}, ${c2.peek()}, ${c3.peek()}, ${m1.peek()}`);\n}\n// => 0: 1, 1, 1, 0.13662528848681\n// => 2: 3, 3, 2, 0.13662528848681\n// => 4: 7, 5, 3, 0.4328468228869129\n```\n\nNote:  Even though there is 1000000 rows of data being potentially generated, only 7 are actually read for this run.\n\n## API Reference\n\n* [Activities](#iterable-activities)\n* [Sensors](#sensors)\n* [Convenience](#convenience)\n\n### Activities\n\n_Functions which alter data inside the dataflow pipe_\n\n<a name=\"concat\" href=\"#concat\">#</a> **concat**(_iterable_, _iterable_): _iterable_ <br>\n<a name=\"concat\" href=\"#concat\">#</a> **concat**(_iterable_): (_iterable_) => _iterable_ <br>\n\nConcatenates two iterables into a single iterable.  Similar to [Array.concat](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/concat).  \n\n```typescript\nconcat([\"a\", \"b\", \"c\"], [\"d\", \"e\", \"f\"]);  // => \"a\", \"b\", \"c\", \"d\", \"e\", \"f\"\n\nconst concatDEF = concat([\"d\", \"e\", \"f\"]);\nconcatDEF([\"a\", \"b\", \"c\"]);  // => \"a\", \"b\", \"c\", \"d\", \"e\", \"f\"\nconcatDEF([\"1\", \"2\", \"3\"]);  // => \"1\", \"2\", \"3\", \"d\", \"e\", \"f\"\n```\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/activities/concat.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/concat.spec.ts)\n\n<a name=\"each\" href=\"#each\">#</a> **each**(_iterable_, _callbackFn_): _iterable_ <br>\n<a name=\"each\" href=\"#each\">#</a> **each**(_callbackFn): (_iterable_) => _iterable_ <br>\n\nPerform callback for `each` row in an iterable.  Cannot alter the iterable value.  Similar to [Array.forEach](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/forEach).  Useful for debugging steps in a [pipe](#pipe).\n\n```typescript\neach([\"a\", \"b\", \"c\"], (row, idx) => console.log(row));  // => \"a\", \"b\", \"c\"\n\nconst logFlow = each(console.log);\nlogFlow([\"a\", \"b\", \"c\"]);  // => \"a\", \"b\", \"c\"\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/activities/each.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/each.spec.ts) \n\n<a name=\"entries\" href=\"#entries\">#</a> **entries**(_iterable_): _iterable_ <br>\n<a name=\"entries\" href=\"#entries\">#</a> **entries**(): (_iterable_) => _iterable_ <br>\n\nPerform callback for `entries` row in an iterable.  Cannot alter the iterable value.  Similar to [Array.entries](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/entries).\n\n```typescript\nentries([\"a\", \"b\", \"c\"]);  // => [0, \"a\"], [1, \"b\"], [2, \"c\"]\n\nconst calcEntries = entries();\ncalcEntries([\"a\", \"b\", \"c\"]);  // => [0, \"a\"], [1, \"b\"], [2, \"c\"]\n```\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/activities/entries.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/entries.spec.ts)\n\n<a name=\"filter\" href=\"#filter\">#</a> **filter**(_iterable_, _condition_): _iterable_ <br>\n<a name=\"filter\" href=\"#filter\">#</a> **filter**(_condition_): (_iterable_) => _iterable_ <br>\n\nFilter iterable based on some `condition`. Similar to [Array.filter](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/filter).\n\n```typescript\nconst words = [\"spray\", \"limit\", \"elite\", \"exuberant\", \"destruction\", \"present\"];\n\nfilter(words, word => word.length > 6);  // => \"exuberant\", \"destruction\", \"present\"\n\nconst smallWords = filter(word => word.length <= 6);\nsmallWords(words);  // => \"spray\", \"limit\", \"elite\"\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/activities/filter.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/filter.spec.ts)\n\n<a name=\"first\" href=\"#first\">#</a> **first**(_iterable_, _number_): _iterable_ <br>\n<a name=\"first\" href=\"#first\">#</a> **first**(_number_): (_iterable_) => _iterable_ <br>\n\nLimit the flow to the first N rows of data.\n\n```typescript\nconst words = [\"spray\", \"limit\", \"elite\", \"exuberant\", \"destruction\", \"present\"];\n\nfirst(words, 3);  // => \"spray\", \"limit\", \"elite\"\n\nconst first2 = first(2);\nfirst2(words);  // => \"spray\", \"limit\"\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/activities/first.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/first.spec.ts)\n\n<a name=\"group\" href=\"#group\">#</a> **group**(_iterable_, _condition_): _iterable_ <br>\n<a name=\"group\" href=\"#group\">#</a> **group**(_condition_): (_iterable_) => _iterable_ <br>\n\nGroups data based on some grouping condition.  Output is in the form {key: groupCondition, value:[...]}, where the key has to be either a `number` or a `string`.\n\n```typescript\nconst words = [\"one\", \"two\", \"three\", \"four\", \"five\", \"six\"];\n\ngroup(words, word => word.length);  // => {key:  3, value: [\"one\", \"two\", \"six\"]}, {key:  4, value: [\"four\", \"five\"]}, { key: 5, value: [\"three\"]}\n\nconst groupByLength = group(word => word.length);\ngroupByLength(words);  // => {key:  3, value: [\"one\", \"two\", \"six\"]}, {key:  4, value: [\"four\", \"five\"]}, { key: 5, value: [\"three\"]}\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/activities/group.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/group.spec.ts)\n\n<a name=\"histogram\" href=\"#histogram\">#</a> **histogram**(_iterable_, _condition_, _options_): _iterable_ <br>\n<a name=\"histogram\" href=\"#histogram\">#</a> **histogram**(_condition_, _options_): (_iterable_) => _iterable_ <br>\n\nGroups data into buckets (or bins) based on numeric ranges.  Output is in the form `{from: numeric, to: numeric, value:[...]}`.\n\nAvailable options are:\n```\n { buckets: number }  // Specify number of buckets / bins\n```\nor\n```\n { min: number, range: number }  // Specify starting bucket (min) and size of bucket (range)\n```\n\n```typescript\nconst data = [1, 12, 13, 13, 3, 14, 19, 6];\nhistogram(data, n => n, { buckets: 3 });  // => {\"from\":1,\"to\":7,\"value\":[1,3,6]},{\"from\":7,\"to\":13,\"value\":[12]},{\"from\":13,\"to\":19,\"value\":[13,13,14,19]}\n\nhistogram(data, n => n, { min: 0, range: 5 });  // => {\"from\":0,\"to\":5,\"value\":[1,3]},{\"from\":5,\"to\":10,\"value\":[6]},{\"from\":10,\"to\":15,\"value\":[12,13,13,14]},{\"from\":15,\"to\":20,\"value\":[19]}\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/activities/histogram.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/histogram.spec.ts)\n\n<a name=\"map\" href=\"#map\">#</a> **map**(_iterable_, _callback_): _iterable_ <br>\n<a name=\"map\" href=\"#map\">#</a> **map**(_callback_): (_iterable_) => _iterable_ <br>\n\nMap data to a new shape via a callback frunction.  Similar to [Array.map](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/map).\n\n```typescript\nmap([{ n: 22 }, { n: 11 }, { n: 33 }], (row, idx) => ({ ...row, index: idx })); // => { n: 22, index: 0 }, { n: 11, index: 1 }, { n: 33, index: 2 }\n\nconst indexData = map((row, idx) => ({ ...row, index: idx + 1 }));\nindexData([{ n: 22 }, { n: 11 }, { n: 33 }]);  // => { n: 22, index: 1 }, { n: 11, index: 2 }, { n: 33, index: 3 }\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/activities/map.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/map.spec.ts)\n\n<a name=\"skip\" href=\"#skip\">#</a> **skip**(_iterable_, _number_): _iterable_ <br>\n<a name=\"skip\" href=\"#skip\">#</a> **skip**(_number_): (_iterable_) => _iterable_ <br>\n\nSkip a set number of rows.\n\n```typescript\nconst words = [\"spray\", \"limit\", \"elite\", \"exuberant\", \"destruction\", \"present\"];\n\nskip(words, 3);  // => \"exuberant\", \"destruction\", \"present\"\n\nconst skip4 = skip(4);\nskip4(words);  // => \"destruction\", \"present\"\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/activities/skip.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/skip.spec.ts)\n\n<a name=\"sort\" href=\"#sort\">#</a> **sort**(_iterable_, _compare_): _iterable_ <br>\n<a name=\"sort\" href=\"#sort\">#</a> **sort**(_compare_): (_iterable_) => _iterable_ <br>\n\nSort iterable based on result of `compare` function (should return -1, 0, 1). Similar to [Array.sort](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/sort).\n\n\n```typescript\nvar numbers = [4, 2, 5, 1, 3];\n\nsort(numbers, (a, b) => a - b);  // => 1, 2, 3, 4, 5\n\nconst reverseSort = sort((a, b) => b - a);\nreverseSort(numbers)  // => 5, 4, 3, 2, 1\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/activities/sort.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/sort.spec.ts)\n\n### Sensors\n\n_A collection of \"Observers\" which can be adapted as functions, activities and sensors_\n\n#### Types / Interfaces\n\n```typescript\nexport interface Observer<T, U> {\n    observe(r: T, idx: number): void;\n    peek(): U;\n}\n```\n\n#### Adapters\n\n<a name=\"sensor\" href=\"#sensor\">#</a> **sensor**(_: _Observer_): _iterable_ <br>\n\nAdapts an observer so it can be used in a pipe.\n\n<a name=\"scalar\" href=\"#scalar\">#</a> **scalar**(_: _Observer_): _any_ <br>\n\nAdapts an observer so it can be called as a regular function.\n\n#### Observers\n\n<a name=\"count\" href=\"#count\">#</a> **count**(): _Observer_ <br>\n\nCounts the number of \"observed\" rows: \n\n```typescript\nconst s1 = count();\nconst s2 = count();\nconst p1 = pipe(\n    sensor(s1),\n    filter(r => r.age > 30),\n    sensor(s2),\n);\nconst data = [...p1(population)];\ns1.peek();  // => 1000;\ns2.peek();  // => 699;\n\nconst doCount = scalar(count());\ndoCount([5, 1, 2, -3, 4]);  // => 5\n```\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/observers/count.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/count.spec.ts)\n\n<a name=\"min\" href=\"#min\">#</a> **min**(): _Observer_ <br>\n<a name=\"min\" href=\"#min\">#</a> **min**(_accessor_): _Observer_ <br>\n\nCalculates minimal value for \"observed\" rows: \n\n```typescript\nconst s1 = min();\nconst s2 = min();\nconst p1 = pipe(\n    sensor(s1),\n    filter(r => r > 3),\n    sensor(s2),\n);\nconst data = [...p1([1, 2, 3, 4, 5, 0])];\ns1.peek()   // => 0\ns2.peek()   // => 4\n\nconst calcMin = scalar(min(row => row.id));\ncalcMin([{ id: 22 }, { id: 44 }, { id: 33 }]); // => 22\n```\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/observers/min.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/min.spec.ts)\n\n<a name=\"max\" href=\"#max\">#</a> **max**(): _Observer_ <br>\n<a name=\"max\" href=\"#max\">#</a> **max**(_accessor_): _Observer_ <br>\n\nCalculates maximum value for \"observed\" rows: \n\n```typescript\nconst s1 = max();\nconst s2 = max();\nconst p1 = pipe(\n    sensor(s1),\n    filter(r => r < 3),\n    sensor(s2),\n);\nconst data = [...p1([1, 2, 3, 4, 5, 0])];\ns1.peek()   // => 5\ns2.peek()   // => 2\n\nconst calcMax = scalar(max(row => row.id));\ncalcMax([{ id: 22 }, { id: 44 }, { id: 33 }]); // => 44\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/observers/max.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/max.spec.ts)\n\n<a name=\"extent\" href=\"#extent\">#</a> **extent**(): _Observer_ <br>\n<a name=\"extent\" href=\"#extent\">#</a> **extent**(_accessor_): _Observer_ <br>\n\nCalculates extent (min + max) values for \"observed\" rows: \n\n```typescript\nconst s1 = extent(r => r.age);\nconst s2 = extent(r => r.age);\nconst p1 = pipe(\n    sensor(s1),\n    filter(r => r.age > 30),\n    sensor(s2),\n);\nconst data = [...p1(population)];\ns1.peek()   // => [16, 66]\ns2.peek()   // => [31, 66]\n\nconst calcExtent = scalar(extent(row => row.id));\ncalcExtent([{ id: 22 }, { id: 44 }, { id: 33 }]); // => [22, 44]\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/observers/extent.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/extent.spec.ts)\n\n<a name=\"mean\" href=\"#mean\">#</a> **mean**(): _Observer_ <br>\n<a name=\"mean\" href=\"#mean\">#</a> **mean**(_accessor_): _Observer_ <br>\n\nCalculates mean (average) value for \"observed\" rows: \n\n```typescript\nconst calcMean = scalar(mean());\ncalcMean([5, -6, 1, 2, -2]))    // => 0\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/observers/mean.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/mean.spec.ts)\n\n<a name=\"median\" href=\"#median\">#</a> **median**(): _Observer_ <br>\n<a name=\"median\" href=\"#median\">#</a> **median**(_accessor_): _Observer_ <br>\n\nCalculates median value for \"observed\" rows: \n\n```typescript\nconst calcMedian = scalar(median());\ncalcMedian([-6, -2, 1, 2, 5])       // => 1\ncalcMedian([5, -6, 1, 2, -2])       // => 1\ncalcMedian([-6, -2, 1, 2, 5, 6])    // => 1.5\ncalcMedian([5, -6, 1, 2, -2, 6])    // => 1.5\ncalcMedian([9])                     // => 9\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/observers/median.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/median.spec.ts)\n\n<a name=\"quartile\" href=\"#quartile\">#</a> **quartile**(): _Observer_ <br>\n<a name=\"quartile\" href=\"#quartile\">#</a> **quartile**(_accessor_): _Observer_ <br>\n\nCalculates quartile value for \"observed\" rows: \n\n```typescript\nconst calcQuartile = scalar(quartile());\ncalcQuartile([6, 7, 15, 36, 39, 40, 41, 42, 43, 47, 49])    // => [6, 15, 40, 43, 49]\ncalcQuartile([7, 15, 36, 39, 40, 41])                       // => [7, 15, 37.5, 40, 41]\ncalcQuartile([1, 22, 133])                                  // => [1, 1, 22, 133, 133]\ncalcQuartile([2, 144, 33])                                  // => [2, 2, 33, 144, 144]\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/observers/quartile.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/quartile.spec.ts)\n\n<a name=\"reduce\" href=\"#reduce\">#</a> **reduce**(_reducer_[, _initialValue_]): _Observer_ <br>\n\nCalculates reduced value for \"observed\" rows: \n\n```typescript\nconst reduceFunc = (prev, row) => prev + row;\nconst calcReduce1 = scalar(reduce(reduceFunc));\nconst calcReduce2 = scalar(reduce(reduceFunc), 10);\n\ncalcReduce1([1, 2, 3, 4, 5])   // => 15\ncalcReduce2([1, 2, 3, 4, 5])   // => 25\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/observers/reduce.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/reduce.spec.ts)\n\n<a name=\"variance\" href=\"#variance\">#</a> **variance**(): _Observer_ <br>\n<a name=\"variance\" href=\"#variance\">#</a> **variance**(_accessor_): _Observer_ <br>\n\nCalculates the [variance](https://en.wikipedia.org/wiki/Variance) for the \"observed\" rows.  If the number of rows is fewer than two numbers, returns undefined.  \n\n```typescript\nconst calcVariance = scalar(variance());\ncalcVariance([5, 1, 2, 3, 4])   // => 2.5\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/observers/variance.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/variance.spec.ts)\n\n<a name=\"deviation\" href=\"#deviation\">#</a> **deviation**(): _Observer_ <br>\n<a name=\"deviation\" href=\"#deviation\">#</a> **deviation**(_accessor_): _Observer_ <br>\n\nCalculates the [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) for the \"observed\" rows.  If the number of rows is fewer than two numbers, returns undefined.  \n\n```typescript\nconst calcDeviation = scalar(deviation());\ncalcDeviation([5, 1, 2, 3, 4])   // => 1.58113883008 == sqrt(2.5)\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/observers/deviation.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/deviation.spec.ts)\n\n<a name=\"distribution\" href=\"#distribution\">#</a> **distribution**(): _Observer_<_number_, { min: _number_, mean: _number_, max: _number_, deviation: _number_, variance: _number_}> <br>\n<a name=\"distribution\" href=\"#distribution\">#</a> **distribution**(_accessor_): _Observer_<_any_, { min: _number_, mean: _number_, max: _number_, deviation: _number_, variance: _number_}> <br>\n\nCalculates a \"distribution\" (a combination of min, max, mean, variance and deviance) of the \"observed\" rows.  If the number of rows is fewer than two numbers, returns undefined.\n\n```typescript\nconst calcDistribution = scalar(distribution());\ncalcDistribution([5, 1, 2, 3, 4]))  // => { min: 1, mean: 3, max: 5, deviation: Math.sqrt(2.5), variance: 2.5}\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/observers/distribution.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/distribution.spec.ts)\n\n### Convenience\n\n_Convenience functions_\n\n<a name=\"pipe\" href=\"#pipe\">#</a> **pipe**(_iterable_, ..._iterableActivity_): _iterable_ <br>\n<a name=\"pipe\" href=\"#pipe\">#</a> **pipe**(_iterable_, ..._iterableActivity_, _scalarActivity_): _scalar_ <br>\n<a name=\"pipe\" href=\"#pipe\">#</a> **pipe**(..._iterableActivity_): _iterableActivity_ <br>\n<a name=\"pipe\" href=\"#pipe\">#</a> **pipe**(..._iterableActivity_, _scalarActivity_): _scalarActivity_ <br>\n\nPipes a series of activities into a single process pipeline.  \n\n```typescript\n// Iterable output  \npipe([0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n    filter(n => n <= 5),\n    map((n, idx) => ({ index: idx, value: n })),\n    filter(row => row.index % 2 === 0),\n    sort((l, r) => l.value - r.value),\n    first(3)\n);  // => { index: 0, value: 0 }, { index: 2, value: 2 }, { index: 4, value: 4 }\n\nconst process = pipe(\n    filter(n => n <= 5),\n    map((n, idx) => ({ index: idx, value: n })),\n    filter(row => row.index % 2 === 0),\n    sort((l, r) => l.value - r.value),\n    first(3)\n);  \nprocess([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])// => { index: 0, value: 0 }, { index: 2, value: 2 }, { index: 4, value: 4 }\n\n// Scalar output  \npipe([0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n    process,\n    max(row => row.value)\n);  // => 4\n\nconst process_2 = pipe(\n    process,\n    min(row => row.value)\n); \nprocess_2([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]);  // => 0\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/utils/pipe.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/pipe.spec.ts)\n\n<a name=\"generate\" href=\"#generate\">#</a> **generate**(_generatorFn_[, _maxIterations_]): _iterable_ <br>\n\nGenerates an iterable data set.  Optionally limits the length to `maxIterations`.\n\n```typescript\n// Iterable output  \ngenerate(Math.random);  // => Random number iterator\ngenerate(Math.random, 100);  // => Random number iterator limited to 100 items\n\n```\n\n[[source]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/src/utils/generate.ts) [[tests]](https://github.com/hpcc-systems/Visualization/blob/trunk/packages/dataflow/tests/generate.spec.ts)\n","readmeFilename":"README.md"}