diff --git a/packages/nightingale-boxplot-track/README.md b/packages/nightingale-boxplot-track/README.md new file mode 100644 index 00000000..a5f4eeb1 --- /dev/null +++ b/packages/nightingale-boxplot-track/README.md @@ -0,0 +1,240 @@ +# nightingale-boxplot-track + +[![Published on NPM](https://img.shields.io/npm/v/@nightingale-elements/nightingale-boxplot-track.svg)](https://www.npmjs.com/package/@nightingale-elements/nightingale-boxplot-track) + +Nightingale boxplot track component is used to display the distribution of a variable at each sequence position using boxplots. It allows displaying multiple distribution datasets, rendered side-by-side at each sequence position. + +Most of the rendering is implemented via HTML canvas, but some non-critical parts are implemented via SVG (e.g. highlights). + +## Usage + +```html + +``` + +#### Setting the data through property + +```typescript +await customElements.whenDefined('nightingale-boxplot-track'); +const track = document.getElementById('track'); +if (track) { + // Showing 2 datasets on a sequence of 3 amino acids: + (track as any).data = [ + { + name: 'Blue dataset', + color: '#0088ff', + positions: [ + { position: 1, values: [0.2, 0.5, 0.7, 0.9] }, + { position: 2, values: [0.1, 0.3, 0.5, 0.8] }, + { position: 3, values: [0.0, 0.2, 0.6, 0.8] }, + ], + }, + { + name: 'Orange dataset', + color: '#ff8800', + positions: [ + { position: 1, values: [0.1, 0.3, 0.5, 0.8] }, + { position: 2, values: [0.0, 0.2, 0.6, 0.8] }, + { position: 3, values: [0.2, 0.5, 0.7, 0.9] }, + ], + }, + ]; +} +``` + +The `data` property expects a value of type `BoxplotData` - an array of datasets, where each dataset has the following structure: + +```typescript +{ + name: string, + color?: string, + positions: Array<{ + position: number, + values: Array, + }>, +} +``` + +The component will compute the median, quartiles, whiskers, and outliers from the values array for each position. + +## Visualization description + +The components displays a boxplot at each sequence position where data have been provided (or multiple boxplots if there are multiple dataset). +Each boxplot shows the following data: + +``` + • ╮┄┄┄┄┄┄┄┄ Maximum + •• │ Outliers high + •• ╯ + ──┬── - Whisker high (highest value <= Q3 + 1.5 * IQR) + │ + │ +┌──┴──┐ - Box high = Q3 +┝━━━━━┥ - Median +│ │ +└──┬──┘ - Box low = Q1 + │ + ──┴── - Whisker low (lowest value >= Q1 - 1.5 * IQR) + ••• ╮ + • │ Outliers low + •• ╯┄┄┄┄┄┄┄┄ Minimum +``` + +\* Q1 = lower quartile, Q3 = upper quartile, IQR = Q3 - Q1 = interquartile range. + +### "zoomed-out" visualization + +When the view becomes too much zoomed-out and boxplots become poorly visible, the component will switch from boxplot visualization to simplified "zoomed-out" visualization. This visualization shows the median at each position in darker color, and optionally a lighter shaded outline representing extremes or whisker range or box range, depending on the `zoomed-out-outline` attribute (default: whisker range). The `zoom-transition-range` attributes controls when exactly this transition happens. If there are multiple datasets, they are overlaid on one another, being distinguishable by their color (rather then displayed side-by-side as boxplots). + +### Downsampling + +When the view becomes over more zoomed-out (column width < 1 screen pixel), the component will downsample the displayed simplified visualization data. This is done to ensure performance for very long sequences and shouldn't be visually noticeable. + +## API Reference + +### Attributes + +#### `y-min?: number, y-max?: number` + +Bottom and top limit for the Y-axis. If either of these is not set, it will be inferred from the data (minimum/maximum of all data values). + +#### `show-axis?: boolean (default: false)` + +Turn on or off displaying the vertical axis. + +The axis is rendered in the left margin of the component. The margin attributes must be set accordingly to provide enough space for the axis (e.g. `margin-left="30"`). + +#### `show-nested-highlights?: boolean (default: false)` + +Turn on or off displaying nested hightlights. Nested hightlights indicate the selected subcolumn (dataset) within the highlighted column (sequence position). This applies only when multiple datasets are displayed. + +#### `zoomed-out-outline?: "extremes" | "whiskers" | "box" | "none" (default: "whiskers")` + +Choose what kind of data is shown as the shaded outline in the simplified zoomed-out view (in addition to the darker lines showing median). + +- `"extremes"` - the outline covers the range between minimum and maximum; +- `"whiskers"` - the outline covers the same range as the whiskers in the boxplot; +- `"box"` - the outline covers the same range as the box in the boxplot (quartiles); +- `"none"` - no outline is rendered. + +#### `column-gap?: number (default: 0.2)` + +Width of the gap between displayed columns, relative to the base width (width of one sequence position). + +(= _Column gap_ / _Base width_ in the figure below) + +#### `box-gap?: number (default: 0.1)` + +Width of the gap between boxes within a column, relative to the column width divided by the number of datasets (= _Box gap_ / (_Column width_ / _N datasets_) = _Box gap_ / (_Box width_ + _Box gap_) in the figure below). + +#### `whisker-width?: number (default: 0.6)` + +Boxplot whisker width, relative to the box width (= _Whisker width_ / _Box width_ in the figure below). + +#### `outlier-jitter-width?: number (default: 0.4)` + +Width of random noise added to the X-position of outliers, relative to the box width (= _Outlier jitter width_ / _Box width_ in the figure below). + +#### `outlier-radius?: number (default: 2)` + +Radius of the circles used to render outliers, in CSS pixels. + +#### `zoom-transition-range?: string (default: "4-5")` + +Base width(s), in CSS pixels, where the transition from "zoomed-out" simplified visualization to "zoomed-in" boxplot visualization happens. + +`zoom-transition-range` can be either one number (for a sharp transition, e.g. `"4"`) or a hyphen-separated range (for a smooth transition, e.g. `"4-5"`). + +If there are multiple datasets, this width(s) will be multiplied by the number of datasets, to compensate for narrower space for each dataset. + +- Use `"0"` to always show "zoomed-in" boxplot visualization. (This is not recommended, as it will prevent downsampling and may cause performance issues. However, you can use sufficiently small values, such as `"0.5-1"`, to achieve the same goal without performance issues). + +* Use `"Infinity"` to always show "zoomed-out" visualization. + +#### Overview of width measurements + +(2 sequence positions with 2 datasets) + +``` + Seq. position 1 Seq. position 2 +<────────────────────><────────────────────> Base width +┄><────────────────><┄┄><────────────────><┄ Column width, Column gap + ><─────><><─────>< ><─────><><─────>< Box width, Box gap* + <───> <───> <───> <───> Whisker width + ──┬── ──┬── ──┬── ──┬── + ┌──┴──┐ ┌──┴──┐ ┌──┴──┐ ┌──┴──┐ + │ │ │ │ │ │ │ │ + ┝━━━━━┥ ┝━━━━━┥ ┝━━━━━┥ ┝━━━━━┥ + │ │ │ │ │ │ │ │ + └──┬──┘ └──┬──┘ └──┬──┘ └──┬──┘ + ──┴── ──┴── ──┴── ──┴── + ••• • • •• • • + • • •• • + <─> <─> <─> <─> Outlier jitter width +``` + +\* Full _Box gap_ between the boxes in a column, 1/2 _Box gap_ before the first and after the last box in a column + +### Events + +As `nightingale-boxplot-track` implements from `withZoom` and `withHighlight`, it will respond to zooming changes, highlight events, and emit events when interacting with boxplots (helpful if you want to display tooltips, integrate with other components etc.). + +When the user clicks on or hovers over the component, the component emits a `CustomEvent` with type `"change"` and with `detail` of the following structure: + +```typescript +{ + eventType: "mouseover" | "mouseout" | "click", + parentEvent?: Event, + feature?: { + type: "boxplot", + /** Position in the sequence */ + position: number, + /** Data for the boxplot at this position from all datasets (one item for each dataset) */ + data: Array<{ + /** Common properties of the whole dataset */ + dataset: { + name: string, + color: string, + }, + /** Boxplot data at this position */ + datum?: { + /** Position in the sequence */ + position: number, + /** All values of the independent variable at this position */ + values: Float32Array, + median: number, + boxLow: number, + boxHigh: number, + whiskerLow: number, + whiskerHigh: number, + minimum: number, + maximum: number, + outliersLow: Float32Array, + outliersHigh: Float32Array, + }, + }>, + /** Index into `data`, indicates which dataset is being pointed at */ + datasetIndex: number, + } | null, +} +``` + +### Other attributes and properties + +This component inherits from `NightingaleElement`. + +The component implements the following mixins: `withCanvas`, `withManager`, `withZoom`, `withResizable`, `withMargin`, `withPosition`, `withDimensions`, `withHighlight`. diff --git a/packages/nightingale-boxplot-track/package.json b/packages/nightingale-boxplot-track/package.json new file mode 100644 index 00000000..6f4f444d --- /dev/null +++ b/packages/nightingale-boxplot-track/package.json @@ -0,0 +1,40 @@ +{ + "name": "@nightingale-elements/nightingale-boxplot-track", + "version": "5.6.0", + "description": "Track for showing distribution of a variable on each residue position via boxplots.", + "files": [ + "dist", + "src" + ], + "main": "dist/index.js", + "module": "dist/index.js", + "type": "module", + "types": "dist/index.d.ts", + "scripts": { + "build": "rollup --config ../../rollup.config.mjs", + "test": "../../node_modules/.bin/jest --config ../../jest.config.js ./tests/*" + }, + "keywords": [ + "nightingale", + "webcomponents", + "customelements" + ], + "repository": { + "type": "git", + "url": "https://github.com/ebi-webcomponents/nightingale.git" + }, + "bugs": { + "url": "https://github.com/ebi-webcomponents/nightingale/issues" + }, + "homepage": "https://ebi-webcomponents.github.io/nightingale/", + "author": "Adam Midlik ", + "license": "MIT", + "publishConfig": { + "access": "public" + }, + "sideEffects": false, + "dependencies": { + "@nightingale-elements/nightingale-new-core": "^5.6.0", + "d3": "7.9.0" + } +} diff --git a/packages/nightingale-boxplot-track/src/downsampling.ts b/packages/nightingale-boxplot-track/src/downsampling.ts new file mode 100644 index 00000000..5598b3a6 --- /dev/null +++ b/packages/nightingale-boxplot-track/src/downsampling.ts @@ -0,0 +1,112 @@ +type PoolingFunction = (a: number, b: number) => number; + +const PoolingFunctions = { + /** Like `Math.max` but handles `NaN` (`max(1, 2) -> 2`, `max(1, NaN) -> 1`, `max(NaN, 2) -> 2`, `max(NaN, NaN) -> NaN`) */ + max(a: number, b: number): number { + if (isNaN(a) || a < b) return b; + else return a; + }, + /** Like `Math.min` but handles `NaN` (`min(1, 2) -> 1`, `min(1, NaN) -> 1`, `min(NaN, 2) -> 2`, `min(NaN, NaN) -> NaN`) */ + min(a: number, b: number): number { + if (isNaN(a) || a > b) return b; + else return a; + }, +} satisfies Record; + +type PoolingMethod = keyof typeof PoolingFunctions; + + +/** Helper object for downsampling 1D number arrays, with caching */ +export class Downsampler { + /** Downsampled versions of the original data (index 1 holds the original data; indices 2, 4, 8 etc. hold increasingly downsampled data) */ + private readonly downsampled: { [scale: number]: Float32Array | undefined } = {}; + + /** Function used to compute downsampled value from multiple source values */ + private readonly poolingFunction: PoolingFunction; + + /** Create a new downsampler for a 1D number array */ + constructor(data: Float32Array, public readonly poolingMethod: PoolingMethod) { + this.downsampled[1] = data; + this.poolingFunction = PoolingFunctions[poolingMethod]; + } + + /** Return the original full-size data */ + getOriginal(): Float32Array { + return this.downsampled[1]!; + } + + /** Get data downsampled so that `downsampledColumnWidth ~ originalColumnWidth * originalData.length / downsampledData.length ~ 1`. + * Return original data if `originalColumnWidth > 1`. */ + getDownsampledForColumnWidth(originalColumnWidth: number): Float32Array { + const targetScale = Downsampler.targetDownsamplingScale(originalColumnWidth); + return this.getDownsampledByScale(targetScale); + } + + /** Get data downsampled by `scale`, with caching. `scale` must be a power of 2, greater or equal to 1 (i.e. 1, 2, 4, 8, 16...). + * Length of the resulting data will be `Math.ceil(originalData.length / scale)` */ + getDownsampledByScale(scale: number): Float32Array { + if (scale < 1) throw new Error(`Downsampler.getDownsampledByScale: invalid scale ${scale}, scale must be a power of 2, greater or equal to 1`); + if (Math.log2(scale) % 1) throw new Error(`Downsampler.getDownsampledByScale: invalid scale ${scale}, scale must be a power of 2, greater or equal to 1`); + const cached = this.downsampled[scale]; + if (cached) { + return cached; + } else { + const srcData = this.getDownsampledByScale(scale / 2); + const result = downsampleNumbers_halve(srcData, this.poolingFunction); + this.downsampled[scale] = result; + return result; + } + } + + /** Return `scale`, a power of 2, such that + * `0.5 < originalColumnWidth*scale <= 1` (or `scale === 1` if `originalColumnWidth > 1`) */ + static targetDownsamplingScale(originalColumnWidth: number): number { + if (originalColumnWidth > 1) { + return 1; + } + const log2scale = -Math.log2(originalColumnWidth); + const log2scaleFloor = Math.floor(log2scale); + return 2 ** log2scaleFloor; + } + + /** Return one or two scales, each being a power of 2, such that: + * - in case of two scales: `1 < originalColumnWidth*scale1 < 2` and `0.5 < originalColumnWidth*scale2 < 1`, `scale1 = 2 * scale2` + * - in case of one scale: `originalColumnWidth*scale === 1` (or `scale === 1` if `originalColumnWidth > 1`) + * + * Weights of the scales sum up to 1 (the closer `originalColumnWidth*scale` is to 1, the higher its weight). + */ + static targetDownsamplingScalesForTransition(originalColumnWidth: number) { + if (originalColumnWidth > 1) { + return [{ scale: 1, weight: 1 }]; + } + const log2scale = -Math.log2(originalColumnWidth); + const log2scaleFloor = Math.floor(log2scale); + const wNext = log2scale - log2scaleFloor; + if (wNext === 0) { + return [{ scale: 2 ** log2scaleFloor, weight: 1 }]; + } else { + return [ + { scale: 2 ** (log2scaleFloor + 1), weight: wNext }, + { scale: 2 ** log2scaleFloor, weight: 1 - wNext }, + ]; + } + } +} + +/** Downsample array of numbers to a new size `ceil(input.length / 2)`. */ +function downsampleNumbers_halve(input: Float32Array, poolingFunction: PoolingFunction): Float32Array { + const w0 = input.length; + const w1Paired = Math.floor(w0 / 2); + const w1 = Math.ceil(w0 / 2); + const out = new Float32Array(w1).fill(NaN); + for (let j = 0; j < w1Paired; j++) { // column index + const old1 = input[2 * j]; + const old2 = input[2 * j + 1]; + out[j] = poolingFunction(old1, old2); + } + if (w0 % 2) { + // Odd length of input array -> just copy last element + out[w1 - 1] = input[w0 - 1]; + } + return out; +} diff --git a/packages/nightingale-boxplot-track/src/index.ts b/packages/nightingale-boxplot-track/src/index.ts new file mode 100644 index 00000000..3020179c --- /dev/null +++ b/packages/nightingale-boxplot-track/src/index.ts @@ -0,0 +1,3 @@ +import NightingaleBoxplotTrack, { } from "./nightingale-boxplot-track"; + +export { NightingaleBoxplotTrack as default }; diff --git a/packages/nightingale-boxplot-track/src/nightingale-boxplot-track.ts b/packages/nightingale-boxplot-track/src/nightingale-boxplot-track.ts new file mode 100644 index 00000000..d5f59cef --- /dev/null +++ b/packages/nightingale-boxplot-track/src/nightingale-boxplot-track.ts @@ -0,0 +1,976 @@ +import NightingaleElement, { + BinarySearch, + createEvent, + customElementOnce, + EnumAttributeConverter, + OptionalNumberAttributeConverter, + Refresher, + Stamp, + withCanvas, + withDimensions, + withHighlight, + withManager, + withMargin, + withPosition, + withResizable, + withZoom, +} from "@nightingale-elements/nightingale-new-core"; +import { axisLeft, BaseType, color, max, min, randomLcg, randomUniform, scaleLinear, select, Selection, TypedArray } from "d3"; +import { html, PropertyValues } from "lit"; +import { property } from "lit/decorators.js"; +import { Downsampler } from "./downsampling"; + + +const ATTRIBUTES_THAT_TRIGGER_REFRESH = new Set([ + "length", "width", "height", + "margin-top", "margin-bottom", "margin-left", "margin-right", "margin-color", + "y-min", "y-max", "show-axis", "zoomed-out-outline", + "column-gap", "box-gap", "whisker-width", "outlier-jitter-width", "outlier-radius", "zoom-transition-range", +] satisfies (keyof NightingaleBoxplotTrack)[]); + + +/** Line width for rectangle stroke, in CSS pixels */ +const LINE_WIDTH = 1; +/** Maximum line width relative to base width (width of one sequence position) (overrides `LINE_WIDTH` when zoomed out too much) */ +const MAX_REL_LINE_WIDTH = 0.2; + +/** Default fill color for boxes (stroke color will be derived from this) */ +const DEFAULT_DATA_COLOR = "#cccccc"; + +/** Approximate width of a column in screen pixels, when showing downsampled data in "background" visualization. + * (higher value means more responsive but lower-resolution visualization). */ +const BG_DOWNSAMPLING_PIXELS_PER_COLUMN = 1; + +/** Return stroke color matching a given fill color (darker version of the same color) */ +function makeStrokeColor(dataColor: string): string | undefined { + return color(dataColor)?.darker(2).formatHex(); +} + + +/** Data for a single boxplot (subcolumn) in the visualization (i.e. a single sequence position for a single dataset) */ +interface BoxplotDatum { + /** Position in the sequence (1-based) */ + position: number, + /** Array of values of the independent variable at the position */ + values: ArrayLike, +} + +/** Data for a single dataset in the visualization (i.e. all sequence positions for a single dataset) */ +export interface BoxplotDataset { + /** Name of the dataset */ + name: string, + /** Color of the dataset visualization */ + color?: string, + /** Array of boxplot data for individual positions in the sequence */ + positions: BoxplotDatum[], +} + +/** Data for `NightingaleBoxplotTrack`. + * A list of one or more datasets, where each dataset contains boxplot data for individual positions in the sequence. + * In case of multiple datasets, the boxplots for each dataset will be shown side-by-side at each sequence position. */ +export type BoxplotData = BoxplotDataset[]; + +/** Options for what kind of data can be shown as the shaded outline in zoomed-out visualization */ +export const ZoomedOutOutlineOptions = ["extremes", "whiskers", "box", "none"] as const; +/** Options for what kind of data can be shown as the shaded outline in zoomed-out visualization */ +export type ZoomedOutOutlineOption = typeof ZoomedOutOutlineOptions[number]; + + +/** A Nightingale track for showing distribution of a variable on each residue position via boxplots */ +@customElementOnce("nightingale-boxplot-track") +export default class NightingaleBoxplotTrack extends withCanvas( + withManager( + withZoom( + withResizable( + withMargin( + withPosition(withDimensions(withHighlight(NightingaleElement))) + ) + ) + ) + ) +) { + /** Bottom limit for Y-axis (default: minimum computed from data). */ + @property({ converter: OptionalNumberAttributeConverter }) + "y-min"?: number; + + /** Top limit for Y-axis (default: maximum computed from data). */ + @property({ converter: OptionalNumberAttributeConverter }) + "y-max"?: number; + + /** Turn on vertical axis. */ + @property({ type: Boolean }) + "show-axis"?: boolean; + + /** Turn on showing nested highlights, which indicate selected subcolumn within a column (in case of multiple datasets). */ + @property({ type: Boolean }) + "show-nested-highlights"?: boolean; + + /** Position of nested highlight in form "position/iDataset", or "" if none. */ + @property({ type: String, reflect: true }) // not using attribute converter here, because change detection would not work correctly + private "nested-highlight": string = ""; + + /** What kind of data should be shown as the shaded outline in zoomed-out visualization. */ + @property({ converter: EnumAttributeConverter(ZoomedOutOutlineOptions, "whiskers") }) + "zoomed-out-outline": ZoomedOutOutlineOption; + + /** Width of the gap between displayed columns, relative to the base width (width of one sequence position) (allowed range: 0-1, default: 0.2). */ + @property({ type: Number }) + "column-gap": number = 0.2; + + /** Width of the gap between boxes within a column, relative to the column width (allowed range: 0-1, default: 0.1). */ + @property({ type: Number }) + "box-gap": number = 0.1; + + /** Boxplot whisker width, relative to the box width (allowed range: 0-1, default: 0.6). */ + @property({ type: Number }) + "whisker-width": number = 0.6; + + /** Width of random noise (jitter) to be added to the X-position of the outliers, relative to the box width (allowed range: 0-1, default: 0.4). */ + @property({ type: Number }) + "outlier-jitter-width": number = 0.4; + + /** Radius for the circles representing outliers, in CSS pixels (default: 2). Set to 0 to supress outlier rendering. */ + @property({ type: Number }) + "outlier-radius": number = 2; + + /** Base width(s), in CSS pixels, where the transition from zoomed-out simplified visualization to zoomed-in boxplot visualization happens. + * Can be either one number (for sharp transition) or a hyphen-separated range. + * If there are multiple datasets, this width(s) will be multiplied by the number of datasets, to compensate for narrower space for each dataset. + * Use "0" to always show zoomed-in visualization (or preferrably "0.5-1" to avoid perfomance issues). + * Use "Infinity" to always show zoomed-out visualization. + * (default: "4-5") */ + @property({ type: String }) + "zoom-transition-range": string = "4-5"; + + + #data?: BoxplotData; + private preprocessedData?: PreprocessedData; + /** D3 selection for SVG group for highlights (excluding nested highlights) */ + protected svgHighlights?: Selection; + /** D3 selection for SVG group for nested highlights */ + protected svgNestedHighlights?: Selection; + /** D3 selection for SVG group for margins */ + protected svgMargins?: Selection; + + + override connectedCallback() { + super.connectedCallback(); + if (this.data) this.createTrack(); + } + + /** Get or set the data for the boxplot track */ + get data(): BoxplotData | undefined { + return this.#data; + } + set data(data: BoxplotData | undefined) { + this.#data = data; + this.preprocessedData = data ? preprocessData(data) : undefined; + this.createTrack(); + } + + /** Return the range of Y values corresponding to bottom and top of the viewport (excluding margins) */ + getYLimits(): [yMin: number, yMax: number] { + const yMin = this["y-min"] ?? this.preprocessedData?.yLimits.min ?? 0; + const yMax = this["y-max"] ?? this.preprocessedData?.yLimits.max ?? 100; + return [yMin, yMax]; + } + + override attributeChangedCallback(name: string, oldValue: string | null, newValue: string | null): void { + super.attributeChangedCallback(name, oldValue, newValue); + if (ATTRIBUTES_THAT_TRIGGER_REFRESH.has(name)) { + this.onDimensionsChange(); + this.createTrack(); + } + } + + protected createTrack() { + if (this.svg) { + this.svg.selectAll("g").remove(); + this.unbindEvents(this.svg); + } + this.svg = select(this as unknown as NightingaleElement) + .selectAll("svg") + .attr("width", this.width) + .attr("height", this.height); + if (this.svg) { // this check is necessary because `svg` setter does not always set + this.bindEvents(this.svg); + this.svgHighlights = this.svg.append("g").attr("class", "highlighted"); + this.svgNestedHighlights = this.svg.append("g").attr("class", "nested-highlighted"); + this.svgMargins = this.svg.append("g").attr("class", "margin"); + if (this["show-axis"]) { + const yScale = scaleLinear(this.getYLimits(), [this.height - this["margin-bottom"], this["margin-top"]]); + this.svg.append("g").attr("class", "y-axis") + .attr("transform", `translate(${this["margin-left"]},0)`) + .call(axisLeft(yScale)); + } + } + } + + protected refresh() { + this.requestDraw(); + this.updateHighlight(); + this.updateNestedHighlight(); + this.updateMargins(); + } + + /** Stamp for tracking changes to the drawing parameters (when the stamp changes, canvas redraw is needed) */ + private readonly _drawStamp = new Stamp(() => { + const stamp: Record = { + "data": this["data"], + "canvasCtx": this["canvasCtx"], + "canvasWidth": this["width"], + "canvasHeight": this["height"], + "canvasScale": this["canvasScale"], + "display-start": this["display-start"], + "display-end": this["display-end"], + }; + for (const attr of ATTRIBUTES_THAT_TRIGGER_REFRESH) { + stamp[attr] = this.getAttribute(attr); + } + return stamp; + }); + + /** Request canvas redraw. */ + private requestDraw = () => this._drawer.requestRefresh(); + private readonly _drawer = Refresher(() => this._draw()); + /** Do not call directly! Call `requestDraw` instead to avoid browser freezing. */ + private _draw(): void { + if (!this._drawStamp.update().changed) return; + this.adjustCanvasCtxLogicalSize(); + if (!this.canvasCtx) return; + this.clearCanvas(this.canvasCtx); + this.drawData(this.canvasCtx); + } + + /** Get a clean auxiliary offscreen canvas for drawing, of the same size as the main canvas. + * Only one offscreen canvas can be use at any time, because it is reused. */ + private getOffscreenCanvas(): [canvas: HTMLCanvasElement, canvasContext?: CanvasRenderingContext2D] { + this._offscreenCanvas ??= document.createElement("canvas"); + const width = this.canvasCtx?.canvas.width ?? 1; + const height = this.canvasCtx?.canvas.height ?? 1; + if (this._offscreenCanvas.width !== width) { + this._offscreenCanvas.width = width; + } + if (this._offscreenCanvas.height !== height) { + this._offscreenCanvas.height = height; + } + const ctx = this._offscreenCanvas.getContext("2d") ?? undefined; + this.clearCanvas(ctx); + return [this._offscreenCanvas, ctx]; + } + private _offscreenCanvas?: HTMLCanvasElement; + + /** Clear the canvas. */ + private clearCanvas(ctx: CanvasRenderingContext2D | undefined) { + if (!ctx) return; + ctx.clearRect(0, 0, ctx.canvas.width, ctx.canvas.height); + } + + /** Draw the data on the canvas. */ + private drawData(ctx: CanvasRenderingContext2D) { + const [fgAlpha, bgAlpha] = this.getFgBgOpacity(); + + // Draw background (zoomed-out) visualization + if (bgAlpha > 0) { + this.drawSimplifiedVisualization(ctx, bgAlpha); + } + + // Draw foreground (zoomed-in) visualization + if (fgAlpha === 1) { + // Draw foreground visualization directly + this.drawBoxplotVisualization(ctx); + } else if (fgAlpha > 0) { + // Draw foreground visualization via an auxiliary canvas (cannot be drawn directly to the main canvas, because overlapping outliers would add up and create opacity > fgAlpha) + const [auxCanvas, auxCtx] = this.getOffscreenCanvas(); + if (auxCtx) { + this.drawBoxplotVisualization(auxCtx); + ctx.globalAlpha = fgAlpha; + ctx.drawImage(auxCanvas, 0, 0); + } + } + } + + /** Draw full boxplot visualization ("foreground" / zoomed-in) */ + private drawBoxplotVisualization(ctx: CanvasRenderingContext2D) { + if (!this.preprocessedData) return; + + const { xColumnLeft, xColumnWidth, xScale, yScale, yMedianExtra, lineWidth, outlierRadius, start, stop } = this.getDrawingMeasurements(); + ctx.lineWidth = lineWidth; + + const nDatasets = this.preprocessedData.datasets.length; + for (let iDataset = 0; iDataset < nDatasets; iDataset++) { + const dataset = this.preprocessedData.datasets[iDataset]; + if (!dataset) continue; + + const dataColor = dataset.color ?? DEFAULT_DATA_COLOR; + const fillColor = dataColor; + const strokeColor = makeStrokeColor(dataColor)!; + + const xBoxOffset = xColumnLeft + (iDataset + 0.5 * this["box-gap"]) * xColumnWidth / nDatasets; + const xBoxWidth = (1 - this["box-gap"]) * xColumnWidth / nDatasets; + const xCenter = xBoxOffset + 0.5 * xBoxWidth; + const xWhiskerLeft = xCenter - 0.5 * this["whisker-width"] * xBoxWidth; + const xWhiskerRight = xCenter + 0.5 * this["whisker-width"] * xBoxWidth; + const xJitterHalfwidth = 0.5 * this["outlier-jitter-width"] * xBoxWidth; + + for (let i = start; i < stop; i++) { + const datum = dataset.positions[i]; + if (!datum) continue; + + const x = xScale(i); + const yMedian = yScale(datum.median); + const yBoxLow = yScale(datum.boxLow); + const yBoxHigh = yScale(datum.boxHigh); + const yWhiskerLow = yScale(datum.whiskerLow); + const yWhiskerHigh = yScale(datum.whiskerHigh); + + ctx.globalAlpha = 1; + + // Whiskers + ctx.strokeStyle = strokeColor; + ctx.beginPath(); + ctx.moveTo(x + xWhiskerLeft, yWhiskerHigh); + ctx.lineTo(x + xWhiskerRight, yWhiskerHigh); + ctx.moveTo(x + xWhiskerLeft, yWhiskerLow); + ctx.lineTo(x + xWhiskerRight, yWhiskerLow); + ctx.moveTo(x + xCenter, yWhiskerHigh); + ctx.lineTo(x + xCenter, yWhiskerLow); + ctx.stroke(); + + // Box + ctx.fillStyle = fillColor; + ctx.strokeStyle = strokeColor; + ctx.fillRect(x + xBoxOffset, yBoxHigh, xBoxWidth, yBoxLow - yBoxHigh); + ctx.strokeRect(x + xBoxOffset, yBoxHigh, xBoxWidth, yBoxLow - yBoxHigh); + + // Median + ctx.fillStyle = strokeColor; + ctx.strokeStyle = strokeColor; + ctx.fillRect(x + xBoxOffset, yMedian - yMedianExtra, xBoxWidth, 2 * yMedianExtra); + ctx.strokeRect(x + xBoxOffset, yMedian - yMedianExtra, xBoxWidth, 2 * yMedianExtra); + + // Outliers + if (outlierRadius > 0) { + const xJitter = xJitterHalfwidth !== 0 ? + randomUniform.source(randomLcg(i * nDatasets + iDataset))(xCenter - xJitterHalfwidth, xCenter + xJitterHalfwidth) + : () => xCenter; + ctx.globalAlpha = 0.25; + ctx.fillStyle = strokeColor; + for (const outlier of datum.outliersHigh) { + ctx.beginPath(); + ctx.arc(x + xJitter(), yScale(outlier), outlierRadius, 0, 2 * Math.PI); + ctx.fill(); + } + for (const outlier of datum.outliersLow) { + ctx.beginPath(); + ctx.arc(x + xJitter(), yScale(outlier), outlierRadius, 0, 2 * Math.PI); + ctx.fill(); + } + } + } + } + } + + /** Draw simplified visualization ("background" / zoomed-out) */ + private drawSimplifiedVisualization(ctx: CanvasRenderingContext2D, alpha: number) { + if (!this.data || !this.preprocessedData) return; + + const { xColumnLeft, xColumnRight, xScale, yScale, yMedianExtra, lineWidth, start, stop } = this.getDrawingMeasurements(); + ctx.lineWidth = lineWidth; + + const originalColumnWidth = (xScale(1) - xScale(0)) / BG_DOWNSAMPLING_PIXELS_PER_COLUMN; + const downsamplingLevels = Downsampler.targetDownsamplingScalesForTransition(originalColumnWidth); + + const nDatasets = this.preprocessedData?.datasets.length ?? 1; + for (let iDataset = 0; iDataset < nDatasets; iDataset++) { + const dataset = this.preprocessedData.datasets[iDataset]; + if (!dataset) continue; + + const { offset, length, downsamplers } = dataset.downsampling; + + const dataColor = dataset.color ?? DEFAULT_DATA_COLOR; + const fillColor = dataColor; + const strokeColor = makeStrokeColor(dataColor)!; + + // Overlay two downsampling levels (e.g. 8x and 4x) to hide the transition between levels and create illusion of smooth zooming + for (const { scale, weight } of downsamplingLevels) { + ctx.lineWidth = (scale === 1) ? 1 : lineWidth; + const medianLow = downsamplers.medianLow.getDownsampledByScale(scale); + const medianHigh = downsamplers.medianHigh.getDownsampledByScale(scale); + const exactScale = length / medianLow.length; // This might be slightly different from `scale` which is always a power of two. + + const yMedianLow = (j: number) => yScale(medianLow[j]) + yMedianExtra; + const yMedianHigh = (j: number) => yScale(medianHigh[j]) - yMedianExtra; + + let yOutlineLow: ((j: number) => number) | undefined; + let yOulineHigh: ((j: number) => number) | undefined; + switch (this["zoomed-out-outline"]) { + case "extremes": { + const minimum = downsamplers.minimum.getDownsampledByScale(scale); + const maximum = downsamplers.maximum.getDownsampledByScale(scale); + yOutlineLow = (j: number) => yScale(minimum[j]); + yOulineHigh = (j: number) => yScale(maximum[j]); + break; + } + case "whiskers": { + const whiskerLow = downsamplers.whiskerLow.getDownsampledByScale(scale); + const whiskerHigh = downsamplers.whiskerHigh.getDownsampledByScale(scale); + yOutlineLow = (j: number) => yScale(whiskerLow[j]); + yOulineHigh = (j: number) => yScale(whiskerHigh[j]); + break; + } + case "box": { + const boxLow = downsamplers.boxLow.getDownsampledByScale(scale); + const boxHigh = downsamplers.boxHigh.getDownsampledByScale(scale); + yOutlineLow = (j: number) => yScale(boxLow[j]); + yOulineHigh = (j: number) => yScale(boxHigh[j]); + break; + } + case "none": { + yOutlineLow = undefined; + yOulineHigh = undefined; + break; + } + } + + // j-prefixed variables refer to indices in the downsampled data + const jStart = Math.max(0, Math.floor((start - offset) / exactScale)); + const jStop = Math.min(medianLow.length, Math.ceil((stop - offset) / exactScale)); + const jSegments = getContiguousSegments(jStart, jStop, j => !isNaN(medianLow[j])); + + const jXScale = (j: number) => xScale(offset + j * exactScale); + + // Shaded outline + if (yOutlineLow && yOulineHigh) { + for (const segment of jSegments) { + ctx.globalAlpha = weightAlpha(0.25 * alpha, weight); + ctx.fillStyle = fillColor; + drawSilhouette(ctx, segment, jXScale, yOutlineLow, yOulineHigh, [xColumnLeft, xColumnRight], "fill"); + } + } + + // Median + for (const segment of jSegments) { + ctx.globalAlpha = weightAlpha(0.5 * alpha, weight); + ctx.fillStyle = strokeColor; + ctx.strokeStyle = strokeColor; + drawSilhouette(ctx, segment, jXScale, yMedianLow, yMedianHigh, [xColumnLeft, xColumnRight], "fill+stroke"); + } + } + } + } + + /** Get a set of shared measurement variables for drawing the boxplot visualizations. */ + private getDrawingMeasurements() { + const canvasScale = this.canvasScale; + const xBaseWidthInCss = this.getSingleBaseWidth(); + const xBaseWidth = canvasScale * xBaseWidthInCss; + const xColumnLeft = xBaseWidth * 0.5 * this["column-gap"]; + const xColumnWidth = xBaseWidth * (1 - this["column-gap"]); + const xColumnRight = xColumnLeft + xColumnWidth; + const xScale = (i: number) => canvasScale * this.getXFromSeqPosition(i); + + const yColumnOffset = canvasScale * this["margin-top"]; + const yColumnHeight = canvasScale * (this["height"] - this["margin-top"] - this["margin-bottom"]); + const yScale = scaleLinear(this.getYLimits(), [yColumnOffset + yColumnHeight, yColumnOffset]); + const yMedianExtra = canvasScale * 1; + + const lineWidth = canvasScale * Math.min(LINE_WIDTH, MAX_REL_LINE_WIDTH * xBaseWidthInCss); + const outlierRadius = canvasScale * this["outlier-radius"]; + + const start = Math.floor(this.getSeqPositionFromX(0) ?? 1); + const stop = Math.floor(this.getSeqPositionFromX(this.width) ?? 1) + 1; + + return { + /** Ratio of canvas logical size versus canvas display size */ + canvasScale, + /** Horizontal offset of the displayed column from the beginning of the slot, in canvas space */ + xColumnLeft, + /** Horizontal offset of the right edge of displayed column from the beginning of the slot, in canvas space */ + xColumnRight, + /** Width of displayed column, in canvas space */ + xColumnWidth, + /** Scale from sequence position to horizontal offset of the slot in canvas space (does not consider column gap) */ + xScale, + /** Scale from value of the independent variable to vertical position in canvas space */ + yScale, + /** Amount for vertically thickening the median rectangle, in canvas space */ + yMedianExtra, + /** Line width for strokes, in canvas space */ + lineWidth, + /** Circle radius for drawing outliers, in canvas space */ + outlierRadius, + /** Sequence position of the first rendered datapoint */ + start, + /** Sequence position of the last rendered datapoint + 1 */ + stop, + }; + } + + /** Compute opacity for the "foreground" (zoomed-in) and "background" (zoomed-out) visualization */ + private getFgBgOpacity(): [fgAlpha: number, bgAlpha: number] { + const nDatasets = this.preprocessedData?.datasets.length ?? 1; + /** Approximate boxplot box width, in CSS pixels. No need to consider column gap and box gap here. */ + const boxWidth = this.getSingleBaseWidth() / nDatasets; + const [transitionMin, transitionMax] = this.getZoomTransitionRange(); + + if (boxWidth <= transitionMin) { + return [0, 1]; + } else if (boxWidth >= transitionMax) { + return [1, 0]; + } else { + const fgAlpha = (boxWidth - transitionMin) / (transitionMax - transitionMin); + return [fgAlpha, 1 - fgAlpha]; + } + } + + private getZoomTransitionRange(): [number, number] { + const nums = this["zoom-transition-range"].split("-").map(Number); + const [a, b] = nums.length >= 2 ? [nums[0], nums[1]] : [nums[0], nums[0]] + if (isNaN(a) || isNaN(b)) return [0, 0]; + return [a, b]; + } + + /** Update the SVG highlights (excluding nested highlights) */ + protected updateHighlight() { + if (!this.svgHighlights) return; + const highlights = this.svgHighlights + .selectAll("rect") + .data(this.highlightedRegion.segments); + + highlights + .enter() + .append("rect") + .style("pointer-events", "none") + .merge(highlights) + .attr("fill", this["highlight-color"]) + .attr("height", this.height) + .attr("x", d => this.getXFromSeqPosition(d.start)) + .attr("width", d => Math.max(0, this.getSingleBaseWidth() * (d.end - d.start + 1))); + + highlights.exit().remove(); + } + + /** Update the SVG nested highlights (indicate the pointed subcolumn within the highlighted column) */ + protected updateNestedHighlight() { + if (!this.svgNestedHighlights) return; + const nestedHighlightLocation = NestedHighlight.parse(this["nested-highlight"]); + const nDatasets = this.preprocessedData?.datasets.length ?? 1; + const showNestedHighlight = this["show-nested-highlights"] && nDatasets > 1 && nestedHighlightLocation !== undefined; + + const highlights = this.svgNestedHighlights + .selectAll("rect") + .data(showNestedHighlight ? [nestedHighlightLocation] : []); + + const baseWidth = this.getSingleBaseWidth(); + const columnOffset = baseWidth * 0.5 * this["column-gap"]; + const columnWidth = baseWidth * (1 - this["column-gap"]); + const datumWidth = columnWidth / nDatasets; + + highlights + .enter() + .append("rect") + .style("pointer-events", "none") + .merge(highlights) + .attr("fill", this["highlight-color"]) + .attr("opacity", this.getFgBgOpacity()[0]) + .attr("height", this.height) + .attr("x", ([position, iDataset]) => this.getXFromSeqPosition(position) + columnOffset + iDataset * datumWidth) + .attr("width", datumWidth); + + highlights.exit().remove(); + } + + /** Update the SVG margins */ + protected updateMargins() { + this.renderMarginOnGroup(this.svgMargins); + } + + override zoomRefreshed() { + super.zoomRefreshed(); + if (this.getWidthWithMargins() > 0) this.refresh(); + } + + override firstUpdated(_changedProperties: PropertyValues) { + super.firstUpdated(_changedProperties); + this.createTrack(); + } + + override render() { + return html` +
+
+ + +
+
+ `; + } + + override onCanvasScaleChange() { + super.onCanvasScaleChange(); + this.refresh(); + } + + + private bindEvents(target: Selection): void { + target.on("click.NightingaleBoxplotTrack", (event: MouseEvent) => this.handleClick(event)); + target.on("mousemove.NightingaleBoxplotTrack", (event: MouseEvent) => this.handleMousemove(event)); + target.on("mouseout.NightingaleBoxplotTrack", (event: MouseEvent) => this.handleMouseout(event)); + } + + private unbindEvents(target: Selection): void { + target.on("click.NightingaleBoxplotTrack", null); + target.on("mousemove.NightingaleBoxplotTrack", null); + target.on("mouseout.NightingaleBoxplotTrack", null); + } + + private handleClick(event: MouseEvent): void { + const pointed = this.getPointedDatum(event.offsetX, event.offsetY); + if (pointed === undefined) { + return; + } + + const withHighlight = this.getAttribute("highlight-event") === "onclick"; + if (withHighlight) { + const iDataset = pointed.feature?.datasetIndex; + this["nested-highlight"] = NestedHighlight.format(iDataset !== undefined ? [pointed.position, iDataset] : undefined); + } + + const customEvent = createEvent( + "click", + pointed.feature, + withHighlight, + true, + pointed.position, + pointed.position, + event.target instanceof HTMLElement ? event.target : undefined, + event, + this, + ); + this.dispatchEvent(customEvent); + } + + private handleMousemove(event: MouseEvent): void { + const pointed = this.getPointedDatum(event.offsetX, event.offsetY); + if (pointed === undefined) { + return; + } + + const withHighlight = this.getAttribute("highlight-event") === "onmouseover"; + if (withHighlight) { + const iDataset = pointed.feature?.datasetIndex; + this["nested-highlight"] = NestedHighlight.format(iDataset !== undefined ? [pointed.position, iDataset] : undefined); + } + + const customEvent = createEvent( + "mouseover", + pointed.feature, + withHighlight, + false, + pointed.position, + pointed.position, + event.target instanceof HTMLElement ? event.target : undefined, + event, + this, + ); + this.dispatchEvent(customEvent); + } + + private handleMouseout(event: MouseEvent): void { + const withHighlight = this.getAttribute("highlight-event") === "onmouseover"; + if (withHighlight) { + this["nested-highlight"] = NestedHighlight.format(undefined); + } + + const customEvent = createEvent( + "mouseout", + null, + withHighlight, + undefined, + undefined, + undefined, + event.target instanceof HTMLElement ? event.target : undefined, + event, + this + ); + this.dispatchEvent(customEvent); + } + + /** Get the datum pointed by the mouse cursor at the given SVG coordinates */ + private getPointedDatum(svgX: number, _svgY: number) { + const continuousPosition = this.getSeqPositionFromX(svgX); + if (continuousPosition === undefined) return undefined; + const position = Math.floor(continuousPosition); + const fractional = continuousPosition - position; + const fractionalWithinColumn = (fractional - 0.5 * this["column-gap"]) / (1 - this["column-gap"]); + const nDatasets = this.preprocessedData?.datasets.length ?? 1; + const iDataset = Math.max(0, Math.min(nDatasets - 1, Math.floor(fractionalWithinColumn * nDatasets))); + + type EventFeatureData = Parameters[1]; + const feature: EventFeatureData = { + type: "boxplot", + position: position, + data: this.preprocessedData?.datasets.map(dataset => ({ + dataset: { name: dataset.name, color: dataset.color ?? DEFAULT_DATA_COLOR }, + datum: dataset?.positions[position], + })) ?? [], + datasetIndex: iDataset, + }; + return { position, feature }; + } +} + + +/** Preprocessed data for the boxplot track */ +type PreprocessedData = ReturnType; + +/** Preprocess data for the boxplot track */ +function preprocessData(data: BoxplotData) { + const preprocessedDatasets = data.map(preprocessDataset); + return { + datasets: preprocessedDatasets, + yLimits: getExtremes(preprocessedDatasets), + } +} + +/** Preprocessed data for a single dataset */ +type PreprocessedDataset = ReturnType; + +/** Preprocess data for a single dataset */ +function preprocessDataset(dataset: BoxplotDataset) { + const preprocessedPositions: PreprocessedPositions = {}; + + for (const datum of dataset.positions) { + preprocessedPositions[datum.position] = preprocessDatum(datum); + } + return { + ...dataset, + positions: preprocessedPositions, + downsampling: getDownsamplersForDataset(preprocessedPositions), + }; +} + +/** Preprocessed data for a single position in a single dataset */ +interface PreprocessedDatum { + /** Position in the sequence */ + position: number, + /** All values at this position, sorted */ + values: Float32Array, + /** Median value */ + median: number, + /** Lower bound of the box (lower quartile) */ + boxLow: number, + /** Upper bound of the box (upper quartile) */ + boxHigh: number, + /** Lower bound of the whisker (lower quartile - 1.5 * IQR) */ + whiskerLow: number, + /** Upper bound of the whisker (upper quartile + 1.5 * IQR) */ + whiskerHigh: number, + /** Minimum value */ + minimum: number, + /** Maximum value */ + maximum: number, + /** Outliers below the whisker */ + outliersLow: Float32Array, + /** Outliers above the whisker */ + outliersHigh: Float32Array, +} + +type PreprocessedPositions = { [position: number]: PreprocessedDatum } + +/** Preprocess data for a single position in a dataset */ +function preprocessDatum(datum: BoxplotDatum): PreprocessedDatum { + const sorted = sortIfNeeded(new Float32Array(datum.values)); + const median = getQuantile(sorted, 0.5); + const q1 = getQuantile(sorted, 0.25); + const q3 = getQuantile(sorted, 0.75); + + const iqr = q3 - q1; + /** Index of the first value >= q1 - 1.5 * IQR (low whisker) */ + const iWhiskerLow = BinarySearch.firstGteqIndex(sorted, q1 - 1.5 * iqr, x => x) + const stop = BinarySearch.firstGteqIndex(sorted, q3 + 1.5 * iqr, x => x); + /** Index of the last value <= q3 + 1.5 * IQR (high whisker) */ + const iWhiskerHigh = (stop >= sorted.length || sorted[stop] > q3 + 1.5 * iqr) ? stop - 1 : stop; + + return { + position: datum.position, + values: sorted, + median: median, + boxLow: q1, + boxHigh: q3, + whiskerLow: sorted[iWhiskerLow], + whiskerHigh: sorted[iWhiskerHigh], + outliersLow: sorted.slice(0, iWhiskerLow), + outliersHigh: sorted.slice(iWhiskerHigh + 1, undefined), + minimum: sorted[0], + maximum: sorted[sorted.length - 1], + }; +} + +/** Check whether `array` is sorted, sort if not. */ +function sortIfNeeded(array: T): T { + if (!arrayIsSorted(array)) { + array.sort(); + } + return array; +} + +/** Decide whether `array` is sorted. */ +function arrayIsSorted(array: TypedArray): boolean { + for (let i = 1, n = array.length; i < n; i++) { + if (array[i - 1] > array[i]) { + return false; + } + } + return true; +} + +/** Get p-quantile of the dataset. Input values must be sorted for this to work. */ +export function getQuantile(sortedValues: ArrayLike, p: number) { + const i_ = (sortedValues.length - 1) * p; + if (i_ >= sortedValues.length - 1) { + return sortedValues[sortedValues.length - 1]; + } + const i = Math.floor(i_); + const q = i_ - i; + return sortedValues[i] * (1 - q) + sortedValues[i + 1] * q; +} + +/** Get automatic Y-axis limits for the given preprocessed data (minimum and maximum value of the whole dataset). */ +function getExtremes(data: PreprocessedDataset[]) { + let min = Infinity; + let max = -Infinity; + + for (const dataset of data) { + for (const position in dataset.positions) { + const datum = dataset.positions[position]; + if (datum.minimum < min) min = datum.minimum; + if (datum.maximum > max) max = datum.maximum; + } + } + + return { + min: min === Infinity ? undefined : min, + max: max === -Infinity ? undefined : max, + }; +} + +/** Get contiguous segments within the integer range [start, stop), where `isPresent` returns true for each index. + * Return segments as an array of [start, stop) pairs. */ +function getContiguousSegments(start: number, stop: number, isPresent: (i: number) => boolean): [start: number, stop: number][] { + const segments: [number, number][] = []; + for (let i = start; i < stop; i++) { + if (isPresent(i)) { + if (i === segments[segments.length - 1]?.[1]) { + segments[segments.length - 1][1]++; + } else { + segments.push([i, i + 1]); + } + } + } + return segments; +} + +/** Draw a silhouette for the given segments. */ +function drawSilhouette( + ctx: CanvasRenderingContext2D, [sStart, sStop]: [number, number], + getX: (i: number) => number, getYLow: (i: number) => number, getYHigh: (i: number) => number, + [columnOffsetLeft, columnOffsetRight]: [number, number], style: "fill" | "stroke" | "fill+stroke" +) { + ctx.beginPath(); + for (let i = sStart; i < sStop; i++) { + const x = getX(i); + const yHigh = getYHigh(i); + ctx.lineTo(x + columnOffsetLeft, yHigh); + ctx.lineTo(x + columnOffsetRight, yHigh); + } + for (let i = sStop - 1; i >= sStart; i--) { + const x = getX(i); + const yLow = getYLow(i); + ctx.lineTo(x + columnOffsetRight, yLow); + ctx.lineTo(x + columnOffsetLeft, yLow); + } + if (style.includes("fill")) ctx.fill(); + if (style.includes("stroke")) ctx.stroke(); +} + +/** Convert preprocessed dataset from array of objects to object of arrays, for downsampling. */ +function datasetToArrays(data: PreprocessedPositions) { + const positions = Object.values(data).map(d => d.position); + const offset = min(positions) ?? 1; + const stop = (max(positions) ?? 0) + 1; + const length = stop - offset; + const median = new Float32Array(length).fill(NaN); + const boxLow = new Float32Array(length).fill(NaN); + const boxHigh = new Float32Array(length).fill(NaN); + const whiskerLow = new Float32Array(length).fill(NaN); + const whiskerHigh = new Float32Array(length).fill(NaN); + const minimum = new Float32Array(length).fill(NaN); + const maximum = new Float32Array(length).fill(NaN); + for (const pos of Object.values(data)) { + median[pos.position - offset] = pos.median; + boxLow[pos.position - offset] = pos.boxLow; + boxHigh[pos.position - offset] = pos.boxHigh; + whiskerLow[pos.position - offset] = pos.whiskerLow; + whiskerHigh[pos.position - offset] = pos.whiskerHigh; + minimum[pos.position - offset] = pos.minimum; + maximum[pos.position - offset] = pos.maximum; + } + return { + offset, + length, + arrays: { median, boxLow, boxHigh, whiskerLow, whiskerHigh, minimum, maximum }, + }; +} + +/** Get a set of downsamplers for each data type (median, minimum, ...) for a preprocessed dataset */ +function getDownsamplersForDataset(data: PreprocessedPositions) { + const { offset, length, arrays } = datasetToArrays(data); + return { + offset, + length, + downsamplers: { + medianLow: new Downsampler(arrays.median, "min"), + medianHigh: new Downsampler(arrays.median, "max"), + boxLow: new Downsampler(arrays.boxLow, "min"), + boxHigh: new Downsampler(arrays.boxHigh, "max"), + whiskerLow: new Downsampler(arrays.whiskerLow, "min"), + whiskerHigh: new Downsampler(arrays.whiskerHigh, "max"), + minimum: new Downsampler(arrays.minimum, "min"), + maximum: new Downsampler(arrays.maximum, "max"), + }, + }; +} + +/** Adjust opacity value (`alpha`) for rendering two overlaying visualizations. + * This is done in such a way that overlaying of two visualizations with weights `weight` and `1-weight` will give the desired total opacity `alpha` in the overlapping area. + * (Naive `alpha*weight` would result in overlapping areas being more transparent than intended). */ +function weightAlpha(alpha: number, weight: number) { + if (alpha === 1) { + // Avoid ill-defined expressions (division by zero) + return (weight > 0) ? 1 : 0; + } + const transpTotal = 1 - alpha; + const transpW = 1 - alpha * weight; + const transpWComplement = 1 - alpha * (1 - weight); + const correctionFactor = Math.sqrt(transpTotal / (transpW * transpWComplement)); + const transpWCorrected = transpW * correctionFactor; + return 1 - transpWCorrected; +} + +/** Location of nested highlight (sequence position and 0-based dataset index within the position). */ +type NestedHighlight = [position: number, iDataset: number] | undefined; +const NestedHighlight = { + /** Format `NestedHighlight` in the form "position/iDataset", or "" if undefined. */ + format(value: NestedHighlight): string { + if (!value) return ""; + return `${value[0]}/${value[1]}`; + }, + /** Format `NestedHighlight` from the form "position/iDataset", or "" if undefined. */ + parse(str: string): NestedHighlight { + if (str.trim() === "") return undefined; + const [a, b] = str.split("/").map(Number); + return [a, b]; + }, +}; diff --git a/packages/nightingale-boxplot-track/tests/mockData/sample-1.json b/packages/nightingale-boxplot-track/tests/mockData/sample-1.json new file mode 100644 index 00000000..4a05c399 --- /dev/null +++ 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+ } + ] +} \ No newline at end of file diff --git a/packages/nightingale-boxplot-track/tests/nightingale-distribution-track.test.ts b/packages/nightingale-boxplot-track/tests/nightingale-distribution-track.test.ts new file mode 100644 index 00000000..9b393d27 --- /dev/null +++ b/packages/nightingale-boxplot-track/tests/nightingale-distribution-track.test.ts @@ -0,0 +1,77 @@ +import { getQuantile } from "../src/nightingale-boxplot-track"; + + +describe("getQuantile", () => { + // Basic quantile calculations + test("calculates median (0.5 quantile) of odd-length array", () => { + expect(getQuantile([1, 2, 3, 4, 5], 0.5)).toEqual(3); + }); + + test("calculates median (0.5 quantile) of even-length array", () => { + expect(getQuantile([1, 2, 3, 4], 0.5)).toEqual(2.5); + }); + + // Edge cases for quantile values + test("returns minimum value for quantile 0", () => { + expect(getQuantile([1, 2, 3, 4, 5], 0)).toEqual(1); + }); + + test("returns maximum value for quantile 1", () => { + expect(getQuantile([1, 2, 3, 4, 5], 1)).toEqual(5); + }); + + // Quartile calculations + test("calculates first quartile (0.25)", () => { + expect(getQuantile([1, 2, 3, 4, 5], 0.25)).toEqual(2); + }); + + test("calculates third quartile (0.75)", () => { + expect(getQuantile([1, 2, 3, 4, 5], 0.75)).toEqual(4); + }); + + // Small arrays + test("handles single-element array", () => { + expect(getQuantile([5], 0.5)).toEqual(5); + expect(getQuantile([5], 0.1)).toEqual(5); + }); + + test("handles two-element array", () => { + expect(getQuantile([1, 5], 0.5)).toEqual(3); + expect(getQuantile([1, 5], 0.1)).toEqual(1.4); + expect(getQuantile([1, 5], 0.25)).toEqual(2); + expect(getQuantile([1, 5], 0.75)).toEqual(4); + expect(getQuantile([1, 5], 0.9)).toEqual(4.6); + }); + + // Negative numbers + test("works with negative values", () => { + expect(getQuantile([-5, -3, 0, 3, 5], 0.5)).toEqual(0); + }); + + test("calculates quantile with all negative values", () => { + expect(getQuantile([-5, -4, -3, -2, -1], 0.5)).toEqual(-3); + }); + + // Floating-point values + test("works with floating-point values", () => { + expect(getQuantile([1.5, 2.5, 3.5, 4.5], 0.5)).toEqual(3); + }); + + // Duplicate values + test("handles array with duplicate values", () => { + expect(getQuantile([1, 2, 2, 2, 5], 0.5)).toEqual(2); + }); + + // Various array sizes + test("calculates quantile for larger array", () => { + const largeArray = Array.from({ length: 101 }, (_, i) => i); + expect(getQuantile(largeArray, 0.5)).toEqual(50); + }); + + test("calculates deciles correctly", () => { + const array = Array.from({ length: 11 }, (_, i) => i); + expect(getQuantile(array, 0.1)).toEqual(1); + expect(getQuantile(array, 0.2)).toEqual(2); + expect(getQuantile(array, 0.9)).toEqual(9); + }); +}); diff --git a/packages/nightingale-boxplot-track/tsconfig.json b/packages/nightingale-boxplot-track/tsconfig.json new file mode 100644 index 00000000..6a62dbc4 --- /dev/null +++ b/packages/nightingale-boxplot-track/tsconfig.json @@ -0,0 +1,7 @@ +{ + "extends": "../../tsconfig.json", + "compilerOptions": { + "outDir": "./dist" + }, + "include": ["./src"] +} diff --git a/packages/nightingale-conservation-track/src/nightingale-conservation-track.ts b/packages/nightingale-conservation-track/src/nightingale-conservation-track.ts index f46389a0..0d505aea 100644 --- a/packages/nightingale-conservation-track/src/nightingale-conservation-track.ts +++ b/packages/nightingale-conservation-track/src/nightingale-conservation-track.ts @@ -2,6 +2,7 @@ import NightingaleElement, { BinarySearch, createEvent, customElementOnce, + EnumAttributeConverter, Refresher, Stamp, withCanvas, @@ -18,12 +19,15 @@ import { html, PropertyValues } from "lit"; import { property } from "lit/decorators.js"; -const ATTRIBUTES_THAT_TRIGGER_REFRESH: string[] = [ +const ATTRIBUTES_THAT_TRIGGER_REFRESH = new Set([ "length", "width", "height", "margin-top", "margin-bottom", "margin-left", "margin-right", "margin-color", "font-family", "min-font-size", "fade-font-size", "max-font-size", -] satisfies (keyof NightingaleConservationTrack)[]; -const ATTRIBUTES_THAT_TRIGGER_DATA_RESET: string[] = ["letter-order"] satisfies (keyof NightingaleConservationTrack)[]; +] satisfies (keyof NightingaleConservationTrack)[]); + +const ATTRIBUTES_THAT_TRIGGER_DATA_RESET = new Set([ + "letter-order", +] satisfies (keyof NightingaleConservationTrack)[]); /** Order of amino acids in a column (top-to-bottom) */ const AMINO_ACID_ORDER = Array.from("HYAVLIMFWSTQNKRDEPCG"); // Old Protvista order: HYSTQNAVLIMFWDEPKRCG @@ -80,9 +84,10 @@ interface YPositions { end: { [letter: string]: number[] }, } -type LetterOrder = "default" | "probability"; +const LetterOrders = ["default", "probability"] as const; +type LetterOrder = typeof LetterOrders[number]; -/** Type for `NightingaleConservationTrack.data`` */ +/** Type for `NightingaleConservationTrack.data` */ export interface SequenceConservationData { /** Sequence number for each position */ index: number[], @@ -104,8 +109,8 @@ export default class NightingaleConservationTrack extends withCanvas( ) ) { /** Order of amino acids within a column (top-to-bottom). default = fixed order based on amino acid groups, probability = on every position sort by descending probability */ - @property({ type: String }) - "letter-order": LetterOrder = "default"; + @property({ converter: EnumAttributeConverter(LetterOrders, "default") }) + "letter-order": LetterOrder; /** Font family for labels (can be a list of multiple font families separated by comma, like in CSS) */ @property({ type: String }) @@ -126,7 +131,8 @@ export default class NightingaleConservationTrack extends withCanvas( #data?: SequenceConservationData; private yPositions?: YPositions; - protected highlighted?: Selection; + protected svgHighlights?: Selection; + protected svgMargins?: Selection; override connectedCallback() { @@ -154,10 +160,10 @@ export default class NightingaleConservationTrack extends withCanvas( override attributeChangedCallback(name: string, oldValue: string | null, newValue: string | null): void { super.attributeChangedCallback(name, oldValue, newValue); - if (ATTRIBUTES_THAT_TRIGGER_DATA_RESET.includes(name)) { + if (ATTRIBUTES_THAT_TRIGGER_DATA_RESET.has(name)) { // Calling `this.data` setter to recompute `this.positions` and run `this.createTrack()` this.data = this.data; // eslint-disable-line no-self-assign - } else if (ATTRIBUTES_THAT_TRIGGER_REFRESH.includes(name)) { + } else if (ATTRIBUTES_THAT_TRIGGER_REFRESH.has(name)) { this.onDimensionsChange(); this.createTrack(); } @@ -174,19 +180,23 @@ export default class NightingaleConservationTrack extends withCanvas( .attr("height", this.height); if (this.svg) { // this check is necessary because `svg` setter does not always set this.bindEvents(this.svg); - this.highlighted = this.svg.append("g").attr("class", "highlighted"); + this.svgHighlights = this.svg.append("g").attr("class", "highlighted"); + this.svgMargins = this.svg.append("g").attr("class", "margin"); } } refresh() { this.requestDraw(); this.updateHighlight(); + this.updateMargins(); } private readonly _drawStamp = new Stamp(() => { const stamp: Record = { "data": this["data"], "canvasCtx": this["canvasCtx"], + "canvasWidth": this["width"], + "canvasHeight": this["height"], "canvasScale": this["canvasScale"], "display-start": this["display-start"], "display-end": this["display-end"], @@ -209,7 +219,6 @@ export default class NightingaleConservationTrack extends withCanvas( this.adjustCanvasCtxLogicalSize(); this.clearCanvas(); this.drawColumns(); - this.drawMargins(); } private clearCanvas() { @@ -270,25 +279,6 @@ export default class NightingaleConservationTrack extends withCanvas( } } - private drawMargins() { - const ctx = this.canvasCtx; - if (!ctx) return; - - const canvasWidth = ctx.canvas.width; - const canvasHeight = ctx.canvas.height; - const marginLeft = this["margin-left"] * this.canvasScale; - const marginRight = this["margin-right"] * this.canvasScale; - const marginTop = this["margin-top"] * this.canvasScale; - const marginBottom = this["margin-bottom"] * this.canvasScale; - - ctx.globalAlpha = 1; - ctx.fillStyle = this["margin-color"]; - ctx.fillRect(0, 0, marginLeft, canvasHeight); - ctx.fillRect(canvasWidth - marginRight, 0, marginRight, canvasHeight); - ctx.fillRect(marginLeft, 0, canvasWidth - marginLeft - marginRight, marginTop); - ctx.fillRect(marginLeft, canvasHeight - marginBottom, canvasWidth - marginLeft - marginRight, marginBottom); - } - private getFontOpacity(baseWidthInCss: number): number { if (baseWidthInCss < this["min-font-size"]) { return 0; @@ -300,8 +290,8 @@ export default class NightingaleConservationTrack extends withCanvas( } protected updateHighlight() { - if (!this.highlighted) return; - const highlights = this.highlighted + if (!this.svgHighlights) return; + const highlights = this.svgHighlights .selectAll("rect") .data(this.highlightedRegion.segments); @@ -318,6 +308,10 @@ export default class NightingaleConservationTrack extends withCanvas( highlights.exit().remove(); } + protected updateMargins() { + this.renderMarginOnGroup(this.svgMargins); + } + override zoomRefreshed() { super.zoomRefreshed(); if (this.getWidthWithMargins() > 0) this.refresh(); diff --git a/packages/nightingale-new-core/src/index.ts b/packages/nightingale-new-core/src/index.ts index 0631b9e5..5ee8e94d 100644 --- a/packages/nightingale-new-core/src/index.ts +++ b/packages/nightingale-new-core/src/index.ts @@ -7,6 +7,7 @@ export { default as withManager } from "./mixins/withManager/index"; export { default as withHighlight } from "./mixins/withHighlight/index"; export { default as withSVGHighlight } from "./mixins/withHighlight/SVG/index"; export { default as withZoom } from "./mixins/withZoom/index"; +export { EnumAttributeConverter, OptionalNumberAttributeConverter } from "./utils/attribute-converters"; export { default as bindEvents, createEvent } from "./utils/bindEvents"; export { contrastingColor, getColor } from "./utils/colors"; export { default as Region } from "./utils/Region"; diff --git a/packages/nightingale-new-core/src/mixins/withZoom/index.ts b/packages/nightingale-new-core/src/mixins/withZoom/index.ts index d070e3b8..49c604c6 100644 --- a/packages/nightingale-new-core/src/mixins/withZoom/index.ts +++ b/packages/nightingale-new-core/src/mixins/withZoom/index.ts @@ -42,7 +42,9 @@ export interface WithZoomInterface extends WithDimensionsInterface, withPosition zoomRefreshed(): void; } -const ATTRIBUTES_THAT_TRIGGER_REFRESH = ["length", "width", "height"]; +const ATTRIBUTES_THAT_TRIGGER_REFRESH = new Set([ + "length", "width", "height", +] satisfies (keyof WithZoomInterface)[]); const withZoom = >( @@ -71,7 +73,7 @@ const withZoom = >( super.attributeChangedCallback(name, oldValue, newValue); const newV = newValue === "null" ? null : newValue; if (oldValue !== newV) { - if (ATTRIBUTES_THAT_TRIGGER_REFRESH.includes(name)) { + if (ATTRIBUTES_THAT_TRIGGER_REFRESH.has(name)) { this.adjustZoomConstraints(); this.adjustZoom(); } diff --git a/packages/nightingale-new-core/src/utils/attribute-converters.ts b/packages/nightingale-new-core/src/utils/attribute-converters.ts new file mode 100644 index 00000000..c7487851 --- /dev/null +++ b/packages/nightingale-new-core/src/utils/attribute-converters.ts @@ -0,0 +1,69 @@ +import { ComplexAttributeConverter } from "lit"; + + +/** + * Attribute converter for attributes of type `number|undefined`. + * Attribute value empty string or `null` is interpreted as `undefined`; non-empty string is parsed as number. + * Example: + * + * ```ts + * class NightingaleExampleTrack { + * ;@property({ converter: OptionalNumberAttributeConverter }) + * "y-min"?: number; + * } + * ``` + * */ +export const OptionalNumberAttributeConverter: ComplexAttributeConverter = { + fromAttribute(str) { + if (!str) { // null or empty string + return undefined; + } + const value = Number(str); + if (isNaN(value)) { + console.warn(`Failed to parse numeric attribute value "${str}"`); + return undefined; + } + return value; + }, + toAttribute(value) { + if (value === undefined) { + return ""; + } else { + return String(value); + } + }, +} + +/** + * Create an attribute converter for attribute that allows a fixed set of string values. + * Passing an invalid value to the attribute will result in a warning and using the default value instead. + * Example: + * + * ```ts + * const Weekdays = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"] as const; + * type Weekday = typeof Weekdays[number]; + * + * class NightingaleExampleTrack { + * ;@property({ converter: EnumAttributeConverter(Weekdays, "Mon") }) + * day: Weekday; + * } + * ``` + * */ +export function EnumAttributeConverter(allowedValues: readonly T[], defaultValue: D): ComplexAttributeConverter { + return { + fromAttribute(str) { + if (!str) { // null or empty string + return defaultValue; + } + if (allowedValues.includes(str as T)) { // valid string + return str as T; + } else { // invalid string + console.warn(`Value "${str}" is not valid for attribute of type ${allowedValues.map(v => `"${v}"`).join(" | ")}. Falling back to default value ("${defaultValue}").`); + return defaultValue; + } + }, + toAttribute(value) { + return value; + }, + }; +} diff --git a/packages/nightingale-new-core/src/utils/bindEvents.ts b/packages/nightingale-new-core/src/utils/bindEvents.ts index 1be62db3..2719d274 100644 --- a/packages/nightingale-new-core/src/utils/bindEvents.ts +++ b/packages/nightingale-new-core/src/utils/bindEvents.ts @@ -1,6 +1,6 @@ -import NightingaleBaseElement from "../nightingale-base-element"; +import { BaseType, Selection } from "d3"; import { WithHighlightInterface } from "../mixins/withHighlight"; -import { Selection, BaseType } from "d3"; +import NightingaleBaseElement from "../nightingale-base-element"; export const HIGHLIGHT_EVENT = "highlight-event"; @@ -20,11 +20,42 @@ type SequenceBaseData = { position: number; aa: string; }; +type BoxplotData = { + type: "boxplot"; + /** Position in the sequence */ + position: number; + /** Data for the boxplot at this position from all datasets (one item for each dataset) */ + data: Array<{ + /** Common properties of the whole dataset */ + dataset: { + name: string; + color: string; + }; + /** Boxplot data at this position */ + datum: { + /** Position in the sequence */ + position: number; + /** All values of the independent variable at this position */ + values: Float32Array; + median: number; + boxLow: number; + boxHigh: number; + whiskerLow: number; + whiskerHigh: number; + minimum: number; + maximum: number; + outliersLow: Float32Array; + outliersHigh: Float32Array; + } | undefined; + }>; + /** Index into `data`, indicates which dataset is being pointed at */ + datasetIndex: number; +}; -type detailInterface = { +type EventDetail = { eventType: EventType; coords: null | [number, number]; - feature?: FeatureData | SequenceBaseData | null; + feature?: FeatureData | SequenceBaseData | BoxplotData | null; target?: HTMLElement; highlight?: string; selectedId?: string | null; @@ -33,7 +64,7 @@ type detailInterface = { export function createEvent( type: EventType, - feature: FeatureData | SequenceBaseData | null = null, + feature: FeatureData | SequenceBaseData | BoxplotData | null = null, withHighlight = false, withId = false, start?: number, @@ -48,7 +79,7 @@ export function createEvent( feature = (feature as FeatureData)?.feature || feature; } - const detail: detailInterface = { + const detail: EventDetail = { eventType: type, feature, target, diff --git a/packages/nightingale-new-core/src/utils/refresher.ts b/packages/nightingale-new-core/src/utils/refresher.ts index 210fc17f..a24b024d 100644 --- a/packages/nightingale-new-core/src/utils/refresher.ts +++ b/packages/nightingale-new-core/src/utils/refresher.ts @@ -64,14 +64,17 @@ export class Stamp { private currentStampValue: Record = {}; /** Update the current stamp value (using the return value of `stampFunction`). - * Return `true` if the stamp value has changed since the last `update`, `false` otherwise. + * Return an object with old and new stamp value and whether it has changed. * (Stamp value comparison is performed by shallow object comparison.) */ update() { const oldValue = this.currentStampValue; this.currentStampValue = this.stampFunction(); return { + /** The old stamp value (before the update) */ oldValue, + /** The new stamp value (after the update) */ newValue: this.currentStampValue, + /** Whether the stamp value has changed */ changed: !objectShallowEquals(oldValue, this.currentStampValue), }; } diff --git a/packages/nightingale-track-canvas/src/nightingale-track-canvas.ts b/packages/nightingale-track-canvas/src/nightingale-track-canvas.ts index 689b0ba4..f86fa645 100644 --- a/packages/nightingale-track-canvas/src/nightingale-track-canvas.ts +++ b/packages/nightingale-track-canvas/src/nightingale-track-canvas.ts @@ -14,6 +14,8 @@ export default class NightingaleTrackCanvas extends withCanvas(NightingaleTrack) /** Feature fragments, stored in a data structure for fast range queries */ private fragmentCollection?: RangeCollection; + protected svgMargins?: Selection; + protected override createTrack() { if (this.svg) { this.svg.selectAll("g").remove(); @@ -26,12 +28,14 @@ export default class NightingaleTrackCanvas extends withCanvas(NightingaleTrack) if (this.svg) { // this check is necessary because `svg` setter does not always set this.bindEvents(this.svg); this.highlighted = this.svg.append("g").attr("class", "highlighted"); + this.svgMargins = this.svg.append("g").attr("class", "margin"); } } override refresh() { super.refresh(); this.requestDraw(); + this.updateMargins(); } override render() { @@ -135,18 +139,10 @@ export default class NightingaleTrackCanvas extends withCanvas(NightingaleTrack) } } } + } - // Draw margins - ctx.globalAlpha = 1; - ctx.fillStyle = this["margin-color"]; - const marginLeft = this["margin-left"] * scale; - const marginRight = this["margin-right"] * scale; - const marginTop = this["margin-top"] * scale; - const marginBottom = this["margin-bottom"] * scale; - ctx.fillRect(0, 0, marginLeft, canvasHeight); - ctx.fillRect(canvasWidth - marginRight, 0, marginRight, canvasHeight); - ctx.fillRect(marginLeft, 0, canvasWidth - marginLeft - marginRight, marginTop); - ctx.fillRect(marginLeft, canvasHeight - marginBottom, canvasWidth - marginLeft - marginRight, marginBottom); + protected updateMargins() { + this.renderMarginOnGroup(this.svgMargins); } private _unknownShapeWarningPrinted = new Set(); diff --git a/packages/nightingale-track/src/nightingale-track.ts b/packages/nightingale-track/src/nightingale-track.ts index 33b0b06a..1c8eb4da 100644 --- a/packages/nightingale-track/src/nightingale-track.ts +++ b/packages/nightingale-track/src/nightingale-track.ts @@ -52,7 +52,10 @@ export type Feature = { }; // TODO: height is not triggering a full redrawn when is changed after first render -const ATTRIBUTES_THAT_TRIGGER_REFRESH = ["length", "width", "height"]; +const ATTRIBUTES_THAT_TRIGGER_REFRESH = new Set([ + "length", "width", "height", + "margin-top", "margin-bottom", "margin-left", "margin-right", "margin-color", +] satisfies (keyof NightingaleTrack)[]); @customElementOnce("nightingale-track") class NightingaleTrack extends withManager( @@ -173,10 +176,7 @@ class NightingaleTrack extends withManager( newValue: string | null ): void { super.attributeChangedCallback(name, oldValue, newValue); - if ( - ATTRIBUTES_THAT_TRIGGER_REFRESH.includes(name) || - name.startsWith("margin-") - ) { + if (ATTRIBUTES_THAT_TRIGGER_REFRESH.has(name)) { this.applyFilters(); this.layoutObj = this.getLayout(); this.createTrack(); diff --git a/stories/18.NightingaleTrackCanvas/NightingaleTrackCanvas.stories.ts b/stories/18.NightingaleTrackCanvas/NightingaleTrackCanvas.stories.ts index 4e178eb3..c34b0cf7 100644 --- a/stories/18.NightingaleTrackCanvas/NightingaleTrackCanvas.stories.ts +++ b/stories/18.NightingaleTrackCanvas/NightingaleTrackCanvas.stories.ts @@ -13,8 +13,8 @@ const DefaultArgs = { "highlight-event": "onmouseover", // "onmouseover"|"onclick" "highlight-color": "#EB3BFF22", "margin-color": "#ffffffdd", // "transparent" - "margin-left": "10", - "margin-right": "10", + "margin-left": 10, + "margin-right": 10, "layout": "non-overlapping", // "default"|"non-overlapping" }; type Args = typeof DefaultArgs; diff --git a/stories/22.NightingaleBoxplotTrack/NightingaleBoxplotTrack.stories.mdx b/stories/22.NightingaleBoxplotTrack/NightingaleBoxplotTrack.stories.mdx new file mode 100644 index 00000000..0cfc0d0d --- /dev/null +++ b/stories/22.NightingaleBoxplotTrack/NightingaleBoxplotTrack.stories.mdx @@ -0,0 +1,35 @@ +import { Meta, Description } from "@storybook/addon-docs"; + +import Readme from "../../packages/nightingale-boxplot-track/README.md"; +import ParentReadme from "../../packages/nightingale-new-core/src/nightingale-base-element.md"; +import dimensionsReadme from "../../packages/nightingale-new-core/src/mixins/withDimensions/README.md"; +import highlightReadme from "../../packages/nightingale-new-core/src/mixins/withHighlight/README.md"; +import managerReadme from "../../packages/nightingale-new-core/src/mixins/withManager/README.md"; +import marginReadme from "../../packages/nightingale-new-core/src/mixins/withMargin/README.md"; +import positionReadme from "../../packages/nightingale-new-core/src/mixins/withPosition/README.md"; +import resizableReadme from "../../packages/nightingale-new-core/src/mixins/withResizable/README.md"; +import zoomReadme from "../../packages/nightingale-new-core/src/mixins/withZoom/README.md"; + + + +{Readme} + +
+ +

Mixins

+ +

+ This component implement the following mixins, and therefore it includes their + attributes and properties. +

+{dimensionsReadme} +{highlightReadme} +{managerReadme} +{marginReadme} +{positionReadme} +{resizableReadme} +{zoomReadme} + +

Parent Class

+ +{ParentReadme} diff --git a/stories/22.NightingaleBoxplotTrack/NightingaleBoxplotTrack.stories.ts b/stories/22.NightingaleBoxplotTrack/NightingaleBoxplotTrack.stories.ts new file mode 100644 index 00000000..e3cde73e --- /dev/null +++ b/stories/22.NightingaleBoxplotTrack/NightingaleBoxplotTrack.stories.ts @@ -0,0 +1,231 @@ +import { type createEvent } from "@nightingale-elements/nightingale-new-core"; +import { type ArgTypes, Meta, Story } from "@storybook/web-components"; +import { html } from "lit-html"; +import "../../packages/nightingale-boxplot-track/src/index"; +import NightingaleBoxplotTrack, { type BoxplotData, ZoomedOutOutlineOptions } from "../../packages/nightingale-boxplot-track/src/nightingale-boxplot-track"; +import sampleBoxplotData from "../../packages/nightingale-boxplot-track/tests/mockData/sample-1.json"; + + +export default { title: "Components/Tracks/Boxplot Track" } as Meta; + + +const DefaultArgs = { + "min-width": 400, + "height": 200, + "highlight-event": "onmouseover", + "highlight-color": "#EB3BFF22", + "margin-color": "#ffffffdd", + "margin-left": 30, + "show-axis": true, + "y-min": 0 as number | undefined, + "y-max": undefined as number | undefined, + "show-nested-highlights": true, + "column-gap": 0.2, + "box-gap": 0.1, + "whisker-width": 0.6, + "outlier-jitter-width": 0.4, + "outlier-radius": 2, + "zoom-transition-range": "4-5", + "zoomed-out-outline": "whiskers", + "tooltips": true, +}; +type Args = typeof DefaultArgs; + +const ArgumentTypes: Partial> = { + "highlight-event": { control: "select", options: ["onmouseover", "onclick"] }, + "y-min": { control: "select", options: [undefined, 0, 100, 200, 300, 400, 500] }, + "y-max": { control: "select", options: [undefined, 0, 100, 200, 300, 400, 500] }, + "column-gap": { control: { type: "number", min: 0, max: 1, step: 0.05 } }, + "box-gap": { control: { type: "number", min: 0, max: 1, step: 0.05 } }, + "whisker-width": { control: { type: "number", min: 0, max: 1, step: 0.05 } }, + "outlier-jitter-width": { control: { type: "number", min: 0, max: 1, step: 0.05 } }, + "outlier-radius": { control: { type: "number", min: 0, step: 0.5 } }, + "zoomed-out-outline": { control: "select", options: ZoomedOutOutlineOptions }, +}; + + +const nDataRepeat = 82; +const sampleSequence = "MALYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIA".repeat(nDataRepeat); + +function prepareBoxplotData(data: { positions: { position: number, values: number[] }[] }): BoxplotData { + const positions = data.positions.map(pos => ({ position: pos.position, values: pos.values.sort() })); // Sorting values for each position is not required by the boxplot track, but can make loading faster. + const shift = data.positions.length; + for (let i = 1; i < nDataRepeat; i++) { + for (const pos of data.positions) { + positions.push({ position: pos.position + i * shift, values: pos.values }); + } + } + + return [ + { + name: "Data1", + color: "#0088ff", + positions: positions, + }, + { + name: "Data2", + color: "#ff8800", + // Dummy data: multiply Data1 by a constant and remove some positions + positions: remove(positions.map(pos => ({ ...pos, values: pos.values.map(v => v * 0.8) })), 1, 7, 8, 9, 10), + }, + ]; +} + +function remove(arr: T[], ...indices: number[]) { + const out = arr.slice(); + for (const i of indices.sort((a, b) => b - a)) { + out.splice(i, 1); + } + return out; +} + + +function nightingaleNavigation(args: Args & { length: number }) { + return html` +
+
+ + +
`; +} + +function nightingaleSequence(args: Args & { length: number }) { + const sequence = sampleSequence; + return html` +
+
+ + +
`; +} + +function nightingaleBoxplotTrack(args: Args & { length: number, id: number }) { + return html` +
+
Boxplot
+ + +
`; +} + +/** This is a demonstration of how to use CustomEvents on the `nightingale-boxplot-track` component. Tooltips are not a part of the component. */ +function handleTooltip(event: CustomEvent, args: Args) { + if (event.detail.eventType !== "mouseover" && event.detail.eventType !== "mouseout") return; + + type EventFeatureData = Parameters[1]; + const feature: EventFeatureData = event.detail.feature; + let tooltipDiv = document.getElementById("boxplot-tooltip"); + if (args.tooltips && feature && "type" in feature && feature.type === "boxplot") { + // Show tooltip + if (!tooltipDiv) { + tooltipDiv = document.createElement("div"); + tooltipDiv.id = "boxplot-tooltip"; + Object.assign(tooltipDiv.style, { position: "fixed", background: "white", border: "1px solid #ccc", padding: "6px", boxShadow: "0 2px 6px rgba(0,0,0,0.2)", zIndex: "666", fontFamily: "sans-serif", fontSize: "12px" }); + document.body.appendChild(tooltipDiv); + } + const rows = [ + ["", ...feature.data.map(d => `${d.dataset.name}`)], + ["nDatapoints", ...feature.data.map(d => d.datum?.values.length ?? "-")], + ["nOutliers", ...feature.data.map(d => d.datum?.outliersHigh && d.datum?.outliersLow ? d.datum.outliersHigh.length + d.datum.outliersLow.length : "-")], + ["Max", ...feature.data.map(d => d.datum?.maximum.toFixed(2) ?? "-")], + ["Median", ...feature.data.map(d => d.datum?.median.toFixed(2) ?? "-")], + ["Min", ...feature.data.map(d => d.datum?.minimum.toFixed(2) ?? "-")], + ]; + const rowsHtml = rows.map((row) => `${row.map((cell, index) => `${cell}`).join("")}`).join(""); + tooltipDiv.innerHTML = `${rowsHtml}
`; + const mouseEvent: MouseEvent | undefined = event.detail.parentEvent; + tooltipDiv.style.left = `${(mouseEvent?.clientX ?? 0) + 10}px`; + tooltipDiv.style.top = `${(mouseEvent?.clientY ?? 0) + 10}px`; + } else { + // Hide tooltip + tooltipDiv?.remove(); + } +} + + +function makeStory(options: { length: number }): Story { + let currentArgs: Args = { ...DefaultArgs }; + + const template: Story = (args: Args) => { + currentArgs = args; + return html` + + Use Ctrl+scroll to zoom. +
+ + +
+ ${nightingaleNavigation({ ...args, length: options.length })} + ${nightingaleSequence({ ...args, length: options.length })} + ${nightingaleBoxplotTrack({ ...args, length: options.length, id: 0 })} +
+ +
`; + } + + const story: Story = template.bind({}); + story.args = { ...DefaultArgs }; + story.argTypes = ArgumentTypes; + const boxplotData = prepareBoxplotData(sampleBoxplotData as any); + + story.play = async () => { + await customElements.whenDefined("nightingale-boxplot-track"); + for (const track of document.getElementsByTagName("nightingale-boxplot-track")) { + (track as NightingaleBoxplotTrack).data = boxplotData; + track.addEventListener("change", event => handleTooltip(event as CustomEvent, currentArgs)); + } + }; + return story; +} + + +export const Boxplot = makeStory({ + length: sampleSequence.length, +});