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chore: merge Fix/742 migrate doctest to jest (#749)
* Remove QuickSelect doctest There are more Jest test cases already. * Remove AverageMedian doctest Already migrated to jest * Migrate doctest for BinaryExponentiationRecursive.js (also remove inline "main" test method) * Migrate doctest for EulersTotient.js (also remove inline "main" test method) * Migrate doctest for PrimeFactors.js (also remove inline "main" test method) * Migrate doctest for BogoSort.js Re-write prototype-polluting helper methods, too. (also remove inline test driver code) * Migrate doctest for BeadSort.js (also remove inline test driver code) * Migrate doctest for BucketSort.js (also remove inline test driver code) * Migrate doctest for CocktailShakerSort.js (also remove inline test driver code) * Migrate doctest for MergeSort.js (also remove inline test driver code) * Migrate doctest for QuickSort.js (also remove inline test driver code) * Migrate doctest for ReverseString.js (also remove inline test driver code) * Migrate doctest for ReverseString.js * Migrate doctest for ValidateEmail.js * Migrate doctest for ConwaysGameOfLife.js (remove the animate code, too) * Remove TernarySearch doctest Already migrated to jest * Migrate doctest for BubbleSort.js (also remove inline test driver code) * Remove doctest from CI and from dependencies relates to #742 fixes #586 * Migrate doctest for RgbHsvConversion.js * Add --fix option to "standard" npm script * Migrate doctest for BreadthFirstSearch.js (also remove inline test driver code) * Migrate doctest for BreadthFirstShortestPath.js (also remove inline test driver code) * Migrate doctest for EulerMethod.js (also remove inline test driver code) Move manual test-code for plotting stuff in the browser in a distinct file, too. Those "*.manual-test.js" files are excluded from the UpdateDirectory.mjs script, as well. * Migrate doctest for Mandelbrot.js (also remove inline test driver code & moved manual drawing test into a *.manual-test.js) * Migrate doctest for FloodFill.js * Migrate doctest for KochSnowflake.js (also move manual drawing test into a *.manual-test.js) * Update npm lockfile * Update README and COMMITTING with a few bits & bobs regarding testing & code quality
This commit is contained in:
Roland Hummel
@ -1,44 +1,25 @@
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/**
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* Flood fill, also called seed fill, is an algorithm that determines and alters the area connected
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* to a given node in a multi-dimensional array with some matching attribute. It is used in the
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* "bucket" fill tool of paint programs to fill connected, similarly-colored areas with a different
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* color. (description adapted from https://en.wikipedia.org/wiki/Flood_fill) (see also:
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* https://www.techiedelight.com/flood-fill-algorithm/).
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* Flood fill.
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*
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* Flood fill, also called seed fill, is an algorithm that determines and alters the area connected to a given node in a
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* multi-dimensional array with some matching attribute. It is used in the "bucket" fill tool of paint programs to fill
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* connected, similarly-colored areas with a different color.
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*
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* (description adapted from https://en.wikipedia.org/wiki/Flood_fill)
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* @see https://www.techiedelight.com/flood-fill-algorithm/
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*/
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const neighbors = [[-1, -1], [-1, 0], [-1, 1], [0, -1], [0, 1], [1, -1], [1, 0], [1, 1]]
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const black = [0, 0, 0]
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const green = [0, 255, 0]
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const violet = [255, 0, 255]
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const white = [255, 255, 255]
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const orange = [255, 128, 0] // eslint-disable-line
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/*
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Doctests
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> testBreadthFirst([1, 1], green, orange, [1, 1]);
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orange
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> testBreadthFirst([1, 1], green, orange, [0, 1]);
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violet
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> testBreadthFirst([1, 1], green, orange, [6, 4]);
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white
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> testDepthFirst([1, 1], green, orange, [1, 1]);
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orange
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> testDepthFirst([1, 1], green, orange, [0, 1]);
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violet
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> testDepthFirst([1, 1], green, orange, [6, 4]);
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white
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*/
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/**
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* Implements the flood fill algorithm through a breadth-first approach using a queue.
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*
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* @param rgbData The image to which the algorithm is applied.
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* @param location The start location on the image.
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* @param targetColor The old color to be replaced.
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* @param replacementColor The new color to replace the old one.
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*/
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function breadthFirstSearch (rgbData, location, targetColor, replacementColor) {
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* Implements the flood fill algorithm through a breadth-first approach using a queue.
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*
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* @param rgbData The image to which the algorithm is applied.
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* @param location The start location on the image.
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* @param targetColor The old color to be replaced.
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* @param replacementColor The new color to replace the old one.
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*/
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export function breadthFirstSearch (rgbData, location, targetColor, replacementColor) {
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if (location[0] < 0 ||
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location[0] >= rgbData.length ||
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location[1] < 0 ||
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@ -55,14 +36,14 @@ function breadthFirstSearch (rgbData, location, targetColor, replacementColor) {
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}
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/**
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* Implements the flood fill algorithm through a depth-first approach using recursion.
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*
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* @param rgbData The image to which the algorithm is applied.
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* @param location The start location on the image.
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* @param targetColor The old color to be replaced.
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* @param replacementColor The new color to replace the old one.
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*/
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function depthFirstSearch (rgbData, location, targetColor, replacementColor) {
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* Implements the flood fill algorithm through a depth-first approach using recursion.
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*
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* @param rgbData The image to which the algorithm is applied.
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* @param location The start location on the image.
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* @param targetColor The old color to be replaced.
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* @param replacementColor The new color to replace the old one.
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*/
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export function depthFirstSearch (rgbData, location, targetColor, replacementColor) {
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if (location[0] < 0 ||
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location[0] >= rgbData.length ||
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location[1] < 0 ||
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@ -74,14 +55,14 @@ function depthFirstSearch (rgbData, location, targetColor, replacementColor) {
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}
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/**
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* Utility-function to implement the breadth-first loop
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*
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* @param rgbData The image to which the algorithm is applied.
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* @param location The start location on the image.
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* @param targetColor The old color to be replaced.
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* @param replacementColor The new color to replace the old one.
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* @param queue The locations that still need to be visited.
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*/
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* Utility-function to implement the breadth-first loop.
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*
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* @param rgbData The image to which the algorithm is applied.
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* @param location The start location on the image.
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* @param targetColor The old color to be replaced.
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* @param replacementColor The new color to replace the old one.
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* @param queue The locations that still need to be visited.
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*/
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function breadthFirstFill (rgbData, location, targetColor, replacementColor, queue) {
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const currentLocation = queue[0]
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queue.shift()
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@ -100,13 +81,13 @@ function breadthFirstFill (rgbData, location, targetColor, replacementColor, que
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}
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/**
|
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* Utility-function to implement the depth-first loop
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*
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* @param rgbData The image to which the algorithm is applied.
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* @param location The start location on the image.
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* @param targetColor The old color to be replaced.
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* @param replacementColor The new color to replace the old one.
|
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*/
|
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* Utility-function to implement the depth-first loop.
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*
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* @param rgbData The image to which the algorithm is applied.
|
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* @param location The start location on the image.
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* @param targetColor The old color to be replaced.
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* @param replacementColor The new color to replace the old one.
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*/
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function depthFirstFill (rgbData, location, targetColor, replacementColor) {
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if (rgbData[location[0]][location[1]] === targetColor) {
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rgbData[location[0]][location[1]] = replacementColor
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@ -120,61 +101,3 @@ function depthFirstFill (rgbData, location, targetColor, replacementColor) {
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}
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}
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}
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/**
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* Generates the rgbData-matrix for the tests
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*
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* @return example rgbData-matrix
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*/
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function generateTestRgbData () {
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const layout = [
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[violet, violet, green, green, black, green, green],
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[violet, green, green, black, green, green, green],
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[green, green, green, black, green, green, green],
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[black, black, green, black, white, white, green],
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[violet, violet, black, violet, violet, white, white],
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[green, green, green, violet, violet, violet, violet],
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[violet, violet, violet, violet, violet, violet, violet]
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]
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// transpose layout-matrix so the x-index comes before the y-index
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const transposed = []
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for (let x = 0; x < layout[0].length; x++) {
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transposed[x] = []
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for (let y = 0; y < layout.length; y++) {
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transposed[x][y] = layout[y][x]
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}
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}
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return transposed
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}
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/**
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* Utility-function to test the function "breadthFirstSearch"
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*
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* @param fillLocation The start location on the image where the flood fill is applied.
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* @param targetColor The old color to be replaced.
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* @param replacementColor The new color to replace the old one.
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* @param testLocation The location of the color to be checked.
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* @return The color at testLocation
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*/
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function testBreadthFirst (fillLocation, targetColor, replacementColor, testLocation) {// eslint-disable-line
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const rgbData = generateTestRgbData()
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breadthFirstSearch(rgbData, fillLocation, targetColor, replacementColor)
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return rgbData[testLocation[0]][testLocation[1]]
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}
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/**
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* Utility-function to test the function "depthFirstSearch"
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*
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* @param fillLocation The start location on the image where the flood fill is applied.
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* @param targetColor The old color to be replaced.
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* @param replacementColor The new color to replace the old one.
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* @param testLocation The location of the color to be checked.
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* @return The color at testLocation
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*/
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function testDepthFirst (fillLocation, targetColor, replacementColor, testLocation) {// eslint-disable-line
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const rgbData = generateTestRgbData()
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depthFirstSearch(rgbData, fillLocation, targetColor, replacementColor)
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return rgbData[testLocation[0]][testLocation[1]]
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}
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80
Recursive/FloodFill.test.js
Normal file
80
Recursive/FloodFill.test.js
Normal file
@ -0,0 +1,80 @@
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import { breadthFirstSearch, depthFirstSearch } from './FloodFill'
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// some constants
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const black = [0, 0, 0]
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const green = [0, 255, 0]
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const violet = [255, 0, 255]
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const white = [255, 255, 255]
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const orange = [255, 128, 0]
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describe('FloodFill', () => {
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it('should calculate the correct colors using breadth-first approach', () => {
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expect(testBreadthFirst([1, 1], green, orange, [1, 1])).toEqual(orange)
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expect(testBreadthFirst([1, 1], green, orange, [0, 1])).toEqual(violet)
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expect(testBreadthFirst([1, 1], green, orange, [6, 4])).toEqual(white)
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})
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it('should calculate the correct colors using depth-first approach', () => {
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expect(testDepthFirst([1, 1], green, orange, [1, 1])).toEqual(orange)
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expect(testDepthFirst([1, 1], green, orange, [0, 1])).toEqual(violet)
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expect(testDepthFirst([1, 1], green, orange, [6, 4])).toEqual(white)
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})
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})
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/**
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* Utility-function to test the function "breadthFirstSearch".
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*
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* @param fillLocation The start location on the image where the flood fill is applied.
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* @param targetColor The old color to be replaced.
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* @param replacementColor The new color to replace the old one.
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* @param testLocation The location of the color to be checked.
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* @return The color at testLocation.
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*/
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function testBreadthFirst (fillLocation, targetColor, replacementColor, testLocation) {
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const rgbData = generateTestRgbData()
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breadthFirstSearch(rgbData, fillLocation, targetColor, replacementColor)
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return rgbData[testLocation[0]][testLocation[1]]
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}
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/**
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* Utility-function to test the function "depthFirstSearch".
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*
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* @param fillLocation The start location on the image where the flood fill is applied.
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* @param targetColor The old color to be replaced.
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* @param replacementColor The new color to replace the old one.
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* @param testLocation The location of the color to be checked.
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* @return The color at testLocation.
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*/
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function testDepthFirst (fillLocation, targetColor, replacementColor, testLocation) {// eslint-disable-line
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const rgbData = generateTestRgbData()
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depthFirstSearch(rgbData, fillLocation, targetColor, replacementColor)
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return rgbData[testLocation[0]][testLocation[1]]
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}
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/**
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* Generates the rgbData-matrix for the tests.
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*
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* @return example rgbData-matrix.
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*/
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function generateTestRgbData () {
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const layout = [
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[violet, violet, green, green, black, green, green],
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[violet, green, green, black, green, green, green],
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[green, green, green, black, green, green, green],
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[black, black, green, black, white, white, green],
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[violet, violet, black, violet, violet, white, white],
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[green, green, green, violet, violet, violet, violet],
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[violet, violet, violet, violet, violet, violet, violet]
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]
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// transpose layout-matrix so the x-index comes before the y-index
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const transposed = []
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for (let x = 0; x < layout[0].length; x++) {
|
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transposed[x] = []
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for (let y = 0; y < layout.length; y++) {
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transposed[x][y] = layout[y][x]
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}
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}
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return transposed
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}
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@ -1,34 +1,20 @@
|
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/**
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* The Koch snowflake is a fractal curve and one of the earliest fractals to have been described.
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* The Koch snowflake can be built up iteratively, in a sequence of stages. The first stage is an
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* equilateral triangle, and each successive stage is formed by adding outward bends to each side of
|
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* the previous stage, making smaller equilateral triangles. This can be achieved through the
|
||||
* following steps for each line: 1. divide the line segment into three segments of equal length. 2.
|
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* draw an equilateral triangle that has the middle segment from step 1 as its base and points
|
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* outward. 3. remove the line segment that is the base of the triangle from step 2. (description
|
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* adapted from https://en.wikipedia.org/wiki/Koch_snowflake ) (for a more detailed explanation and
|
||||
* an implementation in the Processing language, see
|
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* https://natureofcode.com/book/chapter-8-fractals/ #84-the-koch-curve-and-the-arraylist-technique
|
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* ).
|
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*
|
||||
* The Koch snowflake can be built up iteratively, in a sequence of stages. The first stage is an equilateral triangle,
|
||||
* and each successive stage is formed by adding outward bends to each side of the previous stage, making smaller
|
||||
* equilateral triangles. This can be achieved through the following steps for each line:
|
||||
* 1. divide the line segment into three segments of equal length.
|
||||
* 2. draw an equilateral triangle that has the middle segment from step 1 as its base and points outward.
|
||||
* 3. remove the line segment that is the base of the triangle from step 2.
|
||||
*
|
||||
* (description adapted from https://en.wikipedia.org/wiki/Koch_snowflake)
|
||||
* (for a more detailed explanation and an implementation in the Processing language, see
|
||||
* https://natureofcode.com/book/chapter-8-fractals/ #84-the-koch-curve-and-the-arraylist-technique).
|
||||
*/
|
||||
|
||||
/*
|
||||
Doctests
|
||||
Test iterate-method
|
||||
> iterate([new Vector2(0, 0), new Vector2(1, 0)], 1)[0];
|
||||
{"x": 0, "y": 0}
|
||||
> iterate([new Vector2(0, 0), new Vector2(1, 0)], 1)[1];
|
||||
{"x": 1/3, "y": 0}
|
||||
> iterate([new Vector2(0, 0), new Vector2(1, 0)], 1)[2];
|
||||
{"x": 1/2, "y": Math.sin(Math.PI / 3) / 3}
|
||||
> iterate([new Vector2(0, 0), new Vector2(1, 0)], 1)[3];
|
||||
{"x": 2/3, "y": 0}
|
||||
> iterate([new Vector2(0, 0), new Vector2(1, 0)], 1)[4];
|
||||
{"x": 1, "y": 0}
|
||||
*/
|
||||
|
||||
/** Class to handle the vector calculations. */
|
||||
class Vector2 {
|
||||
export class Vector2 {
|
||||
constructor (x, y) {
|
||||
this.x = x
|
||||
this.y = y
|
||||
@ -87,66 +73,15 @@ class Vector2 {
|
||||
}
|
||||
|
||||
/**
|
||||
* Method to render the Koch snowflake to a canvas.
|
||||
* Go through the number of iterations determined by the argument "steps".
|
||||
*
|
||||
* @param canvasWidth The width of the canvas.
|
||||
* @param steps The number of iterations.
|
||||
* @returns The canvas of the rendered Koch snowflake.
|
||||
*/
|
||||
function getKochSnowflake (canvasWidth = 600, steps = 5) {
|
||||
if (canvasWidth <= 0) {
|
||||
throw new Error('canvasWidth should be greater than zero')
|
||||
}
|
||||
|
||||
const offsetX = canvasWidth / 10.0
|
||||
const offsetY = canvasWidth / 3.7
|
||||
const vector1 = new Vector2(offsetX, offsetY)
|
||||
const vector2 =
|
||||
new Vector2(canvasWidth / 2, Math.sin(Math.PI / 3) * canvasWidth * 0.8 + offsetY)
|
||||
const vector3 = new Vector2(canvasWidth - offsetX, offsetY)
|
||||
const initialVectors = []
|
||||
initialVectors.push(vector1)
|
||||
initialVectors.push(vector2)
|
||||
initialVectors.push(vector3)
|
||||
initialVectors.push(vector1)
|
||||
const vectors = iterate(initialVectors, steps)
|
||||
return drawToCanvas(vectors, canvasWidth, canvasWidth)
|
||||
}
|
||||
|
||||
/**
|
||||
* Utility-method to render the Koch snowflake to a canvas.
|
||||
*
|
||||
* @param vectors The vectors defining the edges to be rendered.
|
||||
* @param canvasWidth The width of the canvas.
|
||||
* @param canvasHeight The height of the canvas.
|
||||
* @returns The canvas of the rendered edges.
|
||||
*/
|
||||
function drawToCanvas (vectors, canvasWidth, canvasHeight) {
|
||||
const canvas = document.createElement('canvas')
|
||||
canvas.width = canvasWidth
|
||||
canvas.height = canvasHeight
|
||||
|
||||
// Draw the edges
|
||||
const ctx = canvas.getContext('2d')
|
||||
ctx.beginPath()
|
||||
ctx.moveTo(vectors[0].x, vectors[0].y)
|
||||
for (let i = 1; i < vectors.length; i++) {
|
||||
ctx.lineTo(vectors[i].x, vectors[i].y)
|
||||
}
|
||||
ctx.stroke()
|
||||
|
||||
return canvas
|
||||
}
|
||||
|
||||
/**
|
||||
* Go through the number of iterations determined by the argument "steps". Be careful with high
|
||||
* values (above 5) since the time to calculate increases exponentially.
|
||||
* Be careful with high values (above 5) since the time to calculate increases exponentially.
|
||||
*
|
||||
* @param initialVectors The vectors composing the shape to which the algorithm is applied.
|
||||
* @param steps The number of iterations.
|
||||
* @returns The transformed vectors after the iteration-steps.
|
||||
*/
|
||||
function iterate (initialVectors, steps) {
|
||||
export function iterate (initialVectors, steps) {
|
||||
let vectors = initialVectors
|
||||
for (let i = 0; i < steps; i++) {
|
||||
vectors = iterationStep(vectors)
|
||||
@ -156,9 +91,10 @@ function iterate (initialVectors, steps) {
|
||||
}
|
||||
|
||||
/**
|
||||
* Loops through each pair of adjacent vectors. Each line between two adjacent vectors is divided
|
||||
* into 4 segments by adding 3 additional vectors in-between the original two vectors. The vector
|
||||
* in the middle is constructed through a 60 degree rotation so it is bent outwards.
|
||||
* Loops through each pair of adjacent vectors.
|
||||
*
|
||||
* Each line between two adjacent vectors is divided into 4 segments by adding 3 additional vectors in-between the
|
||||
* original two vectors. The vector in the middle is constructed through a 60 degree rotation so it is bent outwards.
|
||||
*
|
||||
* @param vectors The vectors composing the shape to which the algorithm is applied.
|
||||
* @returns The transformed vectors after the iteration-step.
|
||||
@ -178,9 +114,3 @@ function iterationStep (vectors) {
|
||||
newVectors.push(vectors[vectors.length - 1])
|
||||
return newVectors
|
||||
}
|
||||
|
||||
// plot the results if the script is executed in a browser with a window-object
|
||||
if (typeof window !== 'undefined') {
|
||||
const canvas = getKochSnowflake()
|
||||
document.body.append(canvas)
|
||||
}
|
||||
|
||||
58
Recursive/KochSnowflake.manual-test.js
Normal file
58
Recursive/KochSnowflake.manual-test.js
Normal file
@ -0,0 +1,58 @@
|
||||
import { Vector2, iterate } from './KochSnowflake'
|
||||
|
||||
/**
|
||||
* Method to render the Koch snowflake to a canvas.
|
||||
*
|
||||
* @param canvasWidth The width of the canvas.
|
||||
* @param steps The number of iterations.
|
||||
* @returns The canvas of the rendered Koch snowflake.
|
||||
*/
|
||||
function getKochSnowflake (canvasWidth = 600, steps = 5) {
|
||||
if (canvasWidth <= 0) {
|
||||
throw new Error('canvasWidth should be greater than zero')
|
||||
}
|
||||
|
||||
const offsetX = canvasWidth / 10.0
|
||||
const offsetY = canvasWidth / 3.7
|
||||
const vector1 = new Vector2(offsetX, offsetY)
|
||||
const vector2 = new Vector2(canvasWidth / 2, Math.sin(Math.PI / 3) * canvasWidth * 0.8 + offsetY)
|
||||
const vector3 = new Vector2(canvasWidth - offsetX, offsetY)
|
||||
const initialVectors = []
|
||||
initialVectors.push(vector1)
|
||||
initialVectors.push(vector2)
|
||||
initialVectors.push(vector3)
|
||||
initialVectors.push(vector1)
|
||||
const vectors = iterate(initialVectors, steps)
|
||||
return drawToCanvas(vectors, canvasWidth, canvasWidth)
|
||||
}
|
||||
|
||||
/**
|
||||
* Utility-method to render the Koch snowflake to a canvas.
|
||||
*
|
||||
* @param vectors The vectors defining the edges to be rendered.
|
||||
* @param canvasWidth The width of the canvas.
|
||||
* @param canvasHeight The height of the canvas.
|
||||
* @returns The canvas of the rendered edges.
|
||||
*/
|
||||
function drawToCanvas (vectors, canvasWidth, canvasHeight) {
|
||||
const canvas = document.createElement('canvas')
|
||||
canvas.width = canvasWidth
|
||||
canvas.height = canvasHeight
|
||||
|
||||
// Draw the edges
|
||||
const ctx = canvas.getContext('2d')
|
||||
ctx.beginPath()
|
||||
ctx.moveTo(vectors[0].x, vectors[0].y)
|
||||
for (let i = 1; i < vectors.length; i++) {
|
||||
ctx.lineTo(vectors[i].x, vectors[i].y)
|
||||
}
|
||||
ctx.stroke()
|
||||
|
||||
return canvas
|
||||
}
|
||||
|
||||
// plot the results if the script is executed in a browser with a window-object
|
||||
if (typeof window !== 'undefined') {
|
||||
const canvas = getKochSnowflake()
|
||||
document.body.append(canvas)
|
||||
}
|
||||
20
Recursive/KochSnowflake.test.js
Normal file
20
Recursive/KochSnowflake.test.js
Normal file
@ -0,0 +1,20 @@
|
||||
import { iterate, Vector2 } from './KochSnowflake'
|
||||
|
||||
describe('KochSnowflake', () => {
|
||||
it('should produce the correctly-transformed vectors', () => {
|
||||
expect(iterate([new Vector2(0, 0), new Vector2(1, 0)], 1)[0])
|
||||
.toEqual({ x: 0, y: 0 })
|
||||
|
||||
expect(iterate([new Vector2(0, 0), new Vector2(1, 0)], 1)[1])
|
||||
.toEqual({ x: 1 / 3, y: 0 })
|
||||
|
||||
expect(iterate([new Vector2(0, 0), new Vector2(1, 0)], 1)[2])
|
||||
.toEqual({ x: 1 / 2, y: Math.sin(Math.PI / 3) / 3 })
|
||||
|
||||
expect(iterate([new Vector2(0, 0), new Vector2(1, 0)], 1)[3])
|
||||
.toEqual({ x: 2 / 3, y: 0 })
|
||||
|
||||
expect(iterate([new Vector2(0, 0), new Vector2(1, 0)], 1)[4])
|
||||
.toEqual({ x: 1, y: 0 })
|
||||
})
|
||||
})
|
||||
Reference in New Issue
Block a user