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* Partially apply to a function the first argument and return specialization.
* @param {function} func - function to be specialized.
* @param {string} first - value to be fixed as first positional argument.
* @returns {function} function with the first argument primed.
*/
const partial = (func, first) => {
return (...rest) => {
return func(first, ...rest)
}
}
/**
* Create zipper for pairs that creates a pair object given two parameters.
* @param {string} first - key mapping to the first positional value.
* @param {string} second - key mapping to the second and last positional value.
* @returns {function} that creates a pair object.
*/
const zip_pair = (first, second) => {
return (left, right) => ({[first]: left, [second]: right})
}
/**
* We invented a wheel again - hooray
*
* @param {*} me - maybe an object.
* @param {*} you - maybe an object.
* @returns {bool} true if deeply equal.
*/
const deep_equal = (me, you) => {
if (!is_object(me) || !is_object(you)) return me === you
const [my_keys, your_keys] = [Object.keys(me), Object.keys(you)]
if (my_keys.length !== your_keys.length) return false;
for (const key of my_keys) {
const [my_val, your_val] = [me[key], you[key]]
const are_objects = is_object(my_val) && is_object(your_val)
if (
are_objects && !deep_equal(my_val, your_val) ||
!are_objects && my_val !== your_val
) return false
}
return true
}
/**
* We invented another wheel again - hip, hip, hooray
*
* @param {*} thing - maybe an object.
* @returns {bool} true if given an object.
*/
const is_object = thing => thing != null && typeof thing === 'object'
/**
* Derive an array of objects from pairs that map population samples to distinct features.
*
* The default (but optional) validation ensures:
*
* * population and pairs both are non-empty arrays of non-null values
* * features are unique (first elements of pairs)
* * pairs second entries only provide members of population
*
* @param {array} population - array with all members of the population.
* @param {string} sample - key mapping to the sample from the population.
* @param {string} feature - key mapping to the feature variations.
* @param {array} pairs - pairs of features and array of samples (null for final pair).
* @param {bool} [force=false] - (false does / true does not) validate use.
* @returns {array} of objects that map population samples to distinct features.
*/
const cook = (population, sample, feature, pairs, force=false) => {
if (!force) {
if (!(population instanceof Array)
|| !population.length
|| !(pairs instanceof Array)
|| !pairs.length
) return undefined // population or pairs no arrays or empty
if (population.indexOf(null) !== -1) {
population = population.filter(p => p != null)
}
if (!population.length) return undefined // cleansed population empty
}
derived = [...pairs] // Create a copy of the pairs array
if (!force) {
const features = derived.map(pair => pair[0]).filter((el, ndx, arr) => arr.indexOf(el) === ndx)
if ((pairs.length !== features.length) // simple duplicates
|| (!features.every((xi, i, arr) => arr.every((xj, j) => i === j || !deep_equal(xi, xj)))) // deep duplicates
) return undefined
}
const used = derived.map(pair => pair[1]).filter(arr => arr).flat() // registry of used population members
if (!force) {
if ((used.length > population.length) // over population
|| (used.some(e => population.indexOf(e) === -1)) // non-members
) return undefined
}
const rest = population.filter(m => !used.includes(m)) // set difference
const variation = derived[derived.length-1][0]
derived[(derived.length - 1)] = [variation, rest] // complete population
const make = zip_pair(feature, sample) // primed object creator
derived.forEach((pair, i, seq) => seq[i] = make(...pair))
return derived
}
module.exports = { cook: cook, partial: partial }
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