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5F9992021-01-25 00:00:002021-01-27 00:00:00hospitaliséssupprimé1
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STAY_IDt_startt_endSTAY_DURATION
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Measurement
Measurement
Joining
Joining
BioClean Table
BioClean Table
Concept_Relationship
Concept_Relationship
Concept
Concept
Concepts-sets
Concepts-sets
Joining
Joining
Extract concepts-sets
Extract concepts-sets
Input data in OMOP format
Input data in OMOP format
Editable data available in the library
Editable data available in the lib...
Output data
Output data
Data transformation
Data transformation
Select default concepts-set
Select default concepts-set
Create new concepts-set
Create new concepts-set
Edit default concepts-set
Edit default concepts-set
Normalize units
Normalize units
Detect outliers
Detect outliers
Configuration
Configuration
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Select default configuration
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Create new conguration
Edit default configuration
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Inuput data in OMOP format
Inuput data in OMOP format
Editable data available in the library
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Input data in OMOP format
Input data in OMOP format
Editable data available in the library
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Input data in OMOP format
Input data in OMOP format
Editable data available in the library
Editable data available in the lib...Text is not SVG - cannot display diff --git a/main/_static/biology/prepare_measurement_flowchart.drawio.svg b/main/_static/biology/prepare_measurement_flowchart.drawio.svg new file mode 100644 index 00000000..dfc3db28 --- /dev/null +++ b/main/_static/biology/prepare_measurement_flowchart.drawio.svg @@ -0,0 +1,4 @@ + + + +
measurement
measurement
measurement
measurement
concept
concept
prepare_relationship_table
prepare_relationship_table
concept relationship
concept relationship
ConceptSet
ConceptSet
relationship
relationship

filter_measurement_valid

filter_measurement_by_date

tag_measurement_anomaly

normalize_unit


filter_measurement_valid...
join
join

OMOP



OMOP...

python

python

operation

operation
convert_units
convert_units

function

function diff --git a/main/_static/biology/viz/interactive_distribution.html b/main/_static/biology/viz/interactive_distribution.html new file mode 100644 index 00000000..ea4aa9fe --- /dev/null +++ b/main/_static/biology/viz/interactive_distribution.html @@ -0,0 +1,38 @@ + + + + + + + + + + + +
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+ 1511828488192 + 14749-6 + A8424 + Glucose [Moles/Volume] Serum/Plasma - Numerique + Glucose_Plasma FNA_mmol/L + mmol/l + 103334.0 + 6.667987 + 3.790141 + 0.1 + 4.8 + 5.6 + 7.3 + 160.9 + 1.48 + 11.52 + 0.0 + ALL
+

+ + + diff --git a/main/_static/biology/vocabulary_flowchart.svg b/main/_static/biology/vocabulary_flowchart.svg new file mode 100644 index 00000000..10516a6c --- /dev/null +++ b/main/_static/biology/vocabulary_flowchart.svg @@ -0,0 +1,4 @@ + + + +
LIMS
LIMS
Source vocabulary
Source vocabulary
ATHENA (OHDSI)
ATHENA (OHDSI)
Standard vocabulary
Standard vocabulary
Maps to
Maps to
Has parent item
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+ border-radius: .1rem; + display: block; + margin: 0; + padding: .8rem; + transition: border .25s,box-shadow .25s; +} + +.md-typeset .card-set > .card-content:focus-within, +.md-typeset .card-set > .card-content:hover, +.md-typeset .card-set > .card-content:focus-within, +.md-typeset .card-set > .card-content:hover, +.md-typeset .grid > .card:focus-within, +.md-typeset .grid > .card:hover { + border-color: #0000; + box-shadow: var(--md-shadow-z2); +} + +.md-typeset .card-set > .card-content > hr, +.md-typeset .card-set > .card-content > hr, +.md-typeset .grid > .card > hr { + margin-bottom: 1em; + margin-top: 1em; +} + +.md-typeset .card-set > .card-content > :first-child, +.md-typeset .card-set > .card-content > :first-child, +.md-typeset .grid > .card > :first-child { + margin-top: 0; +} + +.md-typeset .card-set > .card-content > :last-child, +.md-typeset .card-set > .card-content > :last-child, +.md-typeset .grid > .card > :last-child { + margin-bottom: 0; +} + +.md-typeset .card-set > *, +.md-typeset .card-set > .admonition, +.md-typeset .card-set > .highlight > *, +.md-typeset .card-set > .highlighttable, +.md-typeset .card-set > .md-typeset details, +.md-typeset .card-set > details, +.md-typeset .card-set > pre { + margin-bottom: 0; + margin-top: 0; +} + +.md-typeset .card-set > .highlight > pre:only-child, +.md-typeset .card-set > .highlight > pre > code, +.md-typeset .card-set > .highlighttable, +.md-typeset .card-set > .highlighttable > tbody, +.md-typeset .card-set > .highlighttable > tbody > tr, +.md-typeset .card-set > .highlighttable > tbody > tr > .code, +.md-typeset .card-set > .highlighttable > tbody > tr > .code > .highlight, +.md-typeset .card-set > .highlighttable > tbody > tr > .code > .highlight > pre, +.md-typeset .card-set > .highlighttable > tbody > tr > .code > .highlight > pre > code { + height: 100%; +} diff --git a/main/_static/chat_badge.svg b/main/_static/chat_badge.svg new file mode 100644 index 00000000..f08e41ed --- /dev/null +++ b/main/_static/chat_badge.svg @@ -0,0 +1 @@ +chat: ZulipchatZulip diff --git a/main/_static/extra.css b/main/_static/extra.css new file mode 100644 index 00000000..07017a6a --- /dev/null +++ b/main/_static/extra.css @@ -0,0 +1,74 @@ +.md-header__button.md-logo :-webkit-any(img, svg) { + height: 2.5rem; +} + +:root { + --md-admonition-icon--aphp: url('data:image/svg+xml;charset=utf-8,') + } + .md-typeset .admonition.aphp, + .md-typeset details.aphp { + border-color: rgb(0, 107, 182); + } + .md-typeset .aphp > .admonition-title, + .md-typeset .aphp > summary { + background-color: rgba(0, 107, 182, 0.1); + border-color: rgb(0, 107, 182); + } + .md-typeset .aphp > .admonition-title::before, + .md-typeset .aphp > summary::before { + background-color:rgb(0, 107, 182); + -webkit-mask-image: var(--md-admonition-icon--aphp); + mask-image: var(--md-admonition-icon--aphp); + } + +:root { + --md-admonition-icon--edsnlp: url('data:image/svg+xml;charset=utf-8,EDSNLP') + } + .md-typeset .admonition.edsnlp, + .md-typeset details.edsnlp { + border-color: rgb(0, 107, 182); + } + .md-typeset .edsnlp > .admonition-title, + .md-typeset .edsnlp > summary { + background-color: rgba(0, 107, 182, 0.1); + border-color: rgb(0, 107, 182); + } + .md-typeset .edsnlp > .admonition-title::before, + .md-typeset .edsnlp > summary::before { + background-color:rgb(0, 107, 182); + -webkit-mask-image: var(--md-admonition-icon--edsnlp); + mask-image: var(--md-admonition-icon--edsnlp); + } + +:root { + --md-admonition-icon--algos: url('data:image/svg+xml;charset=utf-8,') + } + .md-typeset .admonition.algos, + .md-typeset details.algos { + border-color: rgb(32, 3, 136); + } + .md-typeset .algos > .admonition-title, + .md-typeset .algos > summary { + background-color: rgba(32, 3, 136, 0.1); + border-color: rgb(32, 3, 136); + } + .md-typeset .algos > .admonition-title::before, + .md-typeset .algos > summary::before { + background-color:rgb(32, 3, 136); + -webkit-mask-image: var(--md-admonition-icon--algos); + mask-image: var(--md-admonition-icon--algos); + } + +[data-md-color-scheme="default"] { +--md-primary-fg-color: #006bb6; +--md-primary-fg-color--light: #006bb6; +--md-accent-fg-color: #006bb6; +--md-accent-fg-color--light: #006bb6; +} + +[data-md-color-scheme="slate"] { +--md-primary-fg-color: #006bb6; +--md-primary-fg-color--dark: #006bb6; +--md-accent-fg-color: #006bb6; +--md-accent-fg-color--light: #006bb6; +} diff --git a/main/_static/extra.js b/main/_static/extra.js new file mode 100644 index 00000000..56a82d0f --- /dev/null +++ b/main/_static/extra.js @@ -0,0 +1,19 @@ +const dfs = document.querySelectorAll('.dataframe'); + +// dfs.forEach(df => { +// elem = df; +// parent = df.parentElement + +// while (elem.offsetWidth == parent.offsetWidth) { +// elem = parent +// parent = parent.parentElement +// } +// parent.style.overflowX = 'auto'; +// }); + + +dfs.forEach(df => { + elem = df; + elem.style.overflowX = 'auto'; + elem.style.display = "block"; +}); diff --git a/main/_static/flowchart/criterion.png b/main/_static/flowchart/criterion.png new file mode 100644 index 00000000..ff330949 Binary files /dev/null and b/main/_static/flowchart/criterion.png differ diff --git a/main/_static/flowchart/split.png b/main/_static/flowchart/split.png new file mode 100644 index 00000000..192e1608 Binary files /dev/null and b/main/_static/flowchart/split.png differ diff --git a/main/_static/introduction_image.svg b/main/_static/introduction_image.svg new file mode 100644 index 00000000..f56c9e87 --- /dev/null +++ b/main/_static/introduction_image.svg @@ -0,0 +1 @@ +Age range0%5%10%15%20%25%30%35%40%45%50%% of patients who went through ICU.(0, 40](40, 50](50, 60](60, 70](70, 120]ControlDiab.ControlDiab.ControlDiab.ControlDiab.ControlDiab.ControlDiab.CohortPercentage of patients who went through ICU during their COVID stay,as a function of their age range and diabetic status diff --git a/main/_static/question.png b/main/_static/question.png new file mode 100644 index 00000000..9c05a422 Binary files /dev/null and b/main/_static/question.png differ diff --git a/main/_static/scikit_logo.png b/main/_static/scikit_logo.png new file mode 100644 index 00000000..6c74ddd0 Binary files /dev/null and b/main/_static/scikit_logo.png differ diff --git a/main/_static/scikit_logo.svg b/main/_static/scikit_logo.svg new file mode 100644 index 00000000..6c6977b0 --- /dev/null +++ b/main/_static/scikit_logo.svg @@ -0,0 +1,49 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/main/_static/scikit_logo_text.png b/main/_static/scikit_logo_text.png new file mode 100644 index 00000000..3264eab2 Binary files /dev/null and b/main/_static/scikit_logo_text.png differ diff --git a/main/_static/scikit_logo_text.svg b/main/_static/scikit_logo_text.svg new file mode 100644 index 00000000..bb96bc85 --- /dev/null +++ b/main/_static/scikit_logo_text.svg @@ -0,0 +1,32 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +eds-​scikit + diff --git a/main/_static/suggestion.png b/main/_static/suggestion.png new file mode 100644 index 00000000..d770deb5 Binary files /dev/null and b/main/_static/suggestion.png differ diff --git a/main/_static/termynal/termynal.css b/main/_static/termynal/termynal.css new file mode 100644 index 00000000..affc90e3 --- /dev/null +++ b/main/_static/termynal/termynal.css @@ -0,0 +1,132 @@ +/** + * termynal.js + * + * @author Ines Montani + * @version 0.0.1 + * @license MIT + * + * Modified version from https://github.com/tiangolo/typer + */ + +:root { + --color-bg: #252a33; + --color-text: #eee; + --color-text-subtle: #a2a2a2; +} + +[data-termynal] { + width: auto; + max-width: 100%; + background: var(--color-bg); + color: var(--color-text); + font-size: 18px; + /* font-family: 'Fira Mono', Consolas, Menlo, Monaco, 'Courier New', Courier, monospace; */ + font-family: 'Roboto Mono', 'Fira Mono', Consolas, Menlo, Monaco, 'Courier New', Courier, monospace; + border-radius: 4px; + padding: 75px 45px 35px; + position: relative; + -webkit-box-sizing: border-box; + box-sizing: border-box; +} + +[data-termynal]:before { + content: ''; + position: absolute; + top: 15px; + left: 15px; + display: inline-block; + width: 15px; + height: 15px; + border-radius: 50%; + /* A little hack to display the window buttons in one pseudo element. */ + background: #d9515d; + -webkit-box-shadow: 25px 0 0 #f4c025, 50px 0 0 #3ec930; + box-shadow: 25px 0 0 #f4c025, 50px 0 0 #3ec930; +} + +[data-termynal]:after { + content: 'bash'; + position: absolute; + color: var(--color-text-subtle); + top: 5px; + left: 0; + width: 100%; + text-align: center; +} + +a[data-terminal-control] { + text-align: right; + display: block; + color: #aebbff; +} + +[data-terminal-copy] { + text-align: right; + position: absolute; + top: 5px; + right: 5px; +} + +[data-terminal-copy].md-icon { + color: #aebbff; +} + +[data-ty] { + display: block; + line-height: 2; +} + +[data-ty]:before { + /* Set up defaults and ensure empty lines are displayed. */ + content: ''; + display: inline-block; + vertical-align: middle; +} + +[data-ty="input"]:before, +[data-ty-prompt]:before { + margin-right: 0.75em; + color: var(--color-text-subtle); +} + +[data-ty="input"]:before { + content: '$'; +} + +[data-ty][data-ty-prompt]:before { + content: attr(data-ty-prompt); +} + +[data-ty-cursor]:after { + content: attr(data-ty-cursor); + font-family: monospace; + margin-left: 0.5em; + -webkit-animation: blink 1s infinite; + animation: blink 1s infinite; +} + + +/* Cursor animation */ + +@-webkit-keyframes blink { + 50% { + opacity: 0; + } +} + +@keyframes blink { + 50% { + opacity: 0; + } +} + +/* tooltip */ + +[data-md-state="open"] { + transform: translateY(0); + opacity: 1; + transition: + transform 400ms cubic-bezier(0.075, 0.85, 0.175, 1), + opacity 400ms; + pointer-events: initial; +} diff --git a/main/_static/termynal/termynal.js b/main/_static/termynal/termynal.js new file mode 100644 index 00000000..8a572449 --- /dev/null +++ b/main/_static/termynal/termynal.js @@ -0,0 +1,411 @@ +/** + * termynal.js + * A lightweight, modern and extensible animated terminal window, using + * async/await. + * + * @author Ines Montani + * @version 0.0.1 + * @license MIT + * + * Modified version from https://github.com/tiangolo/typer + * + */ + +'use strict'; + +/** Generate a terminal widget. */ +class Termynal { + /** + * Construct the widget's settings. + * @param {(string|Node)=} container - Query selector or container element. + * @param {Object=} options - Custom settings. + * @param {string} options.prefix - Prefix to use for data attributes. + * @param {number} options.startDelay - Delay before animation, in ms. + * @param {number} options.typeDelay - Delay between each typed character, in ms. + * @param {number} options.lineDelay - Delay between each line, in ms. + * @param {number} options.progressLength - Number of characters displayed as progress bar. + * @param {string} options.progressChar – Character to use for progress bar, defaults to █. + * @param {number} options.progressPercent - Max percent of progress. + * @param {string} options.cursor – Character to use for cursor, defaults to ▋. + * @param {Object[]} lineData - Dynamically loaded line data objects. + * @param {boolean} options.noInit - Don't initialise the animation. + */ + constructor(container = '#termynal', options = {}) { + this.container = (typeof container === 'string') ? document.querySelector(container) : container; + this.pfx = `data-${options.prefix || 'ty'}`; + this.originalStartDelay = this.startDelay = options.startDelay + || parseFloat(this.container.getAttribute(`${this.pfx}-startDelay`)) || 600; + this.originalTypeDelay = this.typeDelay = options.typeDelay + || parseFloat(this.container.getAttribute(`${this.pfx}-typeDelay`)) || 50; + this.originalLineDelay = this.lineDelay = options.lineDelay + || parseFloat(this.container.getAttribute(`${this.pfx}-lineDelay`)) || 500; + this.progressLength = options.progressLength + || parseFloat(this.container.getAttribute(`${this.pfx}-progressLength`)) || 40; + this.progressChar = options.progressChar + || this.container.getAttribute(`${this.pfx}-progressChar`) || '█'; + this.progressPercent = options.progressPercent + || parseFloat(this.container.getAttribute(`${this.pfx}-progressPercent`)) || 100; + this.cursor = options.cursor + || this.container.getAttribute(`${this.pfx}-cursor`) || '▋'; + this.lineData = this.lineDataToElements(options.lineData || []); + this.loadLines() + if (!options.noInit) this.init() + } + + loadLines() { + // Load all the lines and create the container so that the size is fixed + // Otherwise it would be changing and the user viewport would be constantly + // moving as she/he scrolls + const finish = this.generateFinish() + finish.style.visibility = 'hidden' + this.container.appendChild(finish) + // Appends dynamically loaded lines to existing line elements. + this.lines = [...this.container.querySelectorAll(`[${this.pfx}]`)].concat(this.lineData); + for (let line of this.lines) { + line.style.visibility = 'hidden' + this.container.appendChild(line) + } + const restart = this.generateRestart() + restart.style.visibility = 'hidden' + this.container.appendChild(restart) + this.container.setAttribute('data-termynal', ''); + } + + /** + * Initialise the widget, get lines, clear container and start animation. + */ + init() { + /** + * Calculates width and height of Termynal container. + * If container is empty and lines are dynamically loaded, defaults to browser `auto` or CSS. + */ + const containerStyle = getComputedStyle(this.container); + this.container.style.width = containerStyle.width !== '0px' ? + containerStyle.width : undefined; + this.container.style.minHeight = containerStyle.height !== '0px' ? + containerStyle.height : undefined; + + this.container.setAttribute('data-termynal', ''); + this.container.innerHTML = ''; + for (let line of this.lines) { + line.style.visibility = 'visible' + } + this.start(); + } + + + /** + * Start the animation and rener the lines depending on their data attributes. + */ + async start() { + this.addCopy() + this.addFinish() + await this._wait(this.startDelay); + + for (let line of this.lines) { + const type = line.getAttribute(this.pfx); + const delay = line.getAttribute(`${this.pfx}-delay`) || this.lineDelay; + + if (type == 'input') { + line.setAttribute(`${this.pfx}-cursor`, this.cursor); + await this.type(line); + await this._wait(delay); + } + + else if (type == 'progress') { + await this.progress(line); + await this._wait(delay); + } + + else { + this.container.appendChild(line); + await this._wait(delay); + } + + line.removeAttribute(`${this.pfx}-cursor`); + } + this.addRestart() + this.finishElement.style.visibility = 'hidden' + this.lineDelay = this.originalLineDelay + this.typeDelay = this.originalTypeDelay + this.startDelay = this.originalStartDelay + } + + generateRestart() { + const restart = document.createElement('a') + restart.onclick = (e) => { + e.preventDefault() + this.container.innerHTML = '' + this.init() + } + restart.href = '#' + restart.setAttribute('data-terminal-control', '') + restart.innerHTML = "restart ↻" + return restart + } + + generateCopy() { + var dialog = document.getElementsByClassName('md-dialog')[0] + var dialog_text = document.getElementsByClassName('md-dialog__inner md-typeset')[0] + const copy = document.createElement('a') + copy.classList.add("md-clipboard") + copy.classList.add("md-icon") + copy.onclick = (e) => { + e.preventDefault() + var command = '' + for (let line of this.lines) { + if (line.getAttribute("data-ty") == 'input') { + command = command + line.innerHTML + '\n' + } + } + navigator.clipboard.writeText(command) + dialog.setAttribute('data-md-state', 'open'); + dialog_text.innerText = 'Copied to clipboard'; + + setTimeout(function () { + dialog.removeAttribute('data-md-state'); + }, 2000); + } + copy.setAttribute('data-terminal-copy', '') + return copy + } + + generateFinish() { + const finish = document.createElement('a') + finish.onclick = (e) => { + e.preventDefault() + this.lineDelay = 0 + this.typeDelay = 0 + this.startDelay = 0 + } + finish.href = '#' + finish.setAttribute('data-terminal-control', '') + finish.innerHTML = "fast →" + this.finishElement = finish + return finish + } + + addRestart() { + const restart = this.generateRestart() + this.container.appendChild(restart) + } + + addFinish() { + const finish = this.generateFinish() + this.container.appendChild(finish) + } + + addCopy() { + let copy = this.generateCopy() + this.container.appendChild(copy) + } + + /** + * Animate a typed line. + * @param {Node} line - The line element to render. + */ + async type(line) { + const chars = [...line.textContent]; + line.textContent = ''; + this.container.appendChild(line); + + for (let char of chars) { + const delay = line.getAttribute(`${this.pfx}-typeDelay`) || this.typeDelay; + await this._wait(delay); + line.textContent += char; + } + } + + /** + * Animate a progress bar. + * @param {Node} line - The line element to render. + */ + async progress(line) { + const progressLength = line.getAttribute(`${this.pfx}-progressLength`) + || this.progressLength; + const progressChar = line.getAttribute(`${this.pfx}-progressChar`) + || this.progressChar; + const chars = progressChar.repeat(progressLength); + const progressPercent = line.getAttribute(`${this.pfx}-progressPercent`) + || this.progressPercent; + line.textContent = ''; + this.container.appendChild(line); + + for (let i = 1; i < chars.length + 1; i++) { + await this._wait(this.typeDelay) / 4; + const percent = Math.round(i / chars.length * 100); + line.textContent = `${chars.slice(0, i)} ${percent}%`; + if (percent > progressPercent) { + break; + } + } + } + + /** + * Helper function for animation delays, called with `await`. + * @param {number} time - Timeout, in ms. + */ + _wait(time) { + return new Promise(resolve => setTimeout(resolve, time)); + } + + /** + * Converts line data objects into line elements. + * + * @param {Object[]} lineData - Dynamically loaded lines. + * @param {Object} line - Line data object. + * @returns {Element[]} - Array of line elements. + */ + lineDataToElements(lineData) { + return lineData.map(line => { + let div = document.createElement('div'); + div.innerHTML = `${line.value || ''}`; + + return div.firstElementChild; + }); + } + + /** + * Helper function for generating attributes string. + * + * @param {Object} line - Line data object. + * @returns {string} - String of attributes. + */ + _attributes(line) { + let attrs = ''; + for (let prop in line) { + // Custom add class + if (prop === 'class') { + attrs += ` class=${line[prop]} ` + continue + } + if (prop === 'type') { + attrs += `${this.pfx}="${line[prop]}" ` + } else if (prop !== 'value') { + attrs += `${this.pfx}-${prop}="${line[prop]}" ` + } + } + + return attrs; + } +} + +/** +* HTML API: If current script has container(s) specified, initialise Termynal. +*/ +if (document.currentScript.hasAttribute('data-termynal-container')) { + const containers = document.currentScript.getAttribute('data-termynal-container'); + containers.split('|') + .forEach(container => new Termynal(container)) +} + +document.querySelectorAll(".use-termynal").forEach(node => { + node.style.display = "block"; + new Termynal(node, { + lineDelay: 500 + }); +}); +const progressLiteralStart = "---> 100%"; +const promptLiteralStart = "$ "; +const customPromptLiteralStart = "$* "; +const commentPromptLiteralStart = "# "; +const colorOutputLiteralStart = "color:"; +const termynalActivateClass = "termy"; +let termynals = []; + +function createTermynals() { + document + .querySelectorAll(`.${termynalActivateClass} .highlight`) + .forEach(node => { + const text = node.textContent; + const lines = text.split("\n"); + const useLines = []; + let buffer = []; + function saveBuffer() { + if (buffer.length) { + let isBlankSpace = true; + buffer.forEach(line => { + if (line) { + isBlankSpace = false; + } + }); + var dataValue = {}; + if (isBlankSpace) { + dataValue["delay"] = 0; + } + if (buffer[buffer.length - 1] === "") { + // A last single
won't have effect + // so put an additional one + buffer.push(""); + } + + const bufferValue = buffer.join("
"); + dataValue["value"] = bufferValue; + useLines.push(dataValue); + buffer = []; + } + } + for (let line of lines) { + if (line === progressLiteralStart) { + saveBuffer(); + useLines.push({ + type: "progress" + }); + } else if (line.startsWith(promptLiteralStart)) { + saveBuffer(); + const value = line.replace(promptLiteralStart, "").trimEnd(); + useLines.push({ + type: "input", + value: value + }); + } else if (line.startsWith(commentPromptLiteralStart)) { + saveBuffer(); + const value = "💬 " + line.replace(commentPromptLiteralStart, "").trimEnd(); + const color_value = "" + value + "" + useLines.push({ + value: color_value, + class: "termynal-comment", + delay: 0 + }); + } else if (line.startsWith(customPromptLiteralStart)) { + saveBuffer(); + const prompt = line.slice(3, line.indexOf(' ', 3)) + let value = line.slice(line.indexOf(' ', 3)).trimEnd(); + useLines.push({ + type: "input", + value: value, + prompt: prompt + }); + } else if (line.startsWith(colorOutputLiteralStart)) { + let color = line.substring(0, line.indexOf(' ')); + let line_value = line.substring(line.indexOf(' ') + 1); + var color_line = "" + line_value + "" + buffer.push(color_line); + } else { + buffer.push(line); + } + } + saveBuffer(); + const div = document.createElement("div"); + node.replaceWith(div); + const termynal = new Termynal(div, { + lineData: useLines, + noInit: true, + lineDelay: 500 + }); + termynals.push(termynal); + }); +} + +function loadVisibleTermynals() { + termynals = termynals.filter(termynal => { + if (termynal.container.getBoundingClientRect().top - innerHeight <= 0) { + termynal.init(); + return false; + } + return true; + }); +} +window.addEventListener("scroll", loadVisibleTermynals); +createTermynals(); +loadVisibleTermynals(); diff --git a/main/_static/trigger_CI.txt b/main/_static/trigger_CI.txt new file mode 100644 index 00000000..e69de29b diff --git a/main/_static/visit_merging.svg b/main/_static/visit_merging.svg new file mode 100644 index 00000000..7518cad3 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strict';\n\n /**\n * Applies the :focus-visible polyfill at the given scope.\n * A scope in this case is either the top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "(function(global) {\r\n /**\r\n * Polyfill URLSearchParams\r\n *\r\n * Inspired from : https://github.com/WebReflection/url-search-params/blob/master/src/url-search-params.js\r\n */\r\n\r\n var checkIfIteratorIsSupported = function() {\r\n try {\r\n return !!Symbol.iterator;\r\n } catch (error) {\r\n return false;\r\n }\r\n };\r\n\r\n\r\n var iteratorSupported = checkIfIteratorIsSupported();\r\n\r\n var createIterator = function(items) {\r\n var iterator = {\r\n next: function() {\r\n var value = items.shift();\r\n return { done: value === void 0, value: value };\r\n }\r\n };\r\n\r\n if (iteratorSupported) {\r\n iterator[Symbol.iterator] = function() {\r\n return iterator;\r\n };\r\n }\r\n\r\n return iterator;\r\n };\r\n\r\n /**\r\n * Search param name and values should be encoded according to https://url.spec.whatwg.org/#urlencoded-serializing\r\n * encodeURIComponent() produces the same result except encoding spaces as `%20` instead of `+`.\r\n */\r\n var serializeParam = function(value) {\r\n return encodeURIComponent(value).replace(/%20/g, '+');\r\n };\r\n\r\n var deserializeParam = function(value) {\r\n return decodeURIComponent(String(value).replace(/\\+/g, ' '));\r\n };\r\n\r\n var polyfillURLSearchParams = function() {\r\n\r\n var URLSearchParams = function(searchString) {\r\n Object.defineProperty(this, '_entries', { writable: true, value: {} });\r\n var typeofSearchString = typeof searchString;\r\n\r\n if (typeofSearchString === 'undefined') {\r\n // do nothing\r\n } else if (typeofSearchString === 'string') {\r\n if (searchString !== '') {\r\n this._fromString(searchString);\r\n }\r\n } else if (searchString instanceof URLSearchParams) {\r\n var _this = this;\r\n searchString.forEach(function(value, name) {\r\n _this.append(name, value);\r\n });\r\n } else if ((searchString !== null) && (typeofSearchString === 'object')) {\r\n if (Object.prototype.toString.call(searchString) === '[object Array]') {\r\n for (var i = 0; i < searchString.length; i++) {\r\n var entry = searchString[i];\r\n if ((Object.prototype.toString.call(entry) === '[object Array]') || (entry.length !== 2)) {\r\n this.append(entry[0], entry[1]);\r\n } else {\r\n throw new TypeError('Expected [string, any] as entry at index ' + i + ' of URLSearchParams\\'s input');\r\n }\r\n }\r\n } else {\r\n for (var key in searchString) {\r\n if (searchString.hasOwnProperty(key)) {\r\n this.append(key, searchString[key]);\r\n }\r\n }\r\n }\r\n } else {\r\n throw new TypeError('Unsupported input\\'s type for URLSearchParams');\r\n }\r\n };\r\n\r\n var proto = URLSearchParams.prototype;\r\n\r\n proto.append = function(name, value) {\r\n if (name in this._entries) {\r\n this._entries[name].push(String(value));\r\n } else {\r\n this._entries[name] = [String(value)];\r\n }\r\n };\r\n\r\n proto.delete = function(name) {\r\n delete this._entries[name];\r\n };\r\n\r\n proto.get = function(name) {\r\n return (name in this._entries) ? this._entries[name][0] : null;\r\n };\r\n\r\n proto.getAll = function(name) {\r\n return (name in this._entries) ? this._entries[name].slice(0) : [];\r\n };\r\n\r\n proto.has = function(name) {\r\n return (name in this._entries);\r\n };\r\n\r\n proto.set = function(name, value) {\r\n this._entries[name] = [String(value)];\r\n };\r\n\r\n proto.forEach = function(callback, thisArg) {\r\n var entries;\r\n for (var name in this._entries) {\r\n if (this._entries.hasOwnProperty(name)) {\r\n entries = this._entries[name];\r\n for (var i = 0; i < entries.length; i++) {\r\n callback.call(thisArg, entries[i], name, this);\r\n }\r\n }\r\n }\r\n };\r\n\r\n proto.keys = function() {\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push(name);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n proto.values = function() {\r\n var items = [];\r\n this.forEach(function(value) {\r\n items.push(value);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n proto.entries = function() {\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push([name, value]);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n if (iteratorSupported) {\r\n proto[Symbol.iterator] = proto.entries;\r\n }\r\n\r\n proto.toString = function() {\r\n var searchArray = [];\r\n this.forEach(function(value, name) {\r\n searchArray.push(serializeParam(name) + '=' + serializeParam(value));\r\n });\r\n return searchArray.join('&');\r\n };\r\n\r\n\r\n global.URLSearchParams = URLSearchParams;\r\n };\r\n\r\n var checkIfURLSearchParamsSupported = function() {\r\n try {\r\n var URLSearchParams = global.URLSearchParams;\r\n\r\n return (\r\n (new URLSearchParams('?a=1').toString() === 'a=1') &&\r\n (typeof URLSearchParams.prototype.set === 'function') &&\r\n (typeof URLSearchParams.prototype.entries === 'function')\r\n );\r\n } catch (e) {\r\n return false;\r\n }\r\n };\r\n\r\n if (!checkIfURLSearchParamsSupported()) {\r\n polyfillURLSearchParams();\r\n }\r\n\r\n var proto = global.URLSearchParams.prototype;\r\n\r\n if (typeof proto.sort !== 'function') {\r\n proto.sort = function() {\r\n var _this = this;\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push([name, value]);\r\n if (!_this._entries) {\r\n _this.delete(name);\r\n }\r\n });\r\n items.sort(function(a, b) {\r\n if (a[0] < b[0]) {\r\n return -1;\r\n } else if (a[0] > b[0]) {\r\n return +1;\r\n } else {\r\n return 0;\r\n }\r\n });\r\n if (_this._entries) { // force reset because IE keeps keys index\r\n _this._entries = {};\r\n }\r\n for (var i = 0; i < items.length; i++) {\r\n this.append(items[i][0], items[i][1]);\r\n }\r\n };\r\n }\r\n\r\n if (typeof proto._fromString !== 'function') {\r\n Object.defineProperty(proto, '_fromString', {\r\n enumerable: false,\r\n configurable: false,\r\n writable: false,\r\n value: function(searchString) {\r\n if (this._entries) {\r\n this._entries = {};\r\n } else {\r\n var keys = [];\r\n this.forEach(function(value, name) {\r\n keys.push(name);\r\n });\r\n for (var i = 0; i < keys.length; i++) {\r\n this.delete(keys[i]);\r\n }\r\n }\r\n\r\n searchString = searchString.replace(/^\\?/, '');\r\n var attributes = searchString.split('&');\r\n var attribute;\r\n for (var i = 0; i < attributes.length; i++) {\r\n attribute = attributes[i].split('=');\r\n this.append(\r\n deserializeParam(attribute[0]),\r\n (attribute.length > 1) ? deserializeParam(attribute[1]) : ''\r\n );\r\n }\r\n }\r\n });\r\n }\r\n\r\n // HTMLAnchorElement\r\n\r\n})(\r\n (typeof global !== 'undefined') ? global\r\n : ((typeof window !== 'undefined') ? window\r\n : ((typeof self !== 'undefined') ? self : this))\r\n);\r\n\r\n(function(global) {\r\n /**\r\n * Polyfill URL\r\n *\r\n * Inspired from : https://github.com/arv/DOM-URL-Polyfill/blob/master/src/url.js\r\n */\r\n\r\n var checkIfURLIsSupported = function() {\r\n try {\r\n var u = new global.URL('b', 'http://a');\r\n u.pathname = 'c d';\r\n return (u.href === 'http://a/c%20d') && u.searchParams;\r\n } catch (e) {\r\n return false;\r\n }\r\n };\r\n\r\n\r\n var polyfillURL = function() {\r\n var _URL = global.URL;\r\n\r\n var URL = function(url, base) {\r\n if (typeof url !== 'string') url = String(url);\r\n if (base && typeof base !== 'string') base = String(base);\r\n\r\n // Only create another document if the base is different from current location.\r\n var doc = document, baseElement;\r\n if (base && (global.location === void 0 || base !== global.location.href)) {\r\n base = base.toLowerCase();\r\n doc = document.implementation.createHTMLDocument('');\r\n baseElement = doc.createElement('base');\r\n baseElement.href = base;\r\n doc.head.appendChild(baseElement);\r\n try {\r\n if (baseElement.href.indexOf(base) !== 0) throw new Error(baseElement.href);\r\n } catch (err) {\r\n throw new Error('URL unable to set base ' + base + ' due to ' + err);\r\n }\r\n }\r\n\r\n var anchorElement = doc.createElement('a');\r\n anchorElement.href = url;\r\n if (baseElement) {\r\n doc.body.appendChild(anchorElement);\r\n anchorElement.href = anchorElement.href; // force href to refresh\r\n }\r\n\r\n var inputElement = doc.createElement('input');\r\n inputElement.type = 'url';\r\n inputElement.value = url;\r\n\r\n if (anchorElement.protocol === ':' || !/:/.test(anchorElement.href) || (!inputElement.checkValidity() && !base)) {\r\n throw new TypeError('Invalid URL');\r\n }\r\n\r\n Object.defineProperty(this, '_anchorElement', {\r\n value: anchorElement\r\n });\r\n\r\n\r\n // create a linked searchParams which reflect its changes on URL\r\n var searchParams = new global.URLSearchParams(this.search);\r\n var enableSearchUpdate = true;\r\n var enableSearchParamsUpdate = true;\r\n var _this = this;\r\n ['append', 'delete', 'set'].forEach(function(methodName) {\r\n var method = searchParams[methodName];\r\n searchParams[methodName] = function() {\r\n method.apply(searchParams, arguments);\r\n if (enableSearchUpdate) {\r\n enableSearchParamsUpdate = false;\r\n _this.search = searchParams.toString();\r\n enableSearchParamsUpdate = true;\r\n }\r\n };\r\n });\r\n\r\n Object.defineProperty(this, 'searchParams', {\r\n value: searchParams,\r\n enumerable: true\r\n });\r\n\r\n var search = void 0;\r\n Object.defineProperty(this, '_updateSearchParams', {\r\n enumerable: false,\r\n configurable: false,\r\n writable: false,\r\n value: function() {\r\n if (this.search !== search) {\r\n search = this.search;\r\n if (enableSearchParamsUpdate) {\r\n enableSearchUpdate = false;\r\n this.searchParams._fromString(this.search);\r\n enableSearchUpdate = true;\r\n }\r\n }\r\n }\r\n });\r\n };\r\n\r\n var proto = URL.prototype;\r\n\r\n var linkURLWithAnchorAttribute = function(attributeName) {\r\n Object.defineProperty(proto, attributeName, {\r\n get: function() {\r\n return this._anchorElement[attributeName];\r\n },\r\n set: function(value) {\r\n this._anchorElement[attributeName] = value;\r\n },\r\n enumerable: true\r\n });\r\n };\r\n\r\n ['hash', 'host', 'hostname', 'port', 'protocol']\r\n .forEach(function(attributeName) {\r\n linkURLWithAnchorAttribute(attributeName);\r\n });\r\n\r\n Object.defineProperty(proto, 'search', {\r\n get: function() {\r\n return this._anchorElement['search'];\r\n },\r\n set: function(value) {\r\n this._anchorElement['search'] = value;\r\n this._updateSearchParams();\r\n },\r\n enumerable: true\r\n });\r\n\r\n Object.defineProperties(proto, {\r\n\r\n 'toString': {\r\n get: function() {\r\n var _this = this;\r\n return function() {\r\n return _this.href;\r\n };\r\n }\r\n },\r\n\r\n 'href': {\r\n get: function() {\r\n return this._anchorElement.href.replace(/\\?$/, '');\r\n },\r\n set: function(value) {\r\n this._anchorElement.href = value;\r\n this._updateSearchParams();\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'pathname': {\r\n get: function() {\r\n return this._anchorElement.pathname.replace(/(^\\/?)/, '/');\r\n },\r\n set: function(value) {\r\n this._anchorElement.pathname = value;\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'origin': {\r\n get: function() {\r\n // get expected port from protocol\r\n var expectedPort = { 'http:': 80, 'https:': 443, 'ftp:': 21 }[this._anchorElement.protocol];\r\n // add port to origin if, expected port is different than actual port\r\n // and it is not empty f.e http://foo:8080\r\n // 8080 != 80 && 8080 != ''\r\n var addPortToOrigin = this._anchorElement.port != expectedPort &&\r\n this._anchorElement.port !== '';\r\n\r\n return this._anchorElement.protocol +\r\n '//' +\r\n this._anchorElement.hostname +\r\n (addPortToOrigin ? (':' + this._anchorElement.port) : '');\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'password': { // TODO\r\n get: function() {\r\n return '';\r\n },\r\n set: function(value) {\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'username': { // TODO\r\n get: function() {\r\n return '';\r\n },\r\n set: function(value) {\r\n },\r\n enumerable: true\r\n },\r\n });\r\n\r\n URL.createObjectURL = function(blob) {\r\n return _URL.createObjectURL.apply(_URL, arguments);\r\n };\r\n\r\n URL.revokeObjectURL = function(url) {\r\n return _URL.revokeObjectURL.apply(_URL, arguments);\r\n };\r\n\r\n global.URL = URL;\r\n\r\n };\r\n\r\n if (!checkIfURLIsSupported()) {\r\n polyfillURL();\r\n }\r\n\r\n if ((global.location !== void 0) && !('origin' in global.location)) {\r\n var getOrigin = function() {\r\n return global.location.protocol + '//' + global.location.hostname + (global.location.port ? 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'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "Array.prototype.flat||Object.defineProperty(Array.prototype,\"flat\",{configurable:!0,value:function r(){var t=isNaN(arguments[0])?1:Number(arguments[0]);return t?Array.prototype.reduce.call(this,function(a,e){return Array.isArray(e)?a.push.apply(a,r.call(e,t-1)):a.push(e),a},[]):Array.prototype.slice.call(this)},writable:!0}),Array.prototype.flatMap||Object.defineProperty(Array.prototype,\"flatMap\",{configurable:!0,value:function(r){return Array.prototype.map.apply(this,arguments).flat()},writable:!0})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"array-flat-polyfill\"\nimport \"focus-visible\"\nimport \"unfetch/polyfill\"\nimport \"url-polyfill\"\n\nimport {\n EMPTY,\n NEVER,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getOptionalElement,\n requestJSON,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountBackToTop,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantLoading,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget()\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? __search?.index || requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up instant loading, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantLoading({ document$, location$, viewport$ })\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"[href][rel=prev]\")\n if (typeof prev !== \"undefined\")\n prev.click()\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"[href][rel=next]\")\n if (typeof next !== \"undefined\")\n next.click()\n break\n }\n })\n\n/* Set up patches */\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, { viewport$, header$, target$ })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.component$ = component$ /* Component observable */\n", "self.fetch||(self.fetch=function(e,n){return n=n||{},new Promise(function(t,s){var r=new XMLHttpRequest,o=[],u=[],i={},a=function(){return{ok:2==(r.status/100|0),statusText:r.statusText,status:r.status,url:r.responseURL,text:function(){return Promise.resolve(r.responseText)},json:function(){return Promise.resolve(r.responseText).then(JSON.parse)},blob:function(){return Promise.resolve(new Blob([r.response]))},clone:a,headers:{keys:function(){return o},entries:function(){return u},get:function(e){return i[e.toLowerCase()]},has:function(e){return e.toLowerCase()in i}}}};for(var c in r.open(n.method||\"get\",e,!0),r.onload=function(){r.getAllResponseHeaders().replace(/^(.*?):[^\\S\\n]*([\\s\\S]*?)$/gm,function(e,n,t){o.push(n=n.toLowerCase()),u.push([n,t]),i[n]=i[n]?i[n]+\",\"+t:t}),t(a())},r.onerror=s,r.withCredentials=\"include\"==n.credentials,n.headers)r.setRequestHeader(c,n.headers[c]);r.send(n.body||null)})});\n", "import tslib from '../tslib.js';\r\nconst {\r\n __extends,\r\n __assign,\r\n __rest,\r\n __decorate,\r\n __param,\r\n __metadata,\r\n __awaiter,\r\n __generator,\r\n __exportStar,\r\n __createBinding,\r\n __values,\r\n __read,\r\n __spread,\r\n __spreadArrays,\r\n __spreadArray,\r\n __await,\r\n __asyncGenerator,\r\n __asyncDelegator,\r\n __asyncValues,\r\n __makeTemplateObject,\r\n __importStar,\r\n __importDefault,\r\n __classPrivateFieldGet,\r\n __classPrivateFieldSet,\r\n} = tslib;\r\nexport {\r\n __extends,\r\n __assign,\r\n __rest,\r\n __decorate,\r\n __param,\r\n __metadata,\r\n __awaiter,\r\n __generator,\r\n __exportStar,\r\n __createBinding,\r\n __values,\r\n __read,\r\n __spread,\r\n __spreadArrays,\r\n __spreadArray,\r\n __await,\r\n __asyncGenerator,\r\n __asyncDelegator,\r\n __asyncValues,\r\n __makeTemplateObject,\r\n __importStar,\r\n __importDefault,\r\n __classPrivateFieldGet,\r\n __classPrivateFieldSet,\r\n};\r\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n ReplaySubject,\n Subject,\n fromEvent\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch document\n *\n * Documents are implemented as subjects, so all downstream observables are\n * automatically updated when a new document is emitted.\n *\n * @returns Document subject\n */\nexport function watchDocument(): Subject {\n const document$ = new ReplaySubject(1)\n fromEvent(document, \"DOMContentLoaded\", { once: true })\n .subscribe(() => document$.next(document))\n\n /* Return document */\n return document$\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements(\n selector: string, node?: ParentNode\n): T[]\n\nexport function getElements(\n selector: string, node: ParentNode = document\n): T[] {\n return Array.from(node.querySelectorAll(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement(\n selector: string, node?: ParentNode\n): T\n\nexport function getElement(\n selector: string, node: ParentNode = document\n): T {\n const el = getOptionalElement(selector, node)\n if (typeof el === \"undefined\")\n throw new ReferenceError(\n `Missing element: expected \"${selector}\" to be present`\n )\n\n /* Return element */\n return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement(\n selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement(\n selector: string, node: ParentNode = document\n): T | undefined {\n return node.querySelector(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n return document.activeElement instanceof HTMLElement\n ? document.activeElement || undefined\n : undefined\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n debounceTime,\n distinctUntilChanged,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element focus\n *\n * Previously, this function used `focus` and `blur` events to determine whether\n * an element is focused, but this doesn't work if there are focusable elements\n * within the elements itself. A better solutions are `focusin` and `focusout`\n * events, which bubble up the tree and allow for more fine-grained control.\n *\n * `debounceTime` is necessary, because when a focus change happens inside an\n * element, the observable would first emit `false` and then `true` again.\n *\n * @param el - Element\n *\n * @returns Element focus observable\n */\nexport function watchElementFocus(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(document.body, \"focusin\"),\n fromEvent(document.body, \"focusout\")\n )\n .pipe(\n debounceTime(1),\n map(() => {\n const active = getActiveElement()\n return typeof active !== \"undefined\"\n ? el.contains(active)\n : false\n }),\n startWith(el === getActiveElement()),\n distinctUntilChanged()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n animationFrameScheduler,\n auditTime,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementOffset {\n x: number /* Horizontal offset */\n y: number /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffset(\n el: HTMLElement\n): ElementOffset {\n return {\n x: el.offsetLeft,\n y: el.offsetTop\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffset(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(window, \"load\"),\n fromEvent(window, \"resize\")\n )\n .pipe(\n auditTime(0, animationFrameScheduler),\n map(() => getElementOffset(el)),\n startWith(getElementOffset(el))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n animationFrameScheduler,\n auditTime,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\nimport { ElementOffset } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content offset (= scroll offset)\n *\n * @param el - Element\n *\n * @returns Element content offset\n */\nexport function getElementContentOffset(\n el: HTMLElement\n): ElementOffset {\n return {\n x: el.scrollLeft,\n y: el.scrollTop\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element content offset\n *\n * @param el - Element\n *\n * @returns Element content offset observable\n */\nexport function watchElementContentOffset(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(el, \"scroll\"),\n fromEvent(window, \"resize\")\n )\n .pipe(\n auditTime(0, animationFrameScheduler),\n map(() => getElementContentOffset(el)),\n startWith(getElementContentOffset(el))\n )\n}\n", "/**\r\n * A collection of shims that provide minimal functionality of the ES6 collections.\r\n *\r\n * These implementations are not meant to be used outside of the ResizeObserver\r\n * modules as they cover only a limited range of use cases.\r\n */\r\n/* eslint-disable require-jsdoc, valid-jsdoc */\r\nvar MapShim = (function () {\r\n if (typeof Map !== 'undefined') {\r\n return Map;\r\n }\r\n /**\r\n * Returns index in provided array that matches the specified key.\r\n *\r\n * @param {Array} arr\r\n * @param {*} key\r\n * @returns {number}\r\n */\r\n function getIndex(arr, key) {\r\n var result = -1;\r\n arr.some(function (entry, index) {\r\n if (entry[0] === key) {\r\n result = index;\r\n return true;\r\n }\r\n return false;\r\n });\r\n return result;\r\n }\r\n return /** @class */ (function () {\r\n function class_1() {\r\n this.__entries__ = [];\r\n }\r\n Object.defineProperty(class_1.prototype, \"size\", {\r\n /**\r\n * @returns {boolean}\r\n */\r\n get: function () {\r\n return this.__entries__.length;\r\n },\r\n enumerable: true,\r\n configurable: true\r\n });\r\n /**\r\n * @param {*} key\r\n * @returns {*}\r\n */\r\n class_1.prototype.get = function (key) {\r\n var index = getIndex(this.__entries__, key);\r\n var entry = this.__entries__[index];\r\n return entry && entry[1];\r\n };\r\n /**\r\n * @param {*} key\r\n * @param {*} value\r\n * @returns {void}\r\n */\r\n class_1.prototype.set = function (key, value) {\r\n var index = getIndex(this.__entries__, key);\r\n if (~index) {\r\n this.__entries__[index][1] = value;\r\n }\r\n else {\r\n this.__entries__.push([key, value]);\r\n }\r\n };\r\n /**\r\n * @param {*} key\r\n * @returns {void}\r\n */\r\n class_1.prototype.delete = function (key) {\r\n var entries = this.__entries__;\r\n var index = getIndex(entries, key);\r\n if (~index) {\r\n entries.splice(index, 1);\r\n }\r\n };\r\n /**\r\n * @param {*} key\r\n * @returns {void}\r\n */\r\n class_1.prototype.has = function (key) {\r\n return !!~getIndex(this.__entries__, key);\r\n };\r\n /**\r\n * @returns {void}\r\n */\r\n class_1.prototype.clear = function () {\r\n this.__entries__.splice(0);\r\n };\r\n /**\r\n * @param {Function} callback\r\n * @param {*} [ctx=null]\r\n * @returns {void}\r\n */\r\n class_1.prototype.forEach = function (callback, ctx) {\r\n if (ctx === void 0) { ctx = null; }\r\n for (var _i = 0, _a = this.__entries__; _i < _a.length; _i++) {\r\n var entry = _a[_i];\r\n callback.call(ctx, entry[1], entry[0]);\r\n }\r\n };\r\n return class_1;\r\n }());\r\n})();\n\n/**\r\n * Detects whether window and document objects are available in current environment.\r\n */\r\nvar isBrowser = typeof window !== 'undefined' && typeof document !== 'undefined' && window.document === document;\n\n// Returns global object of a current environment.\r\nvar global$1 = (function () {\r\n if (typeof global !== 'undefined' && global.Math === Math) {\r\n return global;\r\n }\r\n if (typeof self !== 'undefined' && self.Math === Math) {\r\n return self;\r\n }\r\n if (typeof window !== 'undefined' && window.Math === Math) {\r\n return window;\r\n }\r\n // eslint-disable-next-line no-new-func\r\n return Function('return this')();\r\n})();\n\n/**\r\n * A shim for the requestAnimationFrame which falls back to the setTimeout if\r\n * first one is not supported.\r\n *\r\n * @returns {number} Requests' identifier.\r\n */\r\nvar requestAnimationFrame$1 = (function () {\r\n if (typeof requestAnimationFrame === 'function') {\r\n // It's required to use a bounded function because IE sometimes throws\r\n // an \"Invalid calling object\" error if rAF is invoked without the global\r\n // object on the left hand side.\r\n return requestAnimationFrame.bind(global$1);\r\n }\r\n return function (callback) { return setTimeout(function () { return callback(Date.now()); }, 1000 / 60); };\r\n})();\n\n// Defines minimum timeout before adding a trailing call.\r\nvar trailingTimeout = 2;\r\n/**\r\n * Creates a wrapper function which ensures that provided callback will be\r\n * invoked only once during the specified delay period.\r\n *\r\n * @param {Function} callback - Function to be invoked after the delay period.\r\n * @param {number} delay - Delay after which to invoke callback.\r\n * @returns {Function}\r\n */\r\nfunction throttle (callback, delay) {\r\n var leadingCall = false, trailingCall = false, lastCallTime = 0;\r\n /**\r\n * Invokes the original callback function and schedules new invocation if\r\n * the \"proxy\" was called during current request.\r\n *\r\n * @returns {void}\r\n */\r\n function resolvePending() {\r\n if (leadingCall) {\r\n leadingCall = false;\r\n callback();\r\n }\r\n if (trailingCall) {\r\n proxy();\r\n }\r\n }\r\n /**\r\n * Callback invoked after the specified delay. It will further postpone\r\n * invocation of the original function delegating it to the\r\n * requestAnimationFrame.\r\n *\r\n * @returns {void}\r\n */\r\n function timeoutCallback() {\r\n requestAnimationFrame$1(resolvePending);\r\n }\r\n /**\r\n * Schedules invocation of the original function.\r\n *\r\n * @returns {void}\r\n */\r\n function proxy() {\r\n var timeStamp = Date.now();\r\n if (leadingCall) {\r\n // Reject immediately following calls.\r\n if (timeStamp - lastCallTime < trailingTimeout) {\r\n return;\r\n }\r\n // Schedule new call to be in invoked when the pending one is resolved.\r\n // This is important for \"transitions\" which never actually start\r\n // immediately so there is a chance that we might miss one if change\r\n // happens amids the pending invocation.\r\n trailingCall = true;\r\n }\r\n else {\r\n leadingCall = true;\r\n trailingCall = false;\r\n setTimeout(timeoutCallback, delay);\r\n }\r\n lastCallTime = timeStamp;\r\n }\r\n return proxy;\r\n}\n\n// Minimum delay before invoking the update of observers.\r\nvar REFRESH_DELAY = 20;\r\n// A list of substrings of CSS properties used to find transition events that\r\n// might affect dimensions of observed elements.\r\nvar transitionKeys = ['top', 'right', 'bottom', 'left', 'width', 'height', 'size', 'weight'];\r\n// Check if MutationObserver is available.\r\nvar mutationObserverSupported = typeof MutationObserver !== 'undefined';\r\n/**\r\n * Singleton controller class which handles updates of ResizeObserver instances.\r\n */\r\nvar ResizeObserverController = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserverController.\r\n *\r\n * @private\r\n */\r\n function ResizeObserverController() {\r\n /**\r\n * Indicates whether DOM listeners have been added.\r\n *\r\n * @private {boolean}\r\n */\r\n this.connected_ = false;\r\n /**\r\n * Tells that controller has subscribed for Mutation Events.\r\n *\r\n * @private {boolean}\r\n */\r\n this.mutationEventsAdded_ = false;\r\n /**\r\n * Keeps reference to the instance of MutationObserver.\r\n *\r\n * @private {MutationObserver}\r\n */\r\n this.mutationsObserver_ = null;\r\n /**\r\n * A list of connected observers.\r\n *\r\n * @private {Array}\r\n */\r\n this.observers_ = [];\r\n this.onTransitionEnd_ = this.onTransitionEnd_.bind(this);\r\n this.refresh = throttle(this.refresh.bind(this), REFRESH_DELAY);\r\n }\r\n /**\r\n * Adds observer to observers list.\r\n *\r\n * @param {ResizeObserverSPI} observer - Observer to be added.\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.addObserver = function (observer) {\r\n if (!~this.observers_.indexOf(observer)) {\r\n this.observers_.push(observer);\r\n }\r\n // Add listeners if they haven't been added yet.\r\n if (!this.connected_) {\r\n this.connect_();\r\n }\r\n };\r\n /**\r\n * Removes observer from observers list.\r\n *\r\n * @param {ResizeObserverSPI} observer - Observer to be removed.\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.removeObserver = function (observer) {\r\n var observers = this.observers_;\r\n var index = observers.indexOf(observer);\r\n // Remove observer if it's present in registry.\r\n if (~index) {\r\n observers.splice(index, 1);\r\n }\r\n // Remove listeners if controller has no connected observers.\r\n if (!observers.length && this.connected_) {\r\n this.disconnect_();\r\n }\r\n };\r\n /**\r\n * Invokes the update of observers. It will continue running updates insofar\r\n * it detects changes.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.refresh = function () {\r\n var changesDetected = this.updateObservers_();\r\n // Continue running updates if changes have been detected as there might\r\n // be future ones caused by CSS transitions.\r\n if (changesDetected) {\r\n this.refresh();\r\n }\r\n };\r\n /**\r\n * Updates every observer from observers list and notifies them of queued\r\n * entries.\r\n *\r\n * @private\r\n * @returns {boolean} Returns \"true\" if any observer has detected changes in\r\n * dimensions of it's elements.\r\n */\r\n ResizeObserverController.prototype.updateObservers_ = function () {\r\n // Collect observers that have active observations.\r\n var activeObservers = this.observers_.filter(function (observer) {\r\n return observer.gatherActive(), observer.hasActive();\r\n });\r\n // Deliver notifications in a separate cycle in order to avoid any\r\n // collisions between observers, e.g. when multiple instances of\r\n // ResizeObserver are tracking the same element and the callback of one\r\n // of them changes content dimensions of the observed target. Sometimes\r\n // this may result in notifications being blocked for the rest of observers.\r\n activeObservers.forEach(function (observer) { return observer.broadcastActive(); });\r\n return activeObservers.length > 0;\r\n };\r\n /**\r\n * Initializes DOM listeners.\r\n *\r\n * @private\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.connect_ = function () {\r\n // Do nothing if running in a non-browser environment or if listeners\r\n // have been already added.\r\n if (!isBrowser || this.connected_) {\r\n return;\r\n }\r\n // Subscription to the \"Transitionend\" event is used as a workaround for\r\n // delayed transitions. This way it's possible to capture at least the\r\n // final state of an element.\r\n document.addEventListener('transitionend', this.onTransitionEnd_);\r\n window.addEventListener('resize', this.refresh);\r\n if (mutationObserverSupported) {\r\n this.mutationsObserver_ = new MutationObserver(this.refresh);\r\n this.mutationsObserver_.observe(document, {\r\n attributes: true,\r\n childList: true,\r\n characterData: true,\r\n subtree: true\r\n });\r\n }\r\n else {\r\n document.addEventListener('DOMSubtreeModified', this.refresh);\r\n this.mutationEventsAdded_ = true;\r\n }\r\n this.connected_ = true;\r\n };\r\n /**\r\n * Removes DOM listeners.\r\n *\r\n * @private\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.disconnect_ = function () {\r\n // Do nothing if running in a non-browser environment or if listeners\r\n // have been already removed.\r\n if (!isBrowser || !this.connected_) {\r\n return;\r\n }\r\n document.removeEventListener('transitionend', this.onTransitionEnd_);\r\n window.removeEventListener('resize', this.refresh);\r\n if (this.mutationsObserver_) {\r\n this.mutationsObserver_.disconnect();\r\n }\r\n if (this.mutationEventsAdded_) {\r\n document.removeEventListener('DOMSubtreeModified', this.refresh);\r\n }\r\n this.mutationsObserver_ = null;\r\n this.mutationEventsAdded_ = false;\r\n this.connected_ = false;\r\n };\r\n /**\r\n * \"Transitionend\" event handler.\r\n *\r\n * @private\r\n * @param {TransitionEvent} event\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.onTransitionEnd_ = function (_a) {\r\n var _b = _a.propertyName, propertyName = _b === void 0 ? '' : _b;\r\n // Detect whether transition may affect dimensions of an element.\r\n var isReflowProperty = transitionKeys.some(function (key) {\r\n return !!~propertyName.indexOf(key);\r\n });\r\n if (isReflowProperty) {\r\n this.refresh();\r\n }\r\n };\r\n /**\r\n * Returns instance of the ResizeObserverController.\r\n *\r\n * @returns {ResizeObserverController}\r\n */\r\n ResizeObserverController.getInstance = function () {\r\n if (!this.instance_) {\r\n this.instance_ = new ResizeObserverController();\r\n }\r\n return this.instance_;\r\n };\r\n /**\r\n * Holds reference to the controller's instance.\r\n *\r\n * @private {ResizeObserverController}\r\n */\r\n ResizeObserverController.instance_ = null;\r\n return ResizeObserverController;\r\n}());\n\n/**\r\n * Defines non-writable/enumerable properties of the provided target object.\r\n *\r\n * @param {Object} target - Object for which to define properties.\r\n * @param {Object} props - Properties to be defined.\r\n * @returns {Object} Target object.\r\n */\r\nvar defineConfigurable = (function (target, props) {\r\n for (var _i = 0, _a = Object.keys(props); _i < _a.length; _i++) {\r\n var key = _a[_i];\r\n Object.defineProperty(target, key, {\r\n value: props[key],\r\n enumerable: false,\r\n writable: false,\r\n configurable: true\r\n });\r\n }\r\n return target;\r\n});\n\n/**\r\n * Returns the global object associated with provided element.\r\n *\r\n * @param {Object} target\r\n * @returns {Object}\r\n */\r\nvar getWindowOf = (function (target) {\r\n // Assume that the element is an instance of Node, which means that it\r\n // has the \"ownerDocument\" property from which we can retrieve a\r\n // corresponding global object.\r\n var ownerGlobal = target && target.ownerDocument && target.ownerDocument.defaultView;\r\n // Return the local global object if it's not possible extract one from\r\n // provided element.\r\n return ownerGlobal || global$1;\r\n});\n\n// Placeholder of an empty content rectangle.\r\nvar emptyRect = createRectInit(0, 0, 0, 0);\r\n/**\r\n * Converts provided string to a number.\r\n *\r\n * @param {number|string} value\r\n * @returns {number}\r\n */\r\nfunction toFloat(value) {\r\n return parseFloat(value) || 0;\r\n}\r\n/**\r\n * Extracts borders size from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @param {...string} positions - Borders positions (top, right, ...)\r\n * @returns {number}\r\n */\r\nfunction getBordersSize(styles) {\r\n var positions = [];\r\n for (var _i = 1; _i < arguments.length; _i++) {\r\n positions[_i - 1] = arguments[_i];\r\n }\r\n return positions.reduce(function (size, position) {\r\n var value = styles['border-' + position + '-width'];\r\n return size + toFloat(value);\r\n }, 0);\r\n}\r\n/**\r\n * Extracts paddings sizes from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @returns {Object} Paddings box.\r\n */\r\nfunction getPaddings(styles) {\r\n var positions = ['top', 'right', 'bottom', 'left'];\r\n var paddings = {};\r\n for (var _i = 0, positions_1 = positions; _i < positions_1.length; _i++) {\r\n var position = positions_1[_i];\r\n var value = styles['padding-' + position];\r\n paddings[position] = toFloat(value);\r\n }\r\n return paddings;\r\n}\r\n/**\r\n * Calculates content rectangle of provided SVG element.\r\n *\r\n * @param {SVGGraphicsElement} target - Element content rectangle of which needs\r\n * to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getSVGContentRect(target) {\r\n var bbox = target.getBBox();\r\n return createRectInit(0, 0, bbox.width, bbox.height);\r\n}\r\n/**\r\n * Calculates content rectangle of provided HTMLElement.\r\n *\r\n * @param {HTMLElement} target - Element for which to calculate the content rectangle.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getHTMLElementContentRect(target) {\r\n // Client width & height properties can't be\r\n // used exclusively as they provide rounded values.\r\n var clientWidth = target.clientWidth, clientHeight = target.clientHeight;\r\n // By this condition we can catch all non-replaced inline, hidden and\r\n // detached elements. Though elements with width & height properties less\r\n // than 0.5 will be discarded as well.\r\n //\r\n // Without it we would need to implement separate methods for each of\r\n // those cases and it's not possible to perform a precise and performance\r\n // effective test for hidden elements. E.g. even jQuery's ':visible' filter\r\n // gives wrong results for elements with width & height less than 0.5.\r\n if (!clientWidth && !clientHeight) {\r\n return emptyRect;\r\n }\r\n var styles = getWindowOf(target).getComputedStyle(target);\r\n var paddings = getPaddings(styles);\r\n var horizPad = paddings.left + paddings.right;\r\n var vertPad = paddings.top + paddings.bottom;\r\n // Computed styles of width & height are being used because they are the\r\n // only dimensions available to JS that contain non-rounded values. It could\r\n // be possible to utilize the getBoundingClientRect if only it's data wasn't\r\n // affected by CSS transformations let alone paddings, borders and scroll bars.\r\n var width = toFloat(styles.width), height = toFloat(styles.height);\r\n // Width & height include paddings and borders when the 'border-box' box\r\n // model is applied (except for IE).\r\n if (styles.boxSizing === 'border-box') {\r\n // Following conditions are required to handle Internet Explorer which\r\n // doesn't include paddings and borders to computed CSS dimensions.\r\n //\r\n // We can say that if CSS dimensions + paddings are equal to the \"client\"\r\n // properties then it's either IE, and thus we don't need to subtract\r\n // anything, or an element merely doesn't have paddings/borders styles.\r\n if (Math.round(width + horizPad) !== clientWidth) {\r\n width -= getBordersSize(styles, 'left', 'right') + horizPad;\r\n }\r\n if (Math.round(height + vertPad) !== clientHeight) {\r\n height -= getBordersSize(styles, 'top', 'bottom') + vertPad;\r\n }\r\n }\r\n // Following steps can't be applied to the document's root element as its\r\n // client[Width/Height] properties represent viewport area of the window.\r\n // Besides, it's as well not necessary as the itself neither has\r\n // rendered scroll bars nor it can be clipped.\r\n if (!isDocumentElement(target)) {\r\n // In some browsers (only in Firefox, actually) CSS width & height\r\n // include scroll bars size which can be removed at this step as scroll\r\n // bars are the only difference between rounded dimensions + paddings\r\n // and \"client\" properties, though that is not always true in Chrome.\r\n var vertScrollbar = Math.round(width + horizPad) - clientWidth;\r\n var horizScrollbar = Math.round(height + vertPad) - clientHeight;\r\n // Chrome has a rather weird rounding of \"client\" properties.\r\n // E.g. for an element with content width of 314.2px it sometimes gives\r\n // the client width of 315px and for the width of 314.7px it may give\r\n // 314px. And it doesn't happen all the time. So just ignore this delta\r\n // as a non-relevant.\r\n if (Math.abs(vertScrollbar) !== 1) {\r\n width -= vertScrollbar;\r\n }\r\n if (Math.abs(horizScrollbar) !== 1) {\r\n height -= horizScrollbar;\r\n }\r\n }\r\n return createRectInit(paddings.left, paddings.top, width, height);\r\n}\r\n/**\r\n * Checks whether provided element is an instance of the SVGGraphicsElement.\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nvar isSVGGraphicsElement = (function () {\r\n // Some browsers, namely IE and Edge, don't have the SVGGraphicsElement\r\n // interface.\r\n if (typeof SVGGraphicsElement !== 'undefined') {\r\n return function (target) { return target instanceof getWindowOf(target).SVGGraphicsElement; };\r\n }\r\n // If it's so, then check that element is at least an instance of the\r\n // SVGElement and that it has the \"getBBox\" method.\r\n // eslint-disable-next-line no-extra-parens\r\n return function (target) { return (target instanceof getWindowOf(target).SVGElement &&\r\n typeof target.getBBox === 'function'); };\r\n})();\r\n/**\r\n * Checks whether provided element is a document element ().\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nfunction isDocumentElement(target) {\r\n return target === getWindowOf(target).document.documentElement;\r\n}\r\n/**\r\n * Calculates an appropriate content rectangle for provided html or svg element.\r\n *\r\n * @param {Element} target - Element content rectangle of which needs to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getContentRect(target) {\r\n if (!isBrowser) {\r\n return emptyRect;\r\n }\r\n if (isSVGGraphicsElement(target)) {\r\n return getSVGContentRect(target);\r\n }\r\n return getHTMLElementContentRect(target);\r\n}\r\n/**\r\n * Creates rectangle with an interface of the DOMRectReadOnly.\r\n * Spec: https://drafts.fxtf.org/geometry/#domrectreadonly\r\n *\r\n * @param {DOMRectInit} rectInit - Object with rectangle's x/y coordinates and dimensions.\r\n * @returns {DOMRectReadOnly}\r\n */\r\nfunction createReadOnlyRect(_a) {\r\n var x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n // If DOMRectReadOnly is available use it as a prototype for the rectangle.\r\n var Constr = typeof DOMRectReadOnly !== 'undefined' ? DOMRectReadOnly : Object;\r\n var rect = Object.create(Constr.prototype);\r\n // Rectangle's properties are not writable and non-enumerable.\r\n defineConfigurable(rect, {\r\n x: x, y: y, width: width, height: height,\r\n top: y,\r\n right: x + width,\r\n bottom: height + y,\r\n left: x\r\n });\r\n return rect;\r\n}\r\n/**\r\n * Creates DOMRectInit object based on the provided dimensions and the x/y coordinates.\r\n * Spec: https://drafts.fxtf.org/geometry/#dictdef-domrectinit\r\n *\r\n * @param {number} x - X coordinate.\r\n * @param {number} y - Y coordinate.\r\n * @param {number} width - Rectangle's width.\r\n * @param {number} height - Rectangle's height.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction createRectInit(x, y, width, height) {\r\n return { x: x, y: y, width: width, height: height };\r\n}\n\n/**\r\n * Class that is responsible for computations of the content rectangle of\r\n * provided DOM element and for keeping track of it's changes.\r\n */\r\nvar ResizeObservation = /** @class */ (function () {\r\n /**\r\n * Creates an instance of ResizeObservation.\r\n *\r\n * @param {Element} target - Element to be observed.\r\n */\r\n function ResizeObservation(target) {\r\n /**\r\n * Broadcasted width of content rectangle.\r\n *\r\n * @type {number}\r\n */\r\n this.broadcastWidth = 0;\r\n /**\r\n * Broadcasted height of content rectangle.\r\n *\r\n * @type {number}\r\n */\r\n this.broadcastHeight = 0;\r\n /**\r\n * Reference to the last observed content rectangle.\r\n *\r\n * @private {DOMRectInit}\r\n */\r\n this.contentRect_ = createRectInit(0, 0, 0, 0);\r\n this.target = target;\r\n }\r\n /**\r\n * Updates content rectangle and tells whether it's width or height properties\r\n * have changed since the last broadcast.\r\n *\r\n * @returns {boolean}\r\n */\r\n ResizeObservation.prototype.isActive = function () {\r\n var rect = getContentRect(this.target);\r\n this.contentRect_ = rect;\r\n return (rect.width !== this.broadcastWidth ||\r\n rect.height !== this.broadcastHeight);\r\n };\r\n /**\r\n * Updates 'broadcastWidth' and 'broadcastHeight' properties with a data\r\n * from the corresponding properties of the last observed content rectangle.\r\n *\r\n * @returns {DOMRectInit} Last observed content rectangle.\r\n */\r\n ResizeObservation.prototype.broadcastRect = function () {\r\n var rect = this.contentRect_;\r\n this.broadcastWidth = rect.width;\r\n this.broadcastHeight = rect.height;\r\n return rect;\r\n };\r\n return ResizeObservation;\r\n}());\n\nvar ResizeObserverEntry = /** @class */ (function () {\r\n /**\r\n * Creates an instance of ResizeObserverEntry.\r\n *\r\n * @param {Element} target - Element that is being observed.\r\n * @param {DOMRectInit} rectInit - Data of the element's content rectangle.\r\n */\r\n function ResizeObserverEntry(target, rectInit) {\r\n var contentRect = createReadOnlyRect(rectInit);\r\n // According to the specification following properties are not writable\r\n // and are also not enumerable in the native implementation.\r\n //\r\n // Property accessors are not being used as they'd require to define a\r\n // private WeakMap storage which may cause memory leaks in browsers that\r\n // don't support this type of collections.\r\n defineConfigurable(this, { target: target, contentRect: contentRect });\r\n }\r\n return ResizeObserverEntry;\r\n}());\n\nvar ResizeObserverSPI = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserver.\r\n *\r\n * @param {ResizeObserverCallback} callback - Callback function that is invoked\r\n * when one of the observed elements changes it's content dimensions.\r\n * @param {ResizeObserverController} controller - Controller instance which\r\n * is responsible for the updates of observer.\r\n * @param {ResizeObserver} callbackCtx - Reference to the public\r\n * ResizeObserver instance which will be passed to callback function.\r\n */\r\n function ResizeObserverSPI(callback, controller, callbackCtx) {\r\n /**\r\n * Collection of resize observations that have detected changes in dimensions\r\n * of elements.\r\n *\r\n * @private {Array}\r\n */\r\n this.activeObservations_ = [];\r\n /**\r\n * Registry of the ResizeObservation instances.\r\n *\r\n * @private {Map}\r\n */\r\n this.observations_ = new MapShim();\r\n if (typeof callback !== 'function') {\r\n throw new TypeError('The callback provided as parameter 1 is not a function.');\r\n }\r\n this.callback_ = callback;\r\n this.controller_ = controller;\r\n this.callbackCtx_ = callbackCtx;\r\n }\r\n /**\r\n * Starts observing provided element.\r\n *\r\n * @param {Element} target - Element to be observed.\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.observe = function (target) {\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n // Do nothing if current environment doesn't have the Element interface.\r\n if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n return;\r\n }\r\n if (!(target instanceof getWindowOf(target).Element)) {\r\n throw new TypeError('parameter 1 is not of type \"Element\".');\r\n }\r\n var observations = this.observations_;\r\n // Do nothing if element is already being observed.\r\n if (observations.has(target)) {\r\n return;\r\n }\r\n observations.set(target, new ResizeObservation(target));\r\n this.controller_.addObserver(this);\r\n // Force the update of observations.\r\n this.controller_.refresh();\r\n };\r\n /**\r\n * Stops observing provided element.\r\n *\r\n * @param {Element} target - Element to stop observing.\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.unobserve = function (target) {\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n // Do nothing if current environment doesn't have the Element interface.\r\n if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n return;\r\n }\r\n if (!(target instanceof getWindowOf(target).Element)) {\r\n throw new TypeError('parameter 1 is not of type \"Element\".');\r\n }\r\n var observations = this.observations_;\r\n // Do nothing if element is not being observed.\r\n if (!observations.has(target)) {\r\n return;\r\n }\r\n observations.delete(target);\r\n if (!observations.size) {\r\n this.controller_.removeObserver(this);\r\n }\r\n };\r\n /**\r\n * Stops observing all elements.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.disconnect = function () {\r\n this.clearActive();\r\n this.observations_.clear();\r\n this.controller_.removeObserver(this);\r\n };\r\n /**\r\n * Collects observation instances the associated element of which has changed\r\n * it's content rectangle.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.gatherActive = function () {\r\n var _this = this;\r\n this.clearActive();\r\n this.observations_.forEach(function (observation) {\r\n if (observation.isActive()) {\r\n _this.activeObservations_.push(observation);\r\n }\r\n });\r\n };\r\n /**\r\n * Invokes initial callback function with a list of ResizeObserverEntry\r\n * instances collected from active resize observations.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.broadcastActive = function () {\r\n // Do nothing if observer doesn't have active observations.\r\n if (!this.hasActive()) {\r\n return;\r\n }\r\n var ctx = this.callbackCtx_;\r\n // Create ResizeObserverEntry instance for every active observation.\r\n var entries = this.activeObservations_.map(function (observation) {\r\n return new ResizeObserverEntry(observation.target, observation.broadcastRect());\r\n });\r\n this.callback_.call(ctx, entries, ctx);\r\n this.clearActive();\r\n };\r\n /**\r\n * Clears the collection of active observations.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.clearActive = function () {\r\n this.activeObservations_.splice(0);\r\n };\r\n /**\r\n * Tells whether observer has active observations.\r\n *\r\n * @returns {boolean}\r\n */\r\n ResizeObserverSPI.prototype.hasActive = function () {\r\n return this.activeObservations_.length > 0;\r\n };\r\n return ResizeObserverSPI;\r\n}());\n\n// Registry of internal observers. If WeakMap is not available use current shim\r\n// for the Map collection as it has all required methods and because WeakMap\r\n// can't be fully polyfilled anyway.\r\nvar observers = typeof WeakMap !== 'undefined' ? new WeakMap() : new MapShim();\r\n/**\r\n * ResizeObserver API. Encapsulates the ResizeObserver SPI implementation\r\n * exposing only those methods and properties that are defined in the spec.\r\n */\r\nvar ResizeObserver = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserver.\r\n *\r\n * @param {ResizeObserverCallback} callback - Callback that is invoked when\r\n * dimensions of the observed elements change.\r\n */\r\n function ResizeObserver(callback) {\r\n if (!(this instanceof ResizeObserver)) {\r\n throw new TypeError('Cannot call a class as a function.');\r\n }\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n var controller = ResizeObserverController.getInstance();\r\n var observer = new ResizeObserverSPI(callback, controller, this);\r\n observers.set(this, observer);\r\n }\r\n return ResizeObserver;\r\n}());\r\n// Expose public methods of ResizeObserver.\r\n[\r\n 'observe',\r\n 'unobserve',\r\n 'disconnect'\r\n].forEach(function (method) {\r\n ResizeObserver.prototype[method] = function () {\r\n var _a;\r\n return (_a = observers.get(this))[method].apply(_a, arguments);\r\n };\r\n});\n\nvar index = (function () {\r\n // Export existing implementation if available.\r\n if (typeof global$1.ResizeObserver !== 'undefined') {\r\n return global$1.ResizeObserver;\r\n }\r\n return ResizeObserver;\r\n})();\n\nexport default index;\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ResizeObserver from \"resize-observer-polyfill\"\nimport {\n NEVER,\n Observable,\n Subject,\n defer,\n filter,\n finalize,\n map,\n merge,\n of,\n shareReplay,\n startWith,\n switchMap,\n tap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementSize {\n width: number /* Element width */\n height: number /* Element height */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Resize observer entry subject\n */\nconst entry$ = new Subject()\n\n/**\n * Resize observer observable\n *\n * This observable will create a `ResizeObserver` on the first subscription\n * and will automatically terminate it when there are no more subscribers.\n * It's quite important to centralize observation in a single `ResizeObserver`,\n * as the performance difference can be quite dramatic, as the link shows.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n new ResizeObserver(entries => {\n for (const entry of entries)\n entry$.next(entry)\n })\n))\n .pipe(\n switchMap(observer => merge(NEVER, of(observer))\n .pipe(\n finalize(() => observer.disconnect())\n )\n ),\n shareReplay(1)\n )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n el: HTMLElement\n): ElementSize {\n return {\n width: el.offsetWidth,\n height: el.offsetHeight\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n el: HTMLElement\n): Observable {\n return observer$\n .pipe(\n tap(observer => observer.observe(el)),\n switchMap(observer => entry$\n .pipe(\n filter(({ target }) => target === el),\n finalize(() => observer.unobserve(el)),\n map(() => getElementSize(el))\n )\n ),\n startWith(getElementSize(el))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ElementSize } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content size (= scroll width and height)\n *\n * @param el - Element\n *\n * @returns Element content size\n */\nexport function getElementContentSize(\n el: HTMLElement\n): ElementSize {\n return {\n width: el.scrollWidth,\n height: el.scrollHeight\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n NEVER,\n Observable,\n Subject,\n defer,\n distinctUntilChanged,\n filter,\n finalize,\n map,\n merge,\n of,\n shareReplay,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport {\n getElementContentSize,\n getElementSize,\n watchElementContentOffset\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Intersection observer entry subject\n */\nconst entry$ = new Subject()\n\n/**\n * Intersection observer observable\n *\n * This observable will create an `IntersectionObserver` on first subscription\n * and will automatically terminate it when there are no more subscribers.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n new IntersectionObserver(entries => {\n for (const entry of entries)\n entry$.next(entry)\n }, {\n threshold: 0\n })\n))\n .pipe(\n switchMap(observer => merge(NEVER, of(observer))\n .pipe(\n finalize(() => observer.disconnect())\n )\n ),\n shareReplay(1)\n )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element visibility\n *\n * @param el - Element\n *\n * @returns Element visibility observable\n */\nexport function watchElementVisibility(\n el: HTMLElement\n): Observable {\n return observer$\n .pipe(\n tap(observer => observer.observe(el)),\n switchMap(observer => entry$\n .pipe(\n filter(({ target }) => target === el),\n finalize(() => observer.unobserve(el)),\n map(({ isIntersecting }) => isIntersecting)\n )\n )\n )\n}\n\n/**\n * Watch element boundary\n *\n * This function returns an observable which emits whether the bottom content\n * boundary (= scroll offset) of an element is within a certain threshold.\n *\n * @param el - Element\n * @param threshold - Threshold\n *\n * @returns Element boundary observable\n */\nexport function watchElementBoundary(\n el: HTMLElement, threshold = 16\n): Observable {\n return watchElementContentOffset(el)\n .pipe(\n map(({ y }) => {\n const visible = getElementSize(el)\n const content = getElementContentSize(el)\n return y >= (\n content.height - visible.height - threshold\n )\n }),\n distinctUntilChanged()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n startWith\n} from \"rxjs\"\n\nimport { getElement } from \"../element\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle\n */\nexport type Toggle =\n | \"drawer\" /* Toggle for drawer */\n | \"search\" /* Toggle for search */\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle map\n */\nconst toggles: Record = {\n drawer: getElement(\"[data-md-toggle=drawer]\"),\n search: getElement(\"[data-md-toggle=search]\")\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the value of a toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value\n */\nexport function getToggle(name: Toggle): boolean {\n return toggles[name].checked\n}\n\n/**\n * Set toggle\n *\n * Simulating a click event seems to be the most cross-browser compatible way\n * of changing the value while also emitting a `change` event. Before, Material\n * used `CustomEvent` to programmatically change the value of a toggle, but this\n * is a much simpler and cleaner solution which doesn't require a polyfill.\n *\n * @param name - Toggle\n * @param value - Toggle value\n */\nexport function setToggle(name: Toggle, value: boolean): void {\n if (toggles[name].checked !== value)\n toggles[name].click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value observable\n */\nexport function watchToggle(name: Toggle): Observable {\n const el = toggles[name]\n return fromEvent(el, \"change\")\n .pipe(\n map(() => el.checked),\n startWith(el.checked)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n share\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../element\"\nimport { getToggle } from \"../toggle\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Keyboard mode\n */\nexport type KeyboardMode =\n | \"global\" /* Global */\n | \"search\" /* Search is open */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Keyboard\n */\nexport interface Keyboard {\n mode: KeyboardMode /* Keyboard mode */\n type: string /* Key type */\n claim(): void /* Key claim */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether an element may receive keyboard input\n *\n * @param el - Element\n * @param type - Key type\n *\n * @returns Test result\n */\nfunction isSusceptibleToKeyboard(\n el: HTMLElement, type: string\n): boolean {\n switch (el.constructor) {\n\n /* Input elements */\n case HTMLInputElement:\n /* @ts-expect-error - omit unnecessary type cast */\n if (el.type === \"radio\")\n return /^Arrow/.test(type)\n else\n return true\n\n /* Select element and textarea */\n case HTMLSelectElement:\n case HTMLTextAreaElement:\n return true\n\n /* Everything else */\n default:\n return el.isContentEditable\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch keyboard\n *\n * @returns Keyboard observable\n */\nexport function watchKeyboard(): Observable {\n return fromEvent(window, \"keydown\")\n .pipe(\n filter(ev => !(ev.metaKey || ev.ctrlKey)),\n map(ev => ({\n mode: getToggle(\"search\") ? \"search\" : \"global\",\n type: ev.key,\n claim() {\n ev.preventDefault()\n ev.stopPropagation()\n }\n } as Keyboard)),\n filter(({ mode, type }) => {\n if (mode === \"global\") {\n const active = getActiveElement()\n if (typeof active !== \"undefined\")\n return !isSusceptibleToKeyboard(active, type)\n }\n return true\n }),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Subject } from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location\n *\n * This function returns a `URL` object (and not `Location`) to normalize the\n * typings across the application. Furthermore, locations need to be tracked\n * without setting them and `Location` is a singleton which represents the\n * current location.\n *\n * @returns URL\n */\nexport function getLocation(): URL {\n return new URL(location.href)\n}\n\n/**\n * Set location\n *\n * @param url - URL to change to\n */\nexport function setLocation(url: URL): void {\n location.href = url.href\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location\n *\n * @returns Location subject\n */\nexport function watchLocation(): Subject {\n return new Subject()\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { JSX as JSXInternal } from \"preact\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * HTML attributes\n */\ntype Attributes =\n & JSXInternal.HTMLAttributes\n & JSXInternal.SVGAttributes\n & Record\n\n/**\n * Child element\n */\ntype Child =\n | HTMLElement\n | Text\n | string\n | number\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Append a child node to an element\n *\n * @param el - Element\n * @param child - Child node(s)\n */\nfunction appendChild(el: HTMLElement, child: Child | Child[]): void {\n\n /* Handle primitive types (including raw HTML) */\n if (typeof child === \"string\" || typeof child === \"number\") {\n el.innerHTML += child.toString()\n\n /* Handle nodes */\n } else if (child instanceof Node) {\n el.appendChild(child)\n\n /* Handle nested children */\n } else if (Array.isArray(child)) {\n for (const node of child)\n appendChild(el, node)\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * JSX factory\n *\n * @template T - Element type\n *\n * @param tag - HTML tag\n * @param attributes - HTML attributes\n * @param children - Child elements\n *\n * @returns Element\n */\nexport function h(\n tag: T, attributes?: Attributes | null, ...children: Child[]\n): HTMLElementTagNameMap[T]\n\nexport function h(\n tag: string, attributes?: Attributes | null, ...children: Child[]\n): T\n\nexport function h(\n tag: string, attributes?: Attributes | null, ...children: Child[]\n): T {\n const el = document.createElement(tag)\n\n /* Set attributes, if any */\n if (attributes)\n for (const attr of Object.keys(attributes))\n if (typeof attributes[attr] !== \"boolean\")\n el.setAttribute(attr, attributes[attr])\n else if (attributes[attr])\n el.setAttribute(attr, \"\")\n\n /* Append child nodes */\n for (const child of children)\n appendChild(el, child)\n\n /* Return element */\n return el as T\n}\n\n/* ----------------------------------------------------------------------------\n * Namespace\n * ------------------------------------------------------------------------- */\n\nexport declare namespace h {\n namespace JSX {\n type Element = HTMLElement\n type IntrinsicElements = JSXInternal.IntrinsicElements\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Truncate a string after the given number of characters\n *\n * This is not a very reasonable approach, since the summaries kind of suck.\n * It would be better to create something more intelligent, highlighting the\n * search occurrences and making a better summary out of it, but this note was\n * written three years ago, so who knows if we'll ever fix it.\n *\n * @param value - Value to be truncated\n * @param n - Number of characters\n *\n * @returns Truncated value\n */\nexport function truncate(value: string, n: number): string {\n let i = n\n if (value.length > i) {\n while (value[i] !== \" \" && --i > 0) { /* keep eating */ }\n return `${value.substring(0, i)}...`\n }\n return value\n}\n\n/**\n * Round a number for display with repository facts\n *\n * This is a reverse-engineered version of GitHub's weird rounding algorithm\n * for stars, forks and all other numbers. While all numbers below `1,000` are\n * returned as-is, bigger numbers are converted to fixed numbers:\n *\n * - `1,049` => `1k`\n * - `1,050` => `1.1k`\n * - `1,949` => `1.9k`\n * - `1,950` => `2k`\n *\n * @param value - Original value\n *\n * @returns Rounded value\n */\nexport function round(value: number): string {\n if (value > 999) {\n const digits = +((value - 950) % 1000 > 99)\n return `${((value + 0.000001) / 1000).toFixed(digits)}k`\n } else {\n return value.toString()\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n shareReplay,\n startWith\n} from \"rxjs\"\n\nimport { getOptionalElement } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location hash\n *\n * @returns Location hash\n */\nexport function getLocationHash(): string {\n return location.hash.substring(1)\n}\n\n/**\n * Set location hash\n *\n * Setting a new fragment identifier via `location.hash` will have no effect\n * if the value doesn't change. When a new fragment identifier is set, we want\n * the browser to target the respective element at all times, which is why we\n * use this dirty little trick.\n *\n * @param hash - Location hash\n */\nexport function setLocationHash(hash: string): void {\n const el = h(\"a\", { href: hash })\n el.addEventListener(\"click\", ev => ev.stopPropagation())\n el.click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location hash\n *\n * @returns Location hash observable\n */\nexport function watchLocationHash(): Observable {\n return fromEvent(window, \"hashchange\")\n .pipe(\n map(getLocationHash),\n startWith(getLocationHash()),\n filter(hash => hash.length > 0),\n shareReplay(1)\n )\n}\n\n/**\n * Watch location target\n *\n * @returns Location target observable\n */\nexport function watchLocationTarget(): Observable {\n return watchLocationHash()\n .pipe(\n map(id => getOptionalElement(`[id=\"${id}\"]`)!),\n filter(el => typeof el !== \"undefined\")\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n fromEvent,\n fromEventPattern,\n map,\n merge,\n startWith,\n switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch media query\n *\n * Note that although `MediaQueryList.addListener` is deprecated we have to\n * use it, because it's the only way to ensure proper downward compatibility.\n *\n * @see https://bit.ly/3dUBH2m - GitHub issue\n *\n * @param query - Media query\n *\n * @returns Media observable\n */\nexport function watchMedia(query: string): Observable {\n const media = matchMedia(query)\n return fromEventPattern(next => (\n media.addListener(() => next(media.matches))\n ))\n .pipe(\n startWith(media.matches)\n )\n}\n\n/**\n * Watch print mode\n *\n * @returns Print observable\n */\nexport function watchPrint(): Observable {\n const media = matchMedia(\"print\")\n return merge(\n fromEvent(window, \"beforeprint\").pipe(map(() => true)),\n fromEvent(window, \"afterprint\").pipe(map(() => false))\n )\n .pipe(\n startWith(media.matches)\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Toggle an observable with a media observable\n *\n * @template T - Data type\n *\n * @param query$ - Media observable\n * @param factory - Observable factory\n *\n * @returns Toggled observable\n */\nexport function at(\n query$: Observable, factory: () => Observable\n): Observable {\n return query$\n .pipe(\n switchMap(active => active ? factory() : EMPTY)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n catchError,\n from,\n map,\n of,\n shareReplay,\n switchMap,\n throwError\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * If the request fails (e.g. when dispatched from `file://` locations), the\n * observable will complete without emitting a value.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Response observable\n */\nexport function request(\n url: URL | string, options: RequestInit = { credentials: \"same-origin\" }\n): Observable {\n return from(fetch(`${url}`, options))\n .pipe(\n catchError(() => EMPTY),\n switchMap(res => res.status !== 200\n ? throwError(() => new Error(res.statusText))\n : of(res)\n )\n )\n}\n\n/**\n * Fetch JSON from the given URL\n *\n * @template T - Data type\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestJSON(\n url: URL | string, options?: RequestInit\n): Observable {\n return request(url, options)\n .pipe(\n switchMap(res => res.json()),\n shareReplay(1)\n )\n}\n\n/**\n * Fetch XML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestXML(\n url: URL | string, options?: RequestInit\n): Observable {\n const dom = new DOMParser()\n return request(url, options)\n .pipe(\n switchMap(res => res.text()),\n map(res => dom.parseFromString(res, \"text/xml\")),\n shareReplay(1)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n defer,\n finalize,\n fromEvent,\n map,\n merge,\n switchMap,\n take,\n throwError\n} from \"rxjs\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create and load a `script` element\n *\n * This function returns an observable that will emit when the script was\n * successfully loaded, or throw an error if it didn't.\n *\n * @param src - Script URL\n *\n * @returns Script observable\n */\nexport function watchScript(src: string): Observable {\n const script = h(\"script\", { src })\n return defer(() => {\n document.head.appendChild(script)\n return merge(\n fromEvent(script, \"load\"),\n fromEvent(script, \"error\")\n .pipe(\n switchMap(() => (\n throwError(() => new ReferenceError(`Invalid script: ${src}`))\n ))\n )\n )\n .pipe(\n map(() => undefined),\n finalize(() => document.head.removeChild(script)),\n take(1)\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport offset\n */\nexport interface ViewportOffset {\n x: number /* Horizontal offset */\n y: number /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport offset\n *\n * On iOS Safari, viewport offset can be negative due to overflow scrolling.\n * As this may induce strange behaviors downstream, we'll just limit it to 0.\n *\n * @returns Viewport offset\n */\nexport function getViewportOffset(): ViewportOffset {\n return {\n x: Math.max(0, scrollX),\n y: Math.max(0, scrollY)\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport offset\n *\n * @returns Viewport offset observable\n */\nexport function watchViewportOffset(): Observable {\n return merge(\n fromEvent(window, \"scroll\", { passive: true }),\n fromEvent(window, \"resize\", { passive: true })\n )\n .pipe(\n map(getViewportOffset),\n startWith(getViewportOffset())\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport size\n */\nexport interface ViewportSize {\n width: number /* Viewport width */\n height: number /* Viewport height */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport size\n *\n * @returns Viewport size\n */\nexport function getViewportSize(): ViewportSize {\n return {\n width: innerWidth,\n height: innerHeight\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport size\n *\n * @returns Viewport size observable\n */\nexport function watchViewportSize(): Observable {\n return fromEvent(window, \"resize\", { passive: true })\n .pipe(\n map(getViewportSize),\n startWith(getViewportSize())\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n map,\n shareReplay\n} from \"rxjs\"\n\nimport {\n ViewportOffset,\n watchViewportOffset\n} from \"../offset\"\nimport {\n ViewportSize,\n watchViewportSize\n} from \"../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport\n */\nexport interface Viewport {\n offset: ViewportOffset /* Viewport offset */\n size: ViewportSize /* Viewport size */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport\n *\n * @returns Viewport observable\n */\nexport function watchViewport(): Observable {\n return combineLatest([\n watchViewportOffset(),\n watchViewportSize()\n ])\n .pipe(\n map(([offset, size]) => ({ offset, size })),\n shareReplay(1)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n distinctUntilKeyChanged,\n map\n} from \"rxjs\"\n\nimport { Header } from \"~/components\"\n\nimport { getElementOffset } from \"../../element\"\nimport { Viewport } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
/* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport relative to element\n *\n * @param el - Element\n * @param options - Options\n *\n * @returns Viewport observable\n */\nexport function watchViewportAt(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n const size$ = viewport$\n .pipe(\n distinctUntilKeyChanged(\"size\")\n )\n\n /* Compute element offset */\n const offset$ = combineLatest([size$, header$])\n .pipe(\n map(() => getElementOffset(el))\n )\n\n /* Compute relative viewport, return hot observable */\n return combineLatest([header$, viewport$, offset$])\n .pipe(\n map(([{ height }, { offset, size }, { x, y }]) => ({\n offset: {\n x: offset.x - x,\n y: offset.y - y + height\n },\n size\n }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n fromEvent,\n map,\n share,\n switchMap,\n tap,\n throttle\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Worker message\n */\nexport interface WorkerMessage {\n type: unknown /* Message type */\n data?: unknown /* Message data */\n}\n\n/**\n * Worker handler\n *\n * @template T - Message type\n */\nexport interface WorkerHandler<\n T extends WorkerMessage\n> {\n tx$: Subject /* Message transmission subject */\n rx$: Observable /* Message receive observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n *\n * @template T - Worker message type\n */\ninterface WatchOptions {\n tx$: Observable /* Message transmission observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch a web worker\n *\n * This function returns an observable that sends all values emitted by the\n * message observable to the web worker. Web worker communication is expected\n * to be bidirectional (request-response) and synchronous. Messages that are\n * emitted during a pending request are throttled, the last one is emitted.\n *\n * @param worker - Web worker\n * @param options - Options\n *\n * @returns Worker message observable\n */\nexport function watchWorker(\n worker: Worker, { tx$ }: WatchOptions\n): Observable {\n\n /* Intercept messages from worker-like objects */\n const rx$ = fromEvent(worker, \"message\")\n .pipe(\n map(({ data }) => data as T)\n )\n\n /* Send and receive messages, return hot observable */\n return tx$\n .pipe(\n throttle(() => rx$, { leading: true, trailing: true }),\n tap(message => worker.postMessage(message)),\n switchMap(() => rx$),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getLocation } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Feature flag\n */\nexport type Flag =\n | \"content.code.annotate\" /* Code annotations */\n | \"header.autohide\" /* Hide header */\n | \"navigation.expand\" /* Automatic expansion */\n | \"navigation.indexes\" /* Section pages */\n | \"navigation.instant\" /* Instant loading */\n | \"navigation.sections\" /* Section navigation */\n | \"navigation.tabs\" /* Tabs navigation */\n | \"navigation.tabs.sticky\" /* Tabs navigation (sticky) */\n | \"navigation.top\" /* Back-to-top button */\n | \"navigation.tracking\" /* Anchor tracking */\n | \"search.highlight\" /* Search highlighting */\n | \"search.share\" /* Search sharing */\n | \"search.suggest\" /* Search suggestions */\n | \"toc.integrate\" /* Integrated table of contents */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Translation\n */\nexport type Translation =\n | \"clipboard.copy\" /* Copy to clipboard */\n | \"clipboard.copied\" /* Copied to clipboard */\n | \"search.config.lang\" /* Search language */\n | \"search.config.pipeline\" /* Search pipeline */\n | \"search.config.separator\" /* Search separator */\n | \"search.placeholder\" /* Search */\n | \"search.result.placeholder\" /* Type to start searching */\n | \"search.result.none\" /* No matching documents */\n | \"search.result.one\" /* 1 matching document */\n | \"search.result.other\" /* # matching documents */\n | \"search.result.more.one\" /* 1 more on this page */\n | \"search.result.more.other\" /* # more on this page */\n | \"search.result.term.missing\" /* Missing */\n | \"select.version.title\" /* Version selector */\n\n/**\n * Translations\n */\nexport type Translations = Record\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Versioning\n */\nexport interface Versioning {\n provider: \"mike\" /* Version provider */\n default?: string /* Default version */\n}\n\n/**\n * Configuration\n */\nexport interface Config {\n base: string /* Base URL */\n features: Flag[] /* Feature flags */\n translations: Translations /* Translations */\n search: string /* Search worker URL */\n version?: Versioning /* Versioning */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration and make base URL absolute\n */\nconst script = getElement(\"#__config\")\nconst config: Config = JSON.parse(script.textContent!)\nconfig.base = `${new URL(config.base, getLocation())}`\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration\n *\n * @returns Global configuration\n */\nexport function configuration(): Config {\n return config\n}\n\n/**\n * Check whether a feature flag is enabled\n *\n * @param flag - Feature flag\n *\n * @returns Test result\n */\nexport function feature(flag: Flag): boolean {\n return config.features.includes(flag)\n}\n\n/**\n * Retrieve the translation for the given key\n *\n * @param key - Key to be translated\n * @param value - Positional value, if any\n *\n * @returns Translation\n */\nexport function translation(\n key: Translation, value?: string | number\n): string {\n return typeof value !== \"undefined\"\n ? config.translations[key].replace(\"#\", value.toString())\n : config.translations[key]\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type\n */\nexport type ComponentType =\n | \"announce\" /* Announcement bar */\n | \"container\" /* Container */\n | \"content\" /* Content */\n | \"dialog\" /* Dialog */\n | \"header\" /* Header */\n | \"header-title\" /* Header title */\n | \"header-topic\" /* Header topic */\n | \"main\" /* Main area */\n | \"outdated\" /* Version warning */\n | \"palette\" /* Color palette */\n | \"search\" /* Search */\n | \"search-query\" /* Search input */\n | \"search-result\" /* Search results */\n | \"search-share\" /* Search sharing */\n | \"search-suggest\" /* Search suggestions */\n | \"sidebar\" /* Sidebar */\n | \"skip\" /* Skip link */\n | \"source\" /* Repository information */\n | \"tabs\" /* Navigation tabs */\n | \"toc\" /* Table of contents */\n | \"top\" /* Back-to-top button */\n\n/**\n * Component\n *\n * @template T - Component type\n * @template U - Reference type\n */\nexport type Component<\n T extends {} = {},\n U extends HTMLElement = HTMLElement\n> =\n T & {\n ref: U /* Component reference */\n }\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type map\n */\ninterface ComponentTypeMap {\n \"announce\": HTMLElement /* Announcement bar */\n \"container\": HTMLElement /* Container */\n \"content\": HTMLElement /* Content */\n \"dialog\": HTMLElement /* Dialog */\n \"header\": HTMLElement /* Header */\n \"header-title\": HTMLElement /* Header title */\n \"header-topic\": HTMLElement /* Header topic */\n \"main\": HTMLElement /* Main area */\n \"outdated\": HTMLElement /* Version warning */\n \"palette\": HTMLElement /* Color palette */\n \"search\": HTMLElement /* Search */\n \"search-query\": HTMLInputElement /* Search input */\n \"search-result\": HTMLElement /* Search results */\n \"search-share\": HTMLAnchorElement /* Search sharing */\n \"search-suggest\": HTMLElement /* Search suggestions */\n \"sidebar\": HTMLElement /* Sidebar */\n \"skip\": HTMLAnchorElement /* Skip link */\n \"source\": HTMLAnchorElement /* Repository information */\n \"tabs\": HTMLElement /* Navigation tabs */\n \"toc\": HTMLElement /* Table of contents */\n \"top\": HTMLAnchorElement /* Back-to-top button */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the element for a given component or throw a reference error\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getComponentElement(\n type: T, node: ParentNode = document\n): ComponentTypeMap[T] {\n return getElement(`[data-md-component=${type}]`, node)\n}\n\n/**\n * Retrieve all elements for a given component\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getComponentElements(\n type: T, node: ParentNode = document\n): ComponentTypeMap[T][] {\n return getElements(`[data-md-component=${type}]`, node)\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n finalize,\n map,\n mergeWith,\n switchMap,\n take,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n getElementContentSize,\n watchElementSize,\n watchElementVisibility\n} from \"~/browser\"\nimport { renderClipboardButton } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n Annotation,\n mountAnnotationList\n} from \"../../annotation\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Code block\n */\nexport interface CodeBlock {\n scrollable: boolean /* Code block overflows */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for Clipboard.js integration\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find candidate list element directly following a code block\n *\n * @param el - Code block element\n *\n * @returns List element or nothing\n */\nfunction findCandidateList(el: HTMLElement): HTMLElement | undefined {\n if (el.nextElementSibling) {\n const sibling = el.nextElementSibling as HTMLElement\n if (sibling.tagName === \"OL\")\n return sibling\n\n /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n else if (sibling.tagName === \"P\" && !sibling.children.length)\n return findCandidateList(sibling)\n }\n\n /* Everything else */\n return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch code block\n *\n * This function monitors size changes of the viewport, as well as switches of\n * content tabs with embedded code blocks, as both may trigger overflow.\n *\n * @param el - Code block element\n *\n * @returns Code block observable\n */\nexport function watchCodeBlock(\n el: HTMLElement\n): Observable {\n return watchElementSize(el)\n .pipe(\n map(({ width }) => {\n const content = getElementContentSize(el)\n return {\n scrollable: content.width > width\n }\n }),\n distinctUntilKeyChanged(\"scrollable\")\n )\n}\n\n/**\n * Mount code block\n *\n * This function ensures that an overflowing code block is focusable through\n * keyboard, so it can be scrolled without a mouse to improve on accessibility.\n * Furthermore, if code annotations are enabled, they are mounted if and only\n * if the code block is currently visible, e.g., not in a hidden content tab.\n *\n * @param el - Code block element\n * @param options - Options\n *\n * @returns Code block and annotation component observable\n */\nexport function mountCodeBlock(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const { matches: hover } = matchMedia(\"(hover)\")\n\n /* Defer mounting of code block - see https://bit.ly/3vHVoVD */\n const factory$ = defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ scrollable }) => {\n if (scrollable && hover)\n el.setAttribute(\"tabindex\", \"0\")\n else\n el.removeAttribute(\"tabindex\")\n })\n\n /* Render button for Clipboard.js integration */\n if (ClipboardJS.isSupported()) {\n const parent = el.closest(\"pre\")!\n parent.id = `__code_${++sequence}`\n parent.insertBefore(\n renderClipboardButton(parent.id),\n el\n )\n }\n\n /* Handle code annotations */\n const container = el.closest(\".highlight\")\n if (container instanceof HTMLElement) {\n const list = findCandidateList(container)\n\n /* Mount code annotations, if enabled */\n if (typeof list !== \"undefined\" && (\n container.classList.contains(\"annotate\") ||\n feature(\"content.code.annotate\")\n )) {\n const annotations$ = mountAnnotationList(list, el, options)\n\n /* Create and return component */\n return watchCodeBlock(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state })),\n mergeWith(\n watchElementSize(container)\n .pipe(\n takeUntil(push$.pipe(takeLast(1))),\n map(({ width, height }) => width && height),\n distinctUntilChanged(),\n switchMap(active => active ? annotations$ : EMPTY)\n )\n )\n )\n }\n }\n\n /* Create and return component */\n return watchCodeBlock(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n\n /* Mount code block on first sight */\n return watchElementVisibility(el)\n .pipe(\n filter(visible => visible),\n take(1),\n switchMap(() => factory$)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render an empty annotation\n *\n * @param id - Annotation identifier\n *\n * @returns Element\n */\nexport function renderAnnotation(id: number): HTMLElement {\n return (\n \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a 'copy-to-clipboard' button\n *\n * @param id - Unique identifier\n *\n * @returns Element\n */\nexport function renderClipboardButton(id: string): HTMLElement {\n return (\n code`}\n >\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { feature, translation } from \"~/_\"\nimport {\n SearchDocument,\n SearchMetadata,\n SearchResultItem\n} from \"~/integrations/search\"\nimport { h, truncate } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Render flag\n */\nconst enum Flag {\n TEASER = 1, /* Render teaser */\n PARENT = 2 /* Render as parent */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper function\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search document\n *\n * @param document - Search document\n * @param flag - Render flags\n *\n * @returns Element\n */\nfunction renderSearchDocument(\n document: SearchDocument & SearchMetadata, flag: Flag\n): HTMLElement {\n const parent = flag & Flag.PARENT\n const teaser = flag & Flag.TEASER\n\n /* Render missing query terms */\n const missing = Object.keys(document.terms)\n .filter(key => !document.terms[key])\n .reduce((list, key) => [\n ...list, {key}, \" \"\n ], [])\n .slice(0, -1)\n\n /* Assemble query string for highlighting */\n const url = new URL(document.location)\n if (feature(\"search.highlight\"))\n url.searchParams.set(\"h\", Object.entries(document.terms)\n .filter(([, match]) => match)\n .reduce((highlight, [value]) => `${highlight} ${value}`.trim(), \"\")\n )\n\n /* Render article or section, depending on flags */\n return (\n \n \n {parent > 0 &&
}\n

{document.title}

\n {teaser > 0 && document.text.length > 0 &&\n

\n {truncate(document.text, 320)}\n

\n }\n {document.tags && document.tags.map(tag => (\n {tag}\n ))}\n {teaser > 0 && missing.length > 0 &&\n

\n {translation(\"search.result.term.missing\")}: {...missing}\n

\n }\n \n \n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search result\n *\n * @param result - Search result\n *\n * @returns Element\n */\nexport function renderSearchResultItem(\n result: SearchResultItem\n): HTMLElement {\n const threshold = result[0].score\n const docs = [...result]\n\n /* Find and extract parent article */\n const parent = docs.findIndex(doc => !doc.location.includes(\"#\"))\n const [article] = docs.splice(parent, 1)\n\n /* Determine last index above threshold */\n let index = docs.findIndex(doc => doc.score < threshold)\n if (index === -1)\n index = docs.length\n\n /* Partition sections */\n const best = docs.slice(0, index)\n const more = docs.slice(index)\n\n /* Render children */\n const children = [\n renderSearchDocument(article, Flag.PARENT | +(!parent && index === 0)),\n ...best.map(section => renderSearchDocument(section, Flag.TEASER)),\n ...more.length ? [\n
\n \n {more.length > 0 && more.length === 1\n ? translation(\"search.result.more.one\")\n : translation(\"search.result.more.other\", more.length)\n }\n \n {...more.map(section => renderSearchDocument(section, Flag.TEASER))}\n
\n ] : []\n ]\n\n /* Render search result */\n return (\n
  • \n {children}\n
    • \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SourceFacts } from \"~/components\"\nimport { h, round } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render repository facts\n *\n * @param facts - Repository facts\n *\n * @returns Element\n */\nexport function renderSourceFacts(facts: SourceFacts): HTMLElement {\n return (\n
      \n {Object.entries(facts).map(([key, value]) => (\n
    • \n {typeof value === \"number\" ? round(value) : value}\n
      • \n ))}\n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a table inside a wrapper to improve scrolling on mobile\n *\n * @param table - Table element\n *\n * @returns Element\n */\nexport function renderTable(table: HTMLElement): HTMLElement {\n return (\n
    \n
    \n {table}\n
    \n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { configuration, translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Version\n */\nexport interface Version {\n version: string /* Version identifier */\n title: string /* Version title */\n aliases: string[] /* Version aliases */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version\n *\n * @param version - Version\n *\n * @returns Element\n */\nfunction renderVersion(version: Version): HTMLElement {\n const config = configuration()\n\n /* Ensure trailing slash, see https://bit.ly/3rL5u3f */\n const url = new URL(`../${version.version}/`, config.base)\n return (\n
  • \n \n {version.title}\n \n
    • \n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version selector\n *\n * @param versions - Versions\n * @param active - Active version\n *\n * @returns Element\n */\nexport function renderVersionSelector(\n versions: Version[], active: Version\n): HTMLElement {\n return (\n
    \n \n {active.title}\n \n
      \n {versions.map(renderVersion)}\n
    \n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n animationFrameScheduler,\n combineLatest,\n defer,\n finalize,\n fromEvent,\n map,\n switchMap,\n take,\n takeLast,\n takeUntil,\n tap,\n throttleTime\n} from \"rxjs\"\n\nimport {\n ElementOffset,\n getElement,\n getElementSize,\n watchElementContentOffset,\n watchElementFocus,\n watchElementOffset,\n watchElementVisibility\n} from \"~/browser\"\n\nimport { Component } from \"../../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Annotation\n */\nexport interface Annotation {\n active: boolean /* Annotation is active */\n offset: ElementOffset /* Annotation offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation observable\n */\nexport function watchAnnotation(\n el: HTMLElement, container: HTMLElement\n): Observable {\n const offset$ = defer(() => combineLatest([\n watchElementOffset(el),\n watchElementContentOffset(container)\n ]))\n .pipe(\n map(([{ x, y }, scroll]) => {\n const { width } = getElementSize(el)\n return ({\n x: x - scroll.x + width / 2,\n y: y - scroll.y\n })\n })\n )\n\n /* Actively watch annotation on focus */\n return watchElementFocus(el)\n .pipe(\n switchMap(active => offset$\n .pipe(\n map(offset => ({ active, offset })),\n take(+!active || Infinity)\n )\n )\n )\n}\n\n/**\n * Mount annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotation(\n el: HTMLElement, container: HTMLElement\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe({\n\n /* Handle emission */\n next({ offset }) {\n el.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n el.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-tooltip-x\")\n el.style.removeProperty(\"--md-tooltip-y\")\n }\n })\n\n /* Start animation only when annotation is visible */\n const done$ = push$.pipe(takeLast(1))\n watchElementVisibility(el)\n .pipe(\n takeUntil(done$)\n )\n .subscribe(visible => {\n el.toggleAttribute(\"data-md-visible\", visible)\n })\n\n /* Track relative origin of tooltip */\n push$\n .pipe(\n throttleTime(500, animationFrameScheduler),\n map(() => container.getBoundingClientRect()),\n map(({ x }) => x)\n )\n .subscribe({\n\n /* Handle emission */\n next(origin) {\n if (origin)\n el.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n else\n el.style.removeProperty(\"--md-tooltip-0\")\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-tooltip-0\")\n }\n })\n\n /* Close open annotation on click */\n const index = getElement(\":scope > :last-child\", el)\n const blur$ = fromEvent(index, \"mousedown\", { once: true })\n push$\n .pipe(\n switchMap(({ active }) => active ? blur$ : EMPTY),\n tap(ev => ev.preventDefault())\n )\n .subscribe(() => el.blur())\n\n /* Create and return component */\n return watchAnnotation(el, container)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n finalize,\n merge,\n share,\n takeLast,\n takeUntil\n} from \"rxjs\"\n\nimport {\n getElement,\n getElements,\n getOptionalElement\n} from \"~/browser\"\nimport { renderAnnotation } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n Annotation,\n mountAnnotation\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find all annotation markers in the given code block\n *\n * @param container - Containing element\n *\n * @returns Annotation markers\n */\nfunction findAnnotationMarkers(container: HTMLElement): Text[] {\n const markers: Text[] = []\n for (const comment of getElements(\".c, .c1, .cm\", container)) {\n let match: RegExpExecArray | null\n\n /* Split text at marker and add to list */\n let text = comment.firstChild as Text\n if (text instanceof Text)\n while ((match = /\\((\\d+)\\)/.exec(text.textContent!))) {\n const marker = text.splitText(match.index)\n text = marker.splitText(match[0].length)\n markers.push(marker)\n }\n }\n return markers\n}\n\n/**\n * Swap the child nodes of two elements\n *\n * @param source - Source element\n * @param target - Target element\n */\nfunction swap(source: HTMLElement, target: HTMLElement): void {\n target.append(...Array.from(source.childNodes))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation list\n *\n * This function analyzes the containing code block and checks for markers\n * referring to elements in the given annotation list. If no markers are found,\n * the list is left untouched. Otherwise, list elements are rendered as\n * annotations inside the code block.\n *\n * @param el - Annotation list element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationList(\n el: HTMLElement, container: HTMLElement, { print$ }: MountOptions\n): Observable> {\n\n /* Find and replace all markers with empty annotations */\n const annotations = new Map()\n for (const marker of findAnnotationMarkers(container)) {\n const [, id] = marker.textContent!.match(/\\((\\d+)\\)/)!\n if (getOptionalElement(`li:nth-child(${id})`, el)) {\n annotations.set(+id, renderAnnotation(+id))\n marker.replaceWith(annotations.get(+id)!)\n }\n }\n\n /* Keep list if there are no annotations to render */\n if (annotations.size === 0)\n return EMPTY\n\n /* Create and return component */\n return defer(() => {\n const done$ = new Subject()\n\n /* Handle print mode - see https://bit.ly/3rgPdpt */\n print$\n .pipe(\n takeUntil(done$.pipe(takeLast(1)))\n )\n .subscribe(active => {\n el.hidden = !active\n\n /* Show annotations in code block or list (print) */\n for (const [id, annotation] of annotations) {\n const inner = getElement(\".md-typeset\", annotation)\n const child = getElement(`li:nth-child(${id})`, el)\n if (!active)\n swap(child, inner)\n else\n swap(inner, child)\n }\n })\n\n /* Create and return component */\n return merge(...[...annotations]\n .map(([, annotation]) => (\n mountAnnotation(annotation, container)\n ))\n )\n .pipe(\n finalize(() => done$.complete()),\n share()\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n map,\n of,\n shareReplay,\n tap\n} from \"rxjs\"\n\nimport { watchScript } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../../_\"\n\nimport themeCSS from \"./index.css\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid diagram\n */\nexport interface Mermaid {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid instance observable\n */\nlet mermaid$: Observable\n\n/**\n * Global index for Mermaid integration\n */\nlet index = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch Mermaid script\n *\n * @returns Mermaid scripts observable\n */\nfunction fetchScripts(): Observable {\n return typeof mermaid === \"undefined\" || mermaid instanceof Element\n ? watchScript(\"https://unpkg.com/mermaid@9.0.1/dist/mermaid.min.js\")\n : of(undefined)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount Mermaid diagram\n *\n * @param el - Code block element\n *\n * @returns Mermaid diagram component observable\n */\nexport function mountMermaid(\n el: HTMLElement\n): Observable> {\n el.classList.remove(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n mermaid$ ||= fetchScripts()\n .pipe(\n tap(() => mermaid.initialize({\n startOnLoad: false,\n themeCSS\n })),\n map(() => undefined),\n shareReplay(1)\n )\n\n /* Render diagram */\n mermaid$.subscribe(() => {\n el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n const id = `__mermaid_${index++}`\n const host = h(\"div\", { class: \"mermaid\" })\n mermaid.mermaidAPI.render(id, el.textContent, (svg: string) => {\n\n /* Create a shadow root and inject diagram */\n const shadow = host.attachShadow({ mode: \"closed\" })\n shadow.innerHTML = svg\n\n /* Replace code block with diagram */\n el.replaceWith(host)\n })\n })\n\n /* Create and return component */\n return mermaid$\n .pipe(\n map(() => ({ ref: el }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n filter,\n finalize,\n map,\n merge,\n tap\n} from \"rxjs\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Details\n */\nexport interface Details {\n action: \"open\" | \"close\" /* Details state */\n reveal?: boolean /* Details is revealed */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch details\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details observable\n */\nexport function watchDetails(\n el: HTMLDetailsElement, { target$, print$ }: WatchOptions\n): Observable
    {\n let open = true\n return merge(\n\n /* Open and focus details on location target */\n target$\n .pipe(\n map(target => target.closest(\"details:not([open])\")!),\n filter(details => el === details),\n map(() => ({\n action: \"open\", reveal: true\n }) as Details)\n ),\n\n /* Open details on print and close afterwards */\n print$\n .pipe(\n filter(active => active || !open),\n tap(() => open = el.open),\n map(active => ({\n action: active ? \"open\" : \"close\"\n }) as Details)\n )\n )\n}\n\n/**\n * Mount details\n *\n * This function ensures that `details` tags are opened on anchor jumps and\n * prior to printing, so the whole content of the page is visible.\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details component observable\n */\nexport function mountDetails(\n el: HTMLDetailsElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject
    ()\n push$.subscribe(({ action, reveal }) => {\n if (action === \"open\")\n el.setAttribute(\"open\", \"\")\n else\n el.removeAttribute(\"open\")\n if (reveal)\n el.scrollIntoView()\n })\n\n /* Create and return component */\n return watchDetails(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, of } from \"rxjs\"\n\nimport { renderTable } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Data table\n */\nexport interface DataTable {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Sentinel for replacement\n */\nconst sentinel = h(\"table\")\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount data table\n *\n * This function wraps a data table in another scrollable container, so it can\n * be smoothly scrolled on smaller screen sizes and won't break the layout.\n *\n * @param el - Data table element\n *\n * @returns Data table component observable\n */\nexport function mountDataTable(\n el: HTMLElement\n): Observable> {\n el.replaceWith(sentinel)\n sentinel.replaceWith(renderTable(el))\n\n /* Create and return component */\n return of({ ref: el })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n animationFrameScheduler,\n asyncScheduler,\n auditTime,\n combineLatest,\n defer,\n finalize,\n fromEvent,\n map,\n merge,\n startWith,\n subscribeOn,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport {\n getElement,\n getElementOffset,\n getElementSize,\n getElements,\n watchElementSize\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content tabs\n */\nexport interface ContentTabs {\n active: HTMLLabelElement /* Active tab label */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n el: HTMLElement\n): Observable {\n const inputs = getElements(\":scope > input\", el)\n const active = inputs.find(input => input.checked) || inputs[0]\n return merge(...inputs.map(input => fromEvent(input, \"change\")\n .pipe(\n map(() => ({\n active: getElement(`label[for=${input.id}]`)\n }) as ContentTabs)\n )\n ))\n .pipe(\n startWith({\n active: getElement(`label[for=${active.id}]`)\n } as ContentTabs)\n )\n}\n\n/**\n * Mount content tabs\n *\n * This function scrolls the active tab into view. While this functionality is\n * provided by browsers as part of `scrollInfoView`, browsers will always also\n * scroll the vertical axis, which we do not want. Thus, we decided to provide\n * this functionality ourselves.\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs component observable\n */\nexport function mountContentTabs(\n el: HTMLElement\n): Observable> {\n const container = getElement(\".tabbed-labels\", el)\n return defer(() => {\n const push$ = new Subject()\n combineLatest([push$, watchElementSize(el)])\n .pipe(\n auditTime(1, animationFrameScheduler),\n takeUntil(push$.pipe(takeLast(1)))\n )\n .subscribe({\n\n /* Handle emission */\n next([{ active }]) {\n const offset = getElementOffset(active)\n const { width } = getElementSize(active)\n\n /* Set tab indicator offset and width */\n el.style.setProperty(\"--md-indicator-x\", `${offset.x}px`)\n el.style.setProperty(\"--md-indicator-width\", `${width}px`)\n\n /* Smoothly scroll container */\n container.scrollTo({\n behavior: \"smooth\",\n left: offset.x\n })\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-indicator-x\")\n el.style.removeProperty(\"--md-indicator-width\")\n }\n })\n\n /* Create and return component */\n return watchContentTabs(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n .pipe(\n subscribeOn(asyncScheduler)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, merge } from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Annotation } from \"../annotation\"\nimport {\n CodeBlock,\n Mermaid,\n mountCodeBlock,\n mountMermaid\n} from \"../code\"\nimport {\n Details,\n mountDetails\n} from \"../details\"\nimport {\n DataTable,\n mountDataTable\n} from \"../table\"\nimport {\n ContentTabs,\n mountContentTabs\n} from \"../tabs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content\n */\nexport type Content =\n | Annotation\n | ContentTabs\n | CodeBlock\n | Mermaid\n | DataTable\n | Details\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount content\n *\n * This function mounts all components that are found in the content of the\n * actual article, including code blocks, data tables and details.\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Content component observable\n */\nexport function mountContent(\n el: HTMLElement, { target$, print$ }: MountOptions\n): Observable> {\n return merge(\n\n /* Code blocks */\n ...getElements(\"pre:not(.mermaid) > code\", el)\n .map(child => mountCodeBlock(child, { print$ })),\n\n /* Mermaid diagrams */\n ...getElements(\"pre.mermaid\", el)\n .map(child => mountMermaid(child)),\n\n /* Data tables */\n ...getElements(\"table:not([class])\", el)\n .map(child => mountDataTable(child)),\n\n /* Details */\n ...getElements(\"details\", el)\n .map(child => mountDetails(child, { target$, print$ })),\n\n /* Content tabs */\n ...getElements(\"[data-tabs]\", el)\n .map(child => mountContentTabs(child))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n delay,\n finalize,\n map,\n merge,\n of,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dialog\n */\nexport interface Dialog {\n message: string /* Dialog message */\n active: boolean /* Dialog is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n alert$: Subject /* Alert subject */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n alert$: Subject /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch dialog\n *\n * @param _el - Dialog element\n * @param options - Options\n *\n * @returns Dialog observable\n */\nexport function watchDialog(\n _el: HTMLElement, { alert$ }: WatchOptions\n): Observable {\n return alert$\n .pipe(\n switchMap(message => merge(\n of(true),\n of(false).pipe(delay(2000))\n )\n .pipe(\n map(active => ({ message, active }))\n )\n )\n )\n}\n\n/**\n * Mount dialog\n *\n * This function reveals the dialog in the right corner when a new alert is\n * emitted through the subject that is passed as part of the options.\n *\n * @param el - Dialog element\n * @param options - Options\n *\n * @returns Dialog component observable\n */\nexport function mountDialog(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const inner = getElement(\".md-typeset\", el)\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ message, active }) => {\n inner.textContent = message\n if (active)\n el.setAttribute(\"data-md-state\", \"open\")\n else\n el.removeAttribute(\"data-md-state\")\n })\n\n /* Create and return component */\n return watchDialog(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatest,\n combineLatestWith,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n map,\n of,\n shareReplay,\n startWith,\n switchMap,\n takeLast,\n takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n watchElementSize,\n watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface Header {\n height: number /* Header visible height */\n hidden: boolean /* Header is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute whether the header is hidden\n *\n * If the user scrolls past a certain threshold, the header can be hidden when\n * scrolling down, and shown when scrolling up.\n *\n * @param options - Options\n *\n * @returns Toggle observable\n */\nfunction isHidden({ viewport$ }: WatchOptions): Observable {\n if (!feature(\"header.autohide\"))\n return of(false)\n\n /* Compute direction and turning point */\n const direction$ = viewport$\n .pipe(\n map(({ offset: { y } }) => y),\n bufferCount(2, 1),\n map(([a, b]) => [a < b, b] as const),\n distinctUntilKeyChanged(0)\n )\n\n /* Compute whether header should be hidden */\n const hidden$ = combineLatest([viewport$, direction$])\n .pipe(\n filter(([{ offset }, [, y]]) => Math.abs(y - offset.y) > 100),\n map(([, [direction]]) => direction),\n distinctUntilChanged()\n )\n\n /* Compute threshold for hiding */\n const search$ = watchToggle(\"search\")\n return combineLatest([viewport$, search$])\n .pipe(\n map(([{ offset }, search]) => offset.y > 400 && !search),\n distinctUntilChanged(),\n switchMap(active => active ? hidden$ : of(false)),\n startWith(false)\n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header observable\n */\nexport function watchHeader(\n el: HTMLElement, options: WatchOptions\n): Observable
    {\n return defer(() => combineLatest([\n watchElementSize(el),\n isHidden(options)\n ]))\n .pipe(\n map(([{ height }, hidden]) => ({\n height,\n hidden\n })),\n distinctUntilChanged((a, b) => (\n a.height === b.height &&\n a.hidden === b.hidden\n )),\n shareReplay(1)\n )\n}\n\n/**\n * Mount header\n *\n * This function manages the different states of the header, i.e. whether it's\n * hidden or rendered with a shadow. This depends heavily on the main area.\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header component observable\n */\nexport function mountHeader(\n el: HTMLElement, { header$, main$ }: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject
    ()\n push$\n .pipe(\n distinctUntilKeyChanged(\"active\"),\n combineLatestWith(header$)\n )\n .subscribe(([{ active }, { hidden }]) => {\n if (active)\n el.setAttribute(\"data-md-state\", hidden ? \"hidden\" : \"shadow\")\n else\n el.removeAttribute(\"data-md-state\")\n })\n\n /* Link to main area */\n main$.subscribe(push$)\n\n /* Create and return component */\n return header$\n .pipe(\n takeUntil(push$.pipe(takeLast(1))),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n distinctUntilKeyChanged,\n finalize,\n map,\n tap\n} from \"rxjs\"\n\nimport {\n Viewport,\n getElementSize,\n getOptionalElement,\n watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Header } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface HeaderTitle {\n active: boolean /* Header title is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header title\n *\n * @param el - Heading element\n * @param options - Options\n *\n * @returns Header title observable\n */\nexport function watchHeaderTitle(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n return watchViewportAt(el, { viewport$, header$ })\n .pipe(\n map(({ offset: { y } }) => {\n const { height } = getElementSize(el)\n return {\n active: y >= height\n }\n }),\n distinctUntilKeyChanged(\"active\")\n )\n}\n\n/**\n * Mount header title\n *\n * This function swaps the header title from the site title to the title of the\n * current page when the user scrolls past the first headline.\n *\n * @param el - Header title element\n * @param options - Options\n *\n * @returns Header title component observable\n */\nexport function mountHeaderTitle(\n el: HTMLElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ active }) => {\n if (active)\n el.setAttribute(\"data-md-state\", \"active\")\n else\n el.removeAttribute(\"data-md-state\")\n })\n\n /* Obtain headline, if any */\n const heading = getOptionalElement(\"article h1\")\n if (typeof heading === \"undefined\")\n return EMPTY\n\n /* Create and return component */\n return watchHeaderTitle(heading, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n map,\n switchMap\n} from \"rxjs\"\n\nimport {\n Viewport,\n watchElementSize\n} from \"~/browser\"\n\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Main area\n */\nexport interface Main {\n offset: number /* Main area top offset */\n height: number /* Main area visible height */\n active: boolean /* Main area is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch main area\n *\n * This function returns an observable that computes the visual parameters of\n * the main area which depends on the viewport vertical offset and height, as\n * well as the height of the header element, if the header is fixed.\n *\n * @param el - Main area element\n * @param options - Options\n *\n * @returns Main area observable\n */\nexport function watchMain(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable
    {\n\n /* Compute necessary adjustment for header */\n const adjust$ = header$\n .pipe(\n map(({ height }) => height),\n distinctUntilChanged()\n )\n\n /* Compute the main area's top and bottom borders */\n const border$ = adjust$\n .pipe(\n switchMap(() => watchElementSize(el)\n .pipe(\n map(({ height }) => ({\n top: el.offsetTop,\n bottom: el.offsetTop + height\n })),\n distinctUntilKeyChanged(\"bottom\")\n )\n )\n )\n\n /* Compute the main area's offset, visible height and if we scrolled past */\n return combineLatest([adjust$, border$, viewport$])\n .pipe(\n map(([header, { top, bottom }, { offset: { y }, size: { height } }]) => {\n height = Math.max(0, height\n - Math.max(0, top - y, header)\n - Math.max(0, height + y - bottom)\n )\n return {\n offset: top - header,\n height,\n active: top - header <= y\n }\n }),\n distinctUntilChanged((a, b) => (\n a.offset === b.offset &&\n a.height === b.height &&\n a.active === b.active\n ))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n asyncScheduler,\n defer,\n finalize,\n fromEvent,\n map,\n mergeMap,\n observeOn,\n of,\n shareReplay,\n startWith,\n tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Palette colors\n */\nexport interface PaletteColor {\n scheme?: string /* Color scheme */\n primary?: string /* Primary color */\n accent?: string /* Accent color */\n}\n\n/**\n * Palette\n */\nexport interface Palette {\n index: number /* Palette index */\n color: PaletteColor /* Palette colors */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch color palette\n *\n * @param inputs - Color palette element\n *\n * @returns Color palette observable\n */\nexport function watchPalette(\n inputs: HTMLInputElement[]\n): Observable {\n const current = __md_get(\"__palette\") || {\n index: inputs.findIndex(input => matchMedia(\n input.getAttribute(\"data-md-color-media\")!\n ).matches)\n }\n\n /* Emit changes in color palette */\n return of(...inputs)\n .pipe(\n mergeMap(input => fromEvent(input, \"change\")\n .pipe(\n map(() => input)\n )\n ),\n startWith(inputs[Math.max(0, current.index)]),\n map(input => ({\n index: inputs.indexOf(input),\n color: {\n scheme: input.getAttribute(\"data-md-color-scheme\"),\n primary: input.getAttribute(\"data-md-color-primary\"),\n accent: input.getAttribute(\"data-md-color-accent\")\n }\n } as Palette)),\n shareReplay(1)\n )\n}\n\n/**\n * Mount color palette\n *\n * @param el - Color palette element\n *\n * @returns Color palette component observable\n */\nexport function mountPalette(\n el: HTMLElement\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(palette => {\n document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n /* Set color palette */\n for (const [key, value] of Object.entries(palette.color))\n document.body.setAttribute(`data-md-color-${key}`, value)\n\n /* Toggle visibility */\n for (let index = 0; index < inputs.length; index++) {\n const label = inputs[index].nextElementSibling\n if (label instanceof HTMLElement)\n label.hidden = palette.index !== index\n }\n\n /* Persist preference in local storage */\n __md_set(\"__palette\", palette)\n })\n\n /* Revert transition durations after color switch */\n push$.pipe(observeOn(asyncScheduler))\n .subscribe(() => {\n document.body.removeAttribute(\"data-md-color-switching\")\n })\n\n /* Create and return component */\n const inputs = getElements(\"input\", el)\n return watchPalette(inputs)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n Observable,\n Subject,\n map,\n tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n alert$: Subject /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Extract text to copy\n *\n * @param el - HTML element\n *\n * @returns Extracted text\n */\nfunction extract(el: HTMLElement): string {\n el.setAttribute(\"data-md-copying\", \"\")\n const text = el.innerText\n el.removeAttribute(\"data-md-copying\")\n return text\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up Clipboard.js integration\n *\n * @param options - Options\n */\nexport function setupClipboardJS(\n { alert$ }: SetupOptions\n): void {\n if (ClipboardJS.isSupported()) {\n new Observable(subscriber => {\n new ClipboardJS(\"[data-clipboard-target], [data-clipboard-text]\", {\n text: el => (\n el.getAttribute(\"data-clipboard-text\")! ||\n extract(getElement(\n el.getAttribute(\"data-clipboard-target\")!\n ))\n )\n })\n .on(\"success\", ev => subscriber.next(ev))\n })\n .pipe(\n tap(ev => {\n const trigger = ev.trigger as HTMLElement\n trigger.focus()\n }),\n map(() => translation(\"clipboard.copied\"))\n )\n .subscribe(alert$)\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map,\n of,\n tap\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport { getElements, requestXML } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = string[]\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Preprocess a list of URLs\n *\n * This function replaces the `site_url` in the sitemap with the actual base\n * URL, to allow instant loading to work in occasions like Netlify previews.\n *\n * @param urls - URLs\n *\n * @returns URL path parts\n */\nfunction preprocess(urls: Sitemap): Sitemap {\n if (urls.length < 2)\n return [\"\"]\n\n /* Take the first two URLs and remove everything after the last slash */\n const [root, next] = [...urls]\n .sort((a, b) => a.length - b.length)\n .map(url => url.replace(/[^/]+$/, \"\"))\n\n /* Compute common prefix */\n let index = 0\n if (root === next)\n index = root.length\n else\n while (root.charCodeAt(index) === next.charCodeAt(index))\n index++\n\n /* Remove common prefix and return in original order */\n return urls.map(url => url.replace(root.slice(0, index), \"\"))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base?: URL): Observable {\n const cached = __md_get(\"__sitemap\", sessionStorage, base)\n if (cached) {\n return of(cached)\n } else {\n const config = configuration()\n return requestXML(new URL(\"sitemap.xml\", base || config.base))\n .pipe(\n map(sitemap => preprocess(getElements(\"loc\", sitemap)\n .map(node => node.textContent!)\n )),\n catchError(() => EMPTY), // @todo refactor instant loading\n defaultIfEmpty([]),\n tap(sitemap => __md_set(\"__sitemap\", sitemap, sessionStorage, base))\n )\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n bufferCount,\n catchError,\n concatMap,\n debounceTime,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n fromEvent,\n map,\n merge,\n of,\n sample,\n share,\n skip,\n skipUntil,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n Viewport,\n ViewportOffset,\n getElements,\n getOptionalElement,\n request,\n setLocation,\n setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\nimport { h } from \"~/utilities\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * History state\n */\nexport interface HistoryState {\n url: URL /* State URL */\n offset?: ViewportOffset /* State viewport offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n document$: Subject /* Document subject */\n location$: Subject /* Location subject */\n viewport$: Observable /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up instant loading\n *\n * When fetching, theoretically, we could use `responseType: \"document\"`, but\n * since all MkDocs links are relative, we need to make sure that the current\n * location matches the document we just loaded. Otherwise any relative links\n * in the document could use the old location.\n *\n * This is the reason why we need to synchronize history events and the process\n * of fetching the document for navigation changes (except `popstate` events):\n *\n * 1. Fetch document via `XMLHTTPRequest`\n * 2. Set new location via `history.pushState`\n * 3. Parse and emit fetched document\n *\n * For `popstate` events, we must not use `history.pushState`, or the forward\n * history will be irreversibly overwritten. In case the request fails, the\n * location change is dispatched regularly.\n *\n * @param options - Options\n */\nexport function setupInstantLoading(\n { document$, location$, viewport$ }: SetupOptions\n): void {\n const config = configuration()\n if (location.protocol === \"file:\")\n return\n\n /* Disable automatic scroll restoration */\n if (\"scrollRestoration\" in history) {\n history.scrollRestoration = \"manual\"\n\n /* Hack: ensure that reloads restore viewport offset */\n fromEvent(window, \"beforeunload\")\n .subscribe(() => {\n history.scrollRestoration = \"auto\"\n })\n }\n\n /* Hack: ensure absolute favicon link to omit 404s when switching */\n const favicon = getOptionalElement(\"link[rel=icon]\")\n if (typeof favicon !== \"undefined\")\n favicon.href = favicon.href\n\n /* Intercept internal navigation */\n const push$ = fetchSitemap()\n .pipe(\n map(paths => paths.map(path => `${new URL(path, config.base)}`)),\n switchMap(urls => fromEvent(document.body, \"click\")\n .pipe(\n filter(ev => !ev.metaKey && !ev.ctrlKey),\n switchMap(ev => {\n if (ev.target instanceof Element) {\n const el = ev.target.closest(\"a\")\n if (el && !el.target) {\n const url = new URL(el.href)\n\n /* Canonicalize URL */\n url.search = \"\"\n url.hash = \"\"\n\n /* Check if URL should be intercepted */\n if (\n url.pathname !== location.pathname &&\n urls.includes(url.toString())\n ) {\n ev.preventDefault()\n return of({\n url: new URL(el.href)\n })\n }\n }\n }\n return NEVER\n })\n )\n ),\n share()\n )\n\n /* Intercept history back and forward */\n const pop$ = fromEvent(window, \"popstate\")\n .pipe(\n filter(ev => ev.state !== null),\n map(ev => ({\n url: new URL(location.href),\n offset: ev.state\n })),\n share()\n )\n\n /* Emit location change */\n merge(push$, pop$)\n .pipe(\n distinctUntilChanged((a, b) => a.url.href === b.url.href),\n map(({ url }) => url)\n )\n .subscribe(location$)\n\n /* Fetch document via `XMLHTTPRequest` */\n const response$ = location$\n .pipe(\n distinctUntilKeyChanged(\"pathname\"),\n switchMap(url => request(url.href)\n .pipe(\n catchError(() => {\n setLocation(url)\n return NEVER\n })\n )\n ),\n share()\n )\n\n /* Set new location via `history.pushState` */\n push$\n .pipe(\n sample(response$)\n )\n .subscribe(({ url }) => {\n history.pushState({}, \"\", `${url}`)\n })\n\n /* Parse and emit fetched document */\n const dom = new DOMParser()\n response$\n .pipe(\n switchMap(res => res.text()),\n map(res => dom.parseFromString(res, \"text/html\"))\n )\n .subscribe(document$)\n\n /* Replace meta tags and components */\n document$\n .pipe(\n skip(1)\n )\n .subscribe(replacement => {\n for (const selector of [\n\n /* Meta tags */\n \"title\",\n \"link[rel=canonical]\",\n \"meta[name=author]\",\n \"meta[name=description]\",\n\n /* Components */\n \"[data-md-component=announce]\",\n \"[data-md-component=container]\",\n \"[data-md-component=header-topic]\",\n \"[data-md-component=outdated]\",\n \"[data-md-component=logo]\",\n \"[data-md-component=skip]\",\n ...feature(\"navigation.tabs.sticky\")\n ? [\"[data-md-component=tabs]\"]\n : []\n ]) {\n const source = getOptionalElement(selector)\n const target = getOptionalElement(selector, replacement)\n if (\n typeof source !== \"undefined\" &&\n typeof target !== \"undefined\"\n ) {\n source.replaceWith(target)\n }\n }\n })\n\n /* Re-evaluate scripts */\n document$\n .pipe(\n skip(1),\n map(() => getComponentElement(\"container\")),\n switchMap(el => getElements(\"script\", el)),\n concatMap(el => {\n const script = h(\"script\")\n if (el.src) {\n for (const name of el.getAttributeNames())\n script.setAttribute(name, el.getAttribute(name)!)\n el.replaceWith(script)\n\n /* Complete when script is loaded */\n return new Observable(observer => {\n script.onload = () => observer.complete()\n })\n\n /* Complete immediately */\n } else {\n script.textContent = el.textContent\n el.replaceWith(script)\n return EMPTY\n }\n })\n )\n .subscribe()\n\n /* Emit history state change */\n merge(push$, pop$)\n .pipe(\n sample(document$)\n )\n .subscribe(({ url, offset }) => {\n if (url.hash && !offset) {\n setLocationHash(url.hash)\n } else {\n window.scrollTo(0, offset?.y || 0)\n }\n })\n\n /* Debounce update of viewport offset */\n viewport$\n .pipe(\n skipUntil(push$),\n debounceTime(250),\n distinctUntilKeyChanged(\"offset\")\n )\n .subscribe(({ offset }) => {\n history.replaceState(offset, \"\")\n })\n\n /* Set viewport offset from history */\n merge(push$, pop$)\n .pipe(\n bufferCount(2, 1),\n filter(([a, b]) => a.url.pathname === b.url.pathname),\n map(([, state]) => state)\n )\n .subscribe(({ offset }) => {\n window.scrollTo(0, offset?.y || 0)\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n parent?: SearchIndexDocument /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n docs: SearchIndexDocument[]\n): SearchDocumentMap {\n const documents = new Map()\n const parents = new Set()\n for (const doc of docs) {\n const [path, hash] = doc.location.split(\"#\")\n\n /* Extract location, title and tags */\n const location = doc.location\n const title = doc.title\n const tags = doc.tags\n\n /* Escape and cleanup text */\n const text = escapeHTML(doc.text)\n .replace(/\\s+(?=[,.:;!?])/g, \"\")\n .replace(/\\s+/g, \" \")\n\n /* Handle section */\n if (hash) {\n const parent = documents.get(path)!\n\n /* Ignore first section, override article */\n if (!parents.has(parent)) {\n parent.title = doc.title\n parent.text = text\n\n /* Remember that we processed the article */\n parents.add(parent)\n\n /* Add subsequent section */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n parent\n })\n }\n\n /* Add article */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n ...tags && { tags }\n })\n }\n }\n return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n const separator = new RegExp(config.separator, \"img\")\n const highlight = (_: unknown, data: string, term: string) => {\n return `${data}${term}`\n }\n\n /* Return factory function */\n return (query: string) => {\n query = query\n .replace(/[\\s*+\\-:~^]+/g, \" \")\n .trim()\n\n /* Create search term match expression */\n const match = new RegExp(`(^|${config.separator})(${\n query\n .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n .replace(separator, \"|\")\n })`, \"img\")\n\n /* Highlight string value */\n return value => (\n escape\n ? escapeHTML(value)\n : value\n )\n .replace(match, highlight)\n .replace(/<\\/mark>(\\s+)]*>/img, \"$1\")\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search transformation function\n *\n * @param value - Query value\n *\n * @returns Transformed query value\n */\nexport type SearchTransformFn = (value: string) => string\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Search for terms in quotation marks and prepend a `+` modifier to denote\n * that the resulting document must contain all terms, converting the query\n * to an `AND` query (as opposed to the default `OR` behavior). While users\n * may expect terms enclosed in quotation marks to map to span queries, i.e.\n * for which order is important, Lunr.js doesn't support them, so the best\n * we can do is to convert the terms to an `AND` query.\n *\n * 2. Replace control characters which are not located at the beginning of the\n * query or preceded by white space, or are not followed by a non-whitespace\n * character or are at the end of the query string. Furthermore, filter\n * unmatched quotation marks.\n *\n * 3. Trim excess whitespace from left and right.\n *\n * @param query - Query value\n *\n * @returns Transformed query value\n */\nexport function defaultTransform(query: string): string {\n return query\n .split(/\"([^\"]+)\"/g) /* => 1 */\n .map((terms, index) => index & 1\n ? terms.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n : terms\n )\n .join(\"\")\n .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\") /* => 2 */\n .trim() /* => 3 */\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SearchIndex, SearchResult } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search message type\n */\nexport const enum SearchMessageType {\n SETUP, /* Search index setup */\n READY, /* Search index ready */\n QUERY, /* Search query */\n RESULT /* Search results */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message containing the data necessary to setup the search index\n */\nexport interface SearchSetupMessage {\n type: SearchMessageType.SETUP /* Message type */\n data: SearchIndex /* Message data */\n}\n\n/**\n * Message indicating the search index is ready\n */\nexport interface SearchReadyMessage {\n type: SearchMessageType.READY /* Message type */\n}\n\n/**\n * Message containing a search query\n */\nexport interface SearchQueryMessage {\n type: SearchMessageType.QUERY /* Message type */\n data: string /* Message data */\n}\n\n/**\n * Message containing results for a search query\n */\nexport interface SearchResultMessage {\n type: SearchMessageType.RESULT /* Message type */\n data: SearchResult /* Message data */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message exchanged with the search worker\n */\nexport type SearchMessage =\n | SearchSetupMessage\n | SearchReadyMessage\n | SearchQueryMessage\n | SearchResultMessage\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Type guard for search setup messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchSetupMessage(\n message: SearchMessage\n): message is SearchSetupMessage {\n return message.type === SearchMessageType.SETUP\n}\n\n/**\n * Type guard for search ready messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchReadyMessage(\n message: SearchMessage\n): message is SearchReadyMessage {\n return message.type === SearchMessageType.READY\n}\n\n/**\n * Type guard for search query messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchQueryMessage(\n message: SearchMessage\n): message is SearchQueryMessage {\n return message.type === SearchMessageType.QUERY\n}\n\n/**\n * Type guard for search result messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchResultMessage(\n message: SearchMessage\n): message is SearchResultMessage {\n return message.type === SearchMessageType.RESULT\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n ObservableInput,\n Subject,\n from,\n map,\n share\n} from \"rxjs\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport { WorkerHandler, watchWorker } from \"~/browser\"\n\nimport { SearchIndex } from \"../../_\"\nimport {\n SearchOptions,\n SearchPipeline\n} from \"../../options\"\nimport {\n SearchMessage,\n SearchMessageType,\n SearchSetupMessage,\n isSearchResultMessage\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search worker\n */\nexport type SearchWorker = WorkerHandler\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search index\n *\n * @param data - Search index\n *\n * @returns Search index\n */\nfunction setupSearchIndex({ config, docs }: SearchIndex): SearchIndex {\n\n /* Override default language with value from translation */\n if (config.lang.length === 1 && config.lang[0] === \"en\")\n config.lang = [\n translation(\"search.config.lang\")\n ]\n\n /* Override default separator with value from translation */\n if (config.separator === \"[\\\\s\\\\-]+\")\n config.separator = translation(\"search.config.separator\")\n\n /* Set pipeline from translation */\n const pipeline = translation(\"search.config.pipeline\")\n .split(/\\s*,\\s*/)\n .filter(Boolean) as SearchPipeline\n\n /* Determine search options */\n const options: SearchOptions = {\n pipeline,\n suggestions: feature(\"search.suggest\")\n }\n\n /* Return search index after defaulting */\n return { config, docs, options }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search worker\n *\n * This function creates a web worker to set up and query the search index,\n * which is done using Lunr.js. The index must be passed as an observable to\n * enable hacks like _localsearch_ via search index embedding as JSON.\n *\n * @param url - Worker URL\n * @param index - Search index observable input\n *\n * @returns Search worker\n */\nexport function setupSearchWorker(\n url: string, index: ObservableInput\n): SearchWorker {\n const config = configuration()\n const worker = new Worker(url)\n\n /* Create communication channels and resolve relative links */\n const tx$ = new Subject()\n const rx$ = watchWorker(worker, { tx$ })\n .pipe(\n map(message => {\n if (isSearchResultMessage(message)) {\n for (const result of message.data.items)\n for (const document of result)\n document.location = `${new URL(document.location, config.base)}`\n }\n return message\n }),\n share()\n )\n\n /* Set up search index */\n from(index)\n .pipe(\n map(data => ({\n type: SearchMessageType.SETUP,\n data: setupSearchIndex(data)\n } as SearchSetupMessage))\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Return search worker */\n return { tx$, rx$ }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Subject,\n catchError,\n combineLatest,\n filter,\n fromEvent,\n map,\n of,\n switchMap\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n getElement,\n getLocation,\n requestJSON,\n setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n Version,\n renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n document$: Subject /* Document subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up version selector\n *\n * @param options - Options\n */\nexport function setupVersionSelector(\n { document$ }: SetupOptions\n): void {\n const config = configuration()\n const versions$ = requestJSON(\n new URL(\"../versions.json\", config.base)\n )\n .pipe(\n catchError(() => EMPTY) // @todo refactor instant loading\n )\n\n /* Determine current version */\n const current$ = versions$\n .pipe(\n map(versions => {\n const [, current] = config.base.match(/([^/]+)\\/?$/)!\n return versions.find(({ version, aliases }) => (\n version === current || aliases.includes(current)\n )) || versions[0]\n })\n )\n\n /* Intercept inter-version navigation */\n combineLatest([versions$, current$])\n .pipe(\n map(([versions, current]) => new Map(versions\n .filter(version => version !== current)\n .map(version => [\n `${new URL(`../${version.version}/`, config.base)}`,\n version\n ])\n )),\n switchMap(urls => fromEvent(document.body, \"click\")\n .pipe(\n filter(ev => !ev.metaKey && !ev.ctrlKey),\n switchMap(ev => {\n if (ev.target instanceof Element) {\n const el = ev.target.closest(\"a\")\n if (el && !el.target && urls.has(el.href)) {\n ev.preventDefault()\n return of(el.href)\n }\n }\n return EMPTY\n }),\n switchMap(url => {\n const { version } = urls.get(url)!\n return fetchSitemap(new URL(url))\n .pipe(\n map(sitemap => {\n const location = getLocation()\n const path = location.href.replace(config.base, \"\")\n return sitemap.includes(path)\n ? new URL(`../${version}/${path}`, config.base)\n : new URL(url)\n })\n )\n })\n )\n )\n )\n .subscribe(url => setLocation(url))\n\n /* Render version selector and warning */\n combineLatest([versions$, current$])\n .subscribe(([versions, current]) => {\n const topic = getElement(\".md-header__topic\")\n topic.appendChild(renderVersionSelector(versions, current))\n })\n\n /* Integrate outdated version banner with instant loading */\n document$.pipe(switchMap(() => current$))\n .subscribe(current => {\n\n /* Check if version state was already determined */\n let outdated = __md_get(\"__outdated\", sessionStorage)\n if (outdated === null) {\n const latest = config.version?.default || \"latest\"\n outdated = !current.aliases.includes(latest)\n\n /* Persist version state in session storage */\n __md_set(\"__outdated\", outdated, sessionStorage)\n }\n\n /* Unhide outdated version banner */\n if (outdated)\n for (const warning of getComponentElements(\"outdated\"))\n warning.hidden = false\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n combineLatest,\n delay,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n finalize,\n fromEvent,\n map,\n merge,\n shareReplay,\n startWith,\n take,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n getLocation,\n setToggle,\n watchElementFocus,\n watchToggle\n} from \"~/browser\"\nimport {\n SearchMessageType,\n SearchQueryMessage,\n SearchWorker,\n defaultTransform,\n isSearchReadyMessage\n} from \"~/integrations\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query\n */\nexport interface SearchQuery {\n value: string /* Query value */\n focus: boolean /* Query focus */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch search query\n *\n * Note that the focus event which triggers re-reading the current query value\n * is delayed by `1ms` so the input's empty state is allowed to propagate.\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query observable\n */\nexport function watchSearchQuery(\n el: HTMLInputElement, { rx$ }: SearchWorker\n): Observable {\n const fn = __search?.transform || defaultTransform\n\n /* Immediately show search dialog */\n const { searchParams } = getLocation()\n if (searchParams.has(\"q\"))\n setToggle(\"search\", true)\n\n /* Intercept query parameter (deep link) */\n const param$ = rx$\n .pipe(\n filter(isSearchReadyMessage),\n take(1),\n map(() => searchParams.get(\"q\") || \"\")\n )\n\n /* Remove query parameter when search is closed */\n watchToggle(\"search\")\n .pipe(\n filter(active => !active),\n take(1)\n )\n .subscribe(() => {\n const url = new URL(location.href)\n url.searchParams.delete(\"q\")\n history.replaceState({}, \"\", `${url}`)\n })\n\n /* Set query from parameter */\n param$.subscribe(value => { // TODO: not ideal - find a better way\n if (value) {\n el.value = value\n el.focus()\n }\n })\n\n /* Intercept focus and input events */\n const focus$ = watchElementFocus(el)\n const value$ = merge(\n fromEvent(el, \"keyup\"),\n fromEvent(el, \"focus\").pipe(delay(1)),\n param$\n )\n .pipe(\n map(() => fn(el.value)),\n startWith(\"\"),\n distinctUntilChanged(),\n )\n\n /* Combine into single observable */\n return combineLatest([value$, focus$])\n .pipe(\n map(([value, focus]) => ({ value, focus })),\n shareReplay(1)\n )\n}\n\n/**\n * Mount search query\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query component observable\n */\nexport function mountSearchQuery(\n el: HTMLInputElement, { tx$, rx$ }: SearchWorker\n): Observable> {\n const push$ = new Subject()\n\n /* Handle value changes */\n push$\n .pipe(\n distinctUntilKeyChanged(\"value\"),\n map(({ value }): SearchQueryMessage => ({\n type: SearchMessageType.QUERY,\n data: value\n }))\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Handle focus changes */\n push$\n .pipe(\n distinctUntilKeyChanged(\"focus\")\n )\n .subscribe(({ focus }) => {\n if (focus) {\n setToggle(\"search\", focus)\n el.placeholder = \"\"\n } else {\n el.placeholder = translation(\"search.placeholder\")\n }\n })\n\n /* Handle reset */\n fromEvent(el.form!, \"reset\")\n .pipe(\n takeUntil(push$.pipe(takeLast(1)))\n )\n .subscribe(() => el.focus())\n\n /* Create and return component */\n return watchSearchQuery(el, { tx$, rx$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n filter,\n finalize,\n map,\n merge,\n of,\n skipUntil,\n switchMap,\n take,\n tap,\n withLatestFrom,\n zipWith\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n getElement,\n watchElementBoundary\n} from \"~/browser\"\nimport {\n SearchResult,\n SearchWorker,\n isSearchReadyMessage,\n isSearchResultMessage\n} from \"~/integrations\"\nimport { renderSearchResultItem } from \"~/templates\"\nimport { round } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n query$: Observable /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search result list\n *\n * This function performs a lazy rendering of the search results, depending on\n * the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchResult(\n el: HTMLElement, { rx$ }: SearchWorker, { query$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n const boundary$ = watchElementBoundary(el.parentElement!)\n .pipe(\n filter(Boolean)\n )\n\n /* Retrieve nested components */\n const meta = getElement(\":scope > :first-child\", el)\n const list = getElement(\":scope > :last-child\", el)\n\n /* Wait until search is ready */\n const ready$ = rx$\n .pipe(\n filter(isSearchReadyMessage),\n take(1)\n )\n\n /* Update search result metadata */\n push$\n .pipe(\n withLatestFrom(query$),\n skipUntil(ready$)\n )\n .subscribe(([{ items }, { value }]) => {\n if (value) {\n switch (items.length) {\n\n /* No results */\n case 0:\n meta.textContent = translation(\"search.result.none\")\n break\n\n /* One result */\n case 1:\n meta.textContent = translation(\"search.result.one\")\n break\n\n /* Multiple result */\n default:\n meta.textContent = translation(\n \"search.result.other\",\n round(items.length)\n )\n }\n } else {\n meta.textContent = translation(\"search.result.placeholder\")\n }\n })\n\n /* Update search result list */\n push$\n .pipe(\n tap(() => list.innerHTML = \"\"),\n switchMap(({ items }) => merge(\n of(...items.slice(0, 10)),\n of(...items.slice(10))\n .pipe(\n bufferCount(4),\n zipWith(boundary$),\n switchMap(([chunk]) => chunk)\n )\n ))\n )\n .subscribe(result => list.appendChild(\n renderSearchResultItem(result)\n ))\n\n /* Filter search result message */\n const result$ = rx$\n .pipe(\n filter(isSearchResultMessage),\n map(({ data }) => data)\n )\n\n /* Create and return component */\n return result$\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n finalize,\n fromEvent,\n map,\n tap\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search sharing\n */\nexport interface SearchShare {\n url: URL /* Deep link for sharing */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n query$: Observable /* Search query observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n query$: Observable /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search sharing\n *\n * @param _el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing observable\n */\nexport function watchSearchShare(\n _el: HTMLElement, { query$ }: WatchOptions\n): Observable {\n return query$\n .pipe(\n map(({ value }) => {\n const url = getLocation()\n url.hash = \"\"\n url.searchParams.delete(\"h\")\n url.searchParams.set(\"q\", value)\n return { url }\n })\n )\n}\n\n/**\n * Mount search sharing\n *\n * @param el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing component observable\n */\nexport function mountSearchShare(\n el: HTMLAnchorElement, options: MountOptions\n): Observable> {\n const push$ = new Subject()\n push$.subscribe(({ url }) => {\n el.setAttribute(\"data-clipboard-text\", el.href)\n el.href = `${url}`\n })\n\n /* Prevent following of link */\n fromEvent(el, \"click\")\n .subscribe(ev => ev.preventDefault())\n\n /* Create and return component */\n return watchSearchShare(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n asyncScheduler,\n combineLatestWith,\n distinctUntilChanged,\n filter,\n finalize,\n fromEvent,\n map,\n merge,\n observeOn,\n tap\n} from \"rxjs\"\n\nimport { Keyboard } from \"~/browser\"\nimport {\n SearchResult,\n SearchWorker,\n isSearchResultMessage\n} from \"~/integrations\"\n\nimport { Component, getComponentElement } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search suggestions\n */\nexport interface SearchSuggest {}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n keyboard$: Observable /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search suggestions\n *\n * This function will perform a lazy rendering of the search results, depending\n * on the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchSuggest(\n el: HTMLElement, { rx$ }: SearchWorker, { keyboard$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n\n /* Retrieve query component and track all changes */\n const query = getComponentElement(\"search-query\")\n const query$ = merge(\n fromEvent(query, \"keydown\"),\n fromEvent(query, \"focus\")\n )\n .pipe(\n observeOn(asyncScheduler),\n map(() => query.value),\n distinctUntilChanged(),\n )\n\n /* Update search suggestions */\n push$\n .pipe(\n combineLatestWith(query$),\n map(([{ suggestions }, value]) => {\n const words = value.split(/([\\s-]+)/)\n if (suggestions?.length && words[words.length - 1]) {\n const last = suggestions[suggestions.length - 1]\n if (last.startsWith(words[words.length - 1]))\n words[words.length - 1] = last\n } else {\n words.length = 0\n }\n return words\n })\n )\n .subscribe(words => el.innerHTML = words\n .join(\"\")\n .replace(/\\s/g, \" \")\n )\n\n /* Set up search keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"search\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Right arrow: accept current suggestion */\n case \"ArrowRight\":\n if (\n el.innerText.length &&\n query.selectionStart === query.value.length\n )\n query.value = el.innerText\n break\n }\n })\n\n /* Filter search result message */\n const result$ = rx$\n .pipe(\n filter(isSearchResultMessage),\n map(({ data }) => data)\n )\n\n /* Create and return component */\n return result$\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(() => ({ ref: el }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n NEVER,\n Observable,\n ObservableInput,\n filter,\n merge,\n mergeWith,\n sample,\n take\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n Keyboard,\n getActiveElement,\n getElements,\n setToggle\n} from \"~/browser\"\nimport {\n SearchIndex,\n SearchResult,\n isSearchQueryMessage,\n isSearchReadyMessage,\n setupSearchWorker\n} from \"~/integrations\"\n\nimport {\n Component,\n getComponentElement,\n getComponentElements\n} from \"../../_\"\nimport {\n SearchQuery,\n mountSearchQuery\n} from \"../query\"\nimport { mountSearchResult } from \"../result\"\nimport {\n SearchShare,\n mountSearchShare\n} from \"../share\"\nimport {\n SearchSuggest,\n mountSearchSuggest\n} from \"../suggest\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search\n */\nexport type Search =\n | SearchQuery\n | SearchResult\n | SearchShare\n | SearchSuggest\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n index$: ObservableInput /* Search index observable */\n keyboard$: Observable /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search\n *\n * This function sets up the search functionality, including the underlying\n * web worker and all keyboard bindings.\n *\n * @param el - Search element\n * @param options - Options\n *\n * @returns Search component observable\n */\nexport function mountSearch(\n el: HTMLElement, { index$, keyboard$ }: MountOptions\n): Observable> {\n const config = configuration()\n try {\n const url = __search?.worker || config.search\n const worker = setupSearchWorker(url, index$)\n\n /* Retrieve query and result components */\n const query = getComponentElement(\"search-query\", el)\n const result = getComponentElement(\"search-result\", el)\n\n /* Re-emit query when search is ready */\n const { tx$, rx$ } = worker\n tx$\n .pipe(\n filter(isSearchQueryMessage),\n sample(rx$.pipe(filter(isSearchReadyMessage))),\n take(1)\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Set up search keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"search\")\n )\n .subscribe(key => {\n const active = getActiveElement()\n switch (key.type) {\n\n /* Enter: go to first (best) result */\n case \"Enter\":\n if (active === query) {\n const anchors = new Map()\n for (const anchor of getElements(\n \":first-child [href]\", result\n )) {\n const article = anchor.firstElementChild!\n anchors.set(anchor, parseFloat(\n article.getAttribute(\"data-md-score\")!\n ))\n }\n\n /* Go to result with highest score, if any */\n if (anchors.size) {\n const [[best]] = [...anchors].sort(([, a], [, b]) => b - a)\n best.click()\n }\n\n /* Otherwise omit form submission */\n key.claim()\n }\n break\n\n /* Escape or Tab: close search */\n case \"Escape\":\n case \"Tab\":\n setToggle(\"search\", false)\n query.blur()\n break\n\n /* Vertical arrows: select previous or next search result */\n case \"ArrowUp\":\n case \"ArrowDown\":\n if (typeof active === \"undefined\") {\n query.focus()\n } else {\n const els = [query, ...getElements(\n \":not(details) > [href], summary, details[open] [href]\",\n result\n )]\n const i = Math.max(0, (\n Math.max(0, els.indexOf(active)) + els.length + (\n key.type === \"ArrowUp\" ? -1 : +1\n )\n ) % els.length)\n els[i].focus()\n }\n\n /* Prevent scrolling of page */\n key.claim()\n break\n\n /* All other keys: hand to search query */\n default:\n if (query !== getActiveElement())\n query.focus()\n }\n })\n\n /* Set up global keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\"),\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Open search and select query */\n case \"f\":\n case \"s\":\n case \"/\":\n query.focus()\n query.select()\n\n /* Prevent scrolling of page */\n key.claim()\n break\n }\n })\n\n /* Create and return component */\n const query$ = mountSearchQuery(query, worker)\n const result$ = mountSearchResult(result, worker, { query$ })\n return merge(query$, result$)\n .pipe(\n mergeWith(\n\n /* Search sharing */\n ...getComponentElements(\"search-share\", el)\n .map(child => mountSearchShare(child, { query$ })),\n\n /* Search suggestions */\n ...getComponentElements(\"search-suggest\", el)\n .map(child => mountSearchSuggest(child, worker, { keyboard$ }))\n )\n )\n\n /* Gracefully handle broken search */\n } catch (err) {\n el.hidden = true\n return NEVER\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n ObservableInput,\n combineLatest,\n filter,\n map,\n startWith\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\nimport {\n SearchIndex,\n setupSearchHighlighter\n} from \"~/integrations\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlighting\n */\nexport interface SearchHighlight {\n nodes: Map /* Map of replacements */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n index$: ObservableInput /* Search index observable */\n location$: Observable /* Location observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search highlighting\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Search highlighting component observable\n */\nexport function mountSearchHiglight(\n el: HTMLElement, { index$, location$ }: MountOptions\n): Observable> {\n return combineLatest([\n index$,\n location$\n .pipe(\n startWith(getLocation()),\n filter(url => !!url.searchParams.get(\"h\"))\n )\n ])\n .pipe(\n map(([index, url]) => setupSearchHighlighter(index.config, true)(\n url.searchParams.get(\"h\")!\n )),\n map(fn => {\n const nodes = new Map()\n\n /* Traverse text nodes and collect matches */\n const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n for (let node = it.nextNode(); node; node = it.nextNode()) {\n if (node.parentElement?.offsetHeight) {\n const original = node.textContent!\n const replaced = fn(original)\n if (replaced.length > original.length)\n nodes.set(node as ChildNode, replaced)\n }\n }\n\n /* Replace original nodes with matches */\n for (const [node, text] of nodes) {\n const { childNodes } = h(\"span\", null, text)\n node.replaceWith(...Array.from(childNodes))\n }\n\n /* Return component */\n return { ref: el, nodes }\n })\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n animationFrameScheduler,\n auditTime,\n combineLatest,\n defer,\n distinctUntilChanged,\n finalize,\n map,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport {\n Viewport,\n getElement,\n getElementOffset\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sidebar\n */\nexport interface Sidebar {\n height: number /* Sidebar height */\n locked: boolean /* Sidebar is locked */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n main$: Observable
    /* Main area observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch sidebar\n *\n * This function returns an observable that computes the visual parameters of\n * the sidebar which depends on the vertical viewport offset, as well as the\n * height of the main area. When the page is scrolled beyond the header, the\n * sidebar is locked and fills the remaining space.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar observable\n */\nexport function watchSidebar(\n el: HTMLElement, { viewport$, main$ }: WatchOptions\n): Observable {\n const parent = el.parentElement!\n const adjust =\n parent.offsetTop -\n parent.parentElement!.offsetTop\n\n /* Compute the sidebar's available height and if it should be locked */\n return combineLatest([main$, viewport$])\n .pipe(\n map(([{ offset, height }, { offset: { y } }]) => {\n height = height\n + Math.min(adjust, Math.max(0, y - offset))\n - adjust\n return {\n height,\n locked: y >= offset + adjust\n }\n }),\n distinctUntilChanged((a, b) => (\n a.height === b.height &&\n a.locked === b.locked\n ))\n )\n}\n\n/**\n * Mount sidebar\n *\n * This function doesn't set the height of the actual sidebar, but of its first\n * child \u2013 the `.md-sidebar__scrollwrap` element in order to mitigiate jittery\n * sidebars when the footer is scrolled into view. At some point we switched\n * from `absolute` / `fixed` positioning to `sticky` positioning, significantly\n * reducing jitter in some browsers (respectively Firefox and Safari) when\n * scrolling from the top. However, top-aligned sticky positioning means that\n * the sidebar snaps to the bottom when the end of the container is reached.\n * This is what leads to the mentioned jitter, as the sidebar's height may be\n * updated too slowly.\n *\n * This behaviour can be mitigiated by setting the height of the sidebar to `0`\n * while preserving the padding, and the height on its first element.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar component observable\n */\nexport function mountSidebar(\n el: HTMLElement, { header$, ...options }: MountOptions\n): Observable> {\n const inner = getElement(\".md-sidebar__scrollwrap\", el)\n const { y } = getElementOffset(inner)\n return defer(() => {\n const push$ = new Subject()\n push$\n .pipe(\n auditTime(0, animationFrameScheduler),\n withLatestFrom(header$)\n )\n .subscribe({\n\n /* Handle emission */\n next([{ height }, { height: offset }]) {\n inner.style.height = `${height - 2 * y}px`\n el.style.top = `${offset}px`\n },\n\n /* Handle complete */\n complete() {\n inner.style.height = \"\"\n el.style.top = \"\"\n }\n })\n\n /* Create and return component */\n return watchSidebar(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Repo, User } from \"github-types\"\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map,\n zip\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * GitHub release (partial)\n */\ninterface Release {\n tag_name: string /* Tag name */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitHub repository facts\n *\n * @param user - GitHub user or organization\n * @param repo - GitHub repository\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitHub(\n user: string, repo?: string\n): Observable {\n if (typeof repo !== \"undefined\") {\n const url = `https://api.github.com/repos/${user}/${repo}`\n return zip(\n\n /* Fetch version */\n requestJSON(`${url}/releases/latest`)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(release => ({\n version: release.tag_name\n })),\n defaultIfEmpty({})\n ),\n\n /* Fetch stars and forks */\n requestJSON(url)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(info => ({\n stars: info.stargazers_count,\n forks: info.forks_count\n })),\n defaultIfEmpty({})\n )\n )\n .pipe(\n map(([release, info]) => ({ ...release, ...info }))\n )\n\n /* User or organization */\n } else {\n const url = `https://api.github.com/users/${user}`\n return requestJSON(url)\n .pipe(\n map(info => ({\n repositories: info.public_repos\n })),\n defaultIfEmpty({})\n )\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ProjectSchema } from \"gitlab\"\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitLab repository facts\n *\n * @param base - GitLab base\n * @param project - GitLab project\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitLab(\n base: string, project: string\n): Observable {\n const url = `https://${base}/api/v4/projects/${encodeURIComponent(project)}`\n return requestJSON(url)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(({ star_count, forks_count }) => ({\n stars: star_count,\n forks: forks_count\n })),\n defaultIfEmpty({})\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable } from \"rxjs\"\n\nimport { fetchSourceFactsFromGitHub } from \"../github\"\nimport { fetchSourceFactsFromGitLab } from \"../gitlab\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository facts for repositories\n */\nexport interface RepositoryFacts {\n stars?: number /* Number of stars */\n forks?: number /* Number of forks */\n version?: string /* Latest version */\n}\n\n/**\n * Repository facts for organizations\n */\nexport interface OrganizationFacts {\n repositories?: number /* Number of repositories */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Repository facts\n */\nexport type SourceFacts =\n | RepositoryFacts\n | OrganizationFacts\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch repository facts\n *\n * @param url - Repository URL\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFacts(\n url: string\n): Observable {\n const [type] = url.match(/(git(?:hub|lab))/i) || []\n switch (type.toLowerCase()) {\n\n /* GitHub repository */\n case \"github\":\n const [, user, repo] = url.match(/^.+github\\.com\\/([^/]+)\\/?([^/]+)?/i)!\n return fetchSourceFactsFromGitHub(user, repo)\n\n /* GitLab repository */\n case \"gitlab\":\n const [, base, slug] = url.match(/^.+?([^/]*gitlab[^/]+)\\/(.+?)\\/?$/i)!\n return fetchSourceFactsFromGitLab(base, slug)\n\n /* Everything else */\n default:\n return EMPTY\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n catchError,\n defer,\n filter,\n finalize,\n map,\n of,\n shareReplay,\n tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\nimport { renderSourceFacts } from \"~/templates\"\n\nimport { Component } from \"../../_\"\nimport {\n SourceFacts,\n fetchSourceFacts\n} from \"../facts\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information\n */\nexport interface Source {\n facts: SourceFacts /* Repository facts */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information observable\n */\nlet fetch$: Observable\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch repository information\n *\n * This function tries to read the repository facts from session storage, and\n * if unsuccessful, fetches them from the underlying provider.\n *\n * @param el - Repository information element\n *\n * @returns Repository information observable\n */\nexport function watchSource(\n el: HTMLAnchorElement\n): Observable {\n return fetch$ ||= defer(() => {\n const cached = __md_get(\"__source\", sessionStorage)\n if (cached)\n return of(cached)\n else\n return fetchSourceFacts(el.href)\n .pipe(\n tap(facts => __md_set(\"__source\", facts, sessionStorage))\n )\n })\n .pipe(\n catchError(() => EMPTY),\n filter(facts => Object.keys(facts).length > 0),\n map(facts => ({ facts })),\n shareReplay(1)\n )\n}\n\n/**\n * Mount repository information\n *\n * @param el - Repository information element\n *\n * @returns Repository information component observable\n */\nexport function mountSource(\n el: HTMLAnchorElement\n): Observable> {\n const inner = getElement(\":scope > :last-child\", el)\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ facts }) => {\n inner.appendChild(renderSourceFacts(facts))\n inner.setAttribute(\"data-md-state\", \"done\")\n })\n\n /* Create and return component */\n return watchSource(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n distinctUntilKeyChanged,\n finalize,\n map,\n of,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n watchElementSize,\n watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Navigation tabs\n */\nexport interface Tabs {\n hidden: boolean /* Navigation tabs are hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch navigation tabs\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs observable\n */\nexport function watchTabs(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n return watchElementSize(document.body)\n .pipe(\n switchMap(() => watchViewportAt(el, { header$, viewport$ })),\n map(({ offset: { y } }) => {\n return {\n hidden: y >= 10\n }\n }),\n distinctUntilKeyChanged(\"hidden\")\n )\n}\n\n/**\n * Mount navigation tabs\n *\n * This function hides the navigation tabs when scrolling past the threshold\n * and makes them reappear in a nice CSS animation when scrolling back up.\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs component observable\n */\nexport function mountTabs(\n el: HTMLElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe({\n\n /* Handle emission */\n next({ hidden }) {\n if (hidden)\n el.setAttribute(\"data-md-state\", \"hidden\")\n else\n el.removeAttribute(\"data-md-state\")\n },\n\n /* Handle complete */\n complete() {\n el.removeAttribute(\"data-md-state\")\n }\n })\n\n /* Create and return component */\n return (\n feature(\"navigation.tabs.sticky\")\n ? of({ hidden: false })\n : watchTabs(el, options)\n )\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatestWith,\n debounceTime,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n finalize,\n map,\n of,\n repeat,\n scan,\n share,\n skip,\n startWith,\n switchMap,\n takeLast,\n takeUntil,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n getElement,\n getElements,\n getLocation,\n getOptionalElement,\n watchElementSize\n} from \"~/browser\"\n\nimport {\n Component,\n getComponentElement\n} from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Table of contents\n */\nexport interface TableOfContents {\n prev: HTMLAnchorElement[][] /* Anchors (previous) */\n next: HTMLAnchorElement[][] /* Anchors (next) */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n target$: Observable /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch table of contents\n *\n * This is effectively a scroll spy implementation which will account for the\n * fixed header and automatically re-calculate anchor offsets when the viewport\n * is resized. The returned observable will only emit if the table of contents\n * needs to be repainted.\n *\n * This implementation tracks an anchor element's entire path starting from its\n * level up to the top-most anchor element, e.g. `[h3, h2, h1]`. Although the\n * Material theme currently doesn't make use of this information, it enables\n * the styling of the entire hierarchy through customization.\n *\n * Note that the current anchor is the last item of the `prev` anchor list.\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents observable\n */\nexport function watchTableOfContents(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n const table = new Map()\n\n /* Compute anchor-to-target mapping */\n const anchors = getElements(\"[href^=\\\\#]\", el)\n for (const anchor of anchors) {\n const id = decodeURIComponent(anchor.hash.substring(1))\n const target = getOptionalElement(`[id=\"${id}\"]`)\n if (typeof target !== \"undefined\")\n table.set(anchor, target)\n }\n\n /* Compute necessary adjustment for header */\n const adjust$ = header$\n .pipe(\n distinctUntilKeyChanged(\"height\"),\n map(({ height }) => {\n const main = getComponentElement(\"main\")\n const grid = getElement(\":scope > :first-child\", main)\n return height + 0.8 * (\n grid.offsetTop -\n main.offsetTop\n )\n }),\n share()\n )\n\n /* Compute partition of previous and next anchors */\n const partition$ = watchElementSize(document.body)\n .pipe(\n distinctUntilKeyChanged(\"height\"),\n\n /* Build index to map anchor paths to vertical offsets */\n switchMap(body => defer(() => {\n let path: HTMLAnchorElement[] = []\n return of([...table].reduce((index, [anchor, target]) => {\n while (path.length) {\n const last = table.get(path[path.length - 1])!\n if (last.tagName >= target.tagName) {\n path.pop()\n } else {\n break\n }\n }\n\n /* If the current anchor is hidden, continue with its parent */\n let offset = target.offsetTop\n while (!offset && target.parentElement) {\n target = target.parentElement\n offset = target.offsetTop\n }\n\n /* Map reversed anchor path to vertical offset */\n return index.set(\n [...path = [...path, anchor]].reverse(),\n offset\n )\n }, new Map()))\n })\n .pipe(\n\n /* Sort index by vertical offset (see https://bit.ly/30z6QSO) */\n map(index => new Map([...index].sort(([, a], [, b]) => a - b))),\n combineLatestWith(adjust$),\n\n /* Re-compute partition when viewport offset changes */\n switchMap(([index, adjust]) => viewport$\n .pipe(\n scan(([prev, next], { offset: { y }, size }) => {\n const last = y + size.height >= Math.floor(body.height)\n\n /* Look forward */\n while (next.length) {\n const [, offset] = next[0]\n if (offset - adjust < y || last) {\n prev = [...prev, next.shift()!]\n } else {\n break\n }\n }\n\n /* Look backward */\n while (prev.length) {\n const [, offset] = prev[prev.length - 1]\n if (offset - adjust >= y && !last) {\n next = [prev.pop()!, ...next]\n } else {\n break\n }\n }\n\n /* Return partition */\n return [prev, next]\n }, [[], [...index]]),\n distinctUntilChanged((a, b) => (\n a[0] === b[0] &&\n a[1] === b[1]\n ))\n )\n )\n )\n )\n )\n\n /* Compute and return anchor list migrations */\n return partition$\n .pipe(\n map(([prev, next]) => ({\n prev: prev.map(([path]) => path),\n next: next.map(([path]) => path)\n })),\n\n /* Extract anchor list migrations */\n startWith({ prev: [], next: [] }),\n bufferCount(2, 1),\n map(([a, b]) => {\n\n /* Moving down */\n if (a.prev.length < b.prev.length) {\n return {\n prev: b.prev.slice(Math.max(0, a.prev.length - 1), b.prev.length),\n next: []\n }\n\n /* Moving up */\n } else {\n return {\n prev: b.prev.slice(-1),\n next: b.next.slice(0, b.next.length - a.next.length)\n }\n }\n })\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount table of contents\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents component observable\n */\nexport function mountTableOfContents(\n el: HTMLElement, { viewport$, header$, target$ }: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ prev, next }) => {\n\n /* Look forward */\n for (const [anchor] of next) {\n anchor.removeAttribute(\"data-md-state\")\n anchor.classList.remove(\n \"md-nav__link--active\"\n )\n }\n\n /* Look backward */\n for (const [index, [anchor]] of prev.entries()) {\n anchor.setAttribute(\"data-md-state\", \"blur\")\n anchor.classList.toggle(\n \"md-nav__link--active\",\n index === prev.length - 1\n )\n }\n })\n\n /* Set up anchor tracking, if enabled */\n if (feature(\"navigation.tracking\"))\n viewport$\n .pipe(\n takeUntil(push$.pipe(takeLast(1))),\n distinctUntilKeyChanged(\"offset\"),\n debounceTime(250),\n skip(1),\n takeUntil(target$.pipe(skip(1))),\n repeat({ delay: 250 }),\n withLatestFrom(push$)\n )\n .subscribe(([, { prev }]) => {\n const url = getLocation()\n\n /* Set hash fragment to active anchor */\n const anchor = prev[prev.length - 1]\n if (anchor && anchor.length) {\n const [active] = anchor\n const { hash } = new URL(active.href)\n if (url.hash !== hash) {\n url.hash = hash\n history.replaceState({}, \"\", `${url}`)\n }\n\n /* Reset anchor when at the top */\n } else {\n url.hash = \"\"\n history.replaceState({}, \"\", `${url}`)\n }\n })\n\n /* Create and return component */\n return watchTableOfContents(el, { viewport$, header$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatest,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n endWith,\n finalize,\n map,\n repeat,\n skip,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { Viewport } from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Back-to-top button\n */\nexport interface BackToTop {\n hidden: boolean /* Back-to-top button is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n main$: Observable
    /* Main area observable */\n target$: Observable /* Location target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n target$: Observable /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch back-to-top\n *\n * @param _el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top observable\n */\nexport function watchBackToTop(\n _el: HTMLElement, { viewport$, main$, target$ }: WatchOptions\n): Observable {\n\n /* Compute direction */\n const direction$ = viewport$\n .pipe(\n map(({ offset: { y } }) => y),\n bufferCount(2, 1),\n map(([a, b]) => a > b && b > 0),\n distinctUntilChanged()\n )\n\n /* Compute whether main area is active */\n const active$ = main$\n .pipe(\n map(({ active }) => active)\n )\n\n /* Compute threshold for hiding */\n return combineLatest([active$, direction$])\n .pipe(\n map(([active, direction]) => !(active && direction)),\n distinctUntilChanged(),\n takeUntil(target$.pipe(skip(1))),\n endWith(true),\n repeat({ delay: 250 }),\n map(hidden => ({ hidden }))\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount back-to-top\n *\n * @param el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top component observable\n */\nexport function mountBackToTop(\n el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n push$.subscribe({\n\n /* Handle emission */\n next({ hidden }) {\n if (hidden) {\n el.setAttribute(\"data-md-state\", \"hidden\")\n el.setAttribute(\"tabindex\", \"-1\")\n el.blur()\n } else {\n el.removeAttribute(\"data-md-state\")\n el.removeAttribute(\"tabindex\")\n }\n },\n\n /* Handle complete */\n complete() {\n el.style.top = \"\"\n el.setAttribute(\"data-md-state\", \"hidden\")\n el.removeAttribute(\"tabindex\")\n }\n })\n\n /* Watch header height */\n header$\n .pipe(\n takeUntil(push$.pipe(endWith(0), takeLast(1))),\n distinctUntilKeyChanged(\"height\")\n )\n .subscribe(({ height }) => {\n el.style.top = `${height + 16}px`\n })\n\n /* Create and return component */\n return watchBackToTop(el, { viewport$, main$, target$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n mergeMap,\n switchMap,\n takeWhile,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n document$: Observable /* Document observable */\n tablet$: Observable /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch indeterminate checkboxes\n *\n * This function replaces the indeterminate \"pseudo state\" with the actual\n * indeterminate state, which is used to keep navigation always expanded.\n *\n * @param options - Options\n */\nexport function patchIndeterminate(\n { document$, tablet$ }: PatchOptions\n): void {\n document$\n .pipe(\n switchMap(() => getElements(\n \"[data-md-state=indeterminate]\"\n )),\n tap(el => {\n el.indeterminate = true\n el.checked = false\n }),\n mergeMap(el => fromEvent(el, \"change\")\n .pipe(\n takeWhile(() => el.hasAttribute(\"data-md-state\")),\n map(() => el)\n )\n ),\n withLatestFrom(tablet$)\n )\n .subscribe(([el, tablet]) => {\n el.removeAttribute(\"data-md-state\")\n if (tablet)\n el.checked = false\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n mergeMap,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n document$: Observable /* Document observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether the given device is an Apple device\n *\n * @returns Test result\n */\nfunction isAppleDevice(): boolean {\n return /(iPad|iPhone|iPod)/.test(navigator.userAgent)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch all elements with `data-md-scrollfix` attributes\n *\n * This is a year-old patch which ensures that overflow scrolling works at the\n * top and bottom of containers on iOS by ensuring a `1px` scroll offset upon\n * the start of a touch event.\n *\n * @see https://bit.ly/2SCtAOO - Original source\n *\n * @param options - Options\n */\nexport function patchScrollfix(\n { document$ }: PatchOptions\n): void {\n document$\n .pipe(\n switchMap(() => getElements(\"[data-md-scrollfix]\")),\n tap(el => el.removeAttribute(\"data-md-scrollfix\")),\n filter(isAppleDevice),\n mergeMap(el => fromEvent(el, \"touchstart\")\n .pipe(\n map(() => el)\n )\n )\n )\n .subscribe(el => {\n const top = el.scrollTop\n\n /* We're at the top of the container */\n if (top === 0) {\n el.scrollTop = 1\n\n /* We're at the bottom of the container */\n } else if (top + el.offsetHeight === el.scrollHeight) {\n el.scrollTop = top - 1\n }\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n delay,\n map,\n of,\n switchMap,\n withLatestFrom\n} from \"rxjs\"\n\nimport {\n Viewport,\n watchToggle\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n viewport$: Observable /* Viewport observable */\n tablet$: Observable /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch the document body to lock when search is open\n *\n * For mobile and tablet viewports, the search is rendered full screen, which\n * leads to scroll leaking when at the top or bottom of the search result. This\n * function locks the body when the search is in full screen mode, and restores\n * the scroll position when leaving.\n *\n * @param options - Options\n */\nexport function patchScrolllock(\n { viewport$, tablet$ }: PatchOptions\n): void {\n combineLatest([watchToggle(\"search\"), tablet$])\n .pipe(\n map(([active, tablet]) => active && !tablet),\n switchMap(active => of(active)\n .pipe(\n delay(active ? 400 : 100)\n )\n ),\n withLatestFrom(viewport$)\n )\n .subscribe(([active, { offset: { y }}]) => {\n if (active) {\n document.body.setAttribute(\"data-md-state\", \"lock\")\n document.body.style.top = `-${y}px`\n } else {\n const value = -1 * parseInt(document.body.style.top, 10)\n document.body.removeAttribute(\"data-md-state\")\n document.body.style.top = \"\"\n if (value)\n window.scrollTo(0, value)\n }\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n Object.entries = function (obj: object) {\n const data: [string, string][] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push([key, obj[key]])\n\n /* Return entries */\n return data\n }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n Object.values = function (obj: object) {\n const data: string[] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n 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t=this;e.touches.length<2&&(this.multipointEnd.dispatch(e,this.element),this.sx2=this.sy2=null),this.x2&&Math.abs(this.x1-this.x2)>30||this.y2&&Math.abs(this.y1-this.y2)>30?(e.direction=this._swipeDirection(this.x1,this.x2,this.y1,this.y2),this.swipeTimeout=setTimeout((function(){t.swipe.dispatch(e,t.element)}),0)):(this.tapTimeout=setTimeout((function(){t._preventTap||t.tap.dispatch(e,t.element),t.isDoubleTap&&(t.doubleTap.dispatch(e,t.element),t.isDoubleTap=!1)}),0),t.isDoubleTap||(t.singleTapTimeout=setTimeout((function(){t.singleTap.dispatch(e,t.element)}),250))),this.touchEnd.dispatch(e,this.element),this.preV.x=0,this.preV.y=0,this.zoom=1,this.pinchStartLen=null,this.x1=this.x2=this.y1=this.y2=null}}},{key:"cancelAll",value:function(){this._preventTap=!0,clearTimeout(this.singleTapTimeout),clearTimeout(this.tapTimeout),clearTimeout(this.longTapTimeout),clearTimeout(this.swipeTimeout)}},{key:"cancel",value:function(e){this.cancelAll(),this.touchCancel.dispatch(e,this.element)}},{key:"_cancelLongTap",value:function(){clearTimeout(this.longTapTimeout)}},{key:"_cancelSingleTap",value:function(){clearTimeout(this.singleTapTimeout)}},{key:"_swipeDirection",value:function(e,t,i,n){return 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h,d=s.targetTouches[0].clientX,c=s.targetTouches[0].clientY,u=M-d,m=z-c;if(Math.abs(u)>Math.abs(m)?(L=!1,I=!0):(I=!1,L=!0),t=P.pageX-O.pageX,E=100*t/l,i=P.pageY-O.pageY,A=100*i/o,L&&f&&(h=1-Math.abs(i)/o,Y.style.opacity=h,e.settings.touchFollowAxis&&(E=0)),I&&(h=1-Math.abs(t)/l,g.style.opacity=h,e.settings.touchFollowAxis&&(A=0)),!f)return v(g,"translate3d(".concat(E,"%, 0, 0)"));v(g,"translate3d(".concat(E,"%, ").concat(A,"%, 0)"))}},touchEnd:function(){if(r){if(p=!1,S||b)return C=w,void(k=T);var t=Math.abs(parseInt(A)),i=Math.abs(parseInt(E));if(!(t>29&&f))return t<29&&i<25?(h(Y,"greset"),Y.style.opacity=1,W(g)):void 0;e.close()}},multipointEnd:function(){setTimeout((function(){b=!1}),50)},multipointStart:function(){b=!0,m=x||1},pinch:function(e){if(!f||p)return!1;b=!0,f.scaleX=f.scaleY=m*e.zoom;var t=m*e.zoom;if(S=!0,t<=1)return S=!1,t=1,k=null,C=null,w=null,T=null,void f.setAttribute("style","");t>4.5&&(t=4.5),f.style.transform="scale3d(".concat(t,", ").concat(t,", 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s.drag(e)}),!1),this.img.addEventListener("click",(function(e){return s.slide.classList.contains("dragging-nav")?(s.zoomOut(),!1):s.zoomedIn?void(s.zoomedIn&&!s.dragging&&s.zoomOut()):s.zoomIn()}),!1),this.img.setZoomEvents=!0}return n(e,[{key:"zoomIn",value:function(){var e=this.widowWidth();if(!(this.zoomedIn||e<=768)){var t=this.img;if(t.setAttribute("data-style",t.getAttribute("style")),t.style.maxWidth=t.naturalWidth+"px",t.style.maxHeight=t.naturalHeight+"px",t.naturalWidth>e){var i=e/2-t.naturalWidth/2;this.setTranslate(this.img.parentNode,i,0)}this.slide.classList.add("zoomed"),this.zoomedIn=!0}}},{key:"zoomOut",value:function(){this.img.parentNode.setAttribute("style",""),this.img.setAttribute("style",this.img.getAttribute("data-style")),this.slide.classList.remove("zoomed"),this.zoomedIn=!1,this.currentX=null,this.currentY=null,this.initialX=null,this.initialY=null,this.xOffset=0,this.yOffset=0,this.onclose&&"function"==typeof this.onclose&&this.onclose()}},{key:"dragStart",value:function(e){e.preventDefault(),this.zoomedIn?("touchstart"===e.type?(this.initialX=e.touches[0].clientX-this.xOffset,this.initialY=e.touches[0].clientY-this.yOffset):(this.initialX=e.clientX-this.xOffset,this.initialY=e.clientY-this.yOffset),e.target===this.img&&(this.active=!0,this.img.classList.add("dragging"))):this.active=!1}},{key:"dragEnd",value:function(e){var t=this;e.preventDefault(),this.initialX=this.currentX,this.initialY=this.currentY,this.active=!1,setTimeout((function(){t.dragging=!1,t.img.isDragging=!1,t.img.classList.remove("dragging")}),100)}},{key:"drag",value:function(e){this.active&&(e.preventDefault(),"touchmove"===e.type?(this.currentX=e.touches[0].clientX-this.initialX,this.currentY=e.touches[0].clientY-this.initialY):(this.currentX=e.clientX-this.initialX,this.currentY=e.clientY-this.initialY),this.xOffset=this.currentX,this.yOffset=this.currentY,this.img.isDragging=!0,this.dragging=!0,this.setTranslate(this.img,this.currentX,this.currentY))}},{key:"onMove",value:function(e){if(this.zoomedIn){var t=e.clientX-this.img.naturalWidth/2,i=e.clientY-this.img.naturalHeight/2;this.setTranslate(this.img,t,i)}}},{key:"setTranslate",value:function(e,t,i){e.style.transform="translate3d("+t+"px, "+i+"px, 0)"}},{key:"widowWidth",value:function(){return window.innerWidth||document.documentElement.clientWidth||document.body.clientWidth}}]),e}(),V=function(){function e(){var i=this,n=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};t(this,e);var s=n.dragEl,l=n.toleranceX,o=void 0===l?40:l,r=n.toleranceY,a=void 0===r?65:r,h=n.slide,d=void 0===h?null:h,c=n.instance,u=void 0===c?null:c;this.el=s,this.active=!1,this.dragging=!1,this.currentX=null,this.currentY=null,this.initialX=null,this.initialY=null,this.xOffset=0,this.yOffset=0,this.direction=null,this.lastDirection=null,this.toleranceX=o,this.toleranceY=a,this.toleranceReached=!1,this.dragContainer=this.el,this.slide=d,this.instance=u,this.el.addEventListener("mousedown",(function(e){return i.dragStart(e)}),!1),this.el.addEventListener("mouseup",(function(e){return i.dragEnd(e)}),!1),this.el.addEventListener("mousemove",(function(e){return i.drag(e)}),!1)}return n(e,[{key:"dragStart",value:function(e){if(this.slide.classList.contains("zoomed"))this.active=!1;else{"touchstart"===e.type?(this.initialX=e.touches[0].clientX-this.xOffset,this.initialY=e.touches[0].clientY-this.yOffset):(this.initialX=e.clientX-this.xOffset,this.initialY=e.clientY-this.yOffset);var t=e.target.nodeName.toLowerCase();e.target.classList.contains("nodrag")||u(e.target,".nodrag")||-1!==["input","select","textarea","button","a"].indexOf(t)?this.active=!1:(e.preventDefault(),(e.target===this.el||"img"!==t&&u(e.target,".gslide-inline"))&&(this.active=!0,this.el.classList.add("dragging"),this.dragContainer=u(e.target,".ginner-container")))}}},{key:"dragEnd",value:function(e){var t=this;e&&e.preventDefault(),this.initialX=0,this.initialY=0,this.currentX=null,this.currentY=null,this.initialX=null,this.initialY=null,this.xOffset=0,this.yOffset=0,this.active=!1,this.doSlideChange&&(this.instance.preventOutsideClick=!0,"right"==this.doSlideChange&&this.instance.prevSlide(),"left"==this.doSlideChange&&this.instance.nextSlide()),this.doSlideClose&&this.instance.close(),this.toleranceReached||this.setTranslate(this.dragContainer,0,0,!0),setTimeout((function(){t.instance.preventOutsideClick=!1,t.toleranceReached=!1,t.lastDirection=null,t.dragging=!1,t.el.isDragging=!1,t.el.classList.remove("dragging"),t.slide.classList.remove("dragging-nav"),t.dragContainer.style.transform="",t.dragContainer.style.transition=""}),100)}},{key:"drag",value:function(e){if(this.active){e.preventDefault(),this.slide.classList.add("dragging-nav"),"touchmove"===e.type?(this.currentX=e.touches[0].clientX-this.initialX,this.currentY=e.touches[0].clientY-this.initialY):(this.currentX=e.clientX-this.initialX,this.currentY=e.clientY-this.initialY),this.xOffset=this.currentX,this.yOffset=this.currentY,this.el.isDragging=!0,this.dragging=!0,this.doSlideChange=!1,this.doSlideClose=!1;var t=Math.abs(this.currentX),i=Math.abs(this.currentY);if(t>0&&t>=Math.abs(this.currentY)&&(!this.lastDirection||"x"==this.lastDirection)){this.yOffset=0,this.lastDirection="x",this.setTranslate(this.dragContainer,this.currentX,0);var n=this.shouldChange();if(!this.instance.settings.dragAutoSnap&&n&&(this.doSlideChange=n),this.instance.settings.dragAutoSnap&&n)return this.instance.preventOutsideClick=!0,this.toleranceReached=!0,this.active=!1,this.instance.preventOutsideClick=!0,this.dragEnd(null),"right"==n&&this.instance.prevSlide(),void("left"==n&&this.instance.nextSlide())}if(this.toleranceY>0&&i>0&&i>=t&&(!this.lastDirection||"y"==this.lastDirection)){this.xOffset=0,this.lastDirection="y",this.setTranslate(this.dragContainer,0,this.currentY);var s=this.shouldClose();return!this.instance.settings.dragAutoSnap&&s&&(this.doSlideClose=!0),void(this.instance.settings.dragAutoSnap&&s&&this.instance.close())}}}},{key:"shouldChange",value:function(){var e=!1;if(Math.abs(this.currentX)>=this.toleranceX){var t=this.currentX>0?"right":"left";("left"==t&&this.slide!==this.slide.parentNode.lastChild||"right"==t&&this.slide!==this.slide.parentNode.firstChild)&&(e=t)}return e}},{key:"shouldClose",value:function(){var e=!1;return Math.abs(this.currentY)>=this.toleranceY&&(e=!0),e}},{key:"setTranslate",value:function(e,t,i){var n=arguments.length>3&&void 0!==arguments[3]&&arguments[3];e.style.transition=n?"all .2s ease":"",e.style.transform="translate3d(".concat(t,"px, ").concat(i,"px, 0)")}}]),e}();function j(e,t,i,n){var s=e.querySelector(".gslide-media"),l=new Image,o="gSlideTitle_"+i,r="gSlideDesc_"+i;l.addEventListener("load",(function(){T(n)&&n()}),!1),l.src=t.href,""!=t.sizes&&""!=t.srcset&&(l.sizes=t.sizes,l.srcset=t.srcset),l.alt="",I(t.alt)||""===t.alt||(l.alt=t.alt),""!==t.title&&l.setAttribute("aria-labelledby",o),""!==t.description&&l.setAttribute("aria-describedby",r),t.hasOwnProperty("_hasCustomWidth")&&t._hasCustomWidth&&(l.style.width=t.width),t.hasOwnProperty("_hasCustomHeight")&&t._hasCustomHeight&&(l.style.height=t.height),s.insertBefore(l,s.firstChild)}function F(e,t,i,n){var s=this,l=e.querySelector(".ginner-container"),o="gvideo"+i,r=e.querySelector(".gslide-media"),a=this.getAllPlayers();h(l,"gvideo-container"),r.insertBefore(m('
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t=e;if(null!==(e=e.toLowerCase()).match(/\.(jpeg|jpg|jpe|gif|png|apn|webp|avif|svg)/))return"image";if(e.match(/(youtube\.com|youtube-nocookie\.com)\/watch\?v=([a-zA-Z0-9\-_]+)/)||e.match(/youtu\.be\/([a-zA-Z0-9\-_]+)/)||e.match(/(youtube\.com|youtube-nocookie\.com)\/embed\/([a-zA-Z0-9\-_]+)/))return"video";if(e.match(/vimeo\.com\/([0-9]*)/))return"video";if(null!==e.match(/\.(mp4|ogg|webm|mov)/))return"video";if(null!==e.match(/\.(mp3|wav|wma|aac|ogg)/))return"audio";if(e.indexOf("#")>-1&&""!==t.split("#").pop().trim())return"inline";return e.indexOf("goajax=true")>-1?"ajax":"external"}},{key:"parseConfig",value:function(e,t){var i=this,n=l({descPosition:t.descPosition},this.defaults);if(L(e)&&!k(e)){O(e,"type")||(O(e,"content")&&e.content?e.type="inline":O(e,"href")&&(e.type=this.sourceType(e.href)));var s=l(n,e);return this.setSize(s,t),s}var r="",a=e.getAttribute("data-glightbox"),h=e.nodeName.toLowerCase();if("a"===h&&(r=e.href),"img"===h&&(r=e.src,n.alt=e.alt),n.href=r,o(n,(function(s,l){O(t,l)&&"width"!==l&&(n[l]=t[l]);var o=e.dataset[l];I(o)||(n[l]=i.sanitizeValue(o))})),n.content&&(n.type="inline"),!n.type&&r&&(n.type=this.sourceType(r)),I(a)){if(!n.title&&"a"==h){var d=e.title;I(d)||""===d||(n.title=d)}if(!n.title&&"img"==h){var c=e.alt;I(c)||""===c||(n.title=c)}}else{var u=[];o(n,(function(e,t){u.push(";\\s?"+t)})),u=u.join("\\s?:|"),""!==a.trim()&&o(n,(function(e,t){var s=a,l=new RegExp("s?"+t+"s?:s?(.*?)("+u+"s?:|$)"),o=s.match(l);if(o&&o.length&&o[1]){var r=o[1].trim().replace(/;\s*$/,"");n[t]=i.sanitizeValue(r)}}))}if(n.description&&"."===n.description.substring(0,1)){var g;try{g=document.querySelector(n.description).innerHTML}catch(e){if(!(e instanceof DOMException))throw e}g&&(n.description=g)}if(!n.description){var v=e.querySelector(".glightbox-desc");v&&(n.description=v.innerHTML)}return this.setSize(n,t,e),this.slideConfig=n,n}},{key:"setSize",value:function(e,t){var i=arguments.length>2&&void 0!==arguments[2]?arguments[2]:null,n="video"==e.type?this.checkSize(t.videosWidth):this.checkSize(t.width),s=this.checkSize(t.height);return e.width=O(e,"width")&&""!==e.width?this.checkSize(e.width):n,e.height=O(e,"height")&&""!==e.height?this.checkSize(e.height):s,i&&"image"==e.type&&(e._hasCustomWidth=!!i.dataset.width,e._hasCustomHeight=!!i.dataset.height),e}},{key:"checkSize",value:function(e){return M(e)?"".concat(e,"px"):e}},{key:"sanitizeValue",value:function(e){return"true"!==e&&"false"!==e?e:"true"===e}}]),e}(),U=function(){function e(i,n,s){t(this,e),this.element=i,this.instance=n,this.index=s}return n(e,[{key:"setContent",value:function(){var e=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:null,i=arguments.length>1&&void 0!==arguments[1]&&arguments[1];if(c(t,"loaded"))return!1;var n=this.instance.settings,s=this.slideConfig,l=w();T(n.beforeSlideLoad)&&n.beforeSlideLoad({index:this.index,slide:t,player:!1});var o=s.type,r=s.descPosition,a=t.querySelector(".gslide-media"),d=t.querySelector(".gslide-title"),u=t.querySelector(".gslide-desc"),g=t.querySelector(".gdesc-inner"),v=i,f="gSlideTitle_"+this.index,p="gSlideDesc_"+this.index;if(T(n.afterSlideLoad)&&(v=function(){T(i)&&i(),n.afterSlideLoad({index:e.index,slide:t,player:e.instance.getSlidePlayerInstance(e.index)})}),""==s.title&&""==s.description?g&&g.parentNode.parentNode.removeChild(g.parentNode):(d&&""!==s.title?(d.id=f,d.innerHTML=s.title):d.parentNode.removeChild(d),u&&""!==s.description?(u.id=p,l&&n.moreLength>0?(s.smallDescription=this.slideShortDesc(s.description,n.moreLength,n.moreText),u.innerHTML=s.smallDescription,this.descriptionEvents(u,s)):u.innerHTML=s.description):u.parentNode.removeChild(u),h(a.parentNode,"desc-".concat(r)),h(g.parentNode,"description-".concat(r))),h(a,"gslide-".concat(o)),h(t,"loaded"),"video"!==o){if("external"!==o)return"inline"===o?(G.apply(this.instance,[t,s,this.index,v]),void(s.draggable&&new V({dragEl:t.querySelector(".gslide-inline"),toleranceX:n.dragToleranceX,toleranceY:n.dragToleranceY,slide:t,instance:this.instance}))):void("image"!==o?T(v)&&v():j(t,s,this.index,(function(){var i=t.querySelector("img");s.draggable&&new V({dragEl:i,toleranceX:n.dragToleranceX,toleranceY:n.dragToleranceY,slide:t,instance:e.instance}),s.zoomable&&i.naturalWidth>i.offsetWidth&&(h(i,"zoomable"),new H(i,t,(function(){e.instance.resize()}))),T(v)&&v()})));Z.apply(this,[t,s,this.index,v])}else F.apply(this.instance,[t,s,this.index,v])}},{key:"slideShortDesc",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:50,i=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=document.createElement("div");n.innerHTML=e;var s=n.innerText,l=i;if((e=s.trim()).length<=t)return e;var o=e.substr(0,t-1);return l?(n=null,o+'... '+i+""):o}},{key:"descriptionEvents",value:function(e,t){var i=this,n=e.querySelector(".desc-more");if(!n)return!1;a("click",{onElement:n,withCallback:function(e,n){e.preventDefault();var s=document.body,l=u(n,".gslide-desc");if(!l)return!1;l.innerHTML=t.description,h(s,"gdesc-open");var o=a("click",{onElement:[s,u(l,".gslide-description")],withCallback:function(e,n){"a"!==e.target.nodeName.toLowerCase()&&(d(s,"gdesc-open"),h(s,"gdesc-closed"),l.innerHTML=t.smallDescription,i.descriptionEvents(l,t),setTimeout((function(){d(s,"gdesc-closed")}),400),o.destroy())}})}})}},{key:"create",value:function(){return m(this.instance.settings.slideHTML)}},{key:"getConfig",value:function(){k(this.element)||this.element.hasOwnProperty("draggable")||(this.element.draggable=this.instance.settings.draggable);var e=new $(this.instance.settings.slideExtraAttributes);return this.slideConfig=e.parseConfig(this.element,this.instance.settings),this.slideConfig}}]),e}(),J=w(),K=null!==w()||void 0!==document.createTouch||"ontouchstart"in window||"onmsgesturechange"in window||navigator.msMaxTouchPoints,Q=document.getElementsByTagName("html")[0],ee={selector:".glightbox",elements:null,skin:"clean",theme:"clean",closeButton:!0,startAt:null,autoplayVideos:!0,autofocusVideos:!0,descPosition:"bottom",width:"900px",height:"506px",videosWidth:"960px",beforeSlideChange:null,afterSlideChange:null,beforeSlideLoad:null,afterSlideLoad:null,slideInserted:null,slideRemoved:null,slideExtraAttributes:null,onOpen:null,onClose:null,loop:!1,zoomable:!0,draggable:!0,dragAutoSnap:!1,dragToleranceX:40,dragToleranceY:65,preload:!0,oneSlidePerOpen:!1,touchNavigation:!0,touchFollowAxis:!0,keyboardNavigation:!0,closeOnOutsideClick:!0,plugins:!1,plyr:{css:"https://cdn.plyr.io/3.6.8/plyr.css",js:"https://cdn.plyr.io/3.6.8/plyr.js",config:{ratio:"16:9",fullscreen:{enabled:!0,iosNative:!0},youtube:{noCookie:!0,rel:0,showinfo:0,iv_load_policy:3},vimeo:{byline:!1,portrait:!1,title:!1,transparent:!1}}},openEffect:"zoom",closeEffect:"zoom",slideEffect:"slide",moreText:"See more",moreLength:60,cssEfects:{fade:{in:"fadeIn",out:"fadeOut"},zoom:{in:"zoomIn",out:"zoomOut"},slide:{in:"slideInRight",out:"slideOutLeft"},slideBack:{in:"slideInLeft",out:"slideOutRight"},none:{in:"none",out:"none"}},svg:{close:'',next:' ',prev:''},slideHTML:'
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n=e.getAttribute("data-gallery");n&&(this.fullElementsList=this.elements,this.elements=this.getGalleryElements(this.elements,n)),I(i)&&(i=this.getElementIndex(e))<0&&(i=0)}M(i)||(i=0),this.build(),g(this.overlay,"none"==this.settings.openEffect?"none":this.settings.cssEfects.fade.in);var s=document.body,l=window.innerWidth-document.documentElement.clientWidth;if(l>0){var o=document.createElement("style");o.type="text/css",o.className="gcss-styles",o.innerText=".gscrollbar-fixer {margin-right: ".concat(l,"px}"),document.head.appendChild(o),h(s,"gscrollbar-fixer")}h(s,"glightbox-open"),h(Q,"glightbox-open"),J&&(h(document.body,"glightbox-mobile"),this.settings.slideEffect="slide"),this.showSlide(i,!0),1==this.elements.length?(h(this.prevButton,"glightbox-button-hidden"),h(this.nextButton,"glightbox-button-hidden")):(d(this.prevButton,"glightbox-button-hidden"),d(this.nextButton,"glightbox-button-hidden")),this.lightboxOpen=!0,this.trigger("open"),T(this.settings.onOpen)&&this.settings.onOpen(),K&&this.settings.touchNavigation&&B(this),this.settings.keyboardNavigation&&X(this)}},{key:"openAt",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0;this.open(null,e)}},{key:"showSlide",value:function(){var e=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:0,i=arguments.length>1&&void 0!==arguments[1]&&arguments[1];f(this.loader),this.index=parseInt(t);var n=this.slidesContainer.querySelector(".current");n&&d(n,"current"),this.slideAnimateOut();var s=this.slidesContainer.querySelectorAll(".gslide")[t];if(c(s,"loaded"))this.slideAnimateIn(s,i),p(this.loader);else{f(this.loader);var l=this.elements[t],o={index:this.index,slide:s,slideNode:s,slideConfig:l.slideConfig,slideIndex:this.index,trigger:l.node,player:null};this.trigger("slide_before_load",o),l.instance.setContent(s,(function(){p(e.loader),e.resize(),e.slideAnimateIn(s,i),e.trigger("slide_after_load",o)}))}this.slideDescription=s.querySelector(".gslide-description"),this.slideDescriptionContained=this.slideDescription&&c(this.slideDescription.parentNode,"gslide-media"),this.settings.preload&&(this.preloadSlide(t+1),this.preloadSlide(t-1)),this.updateNavigationClasses(),this.activeSlide=s}},{key:"preloadSlide",value:function(e){var t=this;if(e<0||e>this.elements.length-1)return!1;if(I(this.elements[e]))return!1;var i=this.slidesContainer.querySelectorAll(".gslide")[e];if(c(i,"loaded"))return!1;var n=this.elements[e],s=n.type,l={index:e,slide:i,slideNode:i,slideConfig:n.slideConfig,slideIndex:e,trigger:n.node,player:null};this.trigger("slide_before_load",l),"video"==s||"external"==s?setTimeout((function(){n.instance.setContent(i,(function(){t.trigger("slide_after_load",l)}))}),200):n.instance.setContent(i,(function(){t.trigger("slide_after_load",l)}))}},{key:"prevSlide",value:function(){this.goToSlide(this.index-1)}},{key:"nextSlide",value:function(){this.goToSlide(this.index+1)}},{key:"goToSlide",value:function(){var e=arguments.length>0&&void 0!==arguments[0]&&arguments[0];if(this.prevActiveSlide=this.activeSlide,this.prevActiveSlideIndex=this.index,!this.loop()&&(e<0||e>this.elements.length-1))return!1;e<0?e=this.elements.length-1:e>=this.elements.length&&(e=0),this.showSlide(e)}},{key:"insertSlide",value:function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:-1;t<0&&(t=this.elements.length);var i=new U(e,this,t),n=i.getConfig(),s=l({},n),o=i.create(),r=this.elements.length-1;s.index=t,s.node=!1,s.instance=i,s.slideConfig=n,this.elements.splice(t,0,s);var a=null,h=null;if(this.slidesContainer){if(t>r)this.slidesContainer.appendChild(o);else{var d=this.slidesContainer.querySelectorAll(".gslide")[t];this.slidesContainer.insertBefore(o,d)}(this.settings.preload&&0==this.index&&0==t||this.index-1==t||this.index+1==t)&&this.preloadSlide(t),0==this.index&&0==t&&(this.index=1),this.updateNavigationClasses(),a=this.slidesContainer.querySelectorAll(".gslide")[t],h=this.getSlidePlayerInstance(t),s.slideNode=a}this.trigger("slide_inserted",{index:t,slide:a,slideNode:a,slideConfig:n,slideIndex:t,trigger:null,player:h}),T(this.settings.slideInserted)&&this.settings.slideInserted({index:t,slide:a,player:h})}},{key:"removeSlide",value:function(){var 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nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}}); \ No newline at end of file diff --git a/main/assets/javascripts/lunr/min/lunr.de.min.js b/main/assets/javascripts/lunr/min/lunr.de.min.js new file mode 100644 index 00000000..f3b5c108 --- /dev/null +++ b/main/assets/javascripts/lunr/min/lunr.de.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `German` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daß dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures für gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines können könnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher solches soll sollte sondern sonst um und uns unse unsem unsen unser unses unter viel vom von vor war waren warst was weg weil weiter welche welchem welchen welcher welches wenn werde werden wie wieder will wir wird wirst wo wollen wollte während würde würden zu zum zur zwar zwischen über".split(" ")),e.Pipeline.registerFunction(e.de.stopWordFilter,"stopWordFilter-de")}}); \ No newline at end of file diff --git a/main/assets/javascripts/lunr/min/lunr.du.min.js b/main/assets/javascripts/lunr/min/lunr.du.min.js new file mode 100644 index 00000000..49a0f3f0 --- /dev/null +++ b/main/assets/javascripts/lunr/min/lunr.du.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Dutch` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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keressünk keresztül ki kívül között közül legalább legyen lehet lehetett lenne lenni lesz lett maga magát majd majd meg mellett mely melyek mert mi mikor milyen minden mindenki mindent mindig mint mintha mit mivel miért most már más másik még míg nagy nagyobb nagyon ne nekem neki nem nincs néha néhány nélkül olyan ott pedig persze rá s saját sem semmi sok sokat sokkal szemben szerint szinte számára talán tehát teljes tovább továbbá több ugyanis utolsó után utána vagy vagyis vagyok valaki valami valamint való van vannak vele vissza viszont volna volt voltak voltam voltunk által általában át én éppen és így õ õk õket össze úgy új újabb újra".split(" ")),e.Pipeline.registerFunction(e.hu.stopWordFilter,"stopWordFilter-hu")}}); \ No newline at end of file diff --git a/main/assets/javascripts/lunr/min/lunr.it.min.js b/main/assets/javascripts/lunr/min/lunr.it.min.js new file mode 100644 index 00000000..344b6a3c --- /dev/null +++ b/main/assets/javascripts/lunr/min/lunr.it.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Italian` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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avevo avrai avranno avrebbe avrebbero avrei avremmo avremo avreste avresti avrete avrà avrò avuta avute avuti avuto c che chi ci coi col come con contro cui da dagl dagli dai dal dall dalla dalle dallo degl degli dei del dell della delle dello di dov dove e ebbe ebbero ebbi ed era erano eravamo eravate eri ero essendo faccia facciamo facciano facciate faccio facemmo facendo facesse facessero facessi facessimo faceste facesti faceva facevamo facevano facevate facevi facevo fai fanno farai faranno farebbe farebbero farei faremmo faremo fareste faresti farete farà farò fece fecero feci fosse fossero fossi fossimo foste fosti fu fui fummo furono gli ha hai hanno ho i il in io l la le lei li lo loro lui ma mi mia mie miei mio ne negl negli nei nel nell nella nelle nello noi non nostra nostre nostri nostro o per perché più quale quanta quante quanti quanto quella quelle quelli quello questa queste questi questo sarai saranno sarebbe sarebbero sarei saremmo saremo sareste saresti sarete sarà sarò se sei si sia siamo siano siate siete sono sta stai stando stanno starai staranno starebbe starebbero starei staremmo staremo stareste staresti starete starà starò stava stavamo stavano stavate stavi stavo stemmo stesse stessero stessi stessimo steste stesti stette stettero stetti stia stiamo stiano stiate sto su sua sue sugl sugli sui sul sull sulla sulle sullo suo suoi ti tra tu tua tue tuo tuoi tutti tutto un una uno vi voi vostra vostre vostri vostro è".split(" ")),e.Pipeline.registerFunction(e.it.stopWordFilter,"stopWordFilter-it")}}); \ No newline at end of file diff --git a/main/assets/javascripts/lunr/min/lunr.ja.min.js b/main/assets/javascripts/lunr/min/lunr.ja.min.js new file mode 100644 index 00000000..5f254ebe --- /dev/null +++ b/main/assets/javascripts/lunr/min/lunr.ja.min.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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i=Dr.limit-Dr.cursor;if(d())Dr.bra=Dr.cursor,Dr.slice_del();else if(Dr.cursor=Dr.limit-i,m())Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,W()&&(Dr.bra=Dr.cursor,Dr.slice_del(),K());else if(Dr.cursor=Dr.limit-i,!K())return!1;return!0}function N(r){if(Dr.ket=Dr.cursor,!z()&&(Dr.cursor=Dr.limit-r,!b()))return!1;var i=Dr.limit-Dr.cursor;return!(!m()&&(Dr.cursor=Dr.limit-i,!d()))&&(Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,W()&&(Dr.bra=Dr.cursor,Dr.slice_del(),K()),!0)}function O(){var r,i=Dr.limit-Dr.cursor;return Dr.ket=Dr.cursor,!(!w()&&(Dr.cursor=Dr.limit-i,!v()))&&(Dr.bra=Dr.cursor,Dr.slice_del(),r=Dr.limit-Dr.cursor,Dr.ket=Dr.cursor,!(!W()||(Dr.bra=Dr.cursor,Dr.slice_del(),!K()))||(Dr.cursor=Dr.limit-r,Dr.ket=Dr.cursor,!(a()||(Dr.cursor=Dr.limit-r,m()||(Dr.cursor=Dr.limit-r,K())))||(Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,W()&&(Dr.bra=Dr.cursor,Dr.slice_del(),K()),!0)))}function Q(){var r,i,e=Dr.limit-Dr.cursor;if(Dr.ket=Dr.cursor,!p()&&(Dr.cursor=Dr.limit-e,!f()&&(Dr.cursor=Dr.limit-e,!_())))return!1;if(Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,r=Dr.limit-Dr.cursor,a())Dr.bra=Dr.cursor,Dr.slice_del(),i=Dr.limit-Dr.cursor,Dr.ket=Dr.cursor,W()||(Dr.cursor=Dr.limit-i);else if(Dr.cursor=Dr.limit-r,!W())return!0;return Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,K(),!0}function R(){var r,i,e=Dr.limit-Dr.cursor;if(Dr.ket=Dr.cursor,W())return Dr.bra=Dr.cursor,Dr.slice_del(),void K();if(Dr.cursor=Dr.limit-e,Dr.ket=Dr.cursor,q())if(Dr.bra=Dr.cursor,Dr.slice_del(),r=Dr.limit-Dr.cursor,Dr.ket=Dr.cursor,d())Dr.bra=Dr.cursor,Dr.slice_del();else{if(Dr.cursor=Dr.limit-r,Dr.ket=Dr.cursor,!a()&&(Dr.cursor=Dr.limit-r,!m())){if(Dr.cursor=Dr.limit-r,Dr.ket=Dr.cursor,!W())return;if(Dr.bra=Dr.cursor,Dr.slice_del(),!K())return}Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,W()&&(Dr.bra=Dr.cursor,Dr.slice_del(),K())}else if(Dr.cursor=Dr.limit-e,!M(e)&&(Dr.cursor=Dr.limit-e,!N(e))){if(Dr.cursor=Dr.limit-e,Dr.ket=Dr.cursor,y())return Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,i=Dr.limit-Dr.cursor,void(a()?(Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,W()&&(Dr.bra=Dr.cursor,Dr.slice_del(),K())):(Dr.cursor=Dr.limit-i,W()?(Dr.bra=Dr.cursor,Dr.slice_del(),K()):(Dr.cursor=Dr.limit-i,K())));if(Dr.cursor=Dr.limit-e,!O()){if(Dr.cursor=Dr.limit-e,d())return Dr.bra=Dr.cursor,void Dr.slice_del();Dr.cursor=Dr.limit-e,K()||(Dr.cursor=Dr.limit-e,Q()||(Dr.cursor=Dr.limit-e,Dr.ket=Dr.cursor,(a()||(Dr.cursor=Dr.limit-e,m()))&&(Dr.bra=Dr.cursor,Dr.slice_del(),Dr.ket=Dr.cursor,W()&&(Dr.bra=Dr.cursor,Dr.slice_del(),K()))))}}}function U(){var r;if(Dr.ket=Dr.cursor,r=Dr.find_among_b(Sr,4))switch(Dr.bra=Dr.cursor,r){case 1:Dr.slice_from("p");break;case 2:Dr.slice_from("ç");break;case 3:Dr.slice_from("t");break;case 4:Dr.slice_from("k")}}function V(){for(;;){var r=Dr.limit-Dr.cursor;if(Dr.in_grouping_b(Wr,97,305)){Dr.cursor=Dr.limit-r;break}if(Dr.cursor=Dr.limit-r,Dr.cursor<=Dr.limit_backward)return!1;Dr.cursor--}return!0}function X(r,i,e){if(Dr.cursor=Dr.limit-r,V()){var n=Dr.limit-Dr.cursor;if(!Dr.eq_s_b(1,i)&&(Dr.cursor=Dr.limit-n,!Dr.eq_s_b(1,e)))return!0;Dr.cursor=Dr.limit-r;var t=Dr.cursor;return Dr.insert(Dr.cursor,Dr.cursor,e),Dr.cursor=t,!1}return!0}function Y(){var r=Dr.limit-Dr.cursor;(Dr.eq_s_b(1,"d")||(Dr.cursor=Dr.limit-r,Dr.eq_s_b(1,"g")))&&X(r,"a","ı")&&X(r,"e","i")&&X(r,"o","u")&&X(r,"ö","ü")}function $(){for(var r,i=Dr.cursor,e=2;;){for(r=Dr.cursor;!Dr.in_grouping(Wr,97,305);){if(Dr.cursor>=Dr.limit)return Dr.cursor=r,!(e>0)&&(Dr.cursor=i,!0);Dr.cursor++}e--}}function rr(r,i,e){for(;!Dr.eq_s(i,e);){if(Dr.cursor>=Dr.limit)return!0;Dr.cursor++}return(tr=i)!=Dr.limit||(Dr.cursor=r,!1)}function ir(){var r=Dr.cursor;return!rr(r,2,"ad")||(Dr.cursor=r,!rr(r,5,"soyad"))}function er(){var r=Dr.cursor;return!ir()&&(Dr.limit_backward=r,Dr.cursor=Dr.limit,Y(),Dr.cursor=Dr.limit,U(),!0)}var nr,tr,ur=[new i("m",-1,-1),new i("n",-1,-1),new i("miz",-1,-1),new i("niz",-1,-1),new i("muz",-1,-1),new i("nuz",-1,-1),new i("müz",-1,-1),new i("nüz",-1,-1),new i("mız",-1,-1),new i("nız",-1,-1)],or=[new i("leri",-1,-1),new i("ları",-1,-1)],sr=[new i("ni",-1,-1),new i("nu",-1,-1),new i("nü",-1,-1),new i("nı",-1,-1)],cr=[new i("in",-1,-1),new i("un",-1,-1),new i("ün",-1,-1),new i("ın",-1,-1)],lr=[new i("a",-1,-1),new i("e",-1,-1)],ar=[new i("na",-1,-1),new i("ne",-1,-1)],mr=[new i("da",-1,-1),new i("ta",-1,-1),new i("de",-1,-1),new i("te",-1,-1)],dr=[new i("nda",-1,-1),new i("nde",-1,-1)],fr=[new i("dan",-1,-1),new i("tan",-1,-1),new i("den",-1,-1),new i("ten",-1,-1)],br=[new i("ndan",-1,-1),new i("nden",-1,-1)],wr=[new i("la",-1,-1),new i("le",-1,-1)],_r=[new i("ca",-1,-1),new i("ce",-1,-1)],kr=[new i("im",-1,-1),new i("um",-1,-1),new i("üm",-1,-1),new i("ım",-1,-1)],pr=[new i("sin",-1,-1),new i("sun",-1,-1),new i("sün",-1,-1),new i("sın",-1,-1)],gr=[new i("iz",-1,-1),new i("uz",-1,-1),new i("üz",-1,-1),new i("ız",-1,-1)],yr=[new i("siniz",-1,-1),new i("sunuz",-1,-1),new i("sünüz",-1,-1),new i("sınız",-1,-1)],zr=[new i("lar",-1,-1),new i("ler",-1,-1)],vr=[new i("niz",-1,-1),new i("nuz",-1,-1),new i("nüz",-1,-1),new i("nız",-1,-1)],hr=[new i("dir",-1,-1),new i("tir",-1,-1),new i("dur",-1,-1),new i("tur",-1,-1),new i("dür",-1,-1),new i("tür",-1,-1),new i("dır",-1,-1),new i("tır",-1,-1)],qr=[new i("casına",-1,-1),new i("cesine",-1,-1)],Cr=[new i("di",-1,-1),new i("ti",-1,-1),new i("dik",-1,-1),new i("tik",-1,-1),new i("duk",-1,-1),new i("tuk",-1,-1),new i("dük",-1,-1),new i("tük",-1,-1),new i("dık",-1,-1),new i("tık",-1,-1),new i("dim",-1,-1),new i("tim",-1,-1),new i("dum",-1,-1),new i("tum",-1,-1),new i("düm",-1,-1),new i("tüm",-1,-1),new i("dım",-1,-1),new i("tım",-1,-1),new i("din",-1,-1),new i("tin",-1,-1),new i("dun",-1,-1),new i("tun",-1,-1),new i("dün",-1,-1),new i("tün",-1,-1),new i("dın",-1,-1),new i("tın",-1,-1),new i("du",-1,-1),new i("tu",-1,-1),new i("dü",-1,-1),new i("tü",-1,-1),new i("dı",-1,-1),new i("tı",-1,-1)],Pr=[new i("sa",-1,-1),new i("se",-1,-1),new i("sak",-1,-1),new i("sek",-1,-1),new i("sam",-1,-1),new i("sem",-1,-1),new i("san",-1,-1),new i("sen",-1,-1)],Fr=[new i("miş",-1,-1),new i("muş",-1,-1),new i("müş",-1,-1),new i("mış",-1,-1)],Sr=[new i("b",-1,1),new i("c",-1,2),new i("d",-1,3),new i("ğ",-1,4)],Wr=[17,65,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,32,8,0,0,0,0,0,0,1],Lr=[1,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,1],xr=[1,64,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1],Ar=[17,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,130],Er=[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1],jr=[17],Tr=[65],Zr=[65],Br=[["a",xr,97,305],["e",Ar,101,252],["ı",Er,97,305],["i",jr,101,105],["o",Tr,111,117],["ö",Zr,246,252],["u",Tr,111,117]],Dr=new e;this.setCurrent=function(r){Dr.setCurrent(r)},this.getCurrent=function(){return Dr.getCurrent()},this.stem=function(){return!!($()&&(Dr.limit_backward=Dr.cursor,Dr.cursor=Dr.limit,J(),Dr.cursor=Dr.limit,nr&&(R(),Dr.cursor=Dr.limit_backward,er())))}};return function(r){return"function"==typeof r.update?r.update(function(r){return n.setCurrent(r),n.stem(),n.getCurrent()}):(n.setCurrent(r),n.stem(),n.getCurrent())}}(),r.Pipeline.registerFunction(r.tr.stemmer,"stemmer-tr"),r.tr.stopWordFilter=r.generateStopWordFilter("acaba altmış altı ama ancak arada aslında ayrıca bana bazı belki ben benden beni benim beri beş bile bin bir biri birkaç birkez birçok birşey birşeyi biz bizden bize bizi bizim bu buna bunda bundan bunlar bunları bunların bunu bunun burada böyle böylece da daha dahi de defa değil diye diğer doksan dokuz dolayı dolayısıyla dört edecek eden ederek edilecek ediliyor edilmesi ediyor elli en etmesi etti ettiği ettiğini eğer gibi göre halen hangi hatta hem henüz hep hepsi her herhangi herkesin hiç hiçbir iki ile ilgili ise itibaren itibariyle için işte kadar karşın katrilyon kendi kendilerine kendini kendisi kendisine kendisini kez ki kim kimden kime kimi kimse kırk milyar milyon mu mü mı nasıl ne neden nedenle nerde nerede nereye niye niçin o olan olarak oldu olduklarını olduğu olduğunu olmadı olmadığı olmak olması olmayan olmaz olsa olsun olup olur olursa oluyor on ona ondan onlar onlardan onları onların onu onun otuz oysa pek rağmen sadece sanki sekiz seksen sen senden seni senin siz sizden sizi sizin tarafından trilyon tüm var vardı ve veya ya yani yapacak yapmak yaptı yaptıkları yaptığı yaptığını yapılan yapılması yapıyor yedi yerine yetmiş yine yirmi yoksa yüz zaten çok çünkü öyle üzere üç şey şeyden şeyi şeyler şu şuna şunda şundan şunları şunu şöyle".split(" ")),r.Pipeline.registerFunction(r.tr.stopWordFilter,"stopWordFilter-tr")}}); \ No newline at end of file diff --git a/main/assets/javascripts/lunr/min/lunr.vi.min.js b/main/assets/javascripts/lunr/min/lunr.vi.min.js new file mode 100644 index 00000000..22aed28c --- /dev/null +++ b/main/assets/javascripts/lunr/min/lunr.vi.min.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.vi=function(){this.pipeline.reset(),this.pipeline.add(e.vi.stopWordFilter,e.vi.trimmer)},e.vi.wordCharacters="[A-Za-ẓ̀͐́͑̉̃̓ÂâÊêÔôĂ-ăĐ-đƠ-ơƯ-ư]",e.vi.trimmer=e.trimmerSupport.generateTrimmer(e.vi.wordCharacters),e.Pipeline.registerFunction(e.vi.trimmer,"trimmer-vi"),e.vi.stopWordFilter=e.generateStopWordFilter("là cái nhưng mà".split(" "))}}); \ No newline at end of file diff --git a/main/assets/javascripts/lunr/min/lunr.zh.min.js b/main/assets/javascripts/lunr/min/lunr.zh.min.js new file mode 100644 index 00000000..7727bbe2 --- /dev/null +++ b/main/assets/javascripts/lunr/min/lunr.zh.min.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r(require("nodejieba")):r()(e.lunr)}(this,function(e){return function(r,t){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==r.version[0];r.zh=function(){this.pipeline.reset(),this.pipeline.add(r.zh.trimmer,r.zh.stopWordFilter,r.zh.stemmer),i?this.tokenizer=r.zh.tokenizer:(r.tokenizer&&(r.tokenizer=r.zh.tokenizer),this.tokenizerFn&&(this.tokenizerFn=r.zh.tokenizer))},r.zh.tokenizer=function(n){if(!arguments.length||null==n||void 0==n)return[];if(Array.isArray(n))return n.map(function(e){return i?new r.Token(e.toLowerCase()):e.toLowerCase()});t&&e.load(t);var o=n.toString().trim().toLowerCase(),s=[];e.cut(o,!0).forEach(function(e){s=s.concat(e.split(" "))}),s=s.filter(function(e){return!!e});var u=0;return s.map(function(e,t){if(i){var n=o.indexOf(e,u),s={};return s.position=[n,e.length],s.index=t,u=n,new r.Token(e,s)}return e})},r.zh.wordCharacters="\\w一-龥",r.zh.trimmer=r.trimmerSupport.generateTrimmer(r.zh.wordCharacters),r.Pipeline.registerFunction(r.zh.trimmer,"trimmer-zh"),r.zh.stemmer=function(){return function(e){return e}}(),r.Pipeline.registerFunction(r.zh.stemmer,"stemmer-zh"),r.zh.stopWordFilter=r.generateStopWordFilter("的 一 不 在 人 有 是 为 以 于 上 他 而 后 之 来 及 了 因 下 可 到 由 这 与 也 此 但 并 个 其 已 无 小 我 们 起 最 再 今 去 好 只 又 或 很 亦 某 把 那 你 乃 它 吧 被 比 别 趁 当 从 到 得 打 凡 儿 尔 该 各 给 跟 和 何 还 即 几 既 看 据 距 靠 啦 了 另 么 每 们 嘛 拿 哪 那 您 凭 且 却 让 仍 啥 如 若 使 谁 虽 随 同 所 她 哇 嗡 往 哪 些 向 沿 哟 用 于 咱 则 怎 曾 至 致 着 诸 自".split(" ")),r.Pipeline.registerFunction(r.zh.stopWordFilter,"stopWordFilter-zh")}}); \ No newline at end of file diff --git a/main/assets/javascripts/lunr/tinyseg.js b/main/assets/javascripts/lunr/tinyseg.js new file mode 100644 index 00000000..167fa6dd --- /dev/null +++ b/main/assets/javascripts/lunr/tinyseg.js @@ -0,0 +1,206 @@ +/** + * export the module via AMD, CommonJS or as a browser global + * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js + */ +;(function (root, factory) { + if (typeof define === 'function' && define.amd) { + // AMD. Register as an anonymous module. + define(factory) + } else if (typeof exports === 'object') { + /** + * Node. Does not work with strict CommonJS, but + * only CommonJS-like environments that support module.exports, + * like Node. + */ + module.exports = factory() + } else { + // Browser globals (root is window) + factory()(root.lunr); + } +}(this, function () { + /** + * Just return a value to define the module export. + * This example returns an object, but the module + * can return a function as the exported value. + */ + + return function(lunr) { + // TinySegmenter 0.1 -- Super compact Japanese tokenizer in Javascript + // (c) 2008 Taku Kudo + // TinySegmenter is freely distributable under the terms of a new BSD licence. + // For details, see http://chasen.org/~taku/software/TinySegmenter/LICENCE.txt + + function TinySegmenter() { + var patterns = { + "[一二三四五六七八九十百千万億兆]":"M", + "[一-龠々〆ヵヶ]":"H", + "[ぁ-ん]":"I", + "[ァ-ヴーｱ-ﾝﾞｰ]":"K", + "[a-zA-Zａ-ｚＡ-Ｚ]":"A", + "[0-9０-９]":"N" + } + this.chartype_ = []; + for (var i in patterns) { + var regexp = new RegExp(i); + this.chartype_.push([regexp, patterns[i]]); + } + + this.BIAS__ = -332 + this.BC1__ = {"HH":6,"II":2461,"KH":406,"OH":-1378}; + this.BC2__ = {"AA":-3267,"AI":2744,"AN":-878,"HH":-4070,"HM":-1711,"HN":4012,"HO":3761,"IA":1327,"IH":-1184,"II":-1332,"IK":1721,"IO":5492,"KI":3831,"KK":-8741,"MH":-3132,"MK":3334,"OO":-2920}; + this.BC3__ = {"HH":996,"HI":626,"HK":-721,"HN":-1307,"HO":-836,"IH":-301,"KK":2762,"MK":1079,"MM":4034,"OA":-1652,"OH":266}; + this.BP1__ = {"BB":295,"OB":304,"OO":-125,"UB":352}; + this.BP2__ = {"BO":60,"OO":-1762}; + this.BQ1__ = {"BHH":1150,"BHM":1521,"BII":-1158,"BIM":886,"BMH":1208,"BNH":449,"BOH":-91,"BOO":-2597,"OHI":451,"OIH":-296,"OKA":1851,"OKH":-1020,"OKK":904,"OOO":2965}; + this.BQ2__ = {"BHH":118,"BHI":-1159,"BHM":466,"BIH":-919,"BKK":-1720,"BKO":864,"OHH":-1139,"OHM":-181,"OIH":153,"UHI":-1146}; + this.BQ3__ = {"BHH":-792,"BHI":2664,"BII":-299,"BKI":419,"BMH":937,"BMM":8335,"BNN":998,"BOH":775,"OHH":2174,"OHM":439,"OII":280,"OKH":1798,"OKI":-793,"OKO":-2242,"OMH":-2402,"OOO":11699}; + this.BQ4__ = {"BHH":-3895,"BIH":3761,"BII":-4654,"BIK":1348,"BKK":-1806,"BMI":-3385,"BOO":-12396,"OAH":926,"OHH":266,"OHK":-2036,"ONN":-973}; 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+ + return this; + } + TinySegmenter.prototype.ctype_ = function(str) { + for (var i in this.chartype_) { + if (str.match(this.chartype_[i][0])) { + return this.chartype_[i][1]; + } + } + return "O"; + } + + TinySegmenter.prototype.ts_ = function(v) { + if (v) { return v; } + return 0; + } + + TinySegmenter.prototype.segment = function(input) { + if (input == null || input == undefined || input == "") { + return []; + } + var result = []; + var seg = ["B3","B2","B1"]; + var ctype = ["O","O","O"]; + var o = input.split(""); + for (i = 0; i < o.length; ++i) { + seg.push(o[i]); + ctype.push(this.ctype_(o[i])) + } + seg.push("E1"); + seg.push("E2"); + seg.push("E3"); + ctype.push("O"); + ctype.push("O"); + ctype.push("O"); + var word = seg[3]; + var p1 = "U"; + var p2 = "U"; + var p3 = "U"; + for (var i = 4; i < seg.length - 3; ++i) { + var score = this.BIAS__; + var w1 = seg[i-3]; + var w2 = seg[i-2]; + var w3 = seg[i-1]; + var w4 = seg[i]; + var w5 = seg[i+1]; + var w6 = seg[i+2]; + var c1 = ctype[i-3]; + var c2 = ctype[i-2]; + var c3 = ctype[i-1]; + var c4 = ctype[i]; + var c5 = ctype[i+1]; + var c6 = ctype[i+2]; + score += this.ts_(this.UP1__[p1]); + score += this.ts_(this.UP2__[p2]); + score += this.ts_(this.UP3__[p3]); + score += this.ts_(this.BP1__[p1 + p2]); + score += this.ts_(this.BP2__[p2 + p3]); + score += this.ts_(this.UW1__[w1]); + score += this.ts_(this.UW2__[w2]); + score += this.ts_(this.UW3__[w3]); + score += this.ts_(this.UW4__[w4]); + score += this.ts_(this.UW5__[w5]); + score += this.ts_(this.UW6__[w6]); + score += this.ts_(this.BW1__[w2 + w3]); + score += this.ts_(this.BW2__[w3 + w4]); + score += this.ts_(this.BW3__[w4 + w5]); + score += this.ts_(this.TW1__[w1 + w2 + w3]); + score += this.ts_(this.TW2__[w2 + w3 + w4]); + score += this.ts_(this.TW3__[w3 + w4 + w5]); + score += this.ts_(this.TW4__[w4 + w5 + w6]); + score += this.ts_(this.UC1__[c1]); + score += this.ts_(this.UC2__[c2]); + score += this.ts_(this.UC3__[c3]); + score += this.ts_(this.UC4__[c4]); + score += this.ts_(this.UC5__[c5]); + score += this.ts_(this.UC6__[c6]); + score += this.ts_(this.BC1__[c2 + c3]); + score += this.ts_(this.BC2__[c3 + c4]); + score += this.ts_(this.BC3__[c4 + c5]); + score += this.ts_(this.TC1__[c1 + c2 + c3]); + score += this.ts_(this.TC2__[c2 + c3 + c4]); + score += this.ts_(this.TC3__[c3 + c4 + c5]); + score += this.ts_(this.TC4__[c4 + c5 + c6]); + // score += this.ts_(this.TC5__[c4 + c5 + c6]); + score += this.ts_(this.UQ1__[p1 + c1]); + score += this.ts_(this.UQ2__[p2 + c2]); + score += this.ts_(this.UQ3__[p3 + c3]); + score += this.ts_(this.BQ1__[p2 + c2 + c3]); + score += this.ts_(this.BQ2__[p2 + c3 + c4]); + score += this.ts_(this.BQ3__[p3 + c2 + c3]); + score += this.ts_(this.BQ4__[p3 + c3 + c4]); + score += this.ts_(this.TQ1__[p2 + c1 + c2 + c3]); + score += this.ts_(this.TQ2__[p2 + c2 + c3 + c4]); + score += this.ts_(this.TQ3__[p3 + c1 + c2 + c3]); + score += this.ts_(this.TQ4__[p3 + c2 + c3 + c4]); + var p = "O"; + if (score > 0) { + result.push(word); + word = ""; + p = "B"; + } + p1 = p2; + p2 = p3; + p3 = p; + word += seg[i]; + } + result.push(word); + + return result; + } + + lunr.TinySegmenter = TinySegmenter; + }; + +})); \ No newline at end of file diff --git a/main/assets/javascripts/lunr/wordcut.js b/main/assets/javascripts/lunr/wordcut.js new file mode 100644 index 00000000..146f4b44 --- /dev/null +++ b/main/assets/javascripts/lunr/wordcut.js @@ -0,0 +1,6708 @@ +(function(f){if(typeof exports==="object"&&typeof module!=="undefined"){module.exports=f()}else if(typeof define==="function"&&define.amd){define([],f)}else{var g;if(typeof window!=="undefined"){g=window}else if(typeof global!=="undefined"){g=global}else if(typeof self!=="undefined"){g=self}else{g=this}(g.lunr || (g.lunr = {})).wordcut = f()}})(function(){var define,module,exports;return (function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o 1; + }) + this.addWords(words, false) + } + if(finalize){ + this.finalizeDict(); + } + }, + + dictSeek: function (l, r, ch, strOffset, pos) { + var ans = null; + while (l <= r) { + var m = Math.floor((l + r) / 2), + dict_item = this.dict[m], + len = dict_item.length; + if (len <= strOffset) { + l = m + 1; + } else { + var ch_ = dict_item[strOffset]; + if (ch_ < ch) { + l = m + 1; + } else if (ch_ > ch) { + r = m - 1; + } else { + ans = m; + if (pos == LEFT) { + r = m - 1; + } else { + l = m + 1; + } + } + } + } + return ans; + }, + + isFinal: function (acceptor) { + return this.dict[acceptor.l].length == acceptor.strOffset; + }, + + createAcceptor: function () { + return { + l: 0, + r: this.dict.length - 1, + strOffset: 0, + isFinal: false, + dict: this, + transit: function (ch) { + return this.dict.transit(this, ch); + }, + isError: false, + tag: "DICT", + w: 1, + type: "DICT" + }; + }, + + transit: function (acceptor, ch) { + var l = this.dictSeek(acceptor.l, + acceptor.r, + ch, + acceptor.strOffset, + LEFT); + if (l !== null) { + var r = this.dictSeek(l, + acceptor.r, + ch, + acceptor.strOffset, + RIGHT); + acceptor.l = l; + acceptor.r = r; + acceptor.strOffset++; + acceptor.isFinal = this.isFinal(acceptor); + } else { + acceptor.isError = true; + } + return acceptor; + }, + + sortuniq: function(a){ + return a.sort().filter(function(item, pos, arr){ + return !pos || item != arr[pos - 1]; + }) + }, + + flatten: function(a){ + //[[1,2],[3]] -> [1,2,3] + return [].concat.apply([], a); + } +}; +module.exports = WordcutDict; + +}).call(this,"/dist/tmp") +},{"glob":16,"path":22}],3:[function(require,module,exports){ +var WordRule = { + createAcceptor: function(tag) { + if (tag["WORD_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + var lch = ch.toLowerCase(); + if (lch >= "a" && lch <= "z") { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "WORD_RULE", + type: "WORD_RULE", + w: 1}; + } +}; + +var NumberRule = { + createAcceptor: function(tag) { + if (tag["NUMBER_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (ch >= "0" && ch <= "9") { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "NUMBER_RULE", + type: "NUMBER_RULE", + w: 1}; + } +}; + +var SpaceRule = { + tag: "SPACE_RULE", + createAcceptor: function(tag) { + + if (tag["SPACE_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (ch == " " || ch == "\t" || ch == "\r" || ch == "\n" || + ch == "\u00A0" || ch=="\u2003"//nbsp and emsp + ) { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: SpaceRule.tag, + w: 1, + type: "SPACE_RULE"}; + } +} + +var SingleSymbolRule = { + tag: "SINSYM", + createAcceptor: function(tag) { + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (this.strOffset == 0 && ch.match(/^[\@\(\)\/\,\-\."`]$/)) { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "SINSYM", + w: 1, + type: "SINSYM"}; + } +} + + +var LatinRules = [WordRule, SpaceRule, SingleSymbolRule, NumberRule]; + +module.exports = LatinRules; + +},{}],4:[function(require,module,exports){ +var _ = require("underscore") + , WordcutCore = require("./wordcut_core"); +var PathInfoBuilder = { + + /* + buildByPartAcceptors: function(path, acceptors, i) { + var + var genInfos = partAcceptors.reduce(function(genInfos, acceptor) { + + }, []); + + return genInfos; + } + */ + + buildByAcceptors: function(path, finalAcceptors, i) { + var self = this; + var infos = finalAcceptors.map(function(acceptor) { + var p = i - acceptor.strOffset + 1 + , _info = path[p]; + + var info = {p: p, + mw: _info.mw + (acceptor.mw === undefined ? 0 : acceptor.mw), + w: acceptor.w + _info.w, + unk: (acceptor.unk ? acceptor.unk : 0) + _info.unk, + type: acceptor.type}; + + if (acceptor.type == "PART") { + for(var j = p + 1; j <= i; j++) { + path[j].merge = p; + } + info.merge = p; + } + + return info; + }); + return infos.filter(function(info) { return info; }); + }, + + fallback: function(path, leftBoundary, text, i) { + var _info = path[leftBoundary]; + if (text[i].match(/[\u0E48-\u0E4E]/)) { + if (leftBoundary != 0) + leftBoundary = path[leftBoundary].p; + return {p: leftBoundary, + mw: 0, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; +/* } else if(leftBoundary > 0 && path[leftBoundary].type !== "UNK") { + leftBoundary = path[leftBoundary].p; + return {p: leftBoundary, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; */ + } else { + return {p: leftBoundary, + mw: _info.mw, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; + } + }, + + build: function(path, finalAcceptors, i, leftBoundary, text) { + var basicPathInfos = this.buildByAcceptors(path, finalAcceptors, i); + if (basicPathInfos.length > 0) { + return basicPathInfos; + } else { + return [this.fallback(path, leftBoundary, text, i)]; + } + } +}; + +module.exports = function() { + return _.clone(PathInfoBuilder); +} + +},{"./wordcut_core":8,"underscore":25}],5:[function(require,module,exports){ +var _ = require("underscore"); + + +var PathSelector = { + selectPath: function(paths) { + var path = paths.reduce(function(selectedPath, path) { + if (selectedPath == null) { + return path; + } else { + if (path.unk < selectedPath.unk) + return path; + if (path.unk == selectedPath.unk) { + if (path.mw < selectedPath.mw) + return path + if (path.mw == selectedPath.mw) { + if (path.w < selectedPath.w) + return path; + } + } + return selectedPath; + } + }, null); + return path; + }, + + createPath: function() { + return [{p:null, w:0, unk:0, type: "INIT", mw:0}]; + } +}; + +module.exports = function() { + return _.clone(PathSelector); +}; + +},{"underscore":25}],6:[function(require,module,exports){ +function isMatch(pat, offset, ch) { + if (pat.length <= offset) + return false; + var _ch = pat[offset]; + return _ch == ch || + (_ch.match(/[กข]/) && ch.match(/[ก-ฮ]/)) || + (_ch.match(/[มบ]/) && ch.match(/[ก-ฮ]/)) || + (_ch.match(/\u0E49/) && ch.match(/[\u0E48-\u0E4B]/)); +} + +var Rule0 = { + pat: "เหก็ม", + createAcceptor: function(tag) { + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (isMatch(Rule0.pat, this.strOffset,ch)) { + this.isFinal = (this.strOffset + 1 == Rule0.pat.length); + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "THAI_RULE", + type: "THAI_RULE", + w: 1}; + } +}; + +var PartRule = { + createAcceptor: function(tag) { + return {strOffset: 0, + patterns: [ + "แก", "เก", "ก้", "กก์", "กา", "กี", "กิ", "กืก" + ], + isFinal: false, + transit: function(ch) { + var offset = this.strOffset; + this.patterns = this.patterns.filter(function(pat) { + return isMatch(pat, offset, ch); + }); + + if (this.patterns.length > 0) { + var len = 1 + offset; + this.isFinal = this.patterns.some(function(pat) { + return pat.length == len; + }); + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "PART", + type: "PART", + unk: 1, + w: 1}; + } +}; + +var ThaiRules = [Rule0, PartRule]; + +module.exports = ThaiRules; + +},{}],7:[function(require,module,exports){ +var sys = require("sys") + , WordcutDict = require("./dict") + , WordcutCore = require("./wordcut_core") + , PathInfoBuilder = require("./path_info_builder") + , PathSelector = require("./path_selector") + , Acceptors = require("./acceptors") + , latinRules = require("./latin_rules") + , thaiRules = require("./thai_rules") + , _ = require("underscore"); + + +var Wordcut = Object.create(WordcutCore); +Wordcut.defaultPathInfoBuilder = PathInfoBuilder; +Wordcut.defaultPathSelector = PathSelector; +Wordcut.defaultAcceptors = Acceptors; +Wordcut.defaultLatinRules = latinRules; +Wordcut.defaultThaiRules = thaiRules; +Wordcut.defaultDict = WordcutDict; + + +Wordcut.initNoDict = function(dict_path) { + var self = this; + self.pathInfoBuilder = new self.defaultPathInfoBuilder; + self.pathSelector = new self.defaultPathSelector; + self.acceptors = new self.defaultAcceptors; + self.defaultLatinRules.forEach(function(rule) { + self.acceptors.creators.push(rule); + }); + self.defaultThaiRules.forEach(function(rule) { + self.acceptors.creators.push(rule); + }); +}; + +Wordcut.init = function(dict_path, withDefault, additionalWords) { + withDefault = withDefault || false; + this.initNoDict(); + var dict = _.clone(this.defaultDict); + dict.init(dict_path, withDefault, additionalWords); + this.acceptors.creators.push(dict); +}; + +module.exports = Wordcut; + +},{"./acceptors":1,"./dict":2,"./latin_rules":3,"./path_info_builder":4,"./path_selector":5,"./thai_rules":6,"./wordcut_core":8,"sys":28,"underscore":25}],8:[function(require,module,exports){ +var WordcutCore = { + + buildPath: function(text) { + var self = this + , path = self.pathSelector.createPath() + , leftBoundary = 0; + self.acceptors.reset(); + for (var i = 0; i < text.length; i++) { + var ch = text[i]; + self.acceptors.transit(ch); + + var possiblePathInfos = self + .pathInfoBuilder + .build(path, + self.acceptors.getFinalAcceptors(), + i, + leftBoundary, + text); + var selectedPath = self.pathSelector.selectPath(possiblePathInfos) + + path.push(selectedPath); + if (selectedPath.type !== "UNK") { + leftBoundary = i; + } + } + return path; + }, + + pathToRanges: function(path) { + var e = path.length - 1 + , ranges = []; + + while (e > 0) { + var info = path[e] + , s = info.p; + + if (info.merge !== undefined && ranges.length > 0) { + var r = ranges[ranges.length - 1]; + r.s = info.merge; + s = r.s; + } else { + ranges.push({s:s, e:e}); + } + e = s; + } + return ranges.reverse(); + }, + + rangesToText: function(text, ranges, delimiter) { + return ranges.map(function(r) { + return text.substring(r.s, r.e); + }).join(delimiter); + }, + + cut: function(text, delimiter) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + return this + .rangesToText(text, ranges, + (delimiter === undefined ? "|" : delimiter)); + }, + + cutIntoRanges: function(text, noText) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + + if (!noText) { + ranges.forEach(function(r) { + r.text = text.substring(r.s, r.e); + }); + } + return ranges; + }, + + cutIntoArray: function(text) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + + return ranges.map(function(r) { + return text.substring(r.s, r.e) + }); + } +}; + +module.exports = WordcutCore; + +},{}],9:[function(require,module,exports){ +// http://wiki.commonjs.org/wiki/Unit_Testing/1.0 +// +// THIS IS NOT TESTED NOR LIKELY TO WORK OUTSIDE V8! +// +// Originally from narwhal.js (http://narwhaljs.org) +// Copyright (c) 2009 Thomas Robinson <280north.com> +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files (the 'Software'), to +// deal in the Software without restriction, including without limitation the +// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +// sell copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions: +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +// AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +// ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +// WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +// when used in node, this will actually load the util module we depend on +// versus loading the builtin util module as happens otherwise +// this is a bug in node module loading as far as I am concerned +var util = require('util/'); + +var pSlice = Array.prototype.slice; +var hasOwn = Object.prototype.hasOwnProperty; + +// 1. The assert module provides functions that throw +// AssertionError's when particular conditions are not met. The +// assert module must conform to the following interface. + +var assert = module.exports = ok; + +// 2. The AssertionError is defined in assert. +// new assert.AssertionError({ message: message, +// actual: actual, +// expected: expected }) + +assert.AssertionError = function AssertionError(options) { + this.name = 'AssertionError'; + this.actual = options.actual; + this.expected = options.expected; + this.operator = options.operator; + if (options.message) { + this.message = options.message; + this.generatedMessage = false; + } else { + this.message = getMessage(this); + this.generatedMessage = true; + } + var stackStartFunction = options.stackStartFunction || fail; + + if (Error.captureStackTrace) { + Error.captureStackTrace(this, stackStartFunction); + } + else { + // non v8 browsers so we can have a stacktrace + var err = new Error(); + if (err.stack) { + var out = err.stack; + + // try to strip useless frames + var fn_name = stackStartFunction.name; + var idx = out.indexOf('\n' + fn_name); + if (idx >= 0) { + // once we have located the function frame + // we need to strip out everything before it (and its line) + var next_line = out.indexOf('\n', idx + 1); + out = out.substring(next_line + 1); + } + + this.stack = out; + } + } +}; + +// assert.AssertionError instanceof Error +util.inherits(assert.AssertionError, Error); + +function replacer(key, value) { + if (util.isUndefined(value)) { + return '' + value; + } + if (util.isNumber(value) && !isFinite(value)) { + return value.toString(); + } + if (util.isFunction(value) || util.isRegExp(value)) { + return value.toString(); + } + return value; +} + +function truncate(s, n) { + if (util.isString(s)) { + return s.length < n ? s : s.slice(0, n); + } else { + return s; + } +} + +function getMessage(self) { + return truncate(JSON.stringify(self.actual, replacer), 128) + ' ' + + self.operator + ' ' + + truncate(JSON.stringify(self.expected, replacer), 128); +} + +// At present only the three keys mentioned above are used and +// understood by the spec. Implementations or sub modules can pass +// other keys to the AssertionError's constructor - they will be +// ignored. + +// 3. All of the following functions must throw an AssertionError +// when a corresponding condition is not met, with a message that +// may be undefined if not provided. All assertion methods provide +// both the actual and expected values to the assertion error for +// display purposes. + +function fail(actual, expected, message, operator, stackStartFunction) { + throw new assert.AssertionError({ + message: message, + actual: actual, + expected: expected, + operator: operator, + stackStartFunction: stackStartFunction + }); +} + +// EXTENSION! allows for well behaved errors defined elsewhere. +assert.fail = fail; + +// 4. Pure assertion tests whether a value is truthy, as determined +// by !!guard. +// assert.ok(guard, message_opt); +// This statement is equivalent to assert.equal(true, !!guard, +// message_opt);. To test strictly for the value true, use +// assert.strictEqual(true, guard, message_opt);. + +function ok(value, message) { + if (!value) fail(value, true, message, '==', assert.ok); +} +assert.ok = ok; + +// 5. The equality assertion tests shallow, coercive equality with +// ==. +// assert.equal(actual, expected, message_opt); + +assert.equal = function equal(actual, expected, message) { + if (actual != expected) fail(actual, expected, message, '==', assert.equal); +}; + +// 6. The non-equality assertion tests for whether two objects are not equal +// with != assert.notEqual(actual, expected, message_opt); + +assert.notEqual = function notEqual(actual, expected, message) { + if (actual == expected) { + fail(actual, expected, message, '!=', assert.notEqual); + } +}; + +// 7. The equivalence assertion tests a deep equality relation. +// assert.deepEqual(actual, expected, message_opt); + +assert.deepEqual = function deepEqual(actual, expected, message) { + if (!_deepEqual(actual, expected)) { + fail(actual, expected, message, 'deepEqual', assert.deepEqual); + } +}; + +function _deepEqual(actual, expected) { + // 7.1. All identical values are equivalent, as determined by ===. + if (actual === expected) { + return true; + + } else if (util.isBuffer(actual) && util.isBuffer(expected)) { + if (actual.length != expected.length) return false; + + for (var i = 0; i < actual.length; i++) { + if (actual[i] !== expected[i]) return false; + } + + return true; + + // 7.2. If the expected value is a Date object, the actual value is + // equivalent if it is also a Date object that refers to the same time. + } else if (util.isDate(actual) && util.isDate(expected)) { + return actual.getTime() === expected.getTime(); + + // 7.3 If the expected value is a RegExp object, the actual value is + // equivalent if it is also a RegExp object with the same source and + // properties (`global`, `multiline`, `lastIndex`, `ignoreCase`). + } else if (util.isRegExp(actual) && util.isRegExp(expected)) { + return actual.source === expected.source && + actual.global === expected.global && + actual.multiline === expected.multiline && + actual.lastIndex === expected.lastIndex && + actual.ignoreCase === expected.ignoreCase; + + // 7.4. Other pairs that do not both pass typeof value == 'object', + // equivalence is determined by ==. + } else if (!util.isObject(actual) && !util.isObject(expected)) { + return actual == expected; + + // 7.5 For all other Object pairs, including Array objects, equivalence is + // determined by having the same number of owned properties (as verified + // with Object.prototype.hasOwnProperty.call), the same set of keys + // (although not necessarily the same order), equivalent values for every + // corresponding key, and an identical 'prototype' property. Note: this + // accounts for both named and indexed properties on Arrays. + } else { + return objEquiv(actual, expected); + } +} + +function isArguments(object) { + return Object.prototype.toString.call(object) == '[object Arguments]'; +} + +function objEquiv(a, b) { + if (util.isNullOrUndefined(a) || util.isNullOrUndefined(b)) + return false; + // an identical 'prototype' property. + if (a.prototype !== b.prototype) return false; + // if one is a primitive, the other must be same + if (util.isPrimitive(a) || util.isPrimitive(b)) { + return a === b; + } + var aIsArgs = isArguments(a), + bIsArgs = isArguments(b); + if ((aIsArgs && !bIsArgs) || (!aIsArgs && bIsArgs)) + return false; + if (aIsArgs) { + a = pSlice.call(a); + b = pSlice.call(b); + return _deepEqual(a, b); + } + var ka = objectKeys(a), + kb = objectKeys(b), + key, i; + // having the same number of owned properties (keys incorporates + // hasOwnProperty) + if (ka.length != kb.length) + return false; + //the same set of keys (although not necessarily the same order), + ka.sort(); + kb.sort(); + //~~~cheap key test + for (i = ka.length - 1; i >= 0; i--) { + if (ka[i] != kb[i]) + return false; + } + //equivalent values for every corresponding key, and + //~~~possibly expensive deep test + for (i = ka.length - 1; i >= 0; i--) { + key = ka[i]; + if (!_deepEqual(a[key], b[key])) return false; + } + return true; +} + +// 8. The non-equivalence assertion tests for any deep inequality. +// assert.notDeepEqual(actual, expected, message_opt); + +assert.notDeepEqual = function notDeepEqual(actual, expected, message) { + if (_deepEqual(actual, expected)) { + fail(actual, expected, message, 'notDeepEqual', assert.notDeepEqual); + } +}; + +// 9. The strict equality assertion tests strict equality, as determined by ===. +// assert.strictEqual(actual, expected, message_opt); + +assert.strictEqual = function strictEqual(actual, expected, message) { + if (actual !== expected) { + fail(actual, expected, message, '===', assert.strictEqual); + } +}; + +// 10. The strict non-equality assertion tests for strict inequality, as +// determined by !==. assert.notStrictEqual(actual, expected, message_opt); + +assert.notStrictEqual = function notStrictEqual(actual, expected, message) { + if (actual === expected) { + fail(actual, expected, message, '!==', assert.notStrictEqual); + } +}; + +function expectedException(actual, expected) { + if (!actual || !expected) { + return false; + } + + if (Object.prototype.toString.call(expected) == '[object RegExp]') { + return expected.test(actual); + } else if (actual instanceof expected) { + return true; + } else if (expected.call({}, actual) === true) { + return true; + } + + return false; +} + +function _throws(shouldThrow, block, expected, message) { + var actual; + + if (util.isString(expected)) { + message = expected; + expected = null; + } + + try { + block(); + } catch (e) { + actual = e; + } + + message = (expected && expected.name ? ' (' + expected.name + ').' : '.') + + (message ? ' ' + message : '.'); + + if (shouldThrow && !actual) { + fail(actual, expected, 'Missing expected exception' + message); + } + + if (!shouldThrow && expectedException(actual, expected)) { + fail(actual, expected, 'Got unwanted exception' + message); + } + + if ((shouldThrow && actual && expected && + !expectedException(actual, expected)) || (!shouldThrow && actual)) { + throw actual; + } +} + +// 11. Expected to throw an error: +// assert.throws(block, Error_opt, message_opt); + +assert.throws = function(block, /*optional*/error, /*optional*/message) { + _throws.apply(this, [true].concat(pSlice.call(arguments))); +}; + +// EXTENSION! This is annoying to write outside this module. +assert.doesNotThrow = function(block, /*optional*/message) { + _throws.apply(this, [false].concat(pSlice.call(arguments))); +}; + +assert.ifError = function(err) { if (err) {throw err;}}; + +var objectKeys = Object.keys || function (obj) { + var keys = []; + for (var key in obj) { + if (hasOwn.call(obj, key)) keys.push(key); + } + return keys; +}; + +},{"util/":28}],10:[function(require,module,exports){ +'use strict'; +module.exports = balanced; +function balanced(a, b, str) { + if (a instanceof RegExp) a = maybeMatch(a, str); + if (b instanceof RegExp) b = maybeMatch(b, str); + + var r = range(a, b, str); + + return r && { + start: r[0], + end: r[1], + pre: str.slice(0, r[0]), + body: str.slice(r[0] + a.length, r[1]), + post: str.slice(r[1] + b.length) + }; +} + +function maybeMatch(reg, str) { + var m = str.match(reg); + return m ? m[0] : null; +} + +balanced.range = range; +function range(a, b, str) { + var begs, beg, left, right, result; + var ai = str.indexOf(a); + var bi = str.indexOf(b, ai + 1); + var i = ai; + + if (ai >= 0 && bi > 0) { + begs = []; + left = str.length; + + while (i >= 0 && !result) { + if (i == ai) { + begs.push(i); + ai = str.indexOf(a, i + 1); + } else if (begs.length == 1) { + result = [ begs.pop(), bi ]; + } else { + beg = begs.pop(); + if (beg < left) { + left = beg; + right = bi; + } + + bi = str.indexOf(b, i + 1); + } + + i = ai < bi && ai >= 0 ? ai : bi; + } + + if (begs.length) { + result = [ left, right ]; + } + } + + return result; +} + +},{}],11:[function(require,module,exports){ +var concatMap = require('concat-map'); +var balanced = require('balanced-match'); + +module.exports = expandTop; + +var escSlash = '\0SLASH'+Math.random()+'\0'; +var escOpen = '\0OPEN'+Math.random()+'\0'; +var escClose = '\0CLOSE'+Math.random()+'\0'; +var escComma = '\0COMMA'+Math.random()+'\0'; +var escPeriod = '\0PERIOD'+Math.random()+'\0'; + +function numeric(str) { + return parseInt(str, 10) == str + ? parseInt(str, 10) + : str.charCodeAt(0); +} + +function escapeBraces(str) { + return str.split('\\\\').join(escSlash) + .split('\\{').join(escOpen) + .split('\\}').join(escClose) + .split('\\,').join(escComma) + .split('\\.').join(escPeriod); +} + +function unescapeBraces(str) { + return str.split(escSlash).join('\\') + .split(escOpen).join('{') + .split(escClose).join('}') + .split(escComma).join(',') + .split(escPeriod).join('.'); +} + + +// Basically just str.split(","), but handling cases +// where we have nested braced sections, which should be +// treated as individual members, like {a,{b,c},d} +function parseCommaParts(str) { + if (!str) + return ['']; + + var parts = []; + var m = balanced('{', '}', str); + + if (!m) + return str.split(','); + + var pre = m.pre; + var body = m.body; + var post = m.post; + var p = pre.split(','); + + p[p.length-1] += '{' + body + '}'; + var postParts = parseCommaParts(post); + if (post.length) { + p[p.length-1] += postParts.shift(); + p.push.apply(p, postParts); + } + + parts.push.apply(parts, p); + + return parts; +} + +function expandTop(str) { + if (!str) + return []; + + // I don't know why Bash 4.3 does this, but it does. + // Anything starting with {} will have the first two bytes preserved + // but *only* at the top level, so {},a}b will not expand to anything, + // but a{},b}c will be expanded to [a}c,abc]. + // One could argue that this is a bug in Bash, but since the goal of + // this module is to match Bash's rules, we escape a leading {} + if (str.substr(0, 2) === '{}') { + str = '\\{\\}' + str.substr(2); + } + + return expand(escapeBraces(str), true).map(unescapeBraces); +} + +function identity(e) { + return e; +} + +function embrace(str) { + return '{' + str + '}'; +} +function isPadded(el) { + return /^-?0\d/.test(el); +} + +function lte(i, y) { + return i <= y; +} +function gte(i, y) { + return i >= y; +} + +function expand(str, isTop) { + var expansions = []; + + var m = balanced('{', '}', str); + if (!m || /\$$/.test(m.pre)) return [str]; + + var isNumericSequence = /^-?\d+\.\.-?\d+(?:\.\.-?\d+)?$/.test(m.body); + var isAlphaSequence = /^[a-zA-Z]\.\.[a-zA-Z](?:\.\.-?\d+)?$/.test(m.body); + var isSequence = isNumericSequence || isAlphaSequence; + var isOptions = m.body.indexOf(',') >= 0; + if (!isSequence && !isOptions) { + // {a},b} + if (m.post.match(/,.*\}/)) { + str = m.pre + '{' + m.body + escClose + m.post; + return expand(str); + } + return [str]; + } + + var n; + if (isSequence) { + n = m.body.split(/\.\./); + } else { + n = parseCommaParts(m.body); + if (n.length === 1) { + // x{{a,b}}y ==> x{a}y x{b}y + n = expand(n[0], false).map(embrace); + if (n.length === 1) { + var post = m.post.length + ? expand(m.post, false) + : ['']; + return post.map(function(p) { + return m.pre + n[0] + p; + }); + } + } + } + + // at this point, n is the parts, and we know it's not a comma set + // with a single entry. + + // no need to expand pre, since it is guaranteed to be free of brace-sets + var pre = m.pre; + var post = m.post.length + ? expand(m.post, false) + : ['']; + + var N; + + if (isSequence) { + var x = numeric(n[0]); + var y = numeric(n[1]); + var width = Math.max(n[0].length, n[1].length) + var incr = n.length == 3 + ? Math.abs(numeric(n[2])) + : 1; + var test = lte; + var reverse = y < x; + if (reverse) { + incr *= -1; + test = gte; + } + var pad = n.some(isPadded); + + N = []; + + for (var i = x; test(i, y); i += incr) { + var c; + if (isAlphaSequence) { + c = String.fromCharCode(i); + if (c === '\\') + c = ''; + } else { + c = String(i); + if (pad) { + var need = width - c.length; + if (need > 0) { + var z = new Array(need + 1).join('0'); + if (i < 0) + c = '-' + z + c.slice(1); + else + c = z + c; + } + } + } + N.push(c); + } + } else { + N = concatMap(n, function(el) { return expand(el, false) }); + } + + for (var j = 0; j < N.length; j++) { + for (var k = 0; k < post.length; k++) { + var expansion = pre + N[j] + post[k]; + if (!isTop || isSequence || expansion) + expansions.push(expansion); + } + } + + return expansions; +} + + +},{"balanced-match":10,"concat-map":13}],12:[function(require,module,exports){ + +},{}],13:[function(require,module,exports){ +module.exports = function (xs, fn) { + var res = []; + for (var i = 0; i < xs.length; i++) { + var x = fn(xs[i], i); + if (isArray(x)) res.push.apply(res, x); + else res.push(x); + } + return res; +}; + +var isArray = Array.isArray || function (xs) { + return Object.prototype.toString.call(xs) === '[object Array]'; +}; + +},{}],14:[function(require,module,exports){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +function EventEmitter() { + this._events = this._events || {}; + this._maxListeners = this._maxListeners || undefined; +} +module.exports = EventEmitter; + +// Backwards-compat with node 0.10.x +EventEmitter.EventEmitter = EventEmitter; + +EventEmitter.prototype._events = undefined; +EventEmitter.prototype._maxListeners = undefined; + +// By default EventEmitters will print a warning if more than 10 listeners are +// added to it. This is a useful default which helps finding memory leaks. +EventEmitter.defaultMaxListeners = 10; + +// Obviously not all Emitters should be limited to 10. This function allows +// that to be increased. Set to zero for unlimited. +EventEmitter.prototype.setMaxListeners = function(n) { + if (!isNumber(n) || n < 0 || isNaN(n)) + throw TypeError('n must be a positive number'); + this._maxListeners = n; + return this; +}; + +EventEmitter.prototype.emit = function(type) { + var er, handler, len, args, i, listeners; + + if (!this._events) + this._events = {}; + + // If there is no 'error' event listener then throw. + if (type === 'error') { + if (!this._events.error || + (isObject(this._events.error) && !this._events.error.length)) { + er = arguments[1]; + if (er instanceof Error) { + throw er; // Unhandled 'error' event + } + throw TypeError('Uncaught, unspecified "error" event.'); + } + } + + handler = this._events[type]; + + if (isUndefined(handler)) + return false; + + if (isFunction(handler)) { + switch (arguments.length) { + // fast cases + case 1: + handler.call(this); + break; + case 2: + handler.call(this, arguments[1]); + break; + case 3: + handler.call(this, arguments[1], arguments[2]); + break; + // slower + default: + len = arguments.length; + args = new Array(len - 1); + for (i = 1; i < len; i++) + args[i - 1] = arguments[i]; + handler.apply(this, args); + } + } else if (isObject(handler)) { + len = arguments.length; + args = new Array(len - 1); + for (i = 1; i < len; i++) + args[i - 1] = arguments[i]; + + listeners = handler.slice(); + len = listeners.length; + for (i = 0; i < len; i++) + listeners[i].apply(this, args); + } + + return true; +}; + +EventEmitter.prototype.addListener = function(type, listener) { + var m; + + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + if (!this._events) + this._events = {}; + + // To avoid recursion in the case that type === "newListener"! Before + // adding it to the listeners, first emit "newListener". + if (this._events.newListener) + this.emit('newListener', type, + isFunction(listener.listener) ? + listener.listener : listener); + + if (!this._events[type]) + // Optimize the case of one listener. Don't need the extra array object. + this._events[type] = listener; + else if (isObject(this._events[type])) + // If we've already got an array, just append. + this._events[type].push(listener); + else + // Adding the second element, need to change to array. + this._events[type] = [this._events[type], listener]; + + // Check for listener leak + if (isObject(this._events[type]) && !this._events[type].warned) { + var m; + if (!isUndefined(this._maxListeners)) { + m = this._maxListeners; + } else { + m = EventEmitter.defaultMaxListeners; + } + + if (m && m > 0 && this._events[type].length > m) { + this._events[type].warned = true; + console.error('(node) warning: possible EventEmitter memory ' + + 'leak detected. %d listeners added. ' + + 'Use emitter.setMaxListeners() to increase limit.', + this._events[type].length); + if (typeof console.trace === 'function') { + // not supported in IE 10 + console.trace(); + } + } + } + + return this; +}; + +EventEmitter.prototype.on = EventEmitter.prototype.addListener; + +EventEmitter.prototype.once = function(type, listener) { + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + var fired = false; + + function g() { + this.removeListener(type, g); + + if (!fired) { + fired = true; + listener.apply(this, arguments); + } + } + + g.listener = listener; + this.on(type, g); + + return this; +}; + +// emits a 'removeListener' event iff the listener was removed +EventEmitter.prototype.removeListener = function(type, listener) { + var list, position, length, i; + + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + if (!this._events || !this._events[type]) + return this; + + list = this._events[type]; + length = list.length; + position = -1; + + if (list === listener || + (isFunction(list.listener) && list.listener === listener)) { + delete this._events[type]; + if (this._events.removeListener) + this.emit('removeListener', type, listener); + + } else if (isObject(list)) { + for (i = length; i-- > 0;) { + if (list[i] === listener || + (list[i].listener && list[i].listener === listener)) { + position = i; + break; + } + } + + if (position < 0) + return this; + + if (list.length === 1) { + list.length = 0; + delete this._events[type]; + } else { + list.splice(position, 1); + } + + if (this._events.removeListener) + this.emit('removeListener', type, listener); + } + + return this; +}; + +EventEmitter.prototype.removeAllListeners = function(type) { + var key, listeners; + + if (!this._events) + return this; + + // not listening for removeListener, no need to emit + if (!this._events.removeListener) { + if (arguments.length === 0) + this._events = {}; + else if (this._events[type]) + delete this._events[type]; + return this; + } + + // emit removeListener for all listeners on all events + if (arguments.length === 0) { + for (key in this._events) { + if (key === 'removeListener') continue; + this.removeAllListeners(key); + } + this.removeAllListeners('removeListener'); + this._events = {}; + return this; + } + + listeners = this._events[type]; + + if (isFunction(listeners)) { + this.removeListener(type, listeners); + } else { + // LIFO order + while (listeners.length) + this.removeListener(type, listeners[listeners.length - 1]); + } + delete this._events[type]; + + return this; +}; + +EventEmitter.prototype.listeners = function(type) { + var ret; + if (!this._events || !this._events[type]) + ret = []; + else if (isFunction(this._events[type])) + ret = [this._events[type]]; + else + ret = this._events[type].slice(); + return ret; +}; + +EventEmitter.listenerCount = function(emitter, type) { + var ret; + if (!emitter._events || !emitter._events[type]) + ret = 0; + else if (isFunction(emitter._events[type])) + ret = 1; + else + ret = emitter._events[type].length; + return ret; +}; + +function isFunction(arg) { + return typeof arg === 'function'; +} + +function isNumber(arg) { + return typeof arg === 'number'; +} + +function isObject(arg) { + return typeof arg === 'object' && arg !== null; +} + +function isUndefined(arg) { + return arg === void 0; +} + +},{}],15:[function(require,module,exports){ +(function (process){ +exports.alphasort = alphasort +exports.alphasorti = alphasorti +exports.setopts = setopts +exports.ownProp = ownProp +exports.makeAbs = makeAbs +exports.finish = finish +exports.mark = mark +exports.isIgnored = isIgnored +exports.childrenIgnored = childrenIgnored + +function ownProp (obj, field) { + return Object.prototype.hasOwnProperty.call(obj, field) +} + +var path = require("path") +var minimatch = require("minimatch") +var isAbsolute = require("path-is-absolute") +var Minimatch = minimatch.Minimatch + +function alphasorti (a, b) { + return a.toLowerCase().localeCompare(b.toLowerCase()) +} + +function alphasort (a, b) { + return a.localeCompare(b) +} + +function setupIgnores (self, options) { + self.ignore = options.ignore || [] + + if (!Array.isArray(self.ignore)) + self.ignore = [self.ignore] + + if (self.ignore.length) { + self.ignore = self.ignore.map(ignoreMap) + } +} + +function ignoreMap (pattern) { + var gmatcher = null + if (pattern.slice(-3) === '/**') { + var gpattern = pattern.replace(/(\/\*\*)+$/, '') + gmatcher = new Minimatch(gpattern) + } + + return { + matcher: new Minimatch(pattern), + gmatcher: gmatcher + } +} + +function setopts (self, pattern, options) { + if (!options) + options = {} + + // base-matching: just use globstar for that. + if (options.matchBase && -1 === pattern.indexOf("/")) { + if (options.noglobstar) { + throw new Error("base matching requires globstar") + } + pattern = "**/" + pattern + } + + self.silent = !!options.silent + self.pattern = pattern + self.strict = options.strict !== false + self.realpath = !!options.realpath + self.realpathCache = options.realpathCache || Object.create(null) + self.follow = !!options.follow + self.dot = !!options.dot + self.mark = !!options.mark + self.nodir = !!options.nodir + if (self.nodir) + self.mark = true + self.sync = !!options.sync + self.nounique = !!options.nounique + self.nonull = !!options.nonull + self.nosort = !!options.nosort + self.nocase = !!options.nocase + self.stat = !!options.stat + self.noprocess = !!options.noprocess + + self.maxLength = options.maxLength || Infinity + self.cache = options.cache || Object.create(null) + self.statCache = options.statCache || Object.create(null) + self.symlinks = options.symlinks || Object.create(null) + + setupIgnores(self, options) + + self.changedCwd = false + var cwd = process.cwd() + if (!ownProp(options, "cwd")) + self.cwd = cwd + else { + self.cwd = options.cwd + self.changedCwd = path.resolve(options.cwd) !== cwd + } + + self.root = options.root || path.resolve(self.cwd, "/") + self.root = path.resolve(self.root) + if (process.platform === "win32") + self.root = self.root.replace(/\\/g, "/") + + self.nomount = !!options.nomount + + // disable comments and negation unless the user explicitly + // passes in false as the option. + options.nonegate = options.nonegate === false ? false : true + options.nocomment = options.nocomment === false ? false : true + deprecationWarning(options) + + self.minimatch = new Minimatch(pattern, options) + self.options = self.minimatch.options +} + +// TODO(isaacs): remove entirely in v6 +// exported to reset in tests +exports.deprecationWarned +function deprecationWarning(options) { + if (!options.nonegate || !options.nocomment) { + if (process.noDeprecation !== true && !exports.deprecationWarned) { + var msg = 'glob WARNING: comments and negation will be disabled in v6' + if (process.throwDeprecation) + throw new Error(msg) + else if (process.traceDeprecation) + console.trace(msg) + else + console.error(msg) + + exports.deprecationWarned = true + } + } +} + +function finish (self) { + var nou = self.nounique + var all = nou ? [] : Object.create(null) + + for (var i = 0, l = self.matches.length; i < l; i ++) { + var matches = self.matches[i] + if (!matches || Object.keys(matches).length === 0) { + if (self.nonull) { + // do like the shell, and spit out the literal glob + var literal = self.minimatch.globSet[i] + if (nou) + all.push(literal) + else + all[literal] = true + } + } else { + // had matches + var m = Object.keys(matches) + if (nou) + all.push.apply(all, m) + else + m.forEach(function (m) { + all[m] = true + }) + } + } + + if (!nou) + all = Object.keys(all) + + if (!self.nosort) + all = all.sort(self.nocase ? alphasorti : alphasort) + + // at *some* point we statted all of these + if (self.mark) { + for (var i = 0; i < all.length; i++) { + all[i] = self._mark(all[i]) + } + if (self.nodir) { + all = all.filter(function (e) { + return !(/\/$/.test(e)) + }) + } + } + + if (self.ignore.length) + all = all.filter(function(m) { + return !isIgnored(self, m) + }) + + self.found = all +} + +function mark (self, p) { + var abs = makeAbs(self, p) + var c = self.cache[abs] + var m = p + if (c) { + var isDir = c === 'DIR' || Array.isArray(c) + var slash = p.slice(-1) === '/' + + if (isDir && !slash) + m += '/' + else if (!isDir && slash) + m = m.slice(0, -1) + + if (m !== p) { + var mabs = makeAbs(self, m) + self.statCache[mabs] = self.statCache[abs] + self.cache[mabs] = self.cache[abs] + } + } + + return m +} + +// lotta situps... +function makeAbs (self, f) { + var abs = f + if (f.charAt(0) === '/') { + abs = path.join(self.root, f) + } else if (isAbsolute(f) || f === '') { + abs = f + } else if (self.changedCwd) { + abs = path.resolve(self.cwd, f) + } else { + abs = path.resolve(f) + } + return abs +} + + +// Return true, if pattern ends with globstar '**', for the accompanying parent directory. +// Ex:- If node_modules/** is the pattern, add 'node_modules' to ignore list along with it's contents +function isIgnored (self, path) { + if (!self.ignore.length) + return false + + return self.ignore.some(function(item) { + return item.matcher.match(path) || !!(item.gmatcher && item.gmatcher.match(path)) + }) +} + +function childrenIgnored (self, path) { + if (!self.ignore.length) + return false + + return self.ignore.some(function(item) { + return !!(item.gmatcher && item.gmatcher.match(path)) + }) +} + +}).call(this,require('_process')) +},{"_process":24,"minimatch":20,"path":22,"path-is-absolute":23}],16:[function(require,module,exports){ +(function (process){ +// Approach: +// +// 1. Get the minimatch set +// 2. For each pattern in the set, PROCESS(pattern, false) +// 3. Store matches per-set, then uniq them +// +// PROCESS(pattern, inGlobStar) +// Get the first [n] items from pattern that are all strings +// Join these together. This is PREFIX. +// If there is no more remaining, then stat(PREFIX) and +// add to matches if it succeeds. END. +// +// If inGlobStar and PREFIX is symlink and points to dir +// set ENTRIES = [] +// else readdir(PREFIX) as ENTRIES +// If fail, END +// +// with ENTRIES +// If pattern[n] is GLOBSTAR +// // handle the case where the globstar match is empty +// // by pruning it out, and testing the resulting pattern +// PROCESS(pattern[0..n] + pattern[n+1 .. $], false) +// // handle other cases. +// for ENTRY in ENTRIES (not dotfiles) +// // attach globstar + tail onto the entry +// // Mark that this entry is a globstar match +// PROCESS(pattern[0..n] + ENTRY + pattern[n .. $], true) +// +// else // not globstar +// for ENTRY in ENTRIES (not dotfiles, unless pattern[n] is dot) +// Test ENTRY against pattern[n] +// If fails, continue +// If passes, PROCESS(pattern[0..n] + item + pattern[n+1 .. $]) +// +// Caveat: +// Cache all stats and readdirs results to minimize syscall. Since all +// we ever care about is existence and directory-ness, we can just keep +// `true` for files, and [children,...] for directories, or `false` for +// things that don't exist. + +module.exports = glob + +var fs = require('fs') +var minimatch = require('minimatch') +var Minimatch = minimatch.Minimatch +var inherits = require('inherits') +var EE = require('events').EventEmitter +var path = require('path') +var assert = require('assert') +var isAbsolute = require('path-is-absolute') +var globSync = require('./sync.js') +var common = require('./common.js') +var alphasort = common.alphasort +var alphasorti = common.alphasorti +var setopts = common.setopts +var ownProp = common.ownProp +var inflight = require('inflight') +var util = require('util') +var childrenIgnored = common.childrenIgnored +var isIgnored = common.isIgnored + +var once = require('once') + +function glob (pattern, options, cb) { + if (typeof options === 'function') cb = options, options = {} + if (!options) options = {} + + if (options.sync) { + if (cb) + throw new TypeError('callback provided to sync glob') + return globSync(pattern, options) + } + + return new Glob(pattern, options, cb) +} + +glob.sync = globSync +var GlobSync = glob.GlobSync = globSync.GlobSync + +// old api surface +glob.glob = glob + +glob.hasMagic = function (pattern, options_) { + var options = util._extend({}, options_) + options.noprocess = true + + var g = new Glob(pattern, options) + var set = g.minimatch.set + if (set.length > 1) + return true + + for (var j = 0; j < set[0].length; j++) { + if (typeof set[0][j] !== 'string') + return true + } + + return false +} + +glob.Glob = Glob +inherits(Glob, EE) +function Glob (pattern, options, cb) { + if (typeof options === 'function') { + cb = options + options = null + } + + if (options && options.sync) { + if (cb) + throw new TypeError('callback provided to sync glob') + return new GlobSync(pattern, options) + } + + if (!(this instanceof Glob)) + return new Glob(pattern, options, cb) + + setopts(this, pattern, options) + this._didRealPath = false + + // process each pattern in the minimatch set + var n = this.minimatch.set.length + + // The matches are stored as {: true,...} so that + // duplicates are automagically pruned. + // Later, we do an Object.keys() on these. + // Keep them as a list so we can fill in when nonull is set. + this.matches = new Array(n) + + if (typeof cb === 'function') { + cb = once(cb) + this.on('error', cb) + this.on('end', function (matches) { + cb(null, matches) + }) + } + + var self = this + var n = this.minimatch.set.length + this._processing = 0 + this.matches = new Array(n) + + this._emitQueue = [] + this._processQueue = [] + this.paused = false + + if (this.noprocess) + return this + + if (n === 0) + return done() + + for (var i = 0; i < n; i ++) { + this._process(this.minimatch.set[i], i, false, done) + } + + function done () { + --self._processing + if (self._processing <= 0) + self._finish() + } +} + +Glob.prototype._finish = function () { + assert(this instanceof Glob) + if (this.aborted) + return + + if (this.realpath && !this._didRealpath) + return this._realpath() + + common.finish(this) + this.emit('end', this.found) +} + +Glob.prototype._realpath = function () { + if (this._didRealpath) + return + + this._didRealpath = true + + var n = this.matches.length + if (n === 0) + return this._finish() + + var self = this + for (var i = 0; i < this.matches.length; i++) + this._realpathSet(i, next) + + function next () { + if (--n === 0) + self._finish() + } +} + +Glob.prototype._realpathSet = function (index, cb) { + var matchset = this.matches[index] + if (!matchset) + return cb() + + var found = Object.keys(matchset) + var self = this + var n = found.length + + if (n === 0) + return cb() + + var set = this.matches[index] = Object.create(null) + found.forEach(function (p, i) { + // If there's a problem with the stat, then it means that + // one or more of the links in the realpath couldn't be + // resolved. just return the abs value in that case. + p = self._makeAbs(p) + fs.realpath(p, self.realpathCache, function (er, real) { + if (!er) + set[real] = true + else if (er.syscall === 'stat') + set[p] = true + else + self.emit('error', er) // srsly wtf right here + + if (--n === 0) { + self.matches[index] = set + cb() + } + }) + }) +} + +Glob.prototype._mark = function (p) { + return common.mark(this, p) +} + +Glob.prototype._makeAbs = function (f) { + return common.makeAbs(this, f) +} + +Glob.prototype.abort = function () { + this.aborted = true + this.emit('abort') +} + +Glob.prototype.pause = function () { + if (!this.paused) { + this.paused = true + this.emit('pause') + } +} + +Glob.prototype.resume = function () { + if (this.paused) { + this.emit('resume') + this.paused = false + if (this._emitQueue.length) { + var eq = this._emitQueue.slice(0) + this._emitQueue.length = 0 + for (var i = 0; i < eq.length; i ++) { + var e = eq[i] + this._emitMatch(e[0], e[1]) + } + } + if (this._processQueue.length) { + var pq = this._processQueue.slice(0) + this._processQueue.length = 0 + for (var i = 0; i < pq.length; i ++) { + var p = pq[i] + this._processing-- + this._process(p[0], p[1], p[2], p[3]) + } + } + } +} + +Glob.prototype._process = function (pattern, index, inGlobStar, cb) { + assert(this instanceof Glob) + assert(typeof cb === 'function') + + if (this.aborted) + return + + this._processing++ + if (this.paused) { + this._processQueue.push([pattern, index, inGlobStar, cb]) + return + } + + //console.error('PROCESS %d', this._processing, pattern) + + // Get the first [n] parts of pattern that are all strings. + var n = 0 + while (typeof pattern[n] === 'string') { + n ++ + } + // now n is the index of the first one that is *not* a string. + + // see if there's anything else + var prefix + switch (n) { + // if not, then this is rather simple + case pattern.length: + this._processSimple(pattern.join('/'), index, cb) + return + + case 0: + // pattern *starts* with some non-trivial item. + // going to readdir(cwd), but not include the prefix in matches. + prefix = null + break + + default: + // pattern has some string bits in the front. + // whatever it starts with, whether that's 'absolute' like /foo/bar, + // or 'relative' like '../baz' + prefix = pattern.slice(0, n).join('/') + break + } + + var remain = pattern.slice(n) + + // get the list of entries. + var read + if (prefix === null) + read = '.' + else if (isAbsolute(prefix) || isAbsolute(pattern.join('/'))) { + if (!prefix || !isAbsolute(prefix)) + prefix = '/' + prefix + read = prefix + } else + read = prefix + + var abs = this._makeAbs(read) + + //if ignored, skip _processing + if (childrenIgnored(this, read)) + return cb() + + var isGlobStar = remain[0] === minimatch.GLOBSTAR + if (isGlobStar) + this._processGlobStar(prefix, read, abs, remain, index, inGlobStar, cb) + else + this._processReaddir(prefix, read, abs, remain, index, inGlobStar, cb) +} + +Glob.prototype._processReaddir = function (prefix, read, abs, remain, index, inGlobStar, cb) { + var self = this + this._readdir(abs, inGlobStar, function (er, entries) { + return self._processReaddir2(prefix, read, abs, remain, index, inGlobStar, entries, cb) + }) +} + +Glob.prototype._processReaddir2 = function (prefix, read, abs, remain, index, inGlobStar, entries, cb) { + + // if the abs isn't a dir, then nothing can match! + if (!entries) + return cb() + + // It will only match dot entries if it starts with a dot, or if + // dot is set. Stuff like @(.foo|.bar) isn't allowed. + var pn = remain[0] + var negate = !!this.minimatch.negate + var rawGlob = pn._glob + var dotOk = this.dot || rawGlob.charAt(0) === '.' + + var matchedEntries = [] + for (var i = 0; i < entries.length; i++) { + var e = entries[i] + if (e.charAt(0) !== '.' || dotOk) { + var m + if (negate && !prefix) { + m = !e.match(pn) + } else { + m = e.match(pn) + } + if (m) + matchedEntries.push(e) + } + } + + //console.error('prd2', prefix, entries, remain[0]._glob, matchedEntries) + + var len = matchedEntries.length + // If there are no matched entries, then nothing matches. + if (len === 0) + return cb() + + // if this is the last remaining pattern bit, then no need for + // an additional stat *unless* the user has specified mark or + // stat explicitly. We know they exist, since readdir returned + // them. + + if (remain.length === 1 && !this.mark && !this.stat) { + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + if (prefix) { + if (prefix !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + + if (e.charAt(0) === '/' && !this.nomount) { + e = path.join(this.root, e) + } + this._emitMatch(index, e) + } + // This was the last one, and no stats were needed + return cb() + } + + // now test all matched entries as stand-ins for that part + // of the pattern. + remain.shift() + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + var newPattern + if (prefix) { + if (prefix !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + this._process([e].concat(remain), index, inGlobStar, cb) + } + cb() +} + +Glob.prototype._emitMatch = function (index, e) { + if (this.aborted) + return + + if (this.matches[index][e]) + return + + if (isIgnored(this, e)) + return + + if (this.paused) { + this._emitQueue.push([index, e]) + return + } + + var abs = this._makeAbs(e) + + if (this.nodir) { + var c = this.cache[abs] + if (c === 'DIR' || Array.isArray(c)) + return + } + + if (this.mark) + e = this._mark(e) + + this.matches[index][e] = true + + var st = this.statCache[abs] + if (st) + this.emit('stat', e, st) + + this.emit('match', e) +} + +Glob.prototype._readdirInGlobStar = function (abs, cb) { + if (this.aborted) + return + + // follow all symlinked directories forever + // just proceed as if this is a non-globstar situation + if (this.follow) + return this._readdir(abs, false, cb) + + var lstatkey = 'lstat\0' + abs + var self = this + var lstatcb = inflight(lstatkey, lstatcb_) + + if (lstatcb) + fs.lstat(abs, lstatcb) + + function lstatcb_ (er, lstat) { + if (er) + return cb() + + var isSym = lstat.isSymbolicLink() + self.symlinks[abs] = isSym + + // If it's not a symlink or a dir, then it's definitely a regular file. + // don't bother doing a readdir in that case. + if (!isSym && !lstat.isDirectory()) { + self.cache[abs] = 'FILE' + cb() + } else + self._readdir(abs, false, cb) + } +} + +Glob.prototype._readdir = function (abs, inGlobStar, cb) { + if (this.aborted) + return + + cb = inflight('readdir\0'+abs+'\0'+inGlobStar, cb) + if (!cb) + return + + //console.error('RD %j %j', +inGlobStar, abs) + if (inGlobStar && !ownProp(this.symlinks, abs)) + return this._readdirInGlobStar(abs, cb) + + if (ownProp(this.cache, abs)) { + var c = this.cache[abs] + if (!c || c === 'FILE') + return cb() + + if (Array.isArray(c)) + return cb(null, c) + } + + var self = this + fs.readdir(abs, readdirCb(this, abs, cb)) +} + +function readdirCb (self, abs, cb) { + return function (er, entries) { + if (er) + self._readdirError(abs, er, cb) + else + self._readdirEntries(abs, entries, cb) + } +} + +Glob.prototype._readdirEntries = function (abs, entries, cb) { + if (this.aborted) + return + + // if we haven't asked to stat everything, then just + // assume that everything in there exists, so we can avoid + // having to stat it a second time. + if (!this.mark && !this.stat) { + for (var i = 0; i < entries.length; i ++) { + var e = entries[i] + if (abs === '/') + e = abs + e + else + e = abs + '/' + e + this.cache[e] = true + } + } + + this.cache[abs] = entries + return cb(null, entries) +} + +Glob.prototype._readdirError = function (f, er, cb) { + if (this.aborted) + return + + // handle errors, and cache the information + switch (er.code) { + case 'ENOTSUP': // https://github.com/isaacs/node-glob/issues/205 + case 'ENOTDIR': // totally normal. means it *does* exist. + this.cache[this._makeAbs(f)] = 'FILE' + break + + case 'ENOENT': // not terribly unusual + case 'ELOOP': + case 'ENAMETOOLONG': + case 'UNKNOWN': + this.cache[this._makeAbs(f)] = false + break + + default: // some unusual error. Treat as failure. + this.cache[this._makeAbs(f)] = false + if (this.strict) { + this.emit('error', er) + // If the error is handled, then we abort + // if not, we threw out of here + this.abort() + } + if (!this.silent) + console.error('glob error', er) + break + } + + return cb() +} + +Glob.prototype._processGlobStar = function (prefix, read, abs, remain, index, inGlobStar, cb) { + var self = this + this._readdir(abs, inGlobStar, function (er, entries) { + self._processGlobStar2(prefix, read, abs, remain, index, inGlobStar, entries, cb) + }) +} + + +Glob.prototype._processGlobStar2 = function (prefix, read, abs, remain, index, inGlobStar, entries, cb) { + //console.error('pgs2', prefix, remain[0], entries) + + // no entries means not a dir, so it can never have matches + // foo.txt/** doesn't match foo.txt + if (!entries) + return cb() + + // test without the globstar, and with every child both below + // and replacing the globstar. + var remainWithoutGlobStar = remain.slice(1) + var gspref = prefix ? [ prefix ] : [] + var noGlobStar = gspref.concat(remainWithoutGlobStar) + + // the noGlobStar pattern exits the inGlobStar state + this._process(noGlobStar, index, false, cb) + + var isSym = this.symlinks[abs] + var len = entries.length + + // If it's a symlink, and we're in a globstar, then stop + if (isSym && inGlobStar) + return cb() + + for (var i = 0; i < len; i++) { + var e = entries[i] + if (e.charAt(0) === '.' && !this.dot) + continue + + // these two cases enter the inGlobStar state + var instead = gspref.concat(entries[i], remainWithoutGlobStar) + this._process(instead, index, true, cb) + + var below = gspref.concat(entries[i], remain) + this._process(below, index, true, cb) + } + + cb() +} + +Glob.prototype._processSimple = function (prefix, index, cb) { + // XXX review this. Shouldn't it be doing the mounting etc + // before doing stat? kinda weird? + var self = this + this._stat(prefix, function (er, exists) { + self._processSimple2(prefix, index, er, exists, cb) + }) +} +Glob.prototype._processSimple2 = function (prefix, index, er, exists, cb) { + + //console.error('ps2', prefix, exists) + + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + // If it doesn't exist, then just mark the lack of results + if (!exists) + return cb() + + if (prefix && isAbsolute(prefix) && !this.nomount) { + var trail = /[\/\\]$/.test(prefix) + if (prefix.charAt(0) === '/') { + prefix = path.join(this.root, prefix) + } else { + prefix = path.resolve(this.root, prefix) + if (trail) + prefix += '/' + } + } + + if (process.platform === 'win32') + prefix = prefix.replace(/\\/g, '/') + + // Mark this as a match + this._emitMatch(index, prefix) + cb() +} + +// Returns either 'DIR', 'FILE', or false +Glob.prototype._stat = function (f, cb) { + var abs = this._makeAbs(f) + var needDir = f.slice(-1) === '/' + + if (f.length > this.maxLength) + return cb() + + if (!this.stat && ownProp(this.cache, abs)) { + var c = this.cache[abs] + + if (Array.isArray(c)) + c = 'DIR' + + // It exists, but maybe not how we need it + if (!needDir || c === 'DIR') + return cb(null, c) + + if (needDir && c === 'FILE') + return cb() + + // otherwise we have to stat, because maybe c=true + // if we know it exists, but not what it is. + } + + var exists + var stat = this.statCache[abs] + if (stat !== undefined) { + if (stat === false) + return cb(null, stat) + else { + var type = stat.isDirectory() ? 'DIR' : 'FILE' + if (needDir && type === 'FILE') + return cb() + else + return cb(null, type, stat) + } + } + + var self = this + var statcb = inflight('stat\0' + abs, lstatcb_) + if (statcb) + fs.lstat(abs, statcb) + + function lstatcb_ (er, lstat) { + if (lstat && lstat.isSymbolicLink()) { + // If it's a symlink, then treat it as the target, unless + // the target does not exist, then treat it as a file. + return fs.stat(abs, function (er, stat) { + if (er) + self._stat2(f, abs, null, lstat, cb) + else + self._stat2(f, abs, er, stat, cb) + }) + } else { + self._stat2(f, abs, er, lstat, cb) + } + } +} + +Glob.prototype._stat2 = function (f, abs, er, stat, cb) { + if (er) { + this.statCache[abs] = false + return cb() + } + + var needDir = f.slice(-1) === '/' + this.statCache[abs] = stat + + if (abs.slice(-1) === '/' && !stat.isDirectory()) + return cb(null, false, stat) + + var c = stat.isDirectory() ? 'DIR' : 'FILE' + this.cache[abs] = this.cache[abs] || c + + if (needDir && c !== 'DIR') + return cb() + + return cb(null, c, stat) +} + +}).call(this,require('_process')) +},{"./common.js":15,"./sync.js":17,"_process":24,"assert":9,"events":14,"fs":12,"inflight":18,"inherits":19,"minimatch":20,"once":21,"path":22,"path-is-absolute":23,"util":28}],17:[function(require,module,exports){ +(function (process){ +module.exports = globSync +globSync.GlobSync = GlobSync + +var fs = require('fs') +var minimatch = require('minimatch') +var Minimatch = minimatch.Minimatch +var Glob = require('./glob.js').Glob +var util = require('util') +var path = require('path') +var assert = require('assert') +var isAbsolute = require('path-is-absolute') +var common = require('./common.js') +var alphasort = common.alphasort +var alphasorti = common.alphasorti +var setopts = common.setopts +var ownProp = common.ownProp +var childrenIgnored = common.childrenIgnored + +function globSync (pattern, options) { + if (typeof options === 'function' || arguments.length === 3) + throw new TypeError('callback provided to sync glob\n'+ + 'See: https://github.com/isaacs/node-glob/issues/167') + + return new GlobSync(pattern, options).found +} + +function GlobSync (pattern, options) { + if (!pattern) + throw new Error('must provide pattern') + + if (typeof options === 'function' || arguments.length === 3) + throw new TypeError('callback provided to sync glob\n'+ + 'See: https://github.com/isaacs/node-glob/issues/167') + + if (!(this instanceof GlobSync)) + return new GlobSync(pattern, options) + + setopts(this, pattern, options) + + if (this.noprocess) + return this + + var n = this.minimatch.set.length + this.matches = new Array(n) + for (var i = 0; i < n; i ++) { + this._process(this.minimatch.set[i], i, false) + } + this._finish() +} + +GlobSync.prototype._finish = function () { + assert(this instanceof GlobSync) + if (this.realpath) { + var self = this + this.matches.forEach(function (matchset, index) { + var set = self.matches[index] = Object.create(null) + for (var p in matchset) { + try { + p = self._makeAbs(p) + var real = fs.realpathSync(p, self.realpathCache) + set[real] = true + } catch (er) { + if (er.syscall === 'stat') + set[self._makeAbs(p)] = true + else + throw er + } + } + }) + } + common.finish(this) +} + + +GlobSync.prototype._process = function (pattern, index, inGlobStar) { + assert(this instanceof GlobSync) + + // Get the first [n] parts of pattern that are all strings. + var n = 0 + while (typeof pattern[n] === 'string') { + n ++ + } + // now n is the index of the first one that is *not* a string. + + // See if there's anything else + var prefix + switch (n) { + // if not, then this is rather simple + case pattern.length: + this._processSimple(pattern.join('/'), index) + return + + case 0: + // pattern *starts* with some non-trivial item. + // going to readdir(cwd), but not include the prefix in matches. + prefix = null + break + + default: + // pattern has some string bits in the front. + // whatever it starts with, whether that's 'absolute' like /foo/bar, + // or 'relative' like '../baz' + prefix = pattern.slice(0, n).join('/') + break + } + + var remain = pattern.slice(n) + + // get the list of entries. + var read + if (prefix === null) + read = '.' + else if (isAbsolute(prefix) || isAbsolute(pattern.join('/'))) { + if (!prefix || !isAbsolute(prefix)) + prefix = '/' + prefix + read = prefix + } else + read = prefix + + var abs = this._makeAbs(read) + + //if ignored, skip processing + if (childrenIgnored(this, read)) + return + + var isGlobStar = remain[0] === minimatch.GLOBSTAR + if (isGlobStar) + this._processGlobStar(prefix, read, abs, remain, index, inGlobStar) + else + this._processReaddir(prefix, read, abs, remain, index, inGlobStar) +} + + +GlobSync.prototype._processReaddir = function (prefix, read, abs, remain, index, inGlobStar) { + var entries = this._readdir(abs, inGlobStar) + + // if the abs isn't a dir, then nothing can match! + if (!entries) + return + + // It will only match dot entries if it starts with a dot, or if + // dot is set. Stuff like @(.foo|.bar) isn't allowed. + var pn = remain[0] + var negate = !!this.minimatch.negate + var rawGlob = pn._glob + var dotOk = this.dot || rawGlob.charAt(0) === '.' + + var matchedEntries = [] + for (var i = 0; i < entries.length; i++) { + var e = entries[i] + if (e.charAt(0) !== '.' || dotOk) { + var m + if (negate && !prefix) { + m = !e.match(pn) + } else { + m = e.match(pn) + } + if (m) + matchedEntries.push(e) + } + } + + var len = matchedEntries.length + // If there are no matched entries, then nothing matches. + if (len === 0) + return + + // if this is the last remaining pattern bit, then no need for + // an additional stat *unless* the user has specified mark or + // stat explicitly. We know they exist, since readdir returned + // them. + + if (remain.length === 1 && !this.mark && !this.stat) { + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + if (prefix) { + if (prefix.slice(-1) !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + + if (e.charAt(0) === '/' && !this.nomount) { + e = path.join(this.root, e) + } + this.matches[index][e] = true + } + // This was the last one, and no stats were needed + return + } + + // now test all matched entries as stand-ins for that part + // of the pattern. + remain.shift() + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + var newPattern + if (prefix) + newPattern = [prefix, e] + else + newPattern = [e] + this._process(newPattern.concat(remain), index, inGlobStar) + } +} + + +GlobSync.prototype._emitMatch = function (index, e) { + var abs = this._makeAbs(e) + if (this.mark) + e = this._mark(e) + + if (this.matches[index][e]) + return + + if (this.nodir) { + var c = this.cache[this._makeAbs(e)] + if (c === 'DIR' || Array.isArray(c)) + return + } + + this.matches[index][e] = true + if (this.stat) + this._stat(e) +} + + +GlobSync.prototype._readdirInGlobStar = function (abs) { + // follow all symlinked directories forever + // just proceed as if this is a non-globstar situation + if (this.follow) + return this._readdir(abs, false) + + var entries + var lstat + var stat + try { + lstat = fs.lstatSync(abs) + } catch (er) { + // lstat failed, doesn't exist + return null + } + + var isSym = lstat.isSymbolicLink() + this.symlinks[abs] = isSym + + // If it's not a symlink or a dir, then it's definitely a regular file. + // don't bother doing a readdir in that case. + if (!isSym && !lstat.isDirectory()) + this.cache[abs] = 'FILE' + else + entries = this._readdir(abs, false) + + return entries +} + +GlobSync.prototype._readdir = function (abs, inGlobStar) { + var entries + + if (inGlobStar && !ownProp(this.symlinks, abs)) + return this._readdirInGlobStar(abs) + + if (ownProp(this.cache, abs)) { + var c = this.cache[abs] + if (!c || c === 'FILE') + return null + + if (Array.isArray(c)) + return c + } + + try { + return this._readdirEntries(abs, fs.readdirSync(abs)) + } catch (er) { + this._readdirError(abs, er) + return null + } +} + +GlobSync.prototype._readdirEntries = function (abs, entries) { + // if we haven't asked to stat everything, then just + // assume that everything in there exists, so we can avoid + // having to stat it a second time. + if (!this.mark && !this.stat) { + for (var i = 0; i < entries.length; i ++) { + var e = entries[i] + if (abs === '/') + e = abs + e + else + e = abs + '/' + e + this.cache[e] = true + } + } + + this.cache[abs] = entries + + // mark and cache dir-ness + return entries +} + +GlobSync.prototype._readdirError = function (f, er) { + // handle errors, and cache the information + switch (er.code) { + case 'ENOTSUP': // https://github.com/isaacs/node-glob/issues/205 + case 'ENOTDIR': // totally normal. means it *does* exist. + this.cache[this._makeAbs(f)] = 'FILE' + break + + case 'ENOENT': // not terribly unusual + case 'ELOOP': + case 'ENAMETOOLONG': + case 'UNKNOWN': + this.cache[this._makeAbs(f)] = false + break + + default: // some unusual error. Treat as failure. + this.cache[this._makeAbs(f)] = false + if (this.strict) + throw er + if (!this.silent) + console.error('glob error', er) + break + } +} + +GlobSync.prototype._processGlobStar = function (prefix, read, abs, remain, index, inGlobStar) { + + var entries = this._readdir(abs, inGlobStar) + + // no entries means not a dir, so it can never have matches + // foo.txt/** doesn't match foo.txt + if (!entries) + return + + // test without the globstar, and with every child both below + // and replacing the globstar. + var remainWithoutGlobStar = remain.slice(1) + var gspref = prefix ? [ prefix ] : [] + var noGlobStar = gspref.concat(remainWithoutGlobStar) + + // the noGlobStar pattern exits the inGlobStar state + this._process(noGlobStar, index, false) + + var len = entries.length + var isSym = this.symlinks[abs] + + // If it's a symlink, and we're in a globstar, then stop + if (isSym && inGlobStar) + return + + for (var i = 0; i < len; i++) { + var e = entries[i] + if (e.charAt(0) === '.' && !this.dot) + continue + + // these two cases enter the inGlobStar state + var instead = gspref.concat(entries[i], remainWithoutGlobStar) + this._process(instead, index, true) + + var below = gspref.concat(entries[i], remain) + this._process(below, index, true) + } +} + +GlobSync.prototype._processSimple = function (prefix, index) { + // XXX review this. Shouldn't it be doing the mounting etc + // before doing stat? kinda weird? + var exists = this._stat(prefix) + + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + // If it doesn't exist, then just mark the lack of results + if (!exists) + return + + if (prefix && isAbsolute(prefix) && !this.nomount) { + var trail = /[\/\\]$/.test(prefix) + if (prefix.charAt(0) === '/') { + prefix = path.join(this.root, prefix) + } else { + prefix = path.resolve(this.root, prefix) + if (trail) + prefix += '/' + } + } + + if (process.platform === 'win32') + prefix = prefix.replace(/\\/g, '/') + + // Mark this as a match + this.matches[index][prefix] = true +} + +// Returns either 'DIR', 'FILE', or false +GlobSync.prototype._stat = function (f) { + var abs = this._makeAbs(f) + var needDir = f.slice(-1) === '/' + + if (f.length > this.maxLength) + return false + + if (!this.stat && ownProp(this.cache, abs)) { + var c = this.cache[abs] + + if (Array.isArray(c)) + c = 'DIR' + + // It exists, but maybe not how we need it + if (!needDir || c === 'DIR') + return c + + if (needDir && c === 'FILE') + return false + + // otherwise we have to stat, because maybe c=true + // if we know it exists, but not what it is. + } + + var exists + var stat = this.statCache[abs] + if (!stat) { + var lstat + try { + lstat = fs.lstatSync(abs) + } catch (er) { + return false + } + + if (lstat.isSymbolicLink()) { + try { + stat = fs.statSync(abs) + } catch (er) { + stat = lstat + } + } else { + stat = lstat + } + } + + this.statCache[abs] = stat + + var c = stat.isDirectory() ? 'DIR' : 'FILE' + this.cache[abs] = this.cache[abs] || c + + if (needDir && c !== 'DIR') + return false + + return c +} + +GlobSync.prototype._mark = function (p) { + return common.mark(this, p) +} + +GlobSync.prototype._makeAbs = function (f) { + return common.makeAbs(this, f) +} + +}).call(this,require('_process')) +},{"./common.js":15,"./glob.js":16,"_process":24,"assert":9,"fs":12,"minimatch":20,"path":22,"path-is-absolute":23,"util":28}],18:[function(require,module,exports){ +(function (process){ +var wrappy = require('wrappy') +var reqs = Object.create(null) +var once = require('once') + +module.exports = wrappy(inflight) + +function inflight (key, cb) { + if (reqs[key]) { + reqs[key].push(cb) + return null + } else { + reqs[key] = [cb] + return makeres(key) + } +} + +function makeres (key) { + return once(function RES () { + var cbs = reqs[key] + var len = cbs.length + var args = slice(arguments) + + // XXX It's somewhat ambiguous whether a new callback added in this + // pass should be queued for later execution if something in the + // list of callbacks throws, or if it should just be discarded. + // However, it's such an edge case that it hardly matters, and either + // choice is likely as surprising as the other. + // As it happens, we do go ahead and schedule it for later execution. + try { + for (var i = 0; i < len; i++) { + cbs[i].apply(null, args) + } + } finally { + if (cbs.length > len) { + // added more in the interim. + // de-zalgo, just in case, but don't call again. + cbs.splice(0, len) + process.nextTick(function () { + RES.apply(null, args) + }) + } else { + delete reqs[key] + } + } + }) +} + +function slice (args) { + var length = args.length + var array = [] + + for (var i = 0; i < length; i++) array[i] = args[i] + return array +} + +}).call(this,require('_process')) +},{"_process":24,"once":21,"wrappy":29}],19:[function(require,module,exports){ +if (typeof Object.create === 'function') { + // implementation from standard node.js 'util' module + module.exports = function inherits(ctor, superCtor) { + ctor.super_ = superCtor + ctor.prototype = Object.create(superCtor.prototype, { + constructor: { + value: ctor, + enumerable: false, + writable: true, + configurable: true + } + }); + }; +} else { + // old school shim for old browsers + module.exports = function inherits(ctor, superCtor) { + ctor.super_ = superCtor + var TempCtor = function () {} + TempCtor.prototype = superCtor.prototype + ctor.prototype = new TempCtor() + ctor.prototype.constructor = ctor + } +} + +},{}],20:[function(require,module,exports){ +module.exports = minimatch +minimatch.Minimatch = Minimatch + +var path = { sep: '/' } +try { + path = require('path') +} catch (er) {} + +var GLOBSTAR = minimatch.GLOBSTAR = Minimatch.GLOBSTAR = {} +var expand = require('brace-expansion') + +var plTypes = { + '!': { open: '(?:(?!(?:', close: '))[^/]*?)'}, + '?': { open: '(?:', close: ')?' }, + '+': { open: '(?:', close: ')+' }, + '*': { open: '(?:', close: ')*' }, + '@': { open: '(?:', close: ')' } +} + +// any single thing other than / +// don't need to escape / when using new RegExp() +var qmark = '[^/]' + +// * => any number of characters +var star = qmark + '*?' + +// ** when dots are allowed. Anything goes, except .. and . +// not (^ or / followed by one or two dots followed by $ or /), +// followed by anything, any number of times. +var twoStarDot = '(?:(?!(?:\\\/|^)(?:\\.{1,2})($|\\\/)).)*?' + +// not a ^ or / followed by a dot, +// followed by anything, any number of times. +var twoStarNoDot = '(?:(?!(?:\\\/|^)\\.).)*?' + +// characters that need to be escaped in RegExp. +var reSpecials = charSet('().*{}+?[]^$\\!') + +// "abc" -> { a:true, b:true, c:true } +function charSet (s) { + return s.split('').reduce(function (set, c) { + set[c] = true + return set + }, {}) +} + +// normalizes slashes. +var slashSplit = /\/+/ + +minimatch.filter = filter +function filter (pattern, options) { + options = options || {} + return function (p, i, list) { + return minimatch(p, pattern, options) + } +} + +function ext (a, b) { + a = a || {} + b = b || {} + var t = {} + Object.keys(b).forEach(function (k) { + t[k] = b[k] + }) + Object.keys(a).forEach(function (k) { + t[k] = a[k] + }) + return t +} + +minimatch.defaults = function (def) { + if (!def || !Object.keys(def).length) return minimatch + + var orig = minimatch + + var m = function minimatch (p, pattern, options) { + return orig.minimatch(p, pattern, ext(def, options)) + } + + m.Minimatch = function Minimatch (pattern, options) { + return new orig.Minimatch(pattern, ext(def, options)) + } + + return m +} + +Minimatch.defaults = function (def) { + if (!def || !Object.keys(def).length) return Minimatch + return minimatch.defaults(def).Minimatch +} + +function minimatch (p, pattern, options) { + if (typeof pattern !== 'string') { + throw new TypeError('glob pattern string required') + } + + if (!options) options = {} + + // shortcut: comments match nothing. + if (!options.nocomment && pattern.charAt(0) === '#') { + return false + } + + // "" only matches "" + if (pattern.trim() === '') return p === '' + + return new Minimatch(pattern, options).match(p) +} + +function Minimatch (pattern, options) { + if (!(this instanceof Minimatch)) { + return new Minimatch(pattern, options) + } + + if (typeof pattern !== 'string') { + throw new TypeError('glob pattern string required') + } + + if (!options) options = {} + pattern = pattern.trim() + + // windows support: need to use /, not \ + if (path.sep !== '/') { + pattern = pattern.split(path.sep).join('/') + } + + this.options = options + this.set = [] + this.pattern = pattern + this.regexp = null + this.negate = false + this.comment = false + this.empty = false + + // make the set of regexps etc. + this.make() +} + +Minimatch.prototype.debug = function () {} + +Minimatch.prototype.make = make +function make () { + // don't do it more than once. + if (this._made) return + + var pattern = this.pattern + var options = this.options + + // empty patterns and comments match nothing. + if (!options.nocomment && pattern.charAt(0) === '#') { + this.comment = true + return + } + if (!pattern) { + this.empty = true + return + } + + // step 1: figure out negation, etc. + this.parseNegate() + + // step 2: expand braces + var set = this.globSet = this.braceExpand() + + if (options.debug) this.debug = console.error + + this.debug(this.pattern, set) + + // step 3: now we have a set, so turn each one into a series of path-portion + // matching patterns. + // These will be regexps, except in the case of "**", which is + // set to the GLOBSTAR object for globstar behavior, + // and will not contain any / characters + set = this.globParts = set.map(function (s) { + return s.split(slashSplit) + }) + + this.debug(this.pattern, set) + + // glob --> regexps + set = set.map(function (s, si, set) { + return s.map(this.parse, this) + }, this) + + this.debug(this.pattern, set) + + // filter out everything that didn't compile properly. + set = set.filter(function (s) { + return s.indexOf(false) === -1 + }) + + this.debug(this.pattern, set) + + this.set = set +} + +Minimatch.prototype.parseNegate = parseNegate +function parseNegate () { + var pattern = this.pattern + var negate = false + var options = this.options + var negateOffset = 0 + + if (options.nonegate) return + + for (var i = 0, l = pattern.length + ; i < l && pattern.charAt(i) === '!' + ; i++) { + negate = !negate + negateOffset++ + } + + if (negateOffset) this.pattern = pattern.substr(negateOffset) + this.negate = negate +} + +// Brace expansion: +// a{b,c}d -> abd acd +// a{b,}c -> abc ac +// a{0..3}d -> a0d a1d a2d a3d +// a{b,c{d,e}f}g -> abg acdfg acefg +// a{b,c}d{e,f}g -> abdeg acdeg abdeg abdfg +// +// Invalid sets are not expanded. +// a{2..}b -> a{2..}b +// a{b}c -> a{b}c +minimatch.braceExpand = function (pattern, options) { + return braceExpand(pattern, options) +} + +Minimatch.prototype.braceExpand = braceExpand + +function braceExpand (pattern, options) { + if (!options) { + if (this instanceof Minimatch) { + options = this.options + } else { + options = {} + } + } + + pattern = typeof pattern === 'undefined' + ? this.pattern : pattern + + if (typeof pattern === 'undefined') { + throw new TypeError('undefined pattern') + } + + if (options.nobrace || + !pattern.match(/\{.*\}/)) { + // shortcut. no need to expand. + return [pattern] + } + + return expand(pattern) +} + +// parse a component of the expanded set. +// At this point, no pattern may contain "/" in it +// so we're going to return a 2d array, where each entry is the full +// pattern, split on '/', and then turned into a regular expression. +// A regexp is made at the end which joins each array with an +// escaped /, and another full one which joins each regexp with |. +// +// Following the lead of Bash 4.1, note that "**" only has special meaning +// when it is the *only* thing in a path portion. Otherwise, any series +// of * is equivalent to a single *. Globstar behavior is enabled by +// default, and can be disabled by setting options.noglobstar. +Minimatch.prototype.parse = parse +var SUBPARSE = {} +function parse (pattern, isSub) { + if (pattern.length > 1024 * 64) { + throw new TypeError('pattern is too long') + } + + var options = this.options + + // shortcuts + if (!options.noglobstar && pattern === '**') return GLOBSTAR + if (pattern === '') return '' + + var re = '' + var hasMagic = !!options.nocase + var escaping = false + // ? => one single character + var patternListStack = [] + var negativeLists = [] + var stateChar + var inClass = false + var reClassStart = -1 + var classStart = -1 + // . and .. never match anything that doesn't start with ., + // even when options.dot is set. + var patternStart = pattern.charAt(0) === '.' ? '' // anything + // not (start or / followed by . or .. followed by / or end) + : options.dot ? '(?!(?:^|\\\/)\\.{1,2}(?:$|\\\/))' + : '(?!\\.)' + var self = this + + function clearStateChar () { + if (stateChar) { + // we had some state-tracking character + // that wasn't consumed by this pass. + switch (stateChar) { + case '*': + re += star + hasMagic = true + break + case '?': + re += qmark + hasMagic = true + break + default: + re += '\\' + stateChar + break + } + self.debug('clearStateChar %j %j', stateChar, re) + stateChar = false + } + } + + for (var i = 0, len = pattern.length, c + ; (i < len) && (c = pattern.charAt(i)) + ; i++) { + this.debug('%s\t%s %s %j', pattern, i, re, c) + + // skip over any that are escaped. + if (escaping && reSpecials[c]) { + re += '\\' + c + escaping = false + continue + } + + switch (c) { + case '/': + // completely not allowed, even escaped. + // Should already be path-split by now. + return false + + case '\\': + clearStateChar() + escaping = true + continue + + // the various stateChar values + // for the "extglob" stuff. + case '?': + case '*': + case '+': + case '@': + case '!': + this.debug('%s\t%s %s %j <-- stateChar', pattern, i, re, c) + + // all of those are literals inside a class, except that + // the glob [!a] means [^a] in regexp + if (inClass) { + this.debug(' in class') + if (c === '!' && i === classStart + 1) c = '^' + re += c + continue + } + + // if we already have a stateChar, then it means + // that there was something like ** or +? in there. + // Handle the stateChar, then proceed with this one. + self.debug('call clearStateChar %j', stateChar) + clearStateChar() + stateChar = c + // if extglob is disabled, then +(asdf|foo) isn't a thing. + // just clear the statechar *now*, rather than even diving into + // the patternList stuff. + if (options.noext) clearStateChar() + continue + + case '(': + if (inClass) { + re += '(' + continue + } + + if (!stateChar) { + re += '\\(' + continue + } + + patternListStack.push({ + type: stateChar, + start: i - 1, + reStart: re.length, + open: plTypes[stateChar].open, + close: plTypes[stateChar].close + }) + // negation is (?:(?!js)[^/]*) + re += stateChar === '!' ? '(?:(?!(?:' : '(?:' + this.debug('plType %j %j', stateChar, re) + stateChar = false + continue + + case ')': + if (inClass || !patternListStack.length) { + re += '\\)' + continue + } + + clearStateChar() + hasMagic = true + var pl = patternListStack.pop() + // negation is (?:(?!js)[^/]*) + // The others are (?:) + re += pl.close + if (pl.type === '!') { + negativeLists.push(pl) + } + pl.reEnd = re.length + continue + + case '|': + if (inClass || !patternListStack.length || escaping) { + re += '\\|' + escaping = false + continue + } + + clearStateChar() + re += '|' + continue + + // these are mostly the same in regexp and glob + case '[': + // swallow any state-tracking char before the [ + clearStateChar() + + if (inClass) { + re += '\\' + c + continue + } + + inClass = true + classStart = i + reClassStart = re.length + re += c + continue + + case ']': + // a right bracket shall lose its special + // meaning and represent itself in + // a bracket expression if it occurs + // first in the list. -- POSIX.2 2.8.3.2 + if (i === classStart + 1 || !inClass) { + re += '\\' + c + escaping = false + continue + } + + // handle the case where we left a class open. + // "[z-a]" is valid, equivalent to "\[z-a\]" + if (inClass) { + // split where the last [ was, make sure we don't have + // an invalid re. if so, re-walk the contents of the + // would-be class to re-translate any characters that + // were passed through as-is + // TODO: It would probably be faster to determine this + // without a try/catch and a new RegExp, but it's tricky + // to do safely. For now, this is safe and works. + var cs = pattern.substring(classStart + 1, i) + try { + RegExp('[' + cs + ']') + } catch (er) { + // not a valid class! + var sp = this.parse(cs, SUBPARSE) + re = re.substr(0, reClassStart) + '\\[' + sp[0] + '\\]' + hasMagic = hasMagic || sp[1] + inClass = false + continue + } + } + + // finish up the class. + hasMagic = true + inClass = false + re += c + continue + + default: + // swallow any state char that wasn't consumed + clearStateChar() + + if (escaping) { + // no need + escaping = false + } else if (reSpecials[c] + && !(c === '^' && inClass)) { + re += '\\' + } + + re += c + + } // switch + } // for + + // handle the case where we left a class open. + // "[abc" is valid, equivalent to "\[abc" + if (inClass) { + // split where the last [ was, and escape it + // this is a huge pita. We now have to re-walk + // the contents of the would-be class to re-translate + // any characters that were passed through as-is + cs = pattern.substr(classStart + 1) + sp = this.parse(cs, SUBPARSE) + re = re.substr(0, reClassStart) + '\\[' + sp[0] + hasMagic = hasMagic || sp[1] + } + + // handle the case where we had a +( thing at the *end* + // of the pattern. + // each pattern list stack adds 3 chars, and we need to go through + // and escape any | chars that were passed through as-is for the regexp. + // Go through and escape them, taking care not to double-escape any + // | chars that were already escaped. + for (pl = patternListStack.pop(); pl; pl = patternListStack.pop()) { + var tail = re.slice(pl.reStart + pl.open.length) + this.debug('setting tail', re, pl) + // maybe some even number of \, then maybe 1 \, followed by a | + tail = tail.replace(/((?:\\{2}){0,64})(\\?)\|/g, function (_, $1, $2) { + if (!$2) { + // the | isn't already escaped, so escape it. + $2 = '\\' + } + + // need to escape all those slashes *again*, without escaping the + // one that we need for escaping the | character. As it works out, + // escaping an even number of slashes can be done by simply repeating + // it exactly after itself. That's why this trick works. + // + // I am sorry that you have to see this. + return $1 + $1 + $2 + '|' + }) + + this.debug('tail=%j\n %s', tail, tail, pl, re) + var t = pl.type === '*' ? star + : pl.type === '?' ? qmark + : '\\' + pl.type + + hasMagic = true + re = re.slice(0, pl.reStart) + t + '\\(' + tail + } + + // handle trailing things that only matter at the very end. + clearStateChar() + if (escaping) { + // trailing \\ + re += '\\\\' + } + + // only need to apply the nodot start if the re starts with + // something that could conceivably capture a dot + var addPatternStart = false + switch (re.charAt(0)) { + case '.': + case '[': + case '(': addPatternStart = true + } + + // Hack to work around lack of negative lookbehind in JS + // A pattern like: *.!(x).!(y|z) needs to ensure that a name + // like 'a.xyz.yz' doesn't match. So, the first negative + // lookahead, has to look ALL the way ahead, to the end of + // the pattern. + for (var n = negativeLists.length - 1; n > -1; n--) { + var nl = negativeLists[n] + + var nlBefore = re.slice(0, nl.reStart) + var nlFirst = re.slice(nl.reStart, nl.reEnd - 8) + var nlLast = re.slice(nl.reEnd - 8, nl.reEnd) + var nlAfter = re.slice(nl.reEnd) + + nlLast += nlAfter + + // Handle nested stuff like *(*.js|!(*.json)), where open parens + // mean that we should *not* include the ) in the bit that is considered + // "after" the negated section. + var openParensBefore = nlBefore.split('(').length - 1 + var cleanAfter = nlAfter + for (i = 0; i < openParensBefore; i++) { + cleanAfter = cleanAfter.replace(/\)[+*?]?/, '') + } + nlAfter = cleanAfter + + var dollar = '' + if (nlAfter === '' && isSub !== SUBPARSE) { + dollar = '$' + } + var newRe = nlBefore + nlFirst + nlAfter + dollar + nlLast + re = newRe + } + + // if the re is not "" at this point, then we need to make sure + // it doesn't match against an empty path part. + // Otherwise a/* will match a/, which it should not. + if (re !== '' && hasMagic) { + re = '(?=.)' + re + } + + if (addPatternStart) { + re = patternStart + re + } + + // parsing just a piece of a larger pattern. + if (isSub === SUBPARSE) { + return [re, hasMagic] + } + + // skip the regexp for non-magical patterns + // unescape anything in it, though, so that it'll be + // an exact match against a file etc. + if (!hasMagic) { + return globUnescape(pattern) + } + + var flags = options.nocase ? 'i' : '' + try { + var regExp = new RegExp('^' + re + '$', flags) + } catch (er) { + // If it was an invalid regular expression, then it can't match + // anything. This trick looks for a character after the end of + // the string, which is of course impossible, except in multi-line + // mode, but it's not a /m regex. + return new RegExp('$.') + } + + regExp._glob = pattern + regExp._src = re + + return regExp +} + +minimatch.makeRe = function (pattern, options) { + return new Minimatch(pattern, options || {}).makeRe() +} + +Minimatch.prototype.makeRe = makeRe +function makeRe () { + if (this.regexp || this.regexp === false) return this.regexp + + // at this point, this.set is a 2d array of partial + // pattern strings, or "**". + // + // It's better to use .match(). This function shouldn't + // be used, really, but it's pretty convenient sometimes, + // when you just want to work with a regex. + var set = this.set + + if (!set.length) { + this.regexp = false + return this.regexp + } + var options = this.options + + var twoStar = options.noglobstar ? star + : options.dot ? twoStarDot + : twoStarNoDot + var flags = options.nocase ? 'i' : '' + + var re = set.map(function (pattern) { + return pattern.map(function (p) { + return (p === GLOBSTAR) ? twoStar + : (typeof p === 'string') ? regExpEscape(p) + : p._src + }).join('\\\/') + }).join('|') + + // must match entire pattern + // ending in a * or ** will make it less strict. + re = '^(?:' + re + ')$' + + // can match anything, as long as it's not this. + if (this.negate) re = '^(?!' + re + ').*$' + + try { + this.regexp = new RegExp(re, flags) + } catch (ex) { + this.regexp = false + } + return this.regexp +} + +minimatch.match = function (list, pattern, options) { + options = options || {} + var mm = new Minimatch(pattern, options) + list = list.filter(function (f) { + return mm.match(f) + }) + if (mm.options.nonull && !list.length) { + list.push(pattern) + } + return list +} + +Minimatch.prototype.match = match +function match (f, partial) { + this.debug('match', f, this.pattern) + // short-circuit in the case of busted things. + // comments, etc. + if (this.comment) return false + if (this.empty) return f === '' + + if (f === '/' && partial) return true + + var options = this.options + + // windows: need to use /, not \ + if (path.sep !== '/') { + f = f.split(path.sep).join('/') + } + + // treat the test path as a set of pathparts. + f = f.split(slashSplit) + this.debug(this.pattern, 'split', f) + + // just ONE of the pattern sets in this.set needs to match + // in order for it to be valid. If negating, then just one + // match means that we have failed. + // Either way, return on the first hit. + + var set = this.set + this.debug(this.pattern, 'set', set) + + // Find the basename of the path by looking for the last non-empty segment + var filename + var i + for (i = f.length - 1; i >= 0; i--) { + filename = f[i] + if (filename) break + } + + for (i = 0; i < set.length; i++) { + var pattern = set[i] + var file = f + if (options.matchBase && pattern.length === 1) { + file = [filename] + } + var hit = this.matchOne(file, pattern, partial) + if (hit) { + if (options.flipNegate) return true + return !this.negate + } + } + + // didn't get any hits. this is success if it's a negative + // pattern, failure otherwise. + if (options.flipNegate) return false + return this.negate +} + +// set partial to true to test if, for example, +// "/a/b" matches the start of "/*/b/*/d" +// Partial means, if you run out of file before you run +// out of pattern, then that's fine, as long as all +// the parts match. +Minimatch.prototype.matchOne = function (file, pattern, partial) { + var options = this.options + + this.debug('matchOne', + { 'this': this, file: file, pattern: pattern }) + + this.debug('matchOne', file.length, pattern.length) + + for (var fi = 0, + pi = 0, + fl = file.length, + pl = pattern.length + ; (fi < fl) && (pi < pl) + ; fi++, pi++) { + this.debug('matchOne loop') + var p = pattern[pi] + var f = file[fi] + + this.debug(pattern, p, f) + + // should be impossible. + // some invalid regexp stuff in the set. + if (p === false) return false + + if (p === GLOBSTAR) { + this.debug('GLOBSTAR', [pattern, p, f]) + + // "**" + // a/**/b/**/c would match the following: + // a/b/x/y/z/c + // a/x/y/z/b/c + // a/b/x/b/x/c + // a/b/c + // To do this, take the rest of the pattern after + // the **, and see if it would match the file remainder. + // If so, return success. + // If not, the ** "swallows" a segment, and try again. + // This is recursively awful. + // + // a/**/b/**/c matching a/b/x/y/z/c + // - a matches a + // - doublestar + // - matchOne(b/x/y/z/c, b/**/c) + // - b matches b + // - doublestar + // - matchOne(x/y/z/c, c) -> no + // - matchOne(y/z/c, c) -> no + // - matchOne(z/c, c) -> no + // - matchOne(c, c) yes, hit + var fr = fi + var pr = pi + 1 + if (pr === pl) { + this.debug('** at the end') + // a ** at the end will just swallow the rest. + // We have found a match. + // however, it will not swallow /.x, unless + // options.dot is set. + // . and .. are *never* matched by **, for explosively + // exponential reasons. + for (; fi < fl; fi++) { + if (file[fi] === '.' || file[fi] === '..' || + (!options.dot && file[fi].charAt(0) === '.')) return false + } + return true + } + + // ok, let's see if we can swallow whatever we can. + while (fr < fl) { + var swallowee = file[fr] + + this.debug('\nglobstar while', file, fr, pattern, pr, swallowee) + + // XXX remove this slice. Just pass the start index. + if (this.matchOne(file.slice(fr), pattern.slice(pr), partial)) { + this.debug('globstar found match!', fr, fl, swallowee) + // found a match. + return true + } else { + // can't swallow "." or ".." ever. + // can only swallow ".foo" when explicitly asked. + if (swallowee === '.' || swallowee === '..' || + (!options.dot && swallowee.charAt(0) === '.')) { + this.debug('dot detected!', file, fr, pattern, pr) + break + } + + // ** swallows a segment, and continue. + this.debug('globstar swallow a segment, and continue') + fr++ + } + } + + // no match was found. + // However, in partial mode, we can't say this is necessarily over. + // If there's more *pattern* left, then + if (partial) { + // ran out of file + this.debug('\n>>> no match, partial?', file, fr, pattern, pr) + if (fr === fl) return true + } + return false + } + + // something other than ** + // non-magic patterns just have to match exactly + // patterns with magic have been turned into regexps. + var hit + if (typeof p === 'string') { + if (options.nocase) { + hit = f.toLowerCase() === p.toLowerCase() + } else { + hit = f === p + } + this.debug('string match', p, f, hit) + } else { + hit = f.match(p) + this.debug('pattern match', p, f, hit) + } + + if (!hit) return false + } + + // Note: ending in / means that we'll get a final "" + // at the end of the pattern. This can only match a + // corresponding "" at the end of the file. + // If the file ends in /, then it can only match a + // a pattern that ends in /, unless the pattern just + // doesn't have any more for it. But, a/b/ should *not* + // match "a/b/*", even though "" matches against the + // [^/]*? pattern, except in partial mode, where it might + // simply not be reached yet. + // However, a/b/ should still satisfy a/* + + // now either we fell off the end of the pattern, or we're done. + if (fi === fl && pi === pl) { + // ran out of pattern and filename at the same time. + // an exact hit! + return true + } else if (fi === fl) { + // ran out of file, but still had pattern left. + // this is ok if we're doing the match as part of + // a glob fs traversal. + return partial + } else if (pi === pl) { + // ran out of pattern, still have file left. + // this is only acceptable if we're on the very last + // empty segment of a file with a trailing slash. + // a/* should match a/b/ + var emptyFileEnd = (fi === fl - 1) && (file[fi] === '') + return emptyFileEnd + } + + // should be unreachable. + throw new Error('wtf?') +} + +// replace stuff like \* with * +function globUnescape (s) { + return s.replace(/\\(.)/g, '$1') +} + +function regExpEscape (s) { + return s.replace(/[-[\]{}()*+?.,\\^$|#\s]/g, '\\$&') +} + +},{"brace-expansion":11,"path":22}],21:[function(require,module,exports){ +var wrappy = require('wrappy') +module.exports = wrappy(once) +module.exports.strict = wrappy(onceStrict) + +once.proto = once(function () { + Object.defineProperty(Function.prototype, 'once', { + value: function () { + return once(this) + }, + configurable: true + }) + + Object.defineProperty(Function.prototype, 'onceStrict', { + value: function () { + return onceStrict(this) + }, + configurable: true + }) +}) + +function once (fn) { + var f = function () { + if (f.called) return f.value + f.called = true + return f.value = fn.apply(this, arguments) + } + f.called = false + return f +} + +function onceStrict (fn) { + var f = function () { + if (f.called) + throw new Error(f.onceError) + f.called = true + return f.value = fn.apply(this, arguments) + } + var name = fn.name || 'Function wrapped with `once`' + f.onceError = name + " shouldn't be called more than once" + f.called = false + return f +} + +},{"wrappy":29}],22:[function(require,module,exports){ +(function (process){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +// resolves . and .. elements in a path array with directory names there +// must be no slashes, empty elements, or device names (c:\) in the array +// (so also no leading and trailing slashes - it does not distinguish +// relative and absolute paths) +function normalizeArray(parts, allowAboveRoot) { + // if the path tries to go above the root, `up` ends up > 0 + var up = 0; + for (var i = parts.length - 1; i >= 0; i--) { + var last = parts[i]; + if (last === '.') { + parts.splice(i, 1); + } else if (last === '..') { + parts.splice(i, 1); + up++; + } else if (up) { + parts.splice(i, 1); + up--; + } + } + + // if the path is allowed to go above the root, restore leading ..s + if (allowAboveRoot) { + for (; up--; up) { + parts.unshift('..'); + } + } + + return parts; +} + +// Split a filename into [root, dir, basename, ext], unix version +// 'root' is just a slash, or nothing. +var splitPathRe = + /^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/; +var splitPath = function(filename) { + return splitPathRe.exec(filename).slice(1); +}; + +// path.resolve([from ...], to) +// posix version +exports.resolve = function() { + var resolvedPath = '', + resolvedAbsolute = false; + + for (var i = arguments.length - 1; i >= -1 && !resolvedAbsolute; i--) { + var path = (i >= 0) ? arguments[i] : process.cwd(); + + // Skip empty and invalid entries + if (typeof path !== 'string') { + throw new TypeError('Arguments to path.resolve must be strings'); + } else if (!path) { + continue; + } + + resolvedPath = path + '/' + resolvedPath; + resolvedAbsolute = path.charAt(0) === '/'; + } + + // At this point the path should be resolved to a full absolute path, but + // handle relative paths to be safe (might happen when process.cwd() fails) + + // Normalize the path + resolvedPath = normalizeArray(filter(resolvedPath.split('/'), function(p) { + return !!p; + }), !resolvedAbsolute).join('/'); + + return ((resolvedAbsolute ? '/' : '') + resolvedPath) || '.'; +}; + +// path.normalize(path) +// posix version +exports.normalize = function(path) { + var isAbsolute = exports.isAbsolute(path), + trailingSlash = substr(path, -1) === '/'; + + // Normalize the path + path = normalizeArray(filter(path.split('/'), function(p) { + return !!p; + }), !isAbsolute).join('/'); + + if (!path && !isAbsolute) { + path = '.'; + } + if (path && trailingSlash) { + path += '/'; + } + + return (isAbsolute ? '/' : '') + path; +}; + +// posix version +exports.isAbsolute = function(path) { + return path.charAt(0) === '/'; +}; + +// posix version +exports.join = function() { + var paths = Array.prototype.slice.call(arguments, 0); + return exports.normalize(filter(paths, function(p, index) { + if (typeof p !== 'string') { + throw new TypeError('Arguments to path.join must be strings'); + } + return p; + }).join('/')); +}; + + +// path.relative(from, to) +// posix version +exports.relative = function(from, to) { + from = exports.resolve(from).substr(1); + to = exports.resolve(to).substr(1); + + function trim(arr) { + var start = 0; + for (; start < arr.length; start++) { + if (arr[start] !== '') break; + } + + var end = arr.length - 1; + for (; end >= 0; end--) { + if (arr[end] !== '') break; + } + + if (start > end) return []; + return arr.slice(start, end - start + 1); + } + + var fromParts = trim(from.split('/')); + var toParts = trim(to.split('/')); + + var length = Math.min(fromParts.length, toParts.length); + var samePartsLength = length; + for (var i = 0; i < length; i++) { + if (fromParts[i] !== toParts[i]) { + samePartsLength = i; + break; + } + } + + var outputParts = []; + for (var i = samePartsLength; i < fromParts.length; i++) { + outputParts.push('..'); + } + + outputParts = outputParts.concat(toParts.slice(samePartsLength)); + + return outputParts.join('/'); +}; + +exports.sep = '/'; +exports.delimiter = ':'; + +exports.dirname = function(path) { + var result = splitPath(path), + root = result[0], + dir = result[1]; + + if (!root && !dir) { + // No dirname whatsoever + return '.'; + } + + if (dir) { + // It has a dirname, strip trailing slash + dir = dir.substr(0, dir.length - 1); + } + + return root + dir; +}; + + +exports.basename = function(path, ext) { + var f = splitPath(path)[2]; + // TODO: make this comparison case-insensitive on windows? + if (ext && f.substr(-1 * ext.length) === ext) { + f = f.substr(0, f.length - ext.length); + } + return f; +}; + + +exports.extname = function(path) { + return splitPath(path)[3]; +}; + +function filter (xs, f) { + if (xs.filter) return xs.filter(f); + var res = []; + for (var i = 0; i < xs.length; i++) { + if (f(xs[i], i, xs)) res.push(xs[i]); + } + return res; +} + +// String.prototype.substr - negative index don't work in IE8 +var substr = 'ab'.substr(-1) === 'b' + ? function (str, start, len) { return str.substr(start, len) } + : function (str, start, len) { + if (start < 0) start = str.length + start; + return str.substr(start, len); + } +; + +}).call(this,require('_process')) +},{"_process":24}],23:[function(require,module,exports){ +(function (process){ +'use strict'; + +function posix(path) { + return path.charAt(0) === '/'; +} + +function win32(path) { + // https://github.com/nodejs/node/blob/b3fcc245fb25539909ef1d5eaa01dbf92e168633/lib/path.js#L56 + var splitDeviceRe = /^([a-zA-Z]:|[\\\/]{2}[^\\\/]+[\\\/]+[^\\\/]+)?([\\\/])?([\s\S]*?)$/; + var result = splitDeviceRe.exec(path); + var device = result[1] || ''; + var isUnc = Boolean(device && device.charAt(1) !== ':'); + + // UNC paths are always absolute + return Boolean(result[2] || isUnc); +} + +module.exports = process.platform === 'win32' ? win32 : posix; +module.exports.posix = posix; +module.exports.win32 = win32; + +}).call(this,require('_process')) +},{"_process":24}],24:[function(require,module,exports){ +// shim for using process in browser +var process = module.exports = {}; + +// cached from whatever global is present so that test runners that stub it +// don't break things. But we need to wrap it in a try catch in case it is +// wrapped in strict mode code which doesn't define any globals. It's inside a +// function because try/catches deoptimize in certain engines. + +var cachedSetTimeout; +var cachedClearTimeout; + +function defaultSetTimout() { + throw new Error('setTimeout has not been defined'); +} +function defaultClearTimeout () { + throw new Error('clearTimeout has not been defined'); +} +(function () { + try { + if (typeof setTimeout === 'function') { + cachedSetTimeout = setTimeout; + } else { + cachedSetTimeout = defaultSetTimout; + } + } catch (e) { + cachedSetTimeout = defaultSetTimout; + } + try { + if (typeof clearTimeout === 'function') { + cachedClearTimeout = clearTimeout; + } else { + cachedClearTimeout = defaultClearTimeout; + } + } catch (e) { + cachedClearTimeout = defaultClearTimeout; + } +} ()) +function runTimeout(fun) { + if (cachedSetTimeout === setTimeout) { + //normal enviroments in sane situations + return setTimeout(fun, 0); + } + // if setTimeout wasn't available but was latter defined + if ((cachedSetTimeout === defaultSetTimout || !cachedSetTimeout) && setTimeout) { + cachedSetTimeout = setTimeout; + return setTimeout(fun, 0); + } + try { + // when when somebody has screwed with setTimeout but no I.E. maddness + return cachedSetTimeout(fun, 0); + } catch(e){ + try { + // When we are in I.E. but the script has been evaled so I.E. doesn't trust the global object when called normally + return cachedSetTimeout.call(null, fun, 0); + } catch(e){ + // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error + return cachedSetTimeout.call(this, fun, 0); + } + } + + +} +function runClearTimeout(marker) { + if (cachedClearTimeout === clearTimeout) { + //normal enviroments in sane situations + return clearTimeout(marker); + } + // if clearTimeout wasn't available but was latter defined + if ((cachedClearTimeout === defaultClearTimeout || !cachedClearTimeout) && clearTimeout) { + cachedClearTimeout = clearTimeout; + return clearTimeout(marker); + } + try { + // when when somebody has screwed with setTimeout but no I.E. maddness + return cachedClearTimeout(marker); + } catch (e){ + try { + // When we are in I.E. but the script has been evaled so I.E. doesn't trust the global object when called normally + return cachedClearTimeout.call(null, marker); + } catch (e){ + // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error. + // Some versions of I.E. have different rules for clearTimeout vs setTimeout + return cachedClearTimeout.call(this, marker); + } + } + + + +} +var queue = []; +var draining = false; +var currentQueue; +var queueIndex = -1; + +function cleanUpNextTick() { + if (!draining || !currentQueue) { + return; + } + draining = false; + if (currentQueue.length) { + queue = currentQueue.concat(queue); + } else { + queueIndex = -1; + } + if (queue.length) { + drainQueue(); + } +} + +function drainQueue() { + if (draining) { + return; + } + var timeout = runTimeout(cleanUpNextTick); + draining = true; + + var len = queue.length; + while(len) { + currentQueue = queue; + queue = []; + while (++queueIndex < len) { + if (currentQueue) { + currentQueue[queueIndex].run(); + } + } + queueIndex = -1; + len = queue.length; + } + currentQueue = null; + draining = false; + runClearTimeout(timeout); +} + +process.nextTick = function (fun) { + var args = new Array(arguments.length - 1); + if (arguments.length > 1) { + for (var i = 1; i < arguments.length; i++) { + args[i - 1] = arguments[i]; + } + } + queue.push(new Item(fun, args)); + if (queue.length === 1 && !draining) { + runTimeout(drainQueue); + } +}; + +// v8 likes predictible objects +function Item(fun, array) { + this.fun = fun; + this.array = array; +} +Item.prototype.run = function () { + this.fun.apply(null, this.array); +}; +process.title = 'browser'; +process.browser = true; +process.env = {}; +process.argv = []; +process.version = ''; // empty string to avoid regexp issues +process.versions = {}; + +function noop() {} + +process.on = noop; +process.addListener = noop; +process.once = noop; +process.off = noop; +process.removeListener = noop; +process.removeAllListeners = noop; +process.emit = noop; +process.prependListener = noop; +process.prependOnceListener = noop; + +process.listeners = function (name) { return [] } + +process.binding = function (name) { + throw new Error('process.binding is not supported'); +}; + +process.cwd = function () { return '/' }; +process.chdir = function (dir) { + throw new Error('process.chdir is not supported'); +}; +process.umask = function() { return 0; }; + +},{}],25:[function(require,module,exports){ +// Underscore.js 1.8.3 +// http://underscorejs.org +// (c) 2009-2015 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors +// Underscore may be freely distributed under the MIT license. + +(function() { + + // Baseline setup + // -------------- + + // Establish the root object, `window` in the browser, or `exports` on the server. + var root = this; + + // Save the previous value of the `_` variable. + var previousUnderscore = root._; + + // Save bytes in the minified (but not gzipped) version: + var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype; + + // Create quick reference variables for speed access to core prototypes. + var + push = ArrayProto.push, + slice = ArrayProto.slice, + toString = ObjProto.toString, + hasOwnProperty = ObjProto.hasOwnProperty; + + // All **ECMAScript 5** native function implementations that we hope to use + // are declared here. + var + nativeIsArray = Array.isArray, + nativeKeys = Object.keys, + nativeBind = FuncProto.bind, + nativeCreate = Object.create; + + // Naked function reference for surrogate-prototype-swapping. + var Ctor = function(){}; + + // Create a safe reference to the Underscore object for use below. + var _ = function(obj) { + if (obj instanceof _) return obj; + if (!(this instanceof _)) return new _(obj); + this._wrapped = obj; + }; + + // Export the Underscore object for **Node.js**, with + // backwards-compatibility for the old `require()` API. If we're in + // the browser, add `_` as a global object. + if (typeof exports !== 'undefined') { + if (typeof module !== 'undefined' && module.exports) { + exports = module.exports = _; + } + exports._ = _; + } else { + root._ = _; + } + + // Current version. + _.VERSION = '1.8.3'; + + // Internal function that returns an efficient (for current engines) version + // of the passed-in callback, to be repeatedly applied in other Underscore + // functions. + var optimizeCb = function(func, context, argCount) { + if (context === void 0) return func; + switch (argCount == null ? 3 : argCount) { + case 1: return function(value) { + return func.call(context, value); + }; + case 2: return function(value, other) { + return func.call(context, value, other); + }; + case 3: return function(value, index, collection) { + return func.call(context, value, index, collection); + }; + case 4: return function(accumulator, value, index, collection) { + return func.call(context, accumulator, value, index, collection); + }; + } + return function() { + return func.apply(context, arguments); + }; + }; + + // A mostly-internal function to generate callbacks that can be applied + // to each element in a collection, returning the desired result — either + // identity, an arbitrary callback, a property matcher, or a property accessor. + var cb = function(value, context, argCount) { + if (value == null) return _.identity; + if (_.isFunction(value)) return optimizeCb(value, context, argCount); + if (_.isObject(value)) return _.matcher(value); + return _.property(value); + }; + _.iteratee = function(value, context) { + return cb(value, context, Infinity); + }; + + // An internal function for creating assigner functions. + var createAssigner = function(keysFunc, undefinedOnly) { + return function(obj) { + var length = arguments.length; + if (length < 2 || obj == null) return obj; + for (var index = 1; index < length; index++) { + var source = arguments[index], + keys = keysFunc(source), + l = keys.length; + for (var i = 0; i < l; i++) { + var key = keys[i]; + if (!undefinedOnly || obj[key] === void 0) obj[key] = source[key]; + } + } + return obj; + }; + }; + + // An internal function for creating a new object that inherits from another. + var baseCreate = function(prototype) { + if (!_.isObject(prototype)) return {}; + if (nativeCreate) return nativeCreate(prototype); + Ctor.prototype = prototype; + var result = new Ctor; + Ctor.prototype = null; + return result; + }; + + var property = function(key) { + return function(obj) { + return obj == null ? void 0 : obj[key]; + }; + }; + + // Helper for collection methods to determine whether a collection + // should be iterated as an array or as an object + // Related: http://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength + // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094 + var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1; + var getLength = property('length'); + var isArrayLike = function(collection) { + var length = getLength(collection); + return typeof length == 'number' && length >= 0 && length <= MAX_ARRAY_INDEX; + }; + + // Collection Functions + // -------------------- + + // The cornerstone, an `each` implementation, aka `forEach`. + // Handles raw objects in addition to array-likes. Treats all + // sparse array-likes as if they were dense. + _.each = _.forEach = function(obj, iteratee, context) { + iteratee = optimizeCb(iteratee, context); + var i, length; + if (isArrayLike(obj)) { + for (i = 0, length = obj.length; i < length; i++) { + iteratee(obj[i], i, obj); + } + } else { + var keys = _.keys(obj); + for (i = 0, length = keys.length; i < length; i++) { + iteratee(obj[keys[i]], keys[i], obj); + } + } + return obj; + }; + + // Return the results of applying the iteratee to each element. + _.map = _.collect = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length, + results = Array(length); + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + results[index] = iteratee(obj[currentKey], currentKey, obj); + } + return results; + }; + + // Create a reducing function iterating left or right. + function createReduce(dir) { + // Optimized iterator function as using arguments.length + // in the main function will deoptimize the, see #1991. + function iterator(obj, iteratee, memo, keys, index, length) { + for (; index >= 0 && index < length; index += dir) { + var currentKey = keys ? keys[index] : index; + memo = iteratee(memo, obj[currentKey], currentKey, obj); + } + return memo; + } + + return function(obj, iteratee, memo, context) { + iteratee = optimizeCb(iteratee, context, 4); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length, + index = dir > 0 ? 0 : length - 1; + // Determine the initial value if none is provided. + if (arguments.length < 3) { + memo = obj[keys ? keys[index] : index]; + index += dir; + } + return iterator(obj, iteratee, memo, keys, index, length); + }; + } + + // **Reduce** builds up a single result from a list of values, aka `inject`, + // or `foldl`. + _.reduce = _.foldl = _.inject = createReduce(1); + + // The right-associative version of reduce, also known as `foldr`. + _.reduceRight = _.foldr = createReduce(-1); + + // Return the first value which passes a truth test. Aliased as `detect`. + _.find = _.detect = function(obj, predicate, context) { + var key; + if (isArrayLike(obj)) { + key = _.findIndex(obj, predicate, context); + } else { + key = _.findKey(obj, predicate, context); + } + if (key !== void 0 && key !== -1) return obj[key]; + }; + + // Return all the elements that pass a truth test. + // Aliased as `select`. + _.filter = _.select = function(obj, predicate, context) { + var results = []; + predicate = cb(predicate, context); + _.each(obj, function(value, index, list) { + if (predicate(value, index, list)) results.push(value); + }); + return results; + }; + + // Return all the elements for which a truth test fails. + _.reject = function(obj, predicate, context) { + return _.filter(obj, _.negate(cb(predicate)), context); + }; + + // Determine whether all of the elements match a truth test. + // Aliased as `all`. + _.every = _.all = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length; + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + if (!predicate(obj[currentKey], currentKey, obj)) return false; + } + return true; + }; + + // Determine if at least one element in the object matches a truth test. + // Aliased as `any`. + _.some = _.any = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length; + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + if (predicate(obj[currentKey], currentKey, obj)) return true; + } + return false; + }; + + // Determine if the array or object contains a given item (using `===`). + // Aliased as `includes` and `include`. + _.contains = _.includes = _.include = function(obj, item, fromIndex, guard) { + if (!isArrayLike(obj)) obj = _.values(obj); + if (typeof fromIndex != 'number' || guard) fromIndex = 0; + return _.indexOf(obj, item, fromIndex) >= 0; + }; + + // Invoke a method (with arguments) on every item in a collection. + _.invoke = function(obj, method) { + var args = slice.call(arguments, 2); + var isFunc = _.isFunction(method); + return _.map(obj, function(value) { + var func = isFunc ? method : value[method]; + return func == null ? func : func.apply(value, args); + }); + }; + + // Convenience version of a common use case of `map`: fetching a property. + _.pluck = function(obj, key) { + return _.map(obj, _.property(key)); + }; + + // Convenience version of a common use case of `filter`: selecting only objects + // containing specific `key:value` pairs. + _.where = function(obj, attrs) { + return _.filter(obj, _.matcher(attrs)); + }; + + // Convenience version of a common use case of `find`: getting the first object + // containing specific `key:value` pairs. + _.findWhere = function(obj, attrs) { + return _.find(obj, _.matcher(attrs)); + }; + + // Return the maximum element (or element-based computation). + _.max = function(obj, iteratee, context) { + var result = -Infinity, lastComputed = -Infinity, + value, computed; + if (iteratee == null && obj != null) { + obj = isArrayLike(obj) ? obj : _.values(obj); + for (var i = 0, length = obj.length; i < length; i++) { + value = obj[i]; + if (value > result) { + result = value; + } + } + } else { + iteratee = cb(iteratee, context); + _.each(obj, function(value, index, list) { + computed = iteratee(value, index, list); + if (computed > lastComputed || computed === -Infinity && result === -Infinity) { + result = value; + lastComputed = computed; + } + }); + } + return result; + }; + + // Return the minimum element (or element-based computation). + _.min = function(obj, iteratee, context) { + var result = Infinity, lastComputed = Infinity, + value, computed; + if (iteratee == null && obj != null) { + obj = isArrayLike(obj) ? obj : _.values(obj); + for (var i = 0, length = obj.length; i < length; i++) { + value = obj[i]; + if (value < result) { + result = value; + } + } + } else { + iteratee = cb(iteratee, context); + _.each(obj, function(value, index, list) { + computed = iteratee(value, index, list); + if (computed < lastComputed || computed === Infinity && result === Infinity) { + result = value; + lastComputed = computed; + } + }); + } + return result; + }; + + // Shuffle a collection, using the modern version of the + // [Fisher-Yates shuffle](http://en.wikipedia.org/wiki/Fisher–Yates_shuffle). + _.shuffle = function(obj) { + var set = isArrayLike(obj) ? obj : _.values(obj); + var length = set.length; + var shuffled = Array(length); + for (var index = 0, rand; index < length; index++) { + rand = _.random(0, index); + if (rand !== index) shuffled[index] = shuffled[rand]; + shuffled[rand] = set[index]; + } + return shuffled; + }; + + // Sample **n** random values from a collection. + // If **n** is not specified, returns a single random element. + // The internal `guard` argument allows it to work with `map`. + _.sample = function(obj, n, guard) { + if (n == null || guard) { + if (!isArrayLike(obj)) obj = _.values(obj); + return obj[_.random(obj.length - 1)]; + } + return _.shuffle(obj).slice(0, Math.max(0, n)); + }; + + // Sort the object's values by a criterion produced by an iteratee. + _.sortBy = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + return _.pluck(_.map(obj, function(value, index, list) { + return { + value: value, + index: index, + criteria: iteratee(value, index, list) + }; + }).sort(function(left, right) { + var a = left.criteria; + var b = right.criteria; + if (a !== b) { + if (a > b || a === void 0) return 1; + if (a < b || b === void 0) return -1; + } + return left.index - right.index; + }), 'value'); + }; + + // An internal function used for aggregate "group by" operations. + var group = function(behavior) { + return function(obj, iteratee, context) { + var result = {}; + iteratee = cb(iteratee, context); + _.each(obj, function(value, index) { + var key = iteratee(value, index, obj); + behavior(result, value, key); + }); + return result; + }; + }; + + // Groups the object's values by a criterion. Pass either a string attribute + // to group by, or a function that returns the criterion. + _.groupBy = group(function(result, value, key) { + if (_.has(result, key)) result[key].push(value); else result[key] = [value]; + }); + + // Indexes the object's values by a criterion, similar to `groupBy`, but for + // when you know that your index values will be unique. + _.indexBy = group(function(result, value, key) { + result[key] = value; + }); + + // Counts instances of an object that group by a certain criterion. Pass + // either a string attribute to count by, or a function that returns the + // criterion. + _.countBy = group(function(result, value, key) { + if (_.has(result, key)) result[key]++; else result[key] = 1; + }); + + // Safely create a real, live array from anything iterable. + _.toArray = function(obj) { + if (!obj) return []; + if (_.isArray(obj)) return slice.call(obj); + if (isArrayLike(obj)) return _.map(obj, _.identity); + return _.values(obj); + }; + + // Return the number of elements in an object. + _.size = function(obj) { + if (obj == null) return 0; + return isArrayLike(obj) ? obj.length : _.keys(obj).length; + }; + + // Split a collection into two arrays: one whose elements all satisfy the given + // predicate, and one whose elements all do not satisfy the predicate. + _.partition = function(obj, predicate, context) { + predicate = cb(predicate, context); + var pass = [], fail = []; + _.each(obj, function(value, key, obj) { + (predicate(value, key, obj) ? pass : fail).push(value); + }); + return [pass, fail]; + }; + + // Array Functions + // --------------- + + // Get the first element of an array. Passing **n** will return the first N + // values in the array. Aliased as `head` and `take`. The **guard** check + // allows it to work with `_.map`. + _.first = _.head = _.take = function(array, n, guard) { + if (array == null) return void 0; + if (n == null || guard) return array[0]; + return _.initial(array, array.length - n); + }; + + // Returns everything but the last entry of the array. Especially useful on + // the arguments object. Passing **n** will return all the values in + // the array, excluding the last N. + _.initial = function(array, n, guard) { + return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n))); + }; + + // Get the last element of an array. Passing **n** will return the last N + // values in the array. + _.last = function(array, n, guard) { + if (array == null) return void 0; + if (n == null || guard) return array[array.length - 1]; + return _.rest(array, Math.max(0, array.length - n)); + }; + + // Returns everything but the first entry of the array. Aliased as `tail` and `drop`. + // Especially useful on the arguments object. Passing an **n** will return + // the rest N values in the array. + _.rest = _.tail = _.drop = function(array, n, guard) { + return slice.call(array, n == null || guard ? 1 : n); + }; + + // Trim out all falsy values from an array. + _.compact = function(array) { + return _.filter(array, _.identity); + }; + + // Internal implementation of a recursive `flatten` function. + var flatten = function(input, shallow, strict, startIndex) { + var output = [], idx = 0; + for (var i = startIndex || 0, length = getLength(input); i < length; i++) { + var value = input[i]; + if (isArrayLike(value) && (_.isArray(value) || _.isArguments(value))) { + //flatten current level of array or arguments object + if (!shallow) value = flatten(value, shallow, strict); + var j = 0, len = value.length; + output.length += len; + while (j < len) { + output[idx++] = value[j++]; + } + } else if (!strict) { + output[idx++] = value; + } + } + return output; + }; + + // Flatten out an array, either recursively (by default), or just one level. + _.flatten = function(array, shallow) { + return flatten(array, shallow, false); + }; + + // Return a version of the array that does not contain the specified value(s). + _.without = function(array) { + return _.difference(array, slice.call(arguments, 1)); + }; + + // Produce a duplicate-free version of the array. If the array has already + // been sorted, you have the option of using a faster algorithm. + // Aliased as `unique`. + _.uniq = _.unique = function(array, isSorted, iteratee, context) { + if (!_.isBoolean(isSorted)) { + context = iteratee; + iteratee = isSorted; + isSorted = false; + } + if (iteratee != null) iteratee = cb(iteratee, context); + var result = []; + var seen = []; + for (var i = 0, length = getLength(array); i < length; i++) { + var value = array[i], + computed = iteratee ? iteratee(value, i, array) : value; + if (isSorted) { + if (!i || seen !== computed) result.push(value); + seen = computed; + } else if (iteratee) { + if (!_.contains(seen, computed)) { + seen.push(computed); + result.push(value); + } + } else if (!_.contains(result, value)) { + result.push(value); + } + } + return result; + }; + + // Produce an array that contains the union: each distinct element from all of + // the passed-in arrays. + _.union = function() { + return _.uniq(flatten(arguments, true, true)); + }; + + // Produce an array that contains every item shared between all the + // passed-in arrays. + _.intersection = function(array) { + var result = []; + var argsLength = arguments.length; + for (var i = 0, length = getLength(array); i < length; i++) { + var item = array[i]; + if (_.contains(result, item)) continue; + for (var j = 1; j < argsLength; j++) { + if (!_.contains(arguments[j], item)) break; + } + if (j === argsLength) result.push(item); + } + return result; + }; + + // Take the difference between one array and a number of other arrays. + // Only the elements present in just the first array will remain. + _.difference = function(array) { + var rest = flatten(arguments, true, true, 1); + return _.filter(array, function(value){ + return !_.contains(rest, value); + }); + }; + + // Zip together multiple lists into a single array -- elements that share + // an index go together. + _.zip = function() { + return _.unzip(arguments); + }; + + // Complement of _.zip. Unzip accepts an array of arrays and groups + // each array's elements on shared indices + _.unzip = function(array) { + var length = array && _.max(array, getLength).length || 0; + var result = Array(length); + + for (var index = 0; index < length; index++) { + result[index] = _.pluck(array, index); + } + return result; + }; + + // Converts lists into objects. Pass either a single array of `[key, value]` + // pairs, or two parallel arrays of the same length -- one of keys, and one of + // the corresponding values. + _.object = function(list, values) { + var result = {}; + for (var i = 0, length = getLength(list); i < length; i++) { + if (values) { + result[list[i]] = values[i]; + } else { + result[list[i][0]] = list[i][1]; + } + } + return result; + }; + + // Generator function to create the findIndex and findLastIndex functions + function createPredicateIndexFinder(dir) { + return function(array, predicate, context) { + predicate = cb(predicate, context); + var length = getLength(array); + var index = dir > 0 ? 0 : length - 1; + for (; index >= 0 && index < length; index += dir) { + if (predicate(array[index], index, array)) return index; + } + return -1; + }; + } + + // Returns the first index on an array-like that passes a predicate test + _.findIndex = createPredicateIndexFinder(1); + _.findLastIndex = createPredicateIndexFinder(-1); + + // Use a comparator function to figure out the smallest index at which + // an object should be inserted so as to maintain order. Uses binary search. + _.sortedIndex = function(array, obj, iteratee, context) { + iteratee = cb(iteratee, context, 1); + var value = iteratee(obj); + var low = 0, high = getLength(array); + while (low < high) { + var mid = Math.floor((low + high) / 2); + if (iteratee(array[mid]) < value) low = mid + 1; else high = mid; + } + return low; + }; + + // Generator function to create the indexOf and lastIndexOf functions + function createIndexFinder(dir, predicateFind, sortedIndex) { + return function(array, item, idx) { + var i = 0, length = getLength(array); + if (typeof idx == 'number') { + if (dir > 0) { + i = idx >= 0 ? idx : Math.max(idx + length, i); + } else { + length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1; + } + } else if (sortedIndex && idx && length) { + idx = sortedIndex(array, item); + return array[idx] === item ? idx : -1; + } + if (item !== item) { + idx = predicateFind(slice.call(array, i, length), _.isNaN); + return idx >= 0 ? idx + i : -1; + } + for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) { + if (array[idx] === item) return idx; + } + return -1; + }; + } + + // Return the position of the first occurrence of an item in an array, + // or -1 if the item is not included in the array. + // If the array is large and already in sort order, pass `true` + // for **isSorted** to use binary search. + _.indexOf = createIndexFinder(1, _.findIndex, _.sortedIndex); + _.lastIndexOf = createIndexFinder(-1, _.findLastIndex); + + // Generate an integer Array containing an arithmetic progression. A port of + // the native Python `range()` function. See + // [the Python documentation](http://docs.python.org/library/functions.html#range). + _.range = function(start, stop, step) { + if (stop == null) { + stop = start || 0; + start = 0; + } + step = step || 1; + + var length = Math.max(Math.ceil((stop - start) / step), 0); + var range = Array(length); + + for (var idx = 0; idx < length; idx++, start += step) { + range[idx] = start; + } + + return range; + }; + + // Function (ahem) Functions + // ------------------ + + // Determines whether to execute a function as a constructor + // or a normal function with the provided arguments + var executeBound = function(sourceFunc, boundFunc, context, callingContext, args) { + if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args); + var self = baseCreate(sourceFunc.prototype); + var result = sourceFunc.apply(self, args); + if (_.isObject(result)) return result; + return self; + }; + + // Create a function bound to a given object (assigning `this`, and arguments, + // optionally). Delegates to **ECMAScript 5**'s native `Function.bind` if + // available. + _.bind = function(func, context) { + if (nativeBind && func.bind === nativeBind) return nativeBind.apply(func, slice.call(arguments, 1)); + if (!_.isFunction(func)) throw new TypeError('Bind must be called on a function'); + var args = slice.call(arguments, 2); + var bound = function() { + return executeBound(func, bound, context, this, args.concat(slice.call(arguments))); + }; + return bound; + }; + + // Partially apply a function by creating a version that has had some of its + // arguments pre-filled, without changing its dynamic `this` context. _ acts + // as a placeholder, allowing any combination of arguments to be pre-filled. + _.partial = function(func) { + var boundArgs = slice.call(arguments, 1); + var bound = function() { + var position = 0, length = boundArgs.length; + var args = Array(length); + for (var i = 0; i < length; i++) { + args[i] = boundArgs[i] === _ ? arguments[position++] : boundArgs[i]; + } + while (position < arguments.length) args.push(arguments[position++]); + return executeBound(func, bound, this, this, args); + }; + return bound; + }; + + // Bind a number of an object's methods to that object. Remaining arguments + // are the method names to be bound. Useful for ensuring that all callbacks + // defined on an object belong to it. + _.bindAll = function(obj) { + var i, length = arguments.length, key; + if (length <= 1) throw new Error('bindAll must be passed function names'); + for (i = 1; i < length; i++) { + key = arguments[i]; + obj[key] = _.bind(obj[key], obj); + } + return obj; + }; + + // Memoize an expensive function by storing its results. + _.memoize = function(func, hasher) { + var memoize = function(key) { + var cache = memoize.cache; + var address = '' + (hasher ? hasher.apply(this, arguments) : key); + if (!_.has(cache, address)) cache[address] = func.apply(this, arguments); + return cache[address]; + }; + memoize.cache = {}; + return memoize; + }; + + // Delays a function for the given number of milliseconds, and then calls + // it with the arguments supplied. + _.delay = function(func, wait) { + var args = slice.call(arguments, 2); + return setTimeout(function(){ + return func.apply(null, args); + }, wait); + }; + + // Defers a function, scheduling it to run after the current call stack has + // cleared. + _.defer = _.partial(_.delay, _, 1); + + // Returns a function, that, when invoked, will only be triggered at most once + // during a given window of time. Normally, the throttled function will run + // as much as it can, without ever going more than once per `wait` duration; + // but if you'd like to disable the execution on the leading edge, pass + // `{leading: false}`. To disable execution on the trailing edge, ditto. + _.throttle = function(func, wait, options) { + var context, args, result; + var timeout = null; + var previous = 0; + if (!options) options = {}; + var later = function() { + previous = options.leading === false ? 0 : _.now(); + timeout = null; + result = func.apply(context, args); + if (!timeout) context = args = null; + }; + return function() { + var now = _.now(); + if (!previous && options.leading === false) previous = now; + var remaining = wait - (now - previous); + context = this; + args = arguments; + if (remaining <= 0 || remaining > wait) { + if (timeout) { + clearTimeout(timeout); + timeout = null; + } + previous = now; + result = func.apply(context, args); + if (!timeout) context = args = null; + } else if (!timeout && options.trailing !== false) { + timeout = setTimeout(later, remaining); + } + return result; + }; + }; + + // Returns a function, that, as long as it continues to be invoked, will not + // be triggered. The function will be called after it stops being called for + // N milliseconds. If `immediate` is passed, trigger the function on the + // leading edge, instead of the trailing. + _.debounce = function(func, wait, immediate) { + var timeout, args, context, timestamp, result; + + var later = function() { + var last = _.now() - timestamp; + + if (last < wait && last >= 0) { + timeout = setTimeout(later, wait - last); + } else { + timeout = null; + if (!immediate) { + result = func.apply(context, args); + if (!timeout) context = args = null; + } + } + }; + + return function() { + context = this; + args = arguments; + timestamp = _.now(); + var callNow = immediate && !timeout; + if (!timeout) timeout = setTimeout(later, wait); + if (callNow) { + result = func.apply(context, args); + context = args = null; + } + + return result; + }; + }; + + // Returns the first function passed as an argument to the second, + // allowing you to adjust arguments, run code before and after, and + // conditionally execute the original function. + _.wrap = function(func, wrapper) { + return _.partial(wrapper, func); + }; + + // Returns a negated version of the passed-in predicate. + _.negate = function(predicate) { + return function() { + return !predicate.apply(this, arguments); + }; + }; + + // Returns a function that is the composition of a list of functions, each + // consuming the return value of the function that follows. + _.compose = function() { + var args = arguments; + var start = args.length - 1; + return function() { + var i = start; + var result = args[start].apply(this, arguments); + while (i--) result = args[i].call(this, result); + return result; + }; + }; + + // Returns a function that will only be executed on and after the Nth call. + _.after = function(times, func) { + return function() { + if (--times < 1) { + return func.apply(this, arguments); + } + }; + }; + + // Returns a function that will only be executed up to (but not including) the Nth call. + _.before = function(times, func) { + var memo; + return function() { + if (--times > 0) { + memo = func.apply(this, arguments); + } + if (times <= 1) func = null; + return memo; + }; + }; + + // Returns a function that will be executed at most one time, no matter how + // often you call it. Useful for lazy initialization. + _.once = _.partial(_.before, 2); + + // Object Functions + // ---------------- + + // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed. + var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString'); + var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString', + 'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString']; + + function collectNonEnumProps(obj, keys) { + var nonEnumIdx = nonEnumerableProps.length; + var constructor = obj.constructor; + var proto = (_.isFunction(constructor) && constructor.prototype) || ObjProto; + + // Constructor is a special case. + var prop = 'constructor'; + if (_.has(obj, prop) && !_.contains(keys, prop)) keys.push(prop); + + while (nonEnumIdx--) { + prop = nonEnumerableProps[nonEnumIdx]; + if (prop in obj && obj[prop] !== proto[prop] && !_.contains(keys, prop)) { + keys.push(prop); + } + } + } + + // Retrieve the names of an object's own properties. + // Delegates to **ECMAScript 5**'s native `Object.keys` + _.keys = function(obj) { + if (!_.isObject(obj)) return []; + if (nativeKeys) return nativeKeys(obj); + var keys = []; + for (var key in obj) if (_.has(obj, key)) keys.push(key); + // Ahem, IE < 9. + if (hasEnumBug) collectNonEnumProps(obj, keys); + return keys; + }; + + // Retrieve all the property names of an object. + _.allKeys = function(obj) { + if (!_.isObject(obj)) return []; + var keys = []; + for (var key in obj) keys.push(key); + // Ahem, IE < 9. + if (hasEnumBug) collectNonEnumProps(obj, keys); + return keys; + }; + + // Retrieve the values of an object's properties. + _.values = function(obj) { + var keys = _.keys(obj); + var length = keys.length; + var values = Array(length); + for (var i = 0; i < length; i++) { + values[i] = obj[keys[i]]; + } + return values; + }; + + // Returns the results of applying the iteratee to each element of the object + // In contrast to _.map it returns an object + _.mapObject = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + var keys = _.keys(obj), + length = keys.length, + results = {}, + currentKey; + for (var index = 0; index < length; index++) { + currentKey = keys[index]; + results[currentKey] = iteratee(obj[currentKey], currentKey, obj); + } + return results; + }; + + // Convert an object into a list of `[key, value]` pairs. + _.pairs = function(obj) { + var keys = _.keys(obj); + var length = keys.length; + var pairs = Array(length); + for (var i = 0; i < length; i++) { + pairs[i] = [keys[i], obj[keys[i]]]; + } + return pairs; + }; + + // Invert the keys and values of an object. The values must be serializable. + _.invert = function(obj) { + var result = {}; + var keys = _.keys(obj); + for (var i = 0, length = keys.length; i < length; i++) { + result[obj[keys[i]]] = keys[i]; + } + return result; + }; + + // Return a sorted list of the function names available on the object. + // Aliased as `methods` + _.functions = _.methods = function(obj) { + var names = []; + for (var key in obj) { + if (_.isFunction(obj[key])) names.push(key); + } + return names.sort(); + }; + + // Extend a given object with all the properties in passed-in object(s). + _.extend = createAssigner(_.allKeys); + + // Assigns a given object with all the own properties in the passed-in object(s) + // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign) + _.extendOwn = _.assign = createAssigner(_.keys); + + // Returns the first key on an object that passes a predicate test + _.findKey = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = _.keys(obj), key; + for (var i = 0, length = keys.length; i < length; i++) { + key = keys[i]; + if (predicate(obj[key], key, obj)) return key; + } + }; + + // Return a copy of the object only containing the whitelisted properties. + _.pick = function(object, oiteratee, context) { + var result = {}, obj = object, iteratee, keys; + if (obj == null) return result; + if (_.isFunction(oiteratee)) { + keys = _.allKeys(obj); + iteratee = optimizeCb(oiteratee, context); + } else { + keys = flatten(arguments, false, false, 1); + iteratee = function(value, key, obj) { return key in obj; }; + obj = Object(obj); + } + for (var i = 0, length = keys.length; i < length; i++) { + var key = keys[i]; + var value = obj[key]; + if (iteratee(value, key, obj)) result[key] = value; + } + return result; + }; + + // Return a copy of the object without the blacklisted properties. + _.omit = function(obj, iteratee, context) { + if (_.isFunction(iteratee)) { + iteratee = _.negate(iteratee); + } else { + var keys = _.map(flatten(arguments, false, false, 1), String); + iteratee = function(value, key) { + return !_.contains(keys, key); + }; + } + return _.pick(obj, iteratee, context); + }; + + // Fill in a given object with default properties. + _.defaults = createAssigner(_.allKeys, true); + + // Creates an object that inherits from the given prototype object. + // If additional properties are provided then they will be added to the + // created object. + _.create = function(prototype, props) { + var result = baseCreate(prototype); + if (props) _.extendOwn(result, props); + return result; + }; + + // Create a (shallow-cloned) duplicate of an object. + _.clone = function(obj) { + if (!_.isObject(obj)) return obj; + return _.isArray(obj) ? obj.slice() : _.extend({}, obj); + }; + + // Invokes interceptor with the obj, and then returns obj. + // The primary purpose of this method is to "tap into" a method chain, in + // order to perform operations on intermediate results within the chain. + _.tap = function(obj, interceptor) { + interceptor(obj); + return obj; + }; + + // Returns whether an object has a given set of `key:value` pairs. + _.isMatch = function(object, attrs) { + var keys = _.keys(attrs), length = keys.length; + if (object == null) return !length; + var obj = Object(object); + for (var i = 0; i < length; i++) { + var key = keys[i]; + if (attrs[key] !== obj[key] || !(key in obj)) return false; + } + return true; + }; + + + // Internal recursive comparison function for `isEqual`. + var eq = function(a, b, aStack, bStack) { + // Identical objects are equal. `0 === -0`, but they aren't identical. + // See the [Harmony `egal` proposal](http://wiki.ecmascript.org/doku.php?id=harmony:egal). + if (a === b) return a !== 0 || 1 / a === 1 / b; + // A strict comparison is necessary because `null == undefined`. + if (a == null || b == null) return a === b; + // Unwrap any wrapped objects. + if (a instanceof _) a = a._wrapped; + if (b instanceof _) b = b._wrapped; + // Compare `[[Class]]` names. + var className = toString.call(a); + if (className !== toString.call(b)) return false; + switch (className) { + // Strings, numbers, regular expressions, dates, and booleans are compared by value. + case '[object RegExp]': + // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i') + case '[object String]': + // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is + // equivalent to `new String("5")`. + return '' + a === '' + b; + case '[object Number]': + // `NaN`s are equivalent, but non-reflexive. + // Object(NaN) is equivalent to NaN + if (+a !== +a) return +b !== +b; + // An `egal` comparison is performed for other numeric values. + return +a === 0 ? 1 / +a === 1 / b : +a === +b; + case '[object Date]': + case '[object Boolean]': + // Coerce dates and booleans to numeric primitive values. Dates are compared by their + // millisecond representations. Note that invalid dates with millisecond representations + // of `NaN` are not equivalent. + return +a === +b; + } + + var areArrays = className === '[object Array]'; + if (!areArrays) { + if (typeof a != 'object' || typeof b != 'object') return false; + + // Objects with different constructors are not equivalent, but `Object`s or `Array`s + // from different frames are. + var aCtor = a.constructor, bCtor = b.constructor; + if (aCtor !== bCtor && !(_.isFunction(aCtor) && aCtor instanceof aCtor && + _.isFunction(bCtor) && bCtor instanceof bCtor) + && ('constructor' in a && 'constructor' in b)) { + return false; + } + } + // Assume equality for cyclic structures. The algorithm for detecting cyclic + // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`. + + // Initializing stack of traversed objects. + // It's done here since we only need them for objects and arrays comparison. + aStack = aStack || []; + bStack = bStack || []; + var length = aStack.length; + while (length--) { + // Linear search. Performance is inversely proportional to the number of + // unique nested structures. + if (aStack[length] === a) return bStack[length] === b; + } + + // Add the first object to the stack of traversed objects. + aStack.push(a); + bStack.push(b); + + // Recursively compare objects and arrays. + if (areArrays) { + // Compare array lengths to determine if a deep comparison is necessary. + length = a.length; + if (length !== b.length) return false; + // Deep compare the contents, ignoring non-numeric properties. + while (length--) { + if (!eq(a[length], b[length], aStack, bStack)) return false; + } + } else { + // Deep compare objects. + var keys = _.keys(a), key; + length = keys.length; + // Ensure that both objects contain the same number of properties before comparing deep equality. + if (_.keys(b).length !== length) return false; + while (length--) { + // Deep compare each member + key = keys[length]; + if (!(_.has(b, key) && eq(a[key], b[key], aStack, bStack))) return false; + } + } + // Remove the first object from the stack of traversed objects. + aStack.pop(); + bStack.pop(); + return true; + }; + + // Perform a deep comparison to check if two objects are equal. + _.isEqual = function(a, b) { + return eq(a, b); + }; + + // Is a given array, string, or object empty? + // An "empty" object has no enumerable own-properties. + _.isEmpty = function(obj) { + if (obj == null) return true; + if (isArrayLike(obj) && (_.isArray(obj) || _.isString(obj) || _.isArguments(obj))) return obj.length === 0; + return _.keys(obj).length === 0; + }; + + // Is a given value a DOM element? + _.isElement = function(obj) { + return !!(obj && obj.nodeType === 1); + }; + + // Is a given value an array? + // Delegates to ECMA5's native Array.isArray + _.isArray = nativeIsArray || function(obj) { + return toString.call(obj) === '[object Array]'; + }; + + // Is a given variable an object? + _.isObject = function(obj) { + var type = typeof obj; + return type === 'function' || type === 'object' && !!obj; + }; + + // Add some isType methods: isArguments, isFunction, isString, isNumber, isDate, isRegExp, isError. + _.each(['Arguments', 'Function', 'String', 'Number', 'Date', 'RegExp', 'Error'], function(name) { + _['is' + name] = function(obj) { + return toString.call(obj) === '[object ' + name + ']'; + }; + }); + + // Define a fallback version of the method in browsers (ahem, IE < 9), where + // there isn't any inspectable "Arguments" type. + if (!_.isArguments(arguments)) { + _.isArguments = function(obj) { + return _.has(obj, 'callee'); + }; + } + + // Optimize `isFunction` if appropriate. Work around some typeof bugs in old v8, + // IE 11 (#1621), and in Safari 8 (#1929). + if (typeof /./ != 'function' && typeof Int8Array != 'object') { + _.isFunction = function(obj) { + return typeof obj == 'function' || false; + }; + } + + // Is a given object a finite number? + _.isFinite = function(obj) { + return isFinite(obj) && !isNaN(parseFloat(obj)); + }; + + // Is the given value `NaN`? (NaN is the only number which does not equal itself). + _.isNaN = function(obj) { + return _.isNumber(obj) && obj !== +obj; + }; + + // Is a given value a boolean? + _.isBoolean = function(obj) { + return obj === true || obj === false || toString.call(obj) === '[object Boolean]'; + }; + + // Is a given value equal to null? + _.isNull = function(obj) { + return obj === null; + }; + + // Is a given variable undefined? + _.isUndefined = function(obj) { + return obj === void 0; + }; + + // Shortcut function for checking if an object has a given property directly + // on itself (in other words, not on a prototype). + _.has = function(obj, key) { + return obj != null && hasOwnProperty.call(obj, key); + }; + + // Utility Functions + // ----------------- + + // Run Underscore.js in *noConflict* mode, returning the `_` variable to its + // previous owner. Returns a reference to the Underscore object. + _.noConflict = function() { + root._ = previousUnderscore; + return this; + }; + + // Keep the identity function around for default iteratees. + _.identity = function(value) { + return value; + }; + + // Predicate-generating functions. Often useful outside of Underscore. + _.constant = function(value) { + return function() { + return value; + }; + }; + + _.noop = function(){}; + + _.property = property; + + // Generates a function for a given object that returns a given property. + _.propertyOf = function(obj) { + return obj == null ? function(){} : function(key) { + return obj[key]; + }; + }; + + // Returns a predicate for checking whether an object has a given set of + // `key:value` pairs. + _.matcher = _.matches = function(attrs) { + attrs = _.extendOwn({}, attrs); + return function(obj) { + return _.isMatch(obj, attrs); + }; + }; + + // Run a function **n** times. + _.times = function(n, iteratee, context) { + var accum = Array(Math.max(0, n)); + iteratee = optimizeCb(iteratee, context, 1); + for (var i = 0; i < n; i++) accum[i] = iteratee(i); + return accum; + }; + + // Return a random integer between min and max (inclusive). + _.random = function(min, max) { + if (max == null) { + max = min; + min = 0; + } + return min + Math.floor(Math.random() * (max - min + 1)); + }; + + // A (possibly faster) way to get the current timestamp as an integer. + _.now = Date.now || function() { + return new Date().getTime(); + }; + + // List of HTML entities for escaping. + var escapeMap = { + '&': '&', + '<': '<', + '>': '>', + '"': '"', + "'": ''', + '`': '`' + }; + var unescapeMap = _.invert(escapeMap); + + // Functions for escaping and unescaping strings to/from HTML interpolation. + var createEscaper = function(map) { + var escaper = function(match) { + return map[match]; + }; + // Regexes for identifying a key that needs to be escaped + var source = '(?:' + _.keys(map).join('|') + ')'; + var testRegexp = RegExp(source); + var replaceRegexp = RegExp(source, 'g'); + return function(string) { + string = string == null ? '' : '' + string; + return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string; + }; + }; + _.escape = createEscaper(escapeMap); + _.unescape = createEscaper(unescapeMap); + + // If the value of the named `property` is a function then invoke it with the + // `object` as context; otherwise, return it. + _.result = function(object, property, fallback) { + var value = object == null ? void 0 : object[property]; + if (value === void 0) { + value = fallback; + } + return _.isFunction(value) ? value.call(object) : value; + }; + + // Generate a unique integer id (unique within the entire client session). + // Useful for temporary DOM ids. + var idCounter = 0; + _.uniqueId = function(prefix) { + var id = ++idCounter + ''; + return prefix ? prefix + id : id; + }; + + // By default, Underscore uses ERB-style template delimiters, change the + // following template settings to use alternative delimiters. + _.templateSettings = { + evaluate : /<%([\s\S]+?)%>/g, + interpolate : /<%=([\s\S]+?)%>/g, + escape : /<%-([\s\S]+?)%>/g + }; + + // When customizing `templateSettings`, if you don't want to define an + // interpolation, evaluation or escaping regex, we need one that is + // guaranteed not to match. + var noMatch = /(.)^/; + + // Certain characters need to be escaped so that they can be put into a + // string literal. + var escapes = { + "'": "'", + '\\': '\\', + '\r': 'r', + '\n': 'n', + '\u2028': 'u2028', + '\u2029': 'u2029' + }; + + var escaper = /\\|'|\r|\n|\u2028|\u2029/g; + + var escapeChar = function(match) { + return '\\' + escapes[match]; + }; + + // JavaScript micro-templating, similar to John Resig's implementation. + // Underscore templating handles arbitrary delimiters, preserves whitespace, + // and correctly escapes quotes within interpolated code. + // NB: `oldSettings` only exists for backwards compatibility. + _.template = function(text, settings, oldSettings) { + if (!settings && oldSettings) settings = oldSettings; + settings = _.defaults({}, settings, _.templateSettings); + + // Combine delimiters into one regular expression via alternation. + var matcher = RegExp([ + (settings.escape || noMatch).source, + (settings.interpolate || noMatch).source, + (settings.evaluate || noMatch).source + ].join('|') + '|$', 'g'); + + // Compile the template source, escaping string literals appropriately. + var index = 0; + var source = "__p+='"; + text.replace(matcher, function(match, escape, interpolate, evaluate, offset) { + source += text.slice(index, offset).replace(escaper, escapeChar); + index = offset + match.length; + + if (escape) { + source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'"; + } else if (interpolate) { + source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'"; + } else if (evaluate) { + source += "';\n" + evaluate + "\n__p+='"; + } + + // Adobe VMs need the match returned to produce the correct offest. + return match; + }); + source += "';\n"; + + // If a variable is not specified, place data values in local scope. + if (!settings.variable) source = 'with(obj||{}){\n' + source + '}\n'; + + source = "var __t,__p='',__j=Array.prototype.join," + + "print=function(){__p+=__j.call(arguments,'');};\n" + + source + 'return __p;\n'; + + try { + var render = new Function(settings.variable || 'obj', '_', source); + } catch (e) { + e.source = source; + throw e; + } + + var template = function(data) { + return render.call(this, data, _); + }; + + // Provide the compiled source as a convenience for precompilation. + var argument = settings.variable || 'obj'; + template.source = 'function(' + argument + '){\n' + source + '}'; + + return template; + }; + + // Add a "chain" function. Start chaining a wrapped Underscore object. + _.chain = function(obj) { + var instance = _(obj); + instance._chain = true; + return instance; + }; + + // OOP + // --------------- + // If Underscore is called as a function, it returns a wrapped object that + // can be used OO-style. This wrapper holds altered versions of all the + // underscore functions. Wrapped objects may be chained. + + // Helper function to continue chaining intermediate results. + var result = function(instance, obj) { + return instance._chain ? _(obj).chain() : obj; + }; + + // Add your own custom functions to the Underscore object. + _.mixin = function(obj) { + _.each(_.functions(obj), function(name) { + var func = _[name] = obj[name]; + _.prototype[name] = function() { + var args = [this._wrapped]; + push.apply(args, arguments); + return result(this, func.apply(_, args)); + }; + }); + }; + + // Add all of the Underscore functions to the wrapper object. + _.mixin(_); + + // Add all mutator Array functions to the wrapper. + _.each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) { + var method = ArrayProto[name]; + _.prototype[name] = function() { + var obj = this._wrapped; + method.apply(obj, arguments); + if ((name === 'shift' || name === 'splice') && obj.length === 0) delete obj[0]; + return result(this, obj); + }; + }); + + // Add all accessor Array functions to the wrapper. + _.each(['concat', 'join', 'slice'], function(name) { + var method = ArrayProto[name]; + _.prototype[name] = function() { + return result(this, method.apply(this._wrapped, arguments)); + }; + }); + + // Extracts the result from a wrapped and chained object. + _.prototype.value = function() { + return this._wrapped; + }; + + // Provide unwrapping proxy for some methods used in engine operations + // such as arithmetic and JSON stringification. + _.prototype.valueOf = _.prototype.toJSON = _.prototype.value; + + _.prototype.toString = function() { + return '' + this._wrapped; + }; + + // AMD registration happens at the end for compatibility with AMD loaders + // that may not enforce next-turn semantics on modules. Even though general + // practice for AMD registration is to be anonymous, underscore registers + // as a named module because, like jQuery, it is a base library that is + // popular enough to be bundled in a third party lib, but not be part of + // an AMD load request. Those cases could generate an error when an + // anonymous define() is called outside of a loader request. + if (typeof define === 'function' && define.amd) { + define('underscore', [], function() { + return _; + }); + } +}.call(this)); + +},{}],26:[function(require,module,exports){ +arguments[4][19][0].apply(exports,arguments) +},{"dup":19}],27:[function(require,module,exports){ +module.exports = function isBuffer(arg) { + return arg && typeof arg === 'object' + && typeof arg.copy === 'function' + && typeof arg.fill === 'function' + && typeof arg.readUInt8 === 'function'; +} +},{}],28:[function(require,module,exports){ +(function (process,global){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +var formatRegExp = /%[sdj%]/g; +exports.format = function(f) { + if (!isString(f)) { + var objects = []; + for (var i = 0; i < arguments.length; i++) { + objects.push(inspect(arguments[i])); + } + return objects.join(' '); + } + + var i = 1; + var args = arguments; + var len = args.length; + var str = String(f).replace(formatRegExp, function(x) { + if (x === '%%') return '%'; + if (i >= len) return x; + switch (x) { + case '%s': return String(args[i++]); + case '%d': return Number(args[i++]); + case '%j': + try { + return JSON.stringify(args[i++]); + } catch (_) { + return '[Circular]'; + } + default: + return x; + } + }); + for (var x = args[i]; i < len; x = args[++i]) { + if (isNull(x) || !isObject(x)) { + str += ' ' + x; + } else { + str += ' ' + inspect(x); + } + } + return str; +}; + + +// Mark that a method should not be used. +// Returns a modified function which warns once by default. +// If --no-deprecation is set, then it is a no-op. +exports.deprecate = function(fn, msg) { + // Allow for deprecating things in the process of starting up. + if (isUndefined(global.process)) { + return function() { + return exports.deprecate(fn, msg).apply(this, arguments); + }; + } + + if (process.noDeprecation === true) { + return fn; + } + + var warned = false; + function deprecated() { + if (!warned) { + if (process.throwDeprecation) { + throw new Error(msg); + } else if (process.traceDeprecation) { + console.trace(msg); + } else { + console.error(msg); + } + warned = true; + } + return fn.apply(this, arguments); + } + + return deprecated; +}; + + +var debugs = {}; +var debugEnviron; +exports.debuglog = function(set) { + if (isUndefined(debugEnviron)) + debugEnviron = process.env.NODE_DEBUG || ''; + set = set.toUpperCase(); + if (!debugs[set]) { + if (new RegExp('\\b' + set + '\\b', 'i').test(debugEnviron)) { + var pid = process.pid; + debugs[set] = function() { + var msg = exports.format.apply(exports, arguments); + console.error('%s %d: %s', set, pid, msg); + }; + } else { + debugs[set] = function() {}; + } + } + return debugs[set]; +}; + + +/** + * Echos the value of a value. Trys to print the value out + * in the best way possible given the different types. + * + * @param {Object} obj The object to print out. + * @param {Object} opts Optional options object that alters the output. + */ +/* legacy: obj, showHidden, depth, colors*/ +function inspect(obj, opts) { + // default options + var ctx = { + seen: [], + stylize: stylizeNoColor + }; + // legacy... + if (arguments.length >= 3) ctx.depth = arguments[2]; + if (arguments.length >= 4) ctx.colors = arguments[3]; + if (isBoolean(opts)) { + // legacy... + ctx.showHidden = opts; + } else if (opts) { + // got an "options" object + exports._extend(ctx, opts); + } + // set default options + if (isUndefined(ctx.showHidden)) ctx.showHidden = false; + if (isUndefined(ctx.depth)) ctx.depth = 2; + if (isUndefined(ctx.colors)) ctx.colors = false; + if (isUndefined(ctx.customInspect)) ctx.customInspect = true; + if (ctx.colors) ctx.stylize = stylizeWithColor; + return formatValue(ctx, obj, ctx.depth); +} +exports.inspect = inspect; + + +// http://en.wikipedia.org/wiki/ANSI_escape_code#graphics +inspect.colors = { + 'bold' : [1, 22], + 'italic' : [3, 23], + 'underline' : [4, 24], + 'inverse' : [7, 27], + 'white' : [37, 39], + 'grey' : [90, 39], + 'black' : [30, 39], + 'blue' : [34, 39], + 'cyan' : [36, 39], + 'green' : [32, 39], + 'magenta' : [35, 39], + 'red' : [31, 39], + 'yellow' : [33, 39] +}; + +// Don't use 'blue' not visible on cmd.exe +inspect.styles = { + 'special': 'cyan', + 'number': 'yellow', + 'boolean': 'yellow', + 'undefined': 'grey', + 'null': 'bold', + 'string': 'green', + 'date': 'magenta', + // "name": intentionally not styling + 'regexp': 'red' +}; + + +function stylizeWithColor(str, styleType) { + var style = inspect.styles[styleType]; + + if (style) { + return '\u001b[' + inspect.colors[style][0] + 'm' + str + + '\u001b[' + inspect.colors[style][1] + 'm'; + } else { + return str; + } +} + + +function stylizeNoColor(str, styleType) { + return str; +} + + +function arrayToHash(array) { + var hash = {}; + + array.forEach(function(val, idx) { + hash[val] = true; + }); + + return hash; +} + + +function formatValue(ctx, value, recurseTimes) { + // Provide a hook for user-specified inspect functions. + // Check that value is an object with an inspect function on it + if (ctx.customInspect && + value && + isFunction(value.inspect) && + // Filter out the util module, it's inspect function is special + value.inspect !== exports.inspect && + // Also filter out any prototype objects using the circular check. + !(value.constructor && value.constructor.prototype === value)) { + var ret = value.inspect(recurseTimes, ctx); + if (!isString(ret)) { + ret = formatValue(ctx, ret, recurseTimes); + } + return ret; + } + + // Primitive types cannot have properties + var primitive = formatPrimitive(ctx, value); + if (primitive) { + return primitive; + } + + // Look up the keys of the object. + var keys = Object.keys(value); + var visibleKeys = arrayToHash(keys); + + if (ctx.showHidden) { + keys = Object.getOwnPropertyNames(value); + } + + // IE doesn't make error fields non-enumerable + // http://msdn.microsoft.com/en-us/library/ie/dww52sbt(v=vs.94).aspx + if (isError(value) + && (keys.indexOf('message') >= 0 || keys.indexOf('description') >= 0)) { + return formatError(value); + } + + // Some type of object without properties can be shortcutted. + if (keys.length === 0) { + if (isFunction(value)) { + var name = value.name ? ': ' + value.name : ''; + return ctx.stylize('[Function' + name + ']', 'special'); + } + if (isRegExp(value)) { + return ctx.stylize(RegExp.prototype.toString.call(value), 'regexp'); + } + if (isDate(value)) { + return ctx.stylize(Date.prototype.toString.call(value), 'date'); + } + if (isError(value)) { + return formatError(value); + } + } + + var base = '', array = false, braces = ['{', '}']; + + // Make Array say that they are Array + if (isArray(value)) { + array = true; + braces = ['[', ']']; + } + + // Make functions say that they are functions + if (isFunction(value)) { + var n = value.name ? ': ' + value.name : ''; + base = ' [Function' + n + ']'; + } + + // Make RegExps say that they are RegExps + if (isRegExp(value)) { + base = ' ' + RegExp.prototype.toString.call(value); + } + + // Make dates with properties first say the date + if (isDate(value)) { + base = ' ' + Date.prototype.toUTCString.call(value); + } + + // Make error with message first say the error + if (isError(value)) { + base = ' ' + formatError(value); + } + + if (keys.length === 0 && (!array || value.length == 0)) { + return braces[0] + base + braces[1]; + } + + if (recurseTimes < 0) { + if (isRegExp(value)) { + return ctx.stylize(RegExp.prototype.toString.call(value), 'regexp'); + } else { + return ctx.stylize('[Object]', 'special'); + } + } + + ctx.seen.push(value); + + var output; + if (array) { + output = formatArray(ctx, value, recurseTimes, visibleKeys, keys); + } else { + output = keys.map(function(key) { + return formatProperty(ctx, value, recurseTimes, visibleKeys, key, array); + }); + } + + ctx.seen.pop(); + + return reduceToSingleString(output, base, braces); +} + + +function formatPrimitive(ctx, value) { + if (isUndefined(value)) + return ctx.stylize('undefined', 'undefined'); + if (isString(value)) { + var simple = '\'' + JSON.stringify(value).replace(/^"|"$/g, '') + .replace(/'/g, "\\'") + .replace(/\\"/g, '"') + '\''; + return ctx.stylize(simple, 'string'); + } + if (isNumber(value)) + return ctx.stylize('' + value, 'number'); + if (isBoolean(value)) + return ctx.stylize('' + value, 'boolean'); + // For some reason typeof null is "object", so special case here. + if (isNull(value)) + return ctx.stylize('null', 'null'); +} + + +function formatError(value) { + return '[' + Error.prototype.toString.call(value) + ']'; +} + + +function formatArray(ctx, value, recurseTimes, visibleKeys, keys) { + var output = []; + for (var i = 0, l = value.length; i < l; ++i) { + if (hasOwnProperty(value, String(i))) { + output.push(formatProperty(ctx, value, recurseTimes, visibleKeys, + String(i), true)); + } else { + output.push(''); + } + } + keys.forEach(function(key) { + if (!key.match(/^\d+$/)) { + output.push(formatProperty(ctx, value, recurseTimes, visibleKeys, + key, true)); + } + }); + return output; +} + + +function formatProperty(ctx, value, recurseTimes, visibleKeys, key, array) { + var name, str, desc; + desc = Object.getOwnPropertyDescriptor(value, key) || { value: value[key] }; + if (desc.get) { + if (desc.set) { + str = ctx.stylize('[Getter/Setter]', 'special'); + } else { + str = ctx.stylize('[Getter]', 'special'); + } + } else { + if (desc.set) { + str = ctx.stylize('[Setter]', 'special'); + } + } + if (!hasOwnProperty(visibleKeys, key)) { + name = '[' + key + ']'; + } + if (!str) { + if (ctx.seen.indexOf(desc.value) < 0) { + if (isNull(recurseTimes)) { + str = formatValue(ctx, desc.value, null); + } else { + str = formatValue(ctx, desc.value, recurseTimes - 1); + } + if (str.indexOf('\n') > -1) { + if (array) { + str = str.split('\n').map(function(line) { + return ' ' + line; + }).join('\n').substr(2); + } else { + str = '\n' + str.split('\n').map(function(line) { + return ' ' + line; + }).join('\n'); + } + } + } else { + str = ctx.stylize('[Circular]', 'special'); + } + } + if (isUndefined(name)) { + if (array && key.match(/^\d+$/)) { + return str; + } + name = JSON.stringify('' + key); + if (name.match(/^"([a-zA-Z_][a-zA-Z_0-9]*)"$/)) { + name = name.substr(1, name.length - 2); + name = ctx.stylize(name, 'name'); + } else { + name = name.replace(/'/g, "\\'") + .replace(/\\"/g, '"') + .replace(/(^"|"$)/g, "'"); + name = ctx.stylize(name, 'string'); + } + } + + return name + ': ' + str; +} + + +function reduceToSingleString(output, base, braces) { + var numLinesEst = 0; + var length = output.reduce(function(prev, cur) { + numLinesEst++; + if (cur.indexOf('\n') >= 0) numLinesEst++; + return prev + cur.replace(/\u001b\[\d\d?m/g, '').length + 1; + }, 0); + + if (length > 60) { + return braces[0] + + (base === '' ? '' : base + '\n ') + + ' ' + + output.join(',\n ') + + ' ' + + braces[1]; + } + + return braces[0] + base + ' ' + output.join(', ') + ' ' + braces[1]; +} + + +// NOTE: These type checking functions intentionally don't use `instanceof` +// because it is fragile and can be easily faked with `Object.create()`. +function isArray(ar) { + return Array.isArray(ar); +} +exports.isArray = isArray; + +function isBoolean(arg) { + return typeof arg === 'boolean'; +} +exports.isBoolean = isBoolean; + +function isNull(arg) { + return arg === null; +} +exports.isNull = isNull; + +function isNullOrUndefined(arg) { + return arg == null; +} +exports.isNullOrUndefined = isNullOrUndefined; + +function isNumber(arg) { + return typeof arg === 'number'; +} +exports.isNumber = isNumber; + +function isString(arg) { + return typeof arg === 'string'; +} +exports.isString = isString; + +function isSymbol(arg) { + return typeof arg === 'symbol'; +} +exports.isSymbol = isSymbol; + +function isUndefined(arg) { + return arg === void 0; +} +exports.isUndefined = isUndefined; + +function isRegExp(re) { + return isObject(re) && objectToString(re) === '[object RegExp]'; +} +exports.isRegExp = isRegExp; + +function isObject(arg) { + return typeof arg === 'object' && arg !== null; +} +exports.isObject = isObject; + +function isDate(d) { + return isObject(d) && objectToString(d) === '[object Date]'; +} +exports.isDate = isDate; + +function isError(e) { + return isObject(e) && + (objectToString(e) === '[object Error]' || e instanceof Error); +} +exports.isError = isError; + +function isFunction(arg) { + return typeof arg === 'function'; +} +exports.isFunction = isFunction; + +function isPrimitive(arg) { + return arg === null || + typeof arg === 'boolean' || + typeof arg === 'number' || + typeof arg === 'string' || + typeof arg === 'symbol' || // ES6 symbol + typeof arg === 'undefined'; +} +exports.isPrimitive = isPrimitive; + +exports.isBuffer = require('./support/isBuffer'); + +function objectToString(o) { + return Object.prototype.toString.call(o); +} + + +function pad(n) { + return n < 10 ? '0' + n.toString(10) : n.toString(10); +} + + +var months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', + 'Oct', 'Nov', 'Dec']; + +// 26 Feb 16:19:34 +function timestamp() { + var d = new Date(); + var time = [pad(d.getHours()), + pad(d.getMinutes()), + pad(d.getSeconds())].join(':'); + return [d.getDate(), months[d.getMonth()], time].join(' '); +} + + +// log is just a thin wrapper to console.log that prepends a timestamp +exports.log = function() { + console.log('%s - %s', timestamp(), exports.format.apply(exports, arguments)); +}; + + +/** + * Inherit the prototype methods from one constructor into another. + * + * The Function.prototype.inherits from lang.js rewritten as a standalone + * function (not on Function.prototype). NOTE: If this file is to be loaded + * during bootstrapping this function needs to be rewritten using some native + * functions as prototype setup using normal JavaScript does not work as + * expected during bootstrapping (see mirror.js in r114903). + * + * @param {function} ctor Constructor function which needs to inherit the + * prototype. + * @param {function} superCtor Constructor function to inherit prototype from. + */ +exports.inherits = require('inherits'); + +exports._extend = function(origin, add) { + // Don't do anything if add isn't an object + if (!add || !isObject(add)) return origin; + + var keys = Object.keys(add); + var i = keys.length; + while (i--) { + origin[keys[i]] = add[keys[i]]; + } + return origin; +}; + +function hasOwnProperty(obj, prop) { + return Object.prototype.hasOwnProperty.call(obj, prop); +} + +}).call(this,require('_process'),typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {}) +},{"./support/isBuffer":27,"_process":24,"inherits":26}],29:[function(require,module,exports){ +// Returns a wrapper function that returns a wrapped callback +// The wrapper function should do some stuff, and return a +// presumably different callback function. +// This makes sure that own properties are retained, so that +// decorations and such are not lost along the way. +module.exports = wrappy +function wrappy (fn, cb) { + if (fn && cb) return wrappy(fn)(cb) + + if (typeof fn !== 'function') + throw new TypeError('need wrapper function') + + Object.keys(fn).forEach(function (k) { + wrapper[k] = fn[k] + }) + + return wrapper + + function wrapper() { + var args = new Array(arguments.length) + for (var i = 0; i < args.length; i++) { + args[i] = arguments[i] + } + var ret = fn.apply(this, args) + var cb = args[args.length-1] + if (typeof ret === 'function' && ret !== cb) { + Object.keys(cb).forEach(function (k) { + ret[k] = cb[k] + }) + } + return ret + } +} + +},{}]},{},[7])(7) +}); \ No newline at end of file diff --git a/main/assets/javascripts/workers/search.2a1c317c.min.js b/main/assets/javascripts/workers/search.2a1c317c.min.js new file mode 100644 index 00000000..59bf8384 --- /dev/null +++ b/main/assets/javascripts/workers/search.2a1c317c.min.js @@ -0,0 +1,48 @@ +(()=>{var ge=Object.create;var W=Object.defineProperty,ye=Object.defineProperties,me=Object.getOwnPropertyDescriptor,ve=Object.getOwnPropertyDescriptors,xe=Object.getOwnPropertyNames,G=Object.getOwnPropertySymbols,Se=Object.getPrototypeOf,X=Object.prototype.hasOwnProperty,Qe=Object.prototype.propertyIsEnumerable;var J=(t,e,r)=>e in t?W(t,e,{enumerable:!0,configurable:!0,writable:!0,value:r}):t[e]=r,M=(t,e)=>{for(var r in e||(e={}))X.call(e,r)&&J(t,r,e[r]);if(G)for(var r of G(e))Qe.call(e,r)&&J(t,r,e[r]);return t},Z=(t,e)=>ye(t,ve(e));var K=(t,e)=>()=>(e||t((e={exports:{}}).exports,e),e.exports);var be=(t,e,r,n)=>{if(e&&typeof e=="object"||typeof e=="function")for(let i of xe(e))!X.call(t,i)&&i!==r&&W(t,i,{get:()=>e[i],enumerable:!(n=me(e,i))||n.enumerable});return t};var H=(t,e,r)=>(r=t!=null?ge(Se(t)):{},be(e||!t||!t.__esModule?W(r,"default",{value:t,enumerable:!0}):r,t));var z=(t,e,r)=>new Promise((n,i)=>{var s=u=>{try{a(r.next(u))}catch(c){i(c)}},o=u=>{try{a(r.throw(u))}catch(c){i(c)}},a=u=>u.done?n(u.value):Promise.resolve(u.value).then(s,o);a((r=r.apply(t,e)).next())});var re=K((ee,te)=>{/** + * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9 + * Copyright (C) 2020 Oliver Nightingale + * @license MIT + */(function(){var t=function(e){var r=new t.Builder;return r.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),r.searchPipeline.add(t.stemmer),e.call(r,r),r.build()};t.version="2.3.9";/*! + * lunr.utils + * Copyright (C) 2020 Oliver Nightingale + */t.utils={},t.utils.warn=function(e){return function(r){e.console&&console.warn&&console.warn(r)}}(this),t.utils.asString=function(e){return e==null?"":e.toString()},t.utils.clone=function(e){if(e==null)return e;for(var r=Object.create(null),n=Object.keys(e),i=0;i0){var h=t.utils.clone(r)||{};h.position=[a,c],h.index=s.length,s.push(new t.Token(n.slice(a,o),h))}a=o+1}}return s},t.tokenizer.separator=/[\s\-]+/;/*! + * lunr.Pipeline + * Copyright (C) 2020 Oliver Nightingale + */t.Pipeline=function(){this._stack=[]},t.Pipeline.registeredFunctions=Object.create(null),t.Pipeline.registerFunction=function(e,r){r in this.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+r),e.label=r,t.Pipeline.registeredFunctions[e.label]=e},t.Pipeline.warnIfFunctionNotRegistered=function(e){var r=e.label&&e.label in this.registeredFunctions;r||t.utils.warn(`Function is not registered with pipeline. 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this.pos1&&(e.backup(),e.emit(t.QueryLexer.TERM)),e.ignore(),e.more())return t.QueryLexer.lexText},t.QueryLexer.lexEditDistance=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.EDIT_DISTANCE),t.QueryLexer.lexText},t.QueryLexer.lexBoost=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.BOOST),t.QueryLexer.lexText},t.QueryLexer.lexEOS=function(e){e.width()>0&&e.emit(t.QueryLexer.TERM)},t.QueryLexer.termSeparator=t.tokenizer.separator,t.QueryLexer.lexText=function(e){for(;;){var r=e.next();if(r==t.QueryLexer.EOS)return t.QueryLexer.lexEOS;if(r.charCodeAt(0)==92){e.escapeCharacter();continue}if(r==":")return t.QueryLexer.lexField;if(r=="~")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexEditDistance;if(r=="^")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexBoost;if(r=="+"&&e.width()===1||r=="-"&&e.width()===1)return e.emit(t.QueryLexer.PRESENCE),t.QueryLexer.lexText;if(r.match(t.QueryLexer.termSeparator))return t.QueryLexer.lexTerm}},t.QueryParser=function(e,r){this.lexer=new t.QueryLexer(e),this.query=r,this.currentClause={},this.lexemeIdx=0},t.QueryParser.prototype.parse=function(){this.lexer.run(),this.lexemes=this.lexer.lexemes;for(var e=t.QueryParser.parseClause;e;)e=e(this);return this.query},t.QueryParser.prototype.peekLexeme=function(){return this.lexemes[this.lexemeIdx]},t.QueryParser.prototype.consumeLexeme=function(){var e=this.peekLexeme();return this.lexemeIdx+=1,e},t.QueryParser.prototype.nextClause=function(){var e=this.currentClause;this.query.clause(e),this.currentClause={}},t.QueryParser.parseClause=function(e){var r=e.peekLexeme();if(r!=null)switch(r.type){case t.QueryLexer.PRESENCE:return t.QueryParser.parsePresence;case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expected either a field or a term, found "+r.type;throw r.str.length>=1&&(n+=" with value '"+r.str+"'"),new t.QueryParseError(n,r.start,r.end)}},t.QueryParser.parsePresence=function(e){var r=e.consumeLexeme();if(r!=null){switch(r.str){case"-":e.currentClause.presence=t.Query.presence.PROHIBITED;break;case"+":e.currentClause.presence=t.Query.presence.REQUIRED;break;default:var n="unrecognised presence operator'"+r.str+"'";throw new t.QueryParseError(n,r.start,r.end)}var i=e.peekLexeme();if(i==null){var n="expecting term or field, found nothing";throw new t.QueryParseError(n,r.start,r.end)}switch(i.type){case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expecting term or field, found '"+i.type+"'";throw new t.QueryParseError(n,i.start,i.end)}}},t.QueryParser.parseField=function(e){var r=e.consumeLexeme();if(r!=null){if(e.query.allFields.indexOf(r.str)==-1){var n=e.query.allFields.map(function(o){return"'"+o+"'"}).join(", "),i="unrecognised field '"+r.str+"', possible fields: "+n;throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.fields=[r.str];var s=e.peekLexeme();if(s==null){var i="expecting term, found nothing";throw new t.QueryParseError(i,r.start,r.end)}switch(s.type){case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var i="expecting term, found '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseTerm=function(e){var r=e.consumeLexeme();if(r!=null){e.currentClause.term=r.str.toLowerCase(),r.str.indexOf("*")!=-1&&(e.currentClause.usePipeline=!1);var n=e.peekLexeme();if(n==null){e.nextClause();return}switch(n.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+n.type+"'";throw new t.QueryParseError(i,n.start,n.end)}}},t.QueryParser.parseEditDistance=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="edit distance must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.editDistance=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseBoost=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="boost must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.boost=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},function(e,r){typeof define=="function"&&define.amd?define(r):typeof ee=="object"?te.exports=r():e.lunr=r()}(this,function(){return t})})()});var q=K((Re,ne)=>{"use strict";/*! + * escape-html + * Copyright(c) 2012-2013 TJ Holowaychuk + * Copyright(c) 2015 Andreas Lubbe + * Copyright(c) 2015 Tiancheng "Timothy" Gu + * MIT Licensed + */var Le=/["'&<>]/;ne.exports=we;function we(t){var e=""+t,r=Le.exec(e);if(!r)return e;var n,i="",s=0,o=0;for(s=r.index;s=0;r--){let n=t[r];typeof n!="object"?n=document.createTextNode(n):n.parentNode&&n.parentNode.removeChild(n),r?e.insertBefore(this.previousSibling,n):e.replaceChild(n,this)}}}));var ie=H(q());function se(t){let e=new Map,r=new Set;for(let n of t){let[i,s]=n.location.split("#"),o=n.location,a=n.title,u=n.tags,c=(0,ie.default)(n.text).replace(/\s+(?=[,.:;!?])/g,"").replace(/\s+/g," ");if(s){let h=e.get(i);r.has(h)?e.set(o,{location:o,title:a,text:c,parent:h}):(h.title=n.title,h.text=c,r.add(h))}else e.set(o,M({location:o,title:a,text:c},u&&{tags:u}))}return e}var oe=H(q());function ae(t,e){let r=new RegExp(t.separator,"img"),n=(i,s,o)=>`${s}${o}`;return i=>{i=i.replace(/[\s*+\-:~^]+/g," ").trim();let s=new RegExp(`(^|${t.separator})(${i.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return o=>(e?(0,oe.default)(o):o).replace(s,n).replace(/<\/mark>(\s+)]*>/img,"$1")}}function ue(t){let e=new lunr.Query(["title","text"]);return new lunr.QueryParser(t,e).parse(),e.clauses}function ce(t,e){var i;let r=new Set(t),n={};for(let s=0;s!n.has(i)))]}var U=class{constructor({config:e,docs:r,options:n}){this.options=n,this.documents=se(r),this.highlight=ae(e,!1),lunr.tokenizer.separator=new RegExp(e.separator),this.index=lunr(function(){e.lang.length===1&&e.lang[0]!=="en"?this.use(lunr[e.lang[0]]):e.lang.length>1&&this.use(lunr.multiLanguage(...e.lang));let i=Ee(["trimmer","stopWordFilter","stemmer"],n.pipeline);for(let s of e.lang.map(o=>o==="en"?lunr:lunr[o]))for(let o of i)this.pipeline.remove(s[o]),this.searchPipeline.remove(s[o]);this.ref("location"),this.field("title",{boost:1e3}),this.field("text"),this.field("tags",{boost:1e6});for(let s of r)this.add(s)})}search(e){if(e)try{let r=this.highlight(e),n=ue(e).filter(o=>o.presence!==lunr.Query.presence.PROHIBITED),i=this.index.search(`${e}*`).reduce((o,{ref:a,score:u,matchData:c})=>{let h=this.documents.get(a);if(typeof h!="undefined"){let{location:y,title:g,text:b,tags:m,parent:Q}=h,p=ce(n,Object.keys(c.metadata)),d=+!Q+ +Object.values(p).every(w=>w);o.push(Z(M({location:y,title:r(g),text:r(b)},m&&{tags:m.map(r)}),{score:u*(1+d),terms:p}))}return o},[]).sort((o,a)=>a.score-o.score).reduce((o,a)=>{let u=this.documents.get(a.location);if(typeof u!="undefined"){let c="parent"in u?u.parent.location:u.location;o.set(c,[...o.get(c)||[],a])}return o},new Map),s;if(this.options.suggestions){let o=this.index.query(a=>{for(let u of n)a.term(u.term,{fields:["title"],presence:lunr.Query.presence.REQUIRED,wildcard:lunr.Query.wildcard.TRAILING})});s=o.length?Object.keys(o[0].matchData.metadata):[]}return M({items:[...i.values()]},typeof s!="undefined"&&{suggestions:s})}catch(r){console.warn(`Invalid query: ${e} \u2013 see https://bit.ly/2s3ChXG`)}return{items:[]}}};var Y;function ke(t){return z(this,null,function*(){let e="../lunr";if(typeof parent!="undefined"&&"IFrameWorker"in parent){let n=document.querySelector("script[src]"),[i]=n.src.split("/worker");e=e.replace("..",i)}let r=[];for(let n of t.lang){switch(n){case"ja":r.push(`${e}/tinyseg.js`);break;case"hi":case"th":r.push(`${e}/wordcut.js`);break}n!=="en"&&r.push(`${e}/min/lunr.${n}.min.js`)}t.lang.length>1&&r.push(`${e}/min/lunr.multi.min.js`),r.length&&(yield importScripts(`${e}/min/lunr.stemmer.support.min.js`,...r))})}function Te(t){return z(this,null,function*(){switch(t.type){case 0:return yield ke(t.data.config),Y=new U(t.data),{type:1};case 2:return{type:3,data:Y?Y.search(t.data):{items:[]}};default:throw new TypeError("Invalid message type")}})}self.lunr=le.default;addEventListener("message",t=>z(void 0,null,function*(){postMessage(yield Te(t.data))}));})(); +//# sourceMappingURL=search.2a1c317c.min.js.map + diff --git a/main/assets/javascripts/workers/search.2a1c317c.min.js.map b/main/assets/javascripts/workers/search.2a1c317c.min.js.map new file mode 100644 index 00000000..06d43304 --- /dev/null +++ b/main/assets/javascripts/workers/search.2a1c317c.min.js.map @@ -0,0 +1,8 @@ +{ + "version": 3, + "sources": ["node_modules/lunr/lunr.js", "node_modules/escape-html/index.js", "src/assets/javascripts/integrations/search/worker/main/index.ts", "src/assets/javascripts/polyfills/index.ts", "src/assets/javascripts/integrations/search/document/index.ts", "src/assets/javascripts/integrations/search/highlighter/index.ts", "src/assets/javascripts/integrations/search/query/_/index.ts", "src/assets/javascripts/integrations/search/_/index.ts"], + "sourceRoot": "../../../..", + "sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n * this.field('title')\n * this.field('body')\n * this.ref('id')\n *\n * documents.forEach(function (doc) {\n * this.add(doc)\n * }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n var builder = new lunr.Builder\n\n builder.pipeline.add(\n lunr.trimmer,\n lunr.stopWordFilter,\n lunr.stemmer\n )\n\n builder.searchPipeline.add(\n lunr.stemmer\n )\n\n config.call(builder, builder)\n return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n /* eslint-disable no-console */\n return function (message) {\n if (global.console && console.warn) {\n console.warn(message)\n }\n }\n /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n if (obj === void 0 || obj === null) {\n return \"\"\n } else {\n return obj.toString()\n }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n if (obj === null || obj === undefined) {\n return obj\n }\n\n var clone = Object.create(null),\n keys = Object.keys(obj)\n\n for (var i = 0; i < keys.length; i++) {\n var key = keys[i],\n val = obj[key]\n\n if (Array.isArray(val)) {\n clone[key] = val.slice()\n continue\n }\n\n if (typeof val === 'string' ||\n typeof val === 'number' ||\n typeof val === 'boolean') {\n clone[key] = val\n continue\n }\n\n throw new TypeError(\"clone is not deep and does not support nested objects\")\n }\n\n return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n this.docRef = docRef\n this.fieldName = fieldName\n this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n var n = s.indexOf(lunr.FieldRef.joiner)\n\n if (n === -1) {\n throw \"malformed field ref string\"\n }\n\n var fieldRef = s.slice(0, n),\n docRef = s.slice(n + 1)\n\n return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n if (this._stringValue == undefined) {\n this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n }\n\n return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n this.elements = Object.create(null)\n\n if (elements) {\n this.length = elements.length\n\n for (var i = 0; i < this.length; i++) {\n this.elements[elements[i]] = true\n }\n } else {\n this.length = 0\n }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n intersect: function (other) {\n return other\n },\n\n union: function () {\n return this\n },\n\n contains: function () {\n return true\n }\n}\n\n/**\n * An empty set that contains no elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.empty = {\n intersect: function () {\n return this\n },\n\n union: function (other) {\n return other\n },\n\n contains: function () {\n return false\n }\n}\n\n/**\n * Returns true if this set contains the specified object.\n *\n * @param {object} object - Object whose presence in this set is to be tested.\n * @returns {boolean} - True if this set contains the specified object.\n */\nlunr.Set.prototype.contains = function (object) {\n return !!this.elements[object]\n}\n\n/**\n * Returns a new set containing only the elements that are present in both\n * this set and the specified set.\n *\n * @param {lunr.Set} other - set to intersect with this set.\n * @returns {lunr.Set} a new set that is the intersection of this and the specified set.\n */\n\nlunr.Set.prototype.intersect = function (other) {\n var a, b, elements, intersection = []\n\n if (other === lunr.Set.complete) {\n return this\n }\n\n if (other === lunr.Set.empty) {\n return other\n }\n\n if (this.length < other.length) {\n a = this\n b = other\n } else {\n a = other\n b = this\n }\n\n elements = Object.keys(a.elements)\n\n for (var i = 0; i < elements.length; i++) {\n var element = elements[i]\n if (element in b.elements) {\n intersection.push(element)\n }\n }\n\n return new lunr.Set (intersection)\n}\n\n/**\n * Returns a new set combining the elements of this and the specified set.\n *\n * @param {lunr.Set} other - set to union with this set.\n * @return {lunr.Set} a new set that is the union of this and the specified set.\n */\n\nlunr.Set.prototype.union = function (other) {\n if (other === lunr.Set.complete) {\n return lunr.Set.complete\n }\n\n if (other === lunr.Set.empty) {\n return this\n }\n\n return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))\n}\n/**\n * A function to calculate the inverse document frequency for\n * a posting. This is shared between the builder and the index\n *\n * @private\n * @param {object} posting - The posting for a given term\n * @param {number} documentCount - The total number of documents.\n */\nlunr.idf = function (posting, documentCount) {\n var documentsWithTerm = 0\n\n for (var fieldName in posting) {\n if (fieldName == '_index') continue // Ignore the term index, its not a field\n documentsWithTerm += Object.keys(posting[fieldName]).length\n }\n\n var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)\n\n return Math.log(1 + Math.abs(x))\n}\n\n/**\n * A token wraps a string representation of a token\n * as it is passed through the text processing pipeline.\n *\n * @constructor\n * @param {string} [str=''] - The string token being wrapped.\n * @param {object} [metadata={}] - Metadata associated with this token.\n */\nlunr.Token = function (str, metadata) {\n this.str = str || \"\"\n this.metadata = metadata || {}\n}\n\n/**\n * Returns the token string that is being wrapped by this object.\n *\n * @returns {string}\n */\nlunr.Token.prototype.toString = function () {\n return this.str\n}\n\n/**\n * A token update function is used when updating or optionally\n * when cloning a token.\n *\n * @callback lunr.Token~updateFunction\n * @param {string} str - The string representation of the token.\n * @param {Object} metadata - All metadata associated with this token.\n */\n\n/**\n * Applies the given function to the wrapped string token.\n *\n * @example\n * token.update(function (str, metadata) {\n * return str.toUpperCase()\n * })\n *\n * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.update = function (fn) {\n this.str = fn(this.str, this.metadata)\n return this\n}\n\n/**\n * Creates a clone of this token. Optionally a function can be\n * applied to the cloned token.\n *\n * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.clone = function (fn) {\n fn = fn || function (s) { return s }\n return new lunr.Token (fn(this.str, this.metadata), this.metadata)\n}\n/*!\n * lunr.tokenizer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A function for splitting a string into tokens ready to be inserted into\n * the search index. Uses `lunr.tokenizer.separator` to split strings, change\n * the value of this property to change how strings are split into tokens.\n *\n * This tokenizer will convert its parameter to a string by calling `toString` and\n * then will split this string on the character in `lunr.tokenizer.separator`.\n * Arrays will have their elements converted to strings and wrapped in a lunr.Token.\n *\n * Optional metadata can be passed to the tokenizer, this metadata will be cloned and\n * added as metadata to every token that is created from the object to be tokenized.\n *\n * @static\n * @param {?(string|object|object[])} obj - The object to convert into tokens\n * @param {?object} metadata - Optional metadata to associate with every token\n * @returns {lunr.Token[]}\n * @see {@link lunr.Pipeline}\n */\nlunr.tokenizer = function (obj, metadata) {\n if (obj == null || obj == undefined) {\n return []\n }\n\n if (Array.isArray(obj)) {\n return obj.map(function (t) {\n return new lunr.Token(\n lunr.utils.asString(t).toLowerCase(),\n lunr.utils.clone(metadata)\n )\n })\n }\n\n var str = obj.toString().toLowerCase(),\n len = str.length,\n tokens = []\n\n for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {\n var char = str.charAt(sliceEnd),\n sliceLength = sliceEnd - sliceStart\n\n if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {\n\n if (sliceLength > 0) {\n var tokenMetadata = lunr.utils.clone(metadata) || {}\n tokenMetadata[\"position\"] = [sliceStart, sliceLength]\n tokenMetadata[\"index\"] = tokens.length\n\n tokens.push(\n new lunr.Token (\n str.slice(sliceStart, sliceEnd),\n tokenMetadata\n )\n )\n }\n\n sliceStart = sliceEnd + 1\n }\n\n }\n\n return tokens\n}\n\n/**\n * The separator used to split a string into tokens. Override this property to change the behaviour of\n * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.\n *\n * @static\n * @see lunr.tokenizer\n */\nlunr.tokenizer.separator = /[\\s\\-]+/\n/*!\n * lunr.Pipeline\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Pipelines maintain an ordered list of functions to be applied to all\n * tokens in documents entering the search index and queries being ran against\n * the index.\n *\n * An instance of lunr.Index created with the lunr shortcut will contain a\n * pipeline with a stop word filter and an English language stemmer. Extra\n * functions can be added before or after either of these functions or these\n * default functions can be removed.\n *\n * When run the pipeline will call each function in turn, passing a token, the\n * index of that token in the original list of all tokens and finally a list of\n * all the original tokens.\n *\n * The output of functions in the pipeline will be passed to the next function\n * in the pipeline. To exclude a token from entering the index the function\n * should return undefined, the rest of the pipeline will not be called with\n * this token.\n *\n * For serialisation of pipelines to work, all functions used in an instance of\n * a pipeline should be registered with lunr.Pipeline. Registered functions can\n * then be loaded. If trying to load a serialised pipeline that uses functions\n * that are not registered an error will be thrown.\n *\n * If not planning on serialising the pipeline then registering pipeline functions\n * is not necessary.\n *\n * @constructor\n */\nlunr.Pipeline = function () {\n this._stack = []\n}\n\nlunr.Pipeline.registeredFunctions = Object.create(null)\n\n/**\n * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token\n * string as well as all known metadata. A pipeline function can mutate the token string\n * or mutate (or add) metadata for a given token.\n *\n * A pipeline function can indicate that the passed token should be discarded by returning\n * null, undefined or an empty string. This token will not be passed to any downstream pipeline\n * functions and will not be added to the index.\n *\n * Multiple tokens can be returned by returning an array of tokens. Each token will be passed\n * to any downstream pipeline functions and all will returned tokens will be added to the index.\n *\n * Any number of pipeline functions may be chained together using a lunr.Pipeline.\n *\n * @interface lunr.PipelineFunction\n * @param {lunr.Token} token - A token from the document being processed.\n * @param {number} i - The index of this token in the complete list of tokens for this document/field.\n * @param {lunr.Token[]} tokens - All tokens for this document/field.\n * @returns {(?lunr.Token|lunr.Token[])}\n */\n\n/**\n * Register a function with the pipeline.\n *\n * Functions that are used in the pipeline should be registered if the pipeline\n * needs to be serialised, or a serialised pipeline needs to be loaded.\n *\n * Registering a function does not add it to a pipeline, functions must still be\n * added to instances of the pipeline for them to be used when running a pipeline.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @param {String} label - The label to register this function with\n */\nlunr.Pipeline.registerFunction = function (fn, label) {\n if (label in this.registeredFunctions) {\n lunr.utils.warn('Overwriting existing registered function: ' + label)\n }\n\n fn.label = label\n lunr.Pipeline.registeredFunctions[fn.label] = fn\n}\n\n/**\n * Warns if the function is not registered as a Pipeline function.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @private\n */\nlunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {\n var isRegistered = fn.label && (fn.label in this.registeredFunctions)\n\n if (!isRegistered) {\n lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\\n', fn)\n }\n}\n\n/**\n * Loads a previously serialised pipeline.\n *\n * All functions to be loaded must already be registered with lunr.Pipeline.\n * If any function from the serialised data has not been registered then an\n * error will be thrown.\n *\n * @param {Object} serialised - The serialised pipeline to load.\n * @returns {lunr.Pipeline}\n */\nlunr.Pipeline.load = function (serialised) {\n var pipeline = new lunr.Pipeline\n\n serialised.forEach(function (fnName) {\n var fn = lunr.Pipeline.registeredFunctions[fnName]\n\n if (fn) {\n pipeline.add(fn)\n } else {\n throw new Error('Cannot load unregistered function: ' + fnName)\n }\n })\n\n return pipeline\n}\n\n/**\n * Adds new functions to the end of the pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.\n */\nlunr.Pipeline.prototype.add = function () {\n var fns = Array.prototype.slice.call(arguments)\n\n fns.forEach(function (fn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n this._stack.push(fn)\n }, this)\n}\n\n/**\n * Adds a single function after a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.after = function (existingFn, newFn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n var pos = this._stack.indexOf(existingFn)\n if (pos == -1) {\n throw new Error('Cannot find existingFn')\n }\n\n pos = pos + 1\n this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Adds a single function before a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.before = function (existingFn, newFn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n var pos = this._stack.indexOf(existingFn)\n if (pos == -1) {\n throw new Error('Cannot find existingFn')\n }\n\n this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Removes a function from the pipeline.\n *\n * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.\n */\nlunr.Pipeline.prototype.remove = function (fn) {\n var pos = this._stack.indexOf(fn)\n if (pos == -1) {\n return\n }\n\n this._stack.splice(pos, 1)\n}\n\n/**\n * Runs the current list of functions that make up the pipeline against the\n * passed tokens.\n *\n * @param {Array} tokens The tokens to run through the pipeline.\n * @returns {Array}\n */\nlunr.Pipeline.prototype.run = function (tokens) {\n var stackLength = this._stack.length\n\n for (var i = 0; i < stackLength; i++) {\n var fn = this._stack[i]\n var memo = []\n\n for (var j = 0; j < tokens.length; j++) {\n var result = fn(tokens[j], j, tokens)\n\n if (result === null || result === void 0 || result === '') continue\n\n if (Array.isArray(result)) {\n for (var k = 0; k < result.length; k++) {\n memo.push(result[k])\n }\n } else {\n memo.push(result)\n }\n }\n\n tokens = memo\n }\n\n return tokens\n}\n\n/**\n * Convenience method for passing a string through a pipeline and getting\n * strings out. This method takes care of wrapping the passed string in a\n * token and mapping the resulting tokens back to strings.\n *\n * @param {string} str - The string to pass through the pipeline.\n * @param {?object} metadata - Optional metadata to associate with the token\n * passed to the pipeline.\n * @returns {string[]}\n */\nlunr.Pipeline.prototype.runString = function (str, metadata) {\n var token = new lunr.Token (str, metadata)\n\n return this.run([token]).map(function (t) {\n return t.toString()\n })\n}\n\n/**\n * Resets the pipeline by removing any existing processors.\n *\n */\nlunr.Pipeline.prototype.reset = function () {\n this._stack = []\n}\n\n/**\n * Returns a representation of the pipeline ready for serialisation.\n *\n * Logs a warning if the function has not been registered.\n *\n * @returns {Array}\n */\nlunr.Pipeline.prototype.toJSON = function () {\n return this._stack.map(function (fn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n\n return fn.label\n })\n}\n/*!\n * lunr.Vector\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A vector is used to construct the vector space of documents and queries. These\n * vectors support operations to determine the similarity between two documents or\n * a document and a query.\n *\n * Normally no parameters are required for initializing a vector, but in the case of\n * loading a previously dumped vector the raw elements can be provided to the constructor.\n *\n * For performance reasons vectors are implemented with a flat array, where an elements\n * index is immediately followed by its value. E.g. [index, value, index, value]. This\n * allows the underlying array to be as sparse as possible and still offer decent\n * performance when being used for vector calculations.\n *\n * @constructor\n * @param {Number[]} [elements] - The flat list of element index and element value pairs.\n */\nlunr.Vector = function (elements) {\n this._magnitude = 0\n this.elements = elements || []\n}\n\n\n/**\n * Calculates the position within the vector to insert a given index.\n *\n * This is used internally by insert and upsert. If there are duplicate indexes then\n * the position is returned as if the value for that index were to be updated, but it\n * is the callers responsibility to check whether there is a duplicate at that index\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @returns {Number}\n */\nlunr.Vector.prototype.positionForIndex = function (index) {\n // For an empty vector the tuple can be inserted at the beginning\n if (this.elements.length == 0) {\n return 0\n }\n\n var start = 0,\n end = this.elements.length / 2,\n sliceLength = end - start,\n pivotPoint = Math.floor(sliceLength / 2),\n pivotIndex = this.elements[pivotPoint * 2]\n\n while (sliceLength > 1) {\n if (pivotIndex < index) {\n start = pivotPoint\n }\n\n if (pivotIndex > index) {\n end = pivotPoint\n }\n\n if (pivotIndex == index) {\n break\n }\n\n sliceLength = end - start\n pivotPoint = start + Math.floor(sliceLength / 2)\n pivotIndex = this.elements[pivotPoint * 2]\n }\n\n if (pivotIndex == index) {\n return pivotPoint * 2\n }\n\n if (pivotIndex > index) {\n return pivotPoint * 2\n }\n\n if (pivotIndex < index) {\n return (pivotPoint + 1) * 2\n }\n}\n\n/**\n * Inserts an element at an index within the vector.\n *\n * Does not allow duplicates, will throw an error if there is already an entry\n * for this index.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n */\nlunr.Vector.prototype.insert = function (insertIdx, val) {\n this.upsert(insertIdx, val, function () {\n throw \"duplicate index\"\n })\n}\n\n/**\n * Inserts or updates an existing index within the vector.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n * @param {function} fn - A function that is called for updates, the existing value and the\n * requested value are passed as arguments\n */\nlunr.Vector.prototype.upsert = function (insertIdx, val, fn) {\n this._magnitude = 0\n var position = this.positionForIndex(insertIdx)\n\n if (this.elements[position] == insertIdx) {\n this.elements[position + 1] = fn(this.elements[position + 1], val)\n } else {\n this.elements.splice(position, 0, insertIdx, val)\n }\n}\n\n/**\n * Calculates the magnitude of this vector.\n *\n * @returns {Number}\n */\nlunr.Vector.prototype.magnitude = function () {\n if (this._magnitude) return this._magnitude\n\n var sumOfSquares = 0,\n elementsLength = this.elements.length\n\n for (var i = 1; i < elementsLength; i += 2) {\n var val = this.elements[i]\n sumOfSquares += val * val\n }\n\n return this._magnitude = Math.sqrt(sumOfSquares)\n}\n\n/**\n * Calculates the dot product of this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The vector to compute the dot product with.\n * @returns {Number}\n */\nlunr.Vector.prototype.dot = function (otherVector) {\n var dotProduct = 0,\n a = this.elements, b = otherVector.elements,\n aLen = a.length, bLen = b.length,\n aVal = 0, bVal = 0,\n i = 0, j = 0\n\n while (i < aLen && j < bLen) {\n aVal = a[i], bVal = b[j]\n if (aVal < bVal) {\n i += 2\n } else if (aVal > bVal) {\n j += 2\n } else if (aVal == bVal) {\n dotProduct += a[i + 1] * b[j + 1]\n i += 2\n j += 2\n }\n }\n\n return dotProduct\n}\n\n/**\n * Calculates the similarity between this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The other vector to calculate the\n * similarity with.\n * @returns {Number}\n */\nlunr.Vector.prototype.similarity = function (otherVector) {\n return this.dot(otherVector) / this.magnitude() || 0\n}\n\n/**\n * Converts the vector to an array of the elements within the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toArray = function () {\n var output = new Array (this.elements.length / 2)\n\n for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {\n output[j] = this.elements[i]\n }\n\n return output\n}\n\n/**\n * A JSON serializable representation of the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toJSON = function () {\n return this.elements\n}\n/* eslint-disable */\n/*!\n * lunr.stemmer\n * Copyright (C) 2020 Oliver Nightingale\n * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt\n */\n\n/**\n * lunr.stemmer is an english language stemmer, this is a JavaScript\n * implementation of the PorterStemmer taken from http://tartarus.org/~martin\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token - The string to stem\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n * @function\n */\nlunr.stemmer = (function(){\n var step2list = {\n \"ational\" : \"ate\",\n \"tional\" : \"tion\",\n \"enci\" : \"ence\",\n \"anci\" : \"ance\",\n \"izer\" : \"ize\",\n \"bli\" : \"ble\",\n \"alli\" : \"al\",\n \"entli\" : \"ent\",\n \"eli\" : \"e\",\n \"ousli\" : \"ous\",\n \"ization\" : \"ize\",\n \"ation\" : \"ate\",\n \"ator\" : \"ate\",\n \"alism\" : \"al\",\n \"iveness\" : \"ive\",\n \"fulness\" : \"ful\",\n \"ousness\" : \"ous\",\n \"aliti\" : \"al\",\n \"iviti\" : \"ive\",\n \"biliti\" : \"ble\",\n \"logi\" : \"log\"\n },\n\n step3list = {\n \"icate\" : \"ic\",\n \"ative\" : \"\",\n \"alize\" : \"al\",\n \"iciti\" : \"ic\",\n \"ical\" : \"ic\",\n \"ful\" : \"\",\n \"ness\" : \"\"\n },\n\n c = \"[^aeiou]\", // consonant\n v = \"[aeiouy]\", // vowel\n C = c + \"[^aeiouy]*\", // consonant sequence\n V = v + \"[aeiou]*\", // vowel sequence\n\n mgr0 = \"^(\" + C + \")?\" + V + C, // [C]VC... is m>0\n meq1 = \"^(\" + C + \")?\" + V + C + \"(\" + V + \")?$\", // [C]VC[V] is m=1\n mgr1 = \"^(\" + C + \")?\" + V + C + V + C, // [C]VCVC... is m>1\n s_v = \"^(\" + C + \")?\" + v; // vowel in stem\n\n var re_mgr0 = new RegExp(mgr0);\n var re_mgr1 = new RegExp(mgr1);\n var re_meq1 = new RegExp(meq1);\n var re_s_v = new RegExp(s_v);\n\n var re_1a = /^(.+?)(ss|i)es$/;\n var re2_1a = /^(.+?)([^s])s$/;\n var re_1b = /^(.+?)eed$/;\n var re2_1b = /^(.+?)(ed|ing)$/;\n var re_1b_2 = /.$/;\n var re2_1b_2 = /(at|bl|iz)$/;\n var re3_1b_2 = new RegExp(\"([^aeiouylsz])\\\\1$\");\n var re4_1b_2 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n var re_1c = /^(.+?[^aeiou])y$/;\n var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;\n\n var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;\n\n var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;\n var re2_4 = /^(.+?)(s|t)(ion)$/;\n\n var re_5 = /^(.+?)e$/;\n var re_5_1 = /ll$/;\n var re3_5 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n var porterStemmer = function porterStemmer(w) {\n var stem,\n suffix,\n firstch,\n re,\n re2,\n re3,\n re4;\n\n if (w.length < 3) { return w; }\n\n firstch = w.substr(0,1);\n if (firstch == \"y\") {\n w = firstch.toUpperCase() + w.substr(1);\n }\n\n // Step 1a\n re = re_1a\n re2 = re2_1a;\n\n if (re.test(w)) { w = w.replace(re,\"$1$2\"); }\n else if (re2.test(w)) { w = w.replace(re2,\"$1$2\"); }\n\n // Step 1b\n re = re_1b;\n re2 = re2_1b;\n if (re.test(w)) {\n var fp = re.exec(w);\n re = re_mgr0;\n if (re.test(fp[1])) {\n re = re_1b_2;\n w = w.replace(re,\"\");\n }\n } else if (re2.test(w)) {\n var fp = re2.exec(w);\n stem = fp[1];\n re2 = re_s_v;\n if (re2.test(stem)) {\n w = stem;\n re2 = re2_1b_2;\n re3 = re3_1b_2;\n re4 = re4_1b_2;\n if (re2.test(w)) { w = w + \"e\"; }\n else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,\"\"); }\n else if (re4.test(w)) { w = w + \"e\"; }\n }\n }\n\n // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)\n re = re_1c;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n w = stem + \"i\";\n }\n\n // Step 2\n re = re_2;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n suffix = fp[2];\n re = re_mgr0;\n if (re.test(stem)) {\n w = stem + step2list[suffix];\n }\n }\n\n // Step 3\n re = re_3;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n suffix = fp[2];\n re = re_mgr0;\n if (re.test(stem)) {\n w = stem + step3list[suffix];\n }\n }\n\n // Step 4\n re = re_4;\n re2 = re2_4;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n re = re_mgr1;\n if (re.test(stem)) {\n w = stem;\n }\n } else if (re2.test(w)) {\n var fp = re2.exec(w);\n stem = fp[1] + fp[2];\n re2 = re_mgr1;\n if (re2.test(stem)) {\n w = stem;\n }\n }\n\n // Step 5\n re = re_5;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n re = re_mgr1;\n re2 = re_meq1;\n re3 = re3_5;\n if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {\n w = stem;\n }\n }\n\n re = re_5_1;\n re2 = re_mgr1;\n if (re.test(w) && re2.test(w)) {\n re = re_1b_2;\n w = w.replace(re,\"\");\n }\n\n // and turn initial Y back to y\n\n if (firstch == \"y\") {\n w = firstch.toLowerCase() + w.substr(1);\n }\n\n return w;\n };\n\n return function (token) {\n return token.update(porterStemmer);\n }\n})();\n\nlunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')\n/*!\n * lunr.stopWordFilter\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.generateStopWordFilter builds a stopWordFilter function from the provided\n * list of stop words.\n *\n * The built in lunr.stopWordFilter is built using this generator and can be used\n * to generate custom stopWordFilters for applications or non English languages.\n *\n * @function\n * @param {Array} token The token to pass through the filter\n * @returns {lunr.PipelineFunction}\n * @see lunr.Pipeline\n * @see lunr.stopWordFilter\n */\nlunr.generateStopWordFilter = function (stopWords) {\n var words = stopWords.reduce(function (memo, stopWord) {\n memo[stopWord] = stopWord\n return memo\n }, {})\n\n return function (token) {\n if (token && words[token.toString()] !== token.toString()) return token\n }\n}\n\n/**\n * lunr.stopWordFilter is an English language stop word list filter, any words\n * contained in the list will not be passed through the filter.\n *\n * This is intended to be used in the Pipeline. If the token does not pass the\n * filter then undefined will be returned.\n *\n * @function\n * @implements {lunr.PipelineFunction}\n * @params {lunr.Token} token - A token to check for being a stop word.\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n */\nlunr.stopWordFilter = lunr.generateStopWordFilter([\n 'a',\n 'able',\n 'about',\n 'across',\n 'after',\n 'all',\n 'almost',\n 'also',\n 'am',\n 'among',\n 'an',\n 'and',\n 'any',\n 'are',\n 'as',\n 'at',\n 'be',\n 'because',\n 'been',\n 'but',\n 'by',\n 'can',\n 'cannot',\n 'could',\n 'dear',\n 'did',\n 'do',\n 'does',\n 'either',\n 'else',\n 'ever',\n 'every',\n 'for',\n 'from',\n 'get',\n 'got',\n 'had',\n 'has',\n 'have',\n 'he',\n 'her',\n 'hers',\n 'him',\n 'his',\n 'how',\n 'however',\n 'i',\n 'if',\n 'in',\n 'into',\n 'is',\n 'it',\n 'its',\n 'just',\n 'least',\n 'let',\n 'like',\n 'likely',\n 'may',\n 'me',\n 'might',\n 'most',\n 'must',\n 'my',\n 'neither',\n 'no',\n 'nor',\n 'not',\n 'of',\n 'off',\n 'often',\n 'on',\n 'only',\n 'or',\n 'other',\n 'our',\n 'own',\n 'rather',\n 'said',\n 'say',\n 'says',\n 'she',\n 'should',\n 'since',\n 'so',\n 'some',\n 'than',\n 'that',\n 'the',\n 'their',\n 'them',\n 'then',\n 'there',\n 'these',\n 'they',\n 'this',\n 'tis',\n 'to',\n 'too',\n 'twas',\n 'us',\n 'wants',\n 'was',\n 'we',\n 'were',\n 'what',\n 'when',\n 'where',\n 'which',\n 'while',\n 'who',\n 'whom',\n 'why',\n 'will',\n 'with',\n 'would',\n 'yet',\n 'you',\n 'your'\n])\n\nlunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')\n/*!\n * lunr.trimmer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.trimmer is a pipeline function for trimming non word\n * characters from the beginning and end of tokens before they\n * enter the index.\n *\n * This implementation may not work correctly for non latin\n * characters and should either be removed or adapted for use\n * with languages with non-latin characters.\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token The token to pass through the filter\n * @returns {lunr.Token}\n * @see lunr.Pipeline\n */\nlunr.trimmer = function (token) {\n return token.update(function (s) {\n return s.replace(/^\\W+/, '').replace(/\\W+$/, '')\n })\n}\n\nlunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')\n/*!\n * lunr.TokenSet\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A token set is used to store the unique list of all tokens\n * within an index. Token sets are also used to represent an\n * incoming query to the index, this query token set and index\n * token set are then intersected to find which tokens to look\n * up in the inverted index.\n *\n * A token set can hold multiple tokens, as in the case of the\n * index token set, or it can hold a single token as in the\n * case of a simple query token set.\n *\n * Additionally token sets are used to perform wildcard matching.\n * Leading, contained and trailing wildcards are supported, and\n * from this edit distance matching can also be provided.\n *\n * Token sets are implemented as a minimal finite state automata,\n * where both common prefixes and suffixes are shared between tokens.\n * This helps to reduce the space used for storing the token set.\n *\n * @constructor\n */\nlunr.TokenSet = function () {\n this.final = false\n this.edges = {}\n this.id = lunr.TokenSet._nextId\n lunr.TokenSet._nextId += 1\n}\n\n/**\n * Keeps track of the next, auto increment, identifier to assign\n * to a new tokenSet.\n *\n * TokenSets require a unique identifier to be correctly minimised.\n *\n * @private\n */\nlunr.TokenSet._nextId = 1\n\n/**\n * Creates a TokenSet instance from the given sorted array of words.\n *\n * @param {String[]} arr - A sorted array of strings to create the set from.\n * @returns {lunr.TokenSet}\n * @throws Will throw an error if the input array is not sorted.\n */\nlunr.TokenSet.fromArray = function (arr) {\n var builder = new lunr.TokenSet.Builder\n\n for (var i = 0, len = arr.length; i < len; i++) {\n builder.insert(arr[i])\n }\n\n builder.finish()\n return builder.root\n}\n\n/**\n * Creates a token set from a query clause.\n *\n * @private\n * @param {Object} clause - A single clause from lunr.Query.\n * @param {string} clause.term - The query clause term.\n * @param {number} [clause.editDistance] - The optional edit distance for the term.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromClause = function (clause) {\n if ('editDistance' in clause) {\n return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)\n } else {\n return lunr.TokenSet.fromString(clause.term)\n }\n}\n\n/**\n * Creates a token set representing a single string with a specified\n * edit distance.\n *\n * Insertions, deletions, substitutions and transpositions are each\n * treated as an edit distance of 1.\n *\n * Increasing the allowed edit distance will have a dramatic impact\n * on the performance of both creating and intersecting these TokenSets.\n * It is advised to keep the edit distance less than 3.\n *\n * @param {string} str - The string to create the token set from.\n * @param {number} editDistance - The allowed edit distance to match.\n * @returns {lunr.Vector}\n */\nlunr.TokenSet.fromFuzzyString = function (str, editDistance) {\n var root = new lunr.TokenSet\n\n var stack = [{\n node: root,\n editsRemaining: editDistance,\n str: str\n }]\n\n while (stack.length) {\n var frame = stack.pop()\n\n // no edit\n if (frame.str.length > 0) {\n var char = frame.str.charAt(0),\n noEditNode\n\n if (char in frame.node.edges) {\n noEditNode = frame.node.edges[char]\n } else {\n noEditNode = new lunr.TokenSet\n frame.node.edges[char] = noEditNode\n }\n\n if (frame.str.length == 1) {\n noEditNode.final = true\n }\n\n stack.push({\n node: noEditNode,\n editsRemaining: frame.editsRemaining,\n str: frame.str.slice(1)\n })\n }\n\n if (frame.editsRemaining == 0) {\n continue\n }\n\n // insertion\n if (\"*\" in frame.node.edges) {\n var insertionNode = frame.node.edges[\"*\"]\n } else {\n var insertionNode = new lunr.TokenSet\n frame.node.edges[\"*\"] = insertionNode\n }\n\n if (frame.str.length == 0) {\n insertionNode.final = true\n }\n\n stack.push({\n node: insertionNode,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str\n })\n\n // deletion\n // can only do a deletion if we have enough edits remaining\n // and if there are characters left to delete in the string\n if (frame.str.length > 1) {\n stack.push({\n node: frame.node,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str.slice(1)\n })\n }\n\n // deletion\n // just removing the last character from the str\n if (frame.str.length == 1) {\n frame.node.final = true\n }\n\n // substitution\n // can only do a substitution if we have enough edits remaining\n // and if there are characters left to substitute\n if (frame.str.length >= 1) {\n if (\"*\" in frame.node.edges) {\n var substitutionNode = frame.node.edges[\"*\"]\n } else {\n var substitutionNode = new lunr.TokenSet\n frame.node.edges[\"*\"] = substitutionNode\n }\n\n if (frame.str.length == 1) {\n substitutionNode.final = true\n }\n\n stack.push({\n node: substitutionNode,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str.slice(1)\n })\n }\n\n // transposition\n // can only do a transposition if there are edits remaining\n // and there are enough characters to transpose\n if (frame.str.length > 1) {\n var charA = frame.str.charAt(0),\n charB = frame.str.charAt(1),\n transposeNode\n\n if (charB in frame.node.edges) {\n transposeNode = frame.node.edges[charB]\n } else {\n transposeNode = new lunr.TokenSet\n frame.node.edges[charB] = transposeNode\n }\n\n if (frame.str.length == 1) {\n transposeNode.final = true\n }\n\n stack.push({\n node: transposeNode,\n editsRemaining: frame.editsRemaining - 1,\n str: charA + frame.str.slice(2)\n })\n }\n }\n\n return root\n}\n\n/**\n * Creates a TokenSet from a string.\n *\n * The string may contain one or more wildcard characters (*)\n * that will allow wildcard matching when intersecting with\n * another TokenSet.\n *\n * @param {string} str - The string to create a TokenSet from.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromString = function (str) {\n var node = new lunr.TokenSet,\n root = node\n\n /*\n * Iterates through all characters within the passed string\n * appending a node for each character.\n *\n * When a wildcard character is found then a self\n * referencing edge is introduced to continually match\n * any number of any characters.\n */\n for (var i = 0, len = str.length; i < len; i++) {\n var char = str[i],\n final = (i == len - 1)\n\n if (char == \"*\") {\n node.edges[char] = node\n node.final = final\n\n } else {\n var next = new lunr.TokenSet\n next.final = final\n\n node.edges[char] = next\n node = next\n }\n }\n\n return root\n}\n\n/**\n * Converts this TokenSet into an array of strings\n * contained within the TokenSet.\n *\n * This is not intended to be used on a TokenSet that\n * contains wildcards, in these cases the results are\n * undefined and are likely to cause an infinite loop.\n *\n * @returns {string[]}\n */\nlunr.TokenSet.prototype.toArray = function () {\n var words = []\n\n var stack = [{\n prefix: \"\",\n node: this\n }]\n\n while (stack.length) {\n var frame = stack.pop(),\n edges = Object.keys(frame.node.edges),\n len = edges.length\n\n if (frame.node.final) {\n /* In Safari, at this point the prefix is sometimes corrupted, see:\n * https://github.com/olivernn/lunr.js/issues/279 Calling any\n * String.prototype method forces Safari to \"cast\" this string to what\n * it's supposed to be, fixing the bug. */\n frame.prefix.charAt(0)\n words.push(frame.prefix)\n }\n\n for (var i = 0; i < len; i++) {\n var edge = edges[i]\n\n stack.push({\n prefix: frame.prefix.concat(edge),\n node: frame.node.edges[edge]\n })\n }\n }\n\n return words\n}\n\n/**\n * Generates a string representation of a TokenSet.\n *\n * This is intended to allow TokenSets to be used as keys\n * in objects, largely to aid the construction and minimisation\n * of a TokenSet. As such it is not designed to be a human\n * friendly representation of the TokenSet.\n *\n * @returns {string}\n */\nlunr.TokenSet.prototype.toString = function () {\n // NOTE: Using Object.keys here as this.edges is very likely\n // to enter 'hash-mode' with many keys being added\n //\n // avoiding a for-in loop here as it leads to the function\n // being de-optimised (at least in V8). From some simple\n // benchmarks the performance is comparable, but allowing\n // V8 to optimize may mean easy performance wins in the future.\n\n if (this._str) {\n return this._str\n }\n\n var str = this.final ? '1' : '0',\n labels = Object.keys(this.edges).sort(),\n len = labels.length\n\n for (var i = 0; i < len; i++) {\n var label = labels[i],\n node = this.edges[label]\n\n str = str + label + node.id\n }\n\n return str\n}\n\n/**\n * Returns a new TokenSet that is the intersection of\n * this TokenSet and the passed TokenSet.\n *\n * This intersection will take into account any wildcards\n * contained within the TokenSet.\n *\n * @param {lunr.TokenSet} b - An other TokenSet to intersect with.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.prototype.intersect = function (b) {\n var output = new lunr.TokenSet,\n frame = undefined\n\n var stack = [{\n qNode: b,\n output: output,\n node: this\n }]\n\n while (stack.length) {\n frame = stack.pop()\n\n // NOTE: As with the #toString method, we are using\n // Object.keys and a for loop instead of a for-in loop\n // as both of these objects enter 'hash' mode, causing\n // the function to be de-optimised in V8\n var qEdges = Object.keys(frame.qNode.edges),\n qLen = qEdges.length,\n nEdges = Object.keys(frame.node.edges),\n nLen = nEdges.length\n\n for (var q = 0; q < qLen; q++) {\n var qEdge = qEdges[q]\n\n for (var n = 0; n < nLen; n++) {\n var nEdge = nEdges[n]\n\n if (nEdge == qEdge || qEdge == '*') {\n var node = frame.node.edges[nEdge],\n qNode = frame.qNode.edges[qEdge],\n final = node.final && qNode.final,\n next = undefined\n\n if (nEdge in frame.output.edges) {\n // an edge already exists for this character\n // no need to create a new node, just set the finality\n // bit unless this node is already final\n next = frame.output.edges[nEdge]\n next.final = next.final || final\n\n } else {\n // no edge exists yet, must create one\n // set the finality bit and insert it\n // into the output\n next = new lunr.TokenSet\n next.final = final\n frame.output.edges[nEdge] = next\n }\n\n stack.push({\n qNode: qNode,\n output: next,\n node: node\n })\n }\n }\n }\n }\n\n return output\n}\nlunr.TokenSet.Builder = function () {\n this.previousWord = \"\"\n this.root = new lunr.TokenSet\n this.uncheckedNodes = []\n this.minimizedNodes = {}\n}\n\nlunr.TokenSet.Builder.prototype.insert = function (word) {\n var node,\n commonPrefix = 0\n\n if (word < this.previousWord) {\n throw new Error (\"Out of order word insertion\")\n }\n\n for (var i = 0; i < word.length && i < this.previousWord.length; i++) {\n if (word[i] != this.previousWord[i]) break\n commonPrefix++\n }\n\n this.minimize(commonPrefix)\n\n if (this.uncheckedNodes.length == 0) {\n node = this.root\n } else {\n node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child\n }\n\n for (var i = commonPrefix; i < word.length; i++) {\n var nextNode = new lunr.TokenSet,\n char = word[i]\n\n node.edges[char] = nextNode\n\n this.uncheckedNodes.push({\n parent: node,\n char: char,\n child: nextNode\n })\n\n node = nextNode\n }\n\n node.final = true\n this.previousWord = word\n}\n\nlunr.TokenSet.Builder.prototype.finish = function () {\n this.minimize(0)\n}\n\nlunr.TokenSet.Builder.prototype.minimize = function (downTo) {\n for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {\n var node = this.uncheckedNodes[i],\n childKey = node.child.toString()\n\n if (childKey in this.minimizedNodes) {\n node.parent.edges[node.char] = this.minimizedNodes[childKey]\n } else {\n // Cache the key for this node since\n // we know it can't change anymore\n node.child._str = childKey\n\n this.minimizedNodes[childKey] = node.child\n }\n\n this.uncheckedNodes.pop()\n }\n}\n/*!\n * lunr.Index\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * An index contains the built index of all documents and provides a query interface\n * to the index.\n *\n * Usually instances of lunr.Index will not be created using this constructor, instead\n * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be\n * used to load previously built and serialized indexes.\n *\n * @constructor\n * @param {Object} attrs - The attributes of the built search index.\n * @param {Object} attrs.invertedIndex - An index of term/field to document reference.\n * @param {Object} attrs.fieldVectors - Field vectors\n * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.\n * @param {string[]} attrs.fields - The names of indexed document fields.\n * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.\n */\nlunr.Index = function (attrs) {\n this.invertedIndex = attrs.invertedIndex\n this.fieldVectors = attrs.fieldVectors\n this.tokenSet = attrs.tokenSet\n this.fields = attrs.fields\n this.pipeline = attrs.pipeline\n}\n\n/**\n * A result contains details of a document matching a search query.\n * @typedef {Object} lunr.Index~Result\n * @property {string} ref - The reference of the document this result represents.\n * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.\n * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.\n */\n\n/**\n * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple\n * query language which itself is parsed into an instance of lunr.Query.\n *\n * For programmatically building queries it is advised to directly use lunr.Query, the query language\n * is best used for human entered text rather than program generated text.\n *\n * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported\n * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'\n * or 'world', though those that contain both will rank higher in the results.\n *\n * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can\n * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding\n * wildcards will increase the number of documents that will be found but can also have a negative\n * impact on query performance, especially with wildcards at the beginning of a term.\n *\n * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term\n * hello in the title field will match this query. Using a field not present in the index will lead\n * to an error being thrown.\n *\n * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term\n * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported\n * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.\n * Avoid large values for edit distance to improve query performance.\n *\n * Each term also supports a presence modifier. By default a term's presence in document is optional, however\n * this can be changed to either required or prohibited. For a term's presence to be required in a document the\n * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and\n * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not\n * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.\n *\n * To escape special characters the backslash character '\\' can be used, this allows searches to include\n * characters that would normally be considered modifiers, e.g. `foo\\~2` will search for a term \"foo~2\" instead\n * of attempting to apply a boost of 2 to the search term \"foo\".\n *\n * @typedef {string} lunr.Index~QueryString\n * @example Simple single term query\n * hello\n * @example Multiple term query\n * hello world\n * @example term scoped to a field\n * title:hello\n * @example term with a boost of 10\n * hello^10\n * @example term with an edit distance of 2\n * hello~2\n * @example terms with presence modifiers\n * -foo +bar baz\n */\n\n/**\n * Performs a search against the index using lunr query syntax.\n *\n * Results will be returned sorted by their score, the most relevant results\n * will be returned first. For details on how the score is calculated, please see\n * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.\n *\n * For more programmatic querying use lunr.Index#query.\n *\n * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.\n * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.search = function (queryString) {\n return this.query(function (query) {\n var parser = new lunr.QueryParser(queryString, query)\n parser.parse()\n })\n}\n\n/**\n * A query builder callback provides a query object to be used to express\n * the query to perform on the index.\n *\n * @callback lunr.Index~queryBuilder\n * @param {lunr.Query} query - The query object to build up.\n * @this lunr.Query\n */\n\n/**\n * Performs a query against the index using the yielded lunr.Query object.\n *\n * If performing programmatic queries against the index, this method is preferred\n * over lunr.Index#search so as to avoid the additional query parsing overhead.\n *\n * A query object is yielded to the supplied function which should be used to\n * express the query to be run against the index.\n *\n * Note that although this function takes a callback parameter it is _not_ an\n * asynchronous operation, the callback is just yielded a query object to be\n * customized.\n *\n * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.query = function (fn) {\n // for each query clause\n // * process terms\n // * expand terms from token set\n // * find matching documents and metadata\n // * get document vectors\n // * score documents\n\n var query = new lunr.Query(this.fields),\n matchingFields = Object.create(null),\n queryVectors = Object.create(null),\n termFieldCache = Object.create(null),\n requiredMatches = Object.create(null),\n prohibitedMatches = Object.create(null)\n\n /*\n * To support field level boosts a query vector is created per\n * field. An empty vector is eagerly created to support negated\n * queries.\n */\n for (var i = 0; i < this.fields.length; i++) {\n queryVectors[this.fields[i]] = new lunr.Vector\n }\n\n fn.call(query, query)\n\n for (var i = 0; i < query.clauses.length; i++) {\n /*\n * Unless the pipeline has been disabled for this term, which is\n * the case for terms with wildcards, we need to pass the clause\n * term through the search pipeline. A pipeline returns an array\n * of processed terms. Pipeline functions may expand the passed\n * term, which means we may end up performing multiple index lookups\n * for a single query term.\n */\n var clause = query.clauses[i],\n terms = null,\n clauseMatches = lunr.Set.empty\n\n if (clause.usePipeline) {\n terms = this.pipeline.runString(clause.term, {\n fields: clause.fields\n })\n } else {\n terms = [clause.term]\n }\n\n for (var m = 0; m < terms.length; m++) {\n var term = terms[m]\n\n /*\n * Each term returned from the pipeline needs to use the same query\n * clause object, e.g. the same boost and or edit distance. The\n * simplest way to do this is to re-use the clause object but mutate\n * its term property.\n */\n clause.term = term\n\n /*\n * From the term in the clause we create a token set which will then\n * be used to intersect the indexes token set to get a list of terms\n * to lookup in the inverted index\n */\n var termTokenSet = lunr.TokenSet.fromClause(clause),\n expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()\n\n /*\n * If a term marked as required does not exist in the tokenSet it is\n * impossible for the search to return any matches. We set all the field\n * scoped required matches set to empty and stop examining any further\n * clauses.\n */\n if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {\n for (var k = 0; k < clause.fields.length; k++) {\n var field = clause.fields[k]\n requiredMatches[field] = lunr.Set.empty\n }\n\n break\n }\n\n for (var j = 0; j < expandedTerms.length; j++) {\n /*\n * For each term get the posting and termIndex, this is required for\n * building the query vector.\n */\n var expandedTerm = expandedTerms[j],\n posting = this.invertedIndex[expandedTerm],\n termIndex = posting._index\n\n for (var k = 0; k < clause.fields.length; k++) {\n /*\n * For each field that this query term is scoped by (by default\n * all fields are in scope) we need to get all the document refs\n * that have this term in that field.\n *\n * The posting is the entry in the invertedIndex for the matching\n * term from above.\n */\n var field = clause.fields[k],\n fieldPosting = posting[field],\n matchingDocumentRefs = Object.keys(fieldPosting),\n termField = expandedTerm + \"/\" + field,\n matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)\n\n /*\n * if the presence of this term is required ensure that the matching\n * documents are added to the set of required matches for this clause.\n *\n */\n if (clause.presence == lunr.Query.presence.REQUIRED) {\n clauseMatches = clauseMatches.union(matchingDocumentsSet)\n\n if (requiredMatches[field] === undefined) {\n requiredMatches[field] = lunr.Set.complete\n }\n }\n\n /*\n * if the presence of this term is prohibited ensure that the matching\n * documents are added to the set of prohibited matches for this field,\n * creating that set if it does not yet exist.\n */\n if (clause.presence == lunr.Query.presence.PROHIBITED) {\n if (prohibitedMatches[field] === undefined) {\n prohibitedMatches[field] = lunr.Set.empty\n }\n\n prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)\n\n /*\n * Prohibited matches should not be part of the query vector used for\n * similarity scoring and no metadata should be extracted so we continue\n * to the next field\n */\n continue\n }\n\n /*\n * The query field vector is populated using the termIndex found for\n * the term and a unit value with the appropriate boost applied.\n * Using upsert because there could already be an entry in the vector\n * for the term we are working with. In that case we just add the scores\n * together.\n */\n queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })\n\n /**\n * If we've already seen this term, field combo then we've already collected\n * the matching documents and metadata, no need to go through all that again\n */\n if (termFieldCache[termField]) {\n continue\n }\n\n for (var l = 0; l < matchingDocumentRefs.length; l++) {\n /*\n * All metadata for this term/field/document triple\n * are then extracted and collected into an instance\n * of lunr.MatchData ready to be returned in the query\n * results\n */\n var matchingDocumentRef = matchingDocumentRefs[l],\n matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),\n metadata = fieldPosting[matchingDocumentRef],\n fieldMatch\n\n if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {\n matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)\n } else {\n fieldMatch.add(expandedTerm, field, metadata)\n }\n\n }\n\n termFieldCache[termField] = true\n }\n }\n }\n\n /**\n * If the presence was required we need to update the requiredMatches field sets.\n * We do this after all fields for the term have collected their matches because\n * the clause terms presence is required in _any_ of the fields not _all_ of the\n * fields.\n */\n if (clause.presence === lunr.Query.presence.REQUIRED) {\n for (var k = 0; k < clause.fields.length; k++) {\n var field = clause.fields[k]\n requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)\n }\n }\n }\n\n /**\n * Need to combine the field scoped required and prohibited\n * matching documents into a global set of required and prohibited\n * matches\n */\n var allRequiredMatches = lunr.Set.complete,\n allProhibitedMatches = lunr.Set.empty\n\n for (var i = 0; i < this.fields.length; i++) {\n var field = this.fields[i]\n\n if (requiredMatches[field]) {\n allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])\n }\n\n if (prohibitedMatches[field]) {\n allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])\n }\n }\n\n var matchingFieldRefs = Object.keys(matchingFields),\n results = [],\n matches = Object.create(null)\n\n /*\n * If the query is negated (contains only prohibited terms)\n * we need to get _all_ fieldRefs currently existing in the\n * index. This is only done when we know that the query is\n * entirely prohibited terms to avoid any cost of getting all\n * fieldRefs unnecessarily.\n *\n * Additionally, blank MatchData must be created to correctly\n * populate the results.\n */\n if (query.isNegated()) {\n matchingFieldRefs = Object.keys(this.fieldVectors)\n\n for (var i = 0; i < matchingFieldRefs.length; i++) {\n var matchingFieldRef = matchingFieldRefs[i]\n var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)\n matchingFields[matchingFieldRef] = new lunr.MatchData\n }\n }\n\n for (var i = 0; i < matchingFieldRefs.length; i++) {\n /*\n * Currently we have document fields that match the query, but we\n * need to return documents. The matchData and scores are combined\n * from multiple fields belonging to the same document.\n *\n * Scores are calculated by field, using the query vectors created\n * above, and combined into a final document score using addition.\n */\n var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),\n docRef = fieldRef.docRef\n\n if (!allRequiredMatches.contains(docRef)) {\n continue\n }\n\n if (allProhibitedMatches.contains(docRef)) {\n continue\n }\n\n var fieldVector = this.fieldVectors[fieldRef],\n score = queryVectors[fieldRef.fieldName].similarity(fieldVector),\n docMatch\n\n if ((docMatch = matches[docRef]) !== undefined) {\n docMatch.score += score\n docMatch.matchData.combine(matchingFields[fieldRef])\n } else {\n var match = {\n ref: docRef,\n score: score,\n matchData: matchingFields[fieldRef]\n }\n matches[docRef] = match\n results.push(match)\n }\n }\n\n /*\n * Sort the results objects by score, highest first.\n */\n return results.sort(function (a, b) {\n return b.score - a.score\n })\n}\n\n/**\n * Prepares the index for JSON serialization.\n *\n * The schema for this JSON blob will be described in a\n * separate JSON schema file.\n *\n * @returns {Object}\n */\nlunr.Index.prototype.toJSON = function () {\n var invertedIndex = Object.keys(this.invertedIndex)\n .sort()\n .map(function (term) {\n return [term, this.invertedIndex[term]]\n }, this)\n\n var fieldVectors = Object.keys(this.fieldVectors)\n .map(function (ref) {\n return [ref, this.fieldVectors[ref].toJSON()]\n }, this)\n\n return {\n version: lunr.version,\n fields: this.fields,\n fieldVectors: fieldVectors,\n invertedIndex: invertedIndex,\n pipeline: this.pipeline.toJSON()\n }\n}\n\n/**\n * Loads a previously serialized lunr.Index\n *\n * @param {Object} serializedIndex - A previously serialized lunr.Index\n * @returns {lunr.Index}\n */\nlunr.Index.load = function (serializedIndex) {\n var attrs = {},\n fieldVectors = {},\n serializedVectors = serializedIndex.fieldVectors,\n invertedIndex = Object.create(null),\n serializedInvertedIndex = serializedIndex.invertedIndex,\n tokenSetBuilder = new lunr.TokenSet.Builder,\n pipeline = lunr.Pipeline.load(serializedIndex.pipeline)\n\n if (serializedIndex.version != lunr.version) {\n lunr.utils.warn(\"Version mismatch when loading serialised index. Current version of lunr '\" + lunr.version + \"' does not match serialized index '\" + serializedIndex.version + \"'\")\n }\n\n for (var i = 0; i < serializedVectors.length; i++) {\n var tuple = serializedVectors[i],\n ref = tuple[0],\n elements = tuple[1]\n\n fieldVectors[ref] = new lunr.Vector(elements)\n }\n\n for (var i = 0; i < serializedInvertedIndex.length; i++) {\n var tuple = serializedInvertedIndex[i],\n term = tuple[0],\n posting = tuple[1]\n\n tokenSetBuilder.insert(term)\n invertedIndex[term] = posting\n }\n\n tokenSetBuilder.finish()\n\n attrs.fields = serializedIndex.fields\n\n attrs.fieldVectors = fieldVectors\n attrs.invertedIndex = invertedIndex\n attrs.tokenSet = tokenSetBuilder.root\n attrs.pipeline = pipeline\n\n return new lunr.Index(attrs)\n}\n/*!\n * lunr.Builder\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Builder performs indexing on a set of documents and\n * returns instances of lunr.Index ready for querying.\n *\n * All configuration of the index is done via the builder, the\n * fields to index, the document reference, the text processing\n * pipeline and document scoring parameters are all set on the\n * builder before indexing.\n *\n * @constructor\n * @property {string} _ref - Internal reference to the document reference field.\n * @property {string[]} _fields - Internal reference to the document fields to index.\n * @property {object} invertedIndex - The inverted index maps terms to document fields.\n * @property {object} documentTermFrequencies - Keeps track of document term frequencies.\n * @property {object} documentLengths - Keeps track of the length of documents added to the index.\n * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.\n * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.\n * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.\n * @property {number} documentCount - Keeps track of the total number of documents indexed.\n * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.\n * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.\n * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.\n * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.\n */\nlunr.Builder = function () {\n this._ref = \"id\"\n this._fields = Object.create(null)\n this._documents = Object.create(null)\n this.invertedIndex = Object.create(null)\n this.fieldTermFrequencies = {}\n this.fieldLengths = {}\n this.tokenizer = lunr.tokenizer\n this.pipeline = new lunr.Pipeline\n this.searchPipeline = new lunr.Pipeline\n this.documentCount = 0\n this._b = 0.75\n this._k1 = 1.2\n this.termIndex = 0\n this.metadataWhitelist = []\n}\n\n/**\n * Sets the document field used as the document reference. Every document must have this field.\n * The type of this field in the document should be a string, if it is not a string it will be\n * coerced into a string by calling toString.\n *\n * The default ref is 'id'.\n *\n * The ref should _not_ be changed during indexing, it should be set before any documents are\n * added to the index. Changing it during indexing can lead to inconsistent results.\n *\n * @param {string} ref - The name of the reference field in the document.\n */\nlunr.Builder.prototype.ref = function (ref) {\n this._ref = ref\n}\n\n/**\n * A function that is used to extract a field from a document.\n *\n * Lunr expects a field to be at the top level of a document, if however the field\n * is deeply nested within a document an extractor function can be used to extract\n * the right field for indexing.\n *\n * @callback fieldExtractor\n * @param {object} doc - The document being added to the index.\n * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.\n * @example Extracting a nested field\n * function (doc) { return doc.nested.field }\n */\n\n/**\n * Adds a field to the list of document fields that will be indexed. Every document being\n * indexed should have this field. Null values for this field in indexed documents will\n * not cause errors but will limit the chance of that document being retrieved by searches.\n *\n * All fields should be added before adding documents to the index. Adding fields after\n * a document has been indexed will have no effect on already indexed documents.\n *\n * Fields can be boosted at build time. This allows terms within that field to have more\n * importance when ranking search results. Use a field boost to specify that matches within\n * one field are more important than other fields.\n *\n * @param {string} fieldName - The name of a field to index in all documents.\n * @param {object} attributes - Optional attributes associated with this field.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.\n * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.\n * @throws {RangeError} fieldName cannot contain unsupported characters '/'\n */\nlunr.Builder.prototype.field = function (fieldName, attributes) {\n if (/\\//.test(fieldName)) {\n throw new RangeError (\"Field '\" + fieldName + \"' contains illegal character '/'\")\n }\n\n this._fields[fieldName] = attributes || {}\n}\n\n/**\n * A parameter to tune the amount of field length normalisation that is applied when\n * calculating relevance scores. A value of 0 will completely disable any normalisation\n * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b\n * will be clamped to the range 0 - 1.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.b = function (number) {\n if (number < 0) {\n this._b = 0\n } else if (number > 1) {\n this._b = 1\n } else {\n this._b = number\n }\n}\n\n/**\n * A parameter that controls the speed at which a rise in term frequency results in term\n * frequency saturation. The default value is 1.2. Setting this to a higher value will give\n * slower saturation levels, a lower value will result in quicker saturation.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.k1 = function (number) {\n this._k1 = number\n}\n\n/**\n * Adds a document to the index.\n *\n * Before adding fields to the index the index should have been fully setup, with the document\n * ref and all fields to index already having been specified.\n *\n * The document must have a field name as specified by the ref (by default this is 'id') and\n * it should have all fields defined for indexing, though null or undefined values will not\n * cause errors.\n *\n * Entire documents can be boosted at build time. Applying a boost to a document indicates that\n * this document should rank higher in search results than other documents.\n *\n * @param {object} doc - The document to add to the index.\n * @param {object} attributes - Optional attributes associated with this document.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.\n */\nlunr.Builder.prototype.add = function (doc, attributes) {\n var docRef = doc[this._ref],\n fields = Object.keys(this._fields)\n\n this._documents[docRef] = attributes || {}\n this.documentCount += 1\n\n for (var i = 0; i < fields.length; i++) {\n var fieldName = fields[i],\n extractor = this._fields[fieldName].extractor,\n field = extractor ? extractor(doc) : doc[fieldName],\n tokens = this.tokenizer(field, {\n fields: [fieldName]\n }),\n terms = this.pipeline.run(tokens),\n fieldRef = new lunr.FieldRef (docRef, fieldName),\n fieldTerms = Object.create(null)\n\n this.fieldTermFrequencies[fieldRef] = fieldTerms\n this.fieldLengths[fieldRef] = 0\n\n // store the length of this field for this document\n this.fieldLengths[fieldRef] += terms.length\n\n // calculate term frequencies for this field\n for (var j = 0; j < terms.length; j++) {\n var term = terms[j]\n\n if (fieldTerms[term] == undefined) {\n fieldTerms[term] = 0\n }\n\n fieldTerms[term] += 1\n\n // add to inverted index\n // create an initial posting if one doesn't exist\n if (this.invertedIndex[term] == undefined) {\n var posting = Object.create(null)\n posting[\"_index\"] = this.termIndex\n this.termIndex += 1\n\n for (var k = 0; k < fields.length; k++) {\n posting[fields[k]] = Object.create(null)\n }\n\n this.invertedIndex[term] = posting\n }\n\n // add an entry for this term/fieldName/docRef to the invertedIndex\n if (this.invertedIndex[term][fieldName][docRef] == undefined) {\n this.invertedIndex[term][fieldName][docRef] = Object.create(null)\n }\n\n // store all whitelisted metadata about this token in the\n // inverted index\n for (var l = 0; l < this.metadataWhitelist.length; l++) {\n var metadataKey = this.metadataWhitelist[l],\n metadata = term.metadata[metadataKey]\n\n if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {\n this.invertedIndex[term][fieldName][docRef][metadataKey] = []\n }\n\n this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)\n }\n }\n\n }\n}\n\n/**\n * Calculates the average document length for this index\n *\n * @private\n */\nlunr.Builder.prototype.calculateAverageFieldLengths = function () {\n\n var fieldRefs = Object.keys(this.fieldLengths),\n numberOfFields = fieldRefs.length,\n accumulator = {},\n documentsWithField = {}\n\n for (var i = 0; i < numberOfFields; i++) {\n var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n field = fieldRef.fieldName\n\n documentsWithField[field] || (documentsWithField[field] = 0)\n documentsWithField[field] += 1\n\n accumulator[field] || (accumulator[field] = 0)\n accumulator[field] += this.fieldLengths[fieldRef]\n }\n\n var fields = Object.keys(this._fields)\n\n for (var i = 0; i < fields.length; i++) {\n var fieldName = fields[i]\n accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]\n }\n\n this.averageFieldLength = accumulator\n}\n\n/**\n * Builds a vector space model of every document using lunr.Vector\n *\n * @private\n */\nlunr.Builder.prototype.createFieldVectors = function () {\n var fieldVectors = {},\n fieldRefs = Object.keys(this.fieldTermFrequencies),\n fieldRefsLength = fieldRefs.length,\n termIdfCache = Object.create(null)\n\n for (var i = 0; i < fieldRefsLength; i++) {\n var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n fieldName = fieldRef.fieldName,\n fieldLength = this.fieldLengths[fieldRef],\n fieldVector = new lunr.Vector,\n termFrequencies = this.fieldTermFrequencies[fieldRef],\n terms = Object.keys(termFrequencies),\n termsLength = terms.length\n\n\n var fieldBoost = this._fields[fieldName].boost || 1,\n docBoost = this._documents[fieldRef.docRef].boost || 1\n\n for (var j = 0; j < termsLength; j++) {\n var term = terms[j],\n tf = termFrequencies[term],\n termIndex = this.invertedIndex[term]._index,\n idf, score, scoreWithPrecision\n\n if (termIdfCache[term] === undefined) {\n idf = lunr.idf(this.invertedIndex[term], this.documentCount)\n termIdfCache[term] = idf\n } else {\n idf = termIdfCache[term]\n }\n\n score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)\n score *= fieldBoost\n score *= docBoost\n scoreWithPrecision = Math.round(score * 1000) / 1000\n // Converts 1.23456789 to 1.234.\n // Reducing the precision so that the vectors take up less\n // space when serialised. Doing it now so that they behave\n // the same before and after serialisation. Also, this is\n // the fastest approach to reducing a number's precision in\n // JavaScript.\n\n fieldVector.insert(termIndex, scoreWithPrecision)\n }\n\n fieldVectors[fieldRef] = fieldVector\n }\n\n this.fieldVectors = fieldVectors\n}\n\n/**\n * Creates a token set of all tokens in the index using lunr.TokenSet\n *\n * @private\n */\nlunr.Builder.prototype.createTokenSet = function () {\n this.tokenSet = lunr.TokenSet.fromArray(\n Object.keys(this.invertedIndex).sort()\n )\n}\n\n/**\n * Builds the index, creating an instance of lunr.Index.\n *\n * This completes the indexing process and should only be called\n * once all documents have been added to the index.\n *\n * @returns {lunr.Index}\n */\nlunr.Builder.prototype.build = function () {\n this.calculateAverageFieldLengths()\n this.createFieldVectors()\n this.createTokenSet()\n\n return new lunr.Index({\n invertedIndex: this.invertedIndex,\n fieldVectors: this.fieldVectors,\n tokenSet: this.tokenSet,\n fields: Object.keys(this._fields),\n pipeline: this.searchPipeline\n })\n}\n\n/**\n * Applies a plugin to the index builder.\n *\n * A plugin is a function that is called with the index builder as its context.\n * Plugins can be used to customise or extend the behaviour of the index\n * in some way. A plugin is just a function, that encapsulated the custom\n * behaviour that should be applied when building the index.\n *\n * The plugin function will be called with the index builder as its argument, additional\n * arguments can also be passed when calling use. The function will be called\n * with the index builder as its context.\n *\n * @param {Function} plugin The plugin to apply.\n */\nlunr.Builder.prototype.use = function (fn) {\n var args = Array.prototype.slice.call(arguments, 1)\n args.unshift(this)\n fn.apply(this, args)\n}\n/**\n * Contains and collects metadata about a matching document.\n * A single instance of lunr.MatchData is returned as part of every\n * lunr.Index~Result.\n *\n * @constructor\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n * @property {object} metadata - A cloned collection of metadata associated with this document.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData = function (term, field, metadata) {\n var clonedMetadata = Object.create(null),\n metadataKeys = Object.keys(metadata || {})\n\n // Cloning the metadata to prevent the original\n // being mutated during match data combination.\n // Metadata is kept in an array within the inverted\n // index so cloning the data can be done with\n // Array#slice\n for (var i = 0; i < metadataKeys.length; i++) {\n var key = metadataKeys[i]\n clonedMetadata[key] = metadata[key].slice()\n }\n\n this.metadata = Object.create(null)\n\n if (term !== undefined) {\n this.metadata[term] = Object.create(null)\n this.metadata[term][field] = clonedMetadata\n }\n}\n\n/**\n * An instance of lunr.MatchData will be created for every term that matches a\n * document. However only one instance is required in a lunr.Index~Result. This\n * method combines metadata from another instance of lunr.MatchData with this\n * objects metadata.\n *\n * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData.prototype.combine = function (otherMatchData) {\n var terms = Object.keys(otherMatchData.metadata)\n\n for (var i = 0; i < terms.length; i++) {\n var term = terms[i],\n fields = Object.keys(otherMatchData.metadata[term])\n\n if (this.metadata[term] == undefined) {\n this.metadata[term] = Object.create(null)\n }\n\n for (var j = 0; j < fields.length; j++) {\n var field = fields[j],\n keys = Object.keys(otherMatchData.metadata[term][field])\n\n if (this.metadata[term][field] == undefined) {\n this.metadata[term][field] = Object.create(null)\n }\n\n for (var k = 0; k < keys.length; k++) {\n var key = keys[k]\n\n if (this.metadata[term][field][key] == undefined) {\n this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]\n } else {\n this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])\n }\n\n }\n }\n }\n}\n\n/**\n * Add metadata for a term/field pair to this instance of match data.\n *\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n */\nlunr.MatchData.prototype.add = function (term, field, metadata) {\n if (!(term in this.metadata)) {\n this.metadata[term] = Object.create(null)\n this.metadata[term][field] = metadata\n return\n }\n\n if (!(field in this.metadata[term])) {\n this.metadata[term][field] = metadata\n return\n }\n\n var metadataKeys = Object.keys(metadata)\n\n for (var i = 0; i < metadataKeys.length; i++) {\n var key = metadataKeys[i]\n\n if (key in this.metadata[term][field]) {\n this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])\n } else {\n this.metadata[term][field][key] = metadata[key]\n }\n }\n}\n/**\n * A lunr.Query provides a programmatic way of defining queries to be performed\n * against a {@link lunr.Index}.\n *\n * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method\n * so the query object is pre-initialized with the right index fields.\n *\n * @constructor\n * @property {lunr.Query~Clause[]} clauses - An array of query clauses.\n * @property {string[]} allFields - An array of all available fields in a lunr.Index.\n */\nlunr.Query = function (allFields) {\n this.clauses = []\n this.allFields = allFields\n}\n\n/**\n * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.\n *\n * This allows wildcards to be added to the beginning and end of a term without having to manually do any string\n * concatenation.\n *\n * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.\n *\n * @constant\n * @default\n * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour\n * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists\n * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example query term with trailing wildcard\n * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })\n * @example query term with leading and trailing wildcard\n * query.term('foo', {\n * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING\n * })\n */\n\nlunr.Query.wildcard = new String (\"*\")\nlunr.Query.wildcard.NONE = 0\nlunr.Query.wildcard.LEADING = 1\nlunr.Query.wildcard.TRAILING = 2\n\n/**\n * Constants for indicating what kind of presence a term must have in matching documents.\n *\n * @constant\n * @enum {number}\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example query term with required presence\n * query.term('foo', { presence: lunr.Query.presence.REQUIRED })\n */\nlunr.Query.presence = {\n /**\n * Term's presence in a document is optional, this is the default value.\n */\n OPTIONAL: 1,\n\n /**\n * Term's presence in a document is required, documents that do not contain\n * this term will not be returned.\n */\n REQUIRED: 2,\n\n /**\n * Term's presence in a document is prohibited, documents that do contain\n * this term will not be returned.\n */\n PROHIBITED: 3\n}\n\n/**\n * A single clause in a {@link lunr.Query} contains a term and details on how to\n * match that term against a {@link lunr.Index}.\n *\n * @typedef {Object} lunr.Query~Clause\n * @property {string[]} fields - The fields in an index this clause should be matched against.\n * @property {number} [boost=1] - Any boost that should be applied when matching this clause.\n * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.\n * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.\n * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.\n * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.\n */\n\n/**\n * Adds a {@link lunr.Query~Clause} to this query.\n *\n * Unless the clause contains the fields to be matched all fields will be matched. In addition\n * a default boost of 1 is applied to the clause.\n *\n * @param {lunr.Query~Clause} clause - The clause to add to this query.\n * @see lunr.Query~Clause\n * @returns {lunr.Query}\n */\nlunr.Query.prototype.clause = function (clause) {\n if (!('fields' in clause)) {\n clause.fields = this.allFields\n }\n\n if (!('boost' in clause)) {\n clause.boost = 1\n }\n\n if (!('usePipeline' in clause)) {\n clause.usePipeline = true\n }\n\n if (!('wildcard' in clause)) {\n clause.wildcard = lunr.Query.wildcard.NONE\n }\n\n if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {\n clause.term = \"*\" + clause.term\n }\n\n if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {\n clause.term = \"\" + clause.term + \"*\"\n }\n\n if (!('presence' in clause)) {\n clause.presence = lunr.Query.presence.OPTIONAL\n }\n\n this.clauses.push(clause)\n\n return this\n}\n\n/**\n * A negated query is one in which every clause has a presence of\n * prohibited. These queries require some special processing to return\n * the expected results.\n *\n * @returns boolean\n */\nlunr.Query.prototype.isNegated = function () {\n for (var i = 0; i < this.clauses.length; i++) {\n if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {\n return false\n }\n }\n\n return true\n}\n\n/**\n * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}\n * to the list of clauses that make up this query.\n *\n * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion\n * to a token or token-like string should be done before calling this method.\n *\n * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an\n * array, each term in the array will share the same options.\n *\n * @param {object|object[]} term - The term(s) to add to the query.\n * @param {object} [options] - Any additional properties to add to the query clause.\n * @returns {lunr.Query}\n * @see lunr.Query#clause\n * @see lunr.Query~Clause\n * @example adding a single term to a query\n * query.term(\"foo\")\n * @example adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard\n * query.term(\"foo\", {\n * fields: [\"title\"],\n * boost: 10,\n * wildcard: lunr.Query.wildcard.TRAILING\n * })\n * @example using lunr.tokenizer to convert a string to tokens before using them as terms\n * query.term(lunr.tokenizer(\"foo bar\"))\n */\nlunr.Query.prototype.term = function (term, options) {\n if (Array.isArray(term)) {\n term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this)\n return this\n }\n\n var clause = options || {}\n clause.term = term.toString()\n\n this.clause(clause)\n\n return this\n}\nlunr.QueryParseError = function (message, start, end) {\n this.name = \"QueryParseError\"\n this.message = message\n this.start = start\n this.end = end\n}\n\nlunr.QueryParseError.prototype = new Error\nlunr.QueryLexer = function (str) {\n this.lexemes = []\n this.str = str\n this.length = str.length\n this.pos = 0\n this.start = 0\n this.escapeCharPositions = []\n}\n\nlunr.QueryLexer.prototype.run = function () {\n var state = lunr.QueryLexer.lexText\n\n while (state) {\n state = state(this)\n }\n}\n\nlunr.QueryLexer.prototype.sliceString = function () {\n var subSlices = [],\n sliceStart = this.start,\n sliceEnd = this.pos\n\n for (var i = 0; i < this.escapeCharPositions.length; i++) {\n sliceEnd = this.escapeCharPositions[i]\n subSlices.push(this.str.slice(sliceStart, sliceEnd))\n sliceStart = sliceEnd + 1\n }\n\n subSlices.push(this.str.slice(sliceStart, this.pos))\n this.escapeCharPositions.length = 0\n\n return subSlices.join('')\n}\n\nlunr.QueryLexer.prototype.emit = function (type) {\n this.lexemes.push({\n type: type,\n str: this.sliceString(),\n start: this.start,\n end: this.pos\n })\n\n this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.escapeCharacter = function () {\n this.escapeCharPositions.push(this.pos - 1)\n this.pos += 1\n}\n\nlunr.QueryLexer.prototype.next = function () {\n if (this.pos >= this.length) {\n return lunr.QueryLexer.EOS\n }\n\n var char = this.str.charAt(this.pos)\n this.pos += 1\n return char\n}\n\nlunr.QueryLexer.prototype.width = function () {\n return this.pos - this.start\n}\n\nlunr.QueryLexer.prototype.ignore = function () {\n if (this.start == this.pos) {\n this.pos += 1\n }\n\n this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.backup = function () {\n this.pos -= 1\n}\n\nlunr.QueryLexer.prototype.acceptDigitRun = function () {\n var char, charCode\n\n do {\n char = this.next()\n charCode = char.charCodeAt(0)\n } while (charCode > 47 && charCode < 58)\n\n if (char != lunr.QueryLexer.EOS) {\n this.backup()\n }\n}\n\nlunr.QueryLexer.prototype.more = function () {\n return this.pos < this.length\n}\n\nlunr.QueryLexer.EOS = 'EOS'\nlunr.QueryLexer.FIELD = 'FIELD'\nlunr.QueryLexer.TERM = 'TERM'\nlunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'\nlunr.QueryLexer.BOOST = 'BOOST'\nlunr.QueryLexer.PRESENCE = 'PRESENCE'\n\nlunr.QueryLexer.lexField = function (lexer) {\n lexer.backup()\n lexer.emit(lunr.QueryLexer.FIELD)\n lexer.ignore()\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexTerm = function (lexer) {\n if (lexer.width() > 1) {\n lexer.backup()\n lexer.emit(lunr.QueryLexer.TERM)\n }\n\n lexer.ignore()\n\n if (lexer.more()) {\n return lunr.QueryLexer.lexText\n }\n}\n\nlunr.QueryLexer.lexEditDistance = function (lexer) {\n lexer.ignore()\n lexer.acceptDigitRun()\n lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexBoost = function (lexer) {\n lexer.ignore()\n lexer.acceptDigitRun()\n lexer.emit(lunr.QueryLexer.BOOST)\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexEOS = function (lexer) {\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n}\n\n// This matches the separator used when tokenising fields\n// within a document. These should match otherwise it is\n// not possible to search for some tokens within a document.\n//\n// It is possible for the user to change the separator on the\n// tokenizer so it _might_ clash with any other of the special\n// characters already used within the search string, e.g. :.\n//\n// This means that it is possible to change the separator in\n// such a way that makes some words unsearchable using a search\n// string.\nlunr.QueryLexer.termSeparator = lunr.tokenizer.separator\n\nlunr.QueryLexer.lexText = function (lexer) {\n while (true) {\n var char = lexer.next()\n\n if (char == lunr.QueryLexer.EOS) {\n return lunr.QueryLexer.lexEOS\n }\n\n // Escape character is '\\'\n if (char.charCodeAt(0) == 92) {\n lexer.escapeCharacter()\n continue\n }\n\n if (char == \":\") {\n return lunr.QueryLexer.lexField\n }\n\n if (char == \"~\") {\n lexer.backup()\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n return lunr.QueryLexer.lexEditDistance\n }\n\n if (char == \"^\") {\n lexer.backup()\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n return lunr.QueryLexer.lexBoost\n }\n\n // \"+\" indicates term presence is required\n // checking for length to ensure that only\n // leading \"+\" are considered\n if (char == \"+\" && lexer.width() === 1) {\n lexer.emit(lunr.QueryLexer.PRESENCE)\n return lunr.QueryLexer.lexText\n }\n\n // \"-\" indicates term presence is prohibited\n // checking for length to ensure that only\n // leading \"-\" are considered\n if (char == \"-\" && lexer.width() === 1) {\n lexer.emit(lunr.QueryLexer.PRESENCE)\n return lunr.QueryLexer.lexText\n }\n\n if (char.match(lunr.QueryLexer.termSeparator)) {\n return lunr.QueryLexer.lexTerm\n }\n }\n}\n\nlunr.QueryParser = function (str, query) {\n this.lexer = new lunr.QueryLexer (str)\n this.query = query\n this.currentClause = {}\n this.lexemeIdx = 0\n}\n\nlunr.QueryParser.prototype.parse = function () {\n this.lexer.run()\n this.lexemes = this.lexer.lexemes\n\n var state = lunr.QueryParser.parseClause\n\n while (state) {\n state = state(this)\n }\n\n return this.query\n}\n\nlunr.QueryParser.prototype.peekLexeme = function () {\n return this.lexemes[this.lexemeIdx]\n}\n\nlunr.QueryParser.prototype.consumeLexeme = function () {\n var lexeme = this.peekLexeme()\n this.lexemeIdx += 1\n return lexeme\n}\n\nlunr.QueryParser.prototype.nextClause = function () {\n var completedClause = this.currentClause\n this.query.clause(completedClause)\n this.currentClause = {}\n}\n\nlunr.QueryParser.parseClause = function (parser) {\n var lexeme = parser.peekLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n switch (lexeme.type) {\n case lunr.QueryLexer.PRESENCE:\n return lunr.QueryParser.parsePresence\n case lunr.QueryLexer.FIELD:\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expected either a field or a term, found \" + lexeme.type\n\n if (lexeme.str.length >= 1) {\n errorMessage += \" with value '\" + lexeme.str + \"'\"\n }\n\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n}\n\nlunr.QueryParser.parsePresence = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n switch (lexeme.str) {\n case \"-\":\n parser.currentClause.presence = lunr.Query.presence.PROHIBITED\n break\n case \"+\":\n parser.currentClause.presence = lunr.Query.presence.REQUIRED\n break\n default:\n var errorMessage = \"unrecognised presence operator'\" + lexeme.str + \"'\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n var errorMessage = \"expecting term or field, found nothing\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.FIELD:\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expecting term or field, found '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseField = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n if (parser.query.allFields.indexOf(lexeme.str) == -1) {\n var possibleFields = parser.query.allFields.map(function (f) { return \"'\" + f + \"'\" }).join(', '),\n errorMessage = \"unrecognised field '\" + lexeme.str + \"', possible fields: \" + possibleFields\n\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.fields = [lexeme.str]\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n var errorMessage = \"expecting term, found nothing\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expecting term, found '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseTerm = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n parser.currentClause.term = lexeme.str.toLowerCase()\n\n if (lexeme.str.indexOf(\"*\") != -1) {\n parser.currentClause.usePipeline = false\n }\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseEditDistance = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n var editDistance = parseInt(lexeme.str, 10)\n\n if (isNaN(editDistance)) {\n var errorMessage = \"edit distance must be numeric\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.editDistance = editDistance\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseBoost = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n var boost = parseInt(lexeme.str, 10)\n\n if (isNaN(boost)) {\n var errorMessage = \"boost must be numeric\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.boost = boost\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\n /**\n * export the module via AMD, CommonJS or as a browser global\n * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js\n */\n ;(function (root, factory) {\n if (typeof define === 'function' && define.amd) {\n // AMD. Register as an anonymous module.\n define(factory)\n } else if (typeof exports === 'object') {\n /**\n * Node. Does not work with strict CommonJS, but\n * only CommonJS-like enviroments that support module.exports,\n * like Node.\n */\n module.exports = factory()\n } else {\n // Browser globals (root is window)\n root.lunr = factory()\n }\n }(this, function () {\n /**\n * Just return a value to define the module export.\n * This example returns an object, but the module\n * can return a function as the exported value.\n */\n return lunr\n }))\n})();\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport lunr from \"lunr\"\n\nimport \"~/polyfills\"\n\nimport { Search, SearchIndexConfig } from \"../../_\"\nimport {\n SearchMessage,\n SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Add support for usage with `iframe-worker` polyfill\n *\n * While `importScripts` is synchronous when executed inside of a web worker,\n * it's not possible to provide a synchronous polyfilled implementation. The\n * cool thing is that awaiting a non-Promise is a noop, so extending the type\n * definition to return a `Promise` shouldn't break anything.\n *\n * @see https://bit.ly/2PjDnXi - GitHub comment\n */\ndeclare global {\n function importScripts(...urls: string[]): Promise | void\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nlet index: Search\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch (= import) multi-language support through `lunr-languages`\n *\n * This function automatically imports the stemmers necessary to process the\n * languages, which are defined through the search index configuration.\n *\n * If the worker runs inside of an `iframe` (when using `iframe-worker` as\n * a shim), the base URL for the stemmers to be loaded must be determined by\n * searching for the first `script` element with a `src` attribute, which will\n * contain the contents of this script.\n *\n * @param config - Search index configuration\n *\n * @returns Promise resolving with no result\n */\nasync function setupSearchLanguages(\n config: SearchIndexConfig\n): Promise {\n let base = \"../lunr\"\n\n /* Detect `iframe-worker` and fix base URL */\n if (typeof parent !== \"undefined\" && \"IFrameWorker\" in parent) {\n const worker = document.querySelector(\"script[src]\")!\n const [path] = worker.src.split(\"/worker\")\n\n /* Prefix base with path */\n base = base.replace(\"..\", path)\n }\n\n /* Add scripts for languages */\n const scripts = []\n for (const lang of config.lang) {\n switch (lang) {\n\n /* Add segmenter for Japanese */\n case \"ja\":\n scripts.push(`${base}/tinyseg.js`)\n break\n\n /* Add segmenter for Hindi and Thai */\n case \"hi\":\n case \"th\":\n scripts.push(`${base}/wordcut.js`)\n break\n }\n\n /* Add language support */\n if (lang !== \"en\")\n scripts.push(`${base}/min/lunr.${lang}.min.js`)\n }\n\n /* Add multi-language support */\n if (config.lang.length > 1)\n scripts.push(`${base}/min/lunr.multi.min.js`)\n\n /* Load scripts synchronously */\n if (scripts.length)\n await importScripts(\n `${base}/min/lunr.stemmer.support.min.js`,\n ...scripts\n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Message handler\n *\n * @param message - Source message\n *\n * @returns Target message\n */\nexport async function handler(\n message: SearchMessage\n): Promise {\n switch (message.type) {\n\n /* Search setup message */\n case SearchMessageType.SETUP:\n await setupSearchLanguages(message.data.config)\n index = new Search(message.data)\n return {\n type: SearchMessageType.READY\n }\n\n /* Search query message */\n case SearchMessageType.QUERY:\n return {\n type: SearchMessageType.RESULT,\n data: index ? index.search(message.data) : { items: [] }\n }\n\n /* All other messages */\n default:\n throw new TypeError(\"Invalid message type\")\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Worker\n * ------------------------------------------------------------------------- */\n\n/* @ts-expect-error - expose Lunr.js in global scope, or stemmers won't work */\nself.lunr = lunr\n\n/* Handle messages */\naddEventListener(\"message\", async ev => {\n postMessage(await handler(ev.data))\n})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n Object.entries = function (obj: object) {\n const data: [string, string][] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push([key, obj[key]])\n\n /* Return entries */\n return data\n }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n Object.values = function (obj: object) {\n const data: string[] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push(obj[key])\n\n /* Return values */\n return data\n }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n /* Polyfill `Element.scrollTo` */\n if (!Element.prototype.scrollTo)\n Element.prototype.scrollTo = function (\n x?: ScrollToOptions | number, y?: number\n ): void {\n if (typeof x === \"object\") {\n this.scrollLeft = x.left!\n this.scrollTop = x.top!\n } else {\n this.scrollLeft = x!\n this.scrollTop = y!\n }\n }\n\n /* Polyfill `Element.replaceWith` */\n if (!Element.prototype.replaceWith)\n Element.prototype.replaceWith = function (\n ...nodes: Array\n ): void {\n const parent = this.parentNode\n if (parent) {\n if (nodes.length === 0)\n parent.removeChild(this)\n\n /* Replace children and create text nodes */\n for (let i = nodes.length - 1; i >= 0; i--) {\n let node = nodes[i]\n if (typeof node !== \"object\")\n node = document.createTextNode(node)\n else if (node.parentNode)\n node.parentNode.removeChild(node)\n\n /* Replace child or insert before previous sibling */\n if (!i)\n parent.replaceChild(node, this)\n else\n parent.insertBefore(this.previousSibling!, node)\n }\n }\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n parent?: SearchIndexDocument /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n docs: SearchIndexDocument[]\n): SearchDocumentMap {\n const documents = new Map()\n const parents = new Set()\n for (const doc of docs) {\n const [path, hash] = doc.location.split(\"#\")\n\n /* Extract location, title and tags */\n const location = doc.location\n const title = doc.title\n const tags = doc.tags\n\n /* Escape and cleanup text */\n const text = escapeHTML(doc.text)\n .replace(/\\s+(?=[,.:;!?])/g, \"\")\n .replace(/\\s+/g, \" \")\n\n /* Handle section */\n if (hash) {\n const parent = documents.get(path)!\n\n /* Ignore first section, override article */\n if (!parents.has(parent)) {\n parent.title = doc.title\n parent.text = text\n\n /* Remember that we processed the article */\n parents.add(parent)\n\n /* Add subsequent section */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n parent\n })\n }\n\n /* Add article */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n ...tags && { tags }\n })\n }\n }\n return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n const separator = new RegExp(config.separator, \"img\")\n const highlight = (_: unknown, data: string, term: string) => {\n return `${data}${term}`\n }\n\n /* Return factory function */\n return (query: string) => {\n query = query\n .replace(/[\\s*+\\-:~^]+/g, \" \")\n .trim()\n\n /* Create search term match expression */\n const match = new RegExp(`(^|${config.separator})(${\n query\n .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n .replace(separator, \"|\")\n })`, \"img\")\n\n /* Highlight string value */\n return value => (\n escape\n ? escapeHTML(value)\n : value\n )\n .replace(match, highlight)\n .replace(/<\\/mark>(\\s+)]*>/img, \"$1\")\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query clause\n */\nexport interface SearchQueryClause {\n presence: lunr.Query.presence /* Clause presence */\n term: string /* Clause term */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search query terms\n */\nexport type SearchQueryTerms = Record\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Parse a search query for analysis\n *\n * @param value - Query value\n *\n * @returns Search query clauses\n */\nexport function parseSearchQuery(\n value: string\n): SearchQueryClause[] {\n const query = new (lunr as any).Query([\"title\", \"text\"])\n const parser = new (lunr as any).QueryParser(value, query)\n\n /* Parse and return query clauses */\n parser.parse()\n return query.clauses\n}\n\n/**\n * Analyze the search query clauses in regard to the search terms found\n *\n * @param query - Search query clauses\n * @param terms - Search terms\n *\n * @returns Search query terms\n */\nexport function getSearchQueryTerms(\n query: SearchQueryClause[], terms: string[]\n): SearchQueryTerms {\n const clauses = new Set(query)\n\n /* Match query clauses against terms */\n const result: SearchQueryTerms = {}\n for (let t = 0; t < terms.length; t++)\n for (const clause of clauses)\n if (terms[t].startsWith(clause.term)) {\n result[clause.term] = true\n clauses.delete(clause)\n }\n\n /* Annotate unmatched non-stopword query clauses */\n for (const clause of clauses)\n if (lunr.stopWordFilter?.(clause.term as any))\n result[clause.term] = false\n\n /* Return query terms */\n return result\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n SearchDocument,\n SearchDocumentMap,\n setupSearchDocumentMap\n} from \"../document\"\nimport {\n SearchHighlightFactoryFn,\n setupSearchHighlighter\n} from \"../highlighter\"\nimport { SearchOptions } from \"../options\"\nimport {\n SearchQueryTerms,\n getSearchQueryTerms,\n parseSearchQuery\n} from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index configuration\n */\nexport interface SearchIndexConfig {\n lang: string[] /* Search languages */\n separator: string /* Search separator */\n}\n\n/**\n * Search index document\n */\nexport interface SearchIndexDocument {\n location: string /* Document location */\n title: string /* Document title */\n text: string /* Document text */\n tags?: string[] /* Document tags */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search index\n *\n * This interfaces describes the format of the `search_index.json` file which\n * is automatically built by the MkDocs search plugin.\n */\nexport interface SearchIndex {\n config: SearchIndexConfig /* Search index configuration */\n docs: SearchIndexDocument[] /* Search index documents */\n options: SearchOptions /* Search options */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search metadata\n */\nexport interface SearchMetadata {\n score: number /* Score (relevance) */\n terms: SearchQueryTerms /* Search query terms */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result document\n */\nexport type SearchResultDocument = SearchDocument & SearchMetadata\n\n/**\n * Search result item\n */\nexport type SearchResultItem = SearchResultDocument[]\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result\n */\nexport interface SearchResult {\n items: SearchResultItem[] /* Search result items */\n suggestions?: string[] /* Search suggestions */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute the difference of two lists of strings\n *\n * @param a - 1st list of strings\n * @param b - 2nd list of strings\n *\n * @returns Difference\n */\nfunction difference(a: string[], b: string[]): string[] {\n const [x, y] = [new Set(a), new Set(b)]\n return [\n ...new Set([...x].filter(value => !y.has(value)))\n ]\n}\n\n/* ----------------------------------------------------------------------------\n * Class\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport class Search {\n\n /**\n * Search document mapping\n *\n * A mapping of URLs (including hash fragments) to the actual articles and\n * sections of the documentation. The search document mapping must be created\n * regardless of whether the index was prebuilt or not, as Lunr.js itself\n * only stores the actual index.\n */\n protected documents: SearchDocumentMap\n\n /**\n * Search highlight factory function\n */\n protected highlight: SearchHighlightFactoryFn\n\n /**\n * The underlying Lunr.js search index\n */\n protected index: lunr.Index\n\n /**\n * Search options\n */\n protected options: SearchOptions\n\n /**\n * Create the search integration\n *\n * @param data - Search index\n */\n public constructor({ config, docs, options }: SearchIndex) {\n this.options = options\n\n /* Set up document map and highlighter factory */\n this.documents = setupSearchDocumentMap(docs)\n this.highlight = setupSearchHighlighter(config, false)\n\n /* Set separator for tokenizer */\n lunr.tokenizer.separator = new RegExp(config.separator)\n\n /* Create search index */\n this.index = lunr(function () {\n\n /* Set up multi-language support */\n if (config.lang.length === 1 && config.lang[0] !== \"en\") {\n this.use((lunr as any)[config.lang[0]])\n } else if (config.lang.length > 1) {\n this.use((lunr as any).multiLanguage(...config.lang))\n }\n\n /* Compute functions to be removed from the pipeline */\n const fns = difference([\n \"trimmer\", \"stopWordFilter\", \"stemmer\"\n ], options.pipeline)\n\n /* Remove functions from the pipeline for registered languages */\n for (const lang of config.lang.map(language => (\n language === \"en\" ? lunr : (lunr as any)[language]\n ))) {\n for (const fn of fns) {\n this.pipeline.remove(lang[fn])\n this.searchPipeline.remove(lang[fn])\n }\n }\n\n /* Set up reference */\n this.ref(\"location\")\n\n /* Set up fields */\n this.field(\"title\", { boost: 1e3 })\n this.field(\"text\")\n this.field(\"tags\", { boost: 1e6 })\n\n /* Index documents */\n for (const doc of docs)\n this.add(doc)\n })\n }\n\n /**\n * Search for matching documents\n *\n * The search index which MkDocs provides is divided up into articles, which\n * contain the whole content of the individual pages, and sections, which only\n * contain the contents of the subsections obtained by breaking the individual\n * pages up at `h1` ... `h6`. As there may be many sections on different pages\n * with identical titles (for example within this very project, e.g. \"Usage\"\n * or \"Installation\"), they need to be put into the context of the containing\n * page. For this reason, section results are grouped within their respective\n * articles which are the top-level results that are returned.\n *\n * @param query - Query value\n *\n * @returns Search results\n */\n public search(query: string): SearchResult {\n if (query) {\n try {\n const highlight = this.highlight(query)\n\n /* Parse query to extract clauses for analysis */\n const clauses = parseSearchQuery(query)\n .filter(clause => (\n clause.presence !== lunr.Query.presence.PROHIBITED\n ))\n\n /* Perform search and post-process results */\n const groups = this.index.search(`${query}*`)\n\n /* Apply post-query boosts based on title and search query terms */\n .reduce((item, { ref, score, matchData }) => {\n const document = this.documents.get(ref)\n if (typeof document !== \"undefined\") {\n const { location, title, text, tags, parent } = document\n\n /* Compute and analyze search query terms */\n const terms = getSearchQueryTerms(\n clauses,\n Object.keys(matchData.metadata)\n )\n\n /* Highlight title and text and apply post-query boosts */\n const boost = +!parent + +Object.values(terms).every(t => t)\n item.push({\n location,\n title: highlight(title),\n text: highlight(text),\n ...tags && { tags: tags.map(highlight) },\n score: score * (1 + boost),\n terms\n })\n }\n return item\n }, [])\n\n /* Sort search results again after applying boosts */\n .sort((a, b) => b.score - a.score)\n\n /* Group search results by page */\n .reduce((items, result) => {\n const document = this.documents.get(result.location)\n if (typeof document !== \"undefined\") {\n const ref = \"parent\" in document\n ? document.parent!.location\n : document.location\n items.set(ref, [...items.get(ref) || [], result])\n }\n return items\n }, new Map())\n\n /* Generate search suggestions, if desired */\n let suggestions: string[] | undefined\n if (this.options.suggestions) {\n const titles = this.index.query(builder => {\n for (const clause of clauses)\n builder.term(clause.term, {\n fields: [\"title\"],\n presence: lunr.Query.presence.REQUIRED,\n wildcard: lunr.Query.wildcard.TRAILING\n })\n })\n\n /* Retrieve suggestions for best match */\n suggestions = titles.length\n ? Object.keys(titles[0].matchData.metadata)\n : []\n }\n\n /* Return items and suggestions */\n return {\n items: [...groups.values()],\n ...typeof suggestions !== \"undefined\" && { suggestions }\n }\n\n /* Log errors to console (for now) */\n } catch {\n console.warn(`Invalid query: ${query} \u2013 see https://bit.ly/2s3ChXG`)\n }\n }\n\n /* Return nothing in case of error or empty query */\n return { items: [] }\n }\n}\n"], + "mappings": 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\ No newline at end of file diff --git a/main/changelog/index.html b/main/changelog/index.html new file mode 100644 index 00000000..b64e9f04 --- /dev/null +++ b/main/changelog/index.html @@ -0,0 +1,1829 @@ + + + + + + + + + +Changelog - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    +

    Changelog

    +

    Unreleased

    +

    Fixed

    +
      +
    • Quartiles computed from plot_concepts_set does not depend on value selection anymore
      • +
    +

    v0.1.8 (2024-06-13)

    +

    Fixed

    +
      +
    • Pyarrow fix now work on spark executors.
      • +
    • Fix OMOP _date columns issue
      • +
    +

    Added

    +
      +
    • omop teva module
      • +
    +

    v0.1.7 (2024-04-12)

    +

    Changed

    +
      +
    • Support for pyarrow > 0.17.0
      • +
    +

    Added

    +
      +
    • biology module refacto
      • +
    • load_koalas() not by default in init.py but called in the improve_performance function
      • +
    • adding app_name in improve_performances to facilitate app monitoring
      • +
    +

    Fixed

    +
      +
    • Generation of an inclusion/exclusion flowchart in plotting
      • +
    • improve_performance moved from init.py to io/improve_performance.py file
      • +
    • Caching in spark instead of koalas to improve speed
      • +
    +

    v0.1.6 (2023-09-27)

    +

    Added

    +
      +
    • Module event_sequences to visualize individual sequences of events.
      • +
    • Module age_pyramid to quickly visualize the age and gender distributions in a cohort.
      • +
    +

    Fixed

    + +

    v0.1.5 (2023-04-05)

    +

    Added

    +
      +
    • BaseData class as a parent class for HiveData, PandasData and PostgresData.
      • +
    • Phentyping class with 4 implemented phenotyes.
      • +
    • Custom logger to display useful information during computation.
      • +
    +

    Fixed

    +
      +
    • Add caching to speedup computations.
      • +
    • Updated method to persist tables as parquet locally, with a support for ORC-stored I2B2 database.
      • +
    +

    v0.1.4 (2023-02-09)

    +

    Added

    +
      +
    • Allow saving DB locally in client or cluster mode.
      • +
    • Add data cleaning function to handle incorrect datetime in spark.
      • +
    • Filter biology config on care site.
      • +
    • Adding person-dependent datetime_ref to plot_age_pyramid.
      • +
    +

    Fixed

    +
      +
    • Consultations date for OMOP & I2B2
      • +
    +

    v0.1.3 (2023-02-02)

    +

    Added

    +
      +
    • New BackendDispatcher to handle framework-specific functions
      • +
    • I2B2 to OMOP connector
      • +
    +

    v0.1.2 (2022-12-05)

    +

    Added

    +
      +
    • Adding CITATION.cff
      • +
    • Using mike as a documentation provider
      • +
    +

    Fixed

    +
      +
    • Correct build to PyPI
      • +
    • Renaming from EDS-Scikit to eds-scikit
      • +
    +

    v0.1.1 (2022-12-02)

    +

    Added

    +
      +
    • Various project metadata
      • +
    • Full CI pipeline
      • +
    • License checker in CI
      • +
    • BackendDispatcher object to help with pandas / koalas manipulation
      • +
    +

    Fixed

    +
      +
    • Broken links in documentation and badges
      • +
    +

    v0.1.0 (2022-12-01)

    +

    Added

    +
      +
    • Initial commit to GitHub
      • +
    +
    +
    +
    + + + Back to top + +
    + +
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    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/contributing/index.html b/main/contributing/index.html new file mode 100644 index 00000000..cc1ce7d5 --- /dev/null +++ b/main/contributing/index.html @@ -0,0 +1,1562 @@ + + + + + + + + + +Contributing - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    Contributing

    +

    We welcome contributions! There are many ways to help. For example, you can:

    +
      +
    1. Help us track bugs by filing issues
      2. +
    2. Suggest and help prioritise new functionalities
      3. +
    3. Develop a new functionality!
      4. +
    4. Help us make the library as straightforward as possible, by simply asking questions on whatever does not seem clear to you.
      5. +
    +

    Please do not hesitate to suggest functionalities you have developed and want to incorporate into eds-scikit. We will be glad to help! +Also, any non-technical contribution (e.g. lists of ICD-10 codes curated for a research project) is also welcome.

    +

    Development installation

    +

    To be able to run the test suite, run the example notebooks and develop your own functionalities, you should clone the repo and install it locally.

    +
    +

    Spark and Java

    +

    To run tests locally, you need to have Spark and Java. Whereas Spark will be installed as a dependency of PySpark, you may need to install Java yourself. Please check to installation procedure.

    +
    +
    +
    # Clone the repository and change directory
+$ git clone https://github.com/aphp/eds-scikit.git
+---> 100%
+$ cd eds-scikit
+
+# Create a virtual environment
+$ python -m venv venv
+$ source venv/bin/activate
+
+# Install dependencies and build resources
+$ pip install -e ".[dev, doc]"
+
+# And switch to a new branch to begin developing
+$ git switch -c "name_of_my_new_branch"
+
    +
    +

    To make sure the pipeline will not fail because of formatting errors, we added pre-commit hooks using the pre-commit Python library. To use it, simply install it:

    +
    +
    $ pre-commit install
+
    +
    +

    The pre-commit hooks defined in the configuration will automatically run when you commit your changes, letting you know if something went wrong.

    +

    The hooks only run on staged changes. To force-run it on all files, run:

    +
    +
    $ pre-commit run --all-files
+---> 100%
+color:green All good !
+
    +
    +

    Proposing a merge request

    +

    At the very least, your changes should :

    +
      +
    • Be well-documented ;
      • +
    • Pass every tests, and preferably implement its own ;
      • +
    • Follow the style guide.
      • +
    +

    Testing your code

    +

    We use the Pytest test suite.

    +

    The following command will run the test suite. Writing your own tests is encouraged!

    +
    python -m pytest ./tests
+
    +

    Most tests are designed to run both with Pandas as Koalas DataFrames as input. However, to gain time, by default only Pandas testing is done. The above line of code is equivalent to

    +
    python -m pytest ./tests -m "not koalas"
+
    +

    However, you can also run tests using only Koalas input:

    +
    python -m pytest ./tests -m "koalas"
+
    +

    or using both inputs:

    +
    python -m pytest ./tests -m ""
+
    +

    Finally when developing, you might be interested to run tests for a single file, or even a single function. To do so:

    +

    python -m pytest ./tests/my_file.py #(1)
+python -m pytest ./tests/my_file.py:my_test_function #(2)
+
    +1. Will run all tests found in this file +2. Will only run "my_test_function"

    +

    Style Guide

    +

    We use Black to reformat the code. While other formatter only enforce PEP8 compliance, Black also makes the code uniform. In short :

    +
    +

    Black reformats entire files in place. It is not configurable.

    +
    +

    Moreover, the CI/CD pipeline enforces a number of checks on the "quality" of the code. To wit, non black-formatted code will make the test pipeline fail. We use pre-commit to keep our codebase clean.

    +

    Refer to the development install tutorial for tips on how to format your files automatically. +Most modern editors propose extensions that will format files on save.

    +
    +

    On conventional commits

    +

    We try to use conventional commits guidelines as much as possible. In short, prepend each commit message with one of the following prefix:

    +
      +
    • fix: when patching a bug
      • +
    • feat: when introducing a new feature
      • +
    • If needed, you can also use one of the following: build:, chore:, ci:, docs:, style:, refactor:, perf:, test
      • +
    +
    +

    Documentation

    +

    Make sure to document your improvements, both within the code with comprehensive docstrings, +as well as in the documentation itself if need be.

    +

    We use MkDocs for eds-scikit's documentation. You can checkout the changes you make with:

    +
    +
    # Install the requirements
+$ pip install ".[doc]"
+---> 100%
+color:green Installation successful
+
+# Run the documentation
+$ mkdocs serve
+
    +
    +

    Go to localhost:8000 to see your changes. MkDocs watches for changes in the documentation folder +and automatically reloads the page.

    +
    +

    Warning

    +

    MkDocs will automaticaly build code documentation by going through every .py file located in the eds_scikit directory (and sub-arborescence). It expects to find a __init__.py file in each directory, so make sure to create one if needed.

    +
    +

    Developing your own methods

    +

    Even though the koalas project aim at covering most pandas functions for spark, there are some discrepancies. For instance, the pd.cut() method has no koalas alternative.

    +

    To ease the development and switch gears efficiently between the two backends, we advice you to use the BackendDispatcher class and its collection of custom methods.

    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/css/ansi-colours.css b/main/css/ansi-colours.css new file mode 100644 index 00000000..42301ef9 --- /dev/null +++ b/main/css/ansi-colours.css @@ -0,0 +1,174 @@ +/*! +* +* IPython notebook +* +*/ +/* CSS font colors for translated ANSI escape sequences */ +/* The color values are a mix of + http://www.xcolors.net/dl/baskerville-ivorylight and + http://www.xcolors.net/dl/euphrasia */ +.ansi-black-fg { + color: #3E424D; +} +.ansi-black-bg { + background-color: #3E424D; +} +.ansi-black-intense-fg { + color: #282C36; +} +.ansi-black-intense-bg { + background-color: #282C36; +} +.ansi-red-fg { + color: #E75C58; +} +.ansi-red-bg { + background-color: #E75C58; +} +.ansi-red-intense-fg { + color: #B22B31; +} +.ansi-red-intense-bg { + background-color: #B22B31; +} +.ansi-green-fg { + color: #00A250; +} +.ansi-green-bg { + background-color: #00A250; +} +.ansi-green-intense-fg { + color: #007427; +} +.ansi-green-intense-bg { + background-color: #007427; +} +.ansi-yellow-fg { + color: #DDB62B; +} +.ansi-yellow-bg { + background-color: #DDB62B; +} +.ansi-yellow-intense-fg { + color: #B27D12; +} +.ansi-yellow-intense-bg { + background-color: #B27D12; +} +.ansi-blue-fg { + color: #208FFB; +} +.ansi-blue-bg { + background-color: #208FFB; +} +.ansi-blue-intense-fg { + color: #0065CA; +} +.ansi-blue-intense-bg { + background-color: #0065CA; +} +.ansi-magenta-fg { + color: #D160C4; +} +.ansi-magenta-bg { + background-color: #D160C4; +} +.ansi-magenta-intense-fg { + color: #A03196; +} +.ansi-magenta-intense-bg { + background-color: #A03196; +} +.ansi-cyan-fg { + color: #60C6C8; +} +.ansi-cyan-bg { + background-color: #60C6C8; +} +.ansi-cyan-intense-fg { + color: #258F8F; +} +.ansi-cyan-intense-bg { + background-color: #258F8F; +} +.ansi-white-fg { + color: #C5C1B4; +} +.ansi-white-bg { + background-color: #C5C1B4; +} +.ansi-white-intense-fg { + color: #A1A6B2; +} +.ansi-white-intense-bg { + background-color: #A1A6B2; +} +.ansi-default-inverse-fg { + color: #FFFFFF; +} +.ansi-default-inverse-bg { + background-color: #000000; +} +.ansi-bold { + font-weight: bold; +} +.ansi-underline { + text-decoration: underline; +} +/* The following styles are deprecated an will be removed in a future version */ +.ansibold { + font-weight: bold; +} +.ansi-inverse { + outline: 0.5px dotted; +} +/* use dark versions for foreground, to improve visibility */ +.ansiblack { + color: black; +} +.ansired { + color: darkred; +} +.ansigreen { + color: darkgreen; +} +.ansiyellow { + color: #c4a000; +} +.ansiblue { + color: darkblue; +} +.ansipurple { + color: darkviolet; +} +.ansicyan { + color: steelblue; +} +.ansigray { + color: gray; +} +/* and light for background, for the same reason */ +.ansibgblack { + background-color: black; +} +.ansibgred { + background-color: red; +} +.ansibggreen { + background-color: green; +} +.ansibgyellow { + background-color: yellow; +} +.ansibgblue { + background-color: blue; +} +.ansibgpurple { + background-color: magenta; +} +.ansibgcyan { + background-color: cyan; +} +.ansibggray { + background-color: gray; +} \ No newline at end of file diff --git a/main/css/jupyter-cells.css b/main/css/jupyter-cells.css new file mode 100644 index 00000000..46def9f9 --- /dev/null +++ b/main/css/jupyter-cells.css @@ -0,0 +1,10 @@ +/* Input cells */ +.input code, .input pre { + background-color: #3333aa11; +} + +/* Output cells */ +.output pre { + background-color: #ececec80; + padding: 10px; +} diff --git a/main/css/pandas-dataframe.css b/main/css/pandas-dataframe.css new file mode 100644 index 00000000..2c18015d --- /dev/null +++ b/main/css/pandas-dataframe.css @@ -0,0 +1,36 @@ +/* Pretty Pandas Dataframes */ +/* Supports mkdocs-material color variables */ +.dataframe { + border: 0; + font-size: smaller; +} +.dataframe tr { + border: none; + background: var(--md-code-bg-color, #ffffff); +} +.dataframe tr:nth-child(even) { + background: var(--md-default-bg-color, #f5f5f5); +} +.dataframe tr:hover { + background-color: var(--md-footer-bg-color--dark, #e1f5fe); +} + +.dataframe thead th { + background: var(--md-default-bg-color, #ffffff); + border-bottom: 1px solid #aaa; + font-weight: bold; +} +.dataframe th { + border: none; + padding-left: 10px; + padding-right: 10px; +} + +.dataframe td{ + /* background: #fff; */ + border: none; + text-align: right; + min-width:5em; + padding-left: 10px; + padding-right: 10px; +} diff --git a/main/datasets/care-site-emergency/index.html b/main/datasets/care-site-emergency/index.html new file mode 100644 index 00000000..d82b89b8 --- /dev/null +++ b/main/datasets/care-site-emergency/index.html @@ -0,0 +1,1461 @@ + + + + + + + + + +Emergency - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    Emergency

    +

    Presentation

    +
    +

    Emergency

    +

    This dataset is useful to extract emergency care sites from AP-HP's CDW

    +
    +

    This dataset contains care sites labelled as emergency.

    +

    Those care sites were extracted and verified by Ariel COHEN, +Judith LEBLANC, and an ER doctor validated them.

    +

    Those emergency care sites are further divised into different categories, +as defined in the concept "EMERGENCY_TYPE".

    +

    The different categories are:

    +
      +
    • Urgences spécialisées
      • +
    • UHCD + Post-urgences
      • +
    • Urgences pédiatriques
      • +
    • Urgences générales adulte
      • +
    • Consultation urgences
      • +
    • SAMU / SMUR
      • +
    +
    +

    Warning

    +

    This dataset was built in 2021.

    +
    +

    Structure and usage

    +

    Internally, the dataset is returned by calling the function get_care_site_emergency_mapping():

    +
    from eds_scikit.resources import registry
+
+df = registry.get("data", function_name="get_care_site_emergency_mapping")()
+
    +

    It should return a Pandas Dataframe with 2 columns:

    +
      +
    • care_site_source_value (OMOP column)
      • +
    • EMERGENCY_TYPE (see above)
      • +
    +

    Use your own data.

    +

    It is as simple as registering a new loading function:

    +
    custom_resources.py
    from eds_scikit.resources import registry
+
+@registry.data("get_care_site_emergency_mapping")
+def get_care_site_emergency_mapping():
+      """
+      Your code here
+      """
+      return df
+
    +

    Then simply import your custom_resources module before running eds-scikit's pipelines, and you're good to go.

    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/datasets/care-site-hierarchy/index.html b/main/datasets/care-site-hierarchy/index.html new file mode 100644 index 00000000..e3bdbb0b --- /dev/null +++ b/main/datasets/care-site-hierarchy/index.html @@ -0,0 +1,1480 @@ + + + + + + + + + +Hierarchy - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    Hierarchy

    +

    Presentation

    +
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    Care sites

    +

    This dataset is useful to link AP-HP's care sites of various levels together

    +
    +

    To generate it, it uses the fact_relationship OMOP table, with the care_site domain and the A is part of B relation. Thus, it generates a wide-type table, effectively flattening out the hierarchical structure of each care site.

    +

    This dataset is useful to find the parent of a care_site, e.g.:

    +
      +
    • in which hospital is this UDS (Unité De Soin) ?
      • +
    • in which UF (Unité Fonctionnelle) is this UMA (Unité Médico-Administrative) ?
      • +
    +

    Structure and usage

    +

    In this dataset each row corresponds to a given care_site and the columns contain +the ids of the parent care_site for several hierarchical level. Those columns are thus values contained in care_site_type_source_value.

    +

    Internally, the dataset is returned by calling the function get_care_site_hierarchy():

    +
    from eds_scikit.resources import registry
+
+df = registry.get("data", function_name="get_care_site_hierarchy")()
+
    +

    Use your own data.

    +

    It is as simple as registering a new loading function:

    +
    custom_resources.py
    from eds_scikit.resources import registry # (1)
+
+@registry.data("get_care_site_hierarchy") # (2)
+def get_care_site_hierarchy():
+      """
+      Your code here
+      """
+      return df
+
    +
      +
    1. The registry instance stores user-defined functions
      2. +
    2. Using this decorator allows to register the function when importing the corresponding file
      3. +
    +

    Then simply import your custom_resources module before running eds-scikit's pipelines, and you're good to go.

    +

    Structure and usage

    +

    Internally, the dataset is returned by calling the function get_care_site_hierarchy(). +It should return a Pandas Dataframe with the following columns:

    +
      +
    • care_site_id (OMOP column): The identifier of the care site
      • +
    • care_site_type_source_value (OMOP column): The type of care site
      • +
    +

    Additionally, it can contains an arbitrary number of columns whose name are values from care_site_type_source_value, and whose values are care_site_id of the corresponding parent structure

    +

    Generation function

    +

    You can generate the dataset on your specific data using this function

    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/datasets/concepts-sets/0.html b/main/datasets/concepts-sets/0.html new file mode 100644 index 00000000..0e4a476b --- /dev/null +++ b/main/datasets/concepts-sets/0.html @@ -0,0 +1,391 @@ + + + + + +Concept sets - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    concepts_set_nameGLIMS_ANABIO_concept_codeconcepts_set_category
    ALAT_Activity['A0002', 'G1804', 'J7373', 'E2067', 'F2629']hepatic_panel
    ASAT_Activity['A0022', 'G1800', 'E2068', 'F2628']hepatic_panel
    Activated_Partial_Thromboplastin_Time['A1792', 'L7286', 'A7748']coagulation
    Adenovirus['I5915', 'I7952', 'J2229', 'J2988', 'J8817', 'K1527', 'J9968']virology
    Albumine_Blood_Concentration['D2358', 'C6841', 'C2102', 'G6616', 'L2260', 'A0006', 'E4799', 'I2013']proteins
    B-HCG_Blood_Concentration['A7426', 'F2353', 'A0164', 'L2277']diabete
    B.pertussis['I7748', 'I7968', 'K1531']virology
    BNP_Concentration['C8189', 'B5596', 'A2128']cardiac_biomarkers
    BNP_and_NTProBNP_Concentration['C8189', 'B5596', 'A2128', 'A7333', 'J7267', 'J7959']cardiac_biomarkers
    Bicarbonate_Blood_Concentration['A0422', 'H9622', 'C6408', 'F4161', 'A2136', 'J7371', 'G2031']ionogram
    C.pneumoniae['I5930', 'I7969', 'J2207', 'K1530', 'J9919']virology
    CRP_Concentration['A0248', 'E6332', 'F5581', 'J7381', 'F2631']inflammatory_panel
    Calcium_Blood_Concentration['C0543', 'D2359', 'A0038', 'H5041', 'F2625', 'L5047', 'A2512', 'A2512', 'A0607']ionogram
    Chloride_Blood_Concentration['A0079', 'J1179', 'F2619']ionogram
    Coronavirus['I5916', 'I5917', 'I5918', 'I5919', 'I7953', 'I7954', 'I7955', 'I7956', 'J2199', 'J2996', 'J2994', 'J2993', 'J2992', 'J8829', 'J8828', 'J8823', 'J8822', 'K1525', 'K1524', 'K1522', 'K1523', 'J9969']virology
    Creatine Kinase['A0090', 'G0171', 'E6330']other
    D-Dimers_Concentration['C7882', 'C7882', 'I8765', 'A0124', 'C0474', 'C0474', 'C0474', 'B4199', 'F5402']coagulation
    EPP_Blood_Concentration['A0250', 'C9874', 'A3758', 'A0004', 'F9978', 'A0005', 'H8137', 'C7087', 'A0003', 'C7088', 'B9456', 'B9455', 'A0008', 'H8138', 'C7089', 'A0007', 'C7090', 'A0010', 'H8139', 'C7091', 'A0009', 'C7092', 'C6525', 'C6524', 'A0415', 'C7093', 'A0414', 'C7094', 'B9458', 'B9457', 'A2113', 'H8140', 'E5327', 'A2112', 'E5328', 'C6536', 'C6535', 'E2398', 'E2399', 'A2115', 'H8141', 'E5329', 'A2114', 'E5330', 'C6538', 'C6537', 'E2400', 'E2401', 'A0130', 'H8142', 'C7100', 'A0129', 'C7101', 'G6942', 'G6941', 'B9460', 'B9459', 'C6596', 'C6595', 'C6598', 'C6597', 'E2402', 'E2403', 'K4483', 'A2118', 'A2117', 'E1847', 'B8047', 'A2127', 'I8076', 'A2126', 'I8077', 'X5093', 'X5094', 'X5091', 'X5092', 'A2279', 'L7258', 'A1361', 'L7259', 'C6909', 'B1727', 'B1725', 'C6924', 'I5139', 'X5097', 'X5098', 'X5095', 'X5096', 'A2278', 'L7260', 'A2277', 'L7261', 'D0265', 'B1728', 'B1726', 'D0267', 'I5140', 'X5101', 'X5102', 'X5099', 'X5100', 'H6397', 'L7262', 'H6396', 'L7263', 'D0266', 'C0616', 'D0268', 'I5141', 'A8775', 'A7816', 'A8776', 'A8777', 'A8778', 'A8779', 'A8780', 'A7330', 'F0748', 'F0749', 'H9656', 'H9657', 'H9658', 'H9659', 'H9660']proteins
    Eosinophil_Polymorphonuclears_Blood_Count['A0150', 'H6730']blood_count_cell
    Ferritin_Concentration['A0123', 'E9865']martial_panel
    Fibrinogen_Concentration['A0126']inflammatory_panel
    Glomerular_Filtration_Rate_EPI_CKD['G6921', 'F8160', 'F9613', 'F9621', 'F9621']renal_panel
    Glomerular_Filtration_Rate_MDRD['F9622', 'G7835', 'B9964', 'A7456', 'A7455', 'H5609']renal_panel
    GGT_Activity['A0131', 'F8184', 'E9771', 'J7370', 'K7045']hepatic_panel
    Glucose_Blood_Concentration['A0141', 'H7323', 'J7401', 'F2622', 'B9553', 'C7236', 'E7312', 'A7338', 'H7324', 'C0565', 'E9889', 'A8424']diabete
    HCO3-_Blood_Concentration['A0420', 'L5018']blood_gas
    HIV Serology['D2865', 'D2867', 'D2845', 'D2864', 'F4252', 'D2866', 'F3257', 'D2869', 'F1705', 'D2846', 'D2847', 'D2844', 'E8605', 'F5401', 'G0175', 'J5891', 'J2672', 'H7667']serology
    HbA1c_Blood_%['B6983', 'A2228', 'A1271', 'E6632', 'I5968']diabete
    Hemoglobin_Blood_Count['A0163', 'H6738']blood_count_cell
    Hepatitis B Serology['D2729', 'D2730', 'E1524', 'F2075', 'D2725', 'I1903', 'D2728', 'D2726', 'D2726', 'F5613', 'D2731', 'D2727', 'G1197', 'G1199', 'G1199', 'J5887', 'J5890', 'J2697', 'L6883', 'J2695', 'L7877', 'D2653', 'D2660', 'D2654', 'D2661', 'D2649', 'D2656', 'H9686', 'H9687', 'I6579', 'I6580', 'D2652', 'D2659', 'D2650', 'D2650', 'D2657', 'D2655', 'D2662', 'D2651', 'D2651', 'D2658', 'F5615', 'F5616', 'G1209', 'G1209', 'G1210', 'G1210', 'I2927', 'I2928', 'J5885', 'J5886', 'J2699', 'J2700']serology
    Hepatitis C Serology['D2780', 'H5078', 'I1846', 'D2777', 'E6503', 'D2778', 'D3088', 'D3088', 'D2774', 'J1518', 'D2776', 'D2771', 'E8372', 'F7465', 'I3151', 'G0173', 'L1237', 'J2678', 'K3833', 'E8373', 'K4228']serology
    IL-1 beta_Blood_Concentration['C9351', 'B8921', 'G4800', 'K3662', 'L2217', 'J9193', 'K3665', 'K3687', 'K3661', 'L2197']inflammatory_biomarkers
    IL-10_Blood_Concentration['B8922', 'C8763', 'K3478', 'L2210', 'J9187', 'K3481', 'K3472', 'K3475', 'L2198']inflammatory_biomarkers
    IL-6_Blood_Concentration['B8929', 'G4799', 'B1910', 'K3467', 'L2205', 'E6992', 'J9190', 'K3456', 'L2193', 'K3435', 'K3460']inflammatory_biomarkers
    Quick_INR_Time['A0269']coagulation
    Influenza A['I5922', 'I7960', 'J2198', 'J2990', 'J8825', 'K1517']virology
    Influenza B['I5923', 'I7961', 'J2203', 'J2985', 'J8819', 'K1513']virology
    L.pneumophila['J2176', 'J3006', 'J8826', 'J9899']virology
    LDH['A0170', 'H5261', 'J7400', 'C8889', 'J1161']other
    Lactate_Gaz_Blood_Concentration['C8697', 'H7748']blood_gas
    Legionella Antigenuria['D1465', 'H6694', 'J7960']antigenury
    Leukocytes_Blood_Count['A0174', 'H6740', 'C8824']blood_count_cell
    Lymphocytes_Blood_Count['A0198', 'H6743']blood_count_cell
    Metapneumovirus['I5920', 'I7958', 'J2200', 'J2991', 'J8824', 'K1519', 'J9965']virology
    Monocytes_Blood_Count['A0210', 'H6747']blood_count_cell
    NTProBNP_Concentration['A7333', 'J7267', 'J7959']cardiac_biomarkers
    Neutrophil_Polymorphonuclears_Blood_Count['A0155', 'H6732']blood_count_cell
    PAL_Activity['A0227', 'F8187', 'E6331', 'F1844']hepatic_panel
    PaCO2_Blood_Concentration['A7305', 'A0630']blood_gas
    PaO2_Blood_Concentration['A7319', 'H7747']blood_gas
    Parainfluenza['I5924', 'I5925', 'I5926', 'I5927', 'I7962', 'I7963', 'I7964', 'I7965', 'J2204', 'J2979', 'J2977', 'J2983', 'J2981', 'J8861', 'J8862', 'J8821', 'J8820', 'K1509', 'K1508', 'K1510', 'K1535', 'J9964']virology
    Phosphates_Blood_Concentration['A0226', 'F8186', 'F2626']proteins
    Platelets_Blood_Count['A0230', 'H6751', 'A1598', 'A1598', 'A2538', 'A2539', 'A2539', 'J4463']blood_count_cell
    Pneumococcal Antigenuria['D2055', 'J7962', 'A2804']antigenury
    Potassium_Blood_Concentration['A2380', 'E2073', 'F2618', 'E2337', 'J1178']ionogram
    Procalcitonin_Blood_Concentration['A1661', 'H5267', 'F2632']inflammatory_biomarkers
    Proteins_Urine_24h_Concentration['A1695', 'A1694', 'A1696', 'C9990', 'C9991', 'J7268', 'J7269', 'C3941', 'E4745', 'G4187', 'F6060']proteins
    Proteins_Blood_Concentration['A7347', 'F5122', 'F2624', 'B9417', 'A0249', 'B3990']inflammatory_panel
    Quick_Prothrombin_Time['A1805', 'E9993']coagulation
    RSV['I5928', 'I7966', 'J2201', 'J2974', 'J8859', 'K1534']virology
    Rhino/Enterovirus['I5921', 'I7959', 'J2197', 'J2973', 'J8858', 'K1515']virology
    SARS-CoV-2['K1108', 'J9791', 'J8706', 'J8827', 'K1520']virology
    SaO2_Blood_Concentration['A7334', 'L5021']blood_gas
    Sodium_Blood_Concentration['A0262', 'J1177', 'F8162', 'F2617']ionogram
    TNF alpha_Blood_Concentration['B8931', 'G4801', 'C9393', 'K3505', 'L2203', 'E6993', 'J9194', 'K3658', 'K3502', 'K3504', 'L2191']inflammatory_biomarkers
    TSH_Concentration['A1831', 'F2150', 'I8385', 'C2666']diabete
    Total_Bilirubin_Concentration['A0029', 'H5264', 'D0189']hepatic_panel
    Transferrin_Saturation_Coefficient['A0278']martial_panel
    Troponine_Concentration['A0283', 'C5560', 'F9934', 'E6954', 'L3534', 'G7716', 'J5184', 'A3832', 'E7249']cardiac_biomarkers
    Urea_Blood_Concentration['A0286', 'G3350', 'J7372', 'F2620']renal_panel
    Venous_Lactate['A0173', 'B9146', 'A9995']other
    pH_Blood['A0221', 'L5017', 'A0219']blood_gas
    \ No newline at end of file diff --git a/main/datasets/concepts-sets/index.html b/main/datasets/concepts-sets/index.html new file mode 100644 index 00000000..38f1cbd2 --- /dev/null +++ b/main/datasets/concepts-sets/index.html @@ -0,0 +1,1541 @@ + + + + + + + + + +Concept sets - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    Concepts-sets

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    A concepts-set is a generic concept that has been deemed appropriate for most biological analyses. It is a group of several biological concepts representing the same biological entity.

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    Concepts-sets

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    This dataset is listing common biological entities in AP-HP's Data Warehouse.

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    Below, one can see the list of default concepts-set provided by the library.

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    Preview

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    concepts_set_nameGLIMS_ANABIO_concept_codeconcepts_set_category
    ALAT_Activity['A0002', 'G1804', 'J7373', 'E2067', 'F2629']hepatic_panel
    ASAT_Activity['A0022', 'G1800', 'E2068', 'F2628']hepatic_panel
    Activated_Partial_Thromboplastin_Time['A1792', 'L7286', 'A7748']coagulation
    Adenovirus['I5915', 'I7952', 'J2229', 'J2988', 'J8817', 'K1527', 'J9968']virology
    Albumine_Blood_Concentration['D2358', 'C6841', 'C2102', 'G6616', 'L2260', 'A0006', 'E4799', 'I2013']proteins
    B-HCG_Blood_Concentration['A7426', 'F2353', 'A0164', 'L2277']diabete
    B.pertussis['I7748', 'I7968', 'K1531']virology
    BNP_Concentration['C8189', 'B5596', 'A2128']cardiac_biomarkers
    BNP_and_NTProBNP_Concentration['C8189', 'B5596', 'A2128', 'A7333', 'J7267', 'J7959']cardiac_biomarkers
    .........
     + +

    You can see the dataset here

    +
    +

    Note

    +

    The concept codes are expressed in the AnaBio and LOINC standard vocabularies (for more information about the vocabularies see the Vocabulary page).

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    + + + Back to top + +
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    + + + + + + + + + + \ No newline at end of file diff --git a/main/datasets/private-resources/index.html b/main/datasets/private-resources/index.html new file mode 100644 index 00000000..f5a0a5e8 --- /dev/null +++ b/main/datasets/private-resources/index.html @@ -0,0 +1,1386 @@ + + + + + + + + + +A note on private resources - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    Resources

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    eds-scikit contains some resources that are stored as is, +either because it comes from manual work, or because its generation might be time and computationally intensive.

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    Private data

    +

    A lot of those resources are specific to AP-HP's CDW, thus are stored on a private repository. If you work on AP-HP's ecosystem, you can install those resources along with eds-scikit via pip install 'eds_scikit[aphp]'.

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    For each resource listed bellow, you will find:

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    • A short description
      • +
    • If relevant, a way to register your function in order to use your own data
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    • If relevant, a link to the generation function
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    Available resources

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    + + + + + + + + + + \ No newline at end of file diff --git a/main/datasets/synthetic-data/index.html b/main/datasets/synthetic-data/index.html new file mode 100644 index 00000000..07b30787 --- /dev/null +++ b/main/datasets/synthetic-data/index.html @@ -0,0 +1,1439 @@ + + + + + + + + + +Synthetic data - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    Small Datasets for testing functionalities

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    Presentation

    +

    eds-scikit was build to work seamlessly on a pre-existing OMOP database. However, the library also provides some toy datasets so that you can try out some features even without having access to a database.

    +

    Usage

    +

    First, you can display all availables synthetic datasets:

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    from eds_scikit import datasets
+
+datasets.list_all_synthetics()
+# Out: ['load_ccam', 'load_consultation_dates', 'load_hierarchy', 'load_icd10', 'load_visit_merging', 'load_stay_duration', 'load_suicide_attempt', 'load_tagging', 'load_biology_data', 'load_event_sequences']
+
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    To load a specific dataset, simply run:

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    data = datasets.load_icd10()
+data
+# Out: ICD10Dataset(condition_occurrence, visit_occurrence)
+
    +

    The data object is similar to objects available in eds_scikit.io, namely:

    + +

    For instance, tables are available as attributes:

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    data.condition_occurrence
+
    +| | person_id | condition_source_value | condition_start_datetime | condition_status_source_value | visit_occurrence_id | +|---|-----------|------------------------|--------------------------|-------------------------------|---------------------| +| 0 | 1 | C10 | 2010-01-01 | DP | 11 | +| 1 | 1 | E112 | 2010-01-01 | DAS | 12 | +| 2 | 1 | D20 | 2012-01-01 | DAS | 13 | +| 3 | 1 | A20 | 2020-01-01 | DP | 14 | +| 4 | 1 | A21 | 2000-01-01 | DP | 15 | +| 5 | 1 | X20 | 2000-01-01 | DP | 16 | +| 6 | 1 | C10 | 2010-01-01 | DP | 16 | +| 7 | 1 | C10 | 2010-01-01 | DP | 17 |

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    As shown in the tutorial, you can now try out the corresponding conditions_from_icd10() function.

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    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/biology/about_measurement/index.html b/main/functionalities/biology/about_measurement/index.html new file mode 100644 index 00000000..ad7000af --- /dev/null +++ b/main/functionalities/biology/about_measurement/index.html @@ -0,0 +1,1473 @@ + + + + + + + + + +About measurement - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    About measurement

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    About measurements table

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    The BioClean module focuses on three OMOP terms:

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    • Measurement is a record obtained through the standardized testing or examination of a person or person's sample.
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    • Concept is a semantic notion that uniquely identify a clinical event. It can group several measurements.
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    • Concept Relationship is a semantic relation between terminologies, allowing to map codes from different terminologies.
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    A fourht term was created to ease the use of the two above:

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    • concepts-set is a generic concept that has been deemed appropriate for most biological analyses. It is a group of several biological concepts representing the same biological entity.
      • +
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    Example:
    +Let's imagine the laboratory X tests the creatinine of Mister A and Mister B in mg/dL and the laboratory Y tests the creatinine of Mister C in µmol/L. In this context, the dataset will contain:

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    • 3 measurements (one for each conducted test)
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    • 2 concepts (one concept for the creatinine tested in mg/dL and another one for the creatinine tested in µmol/L)
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    • 1 concepts-set (it groups the 2 concepts because they are the same biological entity)
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    Vocabulary

    +

    A vocabulary is a terminology system that associates a code to a specific clinical event. One may distinguish two types of vocabularies:

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    Source vocabulary

    +

    The source vocabulary is the vocabulary used in the LIMS (Laboratory Information Management System) software. It is specific to the LIMS and may be different in each laboratory.

    +

    Standard vocabulary

    +

    The standard vocabulary is a unified vocabulary that allows data analysis on a larger scale.

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    • It is classified in chapter.
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    • It has a bigger granularity than the source vocabulary, multiple source codes may be associated to one standard code.
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    Vocabulary flowchart in OMOP

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    Image title

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    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/biology/concepts_sets/0.html b/main/functionalities/biology/concepts_sets/0.html new file mode 100644 index 00000000..2b720aa1 --- /dev/null +++ b/main/functionalities/biology/concepts_sets/0.html @@ -0,0 +1,58 @@ + + + + + +Predefined concepts sets - eds-scikit + + + + + + + + + + + + + + +
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    concepts_set_nameGLIMS_ANABIO_concept_code
    Hemoglobin_Blood_Count['A0163', 'H6738']
    Leukocytes_Blood_Count['A0174', 'H6740', 'C8824']
    Platelets_Blood_Count['A0230', 'H6751', 'A1598', 'A1598', 'A2538', 'A2539', 'A2539', 'J4463']
    Lymphocytes_Blood_Count['A0198', 'H6743']
    Monocytes_Blood_Count['A0210', 'H6747']
    Neutrophil_Polymorphonuclears_Blood_Count['A0155', 'H6732']
    Eosinophil_Polymorphonuclears_Blood_Count['A0150', 'H6730']
    \ No newline at end of file diff --git a/main/functionalities/biology/concepts_sets/1.html b/main/functionalities/biology/concepts_sets/1.html new file mode 100644 index 00000000..048b06e8 --- /dev/null +++ b/main/functionalities/biology/concepts_sets/1.html @@ -0,0 +1,50 @@ + + + + + +Predefined concepts sets - eds-scikit + + + + + + + + + + + + + + +
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    concepts_set_nameGLIMS_ANABIO_concept_code
    Bicarbonate_Blood_Concentration['A0422', 'H9622', 'C6408', 'F4161', 'A2136', 'J7371', 'G2031']
    Calcium_Blood_Concentration['C0543', 'D2359', 'A0038', 'H5041', 'F2625', 'L5047', 'A2512', 'A2512', 'A0607']
    Chloride_Blood_Concentration['A0079', 'J1179', 'F2619']
    Potassium_Blood_Concentration['A2380', 'E2073', 'F2618', 'E2337', 'J1178']
    Sodium_Blood_Concentration['A0262', 'J1177', 'F8162', 'F2617']
    \ No newline at end of file diff --git a/main/functionalities/biology/concepts_sets/10.html b/main/functionalities/biology/concepts_sets/10.html new file mode 100644 index 00000000..6c3b116b --- /dev/null +++ b/main/functionalities/biology/concepts_sets/10.html @@ -0,0 +1,46 @@ + + + + + +Predefined concepts sets - eds-scikit + + + + + + + + + + + + + + +
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    concepts_set_nameGLIMS_ANABIO_concept_code
    B-HCG_Blood_Concentration['A7426', 'F2353', 'A0164', 'L2277']
    Glucose_Blood_Concentration['A0141', 'H7323', 'J7401', 'F2622', 'B9553', 'C7236', 'E7312', 'A7338', 'H7324', 'C0565', 'E9889', 'A8424']
    HbA1c_Blood_%['B6983', 'A2228', 'A1271', 'E6632', 'I5968']
    TSH_Concentration['A1831', 'F2150', 'I8385', 'C2666']
    \ No newline at end of file diff --git a/main/functionalities/biology/concepts_sets/11.html b/main/functionalities/biology/concepts_sets/11.html new file mode 100644 index 00000000..7a711a30 --- /dev/null +++ b/main/functionalities/biology/concepts_sets/11.html @@ -0,0 +1,50 @@ + + + + + +Predefined concepts sets - eds-scikit + + + + + + + + + + + + + + +
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    IL-1 beta_Blood_Concentration['C9351', 'B8921', 'G4800', 'K3662', 'L2217', 'J9193', 'K3665', 'K3687', 'K3661', 'L2197']
    IL-10_Blood_Concentration['B8922', 'C8763', 'K3478', 'L2210', 'J9187', 'K3481', 'K3472', 'K3475', 'L2198']
    IL-6_Blood_Concentration['B8929', 'G4799', 'B1910', 'K3467', 'L2205', 'E6992', 'J9190', 'K3456', 'L2193', 'K3435', 'K3460']
    Procalcitonin_Blood_Concentration['A1661', 'H5267', 'F2632']
    TNF alpha_Blood_Concentration['B8931', 'G4801', 'C9393', 'K3505', 'L2203', 'E6993', 'J9194', 'K3658', 'K3502', 'K3504', 'L2191']
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    HCO3-_Blood_Concentration['A0420', 'L5018']
    Lactate_Gaz_Blood_Concentration['C8697', 'H7748']
    PaCO2_Blood_Concentration['A7305', 'A0630']
    PaO2_Blood_Concentration['A7319', 'H7747']
    SaO2_Blood_Concentration['A7334', 'L5021']
    pH_Blood['A0221', 'L5017', 'A0219']
    \ No newline at end of file diff --git a/main/functionalities/biology/concepts_sets/3.html b/main/functionalities/biology/concepts_sets/3.html new file mode 100644 index 00000000..b7555957 --- /dev/null +++ b/main/functionalities/biology/concepts_sets/3.html @@ -0,0 +1,50 @@ + + + + + +Predefined concepts sets - eds-scikit + + + + + + + + + + + + + + +
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    ALAT_Activity['A0002', 'G1804', 'J7373', 'E2067', 'F2629']
    ASAT_Activity['A0022', 'G1800', 'E2068', 'F2628']
    GGT_Activity['A0131', 'F8184', 'E9771', 'J7370', 'K7045']
    PAL_Activity['A0227', 'F8187', 'E6331', 'F1844']
    Total_Bilirubin_Concentration['A0029', 'H5264', 'D0189']
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    BNP_Concentration['C8189', 'B5596', 'A2128']
    BNP_and_NTProBNP_Concentration['C8189', 'B5596', 'A2128', 'A7333', 'J7267', 'J7959']
    NTProBNP_Concentration['A7333', 'J7267', 'J7959']
    Troponine_Concentration['A0283', 'C5560', 'F9934', 'E6954', 'L3534', 'G7716', 'J5184', 'A3832', 'E7249']
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    CRP_Concentration['A0248', 'E6332', 'F5581', 'J7381', 'F2631']
    Fibrinogen_Concentration['A0126']
    Proteins_Blood_Concentration['A7347', 'F5122', 'F2624', 'B9417', 'A0249', 'B3990']
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    Ferritin_Concentration['A0123', 'E9865']
    Transferrin_Saturation_Coefficient['A0278']
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    Glomerular_Filtration_Rate_EPI_CKD['G6921', 'F8160', 'F9613', 'F9621', 'F9621']
    Glomerular_Filtration_Rate_MDRD['F9622', 'G7835', 'B9964', 'A7456', 'A7455', 'H5609']
    Urea_Blood_Concentration['A0286', 'G3350', 'J7372', 'F2620']
    \ No newline at end of file diff --git a/main/functionalities/biology/concepts_sets/8.html b/main/functionalities/biology/concepts_sets/8.html new file mode 100644 index 00000000..b5d79704 --- /dev/null +++ b/main/functionalities/biology/concepts_sets/8.html @@ -0,0 +1,46 @@ + + + + + +Predefined concepts sets - eds-scikit + + + + + + + + + + + + + + +
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    Activated_Partial_Thromboplastin_Time['A1792', 'L7286', 'A7748']
    D-Dimers_Concentration['C7882', 'C7882', 'I8765', 'A0124', 'C0474', 'C0474', 'C0474', 'B4199', 'F5402']
    Quick_INR_Time['A0269']
    Quick_Prothrombin_Time['A1805', 'E9993']
    \ No newline at end of file diff --git a/main/functionalities/biology/concepts_sets/9.html b/main/functionalities/biology/concepts_sets/9.html new file mode 100644 index 00000000..1ebc7ce1 --- /dev/null +++ b/main/functionalities/biology/concepts_sets/9.html @@ -0,0 +1,46 @@ + + + + + +Predefined concepts sets - eds-scikit + + + + + + + + + + + + + + +
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    Albumine_Blood_Concentration['D2358', 'C6841', 'C2102', 'G6616', 'L2260', 'A0006', 'E4799', 'I2013']
    EPP_Blood_Concentration['A0250', 'C9874', 'A3758', 'A0004', 'F9978', 'A0005', 'H8137', 'C7087', 'A0003', 'C7088', 'B9456', 'B9455', 'A0008', 'H8138', 'C7089', 'A0007'...
    Phosphates_Blood_Concentration['A0226', 'F8186', 'F2626']
    Proteins_Urine_24h_Concentration['A1695', 'A1694', 'A1696', 'C9990', 'C9991', 'J7268', 'J7269', 'C3941', 'E4745', 'G4187', 'F6060']
    \ No newline at end of file diff --git a/main/functionalities/biology/concepts_sets/index.html b/main/functionalities/biology/concepts_sets/index.html new file mode 100644 index 00000000..357e1300 --- /dev/null +++ b/main/functionalities/biology/concepts_sets/index.html @@ -0,0 +1,1728 @@ + + + + + + + + + +Predefined concepts sets - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    Predefined concepts sets

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    Those concept sets can be found in eds_scikit.datasets.default_concepts_sets.

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    How were the code selected ?

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    Each concept set codes were selected by coding system expert and validated through statistical analysis. However new codes may appear or become outdated. Feel free to propose new concept sets or concept codes !

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    Hemoglobin_Blood_Count['A0163', 'H6738']
    Leukocytes_Blood_Count['A0174', 'H6740', 'C8824']
    Platelets_Blood_Count['A0230', 'H6751', 'A1598', 'A1598', 'A2538', 'A2539', 'A2539', 'J4463']
    Lymphocytes_Blood_Count['A0198', 'H6743']
    Monocytes_Blood_Count['A0210', 'H6747']
    Neutrophil_Polymorphonuclears_Blood_Count['A0155', 'H6732']
    ......
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    concepts_set_nameGLIMS_ANABIO_concept_code
    Bicarbonate_Blood_Concentration['A0422', 'H9622', 'C6408', 'F4161', 'A2136', 'J7371', 'G2031']
    Calcium_Blood_Concentration['C0543', 'D2359', 'A0038', 'H5041', 'F2625', 'L5047', 'A2512', 'A2512', 'A0607']
    Chloride_Blood_Concentration['A0079', 'J1179', 'F2619']
    Potassium_Blood_Concentration['A2380', 'E2073', 'F2618', 'E2337', 'J1178']
    ......
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    concepts_set_nameGLIMS_ANABIO_concept_code
    HCO3-_Blood_Concentration['A0420', 'L5018']
    Lactate_Gaz_Blood_Concentration['C8697', 'H7748']
    PaCO2_Blood_Concentration['A7305', 'A0630']
    PaO2_Blood_Concentration['A7319', 'H7747']
    SaO2_Blood_Concentration['A7334', 'L5021']
    ......
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    concepts_set_nameGLIMS_ANABIO_concept_code
    ALAT_Activity['A0002', 'G1804', 'J7373', 'E2067', 'F2629']
    ASAT_Activity['A0022', 'G1800', 'E2068', 'F2628']
    GGT_Activity['A0131', 'F8184', 'E9771', 'J7370', 'K7045']
    PAL_Activity['A0227', 'F8187', 'E6331', 'F1844']
    ......
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    concepts_set_nameGLIMS_ANABIO_concept_code
    BNP_Concentration['C8189', 'B5596', 'A2128']
    BNP_and_NTProBNP_Concentration['C8189', 'B5596', 'A2128', 'A7333', 'J7267', 'J7959']
    NTProBNP_Concentration['A7333', 'J7267', 'J7959']
    ......
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    concepts_set_nameGLIMS_ANABIO_concept_code
    CRP_Concentration['A0248', 'E6332', 'F5581', 'J7381', 'F2631']
    Fibrinogen_Concentration['A0126']
    ......
     +
    +
    + + + + + + + + + + + + + + + + + +
    concepts_set_nameGLIMS_ANABIO_concept_code
    Ferritin_Concentration['A0123', 'E9865']
    ......
     +
    +
    + + + + + + + + + + + + + + + + + + + + + +
    concepts_set_nameGLIMS_ANABIO_concept_code
    Glomerular_Filtration_Rate_EPI_CKD['G6921', 'F8160', 'F9613', 'F9621', 'F9621']
    Glomerular_Filtration_Rate_MDRD['F9622', 'G7835', 'B9964', 'A7456', 'A7455', 'H5609']
    ......
     +
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    + + + + + + + + + + + + + + + + + + + + + + + + + +
    concepts_set_nameGLIMS_ANABIO_concept_code
    Activated_Partial_Thromboplastin_Time['A1792', 'L7286', 'A7748']
    D-Dimers_Concentration['C7882', 'C7882', 'I8765', 'A0124', 'C0474', 'C0474', 'C0474', 'B4199', 'F5402']
    Quick_INR_Time['A0269']
    ......
     +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + +
    concepts_set_nameGLIMS_ANABIO_concept_code
    Albumine_Blood_Concentration['D2358', 'C6841', 'C2102', 'G6616', 'L2260', 'A0006', 'E4799', 'I2013']
    EPP_Blood_Concentration['A0250', 'C9874', 'A3758', 'A0004', 'F9978', 'A0005', 'H8137', 'C7087', 'A0003', 'C7088', 'B9456', 'B9455', 'A0008', 'H8138', 'C7089', 'A0007'...
    Phosphates_Blood_Concentration['A0226', 'F8186', 'F2626']
    ......
     +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + +
    concepts_set_nameGLIMS_ANABIO_concept_code
    B-HCG_Blood_Concentration['A7426', 'F2353', 'A0164', 'L2277']
    Glucose_Blood_Concentration['A0141', 'H7323', 'J7401', 'F2622', 'B9553', 'C7236', 'E7312', 'A7338', 'H7324', 'C0565', 'E9889', 'A8424']
    HbA1c_Blood_%['B6983', 'A2228', 'A1271', 'E6632', 'I5968']
    ......
     +
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    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    concepts_set_nameGLIMS_ANABIO_concept_code
    IL-1 beta_Blood_Concentration['C9351', 'B8921', 'G4800', 'K3662', 'L2217', 'J9193', 'K3665', 'K3687', 'K3661', 'L2197']
    IL-10_Blood_Concentration['B8922', 'C8763', 'K3478', 'L2210', 'J9187', 'K3481', 'K3472', 'K3475', 'L2198']
    IL-6_Blood_Concentration['B8929', 'G4799', 'B1910', 'K3467', 'L2205', 'E6992', 'J9190', 'K3456', 'L2193', 'K3435', 'K3460']
    Procalcitonin_Blood_Concentration['A1661', 'H5267', 'F2632']
    ......
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    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/biology/exploring-relationship/0.html b/main/functionalities/biology/exploring-relationship/0.html new file mode 100644 index 00000000..7a4e3e71 --- /dev/null +++ b/main/functionalities/biology/exploring-relationship/0.html @@ -0,0 +1,74 @@ + + + + + +Terminologies relationships - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    source_concept_idsource_concept_namesource_concept_codestandard_concept_idstandard_concept_namestandard_concept_code
    3xxxxxxxxxxxxCX14xxxxxxxxxxxxA1
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    Terminologies relationships

    +

    Manipulating different code terminologies through OMOP concept and concept_relationship tables can be tricky. This becomes even more pronounced when working with biological measurements that may encompass multiple terminologies, including laboratory, unified, and international terminologies.

    +

    Use prepare_biology_relationship_table to preprocess OMOP concept and concept_relationship into a single table and get a better insight on how terminologies are related.

    +
    +

    Relationship config

    +

    Terminologies mapping from AP-HP database are used by default. See io.settings.measurement_config for mapping details or to modify it.

    +
    +
    from eds_scikit.biology import prepare_biology_relationship_table
+
+biology_relationship_table = prepare_biology_relationship_table(data)
+biology_relationship_table = biology_relationship_table.to_pandas()
+biology_relationship_table.head()
+
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    source_concept_idsource_concept_namesource_concept_codestandard_concept_idstandard_concept_namestandard_concept_code
    3xxxxxxxxxxxxCX14xxxxxxxxxxxxA1
    9xxxxxxxxxxxxZY25xxxxxxxxxxxxA2
    9xxxxxxxxxxxxB3F47xxxxxxxxxxxxD3
    7xxxxxxxxxxxxT324xxxxxxxxxxxxF82
    ..................
     +
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    + + + Back to top + +
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    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/biology/index.html b/main/functionalities/biology/index.html new file mode 100644 index 00000000..f2af8889 --- /dev/null +++ b/main/functionalities/biology/index.html @@ -0,0 +1,1382 @@ + + + + + + + + + +Biology - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    + + + +

    Biology

    +

    The biology module of eds-scikit supports data scientists working on biological data. Its main objectives are to:

    +
      +
    • Provide predefined biology concept sets based on AP-HP coding system
      • +
    • Facilitate codes mapping between different terminologies and referentials
      • +
    • Provide data visualization tools and statistic summary
      • +
    • Allows automatic units conversion from heterogenous units system
      • +
    + +

    +Quick use

    Simple mapping of measurement table codes to ANABIO terminology.

    +Visualizing measurements

    Useful measurement visualizations.

    +Detailed use

    Preparing measurements workflow : codes mapping, units conversion, outliers detection.

    +Terminologies relationships

    Explore concept codes relationships between different terminologies.

    +Predefined concept sets

    Explore predefined concept sets.

    +About measurement table

    Knowledge about measurement table.

    + +
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    + + + Back to top + +
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    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/biology/prepare_measurement/index.html b/main/functionalities/biology/prepare_measurement/index.html new file mode 100644 index 00000000..5adb049e --- /dev/null +++ b/main/functionalities/biology/prepare_measurement/index.html @@ -0,0 +1,1357 @@ + + + + + + + + + +Prepare measurement - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    Prepare measurement

    +

    Prepare measurement flowchart

    +

    The pipeline is structured in 3 stages:

    +
      +
    • Basic preprocessing
      • +
    • Codes mapping
      • +
    • Units conversion
      • +
    +

    Image title

    +
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    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/biology/preparing-measurement/0.html b/main/functionalities/biology/preparing-measurement/0.html new file mode 100644 index 00000000..fd4ca011 --- /dev/null +++ b/main/functionalities/biology/preparing-measurement/0.html @@ -0,0 +1,94 @@ + + + + + +Detailed use - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    concept_setANABIO_concept_codeno_unitsunit_source_valuerange_low_anomaly_countrange_high_anomaly_countmeasurement_countvalue_as_number_countvalue_as_number_meanvalue_as_number_stdvalue_as_number_minvalue_as_number_25%value_as_number_50%value_as_number_75%value_as_number_max
    Glucose_BloodXXXXX100mmol/l155100010005202589
    Glucose_BloodYYYYY50mg/ml2010500050002510020253745
    Glucose_BloodZZZZZ10mmol/l5181000100061046710
    \ No newline at end of file diff --git a/main/functionalities/biology/preparing-measurement/1.html b/main/functionalities/biology/preparing-measurement/1.html new file mode 100644 index 00000000..76f69bda --- /dev/null +++ b/main/functionalities/biology/preparing-measurement/1.html @@ -0,0 +1,94 @@ + + + + + +Detailed use - eds-scikit + + + + + + + + + + + + + + +
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    concept_setANABIO_concept_codeno_unitsunit_source_valuerange_low_anomaly_countrange_high_anomaly_countmeasurement_countvalue_as_number_countvalue_as_number_meanvalue_as_number_stdvalue_as_number_minvalue_as_number_25%value_as_number_50%value_as_number_75%value_as_number_max
    Glucose_BloodXXXXX100mmol/l155100010005202589
    Glucose_BloodYYYYY50mmol/l2010500050005204579
    Glucose_BloodZZZZZ10mmol/l5181000100061046710
    \ No newline at end of file diff --git a/main/functionalities/biology/preparing-measurement/index.html b/main/functionalities/biology/preparing-measurement/index.html new file mode 100644 index 00000000..29851724 --- /dev/null +++ b/main/functionalities/biology/preparing-measurement/index.html @@ -0,0 +1,1670 @@ + + + + + + + + + +Detailed use - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    Detailed use

    +

    This tutorial demonstrates the workflow to prepare the measurement table.

    +
    +

    Big volume

    +

    Measurement table can be large. Do not forget to set proper spark config before loading data.

    +
    +

    Mapping measurement table to ANABIO codes

    +

    Defining Concept-Set

    +

    Here we work with the Glucose pre-defined concept set. See quick-use for an example on how to create a custom concept set.

    +
    from eds_scikit.biology import prepare_measurement_table, ConceptsSet
+
+glucose_blood = ConceptsSet("Glucose_Blood")
+
    +

    Preparing measurement table

    +

    First, we prepare measurements with convert_units = False (as we do not yet know which units are contained in the table).

    +
    from eds_scikit.biology import measurement_values_summary
+
+measurement = prepare_measurement_table(
+    data,
+    start_date="2022-01-01",
+    end_date="2022-05-01",
+    concept_sets=[glucose_blood],
+    convert_units=False,
+    get_all_terminologies=False,
+)
+
    +

    Statistical summary

    +

    A statistical summary by codes allows us to gain insight into value distributions and detect possible heterogeneous units.

    +
    from eds_scikit.biology import measurement_values_summary
+
+stats_summary = measurement_values_summary(
+    measurement,
+    category_cols=["concept_set", "GLIMS_ANABIO_concept_code"],
+    value_column="value_as_number",
+    unit_column="unit_source_value",
+)
+
+stats_summary
+
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    concept_setANABIO_concept_codeno_unitsunit_source_valuerange_low_anomaly_countrange_high_anomaly_countmeasurement_countvalue_as_number_countvalue_as_number_meanvalue_as_number_stdvalue_as_number_minvalue_as_number_25%value_as_number_50%value_as_number_75%value_as_number_max
    Glucose_BloodXXXXX100mmol/l155100010005202589
    Glucose_BloodYYYYY50mg/ml2010500050002510020253745
    .............................................
     +

    Units correction

    +

    To map all units to a common unit base we can use add_conversion and add_target_unit from ConceptSet class.

    +
    glucose_blood.add_conversion("mol", "g", 180)
+glucose_blood.add_target_unit("mmol/l")
+
    +

    We can check the new summary table after units conversion.

    +
    stats_summary = measurement_values_summary(
+    measurement,
+    category_cols=["concept_set", "GLIMS_ANABIO_concept_code"],
+    value_column="value_as_number_normalized",
+    unit_column="unit_source_value_normalized",
+)
+
+stats_summary
+
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    concept_setANABIO_concept_codeno_unitsunit_source_valuerange_low_anomaly_countrange_high_anomaly_countmeasurement_countvalue_as_number_countvalue_as_number_meanvalue_as_number_stdvalue_as_number_minvalue_as_number_25%value_as_number_50%value_as_number_75%value_as_number_max
    Glucose_BloodXXXXX100mmol/l155100010005202589
    Glucose_BloodYYYYY50mmol/l2010500050005204579
    .............................................
     +

    Plot summary

    +

    Once all units are homogeneous, we can generate more detailed dashboard for biology investigation.

    +
    from eds_scikit.biology import plot_biology_summary
+
+measurement = measurement.merge(
+    data.visit_occurrence[["care_site_id", "visit_occurrence_id"]],
+    on="visit_occurrence_id",
+)
+measurement = measurement.merge(
+    data.care_site[["care_site_id", "care_site_short_name"]], on="care_site_id"
+)
+
+plot_biology_summary(measurement, value_column="value_as_number_normalized")
+
    +

    Volumetry dashboard +Distribution dashboard

    +
    +
    +
    + + + Back to top + +
    + +
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    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/biology/quick-use/index.html b/main/functionalities/biology/quick-use/index.html new file mode 100644 index 00000000..9255d3b3 --- /dev/null +++ b/main/functionalities/biology/quick-use/index.html @@ -0,0 +1,1458 @@ + + + + + + + + + +Quick use - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    + + + +

    Quick use

    +

    This tutorial demonstrates how the biology module can be quickly used to map measurement codes.

    +
    +

    Big volume

    +

    Measurement table can be large. Do not forget to set proper spark config before loading data.

    +
    +

    Mapping measurement table to ANABIO codes

    +

    Defining Concept-Set

    +

    To define a concept-set variable you just need to specify a terminology and a set of codes.

    +
    from eds_scikit.biology import prepare_measurement_table, ConceptsSet
+
+custom_leukocytes = ConceptsSet("Custom_Leukocytes")
+
+custom_leukocytes.add_concept_codes(
+    concept_codes=["A0174", "H6740"], terminology="GLIMS_ANABIO"  # (1)
+)
+
+custom_leukocytes.add_concept_codes(
+    concept_codes=["6690-2"], terminology="ITM_LOINC"  # (2)
+)
+
    +
      +
    1. Codes must be given with terminology. Available terminologies can be accessed with eds_scikit.io.settings.measurement_config['source_terminologies']. See. AP-HP biology for details on the AP-HP setting.
      2. +
    2. Codes must be given with terminology. Available terminologies can be accessed with eds_scikit.io.settings.measurement_config['source_terminologies']. See. AP-HP biology for details on the AP-HP setting.
      3. +
    +

    Preparing measurement table

    +

    Then, simply run prepare_measurement_table to select the measurements from your concept set.

    +
    measurement = prepare_measurement_table(
+    data,
+    start_date="2022-01-01",
+    end_date="2022-05-01",
+    concept_sets=[custom_leukocytes],
+    convert_units=False,
+    get_all_terminologies=True,
+)
+
    +
    +
    +
    + + + Back to top + +
    + +
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    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/biology/tutorial/0.html b/main/functionalities/biology/tutorial/0.html new file mode 100644 index 00000000..8883ad50 --- /dev/null +++ b/main/functionalities/biology/tutorial/0.html @@ -0,0 +1,54 @@ + + + + + +Tutorial - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ANALYSES_LABORATOIRE_concept_codeGLIMS_ANABIO_concept_codeGLIMS_LOINC_concept_codeITM_ANABIO_concept_codeITM_LOINC_concept_code
    0C882433256-9UnknownUnknown
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    4E43586690-2UnknownUnknown
    2C909726464-8UnknownUnknown
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    \ No newline at end of file diff --git a/main/functionalities/biology/tutorial/index.html b/main/functionalities/biology/tutorial/index.html new file mode 100644 index 00000000..52f44032 --- /dev/null +++ b/main/functionalities/biology/tutorial/index.html @@ -0,0 +1,1939 @@ + + + + + + + + + +Tutorial - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    You can download this notebook directly here

    +
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    +

    Tutorial - Preparing measurement table

    +

    This tutorial takes you through the entire workflow of the Biology module.

    +
    +
    +
    +
    +
    +
    import eds_scikit
+import pandas as pd
+
    +
    +
    +
    +
    +
    +

    1 - Load data

    +
    +

    Big volume

    +

    Measurement table can be large. Do not forget to set proper spark config.

    +
    +
    +
    +
    +
    +
    +
    to_add_conf = [
+    ("master", "yarn"),
+    ("deploy_mode", "client"),
+    ("spark.driver.memory", ...),
+    ("spark.executor.memory", ...),
+    ("spark.executor.cores", ...),
+    ("spark.executor.memoryOverhead", ...),
+    ("spark.driver.maxResultSize", ...)
+    ...
+]
+
+spark, sc, sql = eds_scikit.improve_performances(to_add_conf=to_add_conf)
+
+from eds_scikit.io.hive import HiveData
+
    +
    +
    +
    +
    +
    data = HiveData(
+    spark_session=spark,
+    database_name="cse_xxxxxxx_xxxxxxx",
+    tables_to_load=[
+        "care_site",
+        "concept",
+        "visit_occurrence",
+        "measurement",
+        "concept_relationship",
+    ],
+)
+
    +
    +
    +
    +
    +
    +

    2 - Quick use : Preparing measurement table

    +
    +
    +
    +
    +
    +
    +

    a) Define biology concept-sets

    +

    In order to work on the measurements of interest, you can extract a list of concepts-sets by:

    +
      +
    • Selecting default concepts-sets provided in the library.
      • +
    • Modifying the codes of a selected default concepts-set.
      • +
    • Creating a concepts-set from scratch.
      • +
    +

    Code selection can be tricky. See Concept codes relationships exploration section for more details on how to select them.

    +
    +
    +
    +
    +
    +
    from eds_scikit.biology import ConceptsSet
+
+# Creating Concept-Set
+custom_leukocytes = ConceptsSet("Custom_Leukocytes")
+
+custom_leukocytes.add_concept_codes(
+    concept_codes=['A0174', 'H6740', 'C8824'], 
+    terminology='GLIMS_ANABIO' 
+)
+custom_leukocytes.add_concept_codes(
+    concept_codes=['6690-2'], 
+    terminology='ITM_LOINC'
+)
+
+# Importing Concept-Set (see. 4.b for details on existing concepts sets)
+glucose_blood = ConceptsSet("Glucose_Blood_Concentration")
+
    +
    +
    +
    +
    +
    concepts_sets = [
+    custom_leukocytes, 
+    glucose_blood
+]
+
    +
    +
    +
    +
    +
    +

    b) Prepare measurements

    +
    +

    Lazy execution

    +

    Execution will be lazy, except if convert_units=True.

    +
    +
    +
    +
    +
    +
    +
    from eds_scikit.biology import prepare_measurement_table
+
    +
    +
    +
    +
    +
    measurement = prepare_measurement_table(data,
+                                        start_date="2022-01-01", end_date="2022-05-01",
+                                        concept_sets=concepts_sets,
+                                        convert_units=False,
+                                        get_all_terminologies=True
+                                       )
+
    +
    +
    +
    +
    +
    +

    Now you have your measurement table mapped with concept set terminology. Next sections are about measurement codes analysis, units and plots.

    +
    +
    +
    +
    +
    +
    +

    3 - Detailed use : Analysing measurement table

    +
    +
    +
    +
    +
    +
    +

    a) Measurements statistics table

    +
    +
    +
    +
    +
    +
    from eds_scikit.biology import measurement_values_summary
+
    +
    +
    +
    +
    +
    stats_summary = measurement_values_summary(measurement, 
+                                           category_cols=["concept_set", "GLIMS_ANABIO_concept_code", "GLIMS_LOINC_concept_code"], 
+                                           value_column="value_as_number", 
+                                           unit_column="unit_source_value")
+
+stats_summary
+
    +
    +
    +
    +
    +
    +

    b) Measurements units correction

    +
    +
    +
    +
    +
    +
    glucose_blood.add_conversion("mol", "g", 180)
+glucose_blood.add_target_unit("mmol/l")
+
+concepts_sets = [glucose_blood, custom_leukocytes]
+
    +
    +
    +
    +
    +
    measurement = prepare_measurement_table(data, 
+                                        start_date="2022-01-01", end_date="2022-05-01",
+                                        concept_sets=concepts_sets,
+                                        convert_units=True, 
+                                        get_all_terminologies=False
+                                       )
+
    +
    +
    +
    +
    +
    stats_summary = measurement_values_summary(measurement, 
+                                           category_cols=["concept_set", "GLIMS_ANABIO_concept_code"], 
+                                           value_column="value_as_number_normalized", #converted
+                                           unit_column="unit_source_value_normalized")
+
+stats_summary
+
    +
    +
    +
    +
    +
    +

    c) Plot biology summary

    +

    Applying plot_biology_summary to computed measurement dataframe, merged with care sites, allows to generate nice exploration plots such as :

    + +
    +
    +
    +
    +
    +
    from eds_scikit.biology import plot_biology_summary
+
    +
    +
    +
    +
    +
    measurement = measurement.merge(data.visit_occurrence[["care_site_id", "visit_occurrence_id"]], on="visit_occurrence_id")
+measurement = measurement.merge(data.care_site[["care_site_id", "care_site_short_name"]], on="care_site_id")
+
    +
    +
    +
    +
    +
    plot_biology_summary(measurement, value_column="value_as_number_normalized") 
+
    +
    +
    +
    +
    +
    +

    4 - Further : Concept Codes, Concepts Sets and Units

    +
    +
    +
    +
    +
    +
    +

    1 - Concept codes relationships exploration

    +

    Concept codes relationships can be tricky to understand and to manipulate. Function prepare_biology_relationship_table allows to build mapping dataframe between main AP-HP biology referential.

    +

    See io.settings.measurement_config["mapping"] and io.settings.measurement_config["source_terminologies"] configurations for mapping details.

    +
    +
    +
    +
    +
    +
    from eds_scikit.biology import prepare_biology_relationship_table
+
+biology_relationship_table = prepare_biology_relationship_table(data)
+biology_relationship_table = biology_relationship_table.to_pandas()
+
    +
    +
    +
    +
    +
    +

    Relationship between codes from different referentials.

    +
    +
    +
    +
    +
    +
    columns = [col for col in biology_relationship_table.columns if "concept_code" in col]
+
+biology_relationship_table[biology_relationship_table.GLIMS_ANABIO_concept_code.isin(['A0174', 'H6740', 'C8824'])][columns].drop_duplicates()
+
    +
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ANALYSES_LABORATOIRE_concept_codeGLIMS_ANABIO_concept_codeGLIMS_LOINC_concept_codeITM_ANABIO_concept_codeITM_LOINC_concept_code
    0C882433256-9UnknownUnknown
    1A01746690-2A01746690-2
    ...............
     +
    +
    +
    +
    +
    +
    biology_relationship_table[biology_relationship_table.GLIMS_LOINC_concept_code.isin(['33256-9', '6690-2', '26464-8'])][columns].drop_duplicates()
+
    +
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ANALYSES_LABORATOIRE_concept_codeGLIMS_ANABIO_concept_codeGLIMS_LOINC_concept_codeITM_ANABIO_concept_codeITM_LOINC_concept_code
    4E43586690-2UnknownUnknown
    2C909726464-8UnknownUnknown
    6K32326690-2UnknownUnknown
    5E695326464-8UnknownUnknown
    1C882433256-9UnknownUnknown
    4E435826464-8UnknownUnknown
    5E69536690-2UnknownUnknown
    7K60946690-2UnknownUnknown
    0C97846690-2C97846690-2
    ...............
     +
    +
    +
    +
    +
    +
    +

    2 - Concepts-Sets

    +

    To get all availables concepts sets see datasets.default_concepts_sets. More details about their definition and how they are build can be found in this section.

    +
    +
    +
    +
    +
    +
    from eds_scikit import datasets
+from eds_scikit.biology import ConceptsSet
+
    +
    +
    +
    +
    +
    print(ConceptsSet("Glucose_Blood_Concentration").concept_codes)
+
    +
    +
    +
    +
    +
    datasets.default_concepts_sets
+
    +
    +
    +
    +
    +
    +

    3 - Units

    +

    Units module makes conversion between units easier. It uses configuration files datasets.units and datasets.elements.

    +
    +
    +
    +
    +
    +
    from eds_scikit import datasets
+
    +
    +
    +
    +
    +
    from eds_scikit.biology import Units
+
    +
    +
    +
    +
    +
    units = Units()
+
+print("L to ml : ", units.convert_unit("L", "ml"))
+print("m/s to m/h : ", units.convert_unit("m/s", "m/h"))
+print("g to mol : ", units.convert_unit("g", "mol"))
+units.add_conversion("mol", "g", 180)
+print("g to mol : ", units.convert_unit("g", "mol"))
+
    +
    +
    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/biology/visualization/0.html b/main/functionalities/biology/visualization/0.html new file mode 100644 index 00000000..306a9c13 --- /dev/null +++ b/main/functionalities/biology/visualization/0.html @@ -0,0 +1,94 @@ + + + + + +Visualizing measurements - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    concept_setANABIO_concept_codeno_unitsunit_source_valuerange_low_anomaly_countrange_high_anomaly_countmeasurement_countvalue_as_number_countvalue_as_number_meanvalue_as_number_stdvalue_as_number_minvalue_as_number_25%value_as_number_50%value_as_number_75%value_as_number_max
    Custom_LeukocytesA0174148x10*9/l8131099118571185721180255075100
    Custom_LeukocytesC8824121x10*9/l11661196118211182120200255075100
    Custom_LeukocytesC978483x10*9/l935902110821108210160255075100
    \ No newline at end of file diff --git a/main/functionalities/biology/visualization/index.html b/main/functionalities/biology/visualization/index.html new file mode 100644 index 00000000..a46ff9e1 --- /dev/null +++ b/main/functionalities/biology/visualization/index.html @@ -0,0 +1,1514 @@ + + + + + + + + + +Visualizing measurements - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
    + +
    + +
    +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    + + + +

    Visualizing measurements

    +

    Once the measurement table has been computed, biology module provides measurement_values_summary and plot_biology_summary to gain better insights into their distribution and volumetry across codes, care sites, and time.

    +

    Statistical summary

    +

    measurement_values_summary generates useful statistics to identify anomalies in measurements associated with a concept set.

    +
    from eds_scikit.biology import measurement_values_summary
+
+stats_summary = measurement_values_summary(
+    measurement,
+    category_cols=[
+        "concept_set",
+        "GLIMS_ANABIO_concept_code",
+        "GLIMS_LOINC_concept_code",
+    ],
+    value_column="value_as_number",
+    unit_column="unit_source_value",
+)
+
+stats_summary
+
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    concept_setANABIO_concept_codeno_unitsunit_source_valuerange_low_anomaly_countrange_high_anomaly_countmeasurement_countvalue_as_number_countvalue_as_number_meanvalue_as_number_stdvalue_as_number_minvalue_as_number_25%value_as_number_50%value_as_number_75%value_as_number_max
    Custom_LeukocytesA0174148x10*9/l8131099118571185721180255075100
    Custom_LeukocytesC8824121x10*9/l11661196118211182120200255075100
    .............................................
     +

    Plot summary

    +

    plot_biology_summary generates a useful dashboard to better understand the volumetry and distribution of codes within the same concept set. The purpose is to identify and correct possible biases associated with sets of codes, time periods, or specific care sites.

    +
    from eds_scikit.biology import plot_biology_summary
+
+# First add 'care_site_short_name' column to measurement table
+measurement = measurement.merge(
+    data.visit_occurrence[["care_site_id", "visit_occurrence_id"]],
+    on="visit_occurrence_id",
+)
+measurement = measurement.merge(
+    data.care_site[["care_site_id", "care_site_short_name"]], on="care_site_id"
+)
+
+plot_biology_summary(measurement, value_column="value_as_number")
+
    +

    Volumetry dashboard +Distribution dashboard

    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/generic/introduction/0.html b/main/functionalities/generic/introduction/0.html new file mode 100644 index 00000000..f8834f7f --- /dev/null +++ b/main/functionalities/generic/introduction/0.html @@ -0,0 +1,92 @@ + + + + + +A gentle demo - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    agecohortepercent_icu
    0(0, 40]Control0.327988
    1(0, 40]Diab.0.445578
    2(40, 50]Control0.263667
    3(40, 50]Diab.0.427203
    4(50, 60]Control0.315931
    5(50, 60]Diab.0.464736
    6(60, 70]Control0.356808
    7(60, 70]Diab.0.474766
    8(70, 120]Control0.159337
    9(70, 120]Diab.0.230180
    \ No newline at end of file diff --git a/main/functionalities/generic/introduction/index.html b/main/functionalities/generic/introduction/index.html new file mode 100644 index 00000000..c8bd8d70 --- /dev/null +++ b/main/functionalities/generic/introduction/index.html @@ -0,0 +1,2144 @@ + + + + + + + + + +A gentle demo - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
    + +
    + +
    +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    + + + + + +
    +
    +
    +

    You can download this notebook directly here

    +
    +
    +
    +
    +
    +
    +

    A gentle demo

    +
    +
    +
    +
    +
    +
    import datetime
+import pandas as pd
+
+import eds_scikit
+
    +
    +
    +
    +
    +
    +
    spark, sc, sql = eds_scikit.improve_performances() # (1)
+
    +
      +
    1. See the welcome page for an explanation of this line
      2. +
    +
    +
    +
    +
    +
    +
    +

    Loading data

    +

    Data loading is made easy by using the HiveData object.
    +Simply give it the name of the database you want to use:

    +
    +
    +
    +
    +
    +
    database_name = "MY_DATABASE_NAME"
+
    +
    +
    +
    +
    +
    from eds_scikit.io import HiveData
+
+data = HiveData(
+    database_name="database_name",
+)
+
    +
    +
    +
    +
    +
    +

    Now your tables are available as Koalas DataFrames: Those are basically Spark DataFrames which allows for the Pandas API to be used on top (see the Project description of eds-scikit's documentation for more informations.)

    +
    +
    +
    +
    +
    +
    +

    What we need to extract:

    +
      +
    • Patients with diabetes
      • +
    • Patients with Covid-19
      • +
    • Visits from those patients, and their ICU/Non-ICU status
      • +
    +

    Let us import what's necessary from eds-scikit:

    +
    +
    +
    +
    +
    +
    from eds_scikit.event import conditions_from_icd10
+from eds_scikit.event.diabetes import (
+    diabetes_from_icd10,
+    DEFAULT_DIABETE_FROM_ICD10_CONFIG,
+)
+from eds_scikit.icu import tag_icu_visit
+
    +
    +
    +
    +
    +
    DATE_MIN = datetime.datetime(2020, 1, 1)
+DATE_MAX = datetime.datetime(2021, 6, 1)
+
    +
    +
    +
    +
    +
    +

    Extracting the diabetic status

    +

    Luckily, a function is available to extract diabetic patients from ICD-10:

    +
    +
    +
    +
    +
    +
    diabetes = diabetes_from_icd10(
+    condition_occurrence=data.condition_occurrence,
+    visit_occurrence=data.visit_occurrence,
+    date_min=DATE_MIN,
+    date_max=DATE_MAX,
+)
+
    +
    +
    +
    +
    +
    +

    We can check the default parameters used here:

    +
    +
    +
    +
    +
    +
    DEFAULT_DIABETE_FROM_ICD10_CONFIG
+
    +
    +
    +
    +
    +
    +
    +{'additional_filtering': {'condition_status_source_value': {'DP', 'DAS'}},
+ 'codes': {'DIABETES_INSIPIDUS': {'code_list': ['E232', 'N251'],
+                                  'code_type': 'exact'},
+           'DIABETES_IN_PREGNANCY': {'code_list': ['O24'],
+                                     'code_type': 'prefix'},
+           'DIABETES_MALNUTRITION': {'code_list': ['E12'],
+                                     'code_type': 'prefix'},
+           'DIABETES_TYPE_I': {'code_list': ['E10'], 'code_type': 'prefix'},
+           'DIABETES_TYPE_II': {'code_list': ['E11'], 'code_type': 'prefix'},
+           'OTHER_DIABETES_MELLITUS': {'code_list': ['E13', 'E14'],
+                                       'code_type': 'prefix'}},
+ 'date_from_visit': True,
+ 'default_code_type': 'prefix'}
+
+
    +
    +
    +
    +
    +
    +
    +
    +
    +

    We are only interested in diabetes mellitus, although we extracted other types of diabetes:

    +
    +
    +
    +
    +
    +
    diabetes.concept.value_counts()
+
    +
    +
    +
    +
    +
    +
    +DIABETES_TYPE_II           117843
+DIABETES_TYPE_I             10597
+OTHER_DIABETES_MELLITUS      6031
+DIABETES_IN_PREGNANCY        2597
+DIABETES_INSIPIDUS           1089
+DIABETES_MALNUTRITION         199
+Name: concept, dtype: int64
+
    +
    +
    +
    +
    +
    +
    +
    +
    +

    We will restrict the types of diabetes used here:

    +
    +
    +
    +
    +
    +
    diabetes_cohort = (
+    diabetes[
+        diabetes.concept.isin(
+            {
+                "DIABETES_TYPE_I",
+                "DIABETES_TYPE_II",
+                "OTHER_DIABETES_MELLITUS",
+            }
+        )
+    ]
+    .person_id.unique()
+    .reset_index()
+)
+diabetes_cohort.loc[:, "HAS_DIABETE"] = True
+
    +
    +
    +
    +
    +
    +

    Extracting the Covid status

    +

    Using the conditions_from_icd10 function, we will extract visits linked to COVID-19:

    +
    +
    +
    +
    +
    +
    codes = dict(
+    COVID=dict(
+        code_list=r"U071[0145]", 
+        code_type="regex",
+    )
+)
+
+covid = conditions_from_icd10(
+    condition_occurrence=data.condition_occurrence,
+    visit_occurrence=data.visit_occurrence,
+    codes=codes,
+    date_min=DATE_MIN,
+    date_max=DATE_MAX,
+)
+
    +
    +
    +
    +
    +
    +

    Now we can go from the visit_occurrence level to the visit_detail level.

    +
    +
    +
    +
    +
    +
    visit_detail_covid = data.visit_detail.merge(
+    covid[["visit_occurrence_id"]],
+    on="visit_occurrence_id",
+    how="inner",
+)
+
    +
    +
    +
    +
    +
    +

    Extracting ICU visits

    +

    What is left to do is to tag each visit as occurring in an ICU or not. This is achieved with the tag_icu_visit.
    +Like many functions in eds-scikit, this function exposes an algo argument, allowing you to choose how the tagging is done.
    +You can check the corresponding documentation to see the availables algos.

    +
    +
    +
    +
    +
    +
    visit_detail_covid = tag_icu_visit(
+    visit_detail=visit_detail_covid,
+    care_site=data.care_site,
+    algo="from_authorisation_type",
+)
+
    +
    +
    +
    +
    +
    visit_detail_covid = visit_detail_covid.merge(
+    diabetes_cohort, on="person_id", how="left"
+)
+
+visit_detail_covid["HAS_DIABETE"].fillna(False, inplace=True)
+visit_detail_covid["IS_ICU"].fillna(False, inplace=True)
+
    +
    +
    +
    +
    +
    +

    Finishing the analysis

    +

    Adding patient's age

    +
    +
    +
    +
    +
    +
    +

    We will add the patient's age at each visit_detail:

    +
    +
    +
    +
    +
    +
    from eds_scikit.utils import datetime_helpers
+
    +
    +
    +
    +
    +
    visit_detail_covid = visit_detail_covid.merge(data.person[['person_id','birth_datetime']], 
+                                              on='person_id', 
+                                              how='inner')
+
+visit_detail_covid["age"] = (
+    datetime_helpers.substract_datetime(
+        visit_detail_covid["visit_detail_start_datetime"],
+        visit_detail_covid["birth_datetime"],
+        out="hours",
+    )
+    / (24 * 365.25)
+)
+
    +
    +
    +
    +
    +
    +

    From distributed Koalas to local Pandas

    +
    +
    +
    +
    +
    +
    +

    All the computing above was done using Koalas DataFrames, which are distributed.
    +Now that we limited our cohort to a manageable size, we can switch to Pandas to finish our analysis.

    +
    +
    +
    +
    +
    +
    visit_detail_covid_pd = visit_detail_covid[
+    ["person_id", "age", "HAS_DIABETE", "IS_ICU"]
+].to_pandas()
+
    +
    +
    +
    +
    +
    +

    Grouping by patient

    +
    +
    +
    +
    +
    +
    stats = (
+    visit_detail_covid_pd[["person_id", "age", "HAS_DIABETE", "IS_ICU"]]
+    .groupby("person_id")
+    .agg(
+        HAS_DIABETE=("HAS_DIABETE", "any"), 
+        IS_ICU=("IS_ICU", "any"), 
+        age=("age", "min"),
+    )
+)
+
    +
    +
    +
    +
    +
    +

    Binning the age into intervals

    +
    +
    +
    +
    +
    +
    stats["age"] = pd.cut(
+    stats.age,
+    bins=[0, 40, 50, 60, 70, 120],
+    labels=["(0, 40]", "(40, 50]", "(50, 60]", "(60, 70]", "(70, 120]"],
+)
+
    +
    +
    +
    +
    +
    +

    Computing the ratio of patients that had an ICU visit

    +
    +
    +
    +
    +
    +
    stats = stats.groupby(["age", "HAS_DIABETE"], as_index=False).apply(
+    lambda x: x["IS_ICU"].sum() / len(x)
+)
+
+stats.columns = ["age", "cohorte", "percent_icu"]
+
+stats["cohorte"] = stats["cohorte"].replace({True: "Diab.", False: "Control"})
+
    +
    +
    +
    +
    +
    +

    Results

    +
    +
    +
    +
    +
    +
    stats
+
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    +
    +
    +
    +

    We can finally plot our results using Altair:

    +
    +
    +
    +
    +
    +
    import altair as alt
+
    +
    +
    +
    +
    +
    bars = (
+    alt.Chart(
+        stats,
+        title=[
+            "Percentage of patients who went through ICU during their COVID stay, ",
+            "as a function of their age range and diabetic status",
+            " ",
+        ],
+    )
+    .mark_bar()
+    .encode(
+        x=alt.X("cohorte:N", title=""),
+        y=alt.Y(
+            "percent_icu",
+            title="% of patients who went through ICU.",
+            axis=alt.Axis(format="%"),
+        ),
+        color=alt.Color("cohorte:N", title="Cohort"),
+        column=alt.Column("age:N", title="Age range"),
+    )
+)
+
+bars = bars.configure_title(anchor="middle", baseline="bottom")
+bars
+
    +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    + +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/generic/io/0.html b/main/functionalities/generic/io/0.html new file mode 100644 index 00000000..07cfe317 --- /dev/null +++ b/main/functionalities/generic/io/0.html @@ -0,0 +1,68 @@ + + + + + +Connectors - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    birth_datetimedeath_datetimegender_source_valuecdm_source
    01946-06-04NaTmORBIS
    11940-01-212018-05-07mORBIS
    21979-04-25NaTmORBIS
    32007-10-13NaTfORBIS
    41964-12-27NaTfORBIS
    \ No newline at end of file diff --git a/main/functionalities/generic/io/index.html b/main/functionalities/generic/io/index.html new file mode 100644 index 00000000..808a1b93 --- /dev/null +++ b/main/functionalities/generic/io/index.html @@ -0,0 +1,1876 @@ + + + + + + + + + +Connectors - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
    + +
    + +
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    + +
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    +
    +
    +
    + + + + + +
    +
    +
    +

    You can download this notebook directly here

    +
    +
    +
    +
    +
    +
    +

    IO: Getting Data

    +
    +
    +
    +
    +
    +
    +

    3 classes are available to facilitate data access:

    +
      +
    • HiveData: Getting data from a Hive cluster, returning Koalas DataFrames
      • +
    • PandasData: Getting data from tables saved on disk, returning Pandas DataFrames
      • +
    • PostgresData: Getting data from a PostGreSQL DB, returning Pandas DataFrames
      • +
    +
    +
    +
    +
    +
    +
    from eds_scikit.io import HiveData, PandasData, PostgresData
+
    +
    +
    +
    +
    +
    +

    Loading from Hive: HiveData

    +

    The HiveData class expects two parameters:

    +
      +
    • A SparkSession variable
      • +
    • The name of the Database to connect to
      • +
    +
    +
    +
    +
    +
    +
    +
    +

    Using Spark kernels

    +

    All kernels designed to use Spark are configured to expose 3 variables at startup:

    +
      +
    • spark, the current SparkSession
      • +
    • sc, the current SparkContext
      • +
    • sql, a function to execute SQL code on the Hive Database.
      • +
    +

    In this case you can just provide the spark variable to HiveData !

    +
    +
    +

    Working with an I2B2 database

    +

    To use a built-in I2B2 to OMOP connector, specify database_type="I2b2" when instantiating HiveData

    +
    +
    +
    +
    +
    +
    +
    +

    If needed, the following snippet allows to create the necessary variables:

    +
    +
    +
    +
    +
    +
    from pyspark import SparkConf, SparkContext
+from pyspark.sql.session import SparkSession
+
+conf = SparkConf()
+sc = SparkContext(conf=conf)
+spark = SparkSession.builder \
+                    .enableHiveSupport() \
+                    .getOrCreate()
+sql = spark.sql
+
    +
    +
    +
    +
    +
    +

    The class HiveData provides a convenient interface to OMOP data stored in Hive.
    +The OMOP tables can be accessed as attribute and they are represented as Koalas DataFrames. +You simply need to mention your Hive database name.

    +
    +
    +
    +
    +
    +
    data = HiveData(
+    "cse_210038_20221219",#DB_NAME,
+    spark,
+    database_type="I2B2",
+)
+
    +
    +
    +
    +
    +
    +

    By default, only a subset of tables are added as attributes:

    +
    +
    +
    +
    +
    +
    data.available_tables
+
    +
    +
    +
    +
    +
    +
    +['concept',
+ 'visit_detail',
+ 'note_deid',
+ 'person',
+ 'care_site',
+ 'visit_occurrence',
+ 'measurement',
+ 'procedure_occurrence',
+ 'condition_occurrence',
+ 'fact_relationship',
+ 'concept_relationship']
+
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Koalas DataFrames, like Spark DataFrames, rely on a lazy execution plan: As long as no data needs to be specifically collected, saved or displayed, no code is executed. It is simply saved for a later execution.
    +The main interest of Koalas DataFrames is that you can use (most of) the Pandas API:

    +
    +
    +
    +
    +
    +
    person = data.person
+person.drop(columns = ['person_id']).head()
+
    +
    +
    +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    birth_datetimedeath_datetimegender_source_valuecdm_source
    01946-06-04NaTmORBIS
    11940-01-212018-05-07mORBIS
    21979-04-25NaTmORBIS
    32007-10-13NaTfORBIS
    4............
     +
    +
    +
    +
    +
    +
    +
    +
    +
    from datetime import datetime
+
+person['is_over_50'] = (person['birth_datetime'] >= datetime(1971,1,1))
+
+stats = (
+    person
+    .groupby('is_over_50')
+    .person_id
+    .count()
+)
+
    +
    +
    +
    +
    +
    +

    Once data has been sufficiently aggregated, it can be converted back to Pandas, e.g. for plotting.

    +
    +
    +
    +
    +
    +
    stats_pd = stats.to_pandas()
+stats_pd
+
    +
    +
    +
    +
    +
    +
    +is_over_50
+True     132794
+False     66808
+Name: person_id, dtype: int64
+
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Similarily, if you want to work on the Spark DataFrame instead, a similar method is available:

    +
    +
    +
    +
    +
    +
    person_spark = person.to_spark()
+
    +
    +
    +
    +
    +
    +

    Persisting/Reading a sample to/from disk: PandasData

    +

    Working with Pandas DataFrame is, when possible, more convenient.
    +You have the possibility to save your database or at least a subset of it.
    +Doing so allows you to work on it later without having to go through Spark again.

    +
    +
    +
    +
    +
    +
    +
    +

    Careful with cohort size

    +

    Do not save it if your cohort is big: This saves all available tables on disk.

    +
    +
    +
    +
    +
    +
    +
    +

    For instance, let us define a dummy subset of 1000 patients:

    +
    +
    +
    +
    +
    +
    visits = data.visit_occurrence
+
+selected_visits = (
+    visits
+    .loc[visits["visit_source_value"] == "urgence"]
+)
+
+sample_patients = (
+    selected_visits["person_id"]
+    .drop_duplicates()
+    .head(1000)
+)
+
    +
    +
    +
    +
    +
    +

    And save every table restricted to this small cohort as a parquet file:

    +
    +
    +
    +
    +
    +
    MY_FOLDER_PATH = "./test_cohort"
+
    +
    +
    +
    +
    +
    import os
+
+folder = os.path.abspath(MY_FOLDER_PATH)
+
+tables_to_save = ["person", "visit_detail", "visit_occurrence"]
+
+data.persist_tables_to_folder(
+    folder,
+    tables=tables_to_save,
+    person_ids=sample_patients
+)
+
    +
    +
    +
    +
    +
    +

    Once you saved some data to disk, a dedicated class can be used to access it:
    +The class PandasData can be used to load OMOP data from a folder containing several parquet files. The tables +are accessed as attributes and are returned as Pandas DataFrame.

    +

    Warning: in this case, the whole table will be loaded into memory on a single jupyter server. Consequently it is advised +to only use this for small datasets.

    +
    +
    +
    +
    +
    +
    data = PandasData(folder)
+
    +
    +
    +
    +
    +
    data.available_tables
+
    +
    +
    +
    +
    +
    +
    +['visit_detail', 'visit_occurrence', 'person']
+
    +
    +
    +
    +
    +
    +
    +
    +
    person = data.person
+print(f"type: {type(person)}")
+print(f"shape: {person.shape}")
+
    +
    +
    +
    +
    +
    +
    +type: <class 'pandas.core.frame.DataFrame'>
+shape: (1000, 5)
+
+
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Loading from PostGres: PostgresData

    +

    OMOP data can be stored in a PostgreSQL database. The PostgresData class provides a convinient interface to it.

    +

    Note : this class relies on the file ~/.pgpass that contains your identifiers for several databases.

    +
    +
    +
    +
    +
    +
    data = PostgresData(
+    dbname=DB, 
+    schema="omop", 
+    user=USER,
+)
+
+data.read_sql("select count(*) from person")
+
    +
    +
    + +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/omop-teva/configuration-omop/index.html b/main/functionalities/omop-teva/configuration-omop/index.html new file mode 100644 index 00000000..2441d37c --- /dev/null +++ b/main/functionalities/omop-teva/configuration-omop/index.html @@ -0,0 +1,1483 @@ + + + + + + + + + +OMOP Teva - Config - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
    + +
    + +
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    + +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    + + + +

    OMOP Teva - Config

    +

    All plots generated by generate_omop_teva are based on the configuration file eds_scikit.plot.default_omop_teva_config.

    +

    Table configuration

    +

    A table configuration is defined by 3 parameters :

    +
      +
    • category columns list
      • +
    • date column
      • +
    • category columns mapping
      • +
    +

    Here is two possible configurations for OMOP condition table :

    +
    +
    +
    +
    "condition_occurrence": {
+    "category_columns": [
+        "visit_occurrence_id",
+        "care_site_short_name",
+        "condition_source_value",
+        "stay_source_value",
+        "visit_source_value",
+        "admission_reason_source_value",
+        "visit_type_source_value",
+        "destination_source_value",
+        "cdm_source",
+    ],
+    "date_column": "condition_start_datetime",
+    "mapper": {
+        "visit_occurrence_id": {"not NaN": ".*"},
+        "condition_source_value": {"not NaN": ".*"},
+    },
+},
+
    +
    +
    +
    "condition_occurrence": {
+    # (1) Some columns were removed .
+    "category_columns": [
+        "visit_occurrence_id",
+        "care_site_short_name",
+        "condition_source_value",
+        "visit_source_value",
+        "visit_type_source_value",
+        "cdm_source",
+    ],
+    # (2) Date column remain the same .
+    "date_column": "condition_start_datetime",
+    "mapper": {
+        "visit_occurrence_id": {"not NaN": ".*"},
+        # (3) Mapping to diabetic conditions .
+        "condition_source_value": {"has_diabete": r"^E10|^E11|^E12|^E13|^E14|O24"},
+    },
+},
+
    +
    +
    +
    +

    Specifying table configuration

    +

    To specify configuration, simply update default_omop_teva_config and pass it to generate_omop_teva.

    +
    from eds_scikit.plot import generate_omop_teva
+from eds_scikit.io.omop_teva_default_config import default_omop_teva_config
+
+omop_teva_config = default_omop_teva_config
+
+condition_mapper = {
+    "condition_source_value": {"has_diabete": r"^E10|^E11|^E12|^E13|^E14|O24"}
+}
+
+omop_teva_config["condition_occurrence"]["mapper"].update(condition_mapper)
+
+start_date, end_date = "2021-01-01", "2021-12-01"
+generate_omop_teva(data=data,
+                   start_date=start_date,
+                   end_date=end_date,
+                   teva_config=omop_teva_config)
+
    +
    +

    Adding a new table in default_omop_teva_config

    +

    Feel free to add any new table in the configuration. Just make sure it has a visit_occurrence_id column.

    +
    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/omop-teva/custom-teva/index.html b/main/functionalities/omop-teva/custom-teva/index.html new file mode 100644 index 00000000..3b6abe48 --- /dev/null +++ b/main/functionalities/omop-teva/custom-teva/index.html @@ -0,0 +1,2808 @@ + + + + + + + + + +Custom Teva - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
    + +
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    + +
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    +
    + +
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    +
    +
    +
    + + + +

    Custom Teva

    +

    OMOP-Teva module can also be applied to any dataframe. User must use reduce_table and visualize_table from eds_scikit.plot.table_viz.

    +
    +

    Image title +

    +
    +
    +
    +

    Make sure to specify categorical columns with less then 50 values.

    +

    Use the function eds_scikit.plot.table_viz.map_column to reduce columns volumetry.

    +
    +
    +Creating synthetic dataset +
    import numpy as np
+import pandas as pd
+
+data = pd.DataFrame(
+    {
+        "id": str(np.arange(1, 1001)),
+        "category_1": np.random.choice(["A", "B", "C"], size=1000, p=[0.4, 0.3, 0.3]),
+        "category_2": np.array([str(i) for i in range(500)] * 2),
+        "location": np.random.choice(
+            ["location 1", "location 2"], size=1000, p=[0.6, 0.4]
+        ),
+        "date": pd.to_datetime(
+            np.random.choice(
+                pd.date_range(start="2021-01-01", end="2022-01-01"), size=1000
+            )
+        ),
+    }
+)
+
    +
    +
    from eds_scikit.plot import reduce_table, visualize_table
+
+data_reduced = reduce_table(
+    data,
+    category_columns=["location", "category_1", "category_2"],
+    date_column="date",
+    start_date="2021-01-01",
+    end_date="2021-12-01",
+    mapper={"category_2": {"even": r"[02468]$", "odd": r"[13579]$"}},
+)
+
+chart = visualize_table(
+    data_reduced, title="synthetic dataframe table", description=True
+)
+
    +

    { + "$schema": "https://vega.github.io/schema/vega-lite/v5.8.0.json", + "config": { + "legend": { + "columns": 4, + "symbolLimit": 0 + }, + "view": { + "continuousHeight": 300, + "continuousWidth": 300 + } + }, + "data": { + "name": "data-6c8e9658b16d48f64ac39e6b052cf917" + }, + "datasets": { + "data-6c8e9658b16d48f64ac39e6b052cf917": [ + { + "category_1": "A", + "category_2": "even", + "count": 8, + "datetime": "2021-01-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "A", + "category_2": "odd", + "count": 11, + "datetime": "2021-01-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "B", + "category_2": "even", + "count": 11, + "datetime": "2021-01-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "B", + "category_2": "odd", + "count": 5, + "datetime": "2021-01-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "C", + "category_2": "even", + "count": 6, + "datetime": "2021-01-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "C", + "category_2": "odd", + "count": 9, + "datetime": "2021-01-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "A", + "category_2": "even", + "count": 12, + "datetime": "2021-01-01T00:00:00", + "location": "location 2" + }, + { + "category_1": "A", + "category_2": "odd", + "count": 9, + "datetime": "2021-01-01T00:00:00", + "location": "location 2" + }, + { + "category_1": "B", + "category_2": "even", + "count": 4, + "datetime": "2021-01-01T00:00:00", + "location": "location 2" + }, + { + "category_1": "B", + "category_2": "odd", + "count": 5, + "datetime": "2021-01-01T00:00:00", + "location": "location 2" + }, + { + "category_1": "C", + "category_2": "even", + "count": 8, + "datetime": "2021-01-01T00:00:00", + "location": "location 2" + }, + { + "category_1": "C", + "category_2": "odd", + "count": 7, + "datetime": "2021-01-01T00:00:00", + "location": "location 2" + }, + { + "category_1": "A", + "category_2": "even", + "count": 11, + "datetime": "2021-02-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "A", + "category_2": "odd", + "count": 8, + "datetime": "2021-02-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "B", + "category_2": "even", + "count": 7, + "datetime": "2021-02-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "B", + "category_2": "odd", + "count": 9, + "datetime": "2021-02-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "C", + "category_2": "even", + "count": 6, + "datetime": "2021-02-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "C", + "category_2": "odd", + "count": 7, + "datetime": "2021-02-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "A", + "category_2": "even", + "count": 6, + "datetime": "2021-02-01T00:00:00", + "location": "location 2" + }, + { + "category_1": "A", + "category_2": "odd", + "count": 6, + "datetime": "2021-02-01T00:00:00", + "location": "location 2" + }, + { + "category_1": "B", + "category_2": "even", + "count": 3, + "datetime": "2021-02-01T00:00:00", + "location": "location 2" + }, + { + "category_1": "B", + "category_2": "odd", + "count": 6, + "datetime": "2021-02-01T00:00:00", + "location": "location 2" + }, + { + "category_1": "C", + "category_2": "even", + "count": 6, + "datetime": "2021-02-01T00:00:00", + "location": "location 2" + }, + { + "category_1": "C", + "category_2": "odd", + "count": 5, + "datetime": "2021-02-01T00:00:00", + "location": "location 2" + }, + { + "category_1": "A", + "category_2": "even", + "count": 12, + "datetime": "2021-03-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "A", + "category_2": "odd", + "count": 13, + "datetime": "2021-03-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "B", + "category_2": "even", + "count": 11, + "datetime": "2021-03-01T00:00:00", + "location": "location 1" + }, + { + "category_1": "B", + "category_2": "odd", + "count": 9, + "datetime": 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    OMOP Teva

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    The OMOP Teva module of eds-scikit supports data scientists working on OMOP data. +OMOP Teva generates an interactive dashboard for each OMOP table, allowing timely visualization of the volumes associated with each combination of values. This provides a general overview of the possible values, their relative importance and allows to detect quickly possible bias.

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    This module is an eds-scikit transposition of EDS-Teva

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    EDS-Teva is a more complete library designed to handle temporal bias in EHR data. See Adjusting for the progressive digitization of health records for a better understanding about those bias.

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    +

    { + "$schema": "https://vega.github.io/schema/vega-lite/v5.8.0.json", + "config": { + "view": { + "continuousHeight": 300, + "continuousWidth": 300 + } + }, + "data": { + "name": "data-54870d255b2c7e93f9c75f03839955f2" + }, + "datasets": { + "data-54870d255b2c7e93f9c75f03839955f2": [ + { + "care_site_short_name": "care site 1", + "count": 17, + "datetime": "2021-01-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 1", + "count": 14, + "datetime": "2021-01-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 1", + "count": 12, + "datetime": "2021-01-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 2", + "count": 6, + "datetime": "2021-01-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 2", + "count": 2, + "datetime": "2021-01-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 2", + "count": 1, + "datetime": "2021-01-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 3", + "count": 6, + "datetime": "2021-01-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 3", + "count": 10, + "datetime": "2021-01-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 3", + "count": 12, + "datetime": "2021-01-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 1", + "count": 24, + "datetime": "2021-02-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 1", + "count": 21, + "datetime": "2021-02-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 1", + "count": 17, + "datetime": "2021-02-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 2", + "count": 4, + "datetime": "2021-02-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 2", + "count": 2, + "datetime": "2021-02-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 3", + "count": 8, + "datetime": "2021-02-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 3", + "count": 5, + "datetime": "2021-02-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 3", + "count": 8, + "datetime": "2021-02-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 1", + "count": 14, + "datetime": "2021-03-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 1", + "count": 13, + "datetime": "2021-03-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 1", + "count": 17, + "datetime": "2021-03-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 2", + "count": 6, + "datetime": "2021-03-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 2", + "count": 4, + "datetime": "2021-03-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 3", + "count": 9, + "datetime": "2021-03-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 3", + "count": 5, + "datetime": "2021-03-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 3", + "count": 6, + "datetime": "2021-03-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 1", + "count": 14, + "datetime": "2021-04-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 1", + "count": 15, + "datetime": "2021-04-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 1", + "count": 8, + "datetime": "2021-04-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 2", + "count": 5, + "datetime": "2021-04-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 2", + "count": 5, + "datetime": "2021-04-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 2", + "count": 2, + "datetime": "2021-04-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 3", + "count": 12, + "datetime": "2021-04-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 3", + "count": 9, + "datetime": "2021-04-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 3", + "count": 4, + "datetime": "2021-04-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 1", + "count": 25, + "datetime": "2021-05-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 1", + "count": 12, + "datetime": "2021-05-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 1", + "count": 15, + "datetime": "2021-05-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 2", + "count": 1, + "datetime": "2021-05-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 2", + "count": 2, + "datetime": "2021-05-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 2", + "count": 2, + "datetime": "2021-05-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 3", + "count": 10, + "datetime": "2021-05-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 3", + "count": 9, + "datetime": "2021-05-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 3", + "count": 8, + "datetime": "2021-05-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 1", + "count": 18, + "datetime": "2021-06-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 1", + "count": 13, + "datetime": "2021-06-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 1", + "count": 14, + "datetime": "2021-06-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 2", + "count": 1, + "datetime": "2021-06-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 2", + "count": 1, + "datetime": "2021-06-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 2", + "count": 4, + "datetime": "2021-06-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 3", + "count": 5, + "datetime": "2021-06-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 3", + "count": 10, + "datetime": "2021-06-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 3", + "count": 6, + "datetime": "2021-06-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 1", + "count": 23, + "datetime": "2021-07-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 1", + "count": 23, + "datetime": "2021-07-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 1", + "count": 11, + "datetime": "2021-07-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 2", + "count": 6, + "datetime": "2021-07-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 2", + "count": 2, + "datetime": "2021-07-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 2", + "count": 3, + "datetime": "2021-07-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 3", + "count": 13, + "datetime": "2021-07-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 3", + "count": 10, + "datetime": "2021-07-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 3", + "count": 5, + "datetime": "2021-07-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 1", + "count": 24, + "datetime": "2021-08-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 1", + "count": 22, + "datetime": "2021-08-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 1", + "count": 10, + "datetime": "2021-08-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 2", + "count": 1, + "datetime": "2021-08-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 2", + "count": 5, + "datetime": "2021-08-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 2", + "count": 3, + "datetime": "2021-08-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 3", + "count": 8, + "datetime": "2021-08-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 3", + "count": 11, + "datetime": "2021-08-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 3", + "count": 9, + "datetime": "2021-08-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 1", + "count": 20, + "datetime": "2021-09-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 1", + "count": 17, + "datetime": "2021-09-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 1", + "count": 21, + "datetime": "2021-09-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 2", + "count": 3, + "datetime": "2021-09-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 2", + "count": 2, + "datetime": "2021-09-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 2", + "count": 2, + "datetime": "2021-09-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 3", + "count": 12, + "datetime": "2021-09-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 3", + "count": 9, + "datetime": "2021-09-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 3", + "count": 6, + "datetime": "2021-09-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 1", + "count": 23, + "datetime": "2021-10-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 1", + "count": 21, + "datetime": "2021-10-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 1", + "count": 19, + "datetime": "2021-10-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 2", + "count": 4, + "datetime": "2021-10-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 2", + "count": 3, + "datetime": "2021-10-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 2", + "count": 3, + "datetime": "2021-10-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 3", + "count": 7, + "datetime": "2021-10-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 3", + "count": 8, + "datetime": "2021-10-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 3", + "count": 8, + "datetime": "2021-10-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 1", + "count": 27, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 1", + "count": 14, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 1", + "count": 10, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 2", + "count": 3, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 2", + "count": 4, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 2", + "count": 1, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 3", + "count": 8, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "MCO" + }, + { + "care_site_short_name": "care site 3", + "count": 6, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 3", + "count": 4, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "PSY" + }, + { + "care_site_short_name": "care site 1", + "count": 1, + "datetime": "2021-12-01T00:00:00", + "stay_source_value": "Other" + }, + { + "care_site_short_name": "care site 1", + "count": 1, + "datetime": "2021-12-01T00:00:00", + "stay_source_value": "PSY" + } + ] + }, + "params": [ + { + "bind": "legend", + "name": "param_7", + "select": { + "clear": "dblclick", + "fields": [ + "care_site_short_name" + ], + "on": "click", + "type": "point" + }, + "views": [ + "view_7" + ] + }, + { + "bind": "legend", + "name": "param_8", + "select": { + "clear": "dblclick", + "fields": [ + "stay_source_value" + ], + "on": "click", + "type": "point" + }, + "views": [ + "view_8" + ] + } + ], + "resolve": { + "scale": { + "color": "independent" + } + }, + "vconcat": [ + { + "hconcat": [ + { + "encoding": { + "color": { + "field": "care_site_short_name", + "scale": { + "domain": [ + "care site 1", + "care site 2", + "care site 3" + ], + "range": [ + "#1f77b4", + "#ff7f0e", + "#2ca02c" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_7", + "value": 1 + }, + "value": 0.3 + }, + "tooltip": [ + { + "field": "care_site_short_name", + "type": "nominal" + } + ], + "x": { + "aggregate": "sum", + "field": "count", + "type": "quantitative" + } + }, + "mark": { + "type": "bar" + }, + "name": "view_7", + "transform": [ + { + "filter": { + "param": "param_8" + } + } + ] + }, + { + "encoding": { + "color": { + "field": "care_site_short_name", + "scale": { + "domain": [ + "care site 1", + "care site 2", + "care site 3" + ], + "range": [ + "#1f77b4", + "#ff7f0e", + "#2ca02c" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_7", + "value": 1 + }, + "value": 0.3 + }, + "x": { + "field": "datetime", + "timeUnit": "yearmonth", + "type": "temporal" + }, + "y": { + "aggregate": "sum", + "axis": { + "format": "s" + }, + "field": "count", + "type": "quantitative" + } + }, + "height": 50, + "mark": { + "type": "line" + }, + "transform": [ + { + "filter": { + "param": "param_8" + } + } + ], + "width": 300 + } + ], + "title": "care_site_short_name" + }, + { + "hconcat": [ + { + "encoding": { + "color": { + "field": "stay_source_value", + "scale": { + "domain": [ + "MCO", + "Other", + "PSY" + ], + "range": [ + "#1f77b4", + "#ff7f0e", + "#2ca02c" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_8", + "value": 1 + }, + "value": 0.3 + }, + "tooltip": [ + { + "field": "stay_source_value", + "type": "nominal" + } + ], + "x": { + "aggregate": "sum", + "field": "count", + "type": "quantitative" + } + }, + "mark": { + "type": "bar" + }, + "name": "view_8", + "transform": [ + { + "filter": { + "param": "param_7" + } + } + ] + }, + { + "encoding": { + "color": { + "field": "stay_source_value", + "scale": { + "domain": [ + "MCO", + "Other", + "PSY" + ], + "range": [ + "#1f77b4", + "#ff7f0e", + "#2ca02c" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_8", + "value": 1 + }, + "value": 0.3 + }, + "x": { + "field": "datetime", + "timeUnit": "yearmonth", + "type": "temporal" + }, + "y": { + "aggregate": "sum", + "axis": { + "format": "s" + }, + "field": "count", + "type": "quantitative" + } + }, + "height": 50, + "mark": { + "type": "line" + }, + "transform": [ + { + "filter": { + "param": "param_7" + } + } + ], + "width": 300 + } + ], + "title": "stay_source_value" + } + ] + }

    + +

    +OMOP Teva - Quick use

    Quick use for OMOP dataset exploration.

    +OMOP Teva - Config

    See how your can configurate your own OMOP dashboards .

    +Custom Teva

    For any dataframe exploration.

    +OMOP Teva - Example

    A toy example of what can be obtained with this module.

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    + + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/omop-teva/omop-teva-example/index.html b/main/functionalities/omop-teva/omop-teva-example/index.html new file mode 100644 index 00000000..10092cf7 --- /dev/null +++ b/main/functionalities/omop-teva/omop-teva-example/index.html @@ -0,0 +1,3700 @@ + + + + + + + + + +OMOP Teva example - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    OMOP Teva example

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    The dashboard below provides an example of OMOP Teva dashboard for the visit_occurrence table.

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    +Explore this dashboard to identify any abnormal data distributions that could lead to bias. +

    Solution : The Psychological department at care site 1 appears to produce unusual NaN values after June.

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    +

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+ "visit_source_value": "hospit" + }, + { + "care_site_short_name": "care site 3", + "count": 1, + "datetime": "2021-10-01T00:00:00", + "stay_source_value": "PSY", + "visit_occurrence_id": "not NaN", + "visit_source_value": "NaN" + }, + { + "care_site_short_name": "care site 3", + "count": 2, + "datetime": "2021-10-01T00:00:00", + "stay_source_value": "PSY", + "visit_occurrence_id": "not NaN", + "visit_source_value": "consult" + }, + { + "care_site_short_name": "care site 3", + "count": 5, + "datetime": "2021-10-01T00:00:00", + "stay_source_value": "PSY", + "visit_occurrence_id": "not NaN", + "visit_source_value": "hospit" + }, + { + "care_site_short_name": "care site 1", + "count": 2, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "MCO", + "visit_occurrence_id": "not NaN", + "visit_source_value": "NaN" + }, + { + "care_site_short_name": "care site 1", + "count": 8, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "MCO", + "visit_occurrence_id": "not NaN", + "visit_source_value": "consult" + }, + { + "care_site_short_name": "care site 1", + "count": 17, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "MCO", + "visit_occurrence_id": "not NaN", + "visit_source_value": "hospit" + }, + { + "care_site_short_name": "care site 1", + "count": 1, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "Other", + "visit_occurrence_id": "not NaN", + "visit_source_value": "NaN" + }, + { + "care_site_short_name": "care site 1", + "count": 7, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "Other", + "visit_occurrence_id": "not NaN", + "visit_source_value": "consult" + }, + { + "care_site_short_name": "care site 1", + "count": 6, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "Other", + "visit_occurrence_id": "not NaN", + "visit_source_value": "hospit" + }, + { + "care_site_short_name": "care site 1", + "count": 10, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "PSY", + "visit_occurrence_id": "not NaN", + "visit_source_value": "NaN" + }, + { + "care_site_short_name": "care site 2", + "count": 2, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "MCO", + "visit_occurrence_id": "not NaN", + "visit_source_value": "consult" + }, + { + "care_site_short_name": "care site 2", + "count": 1, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "MCO", + "visit_occurrence_id": "not NaN", + "visit_source_value": "hospit" + }, + { + "care_site_short_name": "care site 2", + "count": 1, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "Other", + "visit_occurrence_id": "not NaN", + "visit_source_value": "consult" + }, + { + "care_site_short_name": "care site 2", + "count": 3, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "Other", + "visit_occurrence_id": "not NaN", + "visit_source_value": "hospit" + }, + { + "care_site_short_name": "care site 2", + "count": 1, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "PSY", + "visit_occurrence_id": "not NaN", + "visit_source_value": "hospit" + }, + { + "care_site_short_name": "care site 3", + "count": 1, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "MCO", + "visit_occurrence_id": "not NaN", + "visit_source_value": "NaN" + }, + { + "care_site_short_name": "care site 3", + "count": 4, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "MCO", + "visit_occurrence_id": "not NaN", + "visit_source_value": "consult" + }, + { + "care_site_short_name": "care site 3", + "count": 3, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "MCO", + "visit_occurrence_id": "not NaN", + "visit_source_value": "hospit" + }, + { + "care_site_short_name": "care site 3", + "count": 4, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "Other", + "visit_occurrence_id": "not NaN", + "visit_source_value": "consult" + }, + { + "care_site_short_name": "care site 3", + "count": 2, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "Other", + "visit_occurrence_id": "not NaN", + "visit_source_value": "hospit" + }, + { + "care_site_short_name": "care site 3", + "count": 1, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "PSY", + "visit_occurrence_id": "not NaN", + "visit_source_value": "NaN" + }, + { + "care_site_short_name": "care site 3", + "count": 3, + "datetime": "2021-11-01T00:00:00", + "stay_source_value": "PSY", + "visit_occurrence_id": "not NaN", + "visit_source_value": "hospit" + }, + { + "care_site_short_name": "care site 1", + "count": 1, + "datetime": "2021-12-01T00:00:00", + "stay_source_value": "Other", + "visit_occurrence_id": "not NaN", + "visit_source_value": "hospit" + }, + { + "care_site_short_name": "care site 1", + "count": 1, + "datetime": "2021-12-01T00:00:00", + "stay_source_value": "PSY", + "visit_occurrence_id": "not NaN", + "visit_source_value": "NaN" + } + ] + }, + "padding": { + "bottom": 50, + "left": 50, + "right": 50, + "top": 50 + }, + "params": [ + { + "bind": "legend", + "name": "param_17", + "select": { + "clear": "dblclick", + "fields": [ + "visit_occurrence_id" + ], + "on": "click", + "type": "point" + }, + "views": [ + "view_17" + ] + }, + { + "bind": "legend", + "name": "param_18", + "select": { + "clear": "dblclick", + "fields": [ + "care_site_short_name" + ], + "on": "click", + "type": "point" + }, + "views": [ + "view_18" + ] + }, + { + "bind": "legend", + "name": "param_19", + "select": { + "clear": "dblclick", + "fields": [ + "stay_source_value" + ], + "on": "click", + "type": "point" + }, + "views": [ + "view_19" + ] + }, + { + "bind": "legend", + "name": "param_20", + "select": { + "clear": "dblclick", + "fields": [ + "visit_source_value" + ], + "on": "click", + "type": "point" + }, + "views": [ + "view_20" + ] + } + ], + "resolve": { + "scale": { + "color": "independent" + } + }, + "title": { + "fontSize": 25, + "offset": 30, + "subtitle": [ + "ALT + SHIFT to select multiple categories", + "Double-click on legend to unselect", + "Reduce table column and values size for better interactivity" + ], + "subtitleFontSize": 15, + "subtitlePadding": 20, + "text": [ + "visit_occurrence table" + ] + }, + "vconcat": [ + { + "hconcat": [ + { + "encoding": { + "color": { + "field": "visit_occurrence_id", + "scale": { + "domain": [ + "not NaN" + ], + "range": [ + "#1f77b4" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_17", + "value": 1 + }, + "value": 0.3 + }, + "tooltip": [ + { + "field": "visit_occurrence_id", + "type": "nominal" + } + ], + "x": { + "aggregate": "sum", + "field": "count", + "type": "quantitative" + } + }, + "mark": { + "type": "bar" + }, + "name": "view_17", + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_18" + }, + { + "param": "param_19" + } + ] + }, + { + "param": "param_20" + } + ] + } + } + ] + }, + { + "encoding": { + "color": { + "field": "visit_occurrence_id", + "scale": { + "domain": [ + "not NaN" + ], + "range": [ + "#1f77b4" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_17", + "value": 1 + }, + "value": 0.3 + }, + "x": { + "field": "datetime", + "timeUnit": "yearmonth", + "type": "temporal" + }, + "y": { + "aggregate": "sum", + "axis": { + "format": "s" + }, + "field": "count", + "type": "quantitative" + } + }, + "height": 50, + "mark": { + "type": "line" + }, + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_18" + }, + { + "param": "param_19" + } + ] + }, + { + "param": "param_20" + } + ] + } + } + ], + "width": 300 + } + ], + "title": "visit_occurrence_id" + }, + { + "hconcat": [ + { + "encoding": { + "color": { + "field": "care_site_short_name", + "scale": { + "domain": [ + "care site 1", + "care site 2", + "care site 3" + ], + "range": [ + "#1f77b4", + "#ff7f0e", + "#2ca02c" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_18", + "value": 1 + }, + "value": 0.3 + }, + "tooltip": [ + { + "field": "care_site_short_name", + "type": "nominal" + } + ], + "x": { + "aggregate": "sum", + "field": "count", + "type": "quantitative" + } + }, + "mark": { + "type": "bar" + }, + "name": "view_18", + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_17" + }, + { + "param": "param_19" + } + ] + }, + { + "param": "param_20" + } + ] + } + } + ] + }, + { + "encoding": { + "color": { + "field": "care_site_short_name", + "scale": { + "domain": [ + "care site 1", + "care site 2", + "care site 3" + ], + "range": [ + "#1f77b4", + "#ff7f0e", + "#2ca02c" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_18", + "value": 1 + }, + "value": 0.3 + }, + "x": { + "field": "datetime", + "timeUnit": "yearmonth", + "type": "temporal" + }, + "y": { + "aggregate": "sum", + "axis": { + "format": "s" + }, + "field": "count", + "type": "quantitative" + } + }, + "height": 50, + "mark": { + "type": "line" + }, + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_17" + }, + { + "param": "param_19" + } + ] + }, + { + "param": "param_20" + } + ] + } + } + ], + "width": 300 + } + ], + "title": "care_site_short_name" + }, + { + "hconcat": [ + { + "encoding": { + "color": { + "field": "stay_source_value", + "scale": { + "domain": [ + "MCO", + "Other", + "PSY" + ], + "range": [ + "#1f77b4", + "#ff7f0e", + "#2ca02c" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_19", + "value": 1 + }, + "value": 0.3 + }, + "tooltip": [ + { + "field": "stay_source_value", + "type": "nominal" + } + ], + "x": { + "aggregate": "sum", + "field": "count", + "type": "quantitative" + } + }, + "mark": { + "type": "bar" + }, + "name": "view_19", + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_17" + }, + { + "param": "param_18" + } + ] + }, + { + "param": "param_20" + } + ] + } + } + ] + }, + { + "encoding": { + "color": { + "field": "stay_source_value", + "scale": { + "domain": [ + "MCO", + "Other", + "PSY" + ], + "range": [ + "#1f77b4", + "#ff7f0e", + "#2ca02c" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_19", + "value": 1 + }, + "value": 0.3 + }, + "x": { + "field": "datetime", + "timeUnit": "yearmonth", + "type": "temporal" + }, + "y": { + "aggregate": "sum", + "axis": { + "format": "s" + }, + "field": "count", + "type": "quantitative" + } + }, + "height": 50, + "mark": { + "type": "line" + }, + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_17" + }, + { + "param": "param_18" + } + ] + }, + { + "param": "param_20" + } + ] + } + } + ], + "width": 300 + } + ], + "title": "stay_source_value" + }, + { + "hconcat": [ + { + "encoding": { + "color": { + "field": "visit_source_value", + "scale": { + "domain": [ + "consult", + "hospit", + "NaN" + ], + "range": [ + "#1f77b4", + "#ff7f0e", + "black" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_20", + "value": 1 + }, + "value": 0.3 + }, + "tooltip": [ + { + "field": "visit_source_value", + "type": "nominal" + } + ], + "x": { + "aggregate": "sum", + "field": "count", + "type": "quantitative" + } + }, + "mark": { + "type": "bar" + }, + "name": "view_20", + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_17" + }, + { + "param": "param_18" + } + ] + }, + { + "param": "param_19" + } + ] + } + } + ] + }, + { + "encoding": { + "color": { + "field": "visit_source_value", + "scale": { + "domain": [ + "consult", + "hospit", + "NaN" + ], + "range": [ + "#1f77b4", + "#ff7f0e", + "black" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_20", + "value": 1 + }, + "value": 0.3 + }, + "x": { + "field": "datetime", + "timeUnit": "yearmonth", + "type": "temporal" + }, + "y": { + "aggregate": "sum", + "axis": { + "format": "s" + }, + "field": "count", + "type": "quantitative" + } + }, + "height": 50, + "mark": { + "type": "line" + }, + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_17" + }, + { + "param": "param_18" + } + ] + }, + { + "param": "param_19" + } + ] + } + } + ], + "width": 300 + } + ], + "title": "visit_source_value" + } + ] +}

    +
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    + + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/omop-teva/omop_teva.png b/main/functionalities/omop-teva/omop_teva.png new file mode 100644 index 00000000..db4486ab Binary files /dev/null and b/main/functionalities/omop-teva/omop_teva.png differ diff --git a/main/functionalities/omop-teva/quick-use-omop/index.html b/main/functionalities/omop-teva/quick-use-omop/index.html new file mode 100644 index 00000000..86bb0a57 --- /dev/null +++ b/main/functionalities/omop-teva/quick-use-omop/index.html @@ -0,0 +1,26431 @@ + + + + + + + + + +OMOP Teva - Quick use - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    + + + +

    OMOP Teva - Quick use

    +

    This tutorial demonstrates how the OMOP teva module can be quickly used to generate OMOP tables dashboard.

    +

    Simply apply generate_omop_teva function after loading the data. It will create a directory with one HTML per OMOP table.

    +
    +Avoid Jupyter Notebook +

    Koalas framework with high volumetry processing in Jupyter Notebook might cause computationnal delay and memory issues. Prefer spark-submit script to run OMOP Teva.

    +
    +
    +Loading dataset +
    from eds_scikit.io.hive import HiveData
+
+data = HiveData(
+    spark_session=spark,
+    database_name="project_xxxxxxxx",
+    tables_to_load=[
+        "care_site",
+        "visit_occurrence",
+        "concept",
+        "concept_relationship",
+        "note",
+        "procedure_occurrence",
+        "condition_occurrence",
+        "drug_exposure_prescription",
+        "drug_exposure_administration",
+    ],
+)
+
    +
    +
    from eds_scikit.plot import generate_omop_teva
+
+start_date, end_date = "2021-01-01", "2021-12-01"
+generate_omop_teva(data=data, start_date=start_date, end_date=end_date)
+
    +
    +
    +
    +

    { + "$schema": "https://vega.github.io/schema/vega-lite/v5.8.0.json", + "config": { + "view": { + "continuousHeight": 300, + "continuousWidth": 300 + } + }, + "data": { + "name": "data-b12cdc97854a522ae1f1412cfb19ca93" + }, + "datasets": { + "data-b12cdc97854a522ae1f1412cfb19ca93": [ + { + "care_site_short_name": "H\u00f4pital-2", + "count": 1.0, + "datetime": "2011-01-01T00:00:00", + "stay_source_value": "MCO", + "visit_occurrence_id": "not NaN", + "visit_source_value": "urgences" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "count": 1.0, + "datetime": "2011-06-01T00:00:00", + "stay_source_value": "MCO", + "visit_occurrence_id": "not NaN", + "visit_source_value": "consultation" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "count": 4.0, + "datetime": "2011-06-01T00:00:00", + "stay_source_value": "MCO", + "visit_occurrence_id": "not NaN", + "visit_source_value": "hospitalis\u00e9s" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "count": 2.0, + "datetime": "2011-06-01T00:00:00", + "stay_source_value": "MCO", + "visit_occurrence_id": "not NaN", + "visit_source_value": "urgences" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "count": 1.0, + "datetime": "2011-06-01T00:00:00", + "stay_source_value": "Psychiatrie", + "visit_occurrence_id": "not NaN", + "visit_source_value": "hospitalis\u00e9s" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "count": 1.0, + "datetime": "2011-06-01T00:00:00", + "stay_source_value": "SLD", + "visit_occurrence_id": "not NaN", + "visit_source_value": "urgences" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "count": 1.0, + "datetime": "2011-07-01T00:00:00", + "stay_source_value": "MCO", + "visit_occurrence_id": "not NaN", + "visit_source_value": "hospitalis\u00e9s" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "count": 1.0, + "datetime": "2011-07-01T00:00:00", + "stay_source_value": "Psychiatrie", + "visit_occurrence_id": "not NaN", + "visit_source_value": "hospitalis\u00e9s" + }, 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"2019-08-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-1", + "cdm_source": "ORBIS", + "condition_source_value": "not NaN", + "count": 44.0, + "datetime": "2019-08-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-2", + "cdm_source": "AREM", + "condition_source_value": "not NaN", + "count": 16.0, + "datetime": "2019-08-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-2", + "cdm_source": "ORBIS", + "condition_source_value": "not NaN", + "count": 23.0, + "datetime": "2019-08-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "not NaN", + "count": 14.0, + "datetime": "2019-08-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "not NaN", + "count": 23.0, + "datetime": "2019-08-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-1", + "cdm_source": "AREM", + "condition_source_value": "not NaN", + "count": 6.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-1", + "cdm_source": "ORBIS", + "condition_source_value": "not NaN", + "count": 20.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-2", + "cdm_source": "AREM", + "condition_source_value": "not NaN", + "count": 3.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-2", + "cdm_source": "ORBIS", + "condition_source_value": "not NaN", + "count": 6.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "not NaN", + "count": 1.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "not NaN", + "count": 5.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-2", + "cdm_source": "AREM", + "condition_source_value": "not NaN", + "count": 2.0, + "datetime": "2019-10-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "not NaN", + "count": 2.0, + "datetime": "2019-10-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "not NaN", + "count": 1.0, + "datetime": "2019-10-01T00:00:00", + "visit_occurrence_id": "not NaN" + } + ] + }, + "params": [ + { + "bind": "legend", + "name": "param_5", + "select": { + "clear": "dblclick", + "fields": [ + "visit_occurrence_id" + ], + "on": "click", + "type": "point" + }, + "views": [ + "view_5" + ] + }, + { + "bind": "legend", + "name": "param_6", + "select": { + "clear": "dblclick", + "fields": [ + "care_site_short_name" + ], + "on": "click", + "type": "point" + }, + "views": [ + "view_6" + ] + }, + { + "bind": "legend", + "name": "param_7", + "select": { + "clear": "dblclick", + "fields": [ + "condition_source_value" + ], + "on": "click", + "type": "point" + }, + "views": [ + "view_7" + ] + }, + { + "bind": "legend", + "name": "param_8", + "select": { + "clear": "dblclick", + "fields": [ + "cdm_source" + ], + "on": "click", + "type": "point" + }, + "views": [ + "view_8" + ] + } + ], + "resolve": { + "scale": { + "color": "independent" + } + }, + "vconcat": [ + { + "hconcat": [ + { + "encoding": { + "color": { + "field": "visit_occurrence_id", + "scale": { + "domain": [ + "not NaN" + ], + "range": [ + "#1f77b4" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_5", + "value": 1 + }, + "value": 0.3 + }, + "tooltip": [ + { + "field": "visit_occurrence_id", + "type": "nominal" + } + ], + "x": { + "aggregate": "sum", + "field": "count", + "type": "quantitative" + } + }, + "mark": { + "type": "bar" + }, + "name": "view_5", + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_6" + }, + { + "param": "param_7" + } + ] + }, + { + "param": "param_8" + } + ] + } + } + ] + }, + { + "encoding": { + "color": { + "field": "visit_occurrence_id", + "scale": { + "domain": [ + "not NaN" + ], + "range": [ + "#1f77b4" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_5", + "value": 1 + }, + "value": 0.3 + }, + "x": { + "field": "datetime", + "timeUnit": "yearmonth", + "type": "temporal" + }, + "y": { + "aggregate": "sum", + "axis": { + "format": "s" + }, + "field": "count", + "type": "quantitative" + } + }, + "height": 50, + "mark": { + "type": "line" + }, + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_6" + }, + { + "param": "param_7" + } + ] + }, + { + "param": "param_8" + } + ] + } + } + ], + "width": 300 + } + ], + "title": "visit_occurrence_id" + }, + { + "hconcat": [ + { + "encoding": { + "color": { + "field": "care_site_short_name", + "scale": { + "domain": [ + "H\u00f4pital-3", + "H\u00f4pital-2", + "H\u00f4pital-1" + ], + "range": [ + "#1f77b4", + "#ff7f0e", + "#2ca02c" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_6", + "value": 1 + }, + "value": 0.3 + }, + "tooltip": [ + { + "field": "care_site_short_name", + "type": "nominal" + } + ], + "x": { + "aggregate": "sum", + "field": "count", + "type": "quantitative" + } + }, + "mark": { + "type": "bar" + }, + "name": "view_6", + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_5" + }, + { + "param": "param_7" + } + ] + }, + { + "param": "param_8" + } + ] + } + } + ] + }, + { + "encoding": { + "color": { + "field": "care_site_short_name", + "scale": { + "domain": [ + "H\u00f4pital-3", + "H\u00f4pital-2", + "H\u00f4pital-1" + ], + "range": [ + "#1f77b4", + "#ff7f0e", + "#2ca02c" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_6", + "value": 1 + }, + "value": 0.3 + }, + "x": { + "field": "datetime", + "timeUnit": "yearmonth", + "type": "temporal" + }, + "y": { + "aggregate": "sum", + "axis": { + "format": "s" + }, + "field": "count", + "type": "quantitative" + } + }, + "height": 50, + "mark": { + "type": "line" + }, + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_5" + }, + { + "param": "param_7" + } + ] + }, + { + "param": "param_8" + } + ] + } + } + ], + "width": 300 + } + ], + "title": "care_site_short_name" + }, + { + "hconcat": [ + { + "encoding": { + "color": { + "field": "condition_source_value", + "scale": { + "domain": [ + "not NaN" + ], + "range": [ + "#1f77b4" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_7", + "value": 1 + }, + "value": 0.3 + }, + "tooltip": [ + { + "field": "condition_source_value", + "type": "nominal" + } + ], + "x": { + "aggregate": "sum", + "field": "count", + "type": "quantitative" + } + }, + "mark": { + "type": "bar" + }, + "name": "view_7", + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_5" + }, + { + "param": "param_6" + } + ] + }, + { + "param": "param_8" + } + ] + } + } + ] + }, + { + "encoding": { + "color": { + "field": "condition_source_value", + "scale": { + "domain": [ + "not NaN" + ], + "range": [ + "#1f77b4" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_7", + "value": 1 + }, + "value": 0.3 + }, + "x": { + "field": "datetime", + "timeUnit": "yearmonth", + "type": "temporal" + }, + "y": { + "aggregate": "sum", + "axis": { + "format": "s" + }, + "field": "count", + "type": "quantitative" + } + }, + "height": 50, + "mark": { + "type": "line" + }, + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_5" + }, + { + "param": "param_6" + } + ] + }, + { + "param": "param_8" + } + ] + } + } + ], + "width": 300 + } + ], + "title": "condition_source_value" + }, + { + "hconcat": [ + { + "encoding": { + "color": { + "field": "cdm_source", + "scale": { + "domain": [ + "AREM", + "ORBIS" + ], + "range": [ + "#1f77b4", + "#ff7f0e" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_8", + "value": 1 + }, + "value": 0.3 + }, + "tooltip": [ + { + "field": "cdm_source", + "type": "nominal" + } + ], + "x": { + "aggregate": "sum", + "field": "count", + "type": "quantitative" + } + }, + "mark": { + "type": "bar" + }, + "name": "view_8", + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_5" + }, + { + "param": "param_6" + } + ] + }, + { + "param": "param_7" + } + ] + } + } + ] + }, + { + "encoding": { + "color": { + "field": "cdm_source", + "scale": { + "domain": [ + "AREM", + "ORBIS" + ], + "range": [ + "#1f77b4", + "#ff7f0e" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_8", + "value": 1 + }, + "value": 0.3 + }, + "x": { + "field": "datetime", + "timeUnit": "yearmonth", + "type": "temporal" + }, + "y": { + "aggregate": "sum", + "axis": { + "format": "s" + }, + "field": "count", + "type": "quantitative" + } + }, + "height": 50, + "mark": { + "type": "line" + }, + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_5" + }, + { + "param": "param_6" + } + ] + }, + { + "param": "param_7" + } + ] + } + } + ], + "width": 300 + } + ], + "title": "cdm_source" + } + ] +}

    +
    +
    +
    +

    In this example condition_source_value is splited between diabetic and non-diabetic conditions.

    +

    You can modify dashboard configuration by importing eds_scikit.plot.default_omop_teva_config and customizing it. See next section for details on how to do it.

    +
    +

    { + "$schema": "https://vega.github.io/schema/vega-lite/v5.8.0.json", + "config": { + "view": { + "continuousHeight": 300, + "continuousWidth": 300 + } + }, + "data": { + "name": "data-1d0fd0ace6c43f5e025c5daf44a85809" + }, + "datasets": { + "data-1d0fd0ace6c43f5e025c5daf44a85809": [ + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "Other", + "count": 2.0, + "datetime": "2011-06-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "Other", + "count": 4.0, + "datetime": "2011-06-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "has_diabete", + "count": 1.0, + "datetime": "2011-06-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "Other", + "count": 3.0, + "datetime": "2011-07-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "Other", + "count": 11.0, + "datetime": "2011-07-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "has_diabete", + "count": 2.0, + "datetime": "2011-07-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "has_diabete", + "count": 6.0, + "datetime": "2011-07-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "Other", + "count": 1.0, + "datetime": "2011-08-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "Other", + "count": 9.0, + "datetime": "2011-08-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "has_diabete", + "count": 3.0, + "datetime": "2011-08-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "has_diabete", + "count": 2.0, + "datetime": "2011-08-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "Other", + "count": 1.0, + "datetime": "2011-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "Other", + "count": 8.0, + "datetime": "2011-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "has_diabete", + "count": 1.0, + "datetime": "2011-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "has_diabete", + "count": 2.0, + "datetime": "2011-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "Other", + "count": 9.0, + "datetime": "2011-10-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "Other", + "count": 13.0, + "datetime": "2011-10-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "has_diabete", + "count": 7.0, + "datetime": "2011-10-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "has_diabete", + "count": 8.0, + "datetime": "2011-10-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "Other", + "count": 2.0, + "datetime": "2011-11-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "Other", + "count": 24.0, + "datetime": "2011-11-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "has_diabete", + "count": 5.0, + "datetime": "2011-11-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "has_diabete", + "count": 6.0, + 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"2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-1", + "cdm_source": "ORBIS", + "condition_source_value": "Other", + "count": 12.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-1", + "cdm_source": "AREM", + "condition_source_value": "has_diabete", + "count": 4.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-1", + "cdm_source": "ORBIS", + "condition_source_value": "has_diabete", + "count": 8.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-2", + "cdm_source": "AREM", + "condition_source_value": "Other", + "count": 2.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-2", + "cdm_source": "ORBIS", + "condition_source_value": "Other", + "count": 2.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-2", + "cdm_source": "AREM", + "condition_source_value": "has_diabete", + "count": 1.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-2", + "cdm_source": "ORBIS", + "condition_source_value": "has_diabete", + "count": 4.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "Other", + "count": 1.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "Other", + "count": 2.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "has_diabete", + "count": 3.0, + "datetime": "2019-09-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-2", + "cdm_source": "AREM", + "condition_source_value": "Other", + "count": 1.0, + "datetime": "2019-10-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-2", + "cdm_source": "AREM", + "condition_source_value": "has_diabete", + "count": 1.0, + "datetime": "2019-10-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "Other", + "count": 1.0, + "datetime": "2019-10-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "ORBIS", + "condition_source_value": "Other", + "count": 1.0, + "datetime": "2019-10-01T00:00:00", + "visit_occurrence_id": "not NaN" + }, + { + "care_site_short_name": "H\u00f4pital-3", + "cdm_source": "AREM", + "condition_source_value": "has_diabete", + "count": 1.0, + "datetime": "2019-10-01T00:00:00", + "visit_occurrence_id": "not NaN" + } + ] + }, + "params": [ + { + "bind": "legend", + "name": "param_29", + "select": { + "clear": "dblclick", + "fields": [ + "visit_occurrence_id" + ], + "on": "click", + "type": "point" + }, + "views": [ + "view_29" + ] + }, + { + "bind": "legend", + "name": "param_30", + "select": { + "clear": "dblclick", + "fields": [ + "care_site_short_name" + ], + "on": "click", + "type": "point" + }, + "views": [ + "view_30" + ] + }, + { + "bind": "legend", + "name": "param_31", + "select": { + "clear": "dblclick", + "fields": [ + "condition_source_value" + ], + "on": "click", + "type": "point" + }, + "views": [ + "view_31" + ] + }, + { + "bind": "legend", + "name": "param_32", + "select": { + "clear": "dblclick", + "fields": [ + "cdm_source" + ], + "on": "click", + "type": "point" + }, + "views": [ + "view_32" + ] + } + ], + "resolve": { + "scale": { + "color": "independent" + } + }, + "vconcat": [ + { + "hconcat": [ + { + "encoding": { + "color": { + "field": "visit_occurrence_id", + "scale": { + "domain": [ + "not NaN" + ], + "range": [ + "#1f77b4" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_29", + "value": 1 + }, + "value": 0.3 + }, + "tooltip": [ + { + "field": "visit_occurrence_id", + "type": "nominal" + } + ], + "x": { + "aggregate": "sum", + "field": "count", + "type": "quantitative" + } + }, + "mark": { + "type": "bar" + }, + "name": "view_29", + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_30" + }, + { + "param": "param_31" + } + ] + }, + { + "param": "param_32" + } + ] + } + } + ] + }, + { + "encoding": { + "color": { + "field": "visit_occurrence_id", + "scale": { + "domain": [ + "not NaN" + ], + "range": [ + "#1f77b4" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_29", + "value": 1 + }, + "value": 0.3 + }, + "x": { + "field": "datetime", + "timeUnit": "yearmonth", + "type": "temporal" + }, + "y": { + "aggregate": "sum", + "axis": { + "format": "s" + }, + "field": "count", + "type": "quantitative" + } + }, + "height": 50, + "mark": { + "type": "line" + }, + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_30" + }, + { + "param": "param_31" + } + ] + }, + { + "param": "param_32" + } + ] + } + } + ], + "width": 300 + } + ], + "title": "visit_occurrence_id" + }, + { + "hconcat": [ + { + "encoding": { + "color": { + "field": "care_site_short_name", + "scale": { + "domain": [ + "H\u00f4pital-3", + "H\u00f4pital-2", + "H\u00f4pital-1" + ], + "range": [ + "#1f77b4", + "#ff7f0e", + "#2ca02c" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_30", + "value": 1 + }, + "value": 0.3 + }, + "tooltip": [ + { + "field": "care_site_short_name", + "type": "nominal" + } + ], + "x": { + "aggregate": "sum", + "field": "count", + "type": "quantitative" + } + }, + "mark": { + "type": "bar" + }, + "name": "view_30", + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_29" + }, + { + "param": "param_31" + } + ] + }, + { + "param": "param_32" + } + ] + } + } + ] + }, + { + "encoding": { + "color": { + "field": "care_site_short_name", + "scale": { + "domain": [ + "H\u00f4pital-3", + "H\u00f4pital-2", + "H\u00f4pital-1" + ], + "range": [ + "#1f77b4", + "#ff7f0e", + "#2ca02c" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_30", + "value": 1 + }, + "value": 0.3 + }, + "x": { + "field": "datetime", + "timeUnit": "yearmonth", + "type": "temporal" + }, + "y": { + "aggregate": "sum", + "axis": { + "format": "s" + }, + "field": "count", + "type": "quantitative" + } + }, + "height": 50, + "mark": { + "type": "line" + }, + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_29" + }, + { + "param": "param_31" + } + ] + }, + { + "param": "param_32" + } + ] + } + } + ], + "width": 300 + } + ], + "title": "care_site_short_name" + }, + { + "hconcat": [ + { + "encoding": { + "color": { + "field": "condition_source_value", + "scale": { + "domain": [ + "Other", + "has_diabete" + ], + "range": [ + "#1f77b4", + "#ff7f0e" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_31", + "value": 1 + }, + "value": 0.3 + }, + "tooltip": [ + { + "field": "condition_source_value", + "type": "nominal" + } + ], + "x": { + "aggregate": "sum", + "field": "count", + "type": "quantitative" + } + }, + "mark": { + "type": "bar" + }, + "name": "view_31", + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_29" + }, + { + "param": "param_30" + } + ] + }, + { + "param": "param_32" + } + ] + } + } + ] + }, + { + "encoding": { + "color": { + "field": "condition_source_value", + "scale": { + "domain": [ + "Other", + "has_diabete" + ], + "range": [ + "#1f77b4", + "#ff7f0e" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_31", + "value": 1 + }, + "value": 0.3 + }, + "x": { + "field": "datetime", + "timeUnit": "yearmonth", + "type": "temporal" + }, + "y": { + "aggregate": "sum", + "axis": { + "format": "s" + }, + "field": "count", + "type": "quantitative" + } + }, + "height": 50, + "mark": { + "type": "line" + }, + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_29" + }, + { + "param": "param_30" + } + ] + }, + { + "param": "param_32" + } + ] + } + } + ], + "width": 300 + } + ], + "title": "condition_source_value" + }, + { + "hconcat": [ + { + "encoding": { + "color": { + "field": "cdm_source", + "scale": { + "domain": [ + "AREM", + "ORBIS" + ], + "range": [ + "#1f77b4", + "#ff7f0e" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_32", + "value": 1 + }, + "value": 0.3 + }, + "tooltip": [ + { + "field": "cdm_source", + "type": "nominal" + } + ], + "x": { + "aggregate": "sum", + "field": "count", + "type": "quantitative" + } + }, + "mark": { + "type": "bar" + }, + "name": "view_32", + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_29" + }, + { + "param": "param_30" + } + ] + }, + { + "param": "param_31" + } + ] + } + } + ] + }, + { + "encoding": { + "color": { + "field": "cdm_source", + "scale": { + "domain": [ + "AREM", + "ORBIS" + ], + "range": [ + "#1f77b4", + "#ff7f0e" + ] + }, + "type": "nominal" + }, + "opacity": { + "condition": { + "param": "param_32", + "value": 1 + }, + "value": 0.3 + }, + "x": { + "field": "datetime", + "timeUnit": "yearmonth", + "type": "temporal" + }, + "y": { + "aggregate": "sum", + "axis": { + "format": "s" + }, + "field": "count", + "type": "quantitative" + } + }, + "height": 50, + "mark": { + "type": "line" + }, + "transform": [ + { + "filter": { + "and": [ + { + "and": [ + { + "param": "param_29" + }, + { + "param": "param_30" + } + ] + }, + { + "param": "param_31" + } + ] + } + } + ], + "width": 300 + } + ], + "title": "cdm_source" + } + ] +} +

    +
    +
    +
    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/patients-course/consultation_dates/0.html b/main/functionalities/patients-course/consultation_dates/0.html new file mode 100644 index 00000000..f975104d --- /dev/null +++ b/main/functionalities/patients-course/consultation_dates/0.html @@ -0,0 +1,140 @@ + + + + + +Consultation dates - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    vo +

    visit_occurrence DataFrame

    +

    + +TYPE: +DataFrame + +

    +
    note +

    note DataFrame

    +

    + +TYPE: +DataFrame + +

    +
    note_nlp +

    note_nlp DataFrame, used only with the "nlp" algo

    +

    + +TYPE: +Optional[DataFrame] + + +DEFAULT: +None + +

    +
    algo +

    Algorithm(s) to use to determine consultation dates. +Multiple algorithms can be provided as a list. Accepted values are:

    + +

    + +TYPE: +Union[str, List[str]] + + +DEFAULT: +['nlp'] + +

    +
    max_timedelta +

    If two extracted consultations are spaced by less than max_timedelta, +we consider that they correspond to the same event and only keep the first one.

    +

    + +TYPE: +timedelta + + +DEFAULT: +timedelta(days=7) + +

    +
    structured_config +

    A dictionnary of parameters when using the structured algorithm

    +

    + +TYPE: +Dict[str, Any] + + +DEFAULT: +dict() + +

    +
    nlp_config +

    A dictionnary of parameters when using the nlp algorithm

    +

    + +TYPE: +Dict[str, Any] + + +DEFAULT: +dict() + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/consultation_dates/1.html b/main/functionalities/patients-course/consultation_dates/1.html new file mode 100644 index 00000000..49edd9c3 --- /dev/null +++ b/main/functionalities/patients-course/consultation_dates/1.html @@ -0,0 +1,48 @@ + + + + + +Consultation dates - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + +DataFrame + + +

    Event type DataFrame with the following columns:

    +
      +
    • person_id
      • +
    • visit_occurrence_id
      • +
    • CONSULTATION_DATE: corresponds to the note_datetime value of a consultation +report coming from the considered visit.
      • +
    • CONSULTATION_NOTE_ID: the note_id of the corresponding report.
      • +
    • CONSULTATION_DATE_EXTRACTION: the method of extraction
      • +
    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/consultation_dates/2.html b/main/functionalities/patients-course/consultation_dates/2.html new file mode 100644 index 00000000..94138d56 --- /dev/null +++ b/main/functionalities/patients-course/consultation_dates/2.html @@ -0,0 +1,69 @@ + + + + + +Consultation dates - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    note_nlp +

    A DataFrame with (at least) the following columns:

    +
      +
    • note_id
      • +
    • consultation_date
      • +
    • end if using dates_to_keep=first: +end should store the character offset of the extracted date.
      • +
    +

    + +TYPE: +DataFrame + +

    +
    dates_to_keep +

    How to handle multiple consultation dates found in the document:

    +
      +
    • min: keep the oldest one
      • +
    • first: keep the occurrence that appeared first in the text
      • +
    • all: keep all date
      • +
    +

    + +TYPE: +str, optional + + +DEFAULT: +'min' + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/consultation_dates/3.html b/main/functionalities/patients-course/consultation_dates/3.html new file mode 100644 index 00000000..e4708dbc --- /dev/null +++ b/main/functionalities/patients-course/consultation_dates/3.html @@ -0,0 +1,44 @@ + + + + + +Consultation dates - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + +Dataframe + + +

    With 2 added columns corresponding to the following concept:

    +
      +
    • CONSULTATION_DATE, containing the date
      • +
    • CONSULTATION_DATE_EXTRACTION, containing "NLP"
      • +
    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/consultation_dates/4.html b/main/functionalities/patients-course/consultation_dates/4.html new file mode 100644 index 00000000..f9924250 --- /dev/null +++ b/main/functionalities/patients-course/consultation_dates/4.html @@ -0,0 +1,120 @@ + + + + + +Consultation dates - eds-scikit + + + + + + + + + + + + + + +
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    def get_consultation_dates_nlp(
+    note_nlp: DataFrame,
+    dates_to_keep: str = "min",
+) -> DataFrame:
+    """
+    Uses consultation dates extracted *a priori* in consultation reports to infer *true* consultation dates
+
+    Parameters
+    ----------
+    note_nlp : DataFrame
+        A DataFrame with (at least) the following columns:
+
+        - `note_id`
+        - `consultation_date`
+        - `end` **if** using `dates_to_keep=first`:
+        `end` should store the character offset of the extracted date.
+    dates_to_keep : str, optional
+        How to handle multiple consultation dates found in the document:
+
+        - `min`: keep the oldest one
+        - `first`: keep the occurrence that appeared first in the text
+        - `all`: keep all date
+
+    Returns
+    -------
+    Dataframe
+        With 2 added columns corresponding to the following concept:
+
+        - `CONSULTATION_DATE`, containing the date
+        - `CONSULTATION_DATE_EXTRACTION`, containing `"NLP"`
+    """
+
+    if dates_to_keep == "min":
+        dates_per_note = note_nlp.groupby("note_id").agg(
+            CONSULTATION_DATE=("consultation_date", "min"),
+        )
+    elif dates_to_keep == "first":
+        dates_per_note = (
+            note_nlp.sort_values(by="start")
+            .groupby("note_id")
+            .agg(CONSULTATION_DATE=("consultation_date", "first"))
+        )
+    elif dates_to_keep == "all":
+        dates_per_note = note_nlp[["consultation_date", "note_id"]].set_index("note_id")
+        dates_per_note = dates_per_note.rename(
+            columns={"consultation_date": "CONSULTATION_DATE"}
+        )
+    dates_per_note["CONSULTATION_DATE_EXTRACTION"] = "NLP"
+
+    return dates_per_note
+
    \ No newline at end of file diff --git a/main/functionalities/patients-course/consultation_dates/5.html b/main/functionalities/patients-course/consultation_dates/5.html new file mode 100644 index 00000000..36047e9e --- /dev/null +++ b/main/functionalities/patients-course/consultation_dates/5.html @@ -0,0 +1,101 @@ + + + + + +Consultation dates - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    note +

    A note DataFrame with at least the following columns:

    +
      +
    • note_id
      • +
    • note_datetime
      • +
    • note_source_value if kept_note_class_source_value is not None
      • +
    • visit_occurrence_id if kept_visit_source_value is not None
      • +
    +

    + +TYPE: +DataFrame + +

    +
    vo +

    A visit_occurrence DataFrame to provide if kept_visit_source_value is not None, +with at least the following columns:

    +
      +
    • visit_occurrence_id
      • +
    • visit_source_value if kept_visit_source_value is not None
      • +
    +

    + +TYPE: +Optional[DataFrame] + + +DEFAULT: +None + +

    +
    kept_note_class_source_value +

    Value(s) allowed for the note_class_source_value column.

    +

    + +TYPE: +Optional[Union[str, List[str]]] + + +DEFAULT: +'CR-CONS' + +

    +
    kept_visit_source_value +

    Value(s) allowed for the visit_source_value column.

    +

    + +TYPE: +Optional[Union[str, List[str]]], optional + + +DEFAULT: +'consultation externe' + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/consultation_dates/6.html b/main/functionalities/patients-course/consultation_dates/6.html new file mode 100644 index 00000000..5ab47b43 --- /dev/null +++ b/main/functionalities/patients-course/consultation_dates/6.html @@ -0,0 +1,44 @@ + + + + + +Consultation dates - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + +Dataframe + + +

    With 2 added columns corresponding to the following concept:

    +
      +
    • CONSULTATION_DATE, containing the date
      • +
    • CONSULTATION_DATE_EXTRACTION, containing "STRUCTURED"
      • +
    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/consultation_dates/7.html b/main/functionalities/patients-course/consultation_dates/7.html new file mode 100644 index 00000000..acbbb477 --- /dev/null +++ b/main/functionalities/patients-course/consultation_dates/7.html @@ -0,0 +1,158 @@ + + + + + +Consultation dates - eds-scikit + + + + + + + + + + + + + + +
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    def get_consultation_dates_structured(
+    note: DataFrame,
+    vo: Optional[DataFrame] = None,
+    kept_note_class_source_value: Optional[Union[str, List[str]]] = "CR-CONS",
+    kept_visit_source_value: Optional[Union[str, List[str]]] = "consultation externe",
+) -> DataFrame:
+    """
+    Uses `note_datetime` value to infer *true* consultation dates
+
+    Parameters
+    ----------
+    note : DataFrame
+        A `note` DataFrame with at least the following columns:
+
+        - `note_id`
+        - `note_datetime`
+        - `note_source_value` **if** `kept_note_class_source_value is not None`
+        - `visit_occurrence_id` **if** `kept_visit_source_value is not None`
+    vo : Optional[DataFrame]
+        A visit_occurrence DataFrame to provide **if** `kept_visit_source_value is not None`,
+        with at least the following columns:
+
+        - `visit_occurrence_id`
+        - `visit_source_value` **if** `kept_visit_source_value is not None`
+    kept_note_class_source_value : Optional[Union[str, List[str]]]
+        Value(s) allowed for the `note_class_source_value` column.
+    kept_visit_source_value : Optional[Union[str, List[str]]], optional
+        Value(s) allowed for the `visit_source_value` column.
+
+    Returns
+    -------
+    Dataframe
+        With 2 added columns corresponding to the following concept:
+
+        - `CONSULTATION_DATE`, containing the date
+        - `CONSULTATION_DATE_EXTRACTION`, containing `"STRUCTURED"`
+    """
+
+    kept_note = note
+
+    if kept_note_class_source_value is not None:
+        if type(kept_note_class_source_value) == str:
+            kept_note_class_source_value = [kept_note_class_source_value]
+        kept_note = note[
+            note.note_class_source_value.isin(set(kept_note_class_source_value))
+        ]
+
+    if kept_visit_source_value is not None:
+        if type(kept_visit_source_value) == str:
+            kept_visit_source_value = [kept_visit_source_value]
+        kept_note = kept_note.merge(
+            vo[
+                [
+                    "visit_occurrence_id",
+                    "visit_source_value",
+                ]
+            ][vo.visit_source_value.isin(set(kept_visit_source_value))],
+            on="visit_occurrence_id",
+        )
+
+    dates_per_note = kept_note[["note_datetime", "note_id"]].rename(
+        columns={
+            "note_datetime": "CONSULTATION_DATE",
+        }
+    )
+
+    dates_per_note["CONSULTATION_DATE_EXTRACTION"] = "STRUCTURED"
+
+    return dates_per_note.set_index("note_id")
+
    \ No newline at end of file diff --git a/main/functionalities/patients-course/consultation_dates/index.html b/main/functionalities/patients-course/consultation_dates/index.html new file mode 100644 index 00000000..17b5923f --- /dev/null +++ b/main/functionalities/patients-course/consultation_dates/index.html @@ -0,0 +1,1755 @@ + + + + + + + + + +Consultation dates - eds-scikit + + + + + + + + + + + + + + + + + + + +
    +
    +
    + +
    + +
    + +
    +
    +
    +
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    + +
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    +
    + +
    +
    +
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    + + + +

    Consultation dates

    +

    When a patient comes multiple times for consultations, it is often represented as a single visit_occurrence in the CDW. If a clear history of a patient's course is needed, it is then necessary to use proxies in order to access this information. An available proxy to get those consultation dates is to check for the existence of consultation reports and use the associated reports dates.

    +

    To this extend, two methods are available. They can be combined or used separately:

    +
      +
    • Use the note_datetime field associated to each consultation report
      • +
    • Extract the consultation report date by using NLP
      • +
    +
    +

    An important remark

    +

    Be careful when using the note_datetime field as it can represent the date of modification of a document (i.e. it can be modified if the clinician adds some information in it in the future).

    +
    +
    from eds_scikit.io import HiveData
+data = HiveData(DBNAME)
+
    +
    from eds_scikit.event import get_consultation_dates
+
+get_consultation_dates(
+    data.visit_occurrence,
+    note=data.note,
+    note_nlp=note_nlp,
+    algo=["nlp"],
+)
+
    +

    The snippet above required us to generate a note_nlp with a consultation_date column (see below for more informations).

    +
    +

    Consultation pipe

    +

    A consultation date pipeline exists and is particulary suited for this task. + Moreover, methods are available to run an EDS-NLP pipeline on a Pandas, Spark or even Koalas DataFrame !

    +
    +

    We can check the various exposed parameters if needed:

    +
    +
    +

    Extract consultation dates. +See the implementation details of the algo(s) you want to use

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    vo +

    visit_occurrence DataFrame

    +

    + +TYPE: +DataFrame + +

    +
    note +

    note DataFrame

    +

    + +TYPE: +DataFrame + +

    +
    note_nlp +

    note_nlp DataFrame, used only with the "nlp" algo

    +

    + +TYPE: +Optional[DataFrame] + + +DEFAULT: +None + +

    +
    algo +

    Algorithm(s) to use to determine consultation dates. +Multiple algorithms can be provided as a list. Accepted values are:

    + +

    + +TYPE: +Union[str, List[str]] + + +DEFAULT: +['nlp'] + +

    +
    max_timedelta +

    If two extracted consultations are spaced by less than max_timedelta, +we consider that they correspond to the same event and only keep the first one.

    +

    + +TYPE: +timedelta + + +DEFAULT: +timedelta(days=7) + +

    +
    structured_config +

    A dictionnary of parameters when using the structured algorithm

    +

    + +TYPE: +Dict[str, Any] + + +DEFAULT: +dict() + +

    +
    nlp_config +

    A dictionnary of parameters when using the nlp algorithm

    +

    + +TYPE: +Dict[str, Any] + + +DEFAULT: +dict() + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + +DataFrame + + +

    Event type DataFrame with the following columns:

    +
      +
    • person_id
      • +
    • visit_occurrence_id
      • +
    • CONSULTATION_DATE: corresponds to the note_datetime value of a consultation +report coming from the considered visit.
      • +
    • CONSULTATION_NOTE_ID: the note_id of the corresponding report.
      • +
    • CONSULTATION_DATE_EXTRACTION: the method of extraction
      • +
    +
    +
    +
    +

    Availables algorithms (values for "algo")

    +
    +
    +
    +
    +
    +
    +

    Uses consultation dates extracted a priori in consultation reports to infer true consultation dates

    + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    note_nlp +

    A DataFrame with (at least) the following columns:

    +
      +
    • note_id
      • +
    • consultation_date
      • +
    • end if using dates_to_keep=first: +end should store the character offset of the extracted date.
      • +
    +

    + +TYPE: +DataFrame + +

    +
    dates_to_keep +

    How to handle multiple consultation dates found in the document:

    +
      +
    • min: keep the oldest one
      • +
    • first: keep the occurrence that appeared first in the text
      • +
    • all: keep all date
      • +
    +

    + +TYPE: +str, optional + + +DEFAULT: +'min' + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + +Dataframe + + +

    With 2 added columns corresponding to the following concept:

    +
      +
    • CONSULTATION_DATE, containing the date
      • +
    • CONSULTATION_DATE_EXTRACTION, containing "NLP"
      • +
    +
    +
    +Source code in eds_scikit/event/consultations.py +
    ......
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Uses note_datetime value to infer true consultation dates

    + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    note +

    A note DataFrame with at least the following columns:

    +
      +
    • note_id
      • +
    • note_datetime
      • +
    • note_source_value if kept_note_class_source_value is not None
      • +
    • visit_occurrence_id if kept_visit_source_value is not None
      • +
    +

    + +TYPE: +DataFrame + +

    +
    vo +

    A visit_occurrence DataFrame to provide if kept_visit_source_value is not None, +with at least the following columns:

    +
      +
    • visit_occurrence_id
      • +
    • visit_source_value if kept_visit_source_value is not None
      • +
    +

    + +TYPE: +Optional[DataFrame] + + +DEFAULT: +None + +

    +
    kept_note_class_source_value +

    Value(s) allowed for the note_class_source_value column.

    +

    + +TYPE: +Optional[Union[str, List[str]]] + + +DEFAULT: +'CR-CONS' + +

    +
    kept_visit_source_value +

    Value(s) allowed for the visit_source_value column.

    +

    + +TYPE: +Optional[Union[str, List[str]]], optional + + +DEFAULT: +'consultation externe' + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + +Dataframe + + +

    With 2 added columns corresponding to the following concept:

    +
      +
    • CONSULTATION_DATE, containing the date
      • +
    • CONSULTATION_DATE_EXTRACTION, containing "STRUCTURED"
      • +
    +
    +
    +Source code in eds_scikit/event/consultations.py +
    ......
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/0.html b/main/functionalities/patients-course/is_emergency/0.html new file mode 100644 index 00000000..b478e514 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/0.html @@ -0,0 +1,67 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    + +TYPE: +DataFrame + +

    +
    algo +

    Possible values are:

    +
      +
    • "from_mapping" relies on a list of care_site_source_value extracted +by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites +are here further labelled to distinguish the different types of emergency
      • +
    • "from_regex_on_care_site_description": relies on a specific list of RegEx +applied on the description (= simplified care site name) of each care site.
      • +
    • "from_regex_on_parent_UF": relies on a specific list of regular expressions +applied on the description (= simplified care site name) of each UF (Unité Fonctionnelle). +The obtained tag is then propagated to every UF's children.
      • +
    +

    + +TYPE: +str + + +DEFAULT: +'from_mapping' + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/1.html b/main/functionalities/patients-course/is_emergency/1.html new file mode 100644 index 00000000..e095c764 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/1.html @@ -0,0 +1,48 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 to 2 added columns corresponding to the following concepts:

    +
      +
    • "IS_EMERGENCY"
      • +
    • "EMERGENCY_TYPE" (if using algo "from_mapping")
      • +
    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/10.html b/main/functionalities/patients-course/is_emergency/10.html new file mode 100644 index 00000000..14bcfac9 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/10.html @@ -0,0 +1,64 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
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    def from_regex_on_care_site_description(care_site: DataFrame) -> DataFrame:
+    """Use regular expressions on `care_site_name` to decide if it an emergency care site.
+
+    This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes].
+    The regular expression used to detect emergency status is `r"\bURG|\bSAU\b|\bUHCDb\b|\bZHTCD\b"`
+
+    Parameters
+    ----------
+    care_site: DataFrame
+        Should at least contains the `care_site_name` column
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 1 added column corresponding to the following concept:
+
+        - `"IS_EMERGENCY"`
+
+    """
+    return attributes.add_care_site_attributes(
+        care_site, only_attributes=["IS_EMERGENCY"]
+    )
+
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/11.html b/main/functionalities/patients-course/is_emergency/11.html new file mode 100644 index 00000000..e0aadce1 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/11.html @@ -0,0 +1,103 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    visit_detail +

    + +TYPE: +DataFrame + +

    +
    care_site +

    Isn't necessary if the algo "from_vo_visit_source_value" is used

    +

    + +TYPE: +Optional[DataFrame] + + +DEFAULT: +None + +

    +
    visit_occurrence +

    Is mandatory if the algo "from_vo_visit_source_value" is used

    +

    + +TYPE: +Optional[DataFrame] + + +DEFAULT: +None + +

    +
    algo +

    Possible values are:

    +
      +
    • "from_mapping" relies on a list of care_site_source_value extracted +by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites +are here further labelled to distinguish the different types of emergency
      • +
    • "from_regex_on_care_site_description": relies on a specific list of RegEx +applied on the description (= simplified care site name) of each care site.
      • +
    • "from_regex_on_parent_UF": relies on a specific list of regular expressions +applied on the description (= simplified care site name) of each UF (Unité Fonctionnelle). +The obtained tag is then propagated to every UF's children.
      • +
    • "from_vo_visit_source_value": +relies on the parent visit occurrence of each visit detail: +A visit detail will be tagged as emergency if it belongs to a visit occurrence where +visit_occurrence.visit_source_value=='urgence'.
      • +
    +

    + +TYPE: +str + + +DEFAULT: +'from_mapping' + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/12.html b/main/functionalities/patients-course/is_emergency/12.html new file mode 100644 index 00000000..e095c764 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/12.html @@ -0,0 +1,48 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 to 2 added columns corresponding to the following concepts:

    +
      +
    • "IS_EMERGENCY"
      • +
    • "EMERGENCY_TYPE" (if using algo "from_mapping")
      • +
    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/13.html b/main/functionalities/patients-course/is_emergency/13.html new file mode 100644 index 00000000..31ce8808 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/13.html @@ -0,0 +1,58 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_source_value column

    +

    + +TYPE: +DataFrame + +

    +
    version +

    Optional version string for the mapping

    +

    + +TYPE: +Optional[str] + + +DEFAULT: +None + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/14.html b/main/functionalities/patients-course/is_emergency/14.html new file mode 100644 index 00000000..c8032f8c --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/14.html @@ -0,0 +1,48 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 2 added columns corresponding to the following concepts:

    +
      +
    • "IS_EMERGENCY"
      • +
    • "EMERGENCY_TYPE"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/15.html b/main/functionalities/patients-course/is_emergency/15.html new file mode 100644 index 00000000..88f743c3 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/15.html @@ -0,0 +1,138 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
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    @concept_checker(concepts=["IS_EMERGENCY", "EMERGENCY_TYPE"])
+def from_mapping(
+    care_site: DataFrame,
+    version: Optional[str] = None,
+) -> DataFrame:
+    """This algo uses a labelled list of 201 emergency care sites.
+
+    Those care sites were extracted and verified by Ariel COHEN,
+    Judith LEBLANC, and an ER doctor validated them.
+
+    Those emergency care sites are further divised into different categories,
+    as defined in the concept 'EMERGENCY_TYPE'.
+    The different categories are:
+
+    - Urgences spécialisées
+    - UHCD + Post-urgences
+    - Urgences pédiatriques
+    - Urgences générales adulte
+    - Consultation urgences
+    - SAMU / SMUR
+
+    See the dataset [here](/datasets/care-site-emergency)
+
+    Parameters
+    ----------
+    care_site: DataFrame
+        Should at least contains the `care_site_source_value` column
+    version: Optional[str]
+        Optional version string for the mapping
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 2 added columns corresponding to the following concepts:
+
+        - `"IS_EMERGENCY"`
+        - `"EMERGENCY_TYPE"`
+
+    """
+
+    function_name = "get_care_site_emergency_mapping"
+    if version is not None:
+        function_name += f".{version}"
+
+    mapping = registry.get("data", function_name=function_name)()
+
+    # Getting the right framework
+    fw = framework.get_framework(care_site)
+    mapping = framework.to(fw, mapping)
+
+    care_site = care_site.merge(
+        mapping,
+        how="left",
+        on="care_site_source_value",
+    )
+
+    care_site["IS_EMERGENCY"] = care_site["EMERGENCY_TYPE"].notna()
+
+    return care_site
+
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/16.html b/main/functionalities/patients-course/is_emergency/16.html new file mode 100644 index 00000000..2c388b67 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/16.html @@ -0,0 +1,42 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_name column

    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/17.html b/main/functionalities/patients-course/is_emergency/17.html new file mode 100644 index 00000000..97a8b08d --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/17.html @@ -0,0 +1,47 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • 'IS_EMERGENCY'
      • +
    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/18.html b/main/functionalities/patients-course/is_emergency/18.html new file mode 100644 index 00000000..9d33ff00 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/18.html @@ -0,0 +1,68 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
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    @concept_checker(concepts=["IS_EMERGENCY"])
+def from_regex_on_parent_UF(care_site: DataFrame) -> DataFrame:
+    """Use regular expressions on parent UF (Unité Fonctionnelle) to classify emergency care site.
+
+    This relies on [this function][eds_scikit.structures.attributes.get_parent_attributes].
+    The regular expression used to detect emergency status is `r"\bURG|\bSAU\b|\bUHCD\b|\bZHTCD\b"`
+
+    Parameters
+    ----------
+    care_site: DataFrame
+        Should at least contains the `care_site_name` column
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 1 added column corresponding to the following concept:
+
+        - 'IS_EMERGENCY'
+    """
+    return attributes.get_parent_attributes(
+        care_site,
+        only_attributes=["IS_EMERGENCY"],
+        parent_type="Unité Fonctionnelle (UF)",
+    )
+
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/19.html b/main/functionalities/patients-course/is_emergency/19.html new file mode 100644 index 00000000..2c388b67 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/19.html @@ -0,0 +1,42 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_name column

    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/2.html b/main/functionalities/patients-course/is_emergency/2.html new file mode 100644 index 00000000..31ce8808 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/2.html @@ -0,0 +1,58 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_source_value column

    +

    + +TYPE: +DataFrame + +

    +
    version +

    Optional version string for the mapping

    +

    + +TYPE: +Optional[str] + + +DEFAULT: +None + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/20.html b/main/functionalities/patients-course/is_emergency/20.html new file mode 100644 index 00000000..f0546e46 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/20.html @@ -0,0 +1,47 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • "IS_EMERGENCY"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/21.html b/main/functionalities/patients-course/is_emergency/21.html new file mode 100644 index 00000000..14bcfac9 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/21.html @@ -0,0 +1,64 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
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    def from_regex_on_care_site_description(care_site: DataFrame) -> DataFrame:
+    """Use regular expressions on `care_site_name` to decide if it an emergency care site.
+
+    This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes].
+    The regular expression used to detect emergency status is `r"\bURG|\bSAU\b|\bUHCDb\b|\bZHTCD\b"`
+
+    Parameters
+    ----------
+    care_site: DataFrame
+        Should at least contains the `care_site_name` column
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 1 added column corresponding to the following concept:
+
+        - `"IS_EMERGENCY"`
+
+    """
+    return attributes.add_care_site_attributes(
+        care_site, only_attributes=["IS_EMERGENCY"]
+    )
+
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/22.html b/main/functionalities/patients-course/is_emergency/22.html new file mode 100644 index 00000000..b2e77055 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/22.html @@ -0,0 +1,52 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    visit_detail +

    + +TYPE: +DataFrame + +

    +
    visit_occurrence +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/23.html b/main/functionalities/patients-course/is_emergency/23.html new file mode 100644 index 00000000..73d5aa4d --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/23.html @@ -0,0 +1,47 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +visit_detail + +

    Dataframe with added columns corresponding to the following conceps:

    +
      +
    • "IS_EMERGENCY"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/24.html b/main/functionalities/patients-course/is_emergency/24.html new file mode 100644 index 00000000..339fe013 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/24.html @@ -0,0 +1,90 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
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    @concept_checker(concepts=["IS_EMERGENCY"])
+def from_vo_visit_source_value(
+    visit_detail: DataFrame,
+    visit_occurrence: DataFrame,
+) -> DataFrame:
+    """
+    This algo uses the *"Type de dossier"* of each visit detail's parent visit occurrence.
+    Thus, a visit_detail will be tagged with `IS_EMERGENCY=True` iff the visit occurrence it belongs to
+    is an emergency-type visit (meaning that `visit_occurrence.visit_source_value=='urgence'`)
+
+    !!! aphp "Admission through ICU"
+         At AP-HP, when a patient is hospitalized after coming to the ICU, its `visit_source_value`
+         is set from `"urgence"` to `"hospitalisation complète"`. So you should keep in mind
+         that this method doesn't tag those visits as ICU.
+
+    Parameters
+    ----------
+    visit_detail: DataFrame
+    visit_occurrence: DataFrame
+
+    Returns
+    -------
+    visit_detail: DataFrame
+        Dataframe with added columns corresponding to the following conceps:
+
+        - `"IS_EMERGENCY"`
+    """
+    vo_emergency = visit_occurrence[["visit_occurrence_id", "visit_source_value"]]
+    vo_emergency["IS_EMERGENCY"] = visit_occurrence.visit_source_value == "urgence"
+
+    return visit_detail.merge(
+        vo_emergency[["visit_occurrence_id", "IS_EMERGENCY"]],
+        on="visit_occurrence_id",
+        how="left",
+    )
+
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/3.html b/main/functionalities/patients-course/is_emergency/3.html new file mode 100644 index 00000000..c8032f8c --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/3.html @@ -0,0 +1,48 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 2 added columns corresponding to the following concepts:

    +
      +
    • "IS_EMERGENCY"
      • +
    • "EMERGENCY_TYPE"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/4.html b/main/functionalities/patients-course/is_emergency/4.html new file mode 100644 index 00000000..88f743c3 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/4.html @@ -0,0 +1,138 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
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    @concept_checker(concepts=["IS_EMERGENCY", "EMERGENCY_TYPE"])
+def from_mapping(
+    care_site: DataFrame,
+    version: Optional[str] = None,
+) -> DataFrame:
+    """This algo uses a labelled list of 201 emergency care sites.
+
+    Those care sites were extracted and verified by Ariel COHEN,
+    Judith LEBLANC, and an ER doctor validated them.
+
+    Those emergency care sites are further divised into different categories,
+    as defined in the concept 'EMERGENCY_TYPE'.
+    The different categories are:
+
+    - Urgences spécialisées
+    - UHCD + Post-urgences
+    - Urgences pédiatriques
+    - Urgences générales adulte
+    - Consultation urgences
+    - SAMU / SMUR
+
+    See the dataset [here](/datasets/care-site-emergency)
+
+    Parameters
+    ----------
+    care_site: DataFrame
+        Should at least contains the `care_site_source_value` column
+    version: Optional[str]
+        Optional version string for the mapping
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 2 added columns corresponding to the following concepts:
+
+        - `"IS_EMERGENCY"`
+        - `"EMERGENCY_TYPE"`
+
+    """
+
+    function_name = "get_care_site_emergency_mapping"
+    if version is not None:
+        function_name += f".{version}"
+
+    mapping = registry.get("data", function_name=function_name)()
+
+    # Getting the right framework
+    fw = framework.get_framework(care_site)
+    mapping = framework.to(fw, mapping)
+
+    care_site = care_site.merge(
+        mapping,
+        how="left",
+        on="care_site_source_value",
+    )
+
+    care_site["IS_EMERGENCY"] = care_site["EMERGENCY_TYPE"].notna()
+
+    return care_site
+
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/5.html b/main/functionalities/patients-course/is_emergency/5.html new file mode 100644 index 00000000..2c388b67 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/5.html @@ -0,0 +1,42 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_name column

    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/6.html b/main/functionalities/patients-course/is_emergency/6.html new file mode 100644 index 00000000..97a8b08d --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/6.html @@ -0,0 +1,47 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • 'IS_EMERGENCY'
      • +
    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/7.html b/main/functionalities/patients-course/is_emergency/7.html new file mode 100644 index 00000000..9d33ff00 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/7.html @@ -0,0 +1,68 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
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    @concept_checker(concepts=["IS_EMERGENCY"])
+def from_regex_on_parent_UF(care_site: DataFrame) -> DataFrame:
+    """Use regular expressions on parent UF (Unité Fonctionnelle) to classify emergency care site.
+
+    This relies on [this function][eds_scikit.structures.attributes.get_parent_attributes].
+    The regular expression used to detect emergency status is `r"\bURG|\bSAU\b|\bUHCD\b|\bZHTCD\b"`
+
+    Parameters
+    ----------
+    care_site: DataFrame
+        Should at least contains the `care_site_name` column
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 1 added column corresponding to the following concept:
+
+        - 'IS_EMERGENCY'
+    """
+    return attributes.get_parent_attributes(
+        care_site,
+        only_attributes=["IS_EMERGENCY"],
+        parent_type="Unité Fonctionnelle (UF)",
+    )
+
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/8.html b/main/functionalities/patients-course/is_emergency/8.html new file mode 100644 index 00000000..2c388b67 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/8.html @@ -0,0 +1,42 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_name column

    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/9.html b/main/functionalities/patients-course/is_emergency/9.html new file mode 100644 index 00000000..f0546e46 --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/9.html @@ -0,0 +1,47 @@ + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • "IS_EMERGENCY"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_emergency/index.html b/main/functionalities/patients-course/is_emergency/index.html new file mode 100644 index 00000000..55aa734f --- /dev/null +++ b/main/functionalities/patients-course/is_emergency/index.html @@ -0,0 +1,2160 @@ + + + + + + + + + +Emergency Units - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
    + +
    + +
    +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    + + + +

    Emergency Units

    +

    eds-scikit provides a function to tag care sites as being medical emergency units. It also provides a higher-level function to directly tag visits.

    +
    from eds_scikit.io import HiveData
+data = HiveData(DBNAME)
+
    +

    Tagging care sites

    +

    Tagging is done using the tag_emergency_care_site function:

    +
    from eds_scikit.emergency import tag_emergency_care_site
+
    +
    +
    +

    Tag care sites that correspond to medical emergency units.

    +

    The tagging is done by adding a "IS_EMERGENCY" column to the provided DataFrame.

    +

    Some algos can add an additional "EMERGENCY_TYPE" column to the provided DataFrame, +providing a more detailled classification.

    + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    + +TYPE: +DataFrame + +

    +
    algo +

    Possible values are:

    +
      +
    • "from_mapping" relies on a list of care_site_source_value extracted +by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites +are here further labelled to distinguish the different types of emergency
      • +
    • "from_regex_on_care_site_description": relies on a specific list of RegEx +applied on the description (= simplified care site name) of each care site.
      • +
    • "from_regex_on_parent_UF": relies on a specific list of regular expressions +applied on the description (= simplified care site name) of each UF (Unité Fonctionnelle). +The obtained tag is then propagated to every UF's children.
      • +
    +

    + +TYPE: +str + + +DEFAULT: +'from_mapping' + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 to 2 added columns corresponding to the following concepts:

    +
      +
    • "IS_EMERGENCY"
      • +
    • "EMERGENCY_TYPE" (if using algo "from_mapping")
      • +
    +

    + +TYPE: +DataFrame + +

    +
    +
    +

    Simply call the function by providing the necessary data (see below) and by picking the algo

    +
    care_site = tag_emergency_care_site(
+    care_site=data.care_site,
+    algo="from_mapping",
+)
+
    +
    +

    Availables algorithms (values for "algo")

    +
    +
    +
    +
    +
    +
    +

    This algo uses a labelled list of 201 emergency care sites.

    +

    Those care sites were extracted and verified by Ariel COHEN, +Judith LEBLANC, and an ER doctor validated them.

    +

    Those emergency care sites are further divised into different categories, +as defined in the concept 'EMERGENCY_TYPE'. +The different categories are:

    +
      +
    • Urgences spécialisées
      • +
    • UHCD + Post-urgences
      • +
    • Urgences pédiatriques
      • +
    • Urgences générales adulte
      • +
    • Consultation urgences
      • +
    • SAMU / SMUR
      • +
    +

    See the dataset here

    + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_source_value column

    +

    + +TYPE: +DataFrame + +

    +
    version +

    Optional version string for the mapping

    +

    + +TYPE: +Optional[str] + + +DEFAULT: +None + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 2 added columns corresponding to the following concepts:

    +
      +
    • "IS_EMERGENCY"
      • +
    • "EMERGENCY_TYPE"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    +
    +Source code in eds_scikit/emergency/emergency_care_site.py +
    ......
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Use regular expressions on parent UF (Unité Fonctionnelle) to classify emergency care site.

    +

    This relies on this function. +The regular expression used to detect emergency status is r"URG|SAU|UHCD|ZHTCD"

    + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_name column

    +

    + +TYPE: +DataFrame + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • 'IS_EMERGENCY'
      • +
    +

    + +TYPE: +DataFrame + +

    +
    +
    +Source code in eds_scikit/emergency/emergency_care_site.py +
    ......
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Use regular expressions on care_site_name to decide if it an emergency care site.

    +

    This relies on this function. +The regular expression used to detect emergency status is r"URG|SAU|UHCDb|ZHTCD"

    + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_name column

    +

    + +TYPE: +DataFrame + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • "IS_EMERGENCY"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    +
    +Source code in eds_scikit/emergency/emergency_care_site.py +
    ......
    +
    +
    +
    +
    +
    +
    +
    +

    Tagging visits

    +

    Tagging is done using the tag_emergency_visit function:

    +
    from eds_scikit.emergency import tag_emergency_visit
+
    +
    +
    +

    Tag visits that correspond to medical emergency units.

    +

    The tagging is done by adding a "IS_EMERGENCY" column to the provided DataFrame.

    +

    Some algos can add an additional "EMERGENCY_TYPE" column to the provided DataFrame, +providing a more detailled classification.

    +

    It works by either tagging each visit detail's care site, +or by using the visit_occurrence's "visit_source_value".

    + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    visit_detail +

    + +TYPE: +DataFrame + +

    +
    care_site +

    Isn't necessary if the algo "from_vo_visit_source_value" is used

    +

    + +TYPE: +Optional[DataFrame] + + +DEFAULT: +None + +

    +
    visit_occurrence +

    Is mandatory if the algo "from_vo_visit_source_value" is used

    +

    + +TYPE: +Optional[DataFrame] + + +DEFAULT: +None + +

    +
    algo +

    Possible values are:

    +
      +
    • "from_mapping" relies on a list of care_site_source_value extracted +by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites +are here further labelled to distinguish the different types of emergency
      • +
    • "from_regex_on_care_site_description": relies on a specific list of RegEx +applied on the description (= simplified care site name) of each care site.
      • +
    • "from_regex_on_parent_UF": relies on a specific list of regular expressions +applied on the description (= simplified care site name) of each UF (Unité Fonctionnelle). +The obtained tag is then propagated to every UF's children.
      • +
    • "from_vo_visit_source_value": +relies on the parent visit occurrence of each visit detail: +A visit detail will be tagged as emergency if it belongs to a visit occurrence where +visit_occurrence.visit_source_value=='urgence'.
      • +
    +

    + +TYPE: +str + + +DEFAULT: +'from_mapping' + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 to 2 added columns corresponding to the following concepts:

    +
      +
    • "IS_EMERGENCY"
      • +
    • "EMERGENCY_TYPE" (if using algo "from_mapping")
      • +
    +

    + +TYPE: +DataFrame + +

    +
    +
    +

    Simply call the function by providing the necessary data (see below) and by picking the algo

    +
    visit_detail = tag_emergency_visit(
+    visit_detail=data.visit_detail,
+    algo="from_mapping",
+)
+
    +
    +

    Availables algorithms (values for "algo")

    +
    +
    +
    +
    +
    +
    +

    This algo uses a labelled list of 201 emergency care sites.

    +

    Those care sites were extracted and verified by Ariel COHEN, +Judith LEBLANC, and an ER doctor validated them.

    +

    Those emergency care sites are further divised into different categories, +as defined in the concept 'EMERGENCY_TYPE'. +The different categories are:

    +
      +
    • Urgences spécialisées
      • +
    • UHCD + Post-urgences
      • +
    • Urgences pédiatriques
      • +
    • Urgences générales adulte
      • +
    • Consultation urgences
      • +
    • SAMU / SMUR
      • +
    +

    See the dataset here

    + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_source_value column

    +

    + +TYPE: +DataFrame + +

    +
    version +

    Optional version string for the mapping

    +

    + +TYPE: +Optional[str] + + +DEFAULT: +None + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 2 added columns corresponding to the following concepts:

    +
      +
    • "IS_EMERGENCY"
      • +
    • "EMERGENCY_TYPE"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    +
    +Source code in eds_scikit/emergency/emergency_care_site.py +
    ......
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Use regular expressions on parent UF (Unité Fonctionnelle) to classify emergency care site.

    +

    This relies on this function. +The regular expression used to detect emergency status is r"URG|SAU|UHCD|ZHTCD"

    + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_name column

    +

    + +TYPE: +DataFrame + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • 'IS_EMERGENCY'
      • +
    +

    + +TYPE: +DataFrame + +

    +
    +
    +Source code in eds_scikit/emergency/emergency_care_site.py +
    ......
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Use regular expressions on care_site_name to decide if it an emergency care site.

    +

    This relies on this function. +The regular expression used to detect emergency status is r"URG|SAU|UHCDb|ZHTCD"

    + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_name column

    +

    + +TYPE: +DataFrame + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • "IS_EMERGENCY"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    +
    +Source code in eds_scikit/emergency/emergency_care_site.py +
    ......
    +
    +
    +
    +
    +
    +
    +
    +
    +

    This algo uses the "Type de dossier" of each visit detail's parent visit occurrence. +Thus, a visit_detail will be tagged with IS_EMERGENCY=True iff the visit occurrence it belongs to +is an emergency-type visit (meaning that visit_occurrence.visit_source_value=='urgence')

    +
    +

    Admission through ICU

    +

    At AP-HP, when a patient is hospitalized after coming to the ICU, its visit_source_value + is set from "urgence" to "hospitalisation complète". So you should keep in mind + that this method doesn't tag those visits as ICU.

    +
    + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    visit_detail +

    + +TYPE: +DataFrame + +

    +
    visit_occurrence +

    + +TYPE: +DataFrame + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +visit_detail + +

    Dataframe with added columns corresponding to the following conceps:

    +
      +
    • "IS_EMERGENCY"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    +
    +Source code in eds_scikit/emergency/emergency_visit.py +
    ......
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/patients-course/is_icu/0.html b/main/functionalities/patients-course/is_icu/0.html new file mode 100644 index 00000000..0353a54b --- /dev/null +++ b/main/functionalities/patients-course/is_icu/0.html @@ -0,0 +1,61 @@ + + + + + +ICU - eds-scikit + + + + + + + + + + + + + + +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_icu/1.html b/main/functionalities/patients-course/is_icu/1.html new file mode 100644 index 00000000..dd27d905 --- /dev/null +++ b/main/functionalities/patients-course/is_icu/1.html @@ -0,0 +1,47 @@ + + + + + +ICU - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • "IS_ICU"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_icu/2.html b/main/functionalities/patients-course/is_icu/2.html new file mode 100644 index 00000000..60a55c4d --- /dev/null +++ b/main/functionalities/patients-course/is_icu/2.html @@ -0,0 +1,42 @@ + + + + + +ICU - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the place_of_service_source_value column

    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_icu/3.html b/main/functionalities/patients-course/is_icu/3.html new file mode 100644 index 00000000..64372a4c --- /dev/null +++ b/main/functionalities/patients-course/is_icu/3.html @@ -0,0 +1,47 @@ + + + + + +ICU - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 added column corresponding to the following concepts:

    +
      +
    • "IS_ICU"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_icu/4.html b/main/functionalities/patients-course/is_icu/4.html new file mode 100644 index 00000000..fd85b5e4 --- /dev/null +++ b/main/functionalities/patients-course/is_icu/4.html @@ -0,0 +1,120 @@ + + + + + +ICU - eds-scikit + + + + + + + + + + + + + + +
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    @concept_checker(concepts=["IS_ICU"])
+def from_authorisation_type(care_site: DataFrame) -> DataFrame:
+    """This algo uses the `care_site.place_of_service_source_value` columns
+    to retrieve Intensive Care Units.
+
+    The following values are used to tag a care site as ICU:
+
+    - `"REA PED"`
+    - `"REA"`
+    - `"REA ADULTE"`
+    - `"REA NEONAT"`
+    - `"USI"`
+    - `"USI ADULTE"`
+    - `"USI NEONAT"`
+    - `"SC PED"`
+    - `"SC"`
+    - `"SC ADULTE"`
+
+    Parameters
+    ----------
+    care_site: DataFrame
+        Should at least contains the `place_of_service_source_value` column
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 1 added column corresponding to the following concepts:
+
+        - `"IS_ICU"`
+
+    """
+
+    icu_units = set(
+        [
+            "REA PED",
+            "USI",
+            "SC PED",
+            "SC",
+            "REA",
+            "SC ADULTE",
+            "USI ADULTE",
+            "REA ADULTE",
+            "USI NEONAT",
+            "REA NEONAT",
+        ]
+    )
+
+    care_site["IS_ICU"] = care_site["place_of_service_source_value"].isin(icu_units)
+
+    return care_site
+
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_icu/5.html b/main/functionalities/patients-course/is_icu/5.html new file mode 100644 index 00000000..5af44573 --- /dev/null +++ b/main/functionalities/patients-course/is_icu/5.html @@ -0,0 +1,58 @@ + + + + + +ICU - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_name and care_site_type_source_value columns

    +

    + +TYPE: +DataFrame + +

    +
    subset_care_site_type_source_value +

    Acceptable values for care_site_type_source_value

    +

    + +TYPE: +Union[list, set] + + +DEFAULT: +{'UDS'} + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_icu/6.html b/main/functionalities/patients-course/is_icu/6.html new file mode 100644 index 00000000..5ef806fb --- /dev/null +++ b/main/functionalities/patients-course/is_icu/6.html @@ -0,0 +1,47 @@ + + + + + +ICU - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • "IS_ICU"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_icu/7.html b/main/functionalities/patients-course/is_icu/7.html new file mode 100644 index 00000000..c00ad1f1 --- /dev/null +++ b/main/functionalities/patients-course/is_icu/7.html @@ -0,0 +1,106 @@ + + + + + +ICU - eds-scikit + + + + + + + + + + + + + + +
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    def from_regex_on_care_site_description(
+    care_site: DataFrame, subset_care_site_type_source_value: Union[list, set] = {"UDS"}
+) -> DataFrame:
+    """Use regular expressions on `care_site_name` to decide if it an ICU care site.
+
+    This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes].
+    The regular expression used to detect ICU is
+    `r"\bUSI|\bREA[N\s]|\bREA\b|\bUSC\b|SOINS.*INTENSIF|SURV.{0,15}CONT|\bSI\b|\bSC\b"`.
+
+    !!! aphp "Keeping only 'UDS'"
+         At AP-HP, all ICU are **UDS** (*Unité De Soins*).
+         Therefore, this function filters care sites by default to only keep UDS.
+
+    Parameters
+    ----------
+    care_site: DataFrame
+        Should at least contains the `care_site_name` and `care_site_type_source_value` columns
+    subset_care_site_type_source_value: Union[list, set]
+        Acceptable values for `care_site_type_source_value`
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 1 added column corresponding to the following concept:
+
+        - `"IS_ICU"`
+
+    """  # noqa
+
+    care_site = attributes.add_care_site_attributes(
+        care_site, only_attributes=["IS_ICU"]
+    )
+
+    # Filtering matches
+
+    if subset_care_site_type_source_value:
+        care_site["IS_ICU"] = care_site["IS_ICU"] & (
+            care_site.care_site_type_source_value.isin(
+                subset_care_site_type_source_value
+            )
+        )
+
+    return care_site
+
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_icu/8.html b/main/functionalities/patients-course/is_icu/8.html new file mode 100644 index 00000000..83f6322d --- /dev/null +++ b/main/functionalities/patients-course/is_icu/8.html @@ -0,0 +1,72 @@ + + + + + +ICU - eds-scikit + + + + + + + + + + + + + + +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_icu/9.html b/main/functionalities/patients-course/is_icu/9.html new file mode 100644 index 00000000..ee57945d --- /dev/null +++ b/main/functionalities/patients-course/is_icu/9.html @@ -0,0 +1,47 @@ + + + + + +ICU - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +visit_detail + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • "IS_ICU"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/is_icu/index.html b/main/functionalities/patients-course/is_icu/index.html new file mode 100644 index 00000000..ef98f38f --- /dev/null +++ b/main/functionalities/patients-course/is_icu/index.html @@ -0,0 +1,1758 @@ + + + + + + + + + +ICU - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
    + +
    + +
    +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    + + + +

    ICU

    +

    eds-scikit provides a function to tag care sites as being Intensive Care Units. It also provides a higher-level function to directly tag visits.

    +
    from eds_scikit.io import HiveData
+data = HiveData(DBNAME)
+
    +

    Tagging care sites

    +

    Tagging is done using the tag_icu_care_site function:

    +
    from eds_scikit.icu import tag_icu_care_site
+
    +
    +
    +

    Tag care sites that correspond to ICU units.

    +

    The tagging is done by adding a "IS_ICU" column to the provided DataFrame.

    + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    + +TYPE: +DataFrame + +

    +
    algo +

    Possible values are:

    + +

    + +TYPE: +str + + +DEFAULT: +'from_mapping' + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • "IS_ICU"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    +
    +

    Simply call the function by providing the necessary data (see below) and by picking the algo

    +
    care_site = tag_icu_care_site(
+    care_site=data.care_site,
+    algo="from_authorisation_type",
+)
+
    +
    +

    Availables algorithms (values for "algo")

    +
    +
    +
    +
    +
    +
    +

    This algo uses the care_site.place_of_service_source_value columns +to retrieve Intensive Care Units.

    +

    The following values are used to tag a care site as ICU:

    +
      +
    • "REA PED"
      • +
    • "REA"
      • +
    • "REA ADULTE"
      • +
    • "REA NEONAT"
      • +
    • "USI"
      • +
    • "USI ADULTE"
      • +
    • "USI NEONAT"
      • +
    • "SC PED"
      • +
    • "SC"
      • +
    • "SC ADULTE"
      • +
    + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the place_of_service_source_value column

    +

    + +TYPE: +DataFrame + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 added column corresponding to the following concepts:

    +
      +
    • "IS_ICU"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    +
    +Source code in eds_scikit/icu/icu_care_site.py +
    ......
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Use regular expressions on care_site_name to decide if it an ICU care site.

    +

    This relies on this function. +The regular expression used to detect ICU is +r"USI|REA[N\s]|REA|USC|SOINS.*INTENSIF|SURV.{0,15}CONT|SI|SC".

    +
    +

    Keeping only 'UDS'

    +

    At AP-HP, all ICU are UDS (Unité De Soins). + Therefore, this function filters care sites by default to only keep UDS.

    +
    + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_name and care_site_type_source_value columns

    +

    + +TYPE: +DataFrame + +

    +
    subset_care_site_type_source_value +

    Acceptable values for care_site_type_source_value

    +

    + +TYPE: +Union[list, set] + + +DEFAULT: +{'UDS'} + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • "IS_ICU"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    +
    +Source code in eds_scikit/icu/icu_care_site.py +
    ......
    +
    +
    +
    +
    +
    +
    +
    +

    Tagging visits

    +

    Tagging is done using the tag_icu_visit function:

    +
    from eds_scikit.icu import tag_icu_visit
+
    +
    +
    +

    Tag care_sites that correspond to ICU units.

    +

    The tagging is done by adding a "IS_ICU" column to the provided DataFrame.

    +

    It works by tagging each visit detail's care site.

    + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    visit_detail +

    + +TYPE: +DataFrame + +

    +
    care_site +

    + +TYPE: +DataFrame + +

    +
    algo +

    Possible values are:

    + +

    + +TYPE: +str + + +DEFAULT: +'from_authorisation_type' + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +visit_detail + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • "IS_ICU"
      • +
    +

    + +TYPE: +DataFrame + +

    +
    +
    +

    Simply call the function by providing the necessary data (see below) and by picking the algo

    +
    visit_detail = tag_icu_visit(
+    visit_detail=data.visit_detail,
+    algo="from_mapping",
+)
+
    +
    +

    Availables algorithms (values for "algo")

    +

    Those are the same as tag_icu_care_site

    +
    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/patients-course/visit_merging.png b/main/functionalities/patients-course/visit_merging.png new file mode 100644 index 00000000..363081a6 Binary files /dev/null and b/main/functionalities/patients-course/visit_merging.png differ diff --git a/main/functionalities/patients-course/visit_merging/0.html b/main/functionalities/patients-course/visit_merging/0.html new file mode 100644 index 00000000..39e95c16 --- /dev/null +++ b/main/functionalities/patients-course/visit_merging/0.html @@ -0,0 +1,187 @@ + + + + + +Visit merging - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    vo +

    The visit_occurrence DataFrame, with at least the following columns: +- visit_occurrence_id +- person_id +- visit_start_datetime_calc (from preprocessing) +- visit_end_datetime (from preprocessing) +Depending on the input parameters, additional columns may be required: +- care_site_id (if merge_different_hospitals == True) +- visit_source_value (if merge_different_source_values != False) +- row_status_source_value (if remove_deleted_visits= True)

    +

    + +TYPE: +DataFrame + +

    +
    remove_deleted_visits +

    Wether to remove deleted visits from the merging procedure. +Deleted visits are extracted via the row_status_source_value column

    +

    + +TYPE: +bool + + +DEFAULT: +True + +

    +
    long_stay_filtering +

    Filtering method for long and/or non-closed visits. First of all, visits with no starting date +won't be merged with any other visit, and visits with no ending date will have a temporary +"theoretical" ending date set by datetime.now(). That being said, some visits are sometimes years long +because they weren't closed at time. If other visits occurred during this timespan, +they could be all merged into the same stay. To avoid this issue, filtering can be done +depending on the long_stay_filtering value:

    +
      +
    • all: All long stays (closed and open) are removed from the merging procedure
      • +
    • open: Only long open stays are removed from the merging procedure
      • +
    • None: No filtering is done on long visits
      • +
    +

    Long stays are determined by the long_stay_threshold value.

    +

    + +TYPE: +Optional[str] + + +DEFAULT: +'all' + +

    +
    long_stay_threshold +

    Minimum visit duration value to consider a visit as candidate for "long visits filtering"

    +

    + +TYPE: +timedelta + + +DEFAULT: +timedelta(days=365) + +

    +
    open_stay_end_datetime +

    Datetime to use in order to fill the visit_end_datetime of open visits. This is necessary in +order to compute stay duration and to filter long stays. If not provided datetime.now() will be used. +You might provide the extraction date of your data here.

    +

    + +TYPE: +Optional[datetime] + + +DEFAULT: +None + +

    +
    max_timedelta +

    Maximum time difference between the end of a visit and the start of another to consider +them as belonging to the same stay. This duration is internally converted in seconds before +comparing. Thus, if you want e.g. to merge visits happening in two consecutive days, you should use +timedelta(days=2) and NOT timedelta(days=1) in order to take into account extreme cases such as +an first visit ending on Monday at 00h01 AM and another one starting at 23h59 PM on Tuesday

    +

    + +TYPE: +timedelta + + +DEFAULT: +timedelta(days=2) + +

    +
    merge_different_hospitals +

    Wether to allow visits occurring in different hospitals to be merged into a same stay

    +

    + +TYPE: +bool + + +DEFAULT: +False + +

    +
    merge_different_source_values +

    Wether to allow visits with different visit_source_value to be merged into a same stay. Values can be:

    +
      +
    • True: the visit_source_value isn't taken into account for the merging
      • +
    • False: only visits with the same visit_source_value can be merged into a same stay
      • +
    • List[str]: only visits which visit_source_value is in the provided list can be merged together.
      • +
    +

    Warning: You should avoid merging visits where visit_source_value == "hospitalisation incomplète", +because those stays are often never closed.

    +

    + +TYPE: +Union[bool, List[str]] + + +DEFAULT: +['hospitalisés', 'urgence'] + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/visit_merging/1.html b/main/functionalities/patients-course/visit_merging/1.html new file mode 100644 index 00000000..1129e485 --- /dev/null +++ b/main/functionalities/patients-course/visit_merging/1.html @@ -0,0 +1,44 @@ + + + + + +Visit merging - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +vo + +

    Visit occurrence DataFrame with additional STAY_ID column

    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/patients-course/visit_merging/index.html b/main/functionalities/patients-course/visit_merging/index.html new file mode 100644 index 00000000..5a14930a --- /dev/null +++ b/main/functionalities/patients-course/visit_merging/index.html @@ -0,0 +1,1769 @@ + + + + + + + + + +Visit merging - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
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    + +
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    + +
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    + + + +

    Visit merging

    +

    Merging visits into stays

    +

    Presentation of the problem

    +

    In order to have a precise view of each patient's course of care, it can be useful to merge together visit occurrences into stays.

    +

    A crude way of doing so is by using the preceding_visit_occurrence_id column in the visit_occurrence table. However, this column isn't always filled, and a lot of visits would be missed by using only this method.

    +

    The method proposed here relies on how close two visits are in order to put them in the same stay. This is the role of the merge_visits() functions.

    +

    The figure below shows how the merging of visits into stays would occurs

    +
    +

    Image title +

    +
    +
    +

    The merge_visits() function

    +
    from eds_scikit.io import HiveData
+data = HiveData(DBNAME)
+
    +
    from eds_scikit.period.stays import merge_visits
+
+visit_occurrence = merge_visits(visit_occurrence)
+
    +
    +

    Warning

    +

    The snippet above should run as is, however the merge_visits() function provides a lot of parameters that you should check in order to use it properly. Those parameters are described below or in the corresponding code reference

    +
    +
    +
    +

    Merge "close" visit occurrences to consider them as a single stay +by adding a STAY_ID and CONTIGUOUS_STAY_ID columns to the DataFrame.

    +

    The value of these columns will be the visit_occurrence_id of the first (meaning the oldest) +visit of the stay.

    +

    From a temporal point of view, we consider that two visits belong to the same stay if either:

    +
      +
    • They intersect
      • +
    • The time difference between the end of the most recent and the start of the oldest + is lower than max_timedelta (for STAY_ID) or 0 (for CONTIGUOUS_STAY_ID)
      • +
    +

    Additionally, other parameters are available to further adjust the merging rules. See below.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    vo +

    The visit_occurrence DataFrame, with at least the following columns: +- visit_occurrence_id +- person_id +- visit_start_datetime_calc (from preprocessing) +- visit_end_datetime (from preprocessing) +Depending on the input parameters, additional columns may be required: +- care_site_id (if merge_different_hospitals == True) +- visit_source_value (if merge_different_source_values != False) +- row_status_source_value (if remove_deleted_visits= True)

    +

    + +TYPE: +DataFrame + +

    +
    remove_deleted_visits +

    Wether to remove deleted visits from the merging procedure. +Deleted visits are extracted via the row_status_source_value column

    +

    + +TYPE: +bool + + +DEFAULT: +True + +

    +
    long_stay_filtering +

    Filtering method for long and/or non-closed visits. First of all, visits with no starting date +won't be merged with any other visit, and visits with no ending date will have a temporary +"theoretical" ending date set by datetime.now(). That being said, some visits are sometimes years long +because they weren't closed at time. If other visits occurred during this timespan, +they could be all merged into the same stay. To avoid this issue, filtering can be done +depending on the long_stay_filtering value:

    +
      +
    • all: All long stays (closed and open) are removed from the merging procedure
      • +
    • open: Only long open stays are removed from the merging procedure
      • +
    • None: No filtering is done on long visits
      • +
    +

    Long stays are determined by the long_stay_threshold value.

    +

    + +TYPE: +Optional[str] + + +DEFAULT: +'all' + +

    +
    long_stay_threshold +

    Minimum visit duration value to consider a visit as candidate for "long visits filtering"

    +

    + +TYPE: +timedelta + + +DEFAULT: +timedelta(days=365) + +

    +
    open_stay_end_datetime +

    Datetime to use in order to fill the visit_end_datetime of open visits. This is necessary in +order to compute stay duration and to filter long stays. If not provided datetime.now() will be used. +You might provide the extraction date of your data here.

    +

    + +TYPE: +Optional[datetime] + + +DEFAULT: +None + +

    +
    max_timedelta +

    Maximum time difference between the end of a visit and the start of another to consider +them as belonging to the same stay. This duration is internally converted in seconds before +comparing. Thus, if you want e.g. to merge visits happening in two consecutive days, you should use +timedelta(days=2) and NOT timedelta(days=1) in order to take into account extreme cases such as +an first visit ending on Monday at 00h01 AM and another one starting at 23h59 PM on Tuesday

    +

    + +TYPE: +timedelta + + +DEFAULT: +timedelta(days=2) + +

    +
    merge_different_hospitals +

    Wether to allow visits occurring in different hospitals to be merged into a same stay

    +

    + +TYPE: +bool + + +DEFAULT: +False + +

    +
    merge_different_source_values +

    Wether to allow visits with different visit_source_value to be merged into a same stay. Values can be:

    +
      +
    • True: the visit_source_value isn't taken into account for the merging
      • +
    • False: only visits with the same visit_source_value can be merged into a same stay
      • +
    • List[str]: only visits which visit_source_value is in the provided list can be merged together.
      • +
    +

    Warning: You should avoid merging visits where visit_source_value == "hospitalisation incomplète", +because those stays are often never closed.

    +

    + +TYPE: +Union[bool, List[str]] + + +DEFAULT: +['hospitalisés', 'urgence'] + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +vo + +

    Visit occurrence DataFrame with additional STAY_ID column

    +

    + +TYPE: +DataFrame + +

    +
    +

    Examples:

    +
    >>> import pandas as pd
+>>> from datetime import datetime, timedelta
+>>> data = {
+    1 : ['A', 999, datetime(2021,1,1), datetime(2021,1,5), 'hospitalisés'],
+    2 : ['B', 999, datetime(2021,1,4), datetime(2021,1,8), 'hospitalisés'],
+    3 : ['C', 999, datetime(2021,1,12), datetime(2021,1,18), 'hospitalisés'],
+    4 : ['D', 999, datetime(2021,1,13), datetime(2021,1,14), 'urgence'],
+    5 : ['E', 999, datetime(2021,1,19), datetime(2021,1,21), 'hospitalisés'],
+    6 : ['F', 999, datetime(2021,1,25), datetime(2021,1,27), 'hospitalisés'],
+    7 : ['G', 999, datetime(2017,1,1), None, "hospitalisés"]
+}
+>>> vo = pd.DataFrame.from_dict(
+    data,
+    orient="index",
+    columns=[
+        "visit_occurrence_id",
+        "person_id",
+        "visit_start_datetime",
+        "visit_end_datetime",
+        "visit_source_value",
+    ],
+)
+>>> vo
+  visit_occurrence_id  person_id visit_start_datetime visit_end_datetime visit_source_value
+1                   A        999           2021-01-01         2021-01-05       hospitalisés
+2                   B        999           2021-01-04         2021-01-08       hospitalisés
+3                   C        999           2021-01-12         2021-01-18       hospitalisés
+4                   D        999           2021-01-13         2021-01-14            urgence
+5                   E        999           2021-01-19         2021-01-21       hospitalisés
+6                   F        999           2021-01-25         2021-01-27       hospitalisés
+7                   G        999           2017-01-01                NaT       hospitalisés
+
    +
    >>> vo = merge_visits(
+        vo,
+        remove_deleted_visits=True,
+        long_stay_threshold=timedelta(days=365),
+        long_stay_filtering="all",
+        max_timedelta=timedelta(hours=24),
+        merge_different_hospitals=False,
+        merge_different_source_values=["hospitalisés", "urgence"],
+)
+>>> vo
+  visit_occurrence_id  person_id visit_start_datetime visit_end_datetime visit_source_value STAY_ID CONTIGUOUS_STAY_ID
+1                   A        999           2021-01-01         2021-01-05       hospitalisés       A                  A
+2                   B        999           2021-01-04         2021-01-08       hospitalisés       A                  A
+3                   C        999           2021-01-12         2021-01-18       hospitalisés       C                  C
+4                   D        999           2021-01-13         2021-01-14            urgence       C                  C
+5                   E        999           2021-01-19         2021-01-21       hospitalisés       C                  E
+6                   F        999           2021-01-25         2021-01-27       hospitalisés       F                  F
+7                   G        999           2017-01-01                NaT       hospitalisés       G                  G
+
    +
    +

    Computing stay duration

    +

    Presentation of the problem

    +

    Once that visits are grouped into stays, you might want to compute stays duration.

    +

    The get_stays_duration() function

    +
    from eds_scikit.period.stays import get_stays_duration
+
    +

    This function should be used once you called the merge_visits() functions. It adds a STAY_DURATION column.

    +
    vo = get_stays_duration(
+    vo,
+    algo="visits_date_difference",
+    missing_end_date_handling="fill",
+)
+
    +

    There are actually two ways to compute those stays durations. Pick the "algo" value that suits your needs.

    +
    +

    Availables algorithms (values for "algo")

    +
    +
    +
    +

    The stay duration corresponds to the difference between the end datetime of the stay's last visit and the start datetime of the stay's first visit.

    +
    +
    +

    The stay duration corresponds to the sum of the duration of all visits of the stay (and by handling overlapping)

    +
    +
    +
    +
    +

    Please check the documentation for additional parameters.

    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/phenotyping/base/0.html b/main/functionalities/phenotyping/base/0.html new file mode 100644 index 00000000..d9668128 --- /dev/null +++ b/main/functionalities/phenotyping/base/0.html @@ -0,0 +1,108 @@ + + + + + +The `Phenotype` class - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + +TYPE: +BaseData + +

    +
    cancer_types +

    Optional list of cancer types to use for phenotyping

    +

    + +TYPE: + Optional[List[str]] + + +DEFAULT: +None + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + +TYPE: +str + + +DEFAULT: +'patient' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + +TYPE: +bool + + +DEFAULT: +True + +

    +
    threshold +

    Minimal number of events (which definition depends on the level value)

    +

    + +TYPE: +int + + +DEFAULT: +1 + +

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/base/1.html b/main/functionalities/phenotyping/base/1.html new file mode 100644 index 00000000..d5ff373e --- /dev/null +++ b/main/functionalities/phenotyping/base/1.html @@ -0,0 +1,59 @@ + + + + + +The `Phenotype` class - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + +TYPE: +BaseData + +

    +
    name +

    Name of the phenotype. If left to None, +the name of the class will be used instead

    +

    + +TYPE: +Optional[str] + + +DEFAULT: +None + +

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/base/2.html b/main/functionalities/phenotyping/base/2.html new file mode 100644 index 00000000..44a28e5f --- /dev/null +++ b/main/functionalities/phenotyping/base/2.html @@ -0,0 +1,86 @@ + + + + + +The `Phenotype` class - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    output_feature +

    Name of the feature

    +

    + +TYPE: +str + +

    +
    codes +

    Dictionary of codes to provide to the from_codes function

    +

    + +TYPE: +dict + +

    +
    source +

    Either 'icd10' or 'ccam', by default 'icd10'

    +

    + +TYPE: +str + + +DEFAULT: +'icd10' + +

    +
    additional_filtering +

    Dictionary passed to the from_codes functions for filtering

    +

    + +TYPE: +Optional[dict] + + +DEFAULT: +None + +

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/base/3.html b/main/functionalities/phenotyping/base/3.html new file mode 100644 index 00000000..b66cf0ba --- /dev/null +++ b/main/functionalities/phenotyping/base/3.html @@ -0,0 +1,41 @@ + + + + + +The `Phenotype` class - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + +Phenotype + + +

    The current Phenotype object with an additional feature +stored in self.features[output_feature]

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/base/4.html b/main/functionalities/phenotyping/base/4.html new file mode 100644 index 00000000..ab6c5712 --- /dev/null +++ b/main/functionalities/phenotyping/base/4.html @@ -0,0 +1,109 @@ + + + + + +The `Phenotype` class - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    input_feature +

    Name of the input feature

    +

    + +TYPE: +str + +

    +
    output_feature +

    Name of the input feature. If None, will be set to +input_feature + "_agg"

    +

    + +TYPE: +Optional[str] + + +DEFAULT: +None + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + +TYPE: +str + + +DEFAULT: +'patient' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + +TYPE: +bool + + +DEFAULT: +True + +

    +
    threshold +

    Minimal number of events (which definition depends on the level value)

    +

    + +TYPE: +int, optional + + +DEFAULT: +1 + +

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/base/5.html b/main/functionalities/phenotyping/base/5.html new file mode 100644 index 00000000..b66cf0ba --- /dev/null +++ b/main/functionalities/phenotyping/base/5.html @@ -0,0 +1,41 @@ + + + + + +The `Phenotype` class - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + +Phenotype + + +

    The current Phenotype object with an additional feature +stored in self.features[output_feature]

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/base/6.html b/main/functionalities/phenotyping/base/6.html new file mode 100644 index 00000000..507fb398 --- /dev/null +++ b/main/functionalities/phenotyping/base/6.html @@ -0,0 +1,137 @@ + + + + + +The `Phenotype` class - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    input_feature_1 +

    Name of the first input feature

    +

    + +TYPE: +str + +

    +
    input_feature_2 +

    Name of the second input feature

    +

    + +TYPE: +str + +

    +
    output_feature +

    Name of the input feature. If None, will be set to +input_feature + "_agg"

    +

    + +TYPE: +str + + +DEFAULT: +None + +

    +
    how +

    Whether to perform a boolean "AND" or "OR" aggregation

    +

    + +TYPE: +str, optional + + +DEFAULT: +'AND' + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + +TYPE: +str + + +DEFAULT: +'patient' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + +TYPE: +bool + + +DEFAULT: +True + +

    +
    thresholds +

    Repsective threshold for the first and second feature

    +

    + +TYPE: +Tuple[int, int], optional + + +DEFAULT: +(1, 1) + +

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/base/7.html b/main/functionalities/phenotyping/base/7.html new file mode 100644 index 00000000..b66cf0ba --- /dev/null +++ b/main/functionalities/phenotyping/base/7.html @@ -0,0 +1,41 @@ + + + + + +The `Phenotype` class - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + +Phenotype + + +

    The current Phenotype object with an additional feature +stored in self.features[output_feature]

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/base/8.html b/main/functionalities/phenotyping/base/8.html new file mode 100644 index 00000000..5debe312 --- /dev/null +++ b/main/functionalities/phenotyping/base/8.html @@ -0,0 +1,46 @@ + + + + + +The `Phenotype` class - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    key +

    Key of the self.feature dictionary

    +

    + +TYPE: +Optional[str] + + +DEFAULT: +None + +

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/base/9.html b/main/functionalities/phenotyping/base/9.html new file mode 100644 index 00000000..c1d514b3 --- /dev/null +++ b/main/functionalities/phenotyping/base/9.html @@ -0,0 +1,40 @@ + + + + + +The `Phenotype` class - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + +BaseData + + +

    The data object with phenotype added to data.computed

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/base/index.html b/main/functionalities/phenotyping/base/index.html new file mode 100644 index 00000000..eee74bf5 --- /dev/null +++ b/main/functionalities/phenotyping/base/index.html @@ -0,0 +1,2215 @@ + + + + + + + + + +The `Phenotype` class - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
    + +
    + +
    +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    + + + +

    How to use and developp phenotyping algorithms in eds-scikit

    +

    The Phenotype class

    +

    Phenotyping is done via the Phenotype class.

    +

    Using this class, we can add features that will be stored in the features attribute. +Features are DataFrames containing at least a person_id and a phenotype column. Additionaly:

    +
      +
    • If phenotyping at the visit level, features contains a visit_occurrence_id column
      • +
    • If using sub-phenotypes (e.g. types of diabetes, or various cancer localiizations), features contains a subphenotype column.
      • +
    +

    We distinguish 2 main ways of adding features to a Phenotype instance:

    +
      +
    • By querying the database to extract raw features
      • +
    • By aggregating one or multiple existing features
      • +
    +

    Available phenotypes

    +

    eds-scikit is shipped with various phenotyping algorithms. For instance, the CancerFromICD10 class can be used to extract visits or patients with a cancer-related ICD10 code. All those phenotyping algorithms share the same API. We will demonstrate it using the CancerFromICD10 class

    +
    from eds_scikit.io import HiveData
+data = HiveData(DBNAME)
+
    +
    from eds_scikit.phenotype import CancerFromICD10
+
+cancer = CancerFromICD10(data)
+
    +

    To run the phenotyping algorithm, simply run:

    +
    data = cancer.to_data()
+
    +

    This will put the resulting phenotype DataFrame in data.computed["CancerFromICD10"]

    +

    Most available phenotypes share the same parameters:

    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + +TYPE: +BaseData + +

    +
    cancer_types +

    Optional list of cancer types to use for phenotyping

    +

    + +TYPE: + Optional[List[str]] + + +DEFAULT: +None + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + +TYPE: +str + + +DEFAULT: +'patient' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + +TYPE: +bool + + +DEFAULT: +True + +

    +
    threshold +

    Minimal number of events (which definition depends on the level value)

    +

    + +TYPE: +int + + +DEFAULT: +1 + +

    +
    +
    +

    Please look into each algorithm's documentation for further specific details.

    +

    Implement your own phenotyping algorithm

    +

    TO help you implement your own phenotyping algorithm, the Phenotype class exposes method to

    +
      +
    • Easily featch features based on ICD10 and CCAM codes
      • +
    • Easily aggregate feature(s) using simple threshold rules
      • +
    +

    The following paragraph will show how to implement a dummy phenotyping algorithm for moderate to terminal Chronic Kidney Disease (CKD). In short, it will: +- Extract patients with ICD10 code for CKD +- Extract patients with CCAM code for dialysis or kidney transplant +- Aggregate those two feature by keeping patients with both features

    +

    We will start by creating an instance of the Phenotype class:

    +
    from eds_scikit.phenotype import Phenotype
+
+ckd = Phenotype(data, name="DummyCKD")
+
    +

    Next we define the ICD10 and CCAM codes

    +
    +

    Codes formatting

    +

    Under the hood, Phenotype will use the conditions_from_icd10 and procedures_from_ccam functions. Check their documentation for details on how to format the provided codes

    +
    +
    icd10_codes = {
+    "CKD": {"regex": ["N18[345]"]},
+}
+
+ccam_codes = {
+    "dialysis": {"regex": ["JVJB001"]},
+    "transplant": {"exact": ["JAEA003"]},
+}
+
    +

    Finally, we can start designing the phenotyping algorithm:

    +

    Get ICD10 features

    +
    ckd = ckd.add_code_feature(
+    output_feature="icd10",
+    source="icd10",
+    codes=icd10_codes,
+)
+
    +

    Get CCAM features

    +
    ckd = ckd.add_code_feature(
+    output_feature="ccam",
+    source="ccam",
+    codes=ccam_codes,
+)
+
    +

    Aggregate those 2 features

    +
    ckd = ckd.agg_two_features(
+    input_feature_1="icd10",
+    input_feature_2="ccam",
+    output_feature="CKD",
+    how="AND",
+    level="patient",
+    subphenotype=False,
+    thresholds=(1, 1),
+)
+
    +

    The final phenotype DataFrame can now be added to the data object:

    +
    data = ckd.to_data()
+
    +

    It will be available under data.computed.CKD

    +

    Available methods on Phenotype:

    +
    +
    +

    Base class for phenotyping

    + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + +TYPE: +BaseData + +

    +
    name +

    Name of the phenotype. If left to None, +the name of the class will be used instead

    +

    + +TYPE: +Optional[str] + + +DEFAULT: +None + +

    +
    +
    +
    +

    +add_code_feature +

    +
    add_code_feature(output_feature: str, codes: dict, source: str = 'icd10', additional_filtering: Optional[dict] = None)
+
    +
    +

    Adds a feature from either ICD10 or CCAM codes

    + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    output_feature +

    Name of the feature

    +

    + +TYPE: +str + +

    +
    codes +

    Dictionary of codes to provide to the from_codes function

    +

    + +TYPE: +dict + +

    +
    source +

    Either 'icd10' or 'ccam', by default 'icd10'

    +

    + +TYPE: +str + + +DEFAULT: +'icd10' + +

    +
    additional_filtering +

    Dictionary passed to the from_codes functions for filtering

    +

    + +TYPE: +Optional[dict] + + +DEFAULT: +None + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + +Phenotype + + +

    The current Phenotype object with an additional feature +stored in self.features[output_feature]

    +
    +
    +
    +
    +

    +agg_single_feature +

    +
    agg_single_feature(input_feature: str, output_feature: Optional[str] = None, level: str = 'patient', subphenotype: bool = True, threshold: int = 1) -> Phenotype
+
    +
    +

    Simple aggregation rule on a feature:

    +
      +
    • If level="patient", keeps patients with at least threshold + visits showing the (sub)phenotype
      • +
    • If level="visit", keeps visits with at least threshold events + (could be ICD10 codes, NLP features, biology, etc) showing the (sub)phenotype
      • +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    input_feature +

    Name of the input feature

    +

    + +TYPE: +str + +

    +
    output_feature +

    Name of the input feature. If None, will be set to +input_feature + "_agg"

    +

    + +TYPE: +Optional[str] + + +DEFAULT: +None + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + +TYPE: +str + + +DEFAULT: +'patient' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + +TYPE: +bool + + +DEFAULT: +True + +

    +
    threshold +

    Minimal number of events (which definition depends on the level value)

    +

    + +TYPE: +int, optional + + +DEFAULT: +1 + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + +Phenotype + + +

    The current Phenotype object with an additional feature +stored in self.features[output_feature]

    +
    +
    +
    +
    +

    +agg_two_features +

    +
    agg_two_features(input_feature_1: str, input_feature_2: str, output_feature: str = None, how: str = 'AND', level: str = 'patient', subphenotype: bool = True, thresholds: Tuple[int, int] = (1, 1)) -> Phenotype
+
    +
    +
      +
    • +

      If level='patient', keeps a specific patient if

      +
        +
      • At least thresholds[0] visits are found in feature_1 AND/OR
        • +
      • At least thresholds[1] visits are found in feature_2
        • +
      +
      • +
    • +

      If level='visit', keeps a specific visit if

      +
        +
      • At least thresholds[0] events are found in feature_1 AND/OR
        • +
      • At least thresholds[1] events are found in feature_2
        • +
      +
      • +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    input_feature_1 +

    Name of the first input feature

    +

    + +TYPE: +str + +

    +
    input_feature_2 +

    Name of the second input feature

    +

    + +TYPE: +str + +

    +
    output_feature +

    Name of the input feature. If None, will be set to +input_feature + "_agg"

    +

    + +TYPE: +str + + +DEFAULT: +None + +

    +
    how +

    Whether to perform a boolean "AND" or "OR" aggregation

    +

    + +TYPE: +str, optional + + +DEFAULT: +'AND' + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + +TYPE: +str + + +DEFAULT: +'patient' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + +TYPE: +bool + + +DEFAULT: +True + +

    +
    thresholds +

    Repsective threshold for the first and second feature

    +

    + +TYPE: +Tuple[int, int], optional + + +DEFAULT: +(1, 1) + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + +Phenotype + + +

    The current Phenotype object with an additional feature +stored in self.features[output_feature]

    +
    +
    +
    +
    +

    +compute +

    +
    compute(**kwargs)
+
    +
    +

    Fetch all necessary features and perform aggregation

    +
    +
    +
    +

    +to_data +

    +
    to_data(key: Optional[str] = None) -> BaseData
+
    +
    +

    Appends the feature found in self.features[key] to the data object. +If no key is provided, uses the last added feature

    + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    key +

    Key of the self.feature dictionary

    +

    + +TYPE: +Optional[str] + + +DEFAULT: +None + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + +BaseData + + +

    The data object with phenotype added to data.computed

    +
    +
    +
    +
    +
    +

    Citation

    +

    Most available phenotypes implement an algorithm described in an academic paper. When using this algorithm, you can get the BibTex citation of the corrresponding paper by calling the cite method. For instance:

    +
    cancer.cite()
+
    +
    @article{kempf2022impact,
+  title={Impact of two waves of Sars-Cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: A French multicentric cohort study from a large group of University hospitals},
+  author={Kempf, Emmanuelle and Priou, Sonia and Lam{\'e}, Guillaume and Daniel, Christel and Bellamine, Ali and Sommacale, Daniele and Belkacemi, Yazid and Bey, Romain and Galula, Gilles and Taright, Namik and others},
+  journal={International Journal of Cancer},
+  volume={150},
+  number={10},
+  pages={1609--1618},
+  year={2022},
+  publisher={Wiley Online Library}
+}
+
    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/phenotyping/diabetes/index.html b/main/functionalities/phenotyping/diabetes/index.html new file mode 100644 index 00000000..c0950483 --- /dev/null +++ b/main/functionalities/phenotyping/diabetes/index.html @@ -0,0 +1,1373 @@ + + + + + + + + + +Diabetes - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
    + +
    + +
    +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    + + + +

    Diabetes

    +

    Presentation

    +

    For the moment, we provide a diabetes phenotyping function based solely on ICD-10 codes.

    +

    The diabetes_from_icd10() function

    +
    from eds_scikit.io import HiveData
+data = HiveData(DBNAME)
+
    +
    from eds_scikit.event import diabetes_from_icd10
+
+visit_occurrence = diabetes_from_icd10(
+    data.condition_occurrence,
+    data.visit_occurrence,
+)
+
    +

    The snippet above will run as is and add two columns to the condition_occurrence DataFrame:

    +
      +
    • A "concept" column, containing the "DIABETES_FROM_ICD10" value
      • +
    • A "value" column, containing the type of diabetes extracted
      • +
    +

    Please check the code reference for a complete explanation of the function.

    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/phenotyping/phenotypes/cancer/0.html b/main/functionalities/phenotyping/phenotypes/cancer/0.html new file mode 100644 index 00000000..b26e37d4 --- /dev/null +++ b/main/functionalities/phenotyping/phenotypes/cancer/0.html @@ -0,0 +1,108 @@ + + + + + +Cancer - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + +TYPE: +BaseData + +

    +
    cancer_types +

    Optional list of cancer types to use for phenotyping

    +

    + +TYPE: + Optional[List[str]] + + +DEFAULT: +None + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + +TYPE: +str + + +DEFAULT: +'patient' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + +TYPE: +bool + + +DEFAULT: +True + +

    +
    threshold +

    Minimal number of events (which definition depends on the level value)

    +

    + +TYPE: +int + + +DEFAULT: +1 + +

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/phenotypes/cancer/index.html b/main/functionalities/phenotyping/phenotypes/cancer/index.html new file mode 100644 index 00000000..9b7baafb --- /dev/null +++ b/main/functionalities/phenotyping/phenotypes/cancer/index.html @@ -0,0 +1,1632 @@ + + + + + + + + + +Cancer - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
    + +
    + +
    +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    + + + +

    Cancer

    +

    Presentation

    +

    We provide the CancerFromICD10 class to extract visits or patients with cancer related ICD10 code

    +
    +Available cancer types +
      +
    • Anus
      • +
    • Biliary_duct
      • +
    • Bladder
      • +
    • Bowel
      • +
    • Breast
      • +
    • CNS
      • +
    • CUP
      • +
    • Cervix
      • +
    • Colon
      • +
    • Endometrium
      • +
    • Eye
      • +
    • Gastric
      • +
    • Head_neck
      • +
    • Hodgkin_lymphoma
      • +
    • Kidney
      • +
    • Leukemia
      • +
    • Liver
      • +
    • Lung
      • +
    • Melanoma
      • +
    • Mesothelioma
      • +
    • Myeloma
      • +
    • Nonhodgkin_lymphoma
      • +
    • Oesophagus
      • +
    • Osteosarcoma
      • +
    • Other_digestive
      • +
    • Other_endocrinial
      • +
    • Other_gynecology
      • +
    • Other_hematologic_malignancies
      • +
    • Other_pneumology
      • +
    • Other_skin
      • +
    • Other_urothelial
      • +
    • Ovary
      • +
    • PNS
      • +
    • Pancreas
      • +
    • Prostate
      • +
    • Rectum
      • +
    • Soft_tissue
      • +
    • Testis
      • +
    • Thyroid
      • +
    +
    +
    +

    How it works

    +

    The algorithm works by looking for either DP ou DR ICD10 codes associated with cancer. +The codes terminology comes from this article1 and is available under CancerFromICD10.ICD10_CODES

    +
    +

    Usage

    +

    By default, all cancer types mentionned above are extracted

    +
    from eds_scikit.io import HiveData
+data = HiveData(DBNAME)
+
    +
    from eds_scikit.phenotype import CancerFromICD10
+
+cancer = CancerFromICD10(data)
+data = cancer.to_data()
+
    +

    To choose a subset of cancer types, use the cancer_types argument:

    +
    cancer = CancerFromICD10(
+    data,
+    cancer_types=[
+        "Eye",
+        "Liver",
+        "Leukemia",
+    ],
+)
+
    +

    The final phenotype DataFrame is then available at data.computed["CancerFromICD10"]

    +

    Optional parameters

    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + +TYPE: +BaseData + +

    +
    cancer_types +

    Optional list of cancer types to use for phenotyping

    +

    + +TYPE: + Optional[List[str]] + + +DEFAULT: +None + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + +TYPE: +str + + +DEFAULT: +'patient' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + +TYPE: +bool + + +DEFAULT: +True + +

    +
    threshold +

    Minimal number of events (which definition depends on the level value)

    +

    + +TYPE: +int + + +DEFAULT: +1 + +

    +
    +
    +

    Citation

    +

    You can get the BibTex of the corresponding article1 by calling

    +
    cancer.cite()
+
    +
    @article{kempf2022impact,
+  title={Impact of two waves of Sars-Cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: A French multicentric cohort study from a large group of University hospitals},
+  author={Kempf, Emmanuelle and Priou, Sonia and Lam{\'e}, Guillaume and Daniel, Christel and Bellamine, Ali and Sommacale, Daniele and Belkacemi, Yazid and Bey, Romain and Galula, Gilles and Taright, Namik and others},
+  journal={International Journal of Cancer},
+  volume={150},
+  number={10},
+  pages={1609--1618},
+  year={2022},
+  publisher={Wiley Online Library}
+}
+
    +

    Reference

    +

    Check the code reference here for a more detailled look.

    +
    +
    +
      +
    1. +

      Emmanuelle Kempf, Sonia Priou, Guillaume Lamé, Christel Daniel, Ali Bellamine, Daniele Sommacale, Yazid Belkacemi, Romain Bey, Gilles Galula, Namik Taright, and others. Impact of two waves of sars-cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: a french multicentric cohort study from a large group of university hospitals. International Journal of Cancer, 150(10):1609–1618, 2022. ↩↩

      +
      2. +
    +
    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/phenotyping/phenotypes/diabetes/0.html b/main/functionalities/phenotyping/phenotypes/diabetes/0.html new file mode 100644 index 00000000..f9e2a71c --- /dev/null +++ b/main/functionalities/phenotyping/phenotypes/diabetes/0.html @@ -0,0 +1,108 @@ + + + + + +Diabetes - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + +TYPE: +BaseData + +

    +
    diabetes_types +

    Optional list of diabetes types to use for phenotyping

    +

    + +TYPE: + Optional[List[str]] + + +DEFAULT: +None + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + +TYPE: +str + + +DEFAULT: +'visit' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + +TYPE: +bool + + +DEFAULT: +True + +

    +
    threshold +

    Minimal number of events (which definition depends on the level value)

    +

    + +TYPE: +int + + +DEFAULT: +1 + +

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/phenotypes/diabetes/index.html b/main/functionalities/phenotyping/phenotypes/diabetes/index.html new file mode 100644 index 00000000..621bd079 --- /dev/null +++ b/main/functionalities/phenotyping/phenotypes/diabetes/index.html @@ -0,0 +1,1564 @@ + + + + + + + + + +Diabetes - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
    + +
    + +
    +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    + + + +

    Diabetes

    +

    Presentation

    +

    We provide the DiabetesFromICD10 class to extract visits or patients with ICD10 codes related to diabetes

    +
    +Available diabetes types +
      +
    • DIABETES_IN_PREGNANCY
      • +
    • DIABETES_MALNUTRITION
      • +
    • DIABETES_TYPE_I
      • +
    • DIABETES_TYPE_II
      • +
    • OTHER_DIABETES_MELLITUS
      • +
    +
    +
    +

    How it works

    +

    The algorithm works by looking for either DP, DR or DAS ICD10 codes associated with cancer. +Those codes are available under DiabetesFromICD10.ICD10_CODES

    +
    +

    Usage

    +

    By default, all diabetes types mentionned above are extracted

    +
    from eds_scikit.io import HiveData
+data = HiveData(DBNAME)
+
    +
    from eds_scikit.phenotype import DiabetesFromICD10
+
+diabetes = DiabetesFromICD10(data)
+data = diabetes.to_data()
+
    +

    To choose a subset of disorders, use the diabetes_types argument:

    +
    diabetes = DiabetesFromICD10(
+    data,
+    diabetes_types=[
+        "DIABETES_TYPE_I",
+        "DIABETES_IN_PREGNANCY",
+    ],
+)
+
    +

    The final phenotype DataFrame is then available at data.computed["DiabetesFromICD10"]

    +

    Optional parameters

    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + +TYPE: +BaseData + +

    +
    diabetes_types +

    Optional list of diabetes types to use for phenotyping

    +

    + +TYPE: + Optional[List[str]] + + +DEFAULT: +None + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + +TYPE: +str + + +DEFAULT: +'visit' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + +TYPE: +bool + + +DEFAULT: +True + +

    +
    threshold +

    Minimal number of events (which definition depends on the level value)

    +

    + +TYPE: +int + + +DEFAULT: +1 + +

    +
    +
    +

    Reference

    +

    Check the code reference here for a more detailled look.

    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/phenotyping/phenotypes/psychiatric_disorder/0.html b/main/functionalities/phenotyping/phenotypes/psychiatric_disorder/0.html new file mode 100644 index 00000000..cb067d54 --- /dev/null +++ b/main/functionalities/phenotyping/phenotypes/psychiatric_disorder/0.html @@ -0,0 +1,108 @@ + + + + + +Psychiatric disorder - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + +TYPE: +BaseData + +

    +
    disorder_types +

    Optional list of disorder types to use for phenotyping

    +

    + +TYPE: + Optional[List[str]] + + +DEFAULT: +None + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + +TYPE: +str + + +DEFAULT: +'patient' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + +TYPE: +bool + + +DEFAULT: +True + +

    +
    threshold +

    Minimal number of events (which definition depends on the level value)

    +

    + +TYPE: +int + + +DEFAULT: +1 + +

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/phenotypes/psychiatric_disorder/index.html b/main/functionalities/phenotyping/phenotypes/psychiatric_disorder/index.html new file mode 100644 index 00000000..f967c266 --- /dev/null +++ b/main/functionalities/phenotyping/phenotypes/psychiatric_disorder/index.html @@ -0,0 +1,1613 @@ + + + + + + + + + +Psychiatric disorder - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
    + +
    + +
    +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    +
    + +
    +
    +
    +
    +
    + + + +

    Psychiatric disorder

    +

    Presentation

    +

    We provide the PsychiatricDisorderFromICD10 class to extract visits or patients with ICD10 codes related to psychiatric disorders

    +
    +Available disorders +
      +
    • Anxiety Disorders
      • +
    • Bipolar and Related Disorders
      • +
    • Depressive Disorders
      • +
    • Disruptive, Impulse Control and Conduct Disorders
      • +
    • Dissociative Disorders
      • +
    • Feeding and Eating Disorders
      • +
    • Mental Health Symptom
      • +
    • Miscellaneous
      • +
    • Obsessive-Compulsive and Related Disorders
      • +
    • Personality Disorders
      • +
    • Schizophrenia Spectrum and Other Psychotic Disorders
      • +
    • Sleep-Wake Disorders
      • +
    • Somatic Symptom and Related Disorders
      • +
    • Substance-Related and Addictive Disorders
      • +
    • Suicide or Self-Injury
      • +
    • Trauma and Stressor-Related Disorders
      • +
    +
    +
    +

    How it works

    +

    The algorithm works by looking for either DP, DR or DAS ICD10 codes associated with psychiatric disorder. +The codes terminology comes from this article1 and is available under PsychiatricDisorderFromICD10.ICD10_CODES

    +
    +

    Usage

    +

    By default, all cancer types mentionned above are extracted

    +
    from eds_scikit.io import HiveData
+data = HiveData(DBNAME)
+
    +
    from eds_scikit.phenotype import PsychiatricDisorderFromICD10
+
+psy = PsychiatricDisorderFromICD10(data)
+data = psy.to_data()
+
    +

    To choose a subset of disorders, use the disorder_types argument:

    +
    psy = PsychiatricDisorderFromICD10(
+    data,
+    disorder_types=[
+        "Anxiety Disorders",
+        "Trauma and Stressor-Related Disorders",
+    ],
+)
+
    +

    The final phenotype DataFrame is then available at data.computed["PsychiatricDisorderFromICD10"]

    +

    Optional parameters

    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + +TYPE: +BaseData + +

    +
    disorder_types +

    Optional list of disorder types to use for phenotyping

    +

    + +TYPE: + Optional[List[str]] + + +DEFAULT: +None + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + +TYPE: +str + + +DEFAULT: +'patient' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + +TYPE: +bool + + +DEFAULT: +True + +

    +
    threshold +

    Minimal number of events (which definition depends on the level value)

    +

    + +TYPE: +int + + +DEFAULT: +1 + +

    +
    +
    +

    Citation

    +

    You can get the BibTex of the corresponding article1 by calling

    +
    cancer.cite()
+
    +
    @article{2022_covid_4CE,
+    author = {Gutiérrez-Sacristán, Alba and Serret-Larmande, Arnaud and Hutch, Meghan R. and Sáez, Carlos and Aronow, Bruce J. and Bhatnagar, Surbhi and Bonzel, Clara-Lea and Cai, Tianxi and Devkota, Batsal and Hanauer, David A. and Loh, Ne Hooi Will and Luo, Yuan and Moal, Bertrand and Ahooyi, Taha Mohseni and Njoroge, Wanjikũ F. M. and Omenn, Gilbert S. and Sanchez-Pinto, L. Nelson and South, Andrew M. and Sperotto, Francesca and Tan, Amelia L. M. and Taylor, Deanne M. and Verdy, Guillaume and Visweswaran, Shyam and Xia, Zongqi and Zahner, Janet and Avillach, Paul and Bourgeois, Florence T. and Consortium for Clinical Characterization of COVID-19 by EHR (4CE)},
+    title = "{Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic}",
+    journal = {JAMA Network Open},
+    volume = {5},
+    number = {12},
+    pages = {e2246548-e2246548},
+    year = {2022},
+    month = {12},
+    abstract = "{The COVID-19 pandemic has been associated with an increase in mental health diagnoses among adolescents, though the extent of the increase, particularly for severe cases requiring hospitalization, has not been well characterized. Large-scale federated informatics approaches provide the ability to efficiently and securely query health care data sets to assess and monitor hospitalization patterns for mental health conditions among adolescents.To estimate changes in the proportion of hospitalizations associated with mental health conditions among adolescents following onset of the COVID-19 pandemic.This retrospective, multisite cohort study of adolescents 11 to 17 years of age who were hospitalized with at least 1 mental health condition diagnosis between February 1, 2019, and April 30, 2021, used patient-level data from electronic health records of 8 children’s hospitals in the US and France.Change in the monthly proportion of mental health condition–associated hospitalizations between the prepandemic (February 1, 2019, to March 31, 2020) and pandemic (April 1, 2020, to April 30, 2021) periods using interrupted time series analysis.There were 9696 adolescents hospitalized with a mental health condition during the prepandemic period (5966 [61.5\\%] female) and 11 101 during the pandemic period (7603 [68.5\\%] female). The mean (SD) age in the prepandemic cohort was 14.6 (1.9) years and in the pandemic cohort, 14.7 (1.8) years. The most prevalent diagnoses during the pandemic were anxiety (6066 [57.4\\%]), depression (5065 [48.0\\%]), and suicidality or self-injury (4673 [44.2\\%]). There was an increase in the proportions of monthly hospitalizations during the pandemic for anxiety (0.55\\%; 95\\% CI, 0.26\\%-0.84\\%), depression (0.50\\%; 95\\% CI, 0.19\\%-0.79\\%), and suicidality or self-injury (0.38\\%; 95\\% CI, 0.08\\%-0.68\\%). There was an estimated 0.60\\% increase (95\\% CI, 0.31\\%-0.89\\%) overall in the monthly proportion of mental health–associated hospitalizations following onset of the pandemic compared with the prepandemic period.In this cohort study, onset of the COVID-19 pandemic was associated with increased hospitalizations with mental health diagnoses among adolescents. These findings support the need for greater resources within children’s hospitals to care for adolescents with mental health conditions during the pandemic and beyond.}",
+    issn = {2574-3805},
+    doi = {10.1001/jamanetworkopen.2022.46548},
+    url = {https://doi.org/10.1001/jamanetworkopen.2022.46548},
+    eprint = {https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2799437/gutirrezsacristn\_2022\_oi\_221314\_1670339179.72376.pdf},
+}
+
    +

    Reference

    +

    Check the code reference here for a more detailled look.

    +
    +
    +
      +
    1. +

      Alba Gutiérrez-Sacristán, Arnaud Serret-Larmande, Meghan R. Hutch, Carlos Sáez, Bruce J. Aronow, Surbhi Bhatnagar, Clara-Lea Bonzel, Tianxi Cai, Batsal Devkota, David A. Hanauer, Ne Hooi Will Loh, Yuan Luo, Bertrand Moal, Taha Mohseni Ahooyi, Wanjikũ F. M. Njoroge, Gilbert S. Omenn, L. Nelson Sanchez-Pinto, Andrew M. South, Francesca Sperotto, Amelia L. M. Tan, Deanne M. Taylor, Guillaume Verdy, Shyam Visweswaran, Zongqi Xia, Janet Zahner, Paul Avillach, Florence T. Bourgeois, and Consortium for Clinical Characterization of COVID-19 by EHR (4CE). Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic. JAMA Network Open, 5(12):e2246548–e2246548, 12 2022. URL: https://doi.org/10.1001/jamanetworkopen.2022.46548, arXiv:https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2799437/gutirrezsacristn\_2022\_oi\_221314\_1670339179.72376.pdf, doi:10.1001/jamanetworkopen.2022.46548. ↩↩

      +
      2. +
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    + + + Back to top + +
    + +
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    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/phenotyping/phenotypes/suicide_attempt/index.html b/main/functionalities/phenotyping/phenotypes/suicide_attempt/index.html new file mode 100644 index 00000000..74a67cd7 --- /dev/null +++ b/main/functionalities/phenotyping/phenotypes/suicide_attempt/index.html @@ -0,0 +1,1475 @@ + + + + + + + + + +Suicide Attempt - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    Suicide Attempt

    +

    Presentation

    +

    We provide the SuicideAttemptFromICD10 class to extract visits linked to suicide attempt from ICD-10 codes.

    +

    Usage

    +

    As mentionned below, two algorithms ("Haguenoer2008" (default) and "X60-X84") are available

    +
    from eds_scikit.io import HiveData
+data = HiveData(DBNAME)
+
    +
    from eds_scikit.phenotype import SuicideAttemptFromICD10
+
+sa = SuicideAttemptFromICD10(data)
+data = sa.to_data()
+
    +

    The final phenotype DataFrame is then available at data.computed["SuicideAttemptFromICD10_Haguenoer2008"] or data.computed["SuicideAttemptFromICD10_X60_X84"] depending on the used algorithm

    +
    +

    Availables algorithms (values for "algo")

    +

    The ICD10 codes are available under SuicideAttemptFromICD10.ICD10_CODES

    +
    +
    +
    +

    Returns the visits that have at least one ICD code that belongs to the range X60 to X84.

    +
    +
    +

    Returns the visits that follow the definiton of Haguenoer20081. This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84.

    +
    +
    +
    +
    +

    Citation

    +

    When using algo="Haguenoer2008", you can get the BibTex of the corresponding article1 by calling

    +
    sa.cite()
+
    +
    @misc{haguenoer_tentatives_2008,
+    title = {Épidémiologie des tentatives de suicide en région Centre},
+    language = {fr},
+    author = {Haguenoer, Ken and Caille, Agnès and Fillatre, Marc and Lecuyer, Anne Isabelle and Rusch, Emmanuel},
+    year = {2008},
+    pages = {4},
+    howpublished = {\url{https://www.esante-centre.fr/portail_pro/minisite_25/media-files/56393/plaquette-2006-2009.pdf}}
+}
+
    +

    Reference

    +

    Check the code reference here for a more detailled look.

    +
    +
    +
      +
    1. +

      Ken Haguenoer, Agnès Caille, Marc Fillatre, Anne Isabelle Lecuyer, and Emmanuel Rusch. Épidémiologie des tentatives de suicide en région centre. \url https://www.esante-centre.fr/portail_pro/minisite_25/media-files/56393/plaquette-2006-2009.pdf, 2008. ↩↩

      +
      2. +
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    + + + Back to top + +
    + +
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    +
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    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/phenotyping/suicide_attempts/0.html b/main/functionalities/phenotyping/suicide_attempts/0.html new file mode 100644 index 00000000..1aed33d5 --- /dev/null +++ b/main/functionalities/phenotyping/suicide_attempts/0.html @@ -0,0 +1,104 @@ + + + + + +Suicide attempt - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    visit_occurrence +

    + +TYPE: +DataFrame + +

    +
    condition_occurrence +

    + +TYPE: +DataFrame + +

    +
    date_min +

    Minimal starting date (on visit_start_datetime)

    +

    + +TYPE: +Optional[datetime] + + +DEFAULT: +None + +

    +
    date_max +

    Maximal starting date (on visit_start_datetime)

    +

    + +TYPE: +Optional[datetime] + + +DEFAULT: +None + +

    +
    algo +

    Method to use. Available values are:

    +
      +
    • "X60-X84": Will return a the visits that have at least one ICD code that belongs to the range X60 to X84.
      • +
    • "Haguenoer2008": Will return a the visits that follow the definiton of "Haguenoer, Ken, Agnès Caille, Marc Fillatre, Anne Isabelle Lecuyer, et Emmanuel Rusch. « Tentatives de Suicide », 2008, 4.". This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84.
      • +
    +

    + +TYPE: +str + + +DEFAULT: +'X60-X84' + +

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/suicide_attempts/1.html b/main/functionalities/phenotyping/suicide_attempts/1.html new file mode 100644 index 00000000..43d1a1a5 --- /dev/null +++ b/main/functionalities/phenotyping/suicide_attempts/1.html @@ -0,0 +1,44 @@ + + + + + +Suicide attempt - eds-scikit + + + + + + + + + + + + + + +
    + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +visit_occurrence + +

    Tagged with an additional column SUICIDE_ATTEMPT

    +

    + +TYPE: +DataFrame + +

    +
    \ No newline at end of file diff --git a/main/functionalities/phenotyping/suicide_attempts/index.html b/main/functionalities/phenotyping/suicide_attempts/index.html new file mode 100644 index 00000000..cef05a4b --- /dev/null +++ b/main/functionalities/phenotyping/suicide_attempts/index.html @@ -0,0 +1,1499 @@ + + + + + + + + + +Suicide attempt - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    + + + +

    Suicide attempt

    +

    Presentation

    +

    We provide the tag_suicide_attempt() function to extract suicide attempt from ICD-10 codes.

    +

    The tag_suicide_attempt() function

    +
    from eds_scikit.io import HiveData
+data = HiveData(DBNAME)
+
    +
    from eds_scikit.event import tag_suicide_attempt
+
+visit_occurrence = tag_suicide_attempt(
+    data.visit_occurrence,
+    data.condition_occurrence,
+    algo="X60-X84",
+)
+
    +
    +

    Availables algorithms (values for "algo")

    +
    +
    +
    +

    Returns the visits that have at least one ICD code that belongs to the range X60 to X84.

    +
    +
    +

    Returns the visits that follow the definiton of "Haguenoer, Ken, Agnès Caille, Marc Fillatre, Anne Isabelle Lecuyer, et Emmanuel Rusch. « Tentatives de Suicide », 2008, 4.". This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84.

    +
    +
    +
    +
    +

    You can check the documentation of the function for additional parameters:

    +
    +
    +

    Function to return visits that fulfill different definitions of suicide attempt by ICD10.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    visit_occurrence +

    + +TYPE: +DataFrame + +

    +
    condition_occurrence +

    + +TYPE: +DataFrame + +

    +
    date_min +

    Minimal starting date (on visit_start_datetime)

    +

    + +TYPE: +Optional[datetime] + + +DEFAULT: +None + +

    +
    date_max +

    Maximal starting date (on visit_start_datetime)

    +

    + +TYPE: +Optional[datetime] + + +DEFAULT: +None + +

    +
    algo +

    Method to use. Available values are:

    +
      +
    • "X60-X84": Will return a the visits that have at least one ICD code that belongs to the range X60 to X84.
      • +
    • "Haguenoer2008": Will return a the visits that follow the definiton of "Haguenoer, Ken, Agnès Caille, Marc Fillatre, Anne Isabelle Lecuyer, et Emmanuel Rusch. « Tentatives de Suicide », 2008, 4.". This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84.
      • +
    +

    + +TYPE: +str + + +DEFAULT: +'X60-X84' + +

    +
    + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    +visit_occurrence + +

    Tagged with an additional column SUICIDE_ATTEMPT

    +

    + +TYPE: +DataFrame + +

    +
    +
    +

    Tip

    +

    These rules were implemented in the CSE project n°210013

    +
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    + + + Back to top + +
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    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/phenotyping/working-with-codes/index.html b/main/functionalities/phenotyping/working-with-codes/index.html new file mode 100644 index 00000000..e9b366a6 --- /dev/null +++ b/main/functionalities/phenotyping/working-with-codes/index.html @@ -0,0 +1,1371 @@ + + + + + + + + + +Using ICD-10 and CCAM - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    Using ICD-10 and CCAM

    +

    eds-scikit provides two functions to ease the extraction of occurrrences of

    +
      +
    • ICD-10 codes : eds_scikit.event.icd10.conditions_from_icd10
      • +
    • CCAM codes : eds_scikit.event.ccam.procedures_from_ccam
      • +
    +

    These two functions are by design similar. In fact, they call under the hood the same base function.

    +

    Let us see a minimal working example that would allow us to select patients with Deep Vein Thrombosis based on the presence of specific ICD-10 codes.

    +
    from eds_scikit.io import HiveData
+data = HiveData(DBNAME)
+
    +
    codes = dict(
+    DVT=dict(
+        exact=["I81", "O223", "O082", "O871"], regex=["I82[02389]", "I80[12]"]  # (1)
+    )
+)
+
+from eds_scikit.event.icd10 import conditions_from_icd10
+
+DVTs = conditions_from_icd10(
+    condition_occurrence=data.condition_occurrence,
+    visit_occurrence=data.visit_occurrence,
+    codes=codes,
+    date_from_visit=True,
+    additional_filtering=dict(
+        condition_status_source_value={"DP", "DAS"},  # (1)
+    ),
+)
+
    +
      +
    1. Here you can provide either exact, regex or prefix codes
      2. +
    2. With this syntax we will keep only DP (Diagnostic Principal) or DAS (Diagnostic Associé) diagnoses
      3. +
    +

    Of course, you are encouraged to check the documentation of those functions as they provide additional parameters that might be useful depending on your needs.

    +
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    + + + Back to top + +
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    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/plotting/age_pyramid/index.html b/main/functionalities/plotting/age_pyramid/index.html new file mode 100644 index 00000000..d6515898 --- /dev/null +++ b/main/functionalities/plotting/age_pyramid/index.html @@ -0,0 +1,1473 @@ + + + + + + + + + +Age pyramid - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    Visualizing age pyramid

    +

    The age pyramid is helpful to quickly visualize the age and gender distributions in a cohort.

    +

    Load a synthetic dataset

    +

    plot_age_pyramid uses the "person" table:

    +

    from eds_scikit.datasets.synthetic.person import load_person
+
+df_person = load_person()
+df_person.head()
+
    +| | person_id | gender_source_value | birth_datetime | +|---|-----------|---------------------|----------------| +| 0 | 0 | m | 2010-01-01 | +| 1 | 1 | m | 1938-01-01 | +| 2 | 2 | f | 1994-01-01 | +| 3 | 3 | m | 1994-01-01 | +| 4 | 4 | m | 2004-01-01 |

    +

    Visualize age pyramid

    +

    Basic usage

    +

    By default, plot_age_pyramid will compute age as the difference between today and the date of birth:

    +
    from eds_scikit.plot.age_pyramid import plot_age_pyramid
+
+plot_age_pyramid(df_person)
+
    +

    age_pyramid_default

    +

    Advanced parameters

    +

    Further configuration can be provided, including :

    +
      +
    • datetime_ref : Choose the reference to compute the age from. + It can be either a single datetime (string or datetime type), an array of datetime + (one reference for each patient) or a string representing a column of the input dataframe
      • +
    • return_array: If set to True, return a dataframe instead of a chart.
      • +
    +
    import pandas as pd
+from datetime import datetime
+from eds_scikit.plot.age_pyramid import plot_age_pyramid
+
+dates_of_first_visit = pd.Series([datetime(2020, 1, 1)] * df_person.shape[0])
+plot_age_pyramid(df_person, datetime_ref=dates_of_first_visit)
+
    +

    age_pyramid_single_ref.png

    +

    Please check the documentation for further details on the function's parameters.

    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/plotting/age_pyramid_default.png b/main/functionalities/plotting/age_pyramid_default.png new file mode 100644 index 00000000..6297adc6 Binary files /dev/null and b/main/functionalities/plotting/age_pyramid_default.png differ diff --git a/main/functionalities/plotting/age_pyramid_single_ref.png b/main/functionalities/plotting/age_pyramid_single_ref.png new file mode 100644 index 00000000..79710200 Binary files /dev/null and b/main/functionalities/plotting/age_pyramid_single_ref.png differ diff --git a/main/functionalities/plotting/event_sequences/index.html b/main/functionalities/plotting/event_sequences/index.html new file mode 100644 index 00000000..4833953c --- /dev/null +++ b/main/functionalities/plotting/event_sequences/index.html @@ -0,0 +1,1503 @@ + + + + + + + + + +Event sequence - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
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    Visualizing event sequences

    +

    When studying sequences of events (e.g care trajectories, drug sequences, ...), it might be useful to visualize individual sequences. To that end, we provide the plot_event_sequences function to plot individual sequences given an events dataframe.

    +

    Load a synthetic dataset

    +

    An events dataset has been created to illustrate the visualization function. +It can be load as follows :

    +
    from eds_scikit.datasets.synthetic.event_sequences import load_event_sequences
+
+df_events = load_event_sequences()
+
    +

    The df_events dataset contains occurrences of 12 events, derived from 7 events' families ("A", "B", "C", "D", "E", "F", "G).

    +

    Events can be both one-time and continuous.

    +

    An index_date is also provided and refers to the inclusion date of each patient in the cohort.

    +

    The first raws of the dataframe are as follows :

    +

    df_events.head()
+
    +| | person_id | event_family | event | event_start_datetime | event_end_datetime | index_date | +|---|-----------|--------------|-------|----------------------|--------------------|------------| +| 0 | 1 | A | a1 | 2020-01-01 | 2020-01-02 | 2020-01-01 | +| 1 | 1 | A | a2 | 2020-01-03 | 2020-01-04 | 2020-01-01 | +| 2 | 1 | B | b1 | 2020-01-03 | 2020-01-06 | 2020-01-01 | +| 3 | 1 | C | c1 | 2020-01-05 | NaT | 2020-01-01 | +| 4 | 1 | C | c2 | 2020-01-06 | 2020-01-08 | 2020-01-01 |

    +

    Visualize individual sequences

    +

    Basic usage

    +

    Individual sequences of events can be plotted using the plot_event_sequences function :

    +
    from eds_scikit.plot.event_sequences import plot_event_sequences
+
+chart = plot_event_sequences(df_events)
+chart
+
    +
    +

    Image title +

    +
    +
    +

    Advanced parameters

    +

    Further configuration can be provided, including : +- dim_mapping : dictionary to set colors and labels for each event type. +- family_col: column name of events' families. +- list_person_ids: List of specific person_id +- same_x_axis_scale: boolean to set all individual charts to the same scale

    +

    Here we provide an exemple of dim_mapping, and we plot sequences aggregated following the event_family classification.

    +
    dim_mapping = {
+    "a1": {"color": (255, 200, 150), "label": "eventA1"},
+    "a2": {"color": (255, 150, 150), "label": "eventA2"},
+    "a3": {"color": (255, 100, 150), "label": "eventA3"},
+    "b1": {"color": (100, 200, 150), "label": "eventB1"},
+    "c1": {"color": (50, 255, 255), "label": "eventC1"},
+    "c2": {"color": (50, 200, 255), "label": "eventC2"},
+    "c3": {"color": (50, 100, 255), "label": "eventC3"},
+    "d1": {"color": (180, 200, 100), "label": "eventD1"},
+    "d2": {"color": (180, 150, 100), "label": "eventD2"},
+    "e1": {"color": (130, 60, 10), "label": "eventE1"},
+    "f1": {"color": (255, 0, 0), "label": "eventF1"},
+    "g1": {"color": (100, 0, 200), "label": "eventG1"},
+}
+
    +
    plot_event_sequences(
+    df_events,
+    family_col="event_family",
+    dim_mapping=dim_mapping,
+    same_x_axis_scale=True,
+    title="Event sequences",
+)
+
    +
    +

    Image title +

    +
    +
    +

    Please check the documentation for further details on the function's parameters.

    +
    +
    +
    + + + Back to top + +
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    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/functionalities/plotting/event_sequences_agg.png b/main/functionalities/plotting/event_sequences_agg.png new file mode 100644 index 00000000..5a54f018 Binary files /dev/null and b/main/functionalities/plotting/event_sequences_agg.png differ diff --git a/main/functionalities/plotting/event_sequences_raw.png b/main/functionalities/plotting/event_sequences_raw.png new file mode 100644 index 00000000..b58ce887 Binary files /dev/null and b/main/functionalities/plotting/event_sequences_raw.png differ diff --git a/main/functionalities/plotting/flowchart/index.html b/main/functionalities/plotting/flowchart/index.html new file mode 100644 index 00000000..e822e063 --- /dev/null +++ b/main/functionalities/plotting/flowchart/index.html @@ -0,0 +1,3547 @@ + + + + + + + + + +Generating inclusion/exclusion flowchart - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + + + + +
    +
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    +

    You can download this notebook directly here

    +
    +
    +
    +
    +
    +
    +

    Generation of an inclusion/exclusion flowchart

    +

    Inclusion and exclusion flowcharts are one of the key figure to generate when doing medical research. We provide a class to help you in this task.
    +To summarize, you can sequentialy add criteria to the flowchart, by providing

    +
      +
    • A description of the criterion
      • +
    • Which patients check the criterion
      • +
    +

    To make the use of this class easier, each criterion can be build independently. The order will be determined by how you add the criteria to the flowchart. At this step, criteria will be combined to output a corrrect flowchart. Counting (n=...) is done automatically !

    +

    The input data

    +

    Data can be provided in two forms: DataFrame form or Dictionary form.

    +
    +
    +
    +

    In this case, data is provided as a unique DataFrame. One column (by default person_id) +stores the ids that constitutes the initial cohort. A criterion, it this case, will be defined as a boolean column, +where each row is either accepted or rejected. For instance:

    +
    data = pd.DataFrame(
+    dict(
+        person_id=list(range(10)),
+        over_18=5*[True] + 5*[False],
+        diabetes=[True, False, True, False, True, False, True, False, True, False],
+        infarction=[True, True, False, False, True, True, False, False, True, True],
+        final_split=[True] + 9*[False],
+    )
+)
+
    +
    +
    +

    In this case, data is provided as dictionary. Keys represent criteria names, and values +contains the ids constituting the passing cohort. Those ids can be in the form of any iterable +(list, set, Series, ...). The initial cohort should be provided under the initial key. +For instance:

    +
    data = dict(
+    initial = list(range(10)),
+    over_18 = [0,1,2,3,4],
+    diabetes=[0,2,4,6,8],
+    infarction=[0,1,4,5,8,9],
+    final_split=[0],
+)
+
    +
    +
    +
    +
    +
    +
    +
    +
    +
    +

    A step-by-step example

    +

    Let us suppose we have a small cohort of 10 patients. From this cohort, we want to select patients with three consecutive criteria:

    +
      +
    • Patients should be at least 18 years old.
      • +
    • Patients should have Type I or Type II diabetes.
      • +
    • Patients should've had at least one infarction event.
      • +
    +
    +
    +
    +
    +
    +
    +
    +

    On having multiple criteria

    +

    On advantage of this flowchart generation is that it will handle multiple criteria by itself, by computing intersection iteratively

    +
    +
    +
    +
    +
    +
    +
    import pandas as pd
+from eds_scikit.utils.flowchart import Flowchart
+
    +
    +
    +
    +
    +
    +

    So let us describe our initial cohort in the DataFrame form

    +
    +
    +
    +
    +
    +
    from eds_scikit.utils.flowchart import Flowchart
+
+data = pd.DataFrame(
+    dict(
+        person_id=list(range(10)),
+        over_18=5*[True] + 5*[False],
+        diabetes=[True, False, True, False, True, False, True, False, True, False],
+        infarction=[True, True, False, False, True, True, False, False, True, True],
+        final_split=[True] + 9*[False],
+    )
+)
+
    +
    +
    +
    +
    +
    +

    We added an extra final_split column that can, for instance, occur when splitting a cohort into a training and a testing subcohorts. This will result in a split in the flowchart (see below).

    +
    +
    +
    +
    +
    +
    +

    Here we instantiate the Flowchart with the initial cohort:

    +
    +
    +
    +
    +
    +
    F = Flowchart(
+    data=data,
+    initial_description="Initial population",
+)
+
    +
    +
    +
    +
    +
    +

    And we add each criterion with the add_criterion method:

    +
    +
    +
    +
    +
    +
    F.add_criterion(
+    description="Patients over 18 y.o.",
+    excluded_description="",
+    criterion_name="over_18",
+)
+
+F.add_criterion(
+    description="With Type I or II diabetes",
+    excluded_description="",
+    criterion_name="diabetes",
+)
+
+F.add_criterion(
+    description="With infarction",
+    excluded_description="",
+    criterion_name="infarction",
+)
+
    +
    +
    +
    +
    +
    +

    This add_criterion method expects 3 parameters:

    +
      +
    • description : The description to add in the corresponding flowchart's box
      • +
    • excluded_description : The description to add in the excluded box of the flowchart
      • +
    • +

      criterion_name :

      +
        +
      • DataFrame form: The column name of the data object that contains boolean values to discriminate between rows that checks the criterion and rows that doesn't
        • +
      • Dictionary form: The key of the dictionary containing the ids of the passing cohort
        • +
      +
      • +
    +

    If you need to do a final split in your flowchart, you can via the dedicated method:

    +
    +
    +
    +
    +
    +
    F.add_final_split(
+    left_description = "",
+    right_description = "",
+    criterion_name = "final_split",
+    left_title="Cohort 1",
+    right_title="Cohort 2",
+)
+
    +
    +
    +
    +
    +
    +

    At this point, we are ready to generate the flowchart.
    +Just run the following snippet:

    +
    +
    +
    +
    +
    +
    F.generate_flowchart(alternate=True)
+
    +
    +
    +
    +
    +
    + + + + + + + + +2022-11-15T13:25:23.976033 +image/svg+xml + + +Matplotlib v3.5.3, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Finally, you can save your flowchart (in ".png" or ".svg"):

    +
    +
    +
    +
    +
    +
    F.save("my_flowchart.png")
+
    +
    +
    +
    +
    +
    +

    For more details, you can check the code reference of the Flowchart object.

    +
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+
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    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/generate_development.py b/main/generate_development.py new file mode 100644 index 00000000..e7cc7e08 --- /dev/null +++ b/main/generate_development.py @@ -0,0 +1,16 @@ +"""Generate the code reference pages and navigation.""" + +from pathlib import Path + +import mkdocs_gen_files + +files = [ + "changelog.md", + "contributing.md", +] + +for f in files: + path = Path(f) + + with mkdocs_gen_files.open(path, "w") as fd: + fd.write(path.read_text()) diff --git a/main/generate_reference.py b/main/generate_reference.py new file mode 100644 index 00000000..b3863353 --- /dev/null +++ b/main/generate_reference.py @@ -0,0 +1,38 @@ +"""Generate the code reference pages and navigation.""" + +from pathlib import Path + +import mkdocs_gen_files + +nav = mkdocs_gen_files.Nav() + +for path in sorted(Path("eds_scikit").rglob("*.py")): + print(path) + if ".ipynb_checkpoints" in path.parts or "package-override" in path.parts: + continue + module_path = path.relative_to(".").with_suffix("") + doc_path = path.relative_to("eds_scikit").with_suffix(".md") + full_doc_path = Path("reference", doc_path) + + parts = list(module_path.parts) + + if parts[-1] == "__init__": + parts = parts[:-1] + doc_path = doc_path.with_name("index.md") + full_doc_path = full_doc_path.with_name("index.md") + elif parts[-1] == "__main__": + continue + + ident = ".".join(parts) + + nav[parts] = doc_path + + with mkdocs_gen_files.open(full_doc_path, "w") as fd: + print("---\nglightbox: false\n---", file=fd) + print(f"# `{ident}`\n", file=fd) + print("::: " + ident, file=fd) + + mkdocs_gen_files.set_edit_path(full_doc_path, path) + +with mkdocs_gen_files.open("reference/SUMMARY.md", "w") as nav_file: + nav_file.writelines(nav.build_literate_nav()) diff --git a/main/index.html b/main/index.html new file mode 100644 index 00000000..f917390f --- /dev/null +++ b/main/index.html @@ -0,0 +1,1845 @@ + + + + + + + + + +eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + + +

    +eds-scikit +

    +

    Getting started

    +

    eds-scikit is a tool to assist data scientists working on the AP-HP’s Clinical Data Warehouse. It is specifically targeted for OMOP-standardized data to:

    +
      +
    • +

      Ease access and analysis of data

      +
      • +
    • +

      Allow a better transfer of knowledge between projects

      +
      • +
    • +

      Improve research reproduciblity

      +
      • +
    +

    As an example, the following figure was obtained using various functionalities from eds-scikit.

    +
    +

    Image title +

    +
    +
    +
    +

    How was it done ?

    +

    Click on the figure above to jump to the tutorial using various functionalities from eds-scikit, or continue reading the introduction!

    +
    +
    +

    Using eds-scikit with I2B2

    +

    Although designed for OMOP databases, eds-scikit provides a connector for I2B2 databases is available. We don't guarantee its exhaustivity, but it should allow you to use functionnalities of the library seamlessly.

    +
    +

    Quick start

    +

    Installation

    +
    +

    Requirements

    +

    eds-scikit stands on the shoulders of Spark 2.4 which runs on Java 8 and Python ~3.7.1. If you work on AP-HP's CDW, those requirements are already fulfilled, so please disregard the following steps. Else, it is essential to:

    +
      +
    • Install a version of Python ≥ 3.7.1 and < 3.8.
      • +
    • +

      Install OpenJDK 8, an open-source reference implementation of Java 8 wit the following command lines:

      +
      +
      +
      +

      +
      $ sudo apt-get update
+$ sudo apt-get install openjdk-8-jdk
+---> 100%
+
      +

      +

      For more details, check this installation guide

      +
      +
      +

      +
      $ brew tap AdoptOpenJDK/openjdk
+$ brew install --cask adoptopenjdk8
+---> 100%
+
      +

      +

      For more details, check this installation guide

      +
      +
      +

      Follow this installation guide

      +
      +
      +
      +
      • +
    +
    +

    You can install eds-scikit via pip:

    +
    +
    $ pip install eds-scikit
+---> 100%
+color:green Successfully installed eds_scikit !
+
    +
    +
    +

    Possible issue with pip

    +

    If you get an an error during installation, please try downgrading pip via pip install -U "pip<23" before install eds-scikit

    +
    +
    +

    Improving performances on distributed data

    +

    It is highly recommanded (but not mandatory) to use the helper function eds_scikit.improve_performances to optimaly configure PySpark and Koalas. You can simply call + 

    import eds_scikit
+
+spark, sc, sql = eds_scikit.improve_performances()
+
    + The function will return

    +
      +
    • A SparkSession
      • +
    • A SparkContext
      • +
    • An sql function to execute SQL queries
      • +
    +
    +

    A first example: Merging visits together

    +

    Let's tackle a common problem when dealing with clinical data: Merging close/consecutive visits into stays. +As detailled in the dedicated section, eds-scikit is expecting to work with Pandas or Koalas DataFrames. We provide various connectors to facilitate data fetching, namely a Hive connector and a Postgres connector

    +
    +
    +
    +
    from eds_scikit.io import HiveData
+
+data = HiveData(DB_NAME)
+visit_occurrence = data.visit_occurrence  # (1)
+
    +
      +
    1. With this connector, visit_occurrence will be a Pandas DataFrame
      2. +
    +
    +

    I2B2

    +

    If DB_NAME points to an I2B2 database, use data = HiveData(DB_NAME, database_type="I2B2")

    +
    +
    +
    +
    from eds_scikit.io import PostgresData
+
+DB_NAME = "my_db"
+SCHEMA = "my_schema"
+USER = "my_username"
+data = PostgresData(DB_NAME, schema=SCHEMA, user=USER)  # (1)
+visit_occurrence = data.visit_occurrence  # (2)
+
    +
      +
    1. This connector expects a .pgpass file storing the connection parameters
      2. +
    2. With this connector, visit_occurrence will be a Pandas DataFrame
      3. +
    +
    +
    +

    You can use eds-scikit with data from any source, as long as:
    - It follows the OMOP format
+- It is a Pandas or Koalas DataFrame
+
    +
    import pandas as pd
+
+visit_occurrence = pd.read_csv("./data/visit_occurrence.csv")
+

    +
    +
    +
    +
    +visit_occurrence +

    For the sake of the example, only columns of interest are shown here.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    visit_occurrence_idperson_idvisit_start_datetimevisit_end_datetimevisit_source_valuerow_status_source_valuecare_site_id
    0A9992021-01-01 00:00:002021-01-05 00:00:00hospitaliséscourant1
    1B9992021-01-04 00:00:002021-01-08 00:00:00hospitaliséscourant1
    2C9992021-01-12 00:00:002021-01-18 00:00:00hospitaliséscourant1
    3D9992021-01-13 00:00:002021-01-14 00:00:00urgencecourant1
    4E9992021-01-19 00:00:002021-01-21 00:00:00hospitaliséscourant2
    5F9992021-01-25 00:00:002021-01-27 00:00:00hospitaliséssupprimé1
    ........................
     +
    +
    # Importing the desired functions:
+
+from eds_scikit.period.stays import merge_visits, get_stays_duration
+
+# Calling the first function: computing stays
+
+visit_occurrence = merge_visits(visit_occurrence)
+
    +

    As you can see, the function added a STAY_ID concept, grouping visits together

    +
    +visit_occurrence[["visit_occurrence_id","STAY_ID"]] + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    visit_occurrence_idSTAY_ID
    0AA
    1BA
    2CC
    3DC
    4EE
    5FF
    .........
     +
    +
    # Calling the second function: computing stays duration
+stays = get_stays_duration(visit_occurrence, missing_end_date_handling="coerce")
+
    +

    Here, each stay duration was calculated, dealing with potential overlaps and inclusions.:

    +
    +stays + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    STAY_IDt_startt_endSTAY_DURATION
    A2021-01-01 00:00:002021-01-08 00:00:00168
    C2021-01-12 00:00:002021-01-18 00:00:00144
    E2021-01-19 00:00:002021-01-21 00:00:0048
    F2021-01-25 00:00:002021-01-27 00:00:0048
    ............
     +
    +
    +

    About the code above

    +

    As you noticed, the pipeline above is fairly straightforward, needing only the visit_occurrence DataFrame as input. + However, it is also highly customizable, and you should always look into all the various availables options for the functions you're using. For instance, the following parameters could have been used:

    +
    visit_occurrence = merge_visits(
+    visit_occurrence,
+    remove_deleted_visits=True,
+    long_stay_threshold=timedelta(days=365),
+    long_stay_filtering="all",
+    max_timedelta=timedelta(hours=24),
+    merge_different_hospitals=False,
+    merge_different_source_values=["hospitalisés", "urgence"],
+)
+
+stays = get_stays_duration(
+    visit_occurrence, algo="sum_of_visits_duration", missing_end_date_handling="coerce"
+)
+
    +
    +

    A word about AP-HP

    +

    Specifics of AP-HP CDW

    +

    eds-scikit was developped by AP-HP's Data Science team with the help of Inria's Soda team. As such, it is especially well fitted for AP-HP's Data Warehouse. In this doc, we use the following card to mention information that might be useful when using eds-scikit with AP-HP's data:

    +
    +

    Some information

    +

    Here, we might for instance suggest some parameters for a function that should be used given AP-HP's data.

    +
    +

    EDS-NLP

    +

    Also, a rule-based NLP library (EDS-NLP) designed to work on clinical texts was developped in parallel with eds-scikit. We decided not to include EDS-NLP as a dependency. Still, some functions might require an input à la note_nlp: For instance, the current function designed to extract consultation dates from a visit_occurrence car work either on structured data only or with dates extracted in text and compiled in a DataFrame.

    +

    You are free to use the method of your choice to get this DataFrame, as long as it contains the necessary columns as mentionned in the documentation. Note that we mention with the following card the availability of an EDS-NLP dedicated pipeline:

    +
    +

    A dedicated pipe

    +

    For the example above, a consultation date pipeline exists. + Moreover, methods are available to run an EDS-NLP pipeline on a Pandas, Spark or even Koalas DataFrame !

    +
    +

    Contributing to eds-scikit

    +

    We welcome contributions! Fork the project and create a pull request. Take a look at the dedicated page for details.

    +

    Citation

    +

    If you use eds-scikit, please cite us as below.

    +
    @misc{eds-scikit,
+    author = {Petit-Jean, Thomas and Remaki, Adam and Maladière, Vincent and Varoquaux, Gaël and Bey, Romain},
+    doi = {10.5281/zenodo.7401549},
+    title = {eds-scikit: data analysis on OMOP databases},
+    url = {https://github.com/aphp/eds-scikit}
+}
+
    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/js/mkdocs-charts-plugin.js b/main/js/mkdocs-charts-plugin.js new file mode 100644 index 00000000..056a4d42 --- /dev/null +++ b/main/js/mkdocs-charts-plugin.js @@ -0,0 +1,246 @@ +// Adapted from https://github.com/koaning/justcharts/blob/main/justcharts.js +async function fetchSchema(url){ + var resp = await fetch(url); + var schema = await resp.json(); + return schema +} + +function checkNested(obj /*, level1, level2, ... levelN*/) { + var args = Array.prototype.slice.call(arguments, 1); + + for (var i = 0; i < args.length; i++) { + if (!obj || !obj.hasOwnProperty(args[i])) { + return false; + } + obj = obj[args[i]]; + } + return true; + } + + + +function classnameInParents(el, classname) { + // check if class name in any parents + while (el.parentNode) { + el = el.parentNode; + if (el.classList === undefined) { + continue; + } + if (el.classList.contains(classname) ){ + return true; + } + } + return false; +} + +function findElementInParents(el, classname) { + while (el.parentNode) { + el = el.parentNode; + if (el.classList === undefined) { + continue; + } + if (el.classList.contains(classname) ){ + return el; + } + } + return null; +} + +function findProperChartWidth(el) { + + // mkdocs-material theme uses 'md-content' + var parent = findElementInParents(el, "md-content") + + // mkdocs theme uses 'col-md-9' + if (parent === undefined || parent == null) { + var parent = findElementInParents(el, "col-md-9") + } + if (parent === undefined || parent == null) { + // we can't find a suitable content parent + // 800 width is a good default + return '800' + } else { + // Use full width of parent + // Should bparent.offsetWidth - parseFloat(computedStyle.paddingLeft) - parseFloat(computedStyle.paddingRight) e equilavent to width: 100% + computedStyle = getComputedStyle(parent) + return parent.offsetWidth - parseFloat(computedStyle.paddingLeft) - parseFloat(computedStyle.paddingRight) + } +} + +function updateURL(url) { + // detect if absolute UR: + // credits https://stackoverflow.com/a/19709846 + var r = new RegExp('^(?:[a-z]+:)?//', 'i'); + if (r.test(url)) { + return url; + } + + // If 'use_data_path' is set to true + // schema and data urls are relative to + // 'data_path', not the to current page + // We need to update the specified URL + // to point to the actual location relative to current page + // Example: + // Actual location data file: docs/assets/data.csv + // Page: docs/folder/page.md + // data url in page's schema: assets/data.csv + // data_path in plugin settings: "" + // use_data_path in plugin settings: True + // path_to_homepage: ".." (this was detected in plugin on_post_page() event) + // output url: "../assets/data.csv" + if (mkdocs_chart_plugin['use_data_path'] == "True") { + new_url = window.location.href + new_url = new_url.endsWith('/') ? new_url.slice(0, -1) : new_url; + + if (mkdocs_chart_plugin['path_to_homepage'] != "") { + new_url += "/" + mkdocs_chart_plugin['path_to_homepage'] + } + + new_url = new_url.endsWith('/') ? new_url.slice(0, -1) : new_url; + new_url += "/" + url + new_url = new_url.endsWith('/') ? new_url.slice(0, -1) : new_url; + + if (mkdocs_chart_plugin['data_path'] != "") { + new_url += "/" + mkdocs_chart_plugin['data_path'] + } + + return new_url + } + return url; +} + +var vegalite_charts = []; + +function embedChart(block, schema) { + + // Make sure the schema is specified + let baseSchema = { + "$schema": "https://vega.github.io/schema/vega-lite/v5.json", + } + schema = Object.assign({}, baseSchema, schema); + + // If width is not set at all, + // default is set to 'container' + // Note we inserted .. + // So 'container' will use 100% width + if (!('width' in schema)) { + schema.width = mkdocs_chart_plugin['vega_width'] + } + + // Set default height if not specified + // if (!('height' in schema)) { + // schema.height = mkdocs_chart_plugin['default_height'] + // } + + // charts widths are screwed in content tabs (thinks its zero width) + // https://squidfunk.github.io/mkdocs-material/reference/content-tabs/?h= + // we need to set an explicit, absolute width in those cases + // detect if chart is in tabbed-content: + if (classnameInParents(block, "tabbed-content")) { + var chart_width = schema.width || 'notset'; + if (isNaN(chart_width)) { + schema.width = findProperChartWidth(block); + } + } + + // Update URL if 'use_data_path' is configured + if (schema?.data?.url !== undefined) { + schema.data.url = updateURL(schema.data.url) + } + if (schema?.spec?.data?.url !== undefined) { + schema.spec.data.url = updateURL(schema.spec.data.url) + } + // see docs/assets/data/geo_choropleth.json for example + if (schema.transform) { + for (const t of schema.transform) { + if (t?.from?.data?.url !== undefined) { + t.from.data.url = updateURL(t.from.data.url) + } + } + } + + + + + // Save the block and schema + // This way we can re-render the block + // in a different theme + vegalite_charts.push({'block' : block, 'schema': schema}); + + // mkdocs-material has a dark mode + // detect which one is being used + var theme = (document.querySelector('body').getAttribute('data-md-color-scheme') == 'slate') ? mkdocs_chart_plugin['vega_theme_dark'] : mkdocs_chart_plugin['vega_theme']; + + // Render the chart + vegaEmbed(block, schema, { + actions: false, + "theme": theme, + "renderer": mkdocs_chart_plugin['vega_renderer'] + }); +} + +// Adapted from +// https://facelessuser.github.io/pymdown-extensions/extensions/superfences/#uml-diagram-example +// https://github.com/koaning/justcharts/blob/main/justcharts.js +const chartplugin = className => { + + // Find all of our vegalite sources and render them. + const blocks = document.querySelectorAll('vegachart'); + + for (let i = 0; i < blocks.length; i++) { + + const block = blocks[i] + const block_json = JSON.parse(block.textContent); + + // get the vegalite JSON + if ('schema-url' in block_json) { + + var url = updateURL(block_json['schema-url']) + fetchSchema(url).then( + schema => embedChart(block, schema) + ); + } else { + embedChart(block, block_json); + } + + } + } + + +// mkdocs-material has a dark mode including a toggle +// We should watch when dark mode changes and update charts accordingly + +var bodyelement = document.querySelector('body'); +var observer = new MutationObserver(function(mutations) { + mutations.forEach(function(mutation) { + if (mutation.type === "attributes") { + + if (mutation.attributeName == "data-md-color-scheme") { + + var theme = (bodyelement.getAttribute('data-md-color-scheme') == 'slate') ? mkdocs_chart_plugin['vega_theme_dark'] : mkdocs_chart_plugin['vega_theme']; + for (let i = 0; i < vegalite_charts.length; i++) { + vegaEmbed(vegalite_charts[i].block, vegalite_charts[i].schema, { + actions: false, + "theme": theme, + "renderer": mkdocs_chart_plugin['vega_renderer'] + }); + } + } + + } + }); + }); +observer.observe(bodyelement, { +attributes: true //configure it to listen to attribute changes +}); + + +// Load when DOM ready +if (typeof document$ !== "undefined") { + // compatibility with mkdocs-material's instant loading feature + document$.subscribe(function() { + chartplugin("vegalite") + }) +} else { + document.addEventListener("DOMContentLoaded", () => {chartplugin("vegalite")}) +} diff --git a/main/macros.py b/main/macros.py new file mode 100644 index 00000000..fda0c853 --- /dev/null +++ b/main/macros.py @@ -0,0 +1,45 @@ +import os + +import pandas as pd + + +def define_env(env): + + env.variables["import"] = _concat_lines( + "```python", + "import eds_scikit" "```", + ) + env.variables["load_data"] = _concat_lines( + "```python", + "from eds_scikit.io import HiveData", + "data = HiveData(DBNAME)", + "```", + ) + + @env.macro + def link_repo_file(relative_path: str): + BASE_PATH = "https://github.com/aphp/eds-scikit/blob/main" + return os.path.join(BASE_PATH, relative_path) + + @env.macro + def preview_csv(csv_path: str, limit=None): + HARD_LIMIT = 100 + df = pd.read_csv(csv_path)[:HARD_LIMIT] + if limit is not None: + df = df[:limit] + return df.to_markdown(index=False) + + @env.macro + def values_from_csv(csv_path: str, col: str, indent: str = ""): + df = pd.read_csv(csv_path) + values = df[col].drop_duplicates().sort_values().to_list() + + return "".join(f"\n{indent}- {value}" for value in values) + + @env.macro + def test_fct(x): + return x + + +def _concat_lines(*lines): + return "\n".join(lines) diff --git a/main/objects.inv b/main/objects.inv new file mode 100644 index 00000000..2b231895 Binary files /dev/null and b/main/objects.inv differ diff --git a/main/project_description/index.html b/main/project_description/index.html new file mode 100644 index 00000000..19ef577b --- /dev/null +++ b/main/project_description/index.html @@ -0,0 +1,1631 @@ + + + + + + + + + +Project description - eds-scikit + + + + + + + + + + + + + + + + + + + + +
    + +
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    Project description

    +

    Goal

    +

    eds-scikit is a tool to assist datascientists working on the AP-HP's Clinical Data Warehouse. It is specifically targeted for OMOP-standardized data to:

    +
      +
    • Ease access and analysis of data
      • +
    • Allow a better transfer of knowledge between projects
      • +
    • Improve research reproduciblity
      • +
    +

    Main working principles

    +

    Dealing with various data sizes

    +

    Generally, data analysis can be done in two ways:

    +
      +
    • Locally, by loading everything in RAM and working with e.g. Pandas
      • +
    • In a distributed fashion, when dealing with a lot of data, by using e.g. Spark
      • +
    +

    While working with Pandas is often more convenient, its use can be problematic once working with large cohorts. Thus, making eds-scikit a Pandas-only library wasn't conceivable. In order to allow analysis to be conducted at scale, eds-scikit integrates with Koalas.

    +
    +

    Koalas

    +

    Koalas is a library implementing Pandas API on top of Spark. Basically, it allows for functions and methods developped for Pandas DataFrames to work on Spark DataFrames with close to no adjustments.

    +
    +

    Let us see a dummy example where one wants to count the number of visit occurrences per month.

    +
    +
    +
    +

    Suppose we have a Spark visit_occurrence DataFrame: + 

    type(visit_occurrence_spark)
+# Out: pyspark.sql.dataframe.DataFrame
+

    +
    import pyspark.sql.functions as F
+
+
+def get_stats_spark(visit_occurrence):
+    """
+    Computes the number of visits per month
+
+    Parameters
+    ----------
+    visit_occurrence : DataFrame
+
+    Returns
+    -------
+    stats : pd.DataFrame
+    """
+
+    # Adding a month and year column
+    visit_occurrence = visit_occurrence.withColumn(
+        "year", F.year("visit_start_datetime")
+    ).withColumn("month", F.month("visit_start_datetime"))
+
+    # Grouping and filtering
+    stats = (
+        visit_occurrence.groupby(["year", "month"])
+        .count()
+        .filter((F.col("year") >= 2017))
+        .toPandas()
+    )
+
+    return stats
+
+
+stats_from_spark = get_stats_spark(visit_occurrence_spark)
+
    +
    +
    +

    If the selected database contains few enough visits, we may have a visit_occurrence DataFrame small enough to fit in memory as a Pandas DataFrame.

    +
    type(visit_occurrence_pandas)
+# Out: pandas.core.frame.DataFrame
+
    +

    Then run the same analysis:

    +
    def get_stats_pandas(visit_occurrence):
+    """
+    Computes the number of visits per month
+
+    Parameters
+    ----------
+    visit_occurrence : DataFrame
+
+    Returns
+    -------
+    stats : pd.DataFrame
+    """
+
+    # Adding a duration column
+    visit_occurrence["year"] = visit_occurrence["visit_start_datetime"].dt.year
+    visit_occurrence["month"] = visit_occurrence["visit_start_datetime"].dt.month
+
+    # Grouping and filtering
+    stats = (
+        visit_occurrence.groupby(["year", "month"])
+        .visit_occurrence_id.count()
+        .reset_index()
+    )
+
+    stats = stats[stats["year"] >= 2017]
+    stats.columns = ["year", "month", "count"]
+
+    return stats
+
+
+stats_from_pandas = get_stats_pandas(visit_occurrence_pandas)
+
    +
    +
    +
    +

    The two examples above clearly show the syntax differences between using Pandas and using Spark.

    +

    In order for a library to work both with Pandas and Spark, one would need to developp each function twice to accomodate for those two frameworks. Another problem might occur if you are dealing with a huge cohort, forcing you to do your final analysis in a distributed manner via Spark. In that scenario, you coudn't test your code on a small Pandas DataFrame subset.

    +

    The goal of Koalas is precisely to avoid this issue. It aims at allowing code to be written for Pandas DataFrames, and also run with (almost) no adjustements with Spark DataFrame:

    +
    from databricks import koalas as ks
+
+# Converting the Spark DataFrame into a Koalas DataFrame
+visit_occurrence_koalas = visit_occurrence_spark.to_koalas()
+
    +
    +

    Info

    +

    The code above allows the DataFrame to stay distributed —as opposed to applying the .toPandas() method.

    +
    +

    We can now use the function we designed for Pandas with a Koalas DataFrame:

    +
    stats_from_koalas = get_stats_pandas(visit_occurrence_koalas)
+
    +

    Since we aggregated the data, its size is manageable so we can convert it back to Pandas for e.g. plotting

    +
    stats_from_koalas = stats_from_koalas.to_pandas()
+
    +

    Concept

    +

    Most functions developped in the library implements a concept. For sake of clarity let us illustrate this notion with an example:

    +

    The function tag_icu_care_site() can be used to tag a care site as being an ICU or not. We say that it implements the concept "IS_ICU" because it adds a column named "IS_ICU" to the input DataFrame, as it can be seen from the docstring:

    +

    """
+Returns
+-------
+care_site: DataFrame
+    Dataframe with 1 added column corresponding to the following concept:
+    - 'IS_ICU'
+"""
+
    +This follows a wide data format. However, when multiple concepts are added at once, it might be done in a long format, such as with the diabetes_from_icd10() function, which stores the diabetes type in a concept column, and the corresponding ICD-10 code in a value column:

    +
    """
+Returns
+-------
+DataFrame
+    Event DataFrame in **long** format (with a `concept` and a `value` column).
+    The `concept` column contains one of the following:
+    - DIABETES_TYPE_I
+    - DIABETES_TYPE_II
+    - DIABETES_MALNUTRITION
+    - DIABETES_IN_PREGNANCY
+    - OTHER_DIABETES_MELLITUS
+    - DIABETES_INSIPIDUS
+    The `value` column contains the corresponding ICD-10 code that was extracted
+"""
+
    +
    +

    Question

    +

    Check this link for a (very) quick explanation if you aren't familiar with Long vs Wide data format.

    +
    +

    Algo

    +

    Most functions also have an argument called algo, which allows you to choose how a specific concept will be implemented in a function. Let's check the docstring of the same function tag_icu_care_site():

    +

    """
+Parameters
+----------
+care_site: DataFrame
+algo: str
+    Possible values are:
+    - `"from_authorisation_type"`
+    - `"from_regex_on_care_site_description"`
+"""
+
    +The function's signature shows that "from_authorisation_type" is the default algo, used if the algo argument isn't filled by the user.

    +

    In the documentation, the different "algo" values will be displayed as tabs, along with a short description and optional algo-dependant parameters:

    +
    +

    Availables algorithms (values for "algo")

    +
    +
    +
    +

    This "algo" is used by default. +It does yadi yada. +Specific parameters:

    +
      +
    • This first parameter
      • +
    • This second parameter
      • +
    • And also this third one
      • +
    +
    +
    +

    This second "algo" works differently. +It has no additional parameters

    +
    +
    +
    +
    +

    Please check the available algos when using a function from eds-scikit, to understand what each of them is doing and which one might fits you best.

    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
    + + + + + + + + + + \ No newline at end of file diff --git a/main/recipes/small-cohorts/index.html b/main/recipes/small-cohorts/index.html new file mode 100644 index 00000000..13f587ed --- /dev/null +++ b/main/recipes/small-cohorts/index.html @@ -0,0 +1,1783 @@ + + + + + + + + + +Saving small cohorts locally - eds-scikit + + + + + + + + + + + + + + + + + + + + +
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    Saving small cohorts locally

    + + +
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    +

    You can download this notebook directly here

    +
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    +
    +
    +
    +

    Introduction

    +
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    +
    +

    The goal of this small notebook is to show you how to:

    +
      +
    • Work on a big cohort by staying distributed
      • +
    • Do some phenotyping to select a small subcohort
      • +
    • Save this subcohort locally to work on it later
      • +
    +

    As a dummy example, we will select patients that underwent a cardiac transplantation. The selection will be performed by using both ICD-10 and by CCAM terminologies.

    +
    +
    +
    +
    +
    +
    +

    Data Loading

    +
    +
    +
    +
    +
    +
    import eds_scikit
+
+spark, sc, sql = eds_scikit.improve_performances()
+
    +
    +
    +
    +
    +
    DBNAME="YOUR_DATABASE_NAME"
+
    +
    +
    +
    +
    +
    from eds_scikit.io.hive import HiveData
+
+# Data from Hive
+data = HiveData(DBNAME)
+
    +
    +
    +
    +
    +
    +

    Phenotyping

    +
    +
    +
    +
    +
    +
    from eds_scikit.event.ccam import procedures_from_ccam
+from eds_scikit.event.icd10 import conditions_from_icd10
+
    +
    +
    +
    +
    +
    CCAM = dict(
+    HEART_TRANSPLANT = dict(
+        prefix = "DZEA00", # 
+    )
+)
+
+ICD10 = dict(
+    HEART_TRANSPLANT = dict(
+        exact = "Z941", # 
+    )
+)
+
+procedure_occurrence = procedures_from_ccam(
+    procedure_occurrence=data.procedure_occurrence,
+    visit_occurrence=data.visit_occurrence,
+    codes=CCAM,
+    date_from_visit=True,
+)
+
+condition_occurrence = conditions_from_icd10(
+    condition_occurrence=data.condition_occurrence,
+    visit_occurrence=data.visit_occurrence,
+    codes=ICD10,
+    date_from_visit=True,
+    additional_filtering=dict(
+        condition_status_source_value={"DP", "DAS"}, # 
+    )
+)
+
    +
    +
    +
    +
    +
    procedure_occurrence.groupby(["concept","value"]).size()
+
    +
    +
    +
    +
    +
    +
    +concept           value  
+HEART_TRANSPLANT  DZEA002    39
+dtype: int64
+
    +
    +
    +
    +
    +
    +
    +
    +
    condition_occurrence.groupby(["concept","value"]).size()
+
    +
    +
    +
    +
    +
    +
    +concept           value
+HEART_TRANSPLANT  Z941     602
+dtype: int64
+
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Saving to disk

    +
    +
    +
    +
    +
    +
    cohort = set(
+    procedure_occurrence.person_id.to_list() + condition_occurrence.person_id.to_list()
+)
+
    +
    +
    +
    +
    +
    +

    We can check that our cohort is indeed small and can be stored locally without any concerns:

    +
    +
    +
    +
    +
    +
    len(cohort)
+
    +
    +
    +
    +
    +
    +
    +53
+
    +
    +
    +
    +
    +
    +
    +
    +
    +

    And we can also compute a very crude prevalence of heart transplant in our database:

    +
    +
    +
    +
    +
    +
    f"{100 * len(cohort)/len(set(data.procedure_occurrence.person_id.to_list() + data.condition_occurrence.person_id.to_list())):.5f} %"
+
    +
    +
    +
    +
    +
    +
    +'0.06849 %'
+
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Finally let us save the tables we need locally.
    +Under the hood, eds-scikit will only keep data corresponding to the provided cohort.

    +
    +
    +
    +
    +
    +
    import os
+
+folder = os.path.abspath("./heart_transplant_cohort")
+
+tables_to_save = [
+    "person",
+    "visit_detail",
+    "visit_occurrence",
+    "procedure_occurrence",
+    "condition_occurrence",
+]
+
+data.persist_tables_to_folder(
+    folder,
+    tables=tables_to_save,
+    person_ids=cohort,
+)
+
    +
    +
    +
    +
    +
    +
    +Number of unique patients: 53
+writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/person.parquet
+writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/visit_detail.parquet
+writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/visit_occurrence.parquet
+writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/procedure_occurrence.parquet
+writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/condition_occurrence.parquet
+
+
    +
    +
    +
    +
    +
    +
    +
    +
    +

    Using the saved cohort

    +

    Now that our cohort is saved locally, it can be accessed directly by using the PandasData class.
    +Its akin to the HiveData class, except that the loaded tables will be stored directly as Pandas DataFrames, allowing for faster and easier analysis

    +
    +
    +
    +
    +
    +
    from eds_scikit.io.files import PandasData
+
+data = PandasData(folder)
+
    +
    +
    +
    +
    +
    +

    As a sanity check, let us display the number of patient in our saved cohort (we are expecting 30)

    +
    +
    +
    +
    +
    +
    cohort = data.person.person_id.to_list()
+len(cohort)
+
    +
    +
    +
    +
    +
    +
    +53
+
    +
    +
    +
    +
    +
    +
    +
    +
    +

    And the crude prevalence that should now be 100% !

    +
    +
    +
    +
    +
    +
    f"{100 * len(cohort)/len(set(data.procedure_occurrence.person_id.to_list() + data.condition_occurrence.person_id.to_list())):.5f} %"
+
    +
    +
    +
    +
    +
    +
    +'100.00000 %'
+
    +
    +
    +
    +
    +
    +
    +
    +
    + + + Back to top + +
    + +
    +
    +
    +
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    SUMMARY

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    + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/cleaning/cohort/index.html b/main/reference/biology/cleaning/cohort/index.html new file mode 100644 index 00000000..abcaa87a --- /dev/null +++ b/main/reference/biology/cleaning/cohort/index.html @@ -0,0 +1,3974 @@ + + + + + + + + + + + + + + + + cohort - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.biology.cleaning.cohort

    + + +
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    + + + +

    + select_cohort + + +

    +
    select_cohort(measurement: DataFrame, studied_pop: Union[DataFrame, List[int]]) -> DataFrame
+
    + +
    + +

    Select the patient_ids

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    measurement +

    Target DataFrame

    +

    + + TYPE: + DataFrame + +

    +
    studied_pop +

    List of patient_ids to select

    +

    + + TYPE: + Union[DataFrame, List[int]] + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    Filtered DataFrame with selected patients

    +
    + +
    + Source code in eds_scikit/biology/cleaning/cohort.py + 
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    def select_cohort(
+    measurement: DataFrame,
+    studied_pop: Union[DataFrame, List[int]],
+) -> DataFrame:
+    """Select the patient_ids
+
+    Parameters
+    ----------
+    measurement : DataFrame
+        Target DataFrame
+    studied_pop : Union[DataFrame, List[int]]
+        List of patient_ids to select
+
+    Returns
+    -------
+    DataFrame
+        Filtered DataFrame with selected patients
+    """
+    logger.info("Selecting cohort...")
+
+    if isinstance(studied_pop, DataFrame.__args__):
+        filtered_measures = measurement.merge(
+            studied_pop,
+            on="person_id",
+        )
+    else:
+        filtered_measures = measurement[measurement.person_id.isin(studied_pop)]
+
+    return filtered_measures
+
    +
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    + +
    + + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/cleaning/index.html b/main/reference/biology/cleaning/index.html new file mode 100644 index 00000000..2749e218 --- /dev/null +++ b/main/reference/biology/cleaning/index.html @@ -0,0 +1,3781 @@ + + + + + + + + + + + + + + + + `eds_scikit.biology.cleaning` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.biology.cleaning

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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/cleaning/main/index.html b/main/reference/biology/cleaning/main/index.html new file mode 100644 index 00000000..fd3a3281 --- /dev/null +++ b/main/reference/biology/cleaning/main/index.html @@ -0,0 +1,4100 @@ + + + + + + + + + + + + + + + + main - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.biology.cleaning.main

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + bioclean + + +

    +
    bioclean(data: Data, concepts_sets: List[ConceptsSet] = None, start_date: datetime = None, end_date: datetime = None, convert_units: bool = False, studied_cohort: Union[DataFrame, List[int]] = None) -> Data
+
    + +
    + +

    It follows the pipeline explained [here][cleaning]:

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    Instantiated HiveData, PostgresData or PandasData

    +

    + + TYPE: + Data + +

    +
    concepts_sets +

    List of concepts-sets to select

    +

    + + TYPE: + List[ConceptsSet], optional + + + DEFAULT: + None + +

    +
    start_date +

    EXAMPLE: "2019-05-01"

    +

    + + TYPE: + datetime, optional + + + DEFAULT: + None + +

    +
    end_date +

    EXAMPLE: "2022-05-01"

    +

    + + TYPE: + datetime, optional + + + DEFAULT: + None + +

    +
    convert_units +

    If True, convert units based on ConceptsSets Units object. Eager execution., by default False

    +

    + + TYPE: + bool, optional + + + DEFAULT: + False + +

    +
    studied_cohort +

    List of patient_ids to select

    +

    + + TYPE: + Union[DataFrame, np.iterable, set], optional + + + DEFAULT: + None + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + Data + + +

    Same as the input with the transformed bioclean table

    +
    + +
    + Source code in eds_scikit/biology/cleaning/main.py + 
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    def bioclean(
+    data: Data,
+    concepts_sets: List[ConceptsSet] = None,
+    start_date: datetime = None,
+    end_date: datetime = None,
+    convert_units: bool = False,
+    studied_cohort: Union[DataFrame, List[int]] = None,
+) -> Data:
+    """It follows the pipeline explained [here][cleaning]:
+
+    Parameters
+    ----------
+    data : Data
+        Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData]
+    concepts_sets : List[ConceptsSet], optional
+        List of concepts-sets to select
+    start_date : datetime, optional
+        **EXAMPLE**: `"2019-05-01"`
+    end_date : datetime, optional
+        **EXAMPLE**: `"2022-05-01"`
+    convert_units : bool, optional
+        If True, convert units based on ConceptsSets Units object. Eager execution., by default False
+    studied_cohort : Union[DataFrame, np.iterable, set], optional
+        List of patient_ids to select
+
+    Returns
+    -------
+    Data
+        Same as the input with the transformed `bioclean` table
+    """
+
+    if concepts_sets is None:
+        logger.info("No concepts sets provided. Loading default concepts sets.")
+        concepts_sets = fetch_all_concepts_set()
+
+    measurements = prepare_measurement_table(
+        data, start_date, end_date, concepts_sets, False, convert_units
+    )
+    # Filter Measurement.
+    if studied_cohort:
+        measurements = select_cohort(measurements, studied_cohort)
+    # Transform values
+    data.bioclean = measurements
+
+    measurements = measurements.merge(
+        data.visit_occurrence[["care_site_id", "visit_occurrence_id"]],
+        on="visit_occurrence_id",
+    )
+    measurements = measurements.merge(
+        data.care_site[["care_site_id", "care_site_short_name"]], on="care_site_id"
+    )
+    # Plot values
+    value_column = "value_as_number_normalized" if convert_units else "value_as_number"
+    unit_column = (
+        "unit_source_value_normalized" if convert_units else "unit_source_value"
+    )
+
+    plot_biology_summary(measurements, value_column, unit_column)
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
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    +
    + + + + Back to top + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/index.html b/main/reference/biology/index.html new file mode 100644 index 00000000..f87a02fc --- /dev/null +++ b/main/reference/biology/index.html @@ -0,0 +1,3779 @@ + + + + + + + + + + + + + + + + `eds_scikit.biology` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.biology

    + + +
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    +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/utils/check_data/index.html b/main/reference/biology/utils/check_data/index.html new file mode 100644 index 00000000..767dcbf6 --- /dev/null +++ b/main/reference/biology/utils/check_data/index.html @@ -0,0 +1,4133 @@ + + + + + + + + + + + + + + + + check_data - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.biology.utils.check_data

    + + +
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    + + + +

    + check_data_and_select_columns_measurement + + +

    +
    check_data_and_select_columns_measurement(data: Data)
+
    + +
    + +

    Check the required tables and columns in the Data and extract them.

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    Instantiated HiveData, PostgresData or PandasData

    +

    + + TYPE: + Data + +

    +
    + +
    + Source code in eds_scikit/biology/utils/check_data.py + 
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    def check_data_and_select_columns_measurement(data: Data):
+    """Check the required tables and columns in the Data and extract them.
+
+    Parameters
+    ----------
+    data : Data
+         Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData]
+    """
+    check_tables(
+        data,
+        required_tables=[
+            "measurement",
+            "concept",
+            "concept_relationship",
+        ],
+    )
+
+    _measurement_required_columns = [
+        "measurement_id",
+        "person_id",
+        "visit_occurrence_id",
+        "measurement_date",
+        "measurement_datetime",
+        "value_source_value",
+        "value_as_number",
+        "unit_source_value",
+        "row_status_source_value",
+        "measurement_source_concept_id",
+        "range_high",
+        "range_low",
+    ]
+
+    _concept_required_columns = [
+        "concept_id",
+        "concept_name",
+        "concept_code",
+        "vocabulary_id",
+    ]
+
+    _relationship_required_columns = ["concept_id_1", "concept_id_2", "relationship_id"]
+
+    check_columns(
+        data.measurement,
+        required_columns=_measurement_required_columns,
+    )
+    check_columns(data.concept, required_columns=_concept_required_columns)
+    check_columns(
+        data.concept_relationship,
+        required_columns=_relationship_required_columns,
+    )
+
+    measurement = data.measurement
+    concept = data.concept[_concept_required_columns]
+    concept_relationship = data.concept_relationship[_relationship_required_columns]
+
+    return measurement, concept, concept_relationship
+
    +
    +
    + +
    + +
    + + + +

    + check_data_and_select_columns_relationship + + +

    +
    check_data_and_select_columns_relationship(data: Data)
+
    + +
    + +

    Check the required tables and columns in the Data and extract them.

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    Instantiated HiveData, PostgresData or PandasData

    +

    + + TYPE: + Data + +

    +
    + +
    + Source code in eds_scikit/biology/utils/check_data.py + 
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    def check_data_and_select_columns_relationship(data: Data):
+    """Check the required tables and columns in the Data and extract them.
+
+    Parameters
+    ----------
+    data : Data
+         Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData]
+    """
+    check_tables(
+        data,
+        required_tables=[
+            "concept",
+            "concept_relationship",
+        ],
+    )
+
+    _concept_required_columns = [
+        "concept_id",
+        "concept_name",
+        "concept_code",
+        "vocabulary_id",
+    ]
+
+    _relationship_required_columns = [
+        "concept_id_1",
+        "concept_id_2",
+        "relationship_id",
+    ]
+
+    check_columns(data.concept, required_columns=_concept_required_columns)
+    check_columns(
+        data.concept_relationship,
+        required_columns=_relationship_required_columns,
+    )
+
+    concept = data.concept[_concept_required_columns]
+    concept_relationship = data.concept_relationship[_relationship_required_columns]
+
+    return concept, concept_relationship
+
    +
    +
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    + +
    + +
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    +
    + + + + Back to top + + +
    + + + +
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    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/utils/config/index.html b/main/reference/biology/utils/config/index.html new file mode 100644 index 00000000..d02f8bc0 --- /dev/null +++ b/main/reference/biology/utils/config/index.html @@ -0,0 +1,4101 @@ + + + + + + + + + + + + + + + + config - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.biology.utils.config

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + create_config_from_stats + + +

    +
    create_config_from_stats(concepts_sets: List[ConceptsSet], config_name: str, stats_folder: str = 'Biology_summary')
+
    + +
    + +

    Generate the configuration file from a statistical summary. It is needed [here][eds_scikit.biology.cleaning.transform.transform_measurement]

    + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    concepts_sets +

    List of concepts-sets to select

    +

    + + TYPE: + List[ConceptsSet] + +

    +
    config_name +

    Name of the folder where the configuration will be saved.

    +

    + + TYPE: + str + +

    +
    stats_folder +

    Name of the statistical summary folder

    +

    + + TYPE: + str + + + DEFAULT: + 'Biology_summary' + +

    +
    + +
    + Source code in eds_scikit/biology/utils/config.py + 
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    def create_config_from_stats(
+    concepts_sets: List[ConceptsSet],
+    config_name: str,
+    stats_folder: str = "Biology_summary",
+):
+    """Generate the configuration file from a statistical summary. It is needed [here][eds_scikit.biology.cleaning.transform.transform_measurement]
+
+    Parameters
+    ----------
+    concepts_sets : List[ConceptsSet]
+        List of concepts-sets to select
+    config_name : str
+        Name of the folder where the configuration will be saved.
+    stats_folder : str
+        Name of the statistical summary folder
+    """
+    my_custom_config = pd.DataFrame()
+    for concepts_set in concepts_sets:
+        try:
+            stats = pd.read_pickle(
+                "{}/{}/measurement_stats.pkl".format(stats_folder, concepts_set.name)
+            )
+            stats["transformed_unit"] = (
+                stats.groupby("unit_source_value")["count"]
+                .sum("count")
+                .sort_values(ascending=False)
+                .index[0]
+            )
+            stats["concepts_set"] = concepts_set.name
+            stats["Action"] = None
+            stats["Coefficient"] = None
+
+            my_custom_config = pd.concat([my_custom_config, stats])
+        except OSError:
+            logger.error(
+                "{} has no statistical summary saved in {}",
+                concepts_set.name,
+                stats_folder,
+            )
+            pass
+
+    if "care_site_short_name" in my_custom_config.columns:
+        # Keep only the row computed from every care site
+        my_custom_config = my_custom_config[
+            my_custom_config.care_site_short_name == "ALL"
+        ]
+
+    os.makedirs(CONFIGS_PATH, exist_ok=True)
+
+    my_custom_config.to_csv("{}/{}.csv".format(CONFIGS_PATH, config_name), index=False)
+
+    register_configs()
+
    +
    +
    + +
    + +
    + + + +

    + list_all_configs + + +

    +
    list_all_configs() -> List[str]
+
    + +
    + +

    Helper to get the names of all saved biology configurations

    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + List[str] + + +

    The configurations names

    +
    + +
    + Source code in eds_scikit/biology/utils/config.py + 
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    def list_all_configs() -> List[str]:
+    """
+    Helper to get the names of all saved biology configurations
+
+    Returns
+    -------
+    List[str]
+        The configurations names
+    """
+    registered = list(registry.data.get_all().keys())
+    configs = [
+        r.split(".")[-1] for r in registered if r.startswith("get_biology_config")
+    ]
+    return configs
+
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    eds_scikit.biology.utils

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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/utils/prepare_measurement/index.html b/main/reference/biology/utils/prepare_measurement/index.html new file mode 100644 index 00000000..77332198 --- /dev/null +++ b/main/reference/biology/utils/prepare_measurement/index.html @@ -0,0 +1,4149 @@ + + + + + + + + + + + + + + + + prepare_measurement - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.biology.utils.prepare_measurement

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + prepare_measurement_table + + +

    +
    prepare_measurement_table(data: Data, start_date: datetime = None, end_date: datetime = None, concept_sets: List[ConceptsSet] = None, get_all_terminologies = True, convert_units = False, compute_table = False) -> DataFrame
+
    + +
    + +

    Returns filtered measurement table based on validity, date and concept_sets.

    +

    The output format is identical to data.measurement but adding following columns : +- range_high_anomaly, range_low_anomaly +- {terminology}_code based on concept_sets terminologies +- concept_sets +- normalized_units and normalized_values if convert_units==True

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    Instantiated HiveData, PostgresData or PandasData

    +

    + + TYPE: + Data + +

    +
    start_date +

    EXAMPLE: "2019-05-01"

    +

    + + TYPE: + datetime, optional + + + DEFAULT: + None + +

    +
    end_date +

    EXAMPLE: "2022-05-01"

    +

    + + TYPE: + datetime, optional + + + DEFAULT: + None + +

    +
    concept_sets +

    List of concepts-sets to select

    +

    + + TYPE: + List[ConceptsSet], optional + + + DEFAULT: + None + +

    +
    get_all_terminologies +

    If True, all terminologies from settings terminologies will be added, by default True

    +

    + + TYPE: + bool, optional + + + DEFAULT: + True + +

    +
    convert_units +

    If True, convert units based on ConceptsSets Units object. Eager execution., by default False

    +

    + + TYPE: + bool, optional + + + DEFAULT: + False + +

    +
    compute_table +

    If True, compute table then cache it. Useful to prevent spark issues, especially when running in notebooks.

    +

    + + TYPE: + bool, optional + + + DEFAULT: + False + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    Preprocessed measurement dataframe

    +
    + +
    + Source code in eds_scikit/biology/utils/prepare_measurement.py + 
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    def prepare_measurement_table(
+    data: Data,
+    start_date: datetime = None,
+    end_date: datetime = None,
+    concept_sets: List[ConceptsSet] = None,
+    get_all_terminologies=True,
+    convert_units=False,
+    compute_table=False,
+) -> DataFrame:
+    """Returns filtered measurement table based on validity, date and concept_sets.
+
+    The output format is identical to data.measurement but adding following columns :
+    - range_high_anomaly, range_low_anomaly
+    - {terminology}_code based on concept_sets terminologies
+    - concept_sets
+    - normalized_units and normalized_values if convert_units==True
+
+    Parameters
+    ----------
+    data : Data
+        Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData]
+    start_date : datetime, optional
+        **EXAMPLE**: `"2019-05-01"`
+    end_date : datetime, optional
+        **EXAMPLE**: `"2022-05-01"`
+    concept_sets : List[ConceptsSet], optional
+        List of concepts-sets to select
+    get_all_terminologies : bool, optional
+        If True, all terminologies from settings terminologies will be added, by default True
+    convert_units : bool, optional
+        If True, convert units based on ConceptsSets Units object. Eager execution., by default False
+    compute_table : bool, optional
+        If True, compute table then cache it. Useful to prevent spark issues, especially when running in notebooks.
+
+    Returns
+    -------
+    DataFrame
+        Preprocessed measurement dataframe
+    """
+
+    measurement, _, _ = check_data_and_select_columns_measurement(data)
+
+    # measurement preprocessing
+    measurement = filter_measurement_valid(measurement)
+    measurement = filter_measurement_by_date(measurement, start_date, end_date)
+    measurement = normalize_unit(measurement)
+    measurement = tag_measurement_anomaly(measurement)
+
+    # measurement codes mapping
+    biology_relationship_table = prepare_biology_relationship_table(
+        data, concept_sets, get_all_terminologies
+    )
+    measurement = measurement.merge(
+        biology_relationship_table,
+        left_on="measurement_source_concept_id",
+        right_on=f"{mapping[0][0]}_concept_id",
+    )
+
+    if convert_units:
+        logger.info(
+            "Lazy preparation not available if convert_units=True. Table will be computed then cached."
+        )
+        measurement = convert_measurement_units(measurement, concept_sets)
+
+    measurement = cache(measurement)
+    if compute_table or convert_units:
+        measurement.shape
+
+    if is_koalas(measurement):
+        logger.info("Done. Once computed, measurement will be cached.")
+
+    return measurement
+
    +
    +
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    +
    + + + + Back to top + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/utils/prepare_relationship/index.html b/main/reference/biology/utils/prepare_relationship/index.html new file mode 100644 index 00000000..be17c4a3 --- /dev/null +++ b/main/reference/biology/utils/prepare_relationship/index.html @@ -0,0 +1,4698 @@ + + + + + + + + + + + + + + + + prepare_relationship - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.biology.utils.prepare_relationship

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + prepare_relationship_table + + +

    +
    prepare_relationship_table(data: Data, source_terminologies: Dict[str, str], mapping: List[Tuple[str, str, str]]) -> ks.DataFrame
+
    + +
    + +

    Create easy-to-use relationship table based on given terminologies and mapping between them.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    Instantiated [HiveData][edsteva.io.hive.HiveData], [PostgresData][edsteva.io.postgres.PostgresData] or [LocalData][edsteva.io.files.LocalData]

    +

    + + TYPE: + Data + +

    +
    source_terminologies +

    Dictionary of concepts terminologies with their associated regex.

    +

    + + TYPE: + Dict[str, str] + +

    +
    **EXAMPLE + +

    +

    +
    mapping +

    Ordered mapping of terminologies based on concept_relationship table

    +

    + + TYPE: + List[Tuple[str, str, str]] + +

    +
    **EXAMPLE + +

    +

    +
    +
    Output
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    source_concept_idsource_concept_namesource_concept_codestandard_concept_idstandard_concept_namestandard_concept_code
    3xxxxxxxxxxxxCX14xxxxxxxxxxxxA1
    9xxxxxxxxxxxxZY25xxxxxxxxxxxxA2
    9xxxxxxxxxxxxB3F47xxxxxxxxxxxxD3
    7xxxxxxxxxxxxT324xxxxxxxxxxxxF82
    5xxxxxxxxxxxxS231xxxxxxxxxxxxA432
    + +
    + Source code in eds_scikit/biology/utils/prepare_relationship.py + 
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    def prepare_relationship_table(
+    data: Data,
+    source_terminologies: Dict[str, str],
+    mapping: List[Tuple[str, str, str]],
+) -> ks.DataFrame:  # ks or pandas
+    """
+
+    Create easy-to-use relationship table based on given terminologies and mapping between them.
+
+    Parameters
+    ----------
+    data : Data
+        Instantiated [``HiveData``][edsteva.io.hive.HiveData], [``PostgresData``][edsteva.io.postgres.PostgresData] or [``LocalData``][edsteva.io.files.LocalData]
+    source_terminologies : Dict[str, str]
+        Dictionary of concepts terminologies with their associated regex.
+    **EXAMPLE**: `{'source_concept'  : r'src_.{0, 10}_lab', 'standard_concept' : r'std_concept'}`
+    mapping : List[Tuple[str, str, str]]
+        Ordered mapping of terminologies based on concept_relationship table
+    **EXAMPLE**: `[("source_concept", "standard_concept", "Maps to")]`
+
+    Output
+    -------
+    |   source_concept_id | source_concept_name   | source_concept_code   |   standard_concept_id     | standard_concept_name     | standard_concept_code       |
+    |--------------------:|:---------------------:|:---------------------:|:-------------------------:|:-------------------------:|:---------------------------:|
+    |                   3 | xxxxxxxxxxxx          | CX1                   |                         4 | xxxxxxxxxxxx              | A1                          |
+    |                   9 | xxxxxxxxxxxx          | ZY2                   |                         5 | xxxxxxxxxxxx              | A2                          |
+    |                   9 | xxxxxxxxxxxx          | B3F                   |                        47 | xxxxxxxxxxxx              | D3                          |
+    |                   7 | xxxxxxxxxxxx          | T32                   |                         4 | xxxxxxxxxxxx              | F82                         |
+    |                   5 | xxxxxxxxxxxx          | S23                   |                         1 | xxxxxxxxxxxx              | A432                        |
+
+
+    """
+
+    concept, concept_relationship = check_data_and_select_columns_relationship(data)
+
+    concept_by_terminology = {}
+    for terminology, regex in source_terminologies.items():
+        concept_by_terminology[terminology] = (
+            concept[concept.vocabulary_id.str.contains(regex)]
+            .rename(
+                columns={
+                    "concept_id": "{}_concept_id".format(terminology),
+                    "concept_name": "{}_concept_name".format(terminology),
+                    "concept_code": "{}_concept_code".format(terminology),
+                }
+            )
+            .drop(columns="vocabulary_id")
+        )
+    root_terminology = mapping[0][0]
+    relationship_table = concept_by_terminology[root_terminology]
+    # Look over all predefined structured mapping
+    for source, target, relationship_id in mapping:
+        relationship = concept_relationship.rename(
+            columns={
+                "concept_id_1": "{}_concept_id".format(source),
+                "concept_id_2": "{}_concept_id".format(target),
+            }
+        )[concept_relationship.relationship_id == relationship_id].drop(
+            columns="relationship_id"
+        )
+        relationship = relationship.merge(
+            concept_by_terminology[target], on="{}_concept_id".format(target)
+        )
+        relationship_table = relationship_table.merge(
+            relationship, on="{}_concept_id".format(source), how="left"
+        )
+
+    relationship_table = relationship_table.fillna("Unknown")
+
+    return relationship_table
+
    +
    +
    + +
    + +
    + + + +

    + filter_concept_sets_relationship_table + + +

    +
    filter_concept_sets_relationship_table(relationship_table, concept_sets)
+
    + +
    + +

    Filter relationship table using concept_sets concept codes.

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    relationship_table +

    Biology relationship table

    +

    + + TYPE: + DataFrame + +

    +
    concept_sets +

    List of concepts-sets to select

    +

    + + TYPE: + List[ConceptsSet] + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    Filtered biology relationship table

    +
    + +
    + Source code in eds_scikit/biology/utils/prepare_relationship.py + 
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    def filter_concept_sets_relationship_table(relationship_table, concept_sets):
+    """Filter relationship table using concept_sets concept codes.
+
+    Parameters
+    ----------
+    relationship_table : DataFrame
+        Biology relationship table
+    concept_sets : List[ConceptsSet]
+        List of concepts-sets to select
+
+    Returns
+    -------
+    DataFrame
+        Filtered biology relationship table
+    """
+
+    framework = get_framework(relationship_table)
+
+    concept_sets_tables = pd.DataFrame({})
+    for concept_set in concept_sets:
+        concept_set_table = concept_set.get_concept_codes_table()
+        concept_sets_tables = pd.concat(
+            (concept_set_table, concept_sets_tables), axis=0
+        )
+    terminologies = concept_sets_tables.terminology.unique()
+    concept_sets_tables = to(framework, concept_sets_tables)
+    filtered_terminology_table = framework.DataFrame({})
+    for terminology in terminologies:
+        if f"{terminology}_concept_code" in relationship_table.columns:
+            filtered_terminology_table_ = concept_sets_tables[
+                concept_sets_tables.terminology == terminology
+            ].merge(
+                relationship_table,
+                on=f"{terminology}_concept_code",
+                how="left",
+                suffixes=("_x", ""),
+            )
+            filtered_terminology_table_ = filtered_terminology_table_[
+                [
+                    column
+                    for column in filtered_terminology_table_.columns
+                    if not ("_x" in column)
+                ]
+            ]
+            filtered_terminology_table = framework.concat(
+                (filtered_terminology_table_, filtered_terminology_table)
+            ).drop_duplicates()
+
+    return filtered_terminology_table
+
    +
    +
    + +
    + +
    + + + +

    + concept_sets_columns + + +

    +
    concept_sets_columns(relationship_table: DataFrame, concept_sets: List[ConceptsSet], extra_terminologies: List = List[str]) -> List[str]
+
    + +
    + +

    Filter relationship_table keeping concepts_sets terminologies columns.

    + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    relationship_table + +

    + + TYPE: + DataFrame + +

    +
    concept_sets + +

    + + TYPE: + List[ConceptsSet] + +

    +
    extra_terminologies + +

    + + TYPE: + List, optional + + + DEFAULT: + List[str] + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + List[str] + + + +
    + +
    + Source code in eds_scikit/biology/utils/prepare_relationship.py + 
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    def concept_sets_columns(
+    relationship_table: DataFrame,
+    concept_sets: List[ConceptsSet],
+    extra_terminologies: List = List[str],
+) -> List[str]:
+    """Filter relationship_table keeping concepts_sets terminologies columns.
+
+    Parameters
+    ----------
+    relationship_table : DataFrame
+    concept_sets : List[ConceptsSet]
+    extra_terminologies : List, optional
+
+    Returns
+    -------
+    List[str]
+    """
+    keep_terminologies = extra_terminologies
+    for concept_set in concept_sets:
+        keep_terminologies += concept_set.concept_codes.keys()
+
+    keep_columns = []
+    for col in relationship_table.columns:
+        if any([terminology in col for terminology in keep_terminologies]):
+            keep_columns.append(col)
+
+    return keep_columns
+
    +
    +
    + +
    + +
    + + + +

    + prepare_biology_relationship_table + + +

    +
    prepare_biology_relationship_table(data: Data, concept_sets: List[ConceptsSet] = None, get_all_terminologies: bool = True) -> DataFrame
+
    + +
    + +

    Prepare biology relationship table to map concept codes based on settings.source_terminologies and settings.mapping.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    Instantiated HiveData, PostgresData or PandasData

    +

    + + TYPE: + Data + +

    +
    concept_sets +

    List of concepts-sets to select

    +

    + + TYPE: + List[ConceptsSet], optional + + + DEFAULT: + None + +

    +
    get_all_terminologies +

    If True, all terminologies from settings terminologies will be added, by default True

    +

    + + TYPE: + bool, optional + + + DEFAULT: + True + +

    +
    Returns + +

    +

    +
    DataFrame +

    biology_relationship_table to be merged with measurement

    +

    +

    +
    + +
    + Source code in eds_scikit/biology/utils/prepare_relationship.py + 
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    def prepare_biology_relationship_table(
+    data: Data,
+    concept_sets: List[ConceptsSet] = None,
+    get_all_terminologies: bool = True,
+) -> DataFrame:
+    """Prepare biology relationship table to map concept codes based on settings.source_terminologies and settings.mapping.
+
+    Parameters
+    ----------
+    data : Data
+        Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData]
+    concept_sets : List[ConceptsSet], optional
+        List of concepts-sets to select
+    get_all_terminologies : bool, optional
+        If True, all terminologies from settings terminologies will be added, by default True
+    Returns
+    -------
+    DataFrame
+        biology_relationship_table to be merged with measurement
+    """
+    if concept_sets is None and not get_all_terminologies:
+        raise Exception(
+            "get_all_terminologies must be True if no concept_sets provided."
+        )
+
+    biology_relationship_table = prepare_relationship_table(
+        data, source_terminologies, mapping
+    )
+    biology_relationship_table = (
+        filter_concept_sets_relationship_table(biology_relationship_table, concept_sets)
+        if concept_sets
+        else biology_relationship_table
+    )
+
+    keep_columns = (
+        biology_relationship_table.columns
+        if get_all_terminologies
+        else concept_sets_columns(
+            biology_relationship_table,
+            concept_sets,
+            [mapping[0][0], "concept_set"],
+        )
+    )
+    biology_relationship_table = biology_relationship_table[keep_columns]
+
+    return biology_relationship_table
+
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/utils/process_concepts/index.html b/main/reference/biology/utils/process_concepts/index.html new file mode 100644 index 00000000..da8ae3b5 --- /dev/null +++ b/main/reference/biology/utils/process_concepts/index.html @@ -0,0 +1,4028 @@ + + + + + + + + + + + + + + + + process_concepts - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.biology.utils.process_concepts

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + ConceptsSet + + +

    +
    ConceptsSet(name: str)
+
    + +
    + + +

    Class defining the concepts-sets with 2 attributes:

    +
      +
    • name: the name of the concepts-set
      • +
    • concept_codes : the list of concepts codes included in the concepts-set
      • +
    + + +
    + Source code in eds_scikit/biology/utils/process_concepts.py + 
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    def __init__(self, name: str):
+    self.name = name
+    self.units = Units()
+
+    fetched_codes = fetch_concept_codes_from_name(name)
+    if fetched_codes:
+        self.concept_codes = {"GLIMS_ANABIO": fetch_concept_codes_from_name(name)}
+    else:
+        self.concept_codes = {}
+
    +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + + +
    + + + +

    + fetch_all_concepts_set + + +

    +
    fetch_all_concepts_set(concepts_sets_table_name: str = 'default_concepts_sets') -> List[ConceptsSet]
+
    + +
    + +

    Returns a list of all the concepts-sets of the chosen tables. By default, the table is here.

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    concepts_sets_table_name +

    Name of the table to extract concepts-sets from

    +

    + + TYPE: + str, optional + + + DEFAULT: + 'default_concepts_sets' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + List[ConceptsSet] + + +

    The list of all concepts-sets in the selected table

    +
    + +
    + Source code in eds_scikit/biology/utils/process_concepts.py + 
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    def fetch_all_concepts_set(
+    concepts_sets_table_name: str = "default_concepts_sets",
+) -> List[ConceptsSet]:
+    """Returns a list of all the concepts-sets of the chosen tables. By default, the table is [here][concepts-sets].
+
+    Parameters
+    ----------
+    concepts_sets_table_name : str, optional
+        Name of the table to extract concepts-sets from
+
+    Returns
+    -------
+    List[ConceptsSet]
+        The list of all concepts-sets in the selected table
+    """
+    concepts_sets = []
+    default_concepts_sets = getattr(datasets, concepts_sets_table_name)
+    for concepts_set_name in default_concepts_sets.concepts_set_name:
+        concepts_sets.append(ConceptsSet(concepts_set_name))
+    logger.info("Fetch all concepts-sets from table {}", concepts_sets_table_name)
+    return concepts_sets
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/utils/process_measurement/index.html b/main/reference/biology/utils/process_measurement/index.html new file mode 100644 index 00000000..796e24af --- /dev/null +++ b/main/reference/biology/utils/process_measurement/index.html @@ -0,0 +1,4622 @@ + + + + + + + + + + + + + + + + process_measurement - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.biology.utils.process_measurement

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + filter_measurement_valid + + +

    +
    filter_measurement_valid(measurement: DataFrame) -> DataFrame
+
    + +
    + +

    Filter valid observations based on the row_status_source_value column

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    measurement +

    DataFrame to filter

    +

    + + TYPE: + DataFrame + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    DataFrame with valid observations only

    +
    + +
    + Source code in eds_scikit/biology/utils/process_measurement.py + 
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    def filter_measurement_valid(measurement: DataFrame) -> DataFrame:
+    """Filter valid observations based on the `row_status_source_value` column
+
+    Parameters
+    ----------
+    measurement : DataFrame
+        DataFrame to filter
+
+    Returns
+    -------
+    DataFrame
+        DataFrame with valid observations only
+    """
+    check_columns(
+        df=measurement,
+        required_columns=["row_status_source_value"],
+        df_name="measurment",
+    )
+    measurement_valid = measurement[measurement["row_status_source_value"] == "Validé"]
+    measurement_valid = measurement_valid.drop(columns=["row_status_source_value"])
+    return measurement_valid
+
    +
    +
    + +
    + +
    + + + +

    + filter_measurement_by_date + + +

    +
    filter_measurement_by_date(measurement: DataFrame, start_date: datetime = None, end_date: datetime = None) -> DataFrame
+
    + +
    + +

    Filter observations that are inside the selected time window

    + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    measurement +

    DataFrame to filter

    +

    + + TYPE: + DataFrame + +

    +
    start_date +

    EXAMPLE: "2019-05-01"

    +

    + + TYPE: + datetime, optional + + + DEFAULT: + None + +

    +
    end_date +

    EXAMPLE: "2022-05-01"

    +

    + + TYPE: + datetime, optional + + + DEFAULT: + None + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    DataFrame with observations inside the selected time window only

    +
    + +
    + Source code in eds_scikit/biology/utils/process_measurement.py + 
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    def filter_measurement_by_date(
+    measurement: DataFrame, start_date: datetime = None, end_date: datetime = None
+) -> DataFrame:
+    """Filter observations that are inside the selected time window
+
+    Parameters
+    ----------
+    measurement : DataFrame
+        DataFrame to filter
+    start_date : datetime, optional
+        **EXAMPLE**: `"2019-05-01"`
+    end_date : datetime, optional
+        **EXAMPLE**: `"2022-05-01"`
+
+    Returns
+    -------
+    DataFrame
+        DataFrame with observations inside the selected time window only
+    """
+    check_columns(
+        df=measurement, required_columns=["measurement_date"], df_name="measurment"
+    )
+
+    measurement.measurement_date = measurement.measurement_date.astype("datetime64[ns]")
+
+    measurement.dropna(subset=["measurement_date"], inplace=True)
+
+    if start_date:
+        measurement = measurement[measurement["measurement_date"] >= start_date]
+    if end_date:
+        measurement = measurement[measurement["measurement_date"] <= end_date]
+
+    return measurement
+
    +
    +
    + +
    + +
    + + + +

    + tag_measurement_anomaly + + +

    +
    tag_measurement_anomaly(measurement: DataFrame) -> DataFrame
+
    + +
    + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    measurement +

    DataFrame to filter

    +

    + + TYPE: + DataFrame + +

    +
    start_date +

    EXAMPLE: "2019-05-01"

    +

    + + TYPE: + datetime, optional + +

    +
    end_date +

    EXAMPLE: "2022-05-01"

    +

    + + TYPE: + datetime, optional + +

    +
    + +
    + Source code in eds_scikit/biology/utils/process_measurement.py + 
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    def tag_measurement_anomaly(measurement: DataFrame) -> DataFrame:
+    """
+
+    Parameters
+    ----------
+    measurement : DataFrame
+        DataFrame to filter
+    start_date : datetime, optional
+        **EXAMPLE**: `"2019-05-01"`
+    end_date : datetime, optional
+        **EXAMPLE**: `"2022-05-01"`
+
+    Returns
+    -------
+    """
+
+    measurement["range_high_anomaly"] = (~measurement.range_high.isna()) & (
+        measurement["value_as_number"] > measurement["range_high"]
+    )
+    measurement["range_low_anomaly"] = (~measurement.range_low.isna()) & (
+        measurement["value_as_number"] < measurement["range_low"]
+    )
+
+    return measurement
+
    +
    +
    + +
    + +
    + + + +

    + convert_measurement_units + + +

    +
    convert_measurement_units(measurement: DataFrame, concepts_sets: List[ConceptsSet]) -> DataFrame
+
    + +
    + +

    Add value_as_number_normalized, unit_source_value_normalized and factor columns to measurement dataframe based on concepts_sets and units.

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    measurement + +

    + + TYPE: + DataFrame + +

    +
    concepts_sets + +

    + + TYPE: + List[ConceptsSet] + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    Measurement with added columns value_as_number_normalized, unit_source_value_normalized and factor.

    +
    + +
    + Source code in eds_scikit/biology/utils/process_measurement.py + 
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    def convert_measurement_units(
+    measurement: DataFrame, concepts_sets: List[ConceptsSet]
+) -> DataFrame:
+
+    """Add value_as_number_normalized, unit_source_value_normalized and factor columns to measurement dataframe based on concepts_sets and units.
+
+    Parameters
+    ----------
+    measurement : DataFrame
+    concepts_sets : List[ConceptsSet]
+
+    Returns
+    -------
+    DataFrame
+        Measurement with added columns value_as_number_normalized, unit_source_value_normalized and factor.
+    """
+
+    if is_koalas(measurement):
+        measurement = cache(measurement)
+        measurement.shape
+        conversion_table = to(
+            "koalas", get_conversion_table(measurement, concepts_sets)
+        )
+    else:
+        conversion_table = get_conversion_table(measurement, concepts_sets)
+
+    measurement = measurement.merge(
+        conversion_table, on=["concept_set", "unit_source_value"]
+    )
+    measurement["value_as_number_normalized"] = (
+        measurement["value_as_number"] * measurement["factor"]
+    )
+
+    return measurement
+
    +
    +
    + +
    + +
    + + + +

    + get_conversion_table + + +

    +
    get_conversion_table(measurement: DataFrame, concepts_sets: List[ConceptsSet]) -> DataFrame
+
    + +
    + +

    Given measurement dataframe and list of concepts_sets output conversion table to be merged with measurement.

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    measurement + +

    + + TYPE: + DataFrame + +

    +
    concepts_sets + +

    + + TYPE: + List[ConceptsSet] + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    Conversion table to be merged with measurement

    +
    + +
    + Source code in eds_scikit/biology/utils/process_measurement.py + 
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    def get_conversion_table(
+    measurement: DataFrame, concepts_sets: List[ConceptsSet]
+) -> DataFrame:
+
+    """Given measurement dataframe and list of concepts_sets output conversion table to be merged with measurement.
+
+    Parameters
+    ----------
+    measurement : DataFrame
+    concepts_sets : List[ConceptsSet]
+
+    Returns
+    -------
+    DataFrame
+        Conversion table to be merged with measurement
+    """
+    conversion_table = (
+        measurement.groupby("concept_set")["unit_source_value"]
+        .unique()
+        .explode()
+        .to_frame()
+        .reset_index()
+    )
+    conversion_table = to("pandas", conversion_table)
+    conversion_table["unit_source_value_normalized"] = conversion_table[
+        "unit_source_value"
+    ]
+    conversion_table["factor"] = conversion_table.apply(
+        lambda x: 1 if x.unit_source_value_normalized else 0, axis=1
+    )
+
+    for concept_set in concepts_sets:
+        unit_source_value_normalized = concept_set.units.target_unit
+        conversion_table.loc[
+            conversion_table.concept_set == concept_set.name,
+            "unit_source_value_normalized",
+        ] = conversion_table.apply(
+            lambda x: unit_source_value_normalized
+            if concept_set.units.can_be_converted(
+                x.unit_source_value, unit_source_value_normalized
+            )
+            else concept_set.units.get_unit_base(x.unit_source_value),
+            axis=1,
+        )
+        conversion_table.loc[
+            conversion_table.concept_set == concept_set.name, "factor"
+        ] = conversion_table.apply(
+            lambda x: concept_set.units.convert_unit(
+                x.unit_source_value, x.unit_source_value_normalized
+            ),
+            axis=1,
+        )
+
+    conversion_table = conversion_table.fillna(1)
+
+    return conversion_table
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/utils/process_units/index.html b/main/reference/biology/utils/process_units/index.html new file mode 100644 index 00000000..c10d963d --- /dev/null +++ b/main/reference/biology/utils/process_units/index.html @@ -0,0 +1,3791 @@ + + + + + + + + + + + + + + + + process_units - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.biology.utils.process_units

    + + +
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    + + + + Back to top + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/viz/aggregate/index.html b/main/reference/biology/viz/aggregate/index.html new file mode 100644 index 00000000..0803ae15 --- /dev/null +++ b/main/reference/biology/viz/aggregate/index.html @@ -0,0 +1,4401 @@ + + + + + + + + + + + + + + + + aggregate - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + +
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    + + + + + + + + +

    eds_scikit.biology.viz.aggregate

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + aggregate_measurement + + +

    +
    aggregate_measurement(measurement: DataFrame, stats_only: bool, overall_only: bool, value_column: str, unit_column: str, category_columns = [], debug = False)
+
    + +
    + +

    Aggregates measurement dataframe in three descriptive and synthetic dataframe : + - measurement_stats + - measurement_volumetry + - measurement_distribution

    +

    Useful function before plotting.

    + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    measurement +

    description

    +

    + + TYPE: + DataFrame + +

    +
    stats_only +

    description

    +

    + + TYPE: + bool + +

    +
    overall_only +

    description

    +

    + + TYPE: + bool + +

    +
    category_columns +

    description, by default []

    +

    + + TYPE: + list, optional + + + DEFAULT: + [] + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + _type_ + + +

    description

    +
    + +
    + Source code in eds_scikit/biology/viz/aggregate.py + 
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    def aggregate_measurement(
+    measurement: DataFrame,
+    stats_only: bool,
+    overall_only: bool,
+    value_column: str,
+    unit_column: str,
+    category_columns=[],
+    debug=False,
+):
+    """Aggregates measurement dataframe in three descriptive and synthetic dataframe :
+      - measurement_stats
+      - measurement_volumetry
+      - measurement_distribution
+
+    Useful function before plotting.
+
+    Parameters
+    ----------
+    measurement : DataFrame
+        _description_
+    stats_only : bool
+        _description_
+    overall_only : bool
+        _description_
+    category_columns : list, optional
+        _description_, by default []
+
+    Returns
+    -------
+    _type_
+        _description_
+    """
+
+    check_columns(
+        df=measurement,
+        required_columns=[
+            "measurement_id",
+            unit_column,
+            "measurement_date",
+            value_column,
+        ]
+        + category_columns,
+        df_name="measurement",
+    )
+
+    measurement.shape
+
+    # Truncate date
+    measurement["measurement_month"] = (
+        measurement["measurement_date"].astype("datetime64").dt.strftime("%Y-%m")
+    )
+    measurement = measurement.drop(columns=["measurement_date"])
+
+    # Filter measurement with missing values
+    filtered_measurement, missing_value = filter_missing_values(measurement)
+
+    # Compute measurement statistics by code
+    measurement_stats = _describe_measurement_by_code(
+        filtered_measurement,
+        overall_only,
+        value_column,
+        unit_column,
+        category_columns,
+        debug,
+    )
+
+    if stats_only:
+        return {"measurement_stats": measurement_stats}
+
+    # Count measurement by care_site and by code per each month
+    measurement_volumetry = _count_measurement_by_category_and_code_per_month(
+        filtered_measurement,
+        missing_value,
+        value_column,
+        unit_column,
+        category_columns,
+        debug,
+    )
+
+    # Bin measurement values by care_site and by code
+    measurement_distribution = _bin_measurement_value_by_category_and_code(
+        filtered_measurement, value_column, unit_column, category_columns, debug
+    )
+
+    return {
+        "measurement_stats": measurement_stats,
+        "measurement_volumetry": measurement_volumetry,
+        "measurement_distribution": measurement_distribution,
+    }
+
    +
    +
    + +
    + +
    + + + +

    + add_mad_minmax + + +

    +
    add_mad_minmax(measurement: DataFrame, category_cols: List[str], value_column: str = 'value_as_number', unit_column: str = 'unit_source_value') -> DataFrame
+
    + +
    + +

    Add min_value, max_value column to measurement based on MAD criteria.

    + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    measurement +

    measurement dataframe

    +

    + + TYPE: + DataFrame + +

    +
    category_cols +

    measurement category columns to perform the groupby on when computing MAD

    +

    + + TYPE: + List[str] + +

    +
    value_column +

    measurement value column on which MAD will be computed

    +

    + + TYPE: + str + + + DEFAULT: + 'value_as_number' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    measurement dataframe with added columns min_value, max_value

    +
    + +
    + Source code in eds_scikit/biology/viz/aggregate.py + 
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    def add_mad_minmax(
+    measurement: DataFrame,
+    category_cols: List[str],
+    value_column: str = "value_as_number",
+    unit_column: str = "unit_source_value",
+) -> DataFrame:
+    """Add min_value, max_value column to measurement based on MAD criteria.
+
+    Parameters
+    ----------
+    measurement : DataFrame
+        measurement dataframe
+    category_cols : List[str]
+        measurement category columns to perform the groupby on when computing MAD
+    value_column : str
+        measurement value column on which MAD will be computed
+
+    Returns
+    -------
+    DataFrame
+        measurement dataframe with added columns min_value, max_value
+    """
+    measurement_median = (
+        measurement[category_cols + [value_column]]
+        .groupby(
+            category_cols,
+            as_index=False,
+            dropna=False,
+        )
+        .median()
+        .rename(columns={value_column: "median"})
+    )
+
+    # Add median column to the measurement table
+    measurement_median = measurement_median.merge(
+        measurement[
+            category_cols
+            + [
+                value_column,
+            ]
+        ],
+        on=category_cols,
+    )
+
+    # Compute median deviation for each measurement
+    measurement_median["median_deviation"] = abs(
+        measurement_median["median"] - measurement_median[value_column]
+    )
+
+    # Compute MAD per care site and code
+    measurement_mad = (
+        measurement_median[
+            category_cols
+            + [
+                "median",
+                "median_deviation",
+            ]
+        ]
+        .groupby(
+            category_cols
+            + [
+                "median",
+            ],
+            as_index=False,
+            dropna=False,
+        )
+        .median()
+        .rename(columns={"median_deviation": "MAD"})
+    )
+
+    measurement_mad["MAD"] = 1.48 * measurement_mad["MAD"]
+
+    # Add MAD column to the measurement table
+    measurement = measurement_mad.merge(
+        measurement,
+        on=category_cols,
+    )
+
+    # Compute binned value
+    measurement["max_value"] = measurement["median"] + 4 * measurement["MAD"]
+    measurement["min_value"] = measurement["median"] - 4 * measurement["MAD"]
+
+    return measurement
+
    +
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    + + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/viz/index.html b/main/reference/biology/viz/index.html new file mode 100644 index 00000000..8b9b817c --- /dev/null +++ b/main/reference/biology/viz/index.html @@ -0,0 +1,3781 @@ + + + + + + + + + + + + + + + + `eds_scikit.biology.viz` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.biology.viz

    + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/viz/plot/index.html b/main/reference/biology/viz/plot/index.html new file mode 100644 index 00000000..689b3060 --- /dev/null +++ b/main/reference/biology/viz/plot/index.html @@ -0,0 +1,4050 @@ + + + + + + + + + + + + + + + + plot - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.biology.viz.plot

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + plot_concepts_set + + +

    +
    plot_concepts_set(concepts_set_name: str, source_path: str = 'Biology_summary') -> Union[alt.ConcatChart, pd.DataFrame]
+
    + +
    + +

    Plot and save a summary table and 2 interactive dashboards. For more details, have a look on the [visualization section][visualization]

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    concepts_set_name +

    Name of the concepts-set to plot

    +

    + + TYPE: + str + +

    +
    source_path +

    Name of the folder with aggregated data where the plots will be saved

    +

    + + TYPE: + str, optional + + + DEFAULT: + 'Biology_summary' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + List[alt.ConcatChart, pd.DataFrame] + + +

    Altair plots describing the volumetric and the distribution properties of your biological data along with a pandas DataFrame with a statistical summary

    +
    + +
    + Source code in eds_scikit/biology/viz/plot.py + 
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    def plot_concepts_set(
+    concepts_set_name: str,
+    source_path: str = "Biology_summary",
+) -> Union[alt.ConcatChart, pd.DataFrame]:
+    """Plot and save a summary table and 2 interactive dashboards. For more details, have a look on the [visualization section][visualization]
+
+    Parameters
+    ----------
+    concepts_set_name : str
+        Name of the concepts-set to plot
+    source_path : str, optional
+        Name of the folder with aggregated data where the plots will be saved
+
+    Returns
+    -------
+    List[alt.ConcatChart, pd.DataFrame]
+        Altair plots describing the volumetric and the distribution properties of your biological data along with a pandas DataFrame with a statistical summary
+    """
+    if os.path.isdir("{}/{}".format(source_path, concepts_set_name)):
+        if os.path.isfile(
+            "{}/{}/measurement_stats.pkl".format(source_path, concepts_set_name)
+        ):
+            measurement_stats = pd.read_pickle(
+                "{}/{}/measurement_stats.pkl".format(source_path, concepts_set_name)
+            )
+            _save_and_display_table(measurement_stats, source_path, concepts_set_name)
+        if os.path.isfile(
+            "{}/{}/measurement_volumetry.pkl".format(source_path, concepts_set_name)
+        ):
+            measurement_volumetry = pd.read_pickle(
+                "{}/{}/measurement_volumetry.pkl".format(source_path, concepts_set_name)
+            )
+            interactive_volumetry = plot_interactive_volumetry(
+                measurement_volumetry,
+            )
+            _save_and_display_chart(
+                interactive_volumetry,
+                source_path,
+                concepts_set_name,
+                "interactive_volumetry",
+            )
+        if os.path.isfile(
+            "{}/{}/measurement_distribution.pkl".format(source_path, concepts_set_name)
+        ):
+            measurement_distribution = pd.read_pickle(
+                "{}/{}/measurement_distribution.pkl".format(
+                    source_path, concepts_set_name
+                )
+            )
+            interactive_distribution = plot_interactive_distribution(
+                measurement_distribution,
+            )
+            _save_and_display_chart(
+                interactive_distribution,
+                source_path,
+                concepts_set_name,
+                "interactive_distribution",
+            )
+
+    else:
+        logger.error(
+            "The folder {} has not been found",
+            source_path,
+        )
+        raise FileNotFoundError
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/viz/stats_summary/index.html b/main/reference/biology/viz/stats_summary/index.html new file mode 100644 index 00000000..55939d37 --- /dev/null +++ b/main/reference/biology/viz/stats_summary/index.html @@ -0,0 +1,4152 @@ + + + + + + + + + + + + + + + + stats_summary - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + +
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    + + + +
    +
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    + + +
    +
    + + + + + + + + +

    eds_scikit.biology.viz.stats_summary

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + measurement_values_summary + + +

    +
    measurement_values_summary(measurement: DataFrame, category_cols: List[str] = ['concept_set', 'GLIMS_ANABIO_concept_code'], value_column: str = 'value_as_number', unit_column: str = 'unit_source_value') -> DataFrame
+
    + +
    + +

    Compute measurement values and units summary by category_cols.

    + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    measurement +

    measurement dataframe

    +

    + + TYPE: + DataFrame + +

    +
    category_cols +

    columns on which to groupby the summary, by default ["concept_set", "GLIMS_ANABIO_concept_code",]

    +

    + + TYPE: + List[str], optional + + + DEFAULT: + ['concept_set', 'GLIMS_ANABIO_concept_code'] + +

    +
    value_column +

    value column to summarize, by default "value_as_number" but can be value_as_number_normalized if units conversion is applied.

    +

    + + TYPE: + str, optional + + + DEFAULT: + 'value_as_number' + +

    +
    unit_column +

    units column to summarize, by default "unit_source_value" but can be unit_source_value_normalized if units conversion is applied.

    +

    + + TYPE: + str, optional + + + DEFAULT: + 'unit_source_value' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    statistic summary dataframe

    +
    + +
    + Source code in eds_scikit/biology/viz/stats_summary.py + 
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    def measurement_values_summary(
+    measurement: DataFrame,
+    category_cols: List[str] = [
+        "concept_set",
+        "GLIMS_ANABIO_concept_code",
+    ],
+    value_column: str = "value_as_number",
+    unit_column: str = "unit_source_value",
+) -> DataFrame:
+    """Compute measurement values and units summary by category_cols.
+
+    Parameters
+    ----------
+    measurement : DataFrame
+        measurement dataframe
+    category_cols : List[str], optional
+        columns on which to groupby the summary, by default ["concept_set", "GLIMS_ANABIO_concept_code",]
+    value_column : str, optional
+        value column to summarize, by default "value_as_number" but can be value_as_number_normalized if units conversion is applied.
+    unit_column : str, optional
+        units column to summarize, by default "unit_source_value" but can be unit_source_value_normalized if units conversion is applied.
+
+    Returns
+    -------
+    DataFrame
+        statistic summary dataframe
+    """
+
+    measurement.shape
+
+    no_units = (measurement[unit_column] == "non renseigne") | (
+        measurement[unit_column] == "Unkown"
+    )
+
+    stats_summary = (
+        measurement[no_units]
+        .groupby(category_cols)
+        .agg(no_units=("measurement_id", "count"))
+        .reset_index()
+    )
+
+    # Count measurements
+
+    measurement_count = (
+        measurement.groupby([*category_cols, unit_column])
+        .agg(measurement_count=("measurement_id", "count"))
+        .reset_index()
+    )
+
+    stats_summary = stats_summary.merge(
+        measurement_count, how="right", on=category_cols
+    )
+
+    # Describe stats measurements
+
+    measurement_stats = (
+        measurement[~no_units]
+        .groupby([*category_cols, unit_column])[[value_column]]
+        .describe()
+    )
+
+    measurement_stats.columns = [
+        "_".join(map(str, col)) for col in measurement_stats.columns
+    ]
+
+    measurement_stats = measurement_stats.reset_index()
+    stats_summary = measurement_stats.merge(
+        stats_summary, how="left", on=([*category_cols, unit_column])
+    )
+
+    # Count anomalies
+
+    occurrences_to_count = {
+        "range_high_anomaly_count": measurement[~no_units].range_high_anomaly,
+        "range_low_anomaly_count": measurement[~no_units].range_low_anomaly,
+    }
+
+    for key, to_count in occurrences_to_count.items():
+        additional_summary = (
+            measurement[~no_units][to_count]
+            .groupby([*category_cols, unit_column])[["measurement_id"]]
+            .count()
+            .rename(columns={"measurement_id": key})
+            .reset_index()
+        )
+        stats_summary = stats_summary.merge(
+            additional_summary, how="left", on=[*category_cols, unit_column]
+        )
+
+    stats_summary = stats_summary.fillna(0)
+    stats_summary = stats_summary.set_index(
+        [*category_cols, "no_units", unit_column]
+    ).sort_index()
+    stats_summary = stats_summary[
+        [*stats_summary.columns[::-1][:3], *stats_summary.columns[:-3]]
+    ]
+
+    stats_summary = to("pandas", stats_summary)
+
+    return stats_summary
+
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/biology/viz/wrapper/index.html b/main/reference/biology/viz/wrapper/index.html new file mode 100644 index 00000000..01d734f6 --- /dev/null +++ b/main/reference/biology/viz/wrapper/index.html @@ -0,0 +1,4168 @@ + + + + + + + + + + + + + + + + wrapper - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.biology.viz.wrapper

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + plot_biology_summary + + +

    +
    plot_biology_summary(measurement: DataFrame, value_column: str = 'value_as_number', unit_column: str = 'unit_source_value', save_folder_path: str = 'Biology_summary', stats_only: bool = False, terminologies: List[str] = None, debug: bool = False) -> Union[alt.ConcatChart, pd.DataFrame]
+
    + +
    + +

    Aggregate measurements, create plots and saves all the concepts-sets in folder.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    Instantiated HiveData, PostgresData or PandasData

    +

    + + TYPE: + Data + +

    +
    save_folder_path +

    Name of the folder where the plots will be saved

    +

    + + TYPE: + str, optional + + + DEFAULT: + 'Biology_summary' + +

    +
    stats_only +

    If True, it will only aggregate the data for the [summary table][summary-table].

    +

    + + TYPE: + bool, optional + + + DEFAULT: + False + +

    +
    terminologies +

    biology summary only on terminologies codes columns

    +

    + + TYPE: + List[str], optional + + + DEFAULT: + None + +

    +
    value_column +

    value column for distribution summary plot

    +

    + + TYPE: + str, optional + + + DEFAULT: + 'value_as_number' + +

    +
    debug +

    If True, info log will de displayed to follow aggregation steps

    +

    + + TYPE: + bool, optional + + + DEFAULT: + False + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + List[alt.ConcatChart, pd.DataFrame] + + +

    Altair plots describing the volumetric and the distribution properties of your biological data along with a pandas DataFrame with a statistical summary

    +
    + +
    + Source code in eds_scikit/biology/viz/wrapper.py + 
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    def plot_biology_summary(
+    measurement: DataFrame,
+    value_column: str = "value_as_number",
+    unit_column: str = "unit_source_value",
+    save_folder_path: str = "Biology_summary",
+    stats_only: bool = False,
+    terminologies: List[str] = None,
+    debug: bool = False,
+) -> Union[alt.ConcatChart, pd.DataFrame]:
+    """
+    Aggregate measurements, create plots and saves all the concepts-sets in folder.
+
+
+    Parameters
+    ----------
+    data : Data
+         Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData]
+    save_folder_path : str, optional
+        Name of the folder where the plots will be saved
+    stats_only : bool, optional
+        If ``True``, it will only aggregate the data for the [summary table][summary-table].
+    terminologies : List[str], optional
+        biology summary only on terminologies codes columns
+    value_column : str, optional
+        value column for distribution summary plot
+    debug : bool, optional
+        If ``True``, info log will de displayed to follow aggregation steps
+
+    Returns
+    -------
+    List[alt.ConcatChart, pd.DataFrame]
+        Altair plots describing the volumetric and the distribution properties of your biological data along with a pandas DataFrame with a statistical summary
+    """
+
+    if not value_column:
+        raise ValueError(
+            "Must give a 'value_column' parameter. By default, use value_as_number. Or value_as_number_normalized if exists."
+        )
+    if not unit_column:
+        raise ValueError(
+            "Must give a 'unit_column' parameter. By default, use unit_source_value. Or unit_source_value_normalized if exists."
+        )
+
+    if not os.path.isdir(save_folder_path):
+        os.mkdir(save_folder_path)
+        logger.info("{} folder has been created.", save_folder_path)
+
+    if terminologies:
+        measurement = measurement.drop(
+            columns=[f"{col}_concept_code" for col in terminologies]
+        )
+
+    tables_agg = aggregate_measurement(
+        measurement=measurement,
+        value_column=value_column,
+        unit_column=unit_column,
+        stats_only=stats_only,
+        overall_only=stats_only,
+        category_columns=["concept_set", "care_site_short_name"],
+        debug=debug,
+    )
+
+    table_names = list(tables_agg.keys())
+    concept_sets_names = tables_agg[table_names[0]].concept_set.unique()
+
+    for concept_set_name in concept_sets_names:
+
+        concepts_set_path = "{}/{}".format(save_folder_path, concept_set_name)
+        rmtree(concepts_set_path, ignore_errors=True)
+        os.mkdir(concepts_set_path)
+        logger.info(
+            "{}/{} folder has been created.",
+            save_folder_path,
+            concept_set_name,
+        )
+
+        for table_name in table_names:
+            table = tables_agg[table_name].query("concept_set == @concept_set_name")
+            table.to_pickle(
+                "{}/{}/{}.pkl".format(save_folder_path, concept_set_name, table_name)
+            )
+
+        logger.info(
+            "{} has been processed and saved in {}/{} folder.",
+            concept_set_name,
+            save_folder_path,
+            concept_set_name,
+        )
+
+        plot_concepts_set(
+            concepts_set_name=concept_set_name, source_path=save_folder_path
+        )
+
    +
    +
    + +
    + + + +
    + +
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    +
    + + + + Back to top + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/datasets/generation_scripts/care_site_hierarchy/index.html b/main/reference/datasets/generation_scripts/care_site_hierarchy/index.html new file mode 100644 index 00000000..ce57610a --- /dev/null +++ b/main/reference/datasets/generation_scripts/care_site_hierarchy/index.html @@ -0,0 +1,3957 @@ + + + + + + + + + + + + + + + + care_site_hierarchy - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + +
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    + + + + + + + + +

    eds_scikit.datasets.generation_scripts.care_site_hierarchy

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + generate_care_site_hierarchy + + +

    +
    generate_care_site_hierarchy(care_site: framework.DataFrame, fact_relationship: framework.DataFrame, care_site_categories: List[str]) -> None
+
    + +
    + +

    Generate the care site hierarchy dataset.

    + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    The care_site DataFrame

    +

    + + TYPE: + framework.DataFrame + +

    +
    fact_relationship +

    The fact_relationship DataFrame

    +

    + + TYPE: + framework.DataFrame + +

    +
    care_site_categories +

    A list of care_site_type_source_value to use as categories

    +

    + + TYPE: + List[str] + +

    +
    + +
    + Source code in eds_scikit/datasets/generation_scripts/care_site_hierarchy.py + 
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    def generate_care_site_hierarchy(
+    care_site: framework.DataFrame,
+    fact_relationship: framework.DataFrame,
+    care_site_categories: List[str],
+) -> None:  # pragma: no cover
+    """
+    Generate the care site hierarchy dataset.
+
+    Parameters
+    ----------
+    care_site : framework.DataFrame
+        The `care_site` DataFrame
+    fact_relationship : framework.DataFrame
+        The `fact_relationship` DataFrame
+    care_site_categories : List[str]
+        A list of `care_site_type_source_value` to use as categories
+    """
+
+    care_site = _load_care_site_categories(care_site, care_site_categories)
+    relationships = _load_care_site_relationships(fact_relationship)
+
+    care_site = _simplify_care_site_categories(care_site, relationships)
+    care_site_hierarchy = hierarchy.build_hierarchy(care_site, relationships)
+    care_site_hierarchy = _simplify_care_site_hierarchy(care_site_hierarchy)
+    _save_care_site_hierarchy(care_site_hierarchy, DATASET_FOLDER)
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
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    +
    + + + + Back to top + + +
    + + + +
    +
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    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/datasets/generation_scripts/index.html b/main/reference/datasets/generation_scripts/index.html new file mode 100644 index 00000000..f5b2d87c --- /dev/null +++ b/main/reference/datasets/generation_scripts/index.html @@ -0,0 +1,3781 @@ + + + + + + + + + + + + + + + + `eds_scikit.datasets.generation_scripts` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.datasets.generation_scripts

    + + +
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    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
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    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/datasets/index.html b/main/reference/datasets/index.html new file mode 100644 index 00000000..9de9439e --- /dev/null +++ b/main/reference/datasets/index.html @@ -0,0 +1,3858 @@ + + + + + + + + + + + + + + + + `eds_scikit.datasets` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + +
    +
    + + + + + + + + +

    eds_scikit.datasets

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + list_all_synthetics + + +

    +
    list_all_synthetics() -> List[str]
+
    + +
    + +

    Helper to list all available synthetic datasets

    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + List[str] + + +

    List of datasets names

    +
    + +
    + Source code in eds_scikit/datasets/__init__.py + 
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    def list_all_synthetics() -> List[str]:
+    """
+    Helper to list all available synthetic datasets
+
+    Returns
+    -------
+    List[str]
+        List of datasets names
+    """
+    return [func.__name__ for func in __all__]
+
    +
    +
    + +
    + + + +
    + +
    + +
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    +
    + + + + Back to top + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/datasets/synthetic/base_dataset/index.html b/main/reference/datasets/synthetic/base_dataset/index.html new file mode 100644 index 00000000..78fe6a48 --- /dev/null +++ b/main/reference/datasets/synthetic/base_dataset/index.html @@ -0,0 +1,3791 @@ + + + + + + + + + + + + + + + + base_dataset - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.datasets.synthetic.base_dataset

    + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/datasets/synthetic/biology/index.html b/main/reference/datasets/synthetic/biology/index.html new file mode 100644 index 00000000..413fea40 --- /dev/null +++ b/main/reference/datasets/synthetic/biology/index.html @@ -0,0 +1,4015 @@ + + + + + + + + + + + + + + + + biology - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.datasets.synthetic.biology

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + load_biology_data + + +

    +
    load_biology_data(n_entity: int = 5, mean_measurement: int = 10000, n_care_site: int = 5, n_person: int = 5, n_visit_occurrence: int = 5, units: List[str] = ['g', 'g/l', 'mol', 's'], row_status_source_values: List[str] = ['Validé', 'Discontinué', 'Disponible', 'Attendu', 'Confirmé', 'Initial'], t_start: datetime = datetime(2017, 1, 1), t_end: datetime = datetime(2022, 1, 1), seed: int = None)
+
    + +
    + +

    Create a minimalistic dataset for the bioclean function.

    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + biology_dataset + +

    measurement, concept and concept_relationship.

    +

    + + TYPE: + BiologyDataset, a dataclass comprised of + +

    +
    + +
    + Source code in eds_scikit/datasets/synthetic/biology.py + 
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    def load_biology_data(
+    n_entity: int = 5,
+    mean_measurement: int = 10000,
+    n_care_site: int = 5,
+    n_person: int = 5,
+    n_visit_occurrence: int = 5,
+    units: List[str] = ["g", "g/l", "mol", "s"],
+    row_status_source_values: List[str] = [
+        "Validé",
+        "Discontinué",
+        "Disponible",
+        "Attendu",
+        "Confirmé",
+        "Initial",
+    ],
+    t_start: datetime = datetime(2017, 1, 1),
+    t_end: datetime = datetime(2022, 1, 1),
+    seed: int = None,
+):
+    """
+    Create a minimalistic dataset for the `bioclean` function.
+
+    Returns
+    -------
+    biology_dataset: BiologyDataset, a dataclass comprised of
+        measurement, concept and concept_relationship.
+    """
+    if seed:
+        np.random.seed(seed=seed)
+
+    concept, concept_relationship, src_concept_name = _generate_concept(
+        n_entity=n_entity, units=units
+    )
+    measurement = _generate_measurement(
+        t_start=t_start,
+        t_end=t_end,
+        mean_measurement=mean_measurement,
+        units=units,
+        src_concept_name=src_concept_name,
+        n_visit_occurrence=n_visit_occurrence,
+        n_person=n_person,
+        row_status_source_values=row_status_source_values,
+    )
+    care_site = _generate_care_site(n_care_site=n_care_site)
+    visit_occurrence = _generate_visit_occurrence(
+        n_visit_occurrence=n_visit_occurrence, n_care_site=n_care_site
+    )
+
+    return BiologyDataset(
+        measurement=measurement,
+        concept=concept,
+        concept_relationship=concept_relationship,
+        visit_occurrence=visit_occurrence,
+        care_site=care_site,
+        available_tables=[
+            "measurement",
+            "concept",
+            "concept_relationship",
+            "visit_occurrence",
+            "care_site",
+        ],
+        t_start=t_start,
+        t_end=t_end,
+        module="pandas",
+    )
+
    +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/datasets/synthetic/ccam/index.html b/main/reference/datasets/synthetic/ccam/index.html new file mode 100644 index 00000000..d8fb296a --- /dev/null +++ b/main/reference/datasets/synthetic/ccam/index.html @@ -0,0 +1,4031 @@ + + + + + + + + + + + + + + + + ccam - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.datasets.synthetic.ccam

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + load_ccam + + +

    +
    load_ccam()
+
    + +
    + +

    Create a minimalistic dataset for the procedures_from_ccam function.

    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + ccam_dataset + +

    procedure_occurrence and visit_occurrence.

    +

    + + TYPE: + CCAMDataset, a dataclass comprised of + +

    +
    + +
    + Source code in eds_scikit/datasets/synthetic/ccam.py + 
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    def load_ccam():
+    """
+    Create a minimalistic dataset for the `procedures_from_ccam` function.
+
+    Returns
+    -------
+    ccam_dataset: CCAMDataset, a dataclass comprised of
+        procedure_occurrence and visit_occurrence.
+    """
+    person_ids = [1, 1, 2, 3, 4, 5]
+    procedure_source_values = [
+        "DZEA001",
+        "DZEA003",
+        "GFEA004",
+        "EQQF006",
+        "DZEA001",
+        "DZEA001",
+    ]
+    procedure_datetimes = pd.to_datetime(
+        [
+            "2010-01-01",
+            "2010-01-01",
+            "2012-01-01",
+            "2012-01-01",
+            "2012-01-01",
+            "2012-01-01",
+        ]
+    )
+    visit_occurrence_ids = [11, 12, 13, 14, 98, 99]
+    procedure_occurrence = pd.DataFrame(
+        {
+            "person_id": person_ids,
+            "procedure_source_value": procedure_source_values,
+            "procedure_datetime": procedure_datetimes,
+            "visit_occurrence_id": visit_occurrence_ids,
+        }
+    )
+
+    person_ids = [1] * 6
+    visit_occurrence_ids = [11, 12, 13, 14, 98, 99]
+    visit_start_datetimes = pd.to_datetime(
+        [
+            "2010-01-01",
+            "2010-01-01",
+            "2012-01-01",
+            "2020-01-01",
+            "2000-01-01",
+            "2050-01-01",
+        ]
+    )
+    visit_end_datetimes = pd.to_datetime(
+        [
+            "2010-01-01",
+            "2010-01-01",
+            "2012-01-01",
+            "2020-01-01",
+            "2020-01-01",
+            "1900-01-01",
+        ]
+    )
+    visit_occurrence = pd.DataFrame(
+        {
+            "person_id": person_ids,
+            "visit_occurrence_id": visit_occurrence_ids,
+            "visit_start_datetime": visit_start_datetimes,
+            "visit_end_datetime": visit_end_datetimes,
+        }
+    )
+
+    return CCAMDataset(
+        procedure_occurrence=procedure_occurrence,
+        visit_occurrence=visit_occurrence,
+    )
+
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    + + + + Back to top + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/datasets/synthetic/consultation_dates/index.html b/main/reference/datasets/synthetic/consultation_dates/index.html new file mode 100644 index 00000000..12036ae1 --- /dev/null +++ b/main/reference/datasets/synthetic/consultation_dates/index.html @@ -0,0 +1,4045 @@ + + + + + + + + + + + + + + + + consultation_dates - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.datasets.synthetic.consultation_dates

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + load_consultation_dates + + +

    +
    load_consultation_dates()
+
    + +
    + +

    Create a minimalistic dataset for the get_consultation_dates function.

    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + consultation_dataset + +

    visit_occurence, note and note_nlp.

    +

    + + TYPE: + ConsultationDataset, a dataclass comprised of + +

    +
    + +
    + Source code in eds_scikit/datasets/synthetic/consultation_dates.py + 
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    def load_consultation_dates():
+    """
+    Create a minimalistic dataset for the `get_consultation_dates` function.
+
+    Returns
+    -------
+    consultation_dataset: ConsultationDataset, a dataclass comprised of
+        visit_occurence, note and note_nlp.
+    """
+    n_visits = 4
+    visit_occurrence_ids = list(range(n_visits))
+    visit_source_value = [
+        "consultation externe",
+        "consultation externe",
+        "hospitalisation",
+        "consultation externe",
+    ]
+    visit_occurrence = pd.DataFrame(
+        {
+            "visit_occurrence_id": visit_occurrence_ids,
+            "visit_source_value": visit_source_value,
+        }
+    )
+
+    n_notes = 10
+    visit_occurrence_ids = [n_visits * idx // n_notes for idx in range(n_notes)]
+    note_ids = list(range(n_notes))
+    note_datetimes = [1, 1, 5, 6, 7, 1, 1, 2, 3, 8]
+    note_datetimes = [datetime(2020, 1, day) for day in note_datetimes]
+    note_class_source_value = (n_notes // 2) * ["CR-CONS"] + (n_notes // 2) * [
+        "CR-HOSP"
+    ]
+    note = pd.DataFrame(
+        {
+            "visit_occurrence_id": visit_occurrence_ids,
+            "note_id": note_ids,
+            "note_datetime": note_datetimes,
+            "note_class_source_value": note_class_source_value,
+        }
+    )
+
+    n_note_nlp = 20
+    starts = [
+        4,
+        14,
+        0,
+        7,
+        5,
+        11,
+        8,
+        18,
+        6,
+        19,
+        15,
+        9,
+        17,
+        1,
+        12,
+        2,
+        3,
+        16,
+        10,
+        13,
+    ]
+    note_ids = [n_notes * idx // n_note_nlp for idx in range(n_note_nlp)]
+    consultation_dates = 2 * [1, 1, 5, 6, 7, 1, 2, 3, 9, 12]
+    consultation_dates = [datetime(2020, 1, day) for day in consultation_dates]
+    note_nlp = pd.DataFrame(
+        {
+            "note_id": note_ids,
+            "consultation_date": consultation_dates,
+            "start": starts,
+        }
+    )
+
+    return ConsultationDataset(
+        visit_occurrence=visit_occurrence,
+        note=note,
+        note_nlp=note_nlp,
+    )
+
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    + +
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    eds_scikit.datasets.synthetic.event_sequences

    + + +
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    eds_scikit.datasets.synthetic.hierarchy

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    eds_scikit.datasets.synthetic.icd10

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    eds_scikit.datasets.synthetic

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    eds_scikit.datasets.synthetic.person

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    eds_scikit.datasets.synthetic.stay_duration

    + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/datasets/synthetic/suicide_attempt/index.html b/main/reference/datasets/synthetic/suicide_attempt/index.html new file mode 100644 index 00000000..db82ecc0 --- /dev/null +++ b/main/reference/datasets/synthetic/suicide_attempt/index.html @@ -0,0 +1,3791 @@ + + + + + + + + + + + + + + + + suicide_attempt - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.datasets.synthetic.suicide_attempt

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    eds_scikit.datasets.synthetic.tagging

    + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/datasets/synthetic/visit_merging/index.html b/main/reference/datasets/synthetic/visit_merging/index.html new file mode 100644 index 00000000..e4b7c61d --- /dev/null +++ b/main/reference/datasets/synthetic/visit_merging/index.html @@ -0,0 +1,4009 @@ + + + + + + + + + + + + + + + + visit_merging - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.datasets.synthetic.visit_merging

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + load_visit_merging + + +

    +
    load_visit_merging()
+
    + +
    + +

    Create a minimalistic dataset for the visit_merging function.

    + + + + + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + visit_dataset + + +

    + + TYPE: + VisitDataset, a dataclass comprised of + +

    +
    + + visit_occurence. + + + +
    + +
    + Source code in eds_scikit/datasets/synthetic/visit_merging.py + 
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    def load_visit_merging():
+    """
+    Create a minimalistic dataset for the `visit_merging` function.
+
+    Returns
+    -------
+    visit_dataset : VisitDataset, a dataclass comprised of
+    visit_occurence.
+    """
+    visit_occurrence = pd.DataFrame(
+        {
+            "visit_occurrence_id": ["A", "B", "C", "D", "E", "F", "G"],
+            "person_id": ["999"] * 7,
+            "visit_start_datetime": [
+                "2021-01-01",
+                "2021-01-04",
+                "2021-01-12",
+                "2021-01-13",
+                "2021-01-19",
+                "2021-01-25",
+                "2017-01-01",
+            ],
+            "visit_end_datetime": [
+                "2021-01-05",
+                "2021-01-08",
+                "2021-01-18",
+                "2021-01-14",
+                "2021-01-21",
+                "2021-01-27",
+                None,
+            ],
+            "visit_source_value": [
+                "hospitalisés",
+                "hospitalisés",
+                "hospitalisés",
+                "urgence",
+                "hospitalisés",
+                "hospitalisés",
+                "hospitalisés",
+            ],
+            "row_status_source_value": [
+                "supprimé",
+                "courant",
+                "courant",
+                "courant",
+                "courant",
+                "courant",
+                "courant",
+            ],
+            "care_site_id": ["1", "1", "1", "1", "2", "1", "1"],
+        }
+    )
+
+    for col in ["visit_start_datetime", "visit_end_datetime"]:
+        visit_occurrence[col] = pd.to_datetime(visit_occurrence[col])
+
+    return VisitDataset(visit_occurrence=visit_occurrence)
+
    +
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    + +
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    + + + + Back to top + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/emergency/emergency_care_site/index.html b/main/reference/emergency/emergency_care_site/index.html new file mode 100644 index 00000000..5bfdb014 --- /dev/null +++ b/main/reference/emergency/emergency_care_site/index.html @@ -0,0 +1,4534 @@ + + + + + + + + + + + + + + + + emergency_care_site - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.emergency.emergency_care_site

    + + +
    + + + +
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    + + + + + + + + + +
    + + + +

    + tag_emergency_care_site + + +

    +
    tag_emergency_care_site(care_site: DataFrame, algo: str = 'from_mapping') -> DataFrame
+
    + +
    + +

    Tag care sites that correspond to medical emergency units.

    +

    The tagging is done by adding a "IS_EMERGENCY" column to the provided DataFrame.

    +

    Some algos can add an additional "EMERGENCY_TYPE" column to the provided DataFrame, +providing a more detailled classification.

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site + +

    + + TYPE: + DataFrame + +

    +
    algo +

    Possible values are:

    +
      +
    • "from_mapping" relies on a list of care_site_source_value extracted +by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites +are here further labelled to distinguish the different types of emergency
      • +
    • "from_regex_on_care_site_description": relies on a specific list of RegEx +applied on the description (= simplified care site name) of each care site.
      • +
    • "from_regex_on_parent_UF": relies on a specific list of regular expressions +applied on the description (= simplified care site name) of each UF (Unité Fonctionnelle). +The obtained tag is then propagated to every UF's children.
      • +
    +

    + + TYPE: + str + + + DEFAULT: + 'from_mapping' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + care_site + +

    Dataframe with 1 to 2 added columns corresponding to the following concepts:

    +
      +
    • "IS_EMERGENCY"
      • +
    • "EMERGENCY_TYPE" (if using algo "from_mapping")
      • +
    +

    + + TYPE: + DataFrame + +

    +
    + +
    + Source code in eds_scikit/emergency/emergency_care_site.py + 
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    @algo_checker(algos=ALGOS)
+def tag_emergency_care_site(
+    care_site: DataFrame,
+    algo: str = "from_mapping",
+) -> DataFrame:
+    """Tag care sites that correspond to **medical emergency units**.
+
+    The tagging is done by adding a `"IS_EMERGENCY"` column to the provided DataFrame.
+
+    Some algos can add an additional `"EMERGENCY_TYPE"` column to the provided DataFrame,
+    providing a more detailled classification.
+
+    Parameters
+    ----------
+    care_site: DataFrame
+    algo: str
+        Possible values are:
+
+        - [`"from_mapping"`][eds_scikit.emergency.emergency_care_site.from_mapping] relies on a list of `care_site_source_value` extracted
+          by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites
+          are here further labelled to distinguish the different types of emergency
+        - [`"from_regex_on_care_site_description"`][eds_scikit.emergency.emergency_care_site.from_regex_on_care_site_description]: relies on a specific list of RegEx
+          applied on the description (= simplified care site name) of each care site.
+        - [`"from_regex_on_parent_UF"`][eds_scikit.emergency.emergency_care_site.from_regex_on_parent_UF]: relies on a specific list of regular expressions
+          applied on the description (= simplified care site name) of each UF (Unité Fonctionnelle).
+          The obtained tag is then propagated to every UF's children.
+
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 1 to 2 added columns corresponding to the following concepts:
+
+        - `"IS_EMERGENCY"`
+        - `"EMERGENCY_TYPE"` (if using algo `"from_mapping"`)
+
+    """
+    if algo == "from_regex_on_parent_UF":
+        return from_regex_on_parent_UF(care_site)
+    elif algo == "from_regex_on_care_site_description":
+        return from_regex_on_care_site_description(care_site)
+    elif algo.startswith("from_mapping"):
+        return from_mapping(care_site, version=versionize(algo))
+
    +
    +
    + +
    + +
    + + + +

    + from_mapping + + +

    +
    from_mapping(care_site: DataFrame, version: Optional[str] = None) -> DataFrame
+
    + +
    + +

    This algo uses a labelled list of 201 emergency care sites.

    +

    Those care sites were extracted and verified by Ariel COHEN, +Judith LEBLANC, and an ER doctor validated them.

    +

    Those emergency care sites are further divised into different categories, +as defined in the concept 'EMERGENCY_TYPE'. +The different categories are:

    +
      +
    • Urgences spécialisées
      • +
    • UHCD + Post-urgences
      • +
    • Urgences pédiatriques
      • +
    • Urgences générales adulte
      • +
    • Consultation urgences
      • +
    • SAMU / SMUR
      • +
    +

    See the dataset here

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_source_value column

    +

    + + TYPE: + DataFrame + +

    +
    version +

    Optional version string for the mapping

    +

    + + TYPE: + Optional[str] + + + DEFAULT: + None + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + care_site + +

    Dataframe with 2 added columns corresponding to the following concepts:

    +
      +
    • "IS_EMERGENCY"
      • +
    • "EMERGENCY_TYPE"
      • +
    +

    + + TYPE: + DataFrame + +

    +
    + +
    + Source code in eds_scikit/emergency/emergency_care_site.py + 
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    @concept_checker(concepts=["IS_EMERGENCY", "EMERGENCY_TYPE"])
+def from_mapping(
+    care_site: DataFrame,
+    version: Optional[str] = None,
+) -> DataFrame:
+    """This algo uses a labelled list of 201 emergency care sites.
+
+    Those care sites were extracted and verified by Ariel COHEN,
+    Judith LEBLANC, and an ER doctor validated them.
+
+    Those emergency care sites are further divised into different categories,
+    as defined in the concept 'EMERGENCY_TYPE'.
+    The different categories are:
+
+    - Urgences spécialisées
+    - UHCD + Post-urgences
+    - Urgences pédiatriques
+    - Urgences générales adulte
+    - Consultation urgences
+    - SAMU / SMUR
+
+    See the dataset [here](/datasets/care-site-emergency)
+
+    Parameters
+    ----------
+    care_site: DataFrame
+        Should at least contains the `care_site_source_value` column
+    version: Optional[str]
+        Optional version string for the mapping
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 2 added columns corresponding to the following concepts:
+
+        - `"IS_EMERGENCY"`
+        - `"EMERGENCY_TYPE"`
+
+    """
+
+    function_name = "get_care_site_emergency_mapping"
+    if version is not None:
+        function_name += f".{version}"
+
+    mapping = registry.get("data", function_name=function_name)()
+
+    # Getting the right framework
+    fw = framework.get_framework(care_site)
+    mapping = framework.to(fw, mapping)
+
+    care_site = care_site.merge(
+        mapping,
+        how="left",
+        on="care_site_source_value",
+    )
+
+    care_site["IS_EMERGENCY"] = care_site["EMERGENCY_TYPE"].notna()
+
+    return care_site
+
    +
    +
    + +
    + +
    + + + +

    + from_regex_on_care_site_description + + +

    +
    from_regex_on_care_site_description(care_site: DataFrame) -> DataFrame
+
    + +
    + +

    Use regular expressions on care_site_name to decide if it an emergency care site.

    +

    This relies on this function. +The regular expression used to detect emergency status is r"URG|SAU|UHCDb|ZHTCD"

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_name column

    +

    + + TYPE: + DataFrame + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • "IS_EMERGENCY"
      • +
    +

    + + TYPE: + DataFrame + +

    +
    + +
    + Source code in eds_scikit/emergency/emergency_care_site.py + 
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    def from_regex_on_care_site_description(care_site: DataFrame) -> DataFrame:
+    """Use regular expressions on `care_site_name` to decide if it an emergency care site.
+
+    This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes].
+    The regular expression used to detect emergency status is `r"\bURG|\bSAU\b|\bUHCDb\b|\bZHTCD\b"`
+
+    Parameters
+    ----------
+    care_site: DataFrame
+        Should at least contains the `care_site_name` column
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 1 added column corresponding to the following concept:
+
+        - `"IS_EMERGENCY"`
+
+    """
+    return attributes.add_care_site_attributes(
+        care_site, only_attributes=["IS_EMERGENCY"]
+    )
+
    +
    +
    + +
    + +
    + + + +

    + from_regex_on_parent_UF + + +

    +
    from_regex_on_parent_UF(care_site: DataFrame) -> DataFrame
+
    + +
    + +

    Use regular expressions on parent UF (Unité Fonctionnelle) to classify emergency care site.

    +

    This relies on this function. +The regular expression used to detect emergency status is r"URG|SAU|UHCD|ZHTCD"

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_name column

    +

    + + TYPE: + DataFrame + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • 'IS_EMERGENCY'
      • +
    +

    + + TYPE: + DataFrame + +

    +
    + +
    + Source code in eds_scikit/emergency/emergency_care_site.py + 
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    @concept_checker(concepts=["IS_EMERGENCY"])
+def from_regex_on_parent_UF(care_site: DataFrame) -> DataFrame:
+    """Use regular expressions on parent UF (Unité Fonctionnelle) to classify emergency care site.
+
+    This relies on [this function][eds_scikit.structures.attributes.get_parent_attributes].
+    The regular expression used to detect emergency status is `r"\bURG|\bSAU\b|\bUHCD\b|\bZHTCD\b"`
+
+    Parameters
+    ----------
+    care_site: DataFrame
+        Should at least contains the `care_site_name` column
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 1 added column corresponding to the following concept:
+
+        - 'IS_EMERGENCY'
+    """
+    return attributes.get_parent_attributes(
+        care_site,
+        only_attributes=["IS_EMERGENCY"],
+        parent_type="Unité Fonctionnelle (UF)",
+    )
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/emergency/emergency_visit/index.html b/main/reference/emergency/emergency_visit/index.html new file mode 100644 index 00000000..41174df6 --- /dev/null +++ b/main/reference/emergency/emergency_visit/index.html @@ -0,0 +1,4281 @@ + + + + + + + + + + + + + + + + emergency_visit - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.emergency.emergency_visit

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + tag_emergency_visit + + +

    +
    tag_emergency_visit(visit_detail: DataFrame, care_site: Optional[DataFrame] = None, visit_occurrence: Optional[DataFrame] = None, algo: str = 'from_mapping') -> DataFrame
+
    + +
    + +

    Tag visits that correspond to medical emergency units.

    +

    The tagging is done by adding a "IS_EMERGENCY" column to the provided DataFrame.

    +

    Some algos can add an additional "EMERGENCY_TYPE" column to the provided DataFrame, +providing a more detailled classification.

    +

    It works by either tagging each visit detail's care site, +or by using the visit_occurrence's "visit_source_value".

    + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    visit_detail + +

    + + TYPE: + DataFrame + +

    +
    care_site +

    Isn't necessary if the algo "from_vo_visit_source_value" is used

    +

    + + TYPE: + Optional[DataFrame] + + + DEFAULT: + None + +

    +
    visit_occurrence +

    Is mandatory if the algo "from_vo_visit_source_value" is used

    +

    + + TYPE: + Optional[DataFrame] + + + DEFAULT: + None + +

    +
    algo +

    Possible values are:

    +
      +
    • "from_mapping" relies on a list of care_site_source_value extracted +by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites +are here further labelled to distinguish the different types of emergency
      • +
    • "from_regex_on_care_site_description": relies on a specific list of RegEx +applied on the description (= simplified care site name) of each care site.
      • +
    • "from_regex_on_parent_UF": relies on a specific list of regular expressions +applied on the description (= simplified care site name) of each UF (Unité Fonctionnelle). +The obtained tag is then propagated to every UF's children.
      • +
    • "from_vo_visit_source_value": +relies on the parent visit occurrence of each visit detail: +A visit detail will be tagged as emergency if it belongs to a visit occurrence where +visit_occurrence.visit_source_value=='urgence'.
      • +
    +

    + + TYPE: + str + + + DEFAULT: + 'from_mapping' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + care_site + +

    Dataframe with 1 to 2 added columns corresponding to the following concepts:

    +
      +
    • "IS_EMERGENCY"
      • +
    • "EMERGENCY_TYPE" (if using algo "from_mapping")
      • +
    +

    + + TYPE: + DataFrame + +

    +
    + +
    + Source code in eds_scikit/emergency/emergency_visit.py + 
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    @algo_checker(algos=ALGOS)
+def tag_emergency_visit(
+    visit_detail: DataFrame,
+    care_site: Optional[DataFrame] = None,
+    visit_occurrence: Optional[DataFrame] = None,
+    algo: str = "from_mapping",
+) -> DataFrame:
+    """Tag visits that correspond to **medical emergency units**.
+
+    The tagging is done by adding a `"IS_EMERGENCY"` column to the provided DataFrame.
+
+    Some algos can add an additional `"EMERGENCY_TYPE"` column to the provided DataFrame,
+    providing a more detailled classification.
+
+    It works by either [tagging each visit detail's care site][eds_scikit.emergency.emergency_care_site.tag_emergency_care_site],
+    or by using the *visit_occurrence*'s `"visit_source_value"`.
+
+    Parameters
+    ----------
+    visit_detail: DataFrame
+    care_site: DataFrame
+        Isn't necessary if the algo `"from_vo_visit_source_value"` is used
+    visit_occurrence: DataFrame, optional.
+        Is mandatory if the algo `"from_vo_visit_source_value"` is used
+    algo: str
+        Possible values are:
+
+        - [`"from_mapping"`][eds_scikit.emergency.emergency_care_site.from_mapping] relies on a list of `care_site_source_value` extracted
+          by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites
+          are here further labelled to distinguish the different types of emergency
+        - [`"from_regex_on_care_site_description"`][eds_scikit.emergency.emergency_care_site.from_regex_on_care_site_description]: relies on a specific list of RegEx
+          applied on the description (= simplified care site name) of each care site.
+        - [`"from_regex_on_parent_UF"`][eds_scikit.emergency.emergency_care_site.from_regex_on_parent_UF]: relies on a specific list of regular expressions
+          applied on the description (= simplified care site name) of each UF (Unité Fonctionnelle).
+          The obtained tag is then propagated to every UF's children.
+        - [`"from_vo_visit_source_value"`][eds_scikit.emergency.emergency_visit.from_vo_visit_source_value]:
+        relies on the parent visit occurrence of each visit detail:
+          A visit detail will be tagged as emergency if it belongs to a visit occurrence where
+          `visit_occurrence.visit_source_value=='urgence'`.
+
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 1 to 2 added columns corresponding to the following concepts:
+
+        - `"IS_EMERGENCY"`
+        - `"EMERGENCY_TYPE"` (if using algo `"from_mapping"`)
+    """
+
+    if algo == "from_vo_visit_source_value":
+        return from_vo_visit_source_value(visit_detail, visit_occurrence)
+
+    else:
+        initial_care_site_columns = set(care_site.columns)
+        tagged_care_site = tag_emergency_care_site(care_site, algo=algo)
+        to_add_columns = list(
+            set(tagged_care_site) - initial_care_site_columns | set(["care_site_id"])
+        )
+
+        return visit_detail.merge(
+            tagged_care_site[to_add_columns], on="care_site_id", how="left"
+        )
+
    +
    +
    + +
    + +
    + + + +

    + from_vo_visit_source_value + + +

    +
    from_vo_visit_source_value(visit_detail: DataFrame, visit_occurrence: DataFrame) -> DataFrame
+
    + +
    + +

    This algo uses the "Type de dossier" of each visit detail's parent visit occurrence. +Thus, a visit_detail will be tagged with IS_EMERGENCY=True iff the visit occurrence it belongs to +is an emergency-type visit (meaning that visit_occurrence.visit_source_value=='urgence')

    +
    +

    Admission through ICU

    +

    At AP-HP, when a patient is hospitalized after coming to the ICU, its visit_source_value + is set from "urgence" to "hospitalisation complète". So you should keep in mind + that this method doesn't tag those visits as ICU.

    +
    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    visit_detail + +

    + + TYPE: + DataFrame + +

    +
    visit_occurrence + +

    + + TYPE: + DataFrame + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + visit_detail + +

    Dataframe with added columns corresponding to the following conceps:

    +
      +
    • "IS_EMERGENCY"
      • +
    +

    + + TYPE: + DataFrame + +

    +
    + +
    + Source code in eds_scikit/emergency/emergency_visit.py + 
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    @concept_checker(concepts=["IS_EMERGENCY"])
+def from_vo_visit_source_value(
+    visit_detail: DataFrame,
+    visit_occurrence: DataFrame,
+) -> DataFrame:
+    """
+    This algo uses the *"Type de dossier"* of each visit detail's parent visit occurrence.
+    Thus, a visit_detail will be tagged with `IS_EMERGENCY=True` iff the visit occurrence it belongs to
+    is an emergency-type visit (meaning that `visit_occurrence.visit_source_value=='urgence'`)
+
+    !!! aphp "Admission through ICU"
+         At AP-HP, when a patient is hospitalized after coming to the ICU, its `visit_source_value`
+         is set from `"urgence"` to `"hospitalisation complète"`. So you should keep in mind
+         that this method doesn't tag those visits as ICU.
+
+    Parameters
+    ----------
+    visit_detail: DataFrame
+    visit_occurrence: DataFrame
+
+    Returns
+    -------
+    visit_detail: DataFrame
+        Dataframe with added columns corresponding to the following conceps:
+
+        - `"IS_EMERGENCY"`
+    """
+    vo_emergency = visit_occurrence[["visit_occurrence_id", "visit_source_value"]]
+    vo_emergency["IS_EMERGENCY"] = visit_occurrence.visit_source_value == "urgence"
+
+    return visit_detail.merge(
+        vo_emergency[["visit_occurrence_id", "IS_EMERGENCY"]],
+        on="visit_occurrence_id",
+        how="left",
+    )
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
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    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/emergency/index.html b/main/reference/emergency/index.html new file mode 100644 index 00000000..c8a229ad --- /dev/null +++ b/main/reference/emergency/index.html @@ -0,0 +1,3779 @@ + + + + + + + + + + + + + + + + `eds_scikit.emergency` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.emergency

    + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/event/ccam/index.html b/main/reference/event/ccam/index.html new file mode 100644 index 00000000..4f0c342d --- /dev/null +++ b/main/reference/event/ccam/index.html @@ -0,0 +1,4177 @@ + + + + + + + + + + + + + + + + ccam - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.event.ccam

    + + +
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    + + + + + + + + + +
    + + + +

    + procedures_from_ccam + + +

    +
    procedures_from_ccam(procedure_occurrence: DataFrame, visit_occurrence: Optional[DataFrame] = None, codes: Optional[Dict[str, Union[str, List[str]]]] = None, date_from_visit: bool = True, additional_filtering = dict(), date_min: Optional[datetime] = None, date_max: Optional[datetime] = None) -> DataFrame
+
    + +
    + +

    Phenotyping based on CCAM codes.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    procedure_occurrence +

    procedure_occurrence OMOP DataFrame.

    +

    + + TYPE: + DataFrame + +

    +
    visit_occurrence +

    visit_occurrence OMOP DataFrame, only necessary if date_from_visit is set to True.

    +

    + + TYPE: + Optional[DataFrame] + + + DEFAULT: + None + +

    +
    codes +

    Dictionary which values are CCAM codes (as a unique string or as a list) and which keys are +at least one of the following:

    +
      +
    • exact: To match the codes in codes["exact"] exactly
      • +
    • prefix: To match the codes in codes["prefix"] as prefixes
      • +
    • regex: To match the codes in codes["regex"] as regexes +You can combine any of those keys.
      • +
    +

    + + TYPE: + Dict[str, Union[str, List[str]]] + + + DEFAULT: + None + +

    +
    date_from_visit +

    If set to True, uses visit_start_datetime as the code datetime

    +

    + + TYPE: + bool + + + DEFAULT: + True + +

    +
    additional_filtering +

    An optional dictionary to filter the resulting DataFrame.

    +

    Keys should be column names on which to filter, and values should be either

    +
      +
    • A single value
      • +
    • A list or set of values.
      • +
    +

    + + TYPE: + Dict[str, Any] + + + DEFAULT: + dict() + +

    +
    date_min +

    The minimum code datetime to keep. Depends on the date_from_visit flag

    +

    + + TYPE: + Optional[datetime] + + + DEFAULT: + None + +

    +
    date_max +

    The minimum code datetime to keep. Depends on the date_from_visit flag

    +

    + + TYPE: + Optional[datetime] + + + DEFAULT: + None + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    "event" DataFrame including the following columns:

    +
      +
    • t_start: If date_from_visit is set to False, contains procedure_datetime, +else contains visit_start_datetime
      • +
    • t_end: If date_from_visit is set to False, contains procedure_datetime, +else contains visit_end_datetime
      • +
    • concept : contaning values from codes.keys()
      • +
    • value : The extracted CCAM code.
      • +
    • visit_occurrence_id : the visit_occurrence_id from the visit which contains the CCAM code.
      • +
    +
    + +
    + Source code in eds_scikit/event/ccam.py + 
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    def procedures_from_ccam(
+    procedure_occurrence: DataFrame,
+    visit_occurrence: Optional[DataFrame] = None,
+    codes: Optional[Dict[str, Union[str, List[str]]]] = None,
+    date_from_visit: bool = True,
+    additional_filtering=dict(),
+    date_min: Optional[datetime] = None,
+    date_max: Optional[datetime] = None,
+) -> DataFrame:
+    """
+
+    Phenotyping based on CCAM codes.
+
+    Parameters
+    ----------
+    procedure_occurrence : DataFrame
+        `procedure_occurrence` OMOP DataFrame.
+    visit_occurrence : Optional[DataFrame]
+        `visit_occurrence` OMOP DataFrame, only necessary if `date_from_visit` is set to `True`.
+    codes : Dict[str, Union[str, List[str]]]
+        Dictionary which values are CCAM codes (as a unique string or as a list) and which keys are
+        at least one of the following:
+
+        - `exact`: To match the codes in `codes["exact"]` **exactly**
+        - `prefix`: To match the codes in `codes["prefix"]` **as prefixes**
+        - `regex`: To match the codes in `codes["regex"]` **as regexes**
+        You can combine any of those keys.
+    date_from_visit : bool
+        If set to `True`, uses `visit_start_datetime` as the code datetime
+    additional_filtering : Dict[str, Any]
+        An optional dictionary to filter the resulting DataFrame.
+
+        Keys should be column names on which to filter, and values should be either
+
+        - A single value
+        - A list or set of values.
+    date_min : Optional[datetime]
+        The minimum code datetime to keep. **Depends on the `date_from_visit` flag**
+    date_max : Optional[datetime]
+        The minimum code datetime to keep. **Depends on the `date_from_visit` flag**
+
+    Returns
+    -------
+    DataFrame
+        "event" DataFrame including the following columns:
+
+        - `t_start`: If `date_from_visit` is set to `False`, contains `procedure_datetime`,
+        else contains `visit_start_datetime`
+        - `t_end`: If `date_from_visit` is set to `False`, contains `procedure_datetime`,
+        else contains `visit_end_datetime`
+        - `concept` : contaning values from `codes.keys()`
+        - `value` : The extracted CCAM code.
+        - `visit_occurrence_id` : the `visit_occurrence_id` from the visit which contains the CCAM code.
+    """  # noqa: E501
+
+    procedure_columns = dict(
+        code_source_value="procedure_source_value",
+        code_start_datetime="procedure_datetime",
+        code_end_datetime="procedure_datetime",
+    )
+    events = []
+
+    for concept, code_dict in codes.items():
+        tmp_df = event_from_code(
+            df=procedure_occurrence,
+            columns=procedure_columns,
+            visit_occurrence=visit_occurrence,
+            concept=concept,
+            codes=code_dict,
+            date_from_visit=date_from_visit,
+            additional_filtering=additional_filtering,
+            date_min=date_min,
+            date_max=date_max,
+        )
+
+        events.append(tmp_df)
+
+    framework = get_framework(procedure_occurrence)
+    return framework.concat(events)
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/event/consultations/index.html b/main/reference/event/consultations/index.html new file mode 100644 index 00000000..8cb1edda --- /dev/null +++ b/main/reference/event/consultations/index.html @@ -0,0 +1,4743 @@ + + + + + + + + + + + + + + + + consultations - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + +
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    + + + + + + + + +

    eds_scikit.event.consultations

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + get_consultation_dates + + +

    +
    get_consultation_dates(vo: DataFrame, note: DataFrame, note_nlp: Optional[DataFrame] = None, algo: Union[str, List[str]] = ['nlp'], max_timedelta: timedelta = timedelta(days=7), structured_config: Dict[str, Any] = dict(), nlp_config: Dict[str, Any] = dict()) -> DataFrame
+
    + +
    + +

    Extract consultation dates. +See the implementation details of the algo(s) you want to use

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    vo +

    visit_occurrence DataFrame

    +

    + + TYPE: + DataFrame + +

    +
    note +

    note DataFrame

    +

    + + TYPE: + DataFrame + +

    +
    note_nlp +

    note_nlp DataFrame, used only with the "nlp" algo

    +

    + + TYPE: + Optional[DataFrame] + + + DEFAULT: + None + +

    +
    algo +

    Algorithm(s) to use to determine consultation dates. +Multiple algorithms can be provided as a list. Accepted values are:

    + +

    + + TYPE: + Union[str, List[str]] + + + DEFAULT: + ['nlp'] + +

    +
    max_timedelta +

    If two extracted consultations are spaced by less than max_timedelta, +we consider that they correspond to the same event and only keep the first one.

    +

    + + TYPE: + timedelta + + + DEFAULT: + timedelta(days=7) + +

    +
    structured_config +

    A dictionnary of parameters when using the structured algorithm

    +

    + + TYPE: + Dict[str, Any] + + + DEFAULT: + dict() + +

    +
    nlp_config +

    A dictionnary of parameters when using the nlp algorithm

    +

    + + TYPE: + Dict[str, Any] + + + DEFAULT: + dict() + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    Event type DataFrame with the following columns:

    +
      +
    • person_id
      • +
    • visit_occurrence_id
      • +
    • CONSULTATION_DATE: corresponds to the note_datetime value of a consultation +report coming from the considered visit.
      • +
    • CONSULTATION_NOTE_ID: the note_id of the corresponding report.
      • +
    • CONSULTATION_DATE_EXTRACTION: the method of extraction
      • +
    +
    + +
    + Source code in eds_scikit/event/consultations.py + 
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    @concept_checker(
+    concepts=[
+        "CONSULTATION_DATE",
+        "CONSULTATION_ID",
+        "CONSULTATION_DATE_EXTRACTION",
+    ]
+)
+def get_consultation_dates(
+    vo: DataFrame,
+    note: DataFrame,
+    note_nlp: Optional[DataFrame] = None,
+    algo: Union[str, List[str]] = ["nlp"],
+    max_timedelta: timedelta = timedelta(days=7),
+    structured_config: Dict[str, Any] = dict(),
+    nlp_config: Dict[str, Any] = dict(),
+) -> DataFrame:
+    """
+    Extract consultation dates.
+    See the implementation details of the algo(s) you want to use
+
+    Parameters
+    ----------
+    vo : DataFrame
+        `visit_occurrence` DataFrame
+    note : DataFrame
+        `note` DataFrame
+    note_nlp : Optional[DataFrame]
+        `note_nlp` DataFrame, used only with the `"nlp"` algo
+    algo: Union[str, List[str]] = ["nlp"]
+        Algorithm(s) to use to determine consultation dates.
+        Multiple algorithms can be provided as a list. Accepted values are:
+
+        - `"structured"`: See [get_consultation_dates_structured()][eds_scikit.event.consultations.get_consultation_dates_structured]
+        - `"nlp"`: See [get_consultation_dates_nlp()][eds_scikit.event.consultations.get_consultation_dates_nlp]
+    max_timedelta: timedelta = timedelta(days=7)
+        If two extracted consultations are spaced by less than `max_timedelta`,
+        we consider that they correspond to the same event and only keep the first one.
+    structured_config : Dict[str, Any] = dict()
+        A dictionnary of parameters when using the [`structured`][eds_scikit.event.consultations.get_consultation_dates_structured] algorithm
+    nlp_config : Dict[str, Any] = dict()
+        A dictionnary of parameters when using the [`nlp`][eds_scikit.event.consultations.get_consultation_dates_nlp] algorithm
+
+    Returns
+    -------
+    DataFrame
+        Event type DataFrame with the following columns:
+
+        - `person_id`
+        - `visit_occurrence_id`
+        - `CONSULTATION_DATE`: corresponds to the `note_datetime` value of a consultation
+          report coming from the considered visit.
+        - `CONSULTATION_NOTE_ID`: the `note_id` of the corresponding report.
+        - `CONSULTATION_DATE_EXTRACTION`: the method of extraction
+
+    """
+
+    fw = get_framework(vo)
+
+    if type(algo) == str:
+        algo = [algo]
+
+    dates = []
+
+    for a in algo:
+        if a == "structured":
+            dates.append(
+                get_consultation_dates_structured(
+                    vo=vo,
+                    note=note,
+                    **structured_config,
+                )
+            )
+        if a == "nlp":
+            dates.append(
+                get_consultation_dates_nlp(
+                    note_nlp=note_nlp,
+                    **nlp_config,
+                )
+            )
+
+    dates_per_note = (
+        fw.concat(dates)
+        .reset_index()
+        .merge(note[["note_id", "visit_occurrence_id"]], on="note_id", how="inner")
+    )
+
+    # Remove timezone errors from spark
+    dates_per_note["CONSULTATION_DATE"] = dates_per_note["CONSULTATION_DATE"].astype(
+        str
+    )
+
+    dates_per_visit = (
+        dates_per_note.groupby(["visit_occurrence_id", "CONSULTATION_DATE"])[
+            "CONSULTATION_DATE_EXTRACTION"
+        ]
+        .unique()
+        .apply(sorted)
+        .str.join("+")
+    )
+
+    dates_per_visit.name = "CONSULTATION_DATE_EXTRACTION"
+
+    dates_per_visit = bd.add_unique_id(
+        dates_per_visit.reset_index(), col_name="TMP_CONSULTATION_ID"
+    )
+
+    # Convert back to datetime format
+    dates_per_visit["CONSULTATION_DATE"] = bd.to_datetime(
+        dates_per_visit["CONSULTATION_DATE"], errors="coerce"
+    )
+
+    dates_per_visit = clean_consultations(
+        dates_per_visit,
+        max_timedelta,
+    )
+
+    # Equivalent to df.spark.cache() for ks.DataFrame
+    bd.cache(dates_per_visit)
+
+    return dates_per_visit
+
    +
    +
    + +
    + +
    + + + +

    + get_consultation_dates_structured + + +

    +
    get_consultation_dates_structured(note: DataFrame, vo: Optional[DataFrame] = None, kept_note_class_source_value: Optional[Union[str, List[str]]] = 'CR-CONS', kept_visit_source_value: Optional[Union[str, List[str]]] = 'consultation externe') -> DataFrame
+
    + +
    + +

    Uses note_datetime value to infer true consultation dates

    + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    note +

    A note DataFrame with at least the following columns:

    +
      +
    • note_id
      • +
    • note_datetime
      • +
    • note_source_value if kept_note_class_source_value is not None
      • +
    • visit_occurrence_id if kept_visit_source_value is not None
      • +
    +

    + + TYPE: + DataFrame + +

    +
    vo +

    A visit_occurrence DataFrame to provide if kept_visit_source_value is not None, +with at least the following columns:

    +
      +
    • visit_occurrence_id
      • +
    • visit_source_value if kept_visit_source_value is not None
      • +
    +

    + + TYPE: + Optional[DataFrame] + + + DEFAULT: + None + +

    +
    kept_note_class_source_value +

    Value(s) allowed for the note_class_source_value column.

    +

    + + TYPE: + Optional[Union[str, List[str]]] + + + DEFAULT: + 'CR-CONS' + +

    +
    kept_visit_source_value +

    Value(s) allowed for the visit_source_value column.

    +

    + + TYPE: + Optional[Union[str, List[str]]], optional + + + DEFAULT: + 'consultation externe' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + Dataframe + + +

    With 2 added columns corresponding to the following concept:

    +
      +
    • CONSULTATION_DATE, containing the date
      • +
    • CONSULTATION_DATE_EXTRACTION, containing "STRUCTURED"
      • +
    +
    + +
    + Source code in eds_scikit/event/consultations.py + 
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    def get_consultation_dates_structured(
+    note: DataFrame,
+    vo: Optional[DataFrame] = None,
+    kept_note_class_source_value: Optional[Union[str, List[str]]] = "CR-CONS",
+    kept_visit_source_value: Optional[Union[str, List[str]]] = "consultation externe",
+) -> DataFrame:
+    """
+    Uses `note_datetime` value to infer *true* consultation dates
+
+    Parameters
+    ----------
+    note : DataFrame
+        A `note` DataFrame with at least the following columns:
+
+        - `note_id`
+        - `note_datetime`
+        - `note_source_value` **if** `kept_note_class_source_value is not None`
+        - `visit_occurrence_id` **if** `kept_visit_source_value is not None`
+    vo : Optional[DataFrame]
+        A visit_occurrence DataFrame to provide **if** `kept_visit_source_value is not None`,
+        with at least the following columns:
+
+        - `visit_occurrence_id`
+        - `visit_source_value` **if** `kept_visit_source_value is not None`
+    kept_note_class_source_value : Optional[Union[str, List[str]]]
+        Value(s) allowed for the `note_class_source_value` column.
+    kept_visit_source_value : Optional[Union[str, List[str]]], optional
+        Value(s) allowed for the `visit_source_value` column.
+
+    Returns
+    -------
+    Dataframe
+        With 2 added columns corresponding to the following concept:
+
+        - `CONSULTATION_DATE`, containing the date
+        - `CONSULTATION_DATE_EXTRACTION`, containing `"STRUCTURED"`
+    """
+
+    kept_note = note
+
+    if kept_note_class_source_value is not None:
+        if type(kept_note_class_source_value) == str:
+            kept_note_class_source_value = [kept_note_class_source_value]
+        kept_note = note[
+            note.note_class_source_value.isin(set(kept_note_class_source_value))
+        ]
+
+    if kept_visit_source_value is not None:
+        if type(kept_visit_source_value) == str:
+            kept_visit_source_value = [kept_visit_source_value]
+        kept_note = kept_note.merge(
+            vo[
+                [
+                    "visit_occurrence_id",
+                    "visit_source_value",
+                ]
+            ][vo.visit_source_value.isin(set(kept_visit_source_value))],
+            on="visit_occurrence_id",
+        )
+
+    dates_per_note = kept_note[["note_datetime", "note_id"]].rename(
+        columns={
+            "note_datetime": "CONSULTATION_DATE",
+        }
+    )
+
+    dates_per_note["CONSULTATION_DATE_EXTRACTION"] = "STRUCTURED"
+
+    return dates_per_note.set_index("note_id")
+
    +
    +
    + +
    + +
    + + + +

    + get_consultation_dates_nlp + + +

    +
    get_consultation_dates_nlp(note_nlp: DataFrame, dates_to_keep: str = 'min') -> DataFrame
+
    + +
    + +

    Uses consultation dates extracted a priori in consultation reports to infer true consultation dates

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    note_nlp +

    A DataFrame with (at least) the following columns:

    +
      +
    • note_id
      • +
    • consultation_date
      • +
    • end if using dates_to_keep=first: +end should store the character offset of the extracted date.
      • +
    +

    + + TYPE: + DataFrame + +

    +
    dates_to_keep +

    How to handle multiple consultation dates found in the document:

    +
      +
    • min: keep the oldest one
      • +
    • first: keep the occurrence that appeared first in the text
      • +
    • all: keep all date
      • +
    +

    + + TYPE: + str, optional + + + DEFAULT: + 'min' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + Dataframe + + +

    With 2 added columns corresponding to the following concept:

    +
      +
    • CONSULTATION_DATE, containing the date
      • +
    • CONSULTATION_DATE_EXTRACTION, containing "NLP"
      • +
    +
    + +
    + Source code in eds_scikit/event/consultations.py + 
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    def get_consultation_dates_nlp(
+    note_nlp: DataFrame,
+    dates_to_keep: str = "min",
+) -> DataFrame:
+    """
+    Uses consultation dates extracted *a priori* in consultation reports to infer *true* consultation dates
+
+    Parameters
+    ----------
+    note_nlp : DataFrame
+        A DataFrame with (at least) the following columns:
+
+        - `note_id`
+        - `consultation_date`
+        - `end` **if** using `dates_to_keep=first`:
+        `end` should store the character offset of the extracted date.
+    dates_to_keep : str, optional
+        How to handle multiple consultation dates found in the document:
+
+        - `min`: keep the oldest one
+        - `first`: keep the occurrence that appeared first in the text
+        - `all`: keep all date
+
+    Returns
+    -------
+    Dataframe
+        With 2 added columns corresponding to the following concept:
+
+        - `CONSULTATION_DATE`, containing the date
+        - `CONSULTATION_DATE_EXTRACTION`, containing `"NLP"`
+    """
+
+    if dates_to_keep == "min":
+        dates_per_note = note_nlp.groupby("note_id").agg(
+            CONSULTATION_DATE=("consultation_date", "min"),
+        )
+    elif dates_to_keep == "first":
+        dates_per_note = (
+            note_nlp.sort_values(by="start")
+            .groupby("note_id")
+            .agg(CONSULTATION_DATE=("consultation_date", "first"))
+        )
+    elif dates_to_keep == "all":
+        dates_per_note = note_nlp[["consultation_date", "note_id"]].set_index("note_id")
+        dates_per_note = dates_per_note.rename(
+            columns={"consultation_date": "CONSULTATION_DATE"}
+        )
+    dates_per_note["CONSULTATION_DATE_EXTRACTION"] = "NLP"
+
+    return dates_per_note
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/event/diabetes/index.html b/main/reference/event/diabetes/index.html new file mode 100644 index 00000000..2b6ac7aa --- /dev/null +++ b/main/reference/event/diabetes/index.html @@ -0,0 +1,4182 @@ + + + + + + + + + + + + + + + + diabetes - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.event.diabetes

    + + +
    + + + +
    + + + +
    + + + + + + + +
    + + + +

    + DEFAULT_DIABETE_FROM_ICD10_CONFIG + + + + module-attribute + + +

    +
    DEFAULT_DIABETE_FROM_ICD10_CONFIG = dict(codes=dict(DIABETES_TYPE_I=dict(prefix='E10'), DIABETES_TYPE_II=dict(prefix='E11'), DIABETES_MALNUTRITION=dict(prefix='E12'), DIABETES_IN_PREGNANCY=dict(prefix='O24'), OTHER_DIABETES_MELLITUS=dict(prefix=['E13', 'E14']), DIABETES_INSIPIDUS=dict(exact=['E232', 'N251'])), date_from_visit=True, additional_filtering=dict(condition_status_source_value={'DP', 'DAS'}))
+
    + +
    + +

    Default parameters feeded to conditions_from_icd10()

    +
    + +
    + + + +
    + + + +

    + diabetes_from_icd10 + + +

    +
    diabetes_from_icd10(condition_occurrence: DataFrame, visit_occurrence: DataFrame, date_min: Optional[datetime] = None, date_max: Optional[datetime] = None, codes: Dict[str, Union[str, List[str]]] = DEFAULT_DIABETE_FROM_ICD10_CONFIG['codes'], date_from_visit: bool = DEFAULT_DIABETE_FROM_ICD10_CONFIG['date_from_visit'], additional_filtering: Dict[str, Any] = DEFAULT_DIABETE_FROM_ICD10_CONFIG['additional_filtering']) -> DataFrame
+
    + +
    + +

    Wrapper around the conditions_from_icd10() function. +Check the default configuration to see +the used parameters

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    condition_occurrence +

    OMOP-like condition occurrence DataFrame

    +

    + + TYPE: + DataFrame + +

    +
    visit_occurrence +

    OMOP-like visit_occurrence DataFrame

    +

    + + TYPE: + Optional[DataFrame] + +

    +
    date_min +

    Lower temporal bound

    +

    + + TYPE: + Optional[datetime] + + + DEFAULT: + None + +

    +
    date_max +

    Upper temporal bound

    +

    + + TYPE: + Optional[datetime] + + + DEFAULT: + None + +

    +
    codes +

    Dictionary of ICD-10 used for phenotyping

    +

    + + TYPE: + Optional[Dict[str, Union[str, List[str]]]] + + + DEFAULT: + DEFAULT_DIABETE_FROM_ICD10_CONFIG['codes'] + +

    +
    date_from_visit +

    If true, use the visit_[start/end]_datetime for filtering. Else, use condition_start_datetime

    +

    + + TYPE: + bool, by default True + + + DEFAULT: + DEFAULT_DIABETE_FROM_ICD10_CONFIG['date_from_visit'] + +

    +
    additional_filtering +

    A dictionary to perform additional filtering.

    +
      +
    • Each key should be a valid column name from condition_occurrence
      • +
    • Each value should be a value / set of values / list of values +For each pair (key, value), filtering is done as condition_occurrence[condition_occurrence[k].isin(v)]
      • +
    +

    + + TYPE: + Dict[str, Any] + + + DEFAULT: + DEFAULT_DIABETE_FROM_ICD10_CONFIG['additional_filtering'] + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    Event DataFrame in long format (with a concept and a value column). +The concept column contains one of the following:

    +
      +
    • DIABETES_TYPE_I
      • +
    • DIABETES_TYPE_II
      • +
    • DIABETES_MALNUTRITION
      • +
    • DIABETES_IN_PREGNANCY
      • +
    • OTHER_DIABETES_MELLITUS
      • +
    • DIABETES_INSIPIDUS +The value column contains the corresponding ICD-10 code that was extracted
      • +
    +
    + +
    + Source code in eds_scikit/event/diabetes.py + 
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    def diabetes_from_icd10(
+    condition_occurrence: DataFrame,
+    visit_occurrence: DataFrame,
+    date_min: Optional[datetime] = None,
+    date_max: Optional[datetime] = None,
+    codes: Dict[str, Union[str, List[str]]] = DEFAULT_DIABETE_FROM_ICD10_CONFIG[
+        "codes"
+    ],
+    date_from_visit: bool = DEFAULT_DIABETE_FROM_ICD10_CONFIG["date_from_visit"],
+    additional_filtering: Dict[str, Any] = DEFAULT_DIABETE_FROM_ICD10_CONFIG[
+        "additional_filtering"
+    ],
+) -> DataFrame:
+    """
+    Wrapper around the [conditions_from_icd10()][eds_scikit.event.icd10.conditions_from_icd10] function.
+    Check the [default configuration][eds_scikit.event.diabetes.DEFAULT_DIABETE_FROM_ICD10_CONFIG] to see
+    the used parameters
+
+    Parameters
+    ----------
+    condition_occurrence
+        OMOP-like condition occurrence DataFrame
+    visit_occurrence : Optional[DataFrame]
+        OMOP-like visit_occurrence DataFrame
+    date_min : Optional[datetime]
+        Lower temporal bound
+    date_max : Optional[datetime]
+        Upper temporal bound
+    codes : Optional[Dict[str, Union[str, List[str]]]]
+        Dictionary of ICD-10 used for phenotyping
+    date_from_visit : bool, by default True
+        If true, use the `visit_[start/end]_datetime` for filtering. Else, use `condition_start_datetime`
+    additional_filtering : Dict[str, Any]
+        A dictionary to perform additional filtering.
+
+        - **Each key** should be a valid column name from `condition_occurrence`
+        - **Each value** should be a value / set of values / list of values
+        For each pair (key, value), filtering is done as `condition_occurrence[condition_occurrence[k].isin(v)]`
+
+    Returns
+    -------
+    DataFrame
+        Event DataFrame in **long** format (with a `concept` and a `value` column).
+        The `concept` column contains one of the following:
+
+        - DIABETES_TYPE_I
+        - DIABETES_TYPE_II
+        - DIABETES_MALNUTRITION
+        - DIABETES_IN_PREGNANCY
+        - OTHER_DIABETES_MELLITUS
+        - DIABETES_INSIPIDUS
+        The `value` column contains the corresponding ICD-10 code that was extracted
+    """
+    diabetes = conditions_from_icd10(
+        condition_occurrence=condition_occurrence,
+        visit_occurrence=visit_occurrence,
+        date_min=date_min,
+        date_max=date_max,
+        codes=codes,
+        date_from_visit=date_from_visit,
+        additional_filtering=additional_filtering,
+    )
+
+    diabetes["value"] = diabetes["concept"]
+    diabetes["concept"] = "DIABETES_FROM_ICD10"
+
+    return diabetes
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/event/from_code/index.html b/main/reference/event/from_code/index.html new file mode 100644 index 00000000..984d04b0 --- /dev/null +++ b/main/reference/event/from_code/index.html @@ -0,0 +1,4316 @@ + + + + + + + + + + + + + + + + from_code - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.event.from_code

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + event_from_code + + +

    +
    event_from_code(df: DataFrame, columns: Dict[str, str], visit_occurrence: Optional[DataFrame] = None, concept: str = 'ICD10', codes: Optional[Dict[str, Union[str, List[str]]]] = None, date_from_visit: bool = True, additional_filtering: Dict[str, Any] = dict(), date_min: Optional[datetime] = None, date_max: Optional[datetime] = None) -> DataFrame
+
    + +
    + +

    Generic function to filter a DataFrame based on one of its column and an ensemble of codes to select from.

    +

    For instance, this function is called when phenotyping via ICD-10 or CCAM.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    df +

    The DataFrame to filter.

    +

    + + TYPE: + DataFrame + +

    +
    columns +

    Dictionary with the following keys:

    +
      +
    • code_source_value : The column name containing the code to filter
      • +
    • code_start_datetime : The column name containing the starting date
      • +
    • code_end_datetime : The column name containing the ending date
      • +
    +

    + + TYPE: + Dict[str, str] + +

    +
    visit_occurrence +

    The visit_occurrence DataFrame, only necessary if date_from_visit is set to True.

    +

    + + TYPE: + Optional[DataFrame] + + + DEFAULT: + None + +

    +
    concept +

    The name of the extracted concept

    +

    + + TYPE: + str + + + DEFAULT: + 'ICD10' + +

    +
    codes +

    Dictionary which values are codes (as a unique string or as a list) and which keys are +at least one of the following:

    +
      +
    • exact: To match the codes in codes["exact"] exactly
      • +
    • prefix: To match the codes in codes["prefix"] as prefixes
      • +
    • regex: To match the codes in codes["regex"] as regexes +You can combine any of those keys.
      • +
    +

    + + TYPE: + Dict[str, Union[str, List[str]]] + + + DEFAULT: + None + +

    +
    date_from_visit +

    If set to True, uses visit_start_datetime as the code datetime

    +

    + + TYPE: + bool + + + DEFAULT: + True + +

    +
    additional_filtering +

    An optional dictionary to filter the resulting DataFrame. +Keys should be column names on which too filter, and values should be either

    +
      +
    • A single value
      • +
    • A list or set of values.
      • +
    +

    + + TYPE: + Dict[str, Any] + + + DEFAULT: + dict() + +

    +
    date_min +

    The minimum code datetime to keep. Depends on the date_from_visit flag

    +

    + + TYPE: + Optional[datetime] + + + DEFAULT: + None + +

    +
    date_max +

    The minimum code datetime to keep. Depends on the date_from_visit flag

    +

    + + TYPE: + Optional[datetime] + + + DEFAULT: + None + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    A DataFrame containing especially the following columns:

    +
      +
    • t_start
      • +
    • t_end
      • +
    • concept : The provided concept string
      • +
    • value : The matched code
      • +
    +
    + +
    + Source code in eds_scikit/event/from_code.py + 
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    def event_from_code(
+    df: DataFrame,
+    columns: Dict[str, str],
+    visit_occurrence: Optional[DataFrame] = None,
+    concept: str = "ICD10",
+    codes: Optional[Dict[str, Union[str, List[str]]]] = None,
+    date_from_visit: bool = True,
+    additional_filtering: Dict[str, Any] = dict(),
+    date_min: Optional[datetime] = None,
+    date_max: Optional[datetime] = None,
+) -> DataFrame:
+    """
+    Generic function to filter a DataFrame based on one of its column and an ensemble of codes to select from.
+
+    For instance, this function is called when phenotyping via ICD-10 or CCAM.
+
+    Parameters
+    ----------
+    df : DataFrame
+        The DataFrame to filter.
+    columns : Dict[str, str]
+        Dictionary with the following keys:
+
+        - `code_source_value` : The column name containing the code to filter
+        - `code_start_datetime` : The column name containing the starting date
+        - `code_end_datetime` : The column name containing the ending date
+    visit_occurrence : Optional[DataFrame]
+        The `visit_occurrence` DataFrame, only necessary if `date_from_visit` is set to `True`.
+    concept : str
+        The name of the extracted concept
+    codes : Dict[str, Union[str, List[str]]]
+        Dictionary which values are codes (as a unique string or as a list) and which keys are
+        at least one of the following:
+
+        - `exact`: To match the codes in `codes["exact"]` **exactly**
+        - `prefix`: To match the codes in `codes["prefix"]` **as prefixes**
+        - `regex`: To match the codes in `codes["regex"]` **as regexes**
+        You can combine any of those keys.
+    date_from_visit : bool
+        If set to `True`, uses `visit_start_datetime` as the code datetime
+    additional_filtering : Dict[str, Any]
+        An optional dictionary to filter the resulting DataFrame.
+        Keys should be column names on which too filter, and values should be either
+
+        - A single value
+        - A list or set of values.
+    date_min : Optional[datetime]
+        The minimum code datetime to keep. **Depends on the `date_from_visit` flag**
+    date_max : Optional[datetime]
+        The minimum code datetime to keep. **Depends on the `date_from_visit` flag**
+
+    Returns
+    -------
+    DataFrame
+        A DataFrame containing especially the following columns:
+
+        - `t_start`
+        - `t_end`
+        - `concept` : The provided `concept` string
+        - `value` : The matched code
+
+    """
+
+    required_columns = list(columns.values()) + ["visit_occurrence_id", "person_id"]
+    check_columns(df, required_columns=required_columns)
+
+    d_format = {"exact": r"{code}\b", "regex": r"{code}", "prefix": r"\b{code}"}
+    regexes = []
+
+    for code_type, code_list in codes.items():
+
+        if type(code_list) == str:
+            code_list = [code_list]
+        codes_formated = [d_format[code_type].format(code=code) for code in code_list]
+        regexes.append(r"(?:" + "|".join(codes_formated) + ")")
+
+    final_regex = "|".join(regexes)
+
+    mask = df[columns["code_source_value"]].str.contains(final_regex).fillna(False)
+
+    event = df[mask]
+
+    if date_from_visit:
+        if visit_occurrence is None:
+            raise ValueError(
+                "With 'date_from_visit=True', you should provide a 'visit_occurrence' DataFrame."
+            )
+        event = event.merge(
+            visit_occurrence[
+                ["visit_occurrence_id", "visit_start_datetime", "visit_end_datetime"]
+            ],
+            on="visit_occurrence_id",
+            how="inner",
+        ).rename(
+            columns={
+                "visit_start_datetime": "t_start",
+                "visit_end_datetime": "t_end",
+            }
+        )
+
+    else:
+        event.loc[:, "t_start"] = event.loc[:, columns["code_start_datetime"]]
+        event.loc[:, "t_end"] = event.loc[:, columns["code_end_datetime"]]
+        event = event.drop(
+            columns=[columns["code_start_datetime"], columns["code_end_datetime"]]
+        )
+
+    event = _column_filtering(event, filtering_dict=additional_filtering)
+
+    mask = True  # Resetting the mask
+
+    if date_min is not None:
+        mask = mask & (event.t_start >= date_min)
+
+    if date_max is not None:
+        mask = mask & (event.t_start <= date_max)
+
+    if type(mask) != bool:  # We have a Series mask
+        event = event[mask]
+
+    event["concept"] = concept
+
+    return event.rename(columns={columns["code_source_value"]: "value"})[
+        [
+            "person_id",
+            "t_start",
+            "t_end",
+            "concept",
+            "value",
+            "visit_occurrence_id",
+        ]
+        + list(additional_filtering.keys())
+    ].reset_index(drop=True)
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/event/icd10/index.html b/main/reference/event/icd10/index.html new file mode 100644 index 00000000..3dde5ff3 --- /dev/null +++ b/main/reference/event/icd10/index.html @@ -0,0 +1,4192 @@ + + + + + + + + + + + + + + + + icd10 - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.event.icd10

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + conditions_from_icd10 + + +

    +
    conditions_from_icd10(condition_occurrence: DataFrame, visit_occurrence: Optional[DataFrame] = None, codes: Optional[Dict[str, Union[str, List[str]]]] = None, date_from_visit: bool = True, additional_filtering: Dict[str, Any] = None, date_min: Optional[datetime] = None, date_max: Optional[datetime] = None) -> DataFrame
+
    + +
    + +

    Phenotyping based on ICD-10 codes.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    condition_occurrence +

    condition_occurrence OMOP DataFrame.

    +

    + + TYPE: + DataFrame + +

    +
    visit_occurrence +

    visit_occurrence OMOP DataFrame, only necessary if date_from_visit is set to True.

    +

    + + TYPE: + Optional[DataFrame] + + + DEFAULT: + None + +

    +
    codes +

    Dictionary which values are ICD-10 codes (as a unique string or as a list) and which keys are +at least one of the following:

    +
      +
    • exact: To match the codes in codes["exact"] exactly
      • +
    • prefix: To match the codes in codes["prefix"] as prefixes
      • +
    • regex: To match the codes in codes["regex"] as regexes +You can combine any of those keys.
      • +
    +

    + + TYPE: + Dict[str, Union[str, List[str]]] + + + DEFAULT: + None + +

    +
    date_from_visit +

    If set to True, uses visit_start_datetime as the code datetime

    +

    + + TYPE: + bool + + + DEFAULT: + True + +

    +
    additional_filtering +

    An optional dictionary to filter the resulting DataFrame.

    +

    Keys should be column names on which to filter, and values should be either

    +
      +
    • A single value
      • +
    • A list or set of values.
      • +
    +

    Default filetring is condition_status_source_value in {"DP", "DAS", "DR"}

    +

    + + TYPE: + Dict[str, Any] + + + DEFAULT: + None + +

    +
    date_min +

    The minimum code datetime to keep. Depends on the date_from_visit flag

    +

    + + TYPE: + Optional[datetime] + + + DEFAULT: + None + +

    +
    date_max +

    The minimum code datetime to keep. Depends on the date_from_visit flag

    +

    + + TYPE: + Optional[datetime] + + + DEFAULT: + None + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    "event" DataFrame including the following columns:

    +
      +
    • t_start: If date_from_visit is set to False, contains condition_start_datetime, +else contains visit_start_datetime
      • +
    • t_end: If date_from_visit is set to False, contains condition_start_datetime, +else contains visit_end_datetime
      • +
    • concept : contaning values from codes.keys()
      • +
    • value : The extracted ICD-10 code.
      • +
    • visit_occurrence_id : the visit_occurrence_id from the visit which contains the ICD-10 code.
      • +
    +
    + +
    + Source code in eds_scikit/event/icd10.py + 
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    def conditions_from_icd10(
+    condition_occurrence: DataFrame,
+    visit_occurrence: Optional[DataFrame] = None,
+    codes: Optional[Dict[str, Union[str, List[str]]]] = None,
+    date_from_visit: bool = True,
+    additional_filtering: Dict[str, Any] = None,
+    date_min: Optional[datetime] = None,
+    date_max: Optional[datetime] = None,
+) -> DataFrame:
+    """
+
+    Phenotyping based on ICD-10 codes.
+
+    Parameters
+    ----------
+    condition_occurrence : DataFrame
+        `condition_occurrence` OMOP DataFrame.
+    visit_occurrence : Optional[DataFrame]
+        `visit_occurrence` OMOP DataFrame, only necessary if `date_from_visit` is set to `True`.
+    codes : Dict[str, Union[str, List[str]]]
+        Dictionary which values are ICD-10 codes (as a unique string or as a list) and which keys are
+        at least one of the following:
+
+        - `exact`: To match the codes in `codes["exact"]` **exactly**
+        - `prefix`: To match the codes in `codes["prefix"]` **as prefixes**
+        - `regex`: To match the codes in `codes["regex"]` **as regexes**
+        You can combine any of those keys.
+    date_from_visit : bool
+        If set to `True`, uses `visit_start_datetime` as the code datetime
+    additional_filtering : Dict[str, Any]
+        An optional dictionary to filter the resulting DataFrame.
+
+        Keys should be column names on which to filter, and values should be either
+
+        - A single value
+        - A list or set of values.
+
+        Default filetring is condition_status_source_value in {"DP", "DAS", "DR"}
+    date_min : Optional[datetime]
+        The minimum code datetime to keep. **Depends on the `date_from_visit` flag**
+    date_max : Optional[datetime]
+        The minimum code datetime to keep. **Depends on the `date_from_visit` flag**
+
+    Returns
+    -------
+    DataFrame
+        "event" DataFrame including the following columns:
+
+        - `t_start`: If `date_from_visit` is set to `False`, contains `condition_start_datetime`,
+        else contains `visit_start_datetime`
+        - `t_end`: If `date_from_visit` is set to `False`, contains `condition_start_datetime`,
+        else contains `visit_end_datetime`
+        - `concept` : contaning values from `codes.keys()`
+        - `value` : The extracted ICD-10 code.
+        - `visit_occurrence_id` : the `visit_occurrence_id` from the visit which contains the ICD-10 code.
+    """  # noqa: E501
+    if additional_filtering is None:
+        additional_filtering = dict()
+
+    DEFAULT_FILTERING = dict(condition_status_source_value={"DP", "DAS", "DR"})
+    DEFAULT_FILTERING.update(additional_filtering)
+
+    condition_columns = dict(
+        code_source_value="condition_source_value",
+        code_start_datetime="condition_start_datetime",
+        code_end_datetime="condition_start_datetime",
+    )
+    events = []
+
+    for concept, code_dict in codes.items():
+        tmp_df = event_from_code(
+            df=condition_occurrence,
+            columns=condition_columns,
+            visit_occurrence=visit_occurrence,
+            concept=concept,
+            codes=code_dict,
+            date_from_visit=date_from_visit,
+            additional_filtering=DEFAULT_FILTERING,
+            date_min=date_min,
+            date_max=date_max,
+        )
+
+        events.append(tmp_df)
+
+    framework = get_framework(condition_occurrence)
+    return framework.concat(events)
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
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    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/event/index.html b/main/reference/event/index.html new file mode 100644 index 00000000..4c99eb99 --- /dev/null +++ b/main/reference/event/index.html @@ -0,0 +1,3779 @@ + + + + + + + + + + + + + + + + `eds_scikit.event` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.event

    + + +
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    + +
    + +
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    +
    +
    + + + + Back to top + + +
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    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/event/suicide_attempt/index.html b/main/reference/event/suicide_attempt/index.html new file mode 100644 index 00000000..a8405d44 --- /dev/null +++ b/main/reference/event/suicide_attempt/index.html @@ -0,0 +1,4164 @@ + + + + + + + + + + + + + + + + suicide_attempt - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.event.suicide_attempt

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + tag_suicide_attempt + + +

    +
    tag_suicide_attempt(visit_occurrence: DataFrame, condition_occurrence: DataFrame, date_min: Optional[datetime] = None, date_max: Optional[datetime] = None, algo: str = 'X60-X84') -> DataFrame
+
    + +
    + +

    Function to return visits that fulfill different definitions of suicide attempt by ICD10.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    visit_occurrence + +

    + + TYPE: + DataFrame + +

    +
    condition_occurrence + +

    + + TYPE: + DataFrame + +

    +
    date_min +

    Minimal starting date (on visit_start_datetime)

    +

    + + TYPE: + Optional[datetime] + + + DEFAULT: + None + +

    +
    date_max +

    Maximal starting date (on visit_start_datetime)

    +

    + + TYPE: + Optional[datetime] + + + DEFAULT: + None + +

    +
    algo +

    Method to use. Available values are:

    +
      +
    • "X60-X84": Will return a the visits that have at least one ICD code that belongs to the range X60 to X84.
      • +
    • "Haguenoer2008": Will return a the visits that follow the definiton of "Haguenoer, Ken, Agnès Caille, Marc Fillatre, Anne Isabelle Lecuyer, et Emmanuel Rusch. « Tentatives de Suicide », 2008, 4.". This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84.
      • +
    +

    + + TYPE: + str + + + DEFAULT: + 'X60-X84' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + visit_occurrence + +

    Tagged with an additional column SUICIDE_ATTEMPT

    +

    + + TYPE: + DataFrame + +

    +
    +
    +

    Tip

    +

    These rules were implemented in the CSE project n°210013

    +
    + +
    + Source code in eds_scikit/event/suicide_attempt.py + 
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    @concept_checker(concepts=["SUICIDE_ATTEMPT"])
+@algo_checker(algos=ALGOS)
+def tag_suicide_attempt(
+    visit_occurrence: DataFrame,
+    condition_occurrence: DataFrame,
+    date_min: Optional[datetime] = None,
+    date_max: Optional[datetime] = None,
+    algo: str = "X60-X84",
+) -> DataFrame:
+    """
+    Function to return visits that fulfill different definitions of suicide attempt by ICD10.
+
+    Parameters
+    ----------
+    visit_occurrence: DataFrame
+    condition_occurrence: DataFrame
+    date_min: datetime
+        Minimal starting date (on `visit_start_datetime`)
+    date_max: datetime
+        Maximal starting date (on `visit_start_datetime`)
+    algo: str
+        Method to use. Available values are:
+
+        - `"X60-X84"`: Will return a the visits that have at least one ICD code that belongs to the range X60 to X84.
+        - `"Haguenoer2008"`: Will return a the visits that follow the definiton of "*Haguenoer, Ken, Agnès Caille, Marc Fillatre, Anne Isabelle Lecuyer, et Emmanuel Rusch. « Tentatives de Suicide », 2008, 4.*". This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84.
+
+    Returns
+    -------
+    visit_occurrence: DataFrame
+        Tagged with an additional column `SUICIDE_ATTEMPT`
+
+    !!! tip
+         These rules were implemented in the CSE project n°210013
+
+    """
+
+    events_1 = conditions_from_icd10(
+        condition_occurrence,
+        visit_occurrence=visit_occurrence,
+        date_min=date_min,
+        date_max=date_max,
+        **DEFAULT_CONFIG["X60-X84"],
+    )
+
+    events_1 = events_1[
+        ["visit_occurrence_id", "condition_status_source_value"]
+    ].drop_duplicates(subset="visit_occurrence_id")
+    events_1[CONCEPT] = True
+
+    if algo == "X60-X84":
+
+        visit_occurrence_tagged = visit_occurrence.merge(
+            events_1[["visit_occurrence_id", CONCEPT]],
+            on="visit_occurrence_id",
+            how="left",
+        )
+
+        visit_occurrence_tagged[CONCEPT].fillna(False, inplace=True)
+
+        return visit_occurrence_tagged
+
+    if algo == "Haguenoer2008":
+
+        events_1 = events_1[events_1.condition_status_source_value == "DAS"]
+
+        events_2 = conditions_from_icd10(
+            condition_occurrence,
+            visit_occurrence=visit_occurrence,
+            date_min=date_min,
+            date_max=date_max,
+            **DEFAULT_CONFIG[algo],
+        )
+
+        events_2 = events_2[["visit_occurrence_id"]].drop_duplicates()
+        events_2[f"{CONCEPT}_BIS"] = True
+
+        visit_occurrence_tagged = visit_occurrence.merge(
+            events_1[["visit_occurrence_id", CONCEPT]],
+            on="visit_occurrence_id",
+            how="left",
+        ).merge(
+            events_2[["visit_occurrence_id", f"{CONCEPT}_BIS"]],
+            on="visit_occurrence_id",
+            how="left",
+        )
+
+        visit_occurrence_tagged[CONCEPT] = (
+            visit_occurrence_tagged[CONCEPT] & visit_occurrence_tagged[f"{CONCEPT}_BIS"]
+        )
+
+        visit_occurrence_tagged = visit_occurrence_tagged.drop(
+            columns=[f"{CONCEPT}_BIS"]
+        )
+
+        return visit_occurrence_tagged
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/icu/icu_care_site/index.html b/main/reference/icu/icu_care_site/index.html new file mode 100644 index 00000000..bd68828c --- /dev/null +++ b/main/reference/icu/icu_care_site/index.html @@ -0,0 +1,4388 @@ + + + + + + + + + + + + + + + + icu_care_site - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + +
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    + + + + + + + + +

    eds_scikit.icu.icu_care_site

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + tag_icu_care_site + + +

    +
    tag_icu_care_site(care_site: DataFrame, algo: str = 'from_mapping') -> DataFrame
+
    + +
    + +

    Tag care sites that correspond to ICU units.

    +

    The tagging is done by adding a "IS_ICU" column to the provided DataFrame.

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site + +

    + + TYPE: + DataFrame + +

    +
    algo +

    Possible values are:

    + +

    + + TYPE: + str + + + DEFAULT: + 'from_mapping' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • "IS_ICU"
      • +
    +

    + + TYPE: + DataFrame + +

    +
    + +
    + Source code in eds_scikit/icu/icu_care_site.py + 
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    @algo_checker(algos=ALGOS)
+def tag_icu_care_site(
+    care_site: DataFrame,
+    algo: str = "from_mapping",
+) -> DataFrame:
+    """Tag care sites that correspond to **ICU units**.
+
+    The tagging is done by adding a `"IS_ICU"` column to the provided DataFrame.
+
+    Parameters
+    ----------
+    care_site: DataFrame
+    algo: str
+        Possible values are:
+
+        - [`"from_authorisation_type"`][eds_scikit.icu.icu_care_site.from_authorisation_type]
+        - [`"from_regex_on_care_site_description"`][eds_scikit.icu.icu_care_site.from_regex_on_care_site_description]
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 1 added column corresponding to the following concept:
+
+        - `"IS_ICU"`
+    """
+    if algo == "from_authorisation_type":
+        return from_authorisation_type(care_site)
+    elif algo == "from_regex_on_care_site_description":
+        return from_regex_on_care_site_description(care_site)
+
    +
    +
    + +
    + +
    + + + +

    + from_authorisation_type + + +

    +
    from_authorisation_type(care_site: DataFrame) -> DataFrame
+
    + +
    + +

    This algo uses the care_site.place_of_service_source_value columns +to retrieve Intensive Care Units.

    +

    The following values are used to tag a care site as ICU:

    +
      +
    • "REA PED"
      • +
    • "REA"
      • +
    • "REA ADULTE"
      • +
    • "REA NEONAT"
      • +
    • "USI"
      • +
    • "USI ADULTE"
      • +
    • "USI NEONAT"
      • +
    • "SC PED"
      • +
    • "SC"
      • +
    • "SC ADULTE"
      • +
    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the place_of_service_source_value column

    +

    + + TYPE: + DataFrame + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + care_site + +

    Dataframe with 1 added column corresponding to the following concepts:

    +
      +
    • "IS_ICU"
      • +
    +

    + + TYPE: + DataFrame + +

    +
    + +
    + Source code in eds_scikit/icu/icu_care_site.py + 
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    @concept_checker(concepts=["IS_ICU"])
+def from_authorisation_type(care_site: DataFrame) -> DataFrame:
+    """This algo uses the `care_site.place_of_service_source_value` columns
+    to retrieve Intensive Care Units.
+
+    The following values are used to tag a care site as ICU:
+
+    - `"REA PED"`
+    - `"REA"`
+    - `"REA ADULTE"`
+    - `"REA NEONAT"`
+    - `"USI"`
+    - `"USI ADULTE"`
+    - `"USI NEONAT"`
+    - `"SC PED"`
+    - `"SC"`
+    - `"SC ADULTE"`
+
+    Parameters
+    ----------
+    care_site: DataFrame
+        Should at least contains the `place_of_service_source_value` column
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 1 added column corresponding to the following concepts:
+
+        - `"IS_ICU"`
+
+    """
+
+    icu_units = set(
+        [
+            "REA PED",
+            "USI",
+            "SC PED",
+            "SC",
+            "REA",
+            "SC ADULTE",
+            "USI ADULTE",
+            "REA ADULTE",
+            "USI NEONAT",
+            "REA NEONAT",
+        ]
+    )
+
+    care_site["IS_ICU"] = care_site["place_of_service_source_value"].isin(icu_units)
+
+    return care_site
+
    +
    +
    + +
    + +
    + + + +

    + from_regex_on_care_site_description + + +

    +
    from_regex_on_care_site_description(care_site: DataFrame, subset_care_site_type_source_value: Union[list, set] = {'UDS'}) -> DataFrame
+
    + +
    + +

    Use regular expressions on care_site_name to decide if it an ICU care site.

    +

    This relies on this function. +The regular expression used to detect ICU is +r"USI|REA[N\s]|REA|USC|SOINS.*INTENSIF|SURV.{0,15}CONT|SI|SC".

    +
    +

    Keeping only 'UDS'

    +

    At AP-HP, all ICU are UDS (Unité De Soins). + Therefore, this function filters care sites by default to only keep UDS.

    +
    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    Should at least contains the care_site_name and care_site_type_source_value columns

    +

    + + TYPE: + DataFrame + +

    +
    subset_care_site_type_source_value +

    Acceptable values for care_site_type_source_value

    +

    + + TYPE: + Union[list, set] + + + DEFAULT: + {'UDS'} + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + care_site + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • "IS_ICU"
      • +
    +

    + + TYPE: + DataFrame + +

    +
    + +
    + Source code in eds_scikit/icu/icu_care_site.py + 
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    def from_regex_on_care_site_description(
+    care_site: DataFrame, subset_care_site_type_source_value: Union[list, set] = {"UDS"}
+) -> DataFrame:
+    """Use regular expressions on `care_site_name` to decide if it an ICU care site.
+
+    This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes].
+    The regular expression used to detect ICU is
+    `r"\bUSI|\bREA[N\s]|\bREA\b|\bUSC\b|SOINS.*INTENSIF|SURV.{0,15}CONT|\bSI\b|\bSC\b"`.
+
+    !!! aphp "Keeping only 'UDS'"
+         At AP-HP, all ICU are **UDS** (*Unité De Soins*).
+         Therefore, this function filters care sites by default to only keep UDS.
+
+    Parameters
+    ----------
+    care_site: DataFrame
+        Should at least contains the `care_site_name` and `care_site_type_source_value` columns
+    subset_care_site_type_source_value: Union[list, set]
+        Acceptable values for `care_site_type_source_value`
+
+    Returns
+    -------
+    care_site: DataFrame
+        Dataframe with 1 added column corresponding to the following concept:
+
+        - `"IS_ICU"`
+
+    """  # noqa
+
+    care_site = attributes.add_care_site_attributes(
+        care_site, only_attributes=["IS_ICU"]
+    )
+
+    # Filtering matches
+
+    if subset_care_site_type_source_value:
+        care_site["IS_ICU"] = care_site["IS_ICU"] & (
+            care_site.care_site_type_source_value.isin(
+                subset_care_site_type_source_value
+            )
+        )
+
+    return care_site
+
    +
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    + +
    + + + +
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    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/icu/icu_visit/index.html b/main/reference/icu/icu_visit/index.html new file mode 100644 index 00000000..1b88b030 --- /dev/null +++ b/main/reference/icu/icu_visit/index.html @@ -0,0 +1,4013 @@ + + + + + + + + + + + + + + + + icu_visit - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.icu.icu_visit

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + tag_icu_visit + + +

    +
    tag_icu_visit(visit_detail: DataFrame, care_site: DataFrame, algo: str = 'from_authorisation_type') -> DataFrame
+
    + +
    + +

    Tag care_sites that correspond to ICU units.

    +

    The tagging is done by adding a "IS_ICU" column to the provided DataFrame.

    +

    It works by tagging each visit detail's care site.

    + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    visit_detail + +

    + + TYPE: + DataFrame + +

    +
    care_site + +

    + + TYPE: + DataFrame + +

    +
    algo +

    Possible values are:

    + +

    + + TYPE: + str + + + DEFAULT: + 'from_authorisation_type' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + visit_detail + +

    Dataframe with 1 added column corresponding to the following concept:

    +
      +
    • "IS_ICU"
      • +
    +

    + + TYPE: + DataFrame + +

    +
    + +
    + Source code in eds_scikit/icu/icu_visit.py + 
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    @algo_checker(algos=ALGOS)
+def tag_icu_visit(
+    visit_detail: DataFrame,
+    care_site: DataFrame,
+    algo: str = "from_authorisation_type",
+) -> DataFrame:
+    """Tag care_sites that correspond to **ICU units**.
+
+    The tagging is done by adding a `"IS_ICU"` column to the provided DataFrame.
+
+    It works by [tagging each visit detail's care site][eds_scikit.icu.icu_care_site.tag_icu_care_site].
+
+    Parameters
+    ----------
+    visit_detail: DataFrame
+    care_site: DataFrame
+    algo: str
+        Possible values are:
+
+        - [`"from_authorisation_type"`][eds_scikit.icu.icu_care_site.from_authorisation_type]
+        - [`"from_regex_on_care_site_description"`][eds_scikit.icu.icu_care_site.from_regex_on_care_site_description]
+
+    Returns
+    -------
+    visit_detail: DataFrame
+        Dataframe with 1 added column corresponding to the following concept:
+
+        - `"IS_ICU"`
+
+    """
+    tagged_care_site = tag_icu_care_site(care_site, algo=algo)
+
+    return visit_detail.merge(
+        tagged_care_site[["care_site_id", "IS_ICU"]], on="care_site_id", how="left"
+    )
+
    +
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    eds_scikit.icu

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    eds_scikit

    + + +
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    Top-level package for eds_scikit.

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    eds_scikit.io.base

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    eds_scikit.io.data_quality

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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/io/files/index.html b/main/reference/io/files/index.html new file mode 100644 index 00000000..809754d6 --- /dev/null +++ b/main/reference/io/files/index.html @@ -0,0 +1,3961 @@ + + + + + + + + + + + + + + + + files - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.io.files

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + PandasData + + +

    +
    PandasData(folder: str)
+
    + +
    +

    + Bases: BaseData

    + + + +

    Pandas interface to OMOP data stored as local parquet files/folders.

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    folder +

    absolute path to a folder containing several parquet files with OMOP data

    +

    + + TYPE: + str + +

    +
    + +

    Examples:

    + 
    >>> data = PandasData(folder="/export/home/USER/my_data/")
+>>> person = data.person
+>>> person.shape
+(100, 10)
+
    + +
    + Source code in eds_scikit/io/files.py + 
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    def __init__(
+    self,
+    folder: str,
+):
+    """Pandas interface to OMOP data stored as local parquet files/folders.
+
+
+    Parameters
+    ----------
+    folder: str
+        absolute path to a folder containing several parquet files with OMOP data
+
+    Examples
+    --------
+    >>> data = PandasData(folder="/export/home/USER/my_data/")
+    >>> person = data.person
+    >>> person.shape
+    (100, 10)
+
+    """
+    super().__init__()
+    self.folder = folder
+    self.available_tables = self.list_available_tables()
+    self.tables_paths = self.get_table_path()
+    if not self.available_tables:
+        raise ValueError(f"Folder {folder} does not contain any parquet omop data.")
+
    +
    + + + +
    + + + + + + + + + + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/io/hive/index.html b/main/reference/io/hive/index.html new file mode 100644 index 00000000..e1b2d5a6 --- /dev/null +++ b/main/reference/io/hive/index.html @@ -0,0 +1,4617 @@ + + + + + + + + + + + + + + + + hive - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.io.hive

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + HiveData + + +

    +
    HiveData(database_name: str, spark_session: Optional[SparkSession] = None, person_ids: Optional[Iterable[int]] = None, tables_to_load: Optional[Union[Dict[str, Optional[List[str]]], List[str]]] = None, columns_to_load: Optional[Union[Dict[str, Optional[List[str]]], List[str]]] = None, database_type: Optional[str] = 'OMOP', prune_omop_date_columns: bool = True, cache: bool = True)
+
    + +
    +

    + Bases: BaseData

    + + + +

    Spark interface for OMOP data stored in a Hive database.

    +

    This class provides a simple access to data stored in Hive. +Data is returned as koalas dataframes that match the tables +stored in Hive.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    database_name +

    The name of you database in Hive. Ex: "cse_82727572"

    +

    + + TYPE: + str + +

    +
    spark_session +

    If None, a SparkSession will be retrieved or created via SparkSession.builder.enableHiveSupport().getOrCreate()

    +

    + + TYPE: + pyspark.sql.SparkSession + + + DEFAULT: + None + +

    +
    person_ids +

    An iterable of person_id that is used to define a subset of the database.

    +

    + + TYPE: + Optional[Iterable[int]] + + + DEFAULT: + None + +

    +
    tables_to_load +

    deprecated

    +

    + + TYPE: + dict, default + + + DEFAULT: + None + +

    +
    columns_to_load +

    deprecated

    +

    + + TYPE: + dict, default + + + DEFAULT: + None + +

    +
    database_type +

    Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP.

    +

    + + TYPE: + Optional[str] + + + DEFAULT: + 'OMOP' + +

    +
    prune_omop_date_columns +

    In OMOP, most date values are stored both in a <str>_date and <str>_datetime column +Koalas has trouble handling the date time, so we only keep the datetime column

    +

    + + TYPE: + bool + + + DEFAULT: + True + +

    +
    cache +

    Whether to cache each table after preprocessing or not. +Will speed-up subsequent calculations, but can be long/infeasable for very large tables

    +

    + + TYPE: + bool + + + DEFAULT: + True + +

    +
    + + + + + + + + + + + + + + + + + + +
    ATTRIBUTEDESCRIPTION
    person +

    Hive data for table person as a koalas dataframe. +Other OMOP tables can also be accessed as attributes

    +

    + + TYPE: + koalas dataframe + +

    +
    available_tables +

    names of OMOP tables that can be accessed as attributes with this +HiveData object.

    +

    + + TYPE: + list of str + +

    +
    + +

    Examples:

    + 
    data = HiveData(database_name="edsomop_prod_a")
+data.available_tables
+# Out: ["person", "care_site", "condition_occurrence", ... ]
+
+person = data.person
+type(person)
+# Out: databricks.koalas.frame.DataFrame
+
+person["person_id"].count()
+# Out: 12670874
+
    +

    This class can be used to create a subset of data for a given +list of person_id. This is useful because the smaller dataset +can then be used to prototype more rapidly.

    +
    my_person_ids = [9226726, 2092082, ...]
+data = HiveData(
+    spark_session=spark, database_name="edsomop_prod_a", person_ids=my_person_ids
+)
+data.person["person_id"].count()
+# Out: 1000
+
+tables_to_save = ["person", "visit_occurrence"]
+data.persist_tables_to_folder("./cohort_sample_1000", table_names=tables_to_save)
+# Out: writing /export/home/USER/cohort_sample_1000/person.parquet
+# Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet
+# Out: ...
+
    + +
    + Source code in eds_scikit/io/hive.py + 
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    def __init__(
+    self,
+    database_name: str,
+    spark_session: Optional[SparkSession] = None,
+    person_ids: Optional[Iterable[int]] = None,
+    tables_to_load: Optional[
+        Union[Dict[str, Optional[List[str]]], List[str]]
+    ] = None,
+    columns_to_load: Optional[
+        Union[Dict[str, Optional[List[str]]], List[str]]
+    ] = None,
+    database_type: Optional[str] = "OMOP",
+    prune_omop_date_columns: bool = True,
+    cache: bool = True,
+):
+    """Spark interface for OMOP data stored in a Hive database.
+
+    This class provides a simple access to data stored in Hive.
+    Data is returned as koalas dataframes that match the tables
+    stored in Hive.
+
+    Parameters
+    ----------
+    database_name : str
+        The name of you database in Hive. Ex: "cse_82727572"
+    spark_session : pyspark.sql.SparkSession
+        If None, a SparkSession will be retrieved or  created via `SparkSession.builder.enableHiveSupport().getOrCreate()`
+    person_ids : Optional[Iterable[int]]
+        An iterable of `person_id` that is used to define a subset of the database.
+    tables_to_load : dict, default=None
+        *deprecated*
+    columns_to_load : dict, default=None
+        *deprecated*
+    database_type: Optional[str] = 'OMOP'. Must be 'OMOP' or 'I2B2'
+        Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP.
+    prune_omop_date_columns: bool, default=True
+        In OMOP, most date values are stored both in a `<str>_date` and `<str>_datetime` column
+        Koalas has trouble handling the `date` time, so we only keep the `datetime` column
+    cache: bool, default=True
+        Whether to cache each table after preprocessing or not.
+        Will speed-up subsequent calculations, but can be long/infeasable for very large tables
+
+    Attributes
+    ----------
+    person : koalas dataframe
+        Hive data for table `person` as a koalas dataframe.
+        Other OMOP tables can also be accessed as attributes
+    available_tables : list of str
+        names of OMOP tables that can be accessed as attributes with this
+        HiveData object.
+
+    Examples
+    --------
+
+    ```python
+    data = HiveData(database_name="edsomop_prod_a")
+    data.available_tables
+    # Out: ["person", "care_site", "condition_occurrence", ... ]
+
+    person = data.person
+    type(person)
+    # Out: databricks.koalas.frame.DataFrame
+
+    person["person_id"].count()
+    # Out: 12670874
+    ```
+
+    This class can be used to create a subset of data for a given
+    list of `person_id`. This is useful because the smaller dataset
+    can then be used to prototype more rapidly.
+
+    ```python
+    my_person_ids = [9226726, 2092082, ...]
+    data = HiveData(
+        spark_session=spark, database_name="edsomop_prod_a", person_ids=my_person_ids
+    )
+    data.person["person_id"].count()
+    # Out: 1000
+
+    tables_to_save = ["person", "visit_occurrence"]
+    data.persist_tables_to_folder("./cohort_sample_1000", table_names=tables_to_save)
+    # Out: writing /export/home/USER/cohort_sample_1000/person.parquet
+    # Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet
+    # Out: ...
+    ```
+
+    """
+    super().__init__()
+
+    if columns_to_load is not None:
+        logger.warning("'columns_to_load' is deprecated and won't be used")
+
+    if tables_to_load is not None:
+        logger.warning("'tables_to_load' is deprecated and won't be used")
+
+    self.spark_session = (
+        spark_session or SparkSession.builder.enableHiveSupport().getOrCreate()
+    )
+    self.database_name = database_name
+    if database_type not in ["I2B2", "OMOP"]:
+        raise ValueError(
+            f"`database_type` must be either 'I2B2' or 'OMOP'. Got {database_type}"
+        )
+    self.database_type = database_type
+
+    if self.database_type == "I2B2":
+        self.database_source = "cse" if "cse" in self.database_name else "edsprod"
+        self.omop_to_i2b2 = settings.i2b2_tables[self.database_source]
+        self.i2b2_to_omop = defaultdict(list)
+        for omop_table, i2b2_table in self.omop_to_i2b2.items():
+            self.i2b2_to_omop[i2b2_table].append(omop_table)
+
+    self.prune_omop_date_columns = prune_omop_date_columns
+    self.cache = cache
+    self.user = os.environ["USER"]
+    self.person_ids, self.person_ids_df = self._prepare_person_ids(person_ids)
+    self.available_tables = self.list_available_tables()
+    self._tables = {}
+
    +
    + + + +
    + + + + + + + + + +
    + + + +

    + persist_tables_to_folder + + +

    +
    persist_tables_to_folder(folder: str, person_ids: Optional[Iterable[int]] = None, tables: List[str] = None, overwrite: bool = False) -> None
+
    + +
    + +

    Save OMOP tables as parquet files in a given folder.

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    folder +

    path to folder where the tables will be written.

    +

    + + TYPE: + str + +

    +
    +

    person_ids : iterable + person_ids to keep in the subcohort.

    +

    tables : list of str, default None + list of table names to save. Default value is + 🇵🇾data:~eds_scikit.io.settings.default_tables_to_save.

    +

    overwrite : bool, default=False + whether to overwrite files if 'folder' already exists.

    + +
    + Source code in eds_scikit/io/hive.py + 
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    def persist_tables_to_folder(
+    self,
+    folder: str,
+    person_ids: Optional[Iterable[int]] = None,
+    tables: List[str] = None,
+    overwrite: bool = False,
+) -> None:
+    """Save OMOP tables as parquet files in a given folder.
+
+    Parameters
+    ----------
+    folder : str
+        path to folder where the tables will be written.
+
+    person_ids : iterable
+        person_ids to keep in the subcohort.
+
+    tables : list of str, default None
+        list of table names to save. Default value is
+        :py:data:`~eds_scikit.io.settings.default_tables_to_save`.
+
+    overwrite : bool, default=False
+        whether to overwrite files if 'folder' already exists.
+
+    """
+    # Manage tables
+    if tables is None:
+        tables = settings.default_tables_to_save
+
+    unknown_tables = [
+        table for table in tables if table not in self.available_tables
+    ]
+    if unknown_tables:
+        raise ValueError(
+            f"The following tables are not available : {str(unknown_tables)}"
+        )
+
+    # Create folder
+    folder = Path(folder).absolute()
+
+    if folder.exists() and overwrite:
+        shutil.rmtree(folder)
+
+    folder.mkdir(parents=True, mode=0o766)
+
+    assert os.path.exists(folder) and os.path.isdir(
+        folder
+    ), f"Folder {folder} not found."
+
+    # TODO: remove everything in this folder that is a valid
+    # omop table. This prevents a user from having a
+    # folder containing datasets generated from different
+    # patient subsets.
+
+    # TODO: maybe check how much the user wants to persist
+    # to disk. Set a limit on the number of patients in the cohort ?
+
+    if person_ids is not None:
+        person_ids = self._prepare_person_ids(person_ids, return_df=False)
+
+    database_path = self.get_db_path()
+
+    for idx, table in enumerate(tables):
+        if self.database_type == "I2B2":
+            table_path = self._hdfs_write_orc_to_parquet(
+                table, person_ids, overwrite
+            )
+        else:
+            table_path = os.path.join(database_path, table)
+
+        df = self.get_table_from_parquet(table_path, person_ids=person_ids)
+
+        local_file_path = os.path.join(folder, f"{table}.parquet")
+        df.to_parquet(
+            local_file_path,
+            allow_truncated_timestamps=True,
+            coerce_timestamps="ms",
+        )
+        logger.info(
+            f"({idx+1}/{len(tables)}) Table {table} saved at "
+            f"{local_file_path} (N={len(df)})."
+        )
+
    +
    +
    + +
    + +
    + + + +

    + get_db_path + + +

    +
    get_db_path()
+
    + +
    + +

    Get the HDFS path of the database

    + +
    + Source code in eds_scikit/io/hive.py + 
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    def get_db_path(self):
+    """Get the HDFS path of the database"""
+    return (
+        self.spark_session.sql(f"DESCRIBE DATABASE EXTENDED {self.database_name}")
+        .filter("database_description_item=='Location'")
+        .collect()[0]
+        .database_description_value
+    )
+
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    +
    + +
    + + + +
    + +
    + +
    + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/io/i2b2_mapping/index.html b/main/reference/io/i2b2_mapping/index.html new file mode 100644 index 00000000..dbc28bbe --- /dev/null +++ b/main/reference/io/i2b2_mapping/index.html @@ -0,0 +1,4437 @@ + + + + + + + + + + + + + + + + i2b2_mapping - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.io.i2b2_mapping

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + get_i2b2_table + + +

    +
    get_i2b2_table(spark_session: SparkSession, db_name: str, db_source: str, table: str) -> SparkDataFrame
+
    + +
    + +

    Convert a Spark table from i2b2 to OMOP format.

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    db_name +

    Name of the database where the data is stored.

    +

    + + TYPE: + str + +

    +
    table +

    Name of the table to extract.

    +

    + + TYPE: + str + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + df + +

    Spark DataFrame extracted from the i2b2 database given and converted to OMOP standard.

    +

    + + TYPE: + Spark DataFrame + +

    +
    + +
    + Source code in eds_scikit/io/i2b2_mapping.py + 
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    def get_i2b2_table(
+    spark_session: SparkSession, db_name: str, db_source: str, table: str
+) -> SparkDataFrame:
+    """
+    Convert a Spark table from i2b2 to OMOP format.
+
+    Parameters
+    ----------
+    db_name: str
+        Name of the database where the data is stored.
+    table: str
+        Name of the table to extract.
+
+    Returns
+    -------
+    df: Spark DataFrame
+        Spark DataFrame extracted from the i2b2 database given and converted to OMOP standard.
+    """
+
+    i2b2_table_name = i2b2_tables[db_source][table]
+    # Dictionary of omop_col -> i2b2_col
+    columns = i2b2_renaming.get(table)
+
+    # Can be None if creating a table from scratch (e.g. concept_relationship
+    if columns is not None:
+        query = f"describe {db_name}.{i2b2_table_name}"
+        available_columns = set(spark_session.sql(query).toPandas().col_name.tolist())
+        if db_source == "cse":
+            columns.pop("i2b2_action", None)
+        cols = ", ".join(
+            [
+                f"{i2b2} AS {omop}"
+                for omop, i2b2 in columns.items()
+                if i2b2 in available_columns
+            ]
+        )
+        query = f"SELECT {cols} FROM {db_name}.{i2b2_table_name}"
+        df = spark_session.sql(query)
+
+    # Special mapping for i2b2 :
+    # CIM10
+    if table == "condition_occurrence":
+        df = df.withColumn(
+            "condition_source_value",
+            F.substring(F.col("condition_source_value"), 7, 20),
+        )
+
+    # CCAM
+    elif table == "procedure_occurrence":
+        df = df.withColumn(
+            "procedure_source_value",
+            F.substring(F.col("procedure_source_value"), 6, 20),
+        )
+
+    # Visits
+    elif table == "visit_occurrence":
+        df = df.withColumn(
+            "visit_source_value",
+            mapping_dict(visit_type_mapping, "Non Renseigné")(
+                F.col("visit_source_value")
+            ),
+        )
+        if db_source == "cse":
+            df = df.withColumn("row_status_source_value", F.lit("Actif"))
+            df = df.withColumn(
+                "visit_occurrence_source_value", df["visit_occurrence_id"]
+            )
+        else:
+            df = df.withColumn(
+                "row_status_source_value",
+                F.when(
+                    F.col("row_status_source_value").isin([-1, -2]), "supprimé"
+                ).otherwise("Actif"),
+            )
+        # Retrieve Hospital trigram
+        ufr = spark_session.sql(
+            f"SELECT * FROM {db_name}.{i2b2_tables[db_source]['visit_detail']}"
+        )
+        ufr = ufr.withColumn(
+            "care_site_id",
+            F.substring(F.split(F.col("concept_cd"), ":").getItem(1), 1, 3),
+        )
+        ufr = ufr.withColumnRenamed("encounter_num", "visit_occurrence_id")
+        ufr = ufr.drop_duplicates(subset=["visit_occurrence_id"])
+        ufr = ufr.select(["visit_occurrence_id", "care_site_id"])
+        df = df.join(ufr, how="inner", on=["visit_occurrence_id"])
+
+    # Patients
+    elif table == "person":
+        df = df.withColumn(
+            "gender_source_value",
+            mapping_dict(sex_cd_mapping, "Non Renseigné")(F.col("gender_source_value")),
+        )
+
+    # Documents
+    elif table.startswith("note"):
+        df = df.withColumn(
+            "note_class_source_value",
+            F.substring(F.col("note_class_source_value"), 4, 100),
+        )
+        if db_source == "cse":
+            df = df.withColumn("row_status_source_value", F.lit("Actif"))
+        else:
+            df = df.withColumn(
+                "row_status_source_value",
+                F.when(F.col("row_status_source_value") < 0, "SUPP").otherwise("Actif"),
+            )
+
+    # Hospital trigrams
+    elif table == "care_site":
+        df = df.withColumn("care_site_type_source_value", F.lit("Hôpital"))
+        df = df.withColumn(
+            "care_site_source_value",
+            F.split(F.col("care_site_source_value"), ":").getItem(1),
+        )
+        df = df.withColumn(
+            "care_site_id", F.substring(F.col("care_site_source_value"), 1, 3)
+        )
+        df = df.drop_duplicates(subset=["care_site_id"])
+        df = df.withColumn(
+            "care_site_short_name",
+            mapping_dict(dict_code_UFR, "Non Renseigné")(F.col("care_site_id")),
+        )
+
+    # UFR
+    elif table == "visit_detail":
+        df = df.withColumn(
+            "care_site_id", F.split(F.col("care_site_id"), ":").getItem(1)
+        )
+        df = df.withColumn("visit_detail_type_source_value", F.lit("PASS"))
+        df = df.withColumn("row_status_source_value", F.lit("Actif"))
+
+    # measurement
+    elif table == "measurement":
+        df = df.withColumn(
+            "measurement_source_concept_id",
+            F.substring(F.col("measurement_source_concept_id"), 5, 20),
+        ).withColumn("row_status_source_value", F.lit("Validé"))
+
+    # concept
+    elif table == "concept":
+        df = (
+            df.withColumn(
+                "concept_source_value",
+                F.substring(
+                    F.col("concept_source_value"), 5, 20
+                ),  # TODO: use regexp_extract to take substring after ':'
+            )
+            .withColumn("concept_id", F.col("concept_source_value"))
+            .withColumn("concept_code", F.col("concept_id"))
+            .withColumn("vocabulary_id", F.lit("ANABIO"))
+        )
+
+        # Adding LOINC
+        if "get_additional_i2b2_concept" in registry.data.get_all():
+            loinc_pd = registry.get("data", "get_additional_i2b2_concept")()
+            assert len(loinc_pd.columns) == len(df.columns)
+            loinc_pd = loinc_pd[df.columns]  # for columns ordering
+            df = df.union(
+                spark_session.createDataFrame(loinc_pd, df.schema, verifySchema=False)
+            ).cache()
+
+    # fact_relationship
+    elif table == "fact_relationship":
+        # Retrieve UF information
+        df = df.withColumn(
+            "fact_id_1",
+            F.split(F.col("care_site_source_value"), ":").getItem(1),
+        )
+        df = df.withColumn("domain_concept_id_1", F.lit(57))  # Care_site domain
+
+        # Retrieve hospital information
+        df = df.withColumn("fact_id_2", F.substring(F.col("fact_id_1"), 1, 3))
+        df = df.withColumn("domain_concept_id_2", F.lit(57))  # Care_site domain
+        df = df.drop_duplicates(subset=["fact_id_1", "fact_id_2"])
+
+        # Only UF-Hospital relationships in i2b2
+        df = df.withColumn("relationship_concept_id", F.lit(46233688))  # Included in
+
+    elif table == "concept_relationship":
+        data = []
+        schema = T.StructType(
+            [
+                T.StructField("concept_id_1", T.StringType(), True),
+                T.StructField("concept_id_2", T.StringType(), True),
+                T.StructField("relationship_id", T.StringType(), True),
+            ]
+        )
+        if "get_additional_i2b2_concept_relationship" in registry.data.get_all():
+            data = registry.get("data", "get_additional_i2b2_concept_relationship")()
+        df = spark_session.createDataFrame(data, schema).cache()
+    return df
+
    +
    +
    + +
    + +
    + + + +

    + mapping_dict + + +

    +
    mapping_dict(mapping: Dict[str, str], default: str) -> FunctionUDF
+
    + +
    + +

    Returns a function that maps data according to a mapping dictionnary in a Spark DataFrame.

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    mapping +

    Mapping dictionnary

    +

    + + TYPE: + Dict[str, str] + +

    +
    default +

    Value to return if the function input is not find in the mapping dictionnary.

    +

    + + TYPE: + str + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + Callable + + +

    Function that maps the values of Spark DataFrame column.

    +
    + +
    + Source code in eds_scikit/io/i2b2_mapping.py + 
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    def mapping_dict(mapping: Dict[str, str], default: str) -> FunctionUDF:
+    """
+    Returns a function that maps data according to a mapping dictionnary in a Spark DataFrame.
+
+    Parameters
+    ----------
+    mapping: Dict
+        Mapping dictionnary
+    default: str
+        Value to return if the function input is not find in the mapping dictionnary.
+
+    Returns
+    -------
+    Callable
+        Function that maps the values of Spark DataFrame column.
+    """
+
+    def f(x):
+        return mapping.get(x, default)
+
+    return F.udf(f)
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/io/improve_performance/index.html b/main/reference/io/improve_performance/index.html new file mode 100644 index 00000000..7b71ae08 --- /dev/null +++ b/main/reference/io/improve_performance/index.html @@ -0,0 +1,4206 @@ + + + + + + + + + + + + + + + + improve_performance - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.io.improve_performance

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + koalas_options + + +

    +
    koalas_options() -> None
+
    + +
    + +

    Set necessary options to optimise Koalas

    + +
    + Source code in eds_scikit/io/improve_performance.py + 
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    def koalas_options() -> None:
+    """
+    Set necessary options to optimise Koalas
+    """
+
+    # Reloading Koalas to use the new configuration
+    ks = load_koalas()
+
+    ks.set_option("compute.default_index_type", "distributed")
+    ks.set_option("compute.ops_on_diff_frames", True)
+    ks.set_option("display.max_rows", 50)
+
    +
    +
    + +
    + +
    + + + +

    + pyarrow_fix + + +

    +
    pyarrow_fix()
+
    + +
    + +

    Fixing error 'pyarrow has no attributes open_stream' due to PySpark 2 incompatibility with pyarrow > 0.17

    + +
    + Source code in eds_scikit/io/improve_performance.py + 
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    def pyarrow_fix():
+    """
+    Fixing error 'pyarrow has no attributes open_stream' due to PySpark 2 incompatibility with pyarrow > 0.17
+    """
+
+    # Setting path to our patched pyarrow module
+    pyarrow.open_stream = pyarrow.ipc.open_stream
+
+    sys.path.insert(
+        0, (Path(__file__).parent / "package-override").absolute().as_posix()
+    )
+    os.environ["PYTHONPATH"] = ":".join(sys.path)
+
+    # Setting this path for Pyspark executors
+    global spark, sc, sql
+
+    spark = SparkSession.builder.getOrCreate()
+
+    conf = spark.sparkContext.getConf()
+    conf.set(
+        "spark.executorEnv.PYTHONPATH",
+        f"{Path(__file__).parent.parent}/package-override:{conf.get('spark.executorEnv.PYTHONPATH')}",
+    )
+    spark = SparkSession.builder.enableHiveSupport().config(conf=conf).getOrCreate()
+
+    sc = spark.sparkContext
+
+    sql = spark.sql
+
    +
    +
    + +
    + +
    + + + +

    + improve_performances + + +

    +
    improve_performances(to_add_conf: List[Tuple[str, str]] = [], quiet_spark: bool = True, app_name: str = '') -> Tuple[SparkSession, SparkContext, SparkSession.sql]
+
    + +
    + +

    (Re)defines various Spark variable with some configuration changes +to improve performances by enabling Arrow +This has to be done +- Before launching a SparkCOntext +- Before importing Koalas +Those two points are being taken care on this function. +If a SparkSession already exists, it will copy its configuration before +creating a new one

    + + + + + + + + + + + + + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + Tuple of + + + +
    + + - A SparkSession + + + +
    + + - The associated SparkContext + + + +
    + + - The associated + + + +
    + +
    + Source code in eds_scikit/io/improve_performance.py + 
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    def improve_performances(
+    to_add_conf: List[Tuple[str, str]] = [],
+    quiet_spark: bool = True,
+    app_name: str = "",
+) -> Tuple[SparkSession, SparkContext, SparkSession.sql]:
+    """
+    (Re)defines various Spark variable with some configuration changes
+    to improve performances by enabling Arrow
+    This has to be done
+    - Before launching a SparkCOntext
+    - Before importing Koalas
+    Those two points are being taken care on this function.
+    If a SparkSession already exists, it will copy its configuration before
+    creating a new one
+
+    Returns
+    -------
+    Tuple of
+    - A SparkSession
+    - The associated SparkContext
+    - The associated ``sql`` object to run SQL queries
+    """
+
+    # Check if a spark Session is up
+    global spark, sc, sql
+
+    spark = SparkSession.builder.getOrCreate()
+    sc = spark.sparkContext
+
+    if quiet_spark:
+        sc.setLogLevel("ERROR")
+
+    conf = sc.getConf()
+
+    # Synchronizing TimeZone
+    tz = os.environ.get("TZ", "UTC")
+    os.environ["TZ"] = tz
+    time.tzset()
+
+    to_add_conf.extend(
+        [
+            ("spark.app.name", f"{os.environ.get('USER')}_{app_name}_scikit"),
+            ("spark.sql.session.timeZone", tz),
+            ("spark.sql.execution.arrow.enabled", "true"),
+            ("spark.sql.execution.arrow.pyspark.enabled", "true"),
+        ]
+    )
+
+    for key, value in to_add_conf:
+        conf.set(key, value)
+
+    # Stopping context to add necessary env variables
+    sc.stop()
+    spark.stop()
+
+    set_env_variables()
+
+    spark = SparkSession.builder.enableHiveSupport().config(conf=conf).getOrCreate()
+
+    sc = spark.sparkContext
+
+    if quiet_spark:
+        sc.setLogLevel("ERROR")
+
+    sql = spark.sql
+
+    koalas_options()
+
+    return spark, sc, sql
+
    +
    +
    + +
    + + + +
    + +
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    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/io/index.html b/main/reference/io/index.html new file mode 100644 index 00000000..e8fd27db --- /dev/null +++ b/main/reference/io/index.html @@ -0,0 +1,4918 @@ + + + + + + + + + + + + + + + + `eds_scikit.io` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.io

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + PandasData + + +

    +
    PandasData(folder: str)
+
    + +
    +

    + Bases: BaseData

    + + + +

    Pandas interface to OMOP data stored as local parquet files/folders.

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    folder +

    absolute path to a folder containing several parquet files with OMOP data

    +

    + + TYPE: + str + +

    +
    + +

    Examples:

    + 
    >>> data = PandasData(folder="/export/home/USER/my_data/")
+>>> person = data.person
+>>> person.shape
+(100, 10)
+
    + +
    + Source code in eds_scikit/io/files.py + 
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    def __init__(
+    self,
+    folder: str,
+):
+    """Pandas interface to OMOP data stored as local parquet files/folders.
+
+
+    Parameters
+    ----------
+    folder: str
+        absolute path to a folder containing several parquet files with OMOP data
+
+    Examples
+    --------
+    >>> data = PandasData(folder="/export/home/USER/my_data/")
+    >>> person = data.person
+    >>> person.shape
+    (100, 10)
+
+    """
+    super().__init__()
+    self.folder = folder
+    self.available_tables = self.list_available_tables()
+    self.tables_paths = self.get_table_path()
+    if not self.available_tables:
+        raise ValueError(f"Folder {folder} does not contain any parquet omop data.")
+
    +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + +
    + + + +

    + PostgresData + + +

    +
    PostgresData(dbname: Optional[str] = None, schema: Optional[str] = None, user: Optional[str] = None, host: Optional[str] = None, port: Optional[int] = None)
+
    + +
    +

    + Bases: BaseData

    + + + +
    + Source code in eds_scikit/io/postgres.py + 
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    def __init__(
+    self,
+    dbname: Optional[str] = None,
+    schema: Optional[str] = None,
+    user: Optional[str] = None,
+    host: Optional[str] = None,
+    port: Optional[int] = None,
+):
+    (
+        self.host,
+        self.port,
+        self.dbname,
+        self.user,
+    ) = self._find_matching_pgpass_params(host, port, dbname, user)
+    self.schema = schema
+
    +
    + + + +
    + + + + + + + + + +
    + + + +

    + read_sql + + +

    +
    read_sql(sql_query: str, **kwargs) -> pd.DataFrame
+
    + +
    + +

    Execute pandas.read_sql() on the database.

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    sql_query +

    SQL query (postgres flavor)

    +

    + + TYPE: + str + +

    +
    **kwargs +

    additional arguments passed to pandas.read_sql()

    +

    + + DEFAULT: + {} + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + df + + +

    + + TYPE: + pandas.DataFrame + +

    +
    + +
    + Source code in eds_scikit/io/postgres.py + 
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    def read_sql(self, sql_query: str, **kwargs) -> pd.DataFrame:
+    """Execute pandas.read_sql() on the database.
+
+    Parameters
+    ----------
+    sql_query : str
+        SQL query (postgres flavor)
+    **kwargs
+        additional arguments passed to pandas.read_sql()
+
+    Returns
+    -------
+    df : pandas.DataFrame
+
+    """
+    connection_infos = {
+        param: getattr(self, param) for param in ["host", "port", "dbname", "user"]
+    }
+    connection_infos["password"] = pgpasslib.getpass(**connection_infos)
+    connection = pg.connect(**connection_infos)
+    if self.schema:
+        connection.cursor().execute(f"SET SCHEMA '{self.schema}'")
+
+    df = pd.read_sql(sql_query, con=connection, **kwargs)
+
+    connection.close()
+    return df
+
    +
    +
    + +
    + + + +
    + +
    + +
    + +
    + + + +

    + HiveData + + +

    +
    HiveData(database_name: str, spark_session: Optional[SparkSession] = None, person_ids: Optional[Iterable[int]] = None, tables_to_load: Optional[Union[Dict[str, Optional[List[str]]], List[str]]] = None, columns_to_load: Optional[Union[Dict[str, Optional[List[str]]], List[str]]] = None, database_type: Optional[str] = 'OMOP', prune_omop_date_columns: bool = True, cache: bool = True)
+
    + +
    +

    + Bases: BaseData

    + + + +

    Spark interface for OMOP data stored in a Hive database.

    +

    This class provides a simple access to data stored in Hive. +Data is returned as koalas dataframes that match the tables +stored in Hive.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    database_name +

    The name of you database in Hive. Ex: "cse_82727572"

    +

    + + TYPE: + str + +

    +
    spark_session +

    If None, a SparkSession will be retrieved or created via SparkSession.builder.enableHiveSupport().getOrCreate()

    +

    + + TYPE: + pyspark.sql.SparkSession + + + DEFAULT: + None + +

    +
    person_ids +

    An iterable of person_id that is used to define a subset of the database.

    +

    + + TYPE: + Optional[Iterable[int]] + + + DEFAULT: + None + +

    +
    tables_to_load +

    deprecated

    +

    + + TYPE: + dict, default + + + DEFAULT: + None + +

    +
    columns_to_load +

    deprecated

    +

    + + TYPE: + dict, default + + + DEFAULT: + None + +

    +
    database_type +

    Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP.

    +

    + + TYPE: + Optional[str] + + + DEFAULT: + 'OMOP' + +

    +
    prune_omop_date_columns +

    In OMOP, most date values are stored both in a <str>_date and <str>_datetime column +Koalas has trouble handling the date time, so we only keep the datetime column

    +

    + + TYPE: + bool + + + DEFAULT: + True + +

    +
    cache +

    Whether to cache each table after preprocessing or not. +Will speed-up subsequent calculations, but can be long/infeasable for very large tables

    +

    + + TYPE: + bool + + + DEFAULT: + True + +

    +
    + + + + + + + + + + + + + + + + + + +
    ATTRIBUTEDESCRIPTION
    person +

    Hive data for table person as a koalas dataframe. +Other OMOP tables can also be accessed as attributes

    +

    + + TYPE: + koalas dataframe + +

    +
    available_tables +

    names of OMOP tables that can be accessed as attributes with this +HiveData object.

    +

    + + TYPE: + list of str + +

    +
    + +

    Examples:

    + 
    data = HiveData(database_name="edsomop_prod_a")
+data.available_tables
+# Out: ["person", "care_site", "condition_occurrence", ... ]
+
+person = data.person
+type(person)
+# Out: databricks.koalas.frame.DataFrame
+
+person["person_id"].count()
+# Out: 12670874
+
    +

    This class can be used to create a subset of data for a given +list of person_id. This is useful because the smaller dataset +can then be used to prototype more rapidly.

    +
    my_person_ids = [9226726, 2092082, ...]
+data = HiveData(
+    spark_session=spark, database_name="edsomop_prod_a", person_ids=my_person_ids
+)
+data.person["person_id"].count()
+# Out: 1000
+
+tables_to_save = ["person", "visit_occurrence"]
+data.persist_tables_to_folder("./cohort_sample_1000", table_names=tables_to_save)
+# Out: writing /export/home/USER/cohort_sample_1000/person.parquet
+# Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet
+# Out: ...
+
    + +
    + Source code in eds_scikit/io/hive.py + 
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    def __init__(
+    self,
+    database_name: str,
+    spark_session: Optional[SparkSession] = None,
+    person_ids: Optional[Iterable[int]] = None,
+    tables_to_load: Optional[
+        Union[Dict[str, Optional[List[str]]], List[str]]
+    ] = None,
+    columns_to_load: Optional[
+        Union[Dict[str, Optional[List[str]]], List[str]]
+    ] = None,
+    database_type: Optional[str] = "OMOP",
+    prune_omop_date_columns: bool = True,
+    cache: bool = True,
+):
+    """Spark interface for OMOP data stored in a Hive database.
+
+    This class provides a simple access to data stored in Hive.
+    Data is returned as koalas dataframes that match the tables
+    stored in Hive.
+
+    Parameters
+    ----------
+    database_name : str
+        The name of you database in Hive. Ex: "cse_82727572"
+    spark_session : pyspark.sql.SparkSession
+        If None, a SparkSession will be retrieved or  created via `SparkSession.builder.enableHiveSupport().getOrCreate()`
+    person_ids : Optional[Iterable[int]]
+        An iterable of `person_id` that is used to define a subset of the database.
+    tables_to_load : dict, default=None
+        *deprecated*
+    columns_to_load : dict, default=None
+        *deprecated*
+    database_type: Optional[str] = 'OMOP'. Must be 'OMOP' or 'I2B2'
+        Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP.
+    prune_omop_date_columns: bool, default=True
+        In OMOP, most date values are stored both in a `<str>_date` and `<str>_datetime` column
+        Koalas has trouble handling the `date` time, so we only keep the `datetime` column
+    cache: bool, default=True
+        Whether to cache each table after preprocessing or not.
+        Will speed-up subsequent calculations, but can be long/infeasable for very large tables
+
+    Attributes
+    ----------
+    person : koalas dataframe
+        Hive data for table `person` as a koalas dataframe.
+        Other OMOP tables can also be accessed as attributes
+    available_tables : list of str
+        names of OMOP tables that can be accessed as attributes with this
+        HiveData object.
+
+    Examples
+    --------
+
+    ```python
+    data = HiveData(database_name="edsomop_prod_a")
+    data.available_tables
+    # Out: ["person", "care_site", "condition_occurrence", ... ]
+
+    person = data.person
+    type(person)
+    # Out: databricks.koalas.frame.DataFrame
+
+    person["person_id"].count()
+    # Out: 12670874
+    ```
+
+    This class can be used to create a subset of data for a given
+    list of `person_id`. This is useful because the smaller dataset
+    can then be used to prototype more rapidly.
+
+    ```python
+    my_person_ids = [9226726, 2092082, ...]
+    data = HiveData(
+        spark_session=spark, database_name="edsomop_prod_a", person_ids=my_person_ids
+    )
+    data.person["person_id"].count()
+    # Out: 1000
+
+    tables_to_save = ["person", "visit_occurrence"]
+    data.persist_tables_to_folder("./cohort_sample_1000", table_names=tables_to_save)
+    # Out: writing /export/home/USER/cohort_sample_1000/person.parquet
+    # Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet
+    # Out: ...
+    ```
+
+    """
+    super().__init__()
+
+    if columns_to_load is not None:
+        logger.warning("'columns_to_load' is deprecated and won't be used")
+
+    if tables_to_load is not None:
+        logger.warning("'tables_to_load' is deprecated and won't be used")
+
+    self.spark_session = (
+        spark_session or SparkSession.builder.enableHiveSupport().getOrCreate()
+    )
+    self.database_name = database_name
+    if database_type not in ["I2B2", "OMOP"]:
+        raise ValueError(
+            f"`database_type` must be either 'I2B2' or 'OMOP'. Got {database_type}"
+        )
+    self.database_type = database_type
+
+    if self.database_type == "I2B2":
+        self.database_source = "cse" if "cse" in self.database_name else "edsprod"
+        self.omop_to_i2b2 = settings.i2b2_tables[self.database_source]
+        self.i2b2_to_omop = defaultdict(list)
+        for omop_table, i2b2_table in self.omop_to_i2b2.items():
+            self.i2b2_to_omop[i2b2_table].append(omop_table)
+
+    self.prune_omop_date_columns = prune_omop_date_columns
+    self.cache = cache
+    self.user = os.environ["USER"]
+    self.person_ids, self.person_ids_df = self._prepare_person_ids(person_ids)
+    self.available_tables = self.list_available_tables()
+    self._tables = {}
+
    +
    + + + +
    + + + + + + + + + +
    + + + +

    + persist_tables_to_folder + + +

    +
    persist_tables_to_folder(folder: str, person_ids: Optional[Iterable[int]] = None, tables: List[str] = None, overwrite: bool = False) -> None
+
    + +
    + +

    Save OMOP tables as parquet files in a given folder.

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    folder +

    path to folder where the tables will be written.

    +

    + + TYPE: + str + +

    +
    +

    person_ids : iterable + person_ids to keep in the subcohort.

    +

    tables : list of str, default None + list of table names to save. Default value is + 🇵🇾data:~eds_scikit.io.settings.default_tables_to_save.

    +

    overwrite : bool, default=False + whether to overwrite files if 'folder' already exists.

    + +
    + Source code in eds_scikit/io/hive.py + 
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    def persist_tables_to_folder(
+    self,
+    folder: str,
+    person_ids: Optional[Iterable[int]] = None,
+    tables: List[str] = None,
+    overwrite: bool = False,
+) -> None:
+    """Save OMOP tables as parquet files in a given folder.
+
+    Parameters
+    ----------
+    folder : str
+        path to folder where the tables will be written.
+
+    person_ids : iterable
+        person_ids to keep in the subcohort.
+
+    tables : list of str, default None
+        list of table names to save. Default value is
+        :py:data:`~eds_scikit.io.settings.default_tables_to_save`.
+
+    overwrite : bool, default=False
+        whether to overwrite files if 'folder' already exists.
+
+    """
+    # Manage tables
+    if tables is None:
+        tables = settings.default_tables_to_save
+
+    unknown_tables = [
+        table for table in tables if table not in self.available_tables
+    ]
+    if unknown_tables:
+        raise ValueError(
+            f"The following tables are not available : {str(unknown_tables)}"
+        )
+
+    # Create folder
+    folder = Path(folder).absolute()
+
+    if folder.exists() and overwrite:
+        shutil.rmtree(folder)
+
+    folder.mkdir(parents=True, mode=0o766)
+
+    assert os.path.exists(folder) and os.path.isdir(
+        folder
+    ), f"Folder {folder} not found."
+
+    # TODO: remove everything in this folder that is a valid
+    # omop table. This prevents a user from having a
+    # folder containing datasets generated from different
+    # patient subsets.
+
+    # TODO: maybe check how much the user wants to persist
+    # to disk. Set a limit on the number of patients in the cohort ?
+
+    if person_ids is not None:
+        person_ids = self._prepare_person_ids(person_ids, return_df=False)
+
+    database_path = self.get_db_path()
+
+    for idx, table in enumerate(tables):
+        if self.database_type == "I2B2":
+            table_path = self._hdfs_write_orc_to_parquet(
+                table, person_ids, overwrite
+            )
+        else:
+            table_path = os.path.join(database_path, table)
+
+        df = self.get_table_from_parquet(table_path, person_ids=person_ids)
+
+        local_file_path = os.path.join(folder, f"{table}.parquet")
+        df.to_parquet(
+            local_file_path,
+            allow_truncated_timestamps=True,
+            coerce_timestamps="ms",
+        )
+        logger.info(
+            f"({idx+1}/{len(tables)}) Table {table} saved at "
+            f"{local_file_path} (N={len(df)})."
+        )
+
    +
    +
    + +
    + +
    + + + +

    + get_db_path + + +

    +
    get_db_path()
+
    + +
    + +

    Get the HDFS path of the database

    + +
    + Source code in eds_scikit/io/hive.py + 
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    def get_db_path(self):
+    """Get the HDFS path of the database"""
+    return (
+        self.spark_session.sql(f"DESCRIBE DATABASE EXTENDED {self.database_name}")
+        .filter("database_description_item=='Location'")
+        .collect()[0]
+        .database_description_value
+    )
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/io/omop_teva_default_config/index.html b/main/reference/io/omop_teva_default_config/index.html new file mode 100644 index 00000000..f1fe2067 --- /dev/null +++ b/main/reference/io/omop_teva_default_config/index.html @@ -0,0 +1,3789 @@ + + + + + + + + + + + + + + + + omop_teva_default_config - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + +
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    + + + + + + + + +

    eds_scikit.io.omop_teva_default_config

    + + +
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    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/io/postgres/index.html b/main/reference/io/postgres/index.html new file mode 100644 index 00000000..16cfc5a5 --- /dev/null +++ b/main/reference/io/postgres/index.html @@ -0,0 +1,4070 @@ + + + + + + + + + + + + + + + + postgres - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.io.postgres

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + PostgresData + + +

    +
    PostgresData(dbname: Optional[str] = None, schema: Optional[str] = None, user: Optional[str] = None, host: Optional[str] = None, port: Optional[int] = None)
+
    + +
    +

    + Bases: BaseData

    + + + +
    + Source code in eds_scikit/io/postgres.py + 
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    def __init__(
+    self,
+    dbname: Optional[str] = None,
+    schema: Optional[str] = None,
+    user: Optional[str] = None,
+    host: Optional[str] = None,
+    port: Optional[int] = None,
+):
+    (
+        self.host,
+        self.port,
+        self.dbname,
+        self.user,
+    ) = self._find_matching_pgpass_params(host, port, dbname, user)
+    self.schema = schema
+
    +
    + + + +
    + + + + + + + + + +
    + + + +

    + read_sql + + +

    +
    read_sql(sql_query: str, **kwargs) -> pd.DataFrame
+
    + +
    + +

    Execute pandas.read_sql() on the database.

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    sql_query +

    SQL query (postgres flavor)

    +

    + + TYPE: + str + +

    +
    **kwargs +

    additional arguments passed to pandas.read_sql()

    +

    + + DEFAULT: + {} + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + df + + +

    + + TYPE: + pandas.DataFrame + +

    +
    + +
    + Source code in eds_scikit/io/postgres.py + 
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    def read_sql(self, sql_query: str, **kwargs) -> pd.DataFrame:
+    """Execute pandas.read_sql() on the database.
+
+    Parameters
+    ----------
+    sql_query : str
+        SQL query (postgres flavor)
+    **kwargs
+        additional arguments passed to pandas.read_sql()
+
+    Returns
+    -------
+    df : pandas.DataFrame
+
+    """
+    connection_infos = {
+        param: getattr(self, param) for param in ["host", "port", "dbname", "user"]
+    }
+    connection_infos["password"] = pgpasslib.getpass(**connection_infos)
+    connection = pg.connect(**connection_infos)
+    if self.schema:
+        connection.cursor().execute(f"SET SCHEMA '{self.schema}'")
+
+    df = pd.read_sql(sql_query, con=connection, **kwargs)
+
+    connection.close()
+    return df
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/io/settings/index.html b/main/reference/io/settings/index.html new file mode 100644 index 00000000..f852b088 --- /dev/null +++ b/main/reference/io/settings/index.html @@ -0,0 +1,3934 @@ + + + + + + + + + + + + + + + + settings - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + +
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    + + +
    +
    + + + + + + + + +

    eds_scikit.io.settings

    + + +
    + + + +
    + + + +
    + + + + + + + +
    + + + +

    + default_tables_to_save + + + + module-attribute + + +

    +
    default_tables_to_save = ['person', 'visit_occurrence', 'visit_detail', 'condition_occurrence', 'procedure_occurrence', 'care_site', 'concept']
+
    + +
    + +

    The default tables loaded when instanciating a HiveData +or a PostgresData

    +
    + +
    + +
    + + + +

    + tables_to_load + + + + module-attribute + + +

    +
    tables_to_load = {'person': ['person_id', 'location_id', 'year_of_birth', 'month_of_birth', 'day_of_birth', 'birth_datetime', 'death_datetime', 'gender_source_value', 'gender_source_concept_id', 'cdm_source'], 'visit_occurrence': ['visit_occurrence_id', 'person_id', 'visit_occurrence_source_value', 'preceding_visit_occurrence_id', 'care_site_id', 'visit_start_datetime', 'visit_end_datetime', 'visit_source_value', 'visit_source_concept_id', 'visit_type_source_value', 'visit_type_source_concept_id', 'admitted_from_source_value', 'admitted_from_source_concept_id', 'discharge_to_source_value', 'discharge_to_source_concept_id', 'row_status_source_value', 'stay_source_value', 'stay_source_concept_id', 'cdm_source'], 'care_site': ['care_site_id', 'care_site_source_value', 'care_site_name', 'care_site_short_name', 'place_of_service_source_value', 'care_site_type_source_value', 'valid_start_date', 'valid_end_date'], 'visit_detail': ['visit_detail_id', 'visit_occurrence_id', 'person_id', 'preceding_visit_detail_id', 'visit_detail_parent_id', 'care_site_id', 'visit_detail_start_date', 'visit_detail_start_datetime', 'visit_detail_end_date', 'visit_detail_end_datetime', 'visit_detail_source_value', 'visit_detail_source_concept_id', 'visit_detail_type_source_value', 'visit_detail_type_source_concept_id', 'admitted_from_source_value', 'admitted_from_source_concept_id', 'discharge_to_source_value', 'discharge_to_source_concept_id', 'cdm_source'], 'condition_occurrence': ['condition_occurrence_id', 'person_id', 'visit_occurrence_id', 'visit_detail_id', 'condition_start_datetime', 'condition_source_value', 'condition_source_concept_id', 'condition_status_source_value', 'condition_status_source_concept_id', 'cdm_source'], 'procedure_occurrence': ['procedure_occurrence_id', 'person_id', 'visit_occurrence_id', 'visit_detail_id', 'procedure_datetime', 'procedure_source_value', 'procedure_source_concept_id', 'cdm_source'], 'concept': ['concept_id', 'concept_name', 'domain_id', 'vocabulary_id', 'concept_class_id', 'standard_concept', 'concept_code', 'valid_start_date', 'valid_end_date', 'invalid_reason']}
+
    + +
    + +

    The default columns loaded when instanciating a HiveData +or a PostgresData

    +
    + +
    + +
    + + + +

    + measurement_config + + + + module-attribute + + +

    +
    measurement_config = dict(standard_terminologies=['LOINC', 'AnaBio', 'ANABIO', 'ANALYSES_LABORATOIRE'], standard_concept_regex={'LOINC': '[0-9]{2,5}[-][0-9]', 'AnaBio': '[A-Z][0-9]{4}', 'ANABIO': '[A-Z][0-9]{4}'}, source_terminologies={'ANALYSES_LABORATOIRE': 'Analyses Laboratoire', 'GLIMS_ANABIO': 'GLIMS.{0,20}Anabio', 'GLIMS_LOINC': 'GLIMS.{0,20}LOINC', 'ITM_ANABIO': 'ITM - ANABIO', 'ITM_LOINC': 'ITM - LOINC'}, mapping=[('ANALYSES_LABORATOIRE', 'GLIMS_ANABIO', 'Maps to'), ('ANALYSES_LABORATOIRE', 'GLIMS_LOINC', 'Maps to'), ('GLIMS_ANABIO', 'ITM_ANABIO', 'Mapped from'), ('ITM_ANABIO', 'ITM_LOINC', 'Maps to')])
+
    + +
    + +

    AP-HP specific configuration. ITM and GLIMS do not share the same ANABIO-to-LOINC mapping. ITM referential is more reliable but covers less ANABIO codes the GLIMS referential.

    +
    + +
    + + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/period/index.html b/main/reference/period/index.html new file mode 100644 index 00000000..02b19f49 --- /dev/null +++ b/main/reference/period/index.html @@ -0,0 +1,4094 @@ + + + + + + + + + + + + + + + + `eds_scikit.period` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
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    + + + +
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    + + +
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    + + + + + + + + +

    eds_scikit.period

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + tagging + + +

    +
    tagging(tag_to_df: DataFrame, tag_from_df: DataFrame, concept_to_tag: str, tag_to_date_cols: List[str] = ['t_start', 't_end'], tag_from_date_cols: List[str] = ['t_start', 't_end'], algo: str = 'intersection') -> DataFrame
+
    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    tag_to_df + +

    + + TYPE: + DataFrame + +

    +
    tag_from_df + +

    + + TYPE: + DataFrame + +

    +
    concept_to_tag + +

    + + TYPE: + str + +

    +
    tag_to_date_cols + +

    + + TYPE: + List[str], optional + + + DEFAULT: + ['t_start', 't_end'] + +

    +
    tag_from_date_cols + +

    + + TYPE: + List[str], optional + + + DEFAULT: + ['t_start', 't_end'] + +

    +
    algo + +

    + + TYPE: + str, optional + + + DEFAULT: + 'intersection' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + + +
    + +
    + Source code in eds_scikit/period/tagging_functions.py + 
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    def tagging(
+    tag_to_df: DataFrame,
+    tag_from_df: DataFrame,
+    concept_to_tag: str,
+    tag_to_date_cols: List[str] = ["t_start", "t_end"],
+    tag_from_date_cols: List[str] = ["t_start", "t_end"],
+    algo: str = "intersection",
+) -> DataFrame:
+    """
+
+    Parameters
+    ----------
+    tag_to_df : DataFrame
+    tag_from_df : DataFrame
+    concept_to_tag : str
+    tag_to_date_cols : List[str], optional
+    tag_from_date_cols : List[str], optional
+    algo : str, optional
+
+    Returns
+    -------
+    DataFrame
+    """
+    framework = get_framework(tag_to_df)
+
+    tag_to_df = tag_to_df.assign(event_id=tag_to_df.index)
+
+    tag_from = tag_from_df.loc[
+        tag_from_df.concept == concept_to_tag,
+        ["person_id", "value"] + ["t_start", "t_end"],
+    ]
+
+    tmp = (
+        tag_to_df.rename(
+            columns={tag_to_date_cols[0]: "t_start_x", tag_to_date_cols[1]: "t_end_x"}
+        )
+        .merge(
+            tag_from.rename(
+                columns={
+                    tag_from_date_cols[0]: "t_start_y",
+                    tag_from_date_cols[1]: "t_end_y",
+                }
+            ),
+            on="person_id",
+            how="left",
+        )
+        .dropna(subset=["t_start_x", "t_end_x", "t_start_y", "t_end_y"])
+    )
+
+    if len(tmp) == 0:
+        # TODO: is this necessary ?
+        logger.warning("No matching were found between the 2 DataFrames")
+
+        return framework.DataFrame(
+            columns=["person_id", "t_start", "t_end", "concept", "value"]
+        )
+
+    tmp["tag"] = compare_intervals(
+        tmp["t_start_x"],
+        tmp["t_end_x"],
+        tmp["t_start_y"],
+        tmp["t_end_y"],
+        algo=algo,
+    )
+
+    value_col = (
+        "value_y"
+        if (("value" in tag_to_df.columns) and ("value" in tag_from_df.columns))
+        else "value"
+    )
+
+    tags = (
+        tmp.groupby(["event_id", value_col])
+        .tag.any()
+        .unstack()
+        .fillna(False)
+        .reset_index()
+    )
+    tags = tag_to_df[["event_id"]].merge(tags, on="event_id", how="left").fillna(False)
+    tags = tag_to_df.merge(tags, on="event_id", how="left").drop(columns="event_id")
+    return tags
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/period/stays/index.html b/main/reference/period/stays/index.html new file mode 100644 index 00000000..fd76a69e --- /dev/null +++ b/main/reference/period/stays/index.html @@ -0,0 +1,5202 @@ + + + + + + + + + + + + + + + + stays - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + +
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    + + +
    +
    + + + + + + + + +

    eds_scikit.period.stays

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + cleaning + + +

    +
    cleaning(vo, long_stay_threshold: timedelta, long_stay_filtering: Union[str, None], remove_deleted_visits: bool, open_stay_end_datetime: datetime) -> Tuple[DataFrame, DataFrame]
+
    + +
    + +

    Preprocessing of visits before merging them in stays. +The function will split the input vo DataFrame into 2, one that +should undergo the merging procedure, and one that shouldn't. +Depending on the input parameters, 3 type of visits can be prevented to +undergo the merging procedure:

    +
      +
    • Too long visits
      • +
    • Too long AND unclosed visits
      • +
    • Removed visits
      • +
    +

    See the merge_visits() function for details of the parameters

    + +
    + Source code in eds_scikit/period/stays.py + 
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    def cleaning(
+    vo,
+    long_stay_threshold: timedelta,
+    long_stay_filtering: Union[str, None],
+    remove_deleted_visits: bool,
+    open_stay_end_datetime: datetime,
+) -> Tuple[DataFrame, DataFrame]:
+    """
+    Preprocessing of visits before merging them in stays.
+    The function will split the input `vo` DataFrame into 2, one that
+    should undergo the merging procedure, and one that shouldn't.
+    Depending on the input parameters, 3 type of visits can be prevented to
+    undergo the merging procedure:
+
+    - Too long visits
+    - Too long AND unclosed visits
+    - Removed visits
+
+    See the [merge_visits()][eds_scikit.period.stays.merge_visits] function for details of the parameters
+    """
+
+    LONG_STAY_FILTERING_VALUES = ["all", "open", None]
+    DELETED_ROW_VALUE = "supprimé"
+
+    if long_stay_filtering not in LONG_STAY_FILTERING_VALUES:
+        raise ValueError(
+            f"""Unknown value for `long_stay_filtering`.
+            Accepted values are {LONG_STAY_FILTERING_VALUES}"""
+        )
+
+    if remove_deleted_visits:
+        deleted_visit_mask = vo["row_status_source_value"] == DELETED_ROW_VALUE
+    no_starting_date_mask = vo["visit_start_datetime"].isna()
+    no_ending_date_mask = vo["visit_end_datetime"].isna()
+
+    vo[
+        "visit_end_datetime_calc"
+    ] = open_stay_end_datetime  # Cannot use fillna() with datetime in Koalas
+    vo["visit_end_datetime_calc"] = vo["visit_end_datetime"].combine_first(
+        vo["visit_end_datetime_calc"]
+    )
+
+    too_long_stays_mask = (
+        substract_datetime(vo["visit_end_datetime_calc"], vo["visit_start_datetime"])
+        >= long_stay_threshold.total_seconds()
+    )
+
+    mask = no_starting_date_mask
+
+    if long_stay_filtering == "all":
+        mask = mask | too_long_stays_mask
+
+    elif long_stay_filtering == "open":
+        mask = mask | (too_long_stays_mask & no_ending_date_mask)
+
+    if remove_deleted_visits:
+        mask = (mask) | deleted_visit_mask
+
+    return vo[~mask], vo[mask]
+
    +
    +
    + +
    + +
    + + + +

    + merge_visits + + +

    +
    merge_visits(vo: DataFrame, remove_deleted_visits: bool = True, long_stay_threshold: timedelta = timedelta(days=365), long_stay_filtering: Optional[str] = 'all', open_stay_end_datetime: Optional[datetime] = None, max_timedelta: timedelta = timedelta(days=2), merge_different_hospitals: bool = False, merge_different_source_values: Union[bool, List[str]] = ['hospitalisés', 'urgence']) -> DataFrame
+
    + +
    + +

    Merge "close" visit occurrences to consider them as a single stay +by adding a STAY_ID and CONTIGUOUS_STAY_ID columns to the DataFrame.

    +

    The value of these columns will be the visit_occurrence_id of the first (meaning the oldest) +visit of the stay.

    +

    From a temporal point of view, we consider that two visits belong to the same stay if either:

    +
      +
    • They intersect
      • +
    • The time difference between the end of the most recent and the start of the oldest + is lower than max_timedelta (for STAY_ID) or 0 (for CONTIGUOUS_STAY_ID)
      • +
    +

    Additionally, other parameters are available to further adjust the merging rules. See below.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    vo +

    The visit_occurrence DataFrame, with at least the following columns: +- visit_occurrence_id +- person_id +- visit_start_datetime_calc (from preprocessing) +- visit_end_datetime (from preprocessing) +Depending on the input parameters, additional columns may be required: +- care_site_id (if merge_different_hospitals == True) +- visit_source_value (if merge_different_source_values != False) +- row_status_source_value (if remove_deleted_visits= True)

    +

    + + TYPE: + DataFrame + +

    +
    remove_deleted_visits +

    Wether to remove deleted visits from the merging procedure. +Deleted visits are extracted via the row_status_source_value column

    +

    + + TYPE: + bool + + + DEFAULT: + True + +

    +
    long_stay_filtering +

    Filtering method for long and/or non-closed visits. First of all, visits with no starting date +won't be merged with any other visit, and visits with no ending date will have a temporary +"theoretical" ending date set by datetime.now(). That being said, some visits are sometimes years long +because they weren't closed at time. If other visits occurred during this timespan, +they could be all merged into the same stay. To avoid this issue, filtering can be done +depending on the long_stay_filtering value:

    +
      +
    • all: All long stays (closed and open) are removed from the merging procedure
      • +
    • open: Only long open stays are removed from the merging procedure
      • +
    • None: No filtering is done on long visits
      • +
    +

    Long stays are determined by the long_stay_threshold value.

    +

    + + TYPE: + Optional[str] + + + DEFAULT: + 'all' + +

    +
    long_stay_threshold +

    Minimum visit duration value to consider a visit as candidate for "long visits filtering"

    +

    + + TYPE: + timedelta + + + DEFAULT: + timedelta(days=365) + +

    +
    open_stay_end_datetime +

    Datetime to use in order to fill the visit_end_datetime of open visits. This is necessary in +order to compute stay duration and to filter long stays. If not provided datetime.now() will be used. +You might provide the extraction date of your data here.

    +

    + + TYPE: + Optional[datetime] + + + DEFAULT: + None + +

    +
    max_timedelta +

    Maximum time difference between the end of a visit and the start of another to consider +them as belonging to the same stay. This duration is internally converted in seconds before +comparing. Thus, if you want e.g. to merge visits happening in two consecutive days, you should use +timedelta(days=2) and NOT timedelta(days=1) in order to take into account extreme cases such as +an first visit ending on Monday at 00h01 AM and another one starting at 23h59 PM on Tuesday

    +

    + + TYPE: + timedelta + + + DEFAULT: + timedelta(days=2) + +

    +
    merge_different_hospitals +

    Wether to allow visits occurring in different hospitals to be merged into a same stay

    +

    + + TYPE: + bool + + + DEFAULT: + False + +

    +
    merge_different_source_values +

    Wether to allow visits with different visit_source_value to be merged into a same stay. Values can be:

    +
      +
    • True: the visit_source_value isn't taken into account for the merging
      • +
    • False: only visits with the same visit_source_value can be merged into a same stay
      • +
    • List[str]: only visits which visit_source_value is in the provided list can be merged together.
      • +
    +

    Warning: You should avoid merging visits where visit_source_value == "hospitalisation incomplète", +because those stays are often never closed.

    +

    + + TYPE: + Union[bool, List[str]] + + + DEFAULT: + ['hospitalisés', 'urgence'] + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + vo + +

    Visit occurrence DataFrame with additional STAY_ID column

    +

    + + TYPE: + DataFrame + +

    +
    + +

    Examples:

    + 
    >>> import pandas as pd
+>>> from datetime import datetime, timedelta
+>>> data = {
+    1 : ['A', 999, datetime(2021,1,1), datetime(2021,1,5), 'hospitalisés'],
+    2 : ['B', 999, datetime(2021,1,4), datetime(2021,1,8), 'hospitalisés'],
+    3 : ['C', 999, datetime(2021,1,12), datetime(2021,1,18), 'hospitalisés'],
+    4 : ['D', 999, datetime(2021,1,13), datetime(2021,1,14), 'urgence'],
+    5 : ['E', 999, datetime(2021,1,19), datetime(2021,1,21), 'hospitalisés'],
+    6 : ['F', 999, datetime(2021,1,25), datetime(2021,1,27), 'hospitalisés'],
+    7 : ['G', 999, datetime(2017,1,1), None, "hospitalisés"]
+}
+>>> vo = pd.DataFrame.from_dict(
+    data,
+    orient="index",
+    columns=[
+        "visit_occurrence_id",
+        "person_id",
+        "visit_start_datetime",
+        "visit_end_datetime",
+        "visit_source_value",
+    ],
+)
+>>> vo
+  visit_occurrence_id  person_id visit_start_datetime visit_end_datetime visit_source_value
+1                   A        999           2021-01-01         2021-01-05       hospitalisés
+2                   B        999           2021-01-04         2021-01-08       hospitalisés
+3                   C        999           2021-01-12         2021-01-18       hospitalisés
+4                   D        999           2021-01-13         2021-01-14            urgence
+5                   E        999           2021-01-19         2021-01-21       hospitalisés
+6                   F        999           2021-01-25         2021-01-27       hospitalisés
+7                   G        999           2017-01-01                NaT       hospitalisés
+
    + 
    >>> vo = merge_visits(
+        vo,
+        remove_deleted_visits=True,
+        long_stay_threshold=timedelta(days=365),
+        long_stay_filtering="all",
+        max_timedelta=timedelta(hours=24),
+        merge_different_hospitals=False,
+        merge_different_source_values=["hospitalisés", "urgence"],
+)
+>>> vo
+  visit_occurrence_id  person_id visit_start_datetime visit_end_datetime visit_source_value STAY_ID CONTIGUOUS_STAY_ID
+1                   A        999           2021-01-01         2021-01-05       hospitalisés       A                  A
+2                   B        999           2021-01-04         2021-01-08       hospitalisés       A                  A
+3                   C        999           2021-01-12         2021-01-18       hospitalisés       C                  C
+4                   D        999           2021-01-13         2021-01-14            urgence       C                  C
+5                   E        999           2021-01-19         2021-01-21       hospitalisés       C                  E
+6                   F        999           2021-01-25         2021-01-27       hospitalisés       F                  F
+7                   G        999           2017-01-01                NaT       hospitalisés       G                  G
+
    + +
    + Source code in eds_scikit/period/stays.py + 
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    @concept_checker(concepts=["STAY_ID", "CONTIGUOUS_STAY_ID"])
+def merge_visits(
+    vo: DataFrame,
+    remove_deleted_visits: bool = True,
+    long_stay_threshold: timedelta = timedelta(days=365),
+    long_stay_filtering: Optional[str] = "all",
+    open_stay_end_datetime: Optional[datetime] = None,
+    max_timedelta: timedelta = timedelta(days=2),
+    merge_different_hospitals: bool = False,
+    merge_different_source_values: Union[bool, List[str]] = ["hospitalisés", "urgence"],
+) -> DataFrame:
+    """
+    Merge "close" visit occurrences to consider them as a single stay
+    by adding a ``STAY_ID`` and ``CONTIGUOUS_STAY_ID`` columns to the DataFrame.
+
+    The value of these columns will be the `visit_occurrence_id` of the first (meaning the oldest)
+    visit of the stay.
+
+    From a temporal point of view, we consider that two visits belong to the same stay if either:
+
+    - They intersect
+    - The time difference between the end of the most recent and the start of the oldest
+      is lower than ``max_timedelta`` (for ``STAY_ID``) or 0 (for ``CONTIGUOUS_STAY_ID``)
+
+    Additionally, other parameters are available to further adjust the merging rules. See below.
+
+    Parameters
+    ----------
+    vo : DataFrame
+        The ``visit_occurrence`` DataFrame, with at least the following columns:
+        - visit_occurrence_id
+        - person_id
+        - visit_start_datetime_calc (from preprocessing)
+        - visit_end_datetime (from preprocessing)
+        Depending on the input parameters, additional columns may be required:
+        - care_site_id (if ``merge_different_hospitals == True``)
+        - visit_source_value (if ``merge_different_source_values != False``)
+        - row_status_source_value (if ``remove_deleted_visits= True``)
+    remove_deleted_visits: bool
+        Wether to remove deleted visits from the merging procedure.
+        Deleted visits are extracted via the `row_status_source_value` column
+    long_stay_filtering : Optional[str]
+        Filtering method for long and/or non-closed visits. First of all, visits with no starting date
+        won't be merged with any other visit, and visits with no ending date will have a temporary
+        "theoretical" ending date set by ``datetime.now()``. That being said, some visits are sometimes years long
+        because they weren't closed at time. If other visits occurred during this timespan,
+        they could be all merged into the same stay. To avoid this issue, filtering can be done
+        depending on the ``long_stay_filtering`` value:
+
+        - ``all``: All long stays (closed and open) are removed from the merging procedure
+        - ``open``: Only long open stays are removed from the merging procedure
+        - ``None``: No filtering is done on long visits
+
+        Long stays are determined by the ``long_stay_threshold`` value.
+    long_stay_threshold : timedelta
+        Minimum visit duration value to consider a visit as candidate for "long visits filtering"
+    open_stay_end_datetime: Optional[datetime]
+        Datetime to use in order to fill the `visit_end_datetime` of open visits. This is necessary in
+        order to compute stay duration and to filter long stays. If not provided `datetime.now()` will be used.
+        You might provide the extraction date of your data here.
+    max_timedelta : timedelta
+        Maximum time difference between the end of a visit and the start of another to consider
+        them as belonging to the same stay. This duration is internally converted in seconds before
+        comparing. Thus, if you want e.g. to merge visits happening in two consecutive days, you should use
+        `timedelta(days=2)` and NOT `timedelta(days=1)` in order to take into account extreme cases such as
+        an first visit ending on Monday at 00h01 AM and another one starting at 23h59 PM on Tuesday
+    merge_different_hospitals : bool
+        Wether to allow visits occurring in different hospitals to be merged into a same stay
+    merge_different_source_values : Union[bool, List[str]]
+        Wether to allow visits with different `visit_source_value` to be merged into a same stay. Values can be:
+
+        - `True`: the `visit_source_value` isn't taken into account for the merging
+        - `False`: only visits with the same `visit_source_value` can be merged into a same stay
+        - `List[str]`: only visits which `visit_source_value` is in the provided list can be merged together.
+
+        **Warning**: You should avoid merging visits where `visit_source_value == "hospitalisation incomplète"`,
+        because those stays are often never closed.
+
+    Returns
+    -------
+    vo : DataFrame
+        Visit occurrence DataFrame with additional `STAY_ID` column
+
+    Examples
+    --------
+
+    >>> import pandas as pd
+    >>> from datetime import datetime, timedelta
+    >>> data = {
+        1 : ['A', 999, datetime(2021,1,1), datetime(2021,1,5), 'hospitalisés'],
+        2 : ['B', 999, datetime(2021,1,4), datetime(2021,1,8), 'hospitalisés'],
+        3 : ['C', 999, datetime(2021,1,12), datetime(2021,1,18), 'hospitalisés'],
+        4 : ['D', 999, datetime(2021,1,13), datetime(2021,1,14), 'urgence'],
+        5 : ['E', 999, datetime(2021,1,19), datetime(2021,1,21), 'hospitalisés'],
+        6 : ['F', 999, datetime(2021,1,25), datetime(2021,1,27), 'hospitalisés'],
+        7 : ['G', 999, datetime(2017,1,1), None, "hospitalisés"]
+    }
+    >>> vo = pd.DataFrame.from_dict(
+        data,
+        orient="index",
+        columns=[
+            "visit_occurrence_id",
+            "person_id",
+            "visit_start_datetime",
+            "visit_end_datetime",
+            "visit_source_value",
+        ],
+    )
+    >>> vo
+      visit_occurrence_id  person_id visit_start_datetime visit_end_datetime visit_source_value
+    1                   A        999           2021-01-01         2021-01-05       hospitalisés
+    2                   B        999           2021-01-04         2021-01-08       hospitalisés
+    3                   C        999           2021-01-12         2021-01-18       hospitalisés
+    4                   D        999           2021-01-13         2021-01-14            urgence
+    5                   E        999           2021-01-19         2021-01-21       hospitalisés
+    6                   F        999           2021-01-25         2021-01-27       hospitalisés
+    7                   G        999           2017-01-01                NaT       hospitalisés
+
+    >>> vo = merge_visits(
+            vo,
+            remove_deleted_visits=True,
+            long_stay_threshold=timedelta(days=365),
+            long_stay_filtering="all",
+            max_timedelta=timedelta(hours=24),
+            merge_different_hospitals=False,
+            merge_different_source_values=["hospitalisés", "urgence"],
+    )
+    >>> vo
+      visit_occurrence_id  person_id visit_start_datetime visit_end_datetime visit_source_value STAY_ID CONTIGUOUS_STAY_ID
+    1                   A        999           2021-01-01         2021-01-05       hospitalisés       A                  A
+    2                   B        999           2021-01-04         2021-01-08       hospitalisés       A                  A
+    3                   C        999           2021-01-12         2021-01-18       hospitalisés       C                  C
+    4                   D        999           2021-01-13         2021-01-14            urgence       C                  C
+    5                   E        999           2021-01-19         2021-01-21       hospitalisés       C                  E
+    6                   F        999           2021-01-25         2021-01-27       hospitalisés       F                  F
+    7                   G        999           2017-01-01                NaT       hospitalisés       G                  G
+    """
+
+    # Preprocessing
+    vo_to_merge, vo_to_not_merge = cleaning(
+        vo,
+        remove_deleted_visits=remove_deleted_visits,
+        long_stay_threshold=long_stay_threshold,
+        long_stay_filtering=long_stay_filtering,
+        open_stay_end_datetime=open_stay_end_datetime
+        if open_stay_end_datetime is not None
+        else datetime.now(),
+    )
+
+    fw = get_framework(vo_to_merge)
+
+    grouping_keys = ["person_id"]
+
+    if not merge_different_hospitals:
+        grouping_keys.append("care_site_id")
+
+    if not merge_different_source_values:
+        grouping_keys.append("visit_source_value")
+
+    elif type(merge_different_source_values) == list:
+        tmp = fw.DataFrame(
+            data=dict(
+                visit_source_value=merge_different_source_values,
+                grouped_visit_source_value=True,
+            )
+        )
+        vo_to_merge = vo_to_merge.merge(tmp, on="visit_source_value", how="left")
+        vo_to_merge["grouped_visit_source_value"] = vo_to_merge[
+            "grouped_visit_source_value"
+        ].fillna(value=False)
+        grouping_keys.append("grouped_visit_source_value")
+
+    # Cartesian product
+    merged = vo_to_merge.merge(
+        vo_to_merge,
+        on=grouping_keys,
+        how="inner",
+        suffixes=("_1", "_2"),
+    )
+
+    # Keeping only visits where 1 occurs before 2
+    merged = merged[
+        merged["visit_start_datetime_1"] <= merged["visit_start_datetime_2"]
+    ]
+
+    # Checking correct overlap
+    th = max_timedelta.total_seconds()
+
+    merged["overlap"] = substract_datetime(
+        merged["visit_start_datetime_2"], merged["visit_end_datetime_calc_1"]
+    )
+    merged["to_merge"] = (merged["overlap"] <= th).astype(int)
+    merged["contiguous"] = (merged["overlap"] <= 0).astype(int)
+
+    def get_first(
+        merged: DataFrame,
+        contiguous_only: bool = False,
+    ) -> Tuple[DataFrame, DataFrame]:
+        """
+        Returns a boolean flag for each visit, telling if the visit
+        if the first of a stay.
+        The ``contiguous_only`` parameter controls if the visits have to be
+        contiguous in the stay
+        """
+
+        flag_col = "contiguous" if contiguous_only else "to_merge"
+        flag_name = "1_is_first_contiguous" if contiguous_only else "1_is_first"
+        concept_prefix = "CONTIGUOUS_" if contiguous_only else ""
+
+        # If the only previous visit to be merged with is itself, we found our first visit !
+        first_visits = merged.groupby("visit_occurrence_id_2")[flag_col].sum() == 1
+        first_visits.name = flag_name
+
+        # Adding this boolean flag to the merged DataFrame
+        merged = merged.merge(
+            first_visits,
+            left_on="visit_occurrence_id_1",
+            right_index=True,
+            how="inner",
+        )
+
+        # Getting the corresponding first visit
+        first_visit = (
+            merged.sort_values(
+                by=[flag_name, "visit_start_datetime_1"], ascending=[False, False]
+            )
+            .groupby("visit_occurrence_id_2")
+            .first()["visit_occurrence_id_1"]
+            .reset_index()
+            .rename(
+                columns={
+                    "visit_occurrence_id_1": f"{concept_prefix}STAY_ID",
+                    "visit_occurrence_id_2": "visit_occurrence_id",
+                }
+            )
+        )
+
+        return merged, first_visit
+
+    merged, first_contiguous_visit = get_first(merged, contiguous_only=True)
+    merged, first_visit = get_first(merged, contiguous_only=False)
+
+    # Concatenating merge visits with previously discarded ones
+    results = fw.concat(
+        [
+            vo_to_merge.merge(
+                first_visit,
+                on="visit_occurrence_id",
+                how="inner",
+            ).merge(
+                first_contiguous_visit,
+                on="visit_occurrence_id",
+                how="inner",
+            ),
+            vo_to_not_merge,
+        ]
+    )
+
+    # Adding visit_occurrence_id as STAY_ID and CONTIGUOUS_STAY_ID to discarded visits
+    results["STAY_ID"] = results["STAY_ID"].combine_first(
+        results["visit_occurrence_id"]
+    )
+    results["CONTIGUOUS_STAY_ID"] = results["CONTIGUOUS_STAY_ID"].combine_first(
+        results["visit_occurrence_id"]
+    )
+
+    # Removing tmp columns
+
+    vo = vo.drop(columns=["visit_end_datetime_calc"])
+
+    return results.drop(
+        columns=(
+            set(results.columns)
+            & set(["visit_end_datetime_calc", "grouped_visit_source_value"])
+        )
+    )
+
    +
    +
    + +
    + +
    + + + +

    + get_stays_duration + + +

    +
    get_stays_duration(vo: DataFrame, algo: str = 'sum_of_visits_duration', missing_end_date_handling: str = 'fill', open_stay_end_datetime: Optional[datetime] = None) -> DataFrame
+
    + +
    + +

    Computes stay duration. +The input DataFrame should contain the STAY_ID and CONTIGUOUS_STAY_ID columns, +that can be computed via the merge_visits() function.

    + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    vo +

    visit occurrence DataFrame with the STAY_ID and CONTIGUOUS_STAY_ID columns

    +

    + + TYPE: + DataFrame + +

    +
    algo +

    Which algo to use for computing stay durations. Available values are:

    +
      +
    • "sum_of_visits_duration": The stay duration will correspond to the sum of each visit duration in the stay.
      • +
    • "visits_date_difference": The stay duration will correspond to the difference between the end date of the last visit and the start date of the first visit.
      • +
    +

    + + TYPE: + str + + + DEFAULT: + 'sum_of_visits_duration' + +

    +
    missing_end_date_handling +

    How to handle visits with no end date. Available values are:

    +
      +
    • "fill": Missing values are filled with datetime.now()
      • +
    • "coerce": Missing values are handled as such, so duration of stays with open visits will be NaN.
      • +
    +

    + + TYPE: + str + + + DEFAULT: + 'fill' + +

    +
    open_stay_end_datetime +

    Used if missing_end_date_handling == "fill". Provide the datetime with which +open stays should be ended. Leave to None in order to used datetime.now()

    +

    + + TYPE: + Optional[datetime] + + + DEFAULT: + None + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    stay DataFrame with STAY_ID as index, and the following columns:

    +
      +
    • "person_id"
      • +
    • "t_start": The start date of the first visit of the stay
      • +
    • "t_end": The end date of the last visit of the stay
      • +
    • "STAY_DURATION": The duration (in hours) of the stay
      • +
    +
    + + + + + + + + + + + + + + +
    RAISESDESCRIPTION
    + + MissingConceptError + + +

    If STAY_ID and CONTIGUOUS_STAY_ID are not in the input columns.

    +
    + +
    + Source code in eds_scikit/period/stays.py + 
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    @algo_checker(algos=["sum_of_visits_duration", "visits_date_difference"])
+@concept_checker(concepts=["STAY_DURATION"], only_adds_concepts=False)
+def get_stays_duration(
+    vo: DataFrame,
+    algo: str = "sum_of_visits_duration",
+    missing_end_date_handling: str = "fill",
+    open_stay_end_datetime: Optional[datetime] = None,
+) -> DataFrame:
+    """
+    Computes stay duration.
+    The input DataFrame should contain the `STAY_ID` and `CONTIGUOUS_STAY_ID` columns,
+    that can be computed via the `merge_visits()` function.
+
+    Parameters
+    ----------
+    vo : DataFrame
+        visit occurrence DataFrame with the `STAY_ID` and `CONTIGUOUS_STAY_ID` columns
+    algo : str
+        Which algo to use for computing stay durations. Available values are:
+
+        - `"sum_of_visits_duration"`: The stay duration will correspond to the sum of each visit duration in the stay.
+        - `"visits_date_difference"`: The stay duration will correspond to the difference between the end date of the last visit and the start date of the first visit.
+    missing_end_date_handling : str
+        How to handle visits with no end date. Available values are:
+
+        - `"fill"`: Missing values are filled with `datetime.now()`
+        - `"coerce"`: Missing values are handled as such, so duration of stays with open visits will be NaN.
+    open_stay_end_datetime: Optional[datetime]
+        Used if `missing_end_date_handling == "fill"`. Provide the `datetime` with which
+        open stays should be ended. Leave to `None` in order to used `datetime.now()`
+
+    Returns
+    -------
+    DataFrame
+        *stay* DataFrame with `STAY_ID` as index, and the following columns:
+
+        - `"person_id"`
+        - `"t_start"`: The start date of the first visit of the stay
+        - `"t_end"`: The end date of the last visit of the stay
+        - `"STAY_DURATION"`: The duration (in hours) of the stay
+
+    Raises
+    ------
+    MissingConceptError
+        If `STAY_ID` and `CONTIGUOUS_STAY_ID` are not in the input columns.
+    """
+
+    if set(("STAY_ID", "CONTIGUOUS_STAY_ID")) - set(vo.columns):
+        raise MissingConceptError(
+            df_name="visit_occurence",
+            required_concepts=[
+                ("STAY_ID", "should be computed via 'merge_visits'"),
+                ("CONTIGUOUS_STAY_ID", "should be computed via 'merge_visits'"),
+            ],
+        )
+
+    if missing_end_date_handling == "fill":
+        # Cannot use fillna() with datetime in Koalas
+        if open_stay_end_datetime is None:
+            open_stay_end_datetime = datetime.now()
+        vo["visit_end_datetime_calc"] = open_stay_end_datetime
+        vo["visit_end_datetime_calc"] = vo["visit_end_datetime"].combine_first(
+            vo["visit_end_datetime_calc"]
+        )
+    elif missing_end_date_handling == "coerce":
+        vo["visit_end_datetime_calc"] = vo["visit_end_datetime"]
+
+    agg_dict = dict(
+        person_id=("person_id", "first"),
+        t_start=("visit_start_datetime", "min"),
+        t_end=("visit_end_datetime_calc", "max"),
+    )
+
+    if algo == "sum_of_visits_duration":
+
+        agg_dict["STAY_ID"] = ("STAY_ID", "first")
+
+        contiguous_stays = vo.groupby("CONTIGUOUS_STAY_ID").agg(**agg_dict)
+        contiguous_stays["CONTIGUOUS_STAY_DURATION"] = substract_datetime(
+            contiguous_stays["t_end"], contiguous_stays["t_start"], out="hours"
+        )
+
+        agg_dict = dict(
+            person_id=("person_id", "first"),
+            t_start=("t_start", "min"),
+            t_end=("t_end", "max"),
+            STAY_DURATION=("CONTIGUOUS_STAY_DURATION", "sum"),
+        )
+
+        stays = contiguous_stays.groupby("STAY_ID").agg(**agg_dict)
+
+    elif algo == "visits_date_difference":
+
+        stays = vo.groupby("STAY_ID").agg(**agg_dict)
+        stays["STAY_DURATION"] = substract_datetime(
+            stays["t_end"], stays["t_start"], out="hours"
+        )
+
+    if missing_end_date_handling == "coerce":
+        stays.loc[stays["t_end"].isna(), "STAY_DURATION"] = NaN
+
+    return stays
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/period/tagging_functions/index.html b/main/reference/period/tagging_functions/index.html new file mode 100644 index 00000000..ceaab122 --- /dev/null +++ b/main/reference/period/tagging_functions/index.html @@ -0,0 +1,4135 @@ + + + + + + + + + + + + + + + + tagging_functions - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + +
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    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.period.tagging_functions

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + tagging + + +

    +
    tagging(tag_to_df: DataFrame, tag_from_df: DataFrame, concept_to_tag: str, tag_to_date_cols: List[str] = ['t_start', 't_end'], tag_from_date_cols: List[str] = ['t_start', 't_end'], algo: str = 'intersection') -> DataFrame
+
    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    tag_to_df + +

    + + TYPE: + DataFrame + +

    +
    tag_from_df + +

    + + TYPE: + DataFrame + +

    +
    concept_to_tag + +

    + + TYPE: + str + +

    +
    tag_to_date_cols + +

    + + TYPE: + List[str], optional + + + DEFAULT: + ['t_start', 't_end'] + +

    +
    tag_from_date_cols + +

    + + TYPE: + List[str], optional + + + DEFAULT: + ['t_start', 't_end'] + +

    +
    algo + +

    + + TYPE: + str, optional + + + DEFAULT: + 'intersection' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + + +
    + +
    + Source code in eds_scikit/period/tagging_functions.py + 
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    def tagging(
+    tag_to_df: DataFrame,
+    tag_from_df: DataFrame,
+    concept_to_tag: str,
+    tag_to_date_cols: List[str] = ["t_start", "t_end"],
+    tag_from_date_cols: List[str] = ["t_start", "t_end"],
+    algo: str = "intersection",
+) -> DataFrame:
+    """
+
+    Parameters
+    ----------
+    tag_to_df : DataFrame
+    tag_from_df : DataFrame
+    concept_to_tag : str
+    tag_to_date_cols : List[str], optional
+    tag_from_date_cols : List[str], optional
+    algo : str, optional
+
+    Returns
+    -------
+    DataFrame
+    """
+    framework = get_framework(tag_to_df)
+
+    tag_to_df = tag_to_df.assign(event_id=tag_to_df.index)
+
+    tag_from = tag_from_df.loc[
+        tag_from_df.concept == concept_to_tag,
+        ["person_id", "value"] + ["t_start", "t_end"],
+    ]
+
+    tmp = (
+        tag_to_df.rename(
+            columns={tag_to_date_cols[0]: "t_start_x", tag_to_date_cols[1]: "t_end_x"}
+        )
+        .merge(
+            tag_from.rename(
+                columns={
+                    tag_from_date_cols[0]: "t_start_y",
+                    tag_from_date_cols[1]: "t_end_y",
+                }
+            ),
+            on="person_id",
+            how="left",
+        )
+        .dropna(subset=["t_start_x", "t_end_x", "t_start_y", "t_end_y"])
+    )
+
+    if len(tmp) == 0:
+        # TODO: is this necessary ?
+        logger.warning("No matching were found between the 2 DataFrames")
+
+        return framework.DataFrame(
+            columns=["person_id", "t_start", "t_end", "concept", "value"]
+        )
+
+    tmp["tag"] = compare_intervals(
+        tmp["t_start_x"],
+        tmp["t_end_x"],
+        tmp["t_start_y"],
+        tmp["t_end_y"],
+        algo=algo,
+    )
+
+    value_col = (
+        "value_y"
+        if (("value" in tag_to_df.columns) and ("value" in tag_from_df.columns))
+        else "value"
+    )
+
+    tags = (
+        tmp.groupby(["event_id", value_col])
+        .tag.any()
+        .unstack()
+        .fillna(False)
+        .reset_index()
+    )
+    tags = tag_to_df[["event_id"]].merge(tags, on="event_id", how="left").fillna(False)
+    tags = tag_to_df.merge(tags, on="event_id", how="left").drop(columns="event_id")
+    return tags
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/phenotype/base/index.html b/main/reference/phenotype/base/index.html new file mode 100644 index 00000000..90b57ac0 --- /dev/null +++ b/main/reference/phenotype/base/index.html @@ -0,0 +1,5234 @@ + + + + + + + + + + + + + + + + base - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
    + + + + + + +
    + + +
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    + + + + + + +
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    + + + +
    +
    +
    + + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.phenotype.base

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + Features + + +

    +
    Features()
+
    + +
    + + +

    Class used to store features (i.e. DataFrames). Features are +stored in the self._features dictionary.

    + + +
    + Source code in eds_scikit/phenotype/base.py + 
    22
+23
+24
    def __init__(self):
+    self._features = {}
+    self.last_feature = None
+
    +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + +
    + + + +

    + Phenotype + + +

    +
    Phenotype(data: BaseData, name: Optional[str] = None, **kwargs)
+
    + +
    + + +

    Base class for phenotyping

    + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + + TYPE: + BaseData + +

    +
    name +

    Name of the phenotype. If left to None, +the name of the class will be used instead

    +

    + + TYPE: + Optional[str] + + + DEFAULT: + None + +

    +
    + +
    + Source code in eds_scikit/phenotype/base.py + 
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    def __init__(
+    self,
+    data: BaseData,
+    name: Optional[str] = None,
+    **kwargs,
+):
+    """
+    Parameters
+    ----------
+    data : BaseData
+        A BaseData object
+    name : Optional[str]
+        Name of the phenotype. If left to None,
+        the name of the class will be used instead
+    """
+    self.data = data
+    self.features = Features()
+    self.name = (
+        to_valid_variable_name(name)
+        if name is not None
+        else self.__class__.__name__
+    )
+    self.logger = logger.bind(classname=self.name, sep=".")
+
    +
    + + + +
    + + + + + + + + + +
    + + + +

    + add_code_feature + + +

    +
    add_code_feature(output_feature: str, codes: dict, source: str = 'icd10', additional_filtering: Optional[dict] = None)
+
    + +
    + +

    Adds a feature from either ICD10 or CCAM codes

    + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    output_feature +

    Name of the feature

    +

    + + TYPE: + str + +

    +
    codes +

    Dictionary of codes to provide to the from_codes function

    +

    + + TYPE: + dict + +

    +
    source +

    Either 'icd10' or 'ccam', by default 'icd10'

    +

    + + TYPE: + str + + + DEFAULT: + 'icd10' + +

    +
    additional_filtering +

    Dictionary passed to the from_codes functions for filtering

    +

    + + TYPE: + Optional[dict] + + + DEFAULT: + None + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + Phenotype + + +

    The current Phenotype object with an additional feature +stored in self.features[output_feature]

    +
    + +
    + Source code in eds_scikit/phenotype/base.py + 
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    def add_code_feature(
+    self,
+    output_feature: str,
+    codes: dict,
+    source: str = "icd10",
+    additional_filtering: Optional[dict] = None,
+):
+    """
+    Adds a feature from either ICD10 or CCAM codes
+
+    Parameters
+    ----------
+    output_feature : str
+        Name of the feature
+    codes : dict
+        Dictionary of codes to provide to the `from_codes` function
+    source : str,
+        Either 'icd10' or 'ccam', by default 'icd10'
+    additional_filtering : Optional[dict]
+        Dictionary passed to the `from_codes` functions for filtering
+
+    Returns
+    -------
+    Phenotype
+        The current Phenotype object with an additional feature
+        stored in self.features[output_feature]
+
+    """
+    additional_filtering = additional_filtering or dict()
+
+    if source not in ["icd10", "ccam"]:
+        raise ValueError(f"source should be either 'icd10' or 'ccam', got {source}")
+
+    self.logger.info(f"Getting {source.upper()} features...")
+
+    from_code_func = (
+        conditions_from_icd10 if (source == "icd10") else procedures_from_ccam
+    )
+    codes_df = (
+        self.data.condition_occurrence
+        if (source == "icd10")
+        else self.data.procedure_occurrence
+    )
+
+    df = from_code_func(
+        codes_df,
+        codes=codes,
+        additional_filtering=additional_filtering,
+        date_from_visit=False,
+    )
+    df["phenotype"] = self.name
+    df = df.rename(columns={"concept": "subphenotype"})
+
+    bd.cache(df)
+
+    self.features[output_feature] = df
+
+    self.logger.info(
+        f"{source.upper()} features stored in self.features['{output_feature}'] (N = {len(df)})"
+    )
+
+    return self
+
    +
    +
    + +
    + +
    + + + +

    + agg_single_feature + + +

    +
    agg_single_feature(input_feature: str, output_feature: Optional[str] = None, level: str = 'patient', subphenotype: bool = True, threshold: int = 1) -> Phenotype
+
    + +
    + +

    Simple aggregation rule on a feature:

    +
      +
    • If level="patient", keeps patients with at least threshold + visits showing the (sub)phenotype
      • +
    • If level="visit", keeps visits with at least threshold events + (could be ICD10 codes, NLP features, biology, etc) showing the (sub)phenotype
      • +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    input_feature +

    Name of the input feature

    +

    + + TYPE: + str + +

    +
    output_feature +

    Name of the input feature. If None, will be set to +input_feature + "_agg"

    +

    + + TYPE: + Optional[str] + + + DEFAULT: + None + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + + TYPE: + str + + + DEFAULT: + 'patient' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + + TYPE: + bool + + + DEFAULT: + True + +

    +
    threshold +

    Minimal number of events (which definition depends on the level value)

    +

    + + TYPE: + int, optional + + + DEFAULT: + 1 + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + Phenotype + + +

    The current Phenotype object with an additional feature +stored in self.features[output_feature]

    +
    + +
    + Source code in eds_scikit/phenotype/base.py + 
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    def agg_single_feature(
+    self,
+    input_feature: str,
+    output_feature: Optional[str] = None,
+    level: str = "patient",
+    subphenotype: bool = True,
+    threshold: int = 1,
+) -> "Phenotype":
+    """
+    Simple aggregation rule on a feature:
+
+    - If level="patient", keeps patients with at least `threshold`
+      visits showing the (sub)phenotype
+    - If level="visit", keeps visits with at least `threshold` events
+      (could be ICD10 codes, NLP features, biology, etc) showing the (sub)phenotype
+
+    Parameters
+    ----------
+    input_feature : str
+        Name of the input feature
+    output_feature : Optional[str]
+        Name of the input feature. If None, will be set to
+        input_feature + "_agg"
+    level : str
+        On which level to do the aggregation,
+        either "patient" or "visit"
+    subphenotype : bool
+        Whether the threshold should apply to the phenotype
+        ("phenotype" column) of the subphenotype ("subphenotype" column)
+    threshold : int, optional
+        Minimal number of *events* (which definition depends on the `level` value)
+
+    Returns
+    -------
+    Phenotype
+        The current Phenotype object with an additional feature
+        stored in self.features[output_feature]
+
+    """
+    assert level in {"patient", "visit"}
+
+    output_feature = output_feature or f"{input_feature}_agg"
+
+    if input_feature not in self.features:
+        raise ValueError(
+            f"Input feature {input_feature} not found in self.features. "
+            "Maybe you forgot to call self.get_features() ?"
+        )
+
+    # We use `size` below for two reasons
+    # 1) to use it with the `threshold` parameter directly if level == 'visit'
+    # 2) to drop duplicates on the group_cols + ["visit_occurrence_id"] subset
+
+    phenotype_type = "subphenotype" if subphenotype else "phenotype"
+    group_cols = ["person_id", phenotype_type]
+
+    group_visit = (
+        self.features[input_feature]
+        .groupby(group_cols + ["visit_occurrence_id"])
+        .size()
+        .rename("N")  # number of events per visit_occurrence
+        .reset_index()
+    )
+
+    if level == "patient":
+        group_visit = (
+            group_visit.groupby(group_cols)
+            .size()
+            .rename("N")  # number of visits per person
+            .reset_index()
+        )
+
+    group_visit = group_visit[group_visit["N"] >= threshold].drop(columns="N")
+    group_visit["phenotype"] = self.name
+
+    bd.cache(group_visit)
+
+    self.features[output_feature] = group_visit
+
+    self.logger.info(
+        f"Aggregation from {input_feature} stored in self.features['{output_feature}'] "
+        f"(N = {len(group_visit)})"
+    )
+
+    return self
+
    +
    +
    + +
    + +
    + + + +

    + agg_two_features + + +

    +
    agg_two_features(input_feature_1: str, input_feature_2: str, output_feature: str = None, how: str = 'AND', level: str = 'patient', subphenotype: bool = True, thresholds: Tuple[int, int] = (1, 1)) -> Phenotype
+
    + +
    + +
      +
    • +

      If level='patient', keeps a specific patient if

      +
        +
      • At least thresholds[0] visits are found in feature_1 AND/OR
        • +
      • At least thresholds[1] visits are found in feature_2
        • +
      +
      • +
    • +

      If level='visit', keeps a specific visit if

      +
        +
      • At least thresholds[0] events are found in feature_1 AND/OR
        • +
      • At least thresholds[1] events are found in feature_2
        • +
      +
      • +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    input_feature_1 +

    Name of the first input feature

    +

    + + TYPE: + str + +

    +
    input_feature_2 +

    Name of the second input feature

    +

    + + TYPE: + str + +

    +
    output_feature +

    Name of the input feature. If None, will be set to +input_feature + "_agg"

    +

    + + TYPE: + str + + + DEFAULT: + None + +

    +
    how +

    Whether to perform a boolean "AND" or "OR" aggregation

    +

    + + TYPE: + str, optional + + + DEFAULT: + 'AND' + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + + TYPE: + str + + + DEFAULT: + 'patient' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + + TYPE: + bool + + + DEFAULT: + True + +

    +
    thresholds +

    Repsective threshold for the first and second feature

    +

    + + TYPE: + Tuple[int, int], optional + + + DEFAULT: + (1, 1) + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + Phenotype + + +

    The current Phenotype object with an additional feature +stored in self.features[output_feature]

    +
    + +
    + Source code in eds_scikit/phenotype/base.py + 
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    def agg_two_features(
+    self,
+    input_feature_1: str,
+    input_feature_2: str,
+    output_feature: str = None,
+    how: str = "AND",
+    level: str = "patient",
+    subphenotype: bool = True,
+    thresholds: Tuple[int, int] = (1, 1),
+) -> "Phenotype":
+    """
+
+    - If level='patient', keeps a specific patient if
+        - At least `thresholds[0]` visits are found in feature_1 AND/OR
+        - At least `thresholds[1]` visits are found in feature_2
+
+    - If level='visit', keeps a specific visit if
+        - At least `thresholds[0]` events are found in feature_1 AND/OR
+        - At least `thresholds[1]` events are found in feature_2
+
+    Parameters
+    ----------
+    input_feature_1 : str
+        Name of the first input feature
+    input_feature_2 : str
+        Name of the second input feature
+    output_feature : str
+        Name of the input feature. If None, will be set to
+        input_feature + "_agg"
+    how : str, optional
+        Whether to perform a boolean "AND" or "OR" aggregation
+    level : str
+        On which level to do the aggregation,
+        either "patient" or "visit"
+    subphenotype : bool
+        Whether the threshold should apply to the phenotype
+        ("phenotype" column) of the subphenotype ("subphenotype" column)
+    thresholds : Tuple[int, int], optional
+        Repsective threshold for the first and second feature
+
+    Returns
+    -------
+    Phenotype
+        The current Phenotype object with an additional feature
+        stored in self.features[output_feature]
+    """
+
+    self.agg_single_feature(
+        input_feature=input_feature_1,
+        level=level,
+        subphenotype=subphenotype,
+        threshold=thresholds[0],
+    )
+
+    self.agg_single_feature(
+        input_feature=input_feature_2,
+        level=level,
+        subphenotype=subphenotype,
+        threshold=thresholds[1],
+    )
+
+    results_1 = self.features[f"{input_feature_1}_agg"]
+    results_2 = self.features[f"{input_feature_2}_agg"]
+
+    assert set(results_1.columns) == set(results_2.columns)
+
+    if how == "AND":
+        result = results_1.merge(results_2, on=list(results_1.columns), how="inner")
+    elif how == "OR":
+        result = bd.concat(
+            [
+                results_1,
+                results_2,
+            ]
+        ).drop_duplicates()
+    else:
+        raise ValueError(f"'how' options are ('AND', 'OR'), got {how}.")
+
+    bd.cache(result)
+
+    output_feature = output_feature or f"{input_feature_1}_{how}_{input_feature_2}"
+    self.features[output_feature] = result
+
+    self.logger.info(
+        f"Aggregation from {input_feature_1} {how} {input_feature_1} stored in self.features['{output_feature}'] "
+        f"(N = {len(result)})"
+    )
+    return self
+
    +
    +
    + +
    + +
    + + + +

    + compute + + +

    +
    compute(**kwargs)
+
    + +
    + +

    Fetch all necessary features and perform aggregation

    + +
    + Source code in eds_scikit/phenotype/base.py + 
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    def compute(self, **kwargs):
+    """
+    Fetch all necessary features and perform aggregation
+    """
+    raise NotImplementedError()
+
    +
    +
    + +
    + +
    + + + +

    + to_data + + +

    +
    to_data(key: Optional[str] = None) -> BaseData
+
    + +
    + +

    Appends the feature found in self.features[key] to the data object. +If no key is provided, uses the last added feature

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    key +

    Key of the self.feature dictionary

    +

    + + TYPE: + Optional[str] + + + DEFAULT: + None + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + BaseData + + +

    The data object with phenotype added to data.computed

    +
    + +
    + Source code in eds_scikit/phenotype/base.py + 
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    def to_data(self, key: Optional[str] = None) -> BaseData:
+    """
+    Appends the feature found in self.features[key] to the data object.
+    If no key is provided, uses the last added feature
+
+    Parameters
+    ----------
+    key : Optional[str]
+        Key of the self.feature dictionary
+
+    Returns
+    -------
+    BaseData
+        The data object with phenotype added to `data.computed`
+    """
+
+    if not self.features:
+        self.compute()
+
+    if key is None:
+        self.logger.info("No key provided: Using last added feature.")
+        return self._set(self.features.last())
+
+    else:
+        assert (
+            key in self.features
+        ), f"Key {key} not found in features. Available {self.features}"
+        self.logger.info("Using feature {key}")
+        return self._set(self.features[key])
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    + + + +

    + to_valid_variable_name + + +

    +
    to_valid_variable_name(s: str)
+
    + +
    + +

    Converts a string to a valid variable name

    + +
    + Source code in eds_scikit/phenotype/base.py + 
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    def to_valid_variable_name(s: str):
+    """
+    Converts a string to a valid variable name
+    """
+    # Replace non-alphanumeric characters with underscores
+    s = re.sub(r"\W+", "_", s)
+    # Remove leading underscores
+    s = re.sub(r"^_+", "", s)
+    # If the string is empty or starts with a number, prepend an underscore
+    if not s or s[0].isdigit():
+        s = "_" + s
+    return s
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/phenotype/cancer/cancer/index.html b/main/reference/phenotype/cancer/cancer/index.html new file mode 100644 index 00000000..64b036d4 --- /dev/null +++ b/main/reference/phenotype/cancer/cancer/index.html @@ -0,0 +1,4216 @@ + + + + + + + + + + + + + + + + cancer - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.phenotype.cancer.cancer

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + CancerFromICD10 + + +

    +
    CancerFromICD10(data: BaseData, cancer_types: Optional[List[str]] = None, level: str = 'patient', subphenotype: bool = True, threshold: int = 1)
+
    + +
    +

    + Bases: Phenotype

    + + +

    Phenotyping visits or patients using ICD10 cancer codes

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + + TYPE: + BaseData + +

    +
    cancer_types +

    Optional list of cancer types to use for phenotyping

    +

    + + TYPE: + Optional[List[str]] + + + DEFAULT: + None + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + + TYPE: + str + + + DEFAULT: + 'patient' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + + TYPE: + bool + + + DEFAULT: + True + +

    +
    threshold +

    Minimal number of events (which definition depends on the level value)

    +

    + + TYPE: + int + + + DEFAULT: + 1 + +

    +
    + +
    + Source code in eds_scikit/phenotype/cancer/cancer.py + 
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    def __init__(
+    self,
+    data: BaseData,
+    cancer_types: Optional[List[str]] = None,
+    level: str = "patient",
+    subphenotype: bool = True,
+    threshold: int = 1,
+):
+    """
+    Parameters
+    ----------
+    data : BaseData
+        A BaseData object
+    cancer_types :  Optional[List[str]]
+        Optional list of cancer types to use for phenotyping
+    level : str
+        On which level to do the aggregation,
+        either "patient" or "visit"
+    subphenotype : bool
+        Whether the threshold should apply to the phenotype
+        ("phenotype" column) of the subphenotype ("subphenotype" column)
+    threshold : int
+        Minimal number of *events* (which definition depends on the `level` value)
+    """
+    super().__init__(data)
+
+    if cancer_types is None:
+        cancer_types = self.ALL_CANCER_TYPES
+
+    incorrect_cancer_types = set(cancer_types) - set(self.ALL_CANCER_TYPES)
+
+    if incorrect_cancer_types:
+        raise ValueError(
+            f"Incorrect cancer types ({incorrect_cancer_types}). "
+            f"Available cancer types are {self.ALL_CANCER_TYPES}"
+        )
+
+    self.icd10_codes = {
+        k: v for k, v in self.ICD10_CODES.items() if k in cancer_types
+    }
+
+    self.level = level
+    self.subphenotype = subphenotype
+    self.threshold = threshold
+
    +
    + + + +
    + + + + + + + +
    + + + +

    + ICD10_CODES + + + + class-attribute + + +

    +
    ICD10_CODES = {cancer_type: {'prefix': df.code.to_list()} for (cancer_type, df) in ICD10_CODES_DF.groupby('Cancer type')}
+
    + +
    + +

    For each cancer type, contains a set of corresponding ICD10 codes.

    +
    + +
    + +
    + + + +

    + ALL_CANCER_TYPES + + + + class-attribute + + +

    +
    ALL_CANCER_TYPES = list(ICD10_CODES.keys())
+
    + +
    + +

    Available cancer types.

    +
    + +
    + + + +
    + + + +

    + compute + + +

    +
    compute()
+
    + +
    + +

    Fetch all necessary features and perform aggregation

    + +
    + Source code in eds_scikit/phenotype/cancer/cancer.py + 
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    def compute(self):
+    """
+    Fetch all necessary features and perform aggregation
+    """
+    self.add_code_feature(
+        output_feature="icd10",
+        source="icd10",
+        codes=self.icd10_codes,
+        additional_filtering=dict(condition_status_source_value={"DP", "DR"}),
+    )
+
+    self.agg_single_feature(
+        input_feature="icd10",
+        level=self.level,
+        subphenotype=self.subphenotype,
+        threshold=self.threshold,
+    )
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/phenotype/cancer/index.html b/main/reference/phenotype/cancer/index.html new file mode 100644 index 00000000..39245cd0 --- /dev/null +++ b/main/reference/phenotype/cancer/index.html @@ -0,0 +1,3781 @@ + + + + + + + + + + + + + + + + `eds_scikit.phenotype.cancer` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.phenotype.cancer

    + + +
    + + + +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/phenotype/diabetes/diabetes/index.html b/main/reference/phenotype/diabetes/diabetes/index.html new file mode 100644 index 00000000..b2117bcf --- /dev/null +++ b/main/reference/phenotype/diabetes/diabetes/index.html @@ -0,0 +1,4179 @@ + + + + + + + + + + + + + + + + diabetes - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
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    + + + +
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    +
    + + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.phenotype.diabetes.diabetes

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + DiabetesFromICD10 + + +

    +
    DiabetesFromICD10(data, diabetes_types: Optional[List[str]] = None, level: str = 'visit', subphenotype: bool = True, threshold: int = 1)
+
    + +
    +

    + Bases: Phenotype

    + + +

    Phenotyping visits or patients using ICD10 diabetes codes

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + + TYPE: + BaseData + +

    +
    diabetes_types +

    Optional list of diabetes types to use for phenotyping

    +

    + + TYPE: + Optional[List[str]] + + + DEFAULT: + None + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + + TYPE: + str + + + DEFAULT: + 'visit' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + + TYPE: + bool + + + DEFAULT: + True + +

    +
    threshold +

    Minimal number of events (which definition depends on the level value)

    +

    + + TYPE: + int + + + DEFAULT: + 1 + +

    +
    + +
    + Source code in eds_scikit/phenotype/diabetes/diabetes.py + 
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    def __init__(
+    self,
+    data,
+    diabetes_types: Optional[List[str]] = None,
+    level: str = "visit",
+    subphenotype: bool = True,
+    threshold: int = 1,
+):
+    """
+    Parameters
+    ----------
+    data : BaseData
+        A BaseData object
+    diabetes_types :  Optional[List[str]]
+        Optional list of diabetes types to use for phenotyping
+    level : str
+        On which level to do the aggregation,
+        either "patient" or "visit"
+    subphenotype : bool
+        Whether the threshold should apply to the phenotype
+        ("phenotype" column) of the subphenotype ("subphenotype" column)
+    threshold : int
+        Minimal number of *events* (which definition depends on the `level` value)
+    """
+    super().__init__(data)
+
+    if diabetes_types is None:
+        diabetes_types = self.ALL_DIABETES_TYPES
+
+    incorrect_diabetes_types = set(diabetes_types) - set(self.ALL_DIABETES_TYPES)
+
+    if incorrect_diabetes_types:
+        raise ValueError(
+            f"Incorrect diabetes types ({incorrect_diabetes_types}). "
+            f"Available diabetes types are {self.ALL_DIABETES_TYPES}"
+        )
+
+    self.icd10_codes = {
+        k: v for k, v in self.ICD10_CODES.items() if k in diabetes_types
+    }
+
+    self.level = level
+    self.subphenotype = subphenotype
+    self.threshold = threshold
+
    +
    + + + +
    + + + + + + + +
    + + + +

    + ALL_DIABETES_TYPES + + + + class-attribute + + +

    +
    ALL_DIABETES_TYPES = list(ICD10_CODES.keys())
+
    + +
    + +

    Available diabetes types.

    +
    + +
    + + + +
    + + + +

    + compute + + +

    +
    compute()
+
    + +
    + +

    Fetch all necessary features and perform aggregation

    + +
    + Source code in eds_scikit/phenotype/diabetes/diabetes.py + 
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    def compute(self):
+    """
+    Fetch all necessary features and perform aggregation
+    """
+    self.add_code_feature(
+        output_feature="icd10",
+        source="icd10",
+        codes=self.ICD10_CODES,
+        additional_filtering=dict(condition_status_source_value={"DP", "DAS"}),
+    )
+
+    self.agg_single_feature(
+        input_feature="icd10",
+        level=self.level,
+        subphenotype=self.subphenotype,
+        threshold=self.threshold,
+    )
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/phenotype/diabetes/index.html b/main/reference/phenotype/diabetes/index.html new file mode 100644 index 00000000..d7654cf3 --- /dev/null +++ b/main/reference/phenotype/diabetes/index.html @@ -0,0 +1,3781 @@ + + + + + + + + + + + + + + + + `eds_scikit.phenotype.diabetes` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +
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    + + + +
    +
    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.phenotype.diabetes

    + + +
    + + + +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/phenotype/index.html b/main/reference/phenotype/index.html new file mode 100644 index 00000000..8e03ee2d --- /dev/null +++ b/main/reference/phenotype/index.html @@ -0,0 +1,3779 @@ + + + + + + + + + + + + + + + + `eds_scikit.phenotype` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.phenotype

    + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/phenotype/psychiatric_disorder/index.html b/main/reference/phenotype/psychiatric_disorder/index.html new file mode 100644 index 00000000..298cf58d --- /dev/null +++ b/main/reference/phenotype/psychiatric_disorder/index.html @@ -0,0 +1,3781 @@ + + + + + + + + + + + + + + + + `eds_scikit.phenotype.psychiatric_disorder` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.phenotype.psychiatric_disorder

    + + +
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    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/phenotype/psychiatric_disorder/psychiatric_disorder/index.html b/main/reference/phenotype/psychiatric_disorder/psychiatric_disorder/index.html new file mode 100644 index 00000000..570802d6 --- /dev/null +++ b/main/reference/phenotype/psychiatric_disorder/psychiatric_disorder/index.html @@ -0,0 +1,4215 @@ + + + + + + + + + + + + + + + + psychiatric_disorder - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + +
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    + + + + + + + + +

    eds_scikit.phenotype.psychiatric_disorder.psychiatric_disorder

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + PsychiatricDisorderFromICD10 + + +

    +
    PsychiatricDisorderFromICD10(data, disorder_types: Optional[List[str]] = None, level: str = 'patient', subphenotype: bool = True, threshold: int = 1)
+
    + +
    +

    + Bases: Phenotype

    + + +

    Phenotyping visits or patients with psychiatric disorders +using ICD10 codes

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + + TYPE: + BaseData + +

    +
    disorder_types +

    Optional list of disorder types to use for phenotyping

    +

    + + TYPE: + Optional[List[str]] + + + DEFAULT: + None + +

    +
    level +

    On which level to do the aggregation, +either "patient" or "visit"

    +

    + + TYPE: + str + + + DEFAULT: + 'patient' + +

    +
    subphenotype +

    Whether the threshold should apply to the phenotype +("phenotype" column) of the subphenotype ("subphenotype" column)

    +

    + + TYPE: + bool + + + DEFAULT: + True + +

    +
    threshold +

    Minimal number of events (which definition depends on the level value)

    +

    + + TYPE: + int + + + DEFAULT: + 1 + +

    +
    + +
    + Source code in eds_scikit/phenotype/psychiatric_disorder/psychiatric_disorder.py + 
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    def __init__(
+    self,
+    data,
+    disorder_types: Optional[List[str]] = None,
+    level: str = "patient",
+    subphenotype: bool = True,
+    threshold: int = 1,
+):
+    """
+    Parameters
+    ----------
+    data : BaseData
+        A BaseData object
+    disorder_types :  Optional[List[str]]
+        Optional list of disorder types to use for phenotyping
+    level : str
+        On which level to do the aggregation,
+        either "patient" or "visit"
+    subphenotype : bool
+        Whether the threshold should apply to the phenotype
+        ("phenotype" column) of the subphenotype ("subphenotype" column)
+    threshold : int
+        Minimal number of *events* (which definition depends on the `level` value)
+    """
+    super().__init__(data)
+
+    if disorder_types is None:
+        disorder_types = self.ALL_DISORDER_TYPES
+
+    incorrect_disorder_types = set(disorder_types) - set(self.ALL_DISORDER_TYPES)
+
+    if incorrect_disorder_types:
+        raise ValueError(
+            f"Incorrect cancer types ({incorrect_disorder_types}). "
+            f"Available cancer types are {self.ALL_DISORDER_TYPES}"
+        )
+
+    self.icd10_codes = {
+        k: v for k, v in self.ICD10_CODES.items() if k in disorder_types
+    }
+
+    self.level = level
+    self.subphenotype = subphenotype
+    self.threshold = threshold
+
    +
    + + + +
    + + + + + + + +
    + + + +

    + ICD10_CODES + + + + class-attribute + + +

    +
    ICD10_CODES = {disorder_group: {'exact': df.ICD10_Code.to_list()} for (disorder_group, df) in ICD10_CODES_DF.groupby('disorder_group')}
+
    + +
    + +

    ICD10 codes used for phenotyping

    +
    + +
    + +
    + + + +

    + ALL_DISORDER_TYPES + + + + class-attribute + + +

    +
    ALL_DISORDER_TYPES = list(ICD10_CODES.keys())
+
    + +
    + +

    Available disorder types.

    +
    + +
    + + + +
    + + + +

    + compute + + +

    +
    compute()
+
    + +
    + +

    Fetch all necessary features and perform aggregation

    + +
    + Source code in eds_scikit/phenotype/psychiatric_disorder/psychiatric_disorder.py + 
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    def compute(self):
+    """
+    Fetch all necessary features and perform aggregation
+    """
+    self.add_code_feature(
+        output_feature="icd10",
+        source="icd10",
+        codes=self.ICD10_CODES,
+    )
+
+    self.agg_single_feature(
+        input_feature="icd10",
+        level=self.level,
+        subphenotype=self.subphenotype,
+        threshold=self.threshold,
+    )
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/phenotype/suicide_attempt/index.html b/main/reference/phenotype/suicide_attempt/index.html new file mode 100644 index 00000000..a3c1c490 --- /dev/null +++ b/main/reference/phenotype/suicide_attempt/index.html @@ -0,0 +1,3781 @@ + + + + + + + + + + + + + + + + `eds_scikit.phenotype.suicide_attempt` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + +
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    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.phenotype.suicide_attempt

    + + +
    + + + +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/phenotype/suicide_attempt/suicide_attempt/index.html b/main/reference/phenotype/suicide_attempt/suicide_attempt/index.html new file mode 100644 index 00000000..ef8dc085 --- /dev/null +++ b/main/reference/phenotype/suicide_attempt/suicide_attempt/index.html @@ -0,0 +1,4147 @@ + + + + + + + + + + + + + + + + suicide_attempt - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + +
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    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.phenotype.suicide_attempt.suicide_attempt

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + SuicideAttemptFromICD10 + + +

    +
    SuicideAttemptFromICD10(data: BaseData, algo: str = 'Haguenoer2008')
+
    + +
    +

    + Bases: Phenotype

    + + +

    Phenotyping visits related to a suicide attempt. +Two algorithms are available:

    +
      +
    • "X60-X84": The visit needs to have at least one ICD10 code in the range +X60 to X84
      • +
    • "Haguenoer2008": The visit needs to have at least one ICD10 DAS code in the range +X60 to X84, and a ICD10 DP code in the range S to T
      • +
    + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    A BaseData object

    +

    + + TYPE: + BaseData + +

    +
    algo +

    The name of the algorithm. +Should be either "Haguenoer2008" or "X60-X84"

    +

    + + TYPE: + str, optional + + + DEFAULT: + 'Haguenoer2008' + +

    +
    + +
    + Source code in eds_scikit/phenotype/suicide_attempt/suicide_attempt.py + 
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    def __init__(
+    self,
+    data: BaseData,
+    algo: str = "Haguenoer2008",
+):
+    """
+    Parameters
+    ----------
+    data : BaseData
+        A BaseData object
+    algo : str, optional
+        The name of the algorithm.
+        Should be either "Haguenoer2008" or "X60-X84"
+    """
+    super().__init__(
+        data,
+        name=f"SuicideAttemptFromICD10_{algo}",
+    )
+    self.algo = algo
+
    +
    + + + +
    + + + + + + + +
    + + + +

    + ICD10_CODES + + + + class-attribute + + +

    +
    ICD10_CODES = {'X60-X84': dict(codes={'X60-X84': dict(regex=['X[67]', 'X8[0-4]'])}), 'Haguenoer2008': dict(codes={'Haguenoer2008': dict(regex=['S', 'T[0-9]'])}, additional_filtering=dict(condition_status_source_value='DP'))}
+
    + +
    + +

    ICD10 codes used by both algorithms

    +
    + +
    + + + +
    + + + +

    + compute + + +

    +
    compute()
+
    + +
    + +

    Fetch and aggregate features

    + +
    + Source code in eds_scikit/phenotype/suicide_attempt/suicide_attempt.py + 
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    def compute(self):
+    """
+    Fetch and aggregate features
+    """
+
+    if self.algo == "X60-X84":
+
+        self.add_code_feature(
+            output_feature="X60-X84",
+            source="icd10",
+            codes=self.ICD10_CODES["X60-X84"]["codes"],
+        )
+
+        self.agg_single_feature(
+            "X60-X84",
+            level="visit",
+            subphenotype=False,
+            threshold=1,
+        )
+
+    elif self.algo == "Haguenoer2008":
+
+        self.add_code_feature(
+            output_feature="X60-X84",
+            source="icd10",
+            codes=self.ICD10_CODES["X60-X84"]["codes"],
+            additional_filtering=dict(condition_status_source_value="DAS"),
+        )
+
+        self.add_code_feature(
+            output_feature="DP",
+            source="icd10",
+            codes=self.ICD10_CODES["Haguenoer2008"]["codes"],
+            additional_filtering=self.ICD10_CODES["Haguenoer2008"][
+                "additional_filtering"
+            ],
+        )
+
+        self.agg_two_features(
+            "X60-X84",
+            "DP",
+            output_feature="Haguenoer2008",
+            how="AND",
+            level="visit",
+            subphenotype=False,
+            thresholds=(1, 1),
+        )
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + + + +
    + +
    + +
    + + +
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    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/plot/age_pyramid/index.html b/main/reference/plot/age_pyramid/index.html new file mode 100644 index 00000000..7e656861 --- /dev/null +++ b/main/reference/plot/age_pyramid/index.html @@ -0,0 +1,4193 @@ + + + + + + + + + + + + + + + + age_pyramid - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + +
    +
    + + + + + + + + +

    eds_scikit.plot.age_pyramid

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + plot_age_pyramid + + +

    +
    plot_age_pyramid(person: DataFrame, datetime_ref: datetime = None, return_array: bool = False) -> Tuple[alt.ConcatChart, Series]
+
    + +
    + +

    Plot an age pyramid from a 'person' pandas DataFrame.

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    person +

    The person table. Must have the following columns: +- birth_datetime, dtype : datetime or str +- person_id, dtype : any +- gender_source_value, dtype : str, {'m', 'f'}

    +

    + + TYPE: + pd.DataFrame (ks.DataFrame not supported), + +

    +
    +

    datetime_ref : Union[datetime, str], default None + The reference date to compute population age from. + If a string, it searches for a column with the same name in the person table: each patient has his own datetime reference. + If a datetime, the reference datetime is the same for all patients. + If set to None, datetime.today() will be used instead.

    +

    filename : str, default None + The path to save figure at.

    +

    savefig : bool, default False + If set to True, filename must be set. + The plot will be saved at the specified filename.

    +

    return_array : bool, default False + If set to True, return chart and its pd.Dataframe representation.

    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + chart + +

    If savefig set to True, returns None.

    +

    + + TYPE: + alt.ConcatChart + +

    +
    +

    group_gender_age : Series, + The total number of patients grouped by gender and binned age.

    + +
    + Source code in eds_scikit/plot/age_pyramid.py + 
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    def plot_age_pyramid(
+    person: DataFrame,
+    datetime_ref: datetime = None,
+    return_array: bool = False,
+) -> Tuple[alt.ConcatChart, Series]:
+    """Plot an age pyramid from a 'person' pandas DataFrame.
+
+    Parameters
+    ----------
+    person : pd.DataFrame (ks.DataFrame not supported),
+        The person table. Must have the following columns:
+        - `birth_datetime`, dtype : datetime or str
+        - `person_id`, dtype : any
+        - `gender_source_value`, dtype : str, {'m', 'f'}
+
+    datetime_ref : Union[datetime, str], default None
+        The reference date to compute population age from.
+        If a string, it searches for a column with the same name in the person table: each patient has his own datetime reference.
+        If a datetime, the reference datetime is the same for all patients.
+        If set to None, datetime.today() will be used instead.
+
+    filename : str, default None
+        The path to save figure at.
+
+    savefig : bool, default False
+        If set to True, filename must be set.
+        The plot will be saved at the specified filename.
+
+    return_array : bool, default False
+        If set to True, return chart and its pd.Dataframe representation.
+
+    Returns
+    -------
+    chart : alt.ConcatChart,
+        If savefig set to True, returns None.
+
+    group_gender_age : Series,
+        The total number of patients grouped by gender and binned age.
+    """
+    check_columns(person, ["person_id", "birth_datetime", "gender_source_value"])
+
+    datetime_ref_raw = copy(datetime_ref)
+
+    if datetime_ref is None:
+        datetime_ref = datetime.today()
+    elif isinstance(datetime_ref, datetime):
+        datetime_ref = pd.to_datetime(datetime_ref)
+    elif isinstance(datetime_ref, str):
+        # A string type for datetime_ref could be either
+        # a column name or a datetime in string format.
+        if datetime_ref in person.columns:
+            datetime_ref = person[datetime_ref]
+        else:
+            datetime_ref = pd.to_datetime(
+                datetime_ref, errors="coerce"
+            )  # In case of error, will return NaT
+            if pd.isnull(datetime_ref):
+                raise ValueError(
+                    f"`datetime_ref` must either be a column name or parseable date, "
+                    f"got string '{datetime_ref_raw}'"
+                )
+    else:
+        raise TypeError(
+            f"`datetime_ref` must be either None, a parseable string date"
+            f", a column name or a datetime. Got type: {type(datetime_ref)}, {datetime_ref}"
+        )
+
+    cols_to_keep = ["person_id", "birth_datetime", "gender_source_value"]
+    person_ = bd.to_pandas(person[cols_to_keep])
+
+    person_["age"] = (datetime_ref - person_["birth_datetime"]).dt.total_seconds()
+    person_["age"] /= 365 * 24 * 3600
+
+    # Remove outliers
+    mask_age_inliners = (person_["age"] > 0) & (person_["age"] < 125)
+    n_outliers = (~mask_age_inliners).sum()
+    if n_outliers > 0:
+        perc_outliers = 100 * n_outliers / person_.shape[0]
+        logger.warning(
+            f"{n_outliers} ({perc_outliers:.4f}%) individuals' "
+            "age is out of the (0, 125) interval, we skip them."
+        )
+    person_ = person_.loc[mask_age_inliners]
+
+    # Aggregate rare age categories
+    mask_rare_age_agg = person_["age"] > 90
+    person_.loc[mask_rare_age_agg, "age"] = 99
+
+    bins = np.arange(0, 100, 10)
+    labels = [f"{left}-{right}" for left, right in zip(bins[:-1], bins[1:])]
+    person_["age_bins"] = pd.cut(person_["age"], bins=bins, labels=labels)
+
+    person_ = person_.loc[person_["gender_source_value"].isin(["m", "f"])]
+    group_gender_age = person_.groupby(["gender_source_value", "age_bins"])[
+        "person_id"
+    ].count()
+
+    male = group_gender_age["m"].reset_index()
+    female = group_gender_age["f"].reset_index()
+
+    left = (
+        alt.Chart(male)
+        .mark_bar()
+        .encode(
+            y=alt.Y("age_bins", axis=None, sort=alt.SortOrder("descending")),
+            x=alt.X("person_id", sort=alt.SortOrder("descending")),
+        )
+        .properties(title="Male")
+    )
+
+    right = (
+        alt.Chart(female)
+        .mark_bar(color="coral")
+        .encode(
+            y=alt.Y("age_bins", axis=None, sort=alt.SortOrder("descending")),
+            x=alt.X("person_id", title="N"),
+        )
+        .properties(title="Female")
+    )
+
+    middle = (
+        alt.Chart(male)
+        .mark_text()
+        .encode(
+            y=alt.Y("age_bins", axis=None, sort=alt.SortOrder("descending")),
+            text=alt.Text("age_bins"),
+        )
+    )
+
+    chart = alt.concat(left, middle, right, spacing=5)
+
+    if return_array:
+        return group_gender_age
+
+    return chart
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
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    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/plot/altair_utils/index.html b/main/reference/plot/altair_utils/index.html new file mode 100644 index 00000000..6d1e8242 --- /dev/null +++ b/main/reference/plot/altair_utils/index.html @@ -0,0 +1,4143 @@ + + + + + + + + + + + + + + + + altair_utils - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.plot.altair_utils

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + generate_cyclic_colors + + +

    +
    generate_cyclic_colors(N: int) -> List[str]
+
    + +
    + +

    Given an interger of N values, return a cyclic list +of size N with repeated 20 altair standards colors.

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    N + +

    + + TYPE: + int + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + List[str] + + + +
    + +
    + Source code in eds_scikit/plot/altair_utils.py + 
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    def generate_cyclic_colors(N: int) -> List[str]:
+    """Given an interger of N values, return a cyclic list
+    of size N with repeated 20 altair standards colors.
+
+    Parameters
+    ----------
+    N : int
+
+    Returns
+    -------
+    List[str]
+
+    """
+    category20_colors = [
+        "#1f77b4",
+        "#ff7f0e",
+        "#2ca02c",
+        "#d62728",
+        "#9467bd",
+        "#8c564b",
+        "#e377c2",
+        "#7f7f7f",
+        "#bcbd22",
+        "#17becf",
+        "#aec7e8",
+        "#ffbb78",
+        "#98df8a",
+        "#ff9896",
+        "#c5b0d5",
+        "#c49c94",
+        "#f7b6d2",
+        "#c7c7c7",
+        "#dbdb8d",
+        "#9edae5",
+    ]
+    num_colors = len(category20_colors)
+    return [category20_colors[i % num_colors] for i in range(N)]
+
    +
    +
    + +
    + +
    + + + +

    + generate_color_map + + +

    +
    generate_color_map(df: DataFrame, col: str) -> alt.Scale
+
    + +
    + +

    Given a dataframe and a column name, +generate an altair color scale for visualization purpose.

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    df + +

    + + TYPE: + DataFrame + +

    +
    col + +

    + + TYPE: + str + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + alt.Scale + + + +
    + +
    + Source code in eds_scikit/plot/altair_utils.py + 
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    def generate_color_map(df: DataFrame, col: str) -> alt.Scale:
+    """Given a dataframe and a column name,
+    generate an altair color scale for visualization purpose.
+
+    Parameters
+    ----------
+    df : DataFrame
+    col : str
+
+    Returns
+    -------
+    alt.Scale
+    """
+
+    check_columns(
+        df,
+        required_columns=[col],
+    )
+
+    category_values = df[col].fillna("NaN").unique().tolist()
+    if "NaN" in category_values:
+        category_colors = generate_cyclic_colors(len(category_values) - 1)
+        category_values = [
+            category_value
+            for category_value in category_values
+            if category_value != "NaN"
+        ]
+        domain = [*category_values, "NaN"]
+        range = [*category_colors, "black"]
+    else:
+        category_colors = generate_cyclic_colors(len(category_values))
+        domain = category_values
+        range = category_colors
+
+    color_scale = alt.Scale(domain=domain, range=range)
+    return color_scale
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/plot/event_sequences/index.html b/main/reference/plot/event_sequences/index.html new file mode 100644 index 00000000..2788653f --- /dev/null +++ b/main/reference/plot/event_sequences/index.html @@ -0,0 +1,4584 @@ + + + + + + + + + + + + + + + + event_sequences - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + +
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    + + + +
    +
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    + + +
    +
    + + + + + + + + +

    eds_scikit.plot.event_sequences

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + plot_event_sequences + + +

    +
    plot_event_sequences(df_events: pd.DataFrame, event_col: Optional[str] = 'event', event_start_datetime_col: Optional[str] = 'event_start_datetime', event_end_datetime_col: Optional[str] = 'event_end_datetime', dim_mapping: Optional[Dict[str, Dict[str, Union[Tuple[int], str]]]] = None, index_date_col: Optional[str] = None, family_col: Optional[str] = None, family_to_index: Optional[Dict[str, int]] = None, list_person_ids: Optional[List[str]] = None, same_x_axis_scale: Optional[bool] = False, subplot_height: Optional[int] = 200, subplot_width: Optional[int] = 500, point_size: Optional[int] = 400, bar_height: Optional[int] = 20, title: Optional[str] = None, seed: Optional[int] = 0) -> alt.VConcatChart
+
    + +
    + +

    Plots individual sequences from an events DataFrame. Each event must be recorded with a start date, a name and a person_id. +Events can be both one-time (only start date given) or longitudinal (both start and end dates). +Events can also be aggregated in families using the family_col argument. +Finally, events labelling and colors can be manually set by providing a dim_mapping dictionary.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    df_events +

    DataFrame gathering the events information. Must contain at least person_id, event, t_start and t_end columns.

    +

    + + TYPE: + pd.DataFrame + +

    +
    event_col +

    Column name of the events.

    +

    + + TYPE: + Optional[str] + + + DEFAULT: + 'event' + +

    +
    event_start_datetime_col +

    Column name of the event start datetime.

    +

    + + TYPE: + Optional[str] + + + DEFAULT: + 'event_start_datetime' + +

    +
    event_end_datetime_col +

    Column name of the event end datetime.

    +

    + + TYPE: + Optional[str] + + + DEFAULT: + 'event_end_datetime' + +

    +
    dim_mapping +

    Mapping dictionary to provide plotting details on events. Must be of type : +

    dim_labelling = {
+"event_1": {"color": (255, 200, 150), "label": "Event 1"},
+"event_2": {"color": (200, 255, 150), "label": "Event 2"},
+}
+

    +

    + + TYPE: + Optional[Dict[str, Dict[str, Union[Tuple[int], str]]]] + + + DEFAULT: + None + +

    +
    index_date_col +

    Column name of the index date to compute relative datetimes for events. For example, it could be the date of inclusion for each patient.

    +

    + + TYPE: + Optional[str] + + + DEFAULT: + None + +

    +
    family_col +

    Column name of family events. Events of a given family will be plot on the same row.

    +

    + + TYPE: + Optional[str] + + + DEFAULT: + None + +

    +
    family_to_index +

    Dictionary mapping event family names to ordering indices.

    +

    + + TYPE: + Optional[Dict[str, int]] + + + DEFAULT: + None + +

    +
    list_person_ids +

    List of person_ids to plot. If None given, only the first three individual sequences will be plot.

    +

    + + TYPE: + Optional[List[str]] + + + DEFAULT: + None + +

    +
    same_x_axis_scale +

    Whether to use the same axis scale for all sequences.

    +

    + + TYPE: + Optional[bool] + + + DEFAULT: + False + +

    +
    subplot_height +

    Height of each plot.

    +

    + + TYPE: + Optional[int] + + + DEFAULT: + 200 + +

    +
    subplot_width +

    Width of each plot.

    +

    + + TYPE: + Optional[int] + + + DEFAULT: + 500 + +

    +
    point_size +

    Size of points for one-time events.

    +

    + + TYPE: + Optional[int] + + + DEFAULT: + 400 + +

    +
    bar_height +

    Height of bars for continuous events.

    +

    + + TYPE: + Optional[int] + + + DEFAULT: + 20 + +

    +
    title +

    Chart title.

    +

    + + TYPE: + Optional[str] + + + DEFAULT: + None + +

    +
    seed +

    Seed to randomly draw colors when not provided.

    +

    + + TYPE: + Optional[int] + + + DEFAULT: + 0 + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + chart + +

    Chart with the plotted individual event sequences.

    +

    + + TYPE: + alt.VConcatChart + +

    +
    + +
    + Source code in eds_scikit/plot/event_sequences.py + 
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    def plot_event_sequences(
+    df_events: pd.DataFrame,
+    event_col: Optional[str] = "event",
+    event_start_datetime_col: Optional[str] = "event_start_datetime",
+    event_end_datetime_col: Optional[str] = "event_end_datetime",
+    dim_mapping: Optional[Dict[str, Dict[str, Union[Tuple[int], str]]]] = None,
+    index_date_col: Optional[str] = None,
+    family_col: Optional[str] = None,
+    family_to_index: Optional[Dict[str, int]] = None,
+    list_person_ids: Optional[List[str]] = None,
+    same_x_axis_scale: Optional[bool] = False,
+    subplot_height: Optional[int] = 200,
+    subplot_width: Optional[int] = 500,
+    point_size: Optional[int] = 400,
+    bar_height: Optional[int] = 20,
+    title: Optional[str] = None,
+    seed: Optional[int] = 0,
+) -> alt.VConcatChart:
+    """
+    Plots individual sequences from an events DataFrame. Each event must be recorded with a start date, a name and a `person_id`.
+    Events can be both one-time (only start date given) or longitudinal (both start and end dates).
+    Events can also be aggregated in families using the `family_col` argument.
+    Finally, events labelling and colors can be manually set by providing a `dim_mapping` dictionary.
+
+    Parameters
+    ----------
+    df_events: pd.DataFrame
+        DataFrame gathering the events information. Must contain at least `person_id`, event, t_start and t_end columns.
+    event_col: Optional[str] = "event"
+        Column name of the events.
+    event_start_datetime_col: Optional[str] = "event_start_datetime"
+        Column name of the event start datetime.
+    event_end_datetime_col: Optional[str] = "event_end_datetime"
+        Column name of the event end datetime.
+    dim_mapping: Optional[Dict[str,Dict[str,Union[tuple(int),str]]]] = None
+        Mapping dictionary to provide plotting details on events. Must be of type :
+        ```python
+        dim_labelling = {
+            "event_1": {"color": (255, 200, 150), "label": "Event 1"},
+            "event_2": {"color": (200, 255, 150), "label": "Event 2"},
+        }
+        ```
+    index_date_col: Optional[str] = None
+        Column name of the index date to compute relative datetimes for events. For example, it could be the date of inclusion for each patient.
+    family_col: Optional[str] = None
+        Column name of family events. Events of a given family will be plot on the same row.
+    family_to_index: Optional[Dict[str,int]] = None
+        Dictionary mapping event family names to ordering indices.
+    list_person_ids: Optional[List[str]] = None
+        List of person_ids to plot. If None given, only the first three individual sequences will be plot.
+    same_x_axis_scale: Optional[bool] = False
+        Whether to use the same axis scale for all sequences.
+    subplot_height: Optional[int] = 200
+        Height of each plot.
+    subplot_width: Optional[int] = 500
+        Width of each plot.
+    point_size: Optional[int] = 400
+        Size of points for one-time events.
+    bar_height: Optional[int] = 20
+        Height of bars for continuous events.
+    title: Optional[str] = None
+        Chart title.
+    seed: int = 0
+        Seed to randomly draw colors when not provided.
+
+    Returns
+    -------
+    chart: alt.VConcatChart
+        Chart with the plotted individual event sequences.
+    """
+    rng = np.random.RandomState(seed)
+
+    # Check required columns
+    required_columns = [
+        "person_id",
+        event_col,
+        event_start_datetime_col,
+        event_end_datetime_col,
+    ]
+
+    if index_date_col is not None:
+        required_columns.append(index_date_col)
+
+    if family_col is not None:
+        required_columns.append(family_col)
+
+    check_columns(df_events, required_columns=required_columns)
+
+    # Pre-selection of the sequences to plot
+    if list_person_ids is None:
+        list_person_ids = df_events.person_id.unique()[:3]
+    df_events = df_events.query("person_id in @list_person_ids")
+
+    # Ordering
+    order = {val: idx for idx, val in enumerate(list_person_ids)}
+    df_events = df_events.sort_values(by="person_id", key=lambda x: x.map(order))
+
+    # Encoding events start and end dates
+    if index_date_col is not None:
+        df_events["relative_event_start"] = (
+            df_events[event_start_datetime_col] - df_events[index_date_col]
+        ).dt.days.astype(int)
+
+        df_events["event_duration"] = (
+            (df_events[event_end_datetime_col] - df_events[event_start_datetime_col])
+            .dt.days.fillna(1)
+            .astype(int)
+        )
+
+        df_events["relative_event_end"] = (
+            df_events.relative_event_start + df_events.event_duration
+        )
+        x_encoding = "relative_event_start:Q"
+        x2_encoding = "relative_event_end:Q"
+
+    else:
+        x_encoding = f"{event_start_datetime_col}:T"
+        x2_encoding = f"{event_end_datetime_col}:T"
+
+    # Ordering events
+    if family_col is not None:
+        if family_to_index is None:
+            family_to_index = {
+                v: k for k, v in enumerate(df_events[family_col].unique())
+            }
+
+        df_events["dim_id"] = df_events[family_col].map(family_to_index)
+    else:
+        _, classes = np.unique(df_events[event_col], return_inverse=True)
+        df_events["dim_id"] = classes
+
+    # Mapping events towards colors and labels
+    if dim_mapping is not None:
+        df_events["dim_label"] = df_events[event_col].apply(
+            lambda x: dim_mapping[x]["label"]
+        )
+        labels = []
+        colors = []
+        for event in dim_mapping.keys():
+            labels.append(dim_mapping[event]["label"])
+            colors.append(f"rgb{dim_mapping[event]['color']}")
+
+    else:
+        df_events["dim_label"] = df_events[event_col]
+        labels = list(df_events["dim_label"].unique())
+        colors = [f"rgb{tuple(rng.randint(0, 255, size=3))}" for _ in labels]
+
+    # Base chart
+    raw = alt.Chart(df_events).encode(
+        x=alt.X(x_encoding),
+        y=alt.Y("dim_id:O", title=""),
+        color=alt.Color(
+            "dim_label:O",
+            scale=alt.Scale(domain=labels, range=colors),
+            title="Event type",
+        ),
+    )
+
+    # One-time events
+    point_dim = (
+        raw.transform_filter(
+            {"not": alt.FieldValidPredicate(field=event_end_datetime_col, valid=True)}
+        )
+        .mark_point(filled=True, size=point_size, cursor="pointer")
+        .encode(
+            tooltip=[f"{event_col}", f"{event_start_datetime_col}"],
+        )
+    )
+
+    # Continuous events
+    continuous_dim = (
+        raw.transform_filter(
+            alt.FieldValidPredicate(event_end_datetime_col, valid=True)
+        )
+        .mark_bar(
+            filled=True,
+            cursor="pointer",
+            cornerRadius=bar_height / 2,
+            height=bar_height,
+        )
+        .encode(
+            x2=x2_encoding,
+            tooltip=[
+                f"{event_col}",
+                f"{event_start_datetime_col}",
+                f"{event_end_datetime_col}",
+            ],
+        )
+    )
+
+    # Aggregation
+    base = (point_dim + continuous_dim).properties(
+        width=subplot_width,
+        height=subplot_height,
+    )
+
+    # Vertical concatenation of all patients' sequences
+    chart = (
+        alt.vconcat()
+        .configure_legend(labelFontSize=13, symbolSize=150, titleFontSize=15)
+        .configure_axisY(disable=True)
+    )
+
+    for person_id in df_events.person_id.unique():
+        chart &= base.transform_filter(
+            alt.expr.datum.person_id == person_id
+        ).properties(title=f"Sequence of patient {person_id}")
+
+    if same_x_axis_scale:
+        chart = chart.resolve_scale(x="shared")
+
+    if title is not None:
+        chart = chart.properties(title=title)
+
+    return chart
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/plot/index.html b/main/reference/plot/index.html new file mode 100644 index 00000000..0279c330 --- /dev/null +++ b/main/reference/plot/index.html @@ -0,0 +1,3779 @@ + + + + + + + + + + + + + + + + `eds_scikit.plot` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + +
    +
    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.plot

    + + +
    + + + +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/plot/omop_teva/index.html b/main/reference/plot/omop_teva/index.html new file mode 100644 index 00000000..e8630e87 --- /dev/null +++ b/main/reference/plot/omop_teva/index.html @@ -0,0 +1,4207 @@ + + + + + + + + + + + + + + + + omop_teva - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
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    + + + +
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    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.plot.omop_teva

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + generate_omop_teva + + +

    +
    generate_omop_teva(data: HiveData, start_date: str, end_date: str, teva_config: dict = default_omop_teva_config, output_dir = 'omop_teva')
+
    + +
    + +

    Generate OMOP TEVA folder.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    data +

    Must contain the visit_occurrence table.

    +

    + + TYPE: + HiveData + +

    +
    start_date +

    The start date for data extraction.

    +

    + + TYPE: + str + +

    +
    end_date +

    The end date for data extraction.

    +

    + + TYPE: + str + +

    +
    teva_config +

    OMOP TEVA configuration, by default default_omop_teva_config. Must start with visit_occurrence configuration.

    +

    + + TYPE: + dict, optional + + + DEFAULT: + default_omop_teva_config + +

    +
    output_dir +

    Output directory path, by default "omop_teva".

    +

    + + TYPE: + str, optional + + + DEFAULT: + 'omop_teva' + +

    +
    + +

    Examples:

    +

    Example configuration for teva_config:

    +

    default_omop_teva_config = { + "visit_occurrence": { + "category_columns": [ + "visit_occurrence_id", + "care_site_short_name", + "stay_source_value" + ], + "date_column": "visit_start_datetime", + "mapper": { + "visit_occurrence_id": {"not NaN": "."} + } + }, + "other_table": { + "category_columns": [ + "visit_occurrence_id", + "column A", + "column B", + "column C" + ], + "date_column": "column_datetime", + "mapper": { + "column A": {"not NaN": "."}, + "column B": {"X type": "X.*", "Y type": "Y"} + } + } + ... +}

    + +
    + Source code in eds_scikit/plot/omop_teva.py + 
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    def generate_omop_teva(
+    data: HiveData,
+    start_date: str,
+    end_date: str,
+    teva_config: dict = default_omop_teva_config,
+    output_dir="omop_teva",
+):
+    """
+    Generate OMOP TEVA folder.
+
+    Parameters
+    ----------
+    data : HiveData
+        Must contain the visit_occurrence table.
+    start_date : str
+        The start date for data extraction.
+    end_date : str
+        The end date for data extraction.
+    teva_config : dict, optional
+        OMOP TEVA configuration, by default `default_omop_teva_config`. Must start with visit_occurrence configuration.
+    output_dir : str, optional
+        Output directory path, by default "omop_teva".
+
+    Examples
+    --------
+    Example configuration for `teva_config`:
+
+    default_omop_teva_config = {
+        "visit_occurrence": {
+            "category_columns": [
+                "visit_occurrence_id",
+                "care_site_short_name",
+                "stay_source_value"
+            ],
+            "date_column": "visit_start_datetime",
+            "mapper": {
+                "visit_occurrence_id": {"not NaN": ".*"}
+            }
+        },
+        "other_table": {
+            "category_columns": [
+                "visit_occurrence_id",
+                "column A",
+                "column B",
+                "column C"
+            ],
+            "date_column": "column_datetime",
+            "mapper": {
+                "column A": {"not NaN": ".*"},
+                "column B": {"X type": "X.*", "Y type": "Y"}
+            }
+        }
+        ...
+    }
+    """
+    if not os.path.exists(f"{output_dir}/"):
+        os.makedirs(f"{output_dir}/")
+
+    # First, preprocess visit_occurrence which will be merged with remaining config tables
+    try:
+        visit_occurrence = data.visit_occurrence
+        visit_occurrence = visit_occurrence.merge(
+            data.care_site[["care_site_id", "care_site_short_name"]], on="care_site_id"
+        )
+        teva_config["visit_occurrence"]
+    except AttributeError:
+        raise Exception(
+            "No visit_occurrence or care_site table in input data object. visit_occurrence and care_site table must be provided."
+        )
+
+    # Iterate config tables
+    for table_name, config in teva_config.items():
+
+        logger.info(f"Starting {table_name} processing.")
+
+        if table_name == "visit_occurrence":
+            visit_columns = [
+                *config["category_columns"],
+                config["date_column"],
+                "visit_occurrence_id",
+            ]
+            visit_columns = list(
+                set(visit_columns).intersection(visit_occurrence.columns)
+            )
+            visit_occurrence = visit_occurrence[visit_columns]
+            table = visit_occurrence.copy()
+        else:
+            try:
+                table = data._read_table(table_name)
+                drop_columns = (
+                    set(visit_occurrence.columns).intersection(table.columns)
+                ).difference(["visit_occurrence_id"])
+                if drop_columns:
+                    table = table.merge(
+                        visit_occurrence.drop(columns=drop_columns),
+                        on="visit_occurrence_id",
+                        how="left",
+                    )
+                else:
+                    table = table.merge(
+                        visit_occurrence, on="visit_occurrence_id", how="left"
+                    )
+            except AttributeError:
+                logger.warning(
+                    f"No {table_name} table in input data object. Skipping {table_name}."
+                )
+                continue
+        # Compute reduced table representation
+        table["visit_occurrence_id"] = table["visit_occurrence_id"].astype(str)
+
+        table_count = reduce_table(
+            table, start_date=start_date, end_date=end_date, **config
+        )
+        table_count = table_count[~(table_count == 0).any(axis=1)]
+        # Compute associated chart
+        chart = visualize_table(table_count, title=f"{table_name} table dashboard")
+        # Save computations
+        save_pickle(f"{output_dir}/{table_name}_count", table_count)
+        chart.save(f"{output_dir}/{table_name}_chart.html")
+        logger.info(f"{table_name} processing done.")
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/plot/table_viz/index.html b/main/reference/plot/table_viz/index.html new file mode 100644 index 00000000..614c050c --- /dev/null +++ b/main/reference/plot/table_viz/index.html @@ -0,0 +1,4984 @@ + + + + + + + + + + + + + + + + table_viz - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + +
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    + + +
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    + + + + + + + + +

    eds_scikit.plot.table_viz

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + map_column + + +

    +
    map_column(table: DataFrame, mapping: dict, src_column: str, target_column: str, drop: bool = True) -> DataFrame
+
    + +
    + +

    Map a dataframe column given a mapping dictionnary (of regex). +If src_column == target_column, src_column will be renamed.

    +
    Parameter"
    +

    table : DataFrame +mapping : dict + EXAMPLE: {"column name" : {"CR" : r"^CR", "CRH" : r"^CRH"}} +src_column : str +target_column : str

    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    Dataframe with mapped column

    +
    + +
    + Source code in eds_scikit/plot/table_viz.py + 
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    def map_column(
+    table: DataFrame,
+    mapping: dict,
+    src_column: str,
+    target_column: str,
+    drop: bool = True,
+) -> DataFrame:
+    """Map a dataframe column given a mapping dictionnary (of regex).
+    If ```src_column == target_column```, src_column will be renamed.
+
+    Parameter"
+    ----------
+    table : DataFrame
+    mapping : dict
+        **EXAMPLE**: `{"column name" : {"CR" : r"^CR", "CRH" : r"^CRH"}}`
+    src_column : str
+    target_column : str
+
+    Returns
+    -------
+    DataFrame
+        Dataframe with mapped column
+    """
+    check_columns(
+        table,
+        required_columns=[src_column],
+    )
+
+    remove_columns = []
+
+    if src_column == target_column:
+        table[src_column + "_src"] = table[src_column]
+        src_column = src_column + "_src"
+        remove_columns += [src_column]
+    table[target_column] = "Other"
+    table.loc[table[src_column].isna(), target_column] = "NaN"
+    for target, regex in mapping.items():
+        table.loc[
+            table[src_column].str.contains(regex, case=False, regex=True, na=False),
+            target_column,
+        ] = target
+
+    if drop:
+        table = table[set(table.columns).difference(remove_columns)]
+
+    return table
+
    +
    +
    + +
    + +
    + + + +

    + preprocess_table + + +

    +
    preprocess_table(table: DataFrame, category_columns: List[str], date_column: str, start_date: str, end_date: str, mapper: dict = None) -> DataFrame
+
    + +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    table +

    Input dataframe to be reduced.

    +

    + + TYPE: + DataFrame + +

    +
    category_columns +

    Columns to perform reduction on.

    +

    + + TYPE: + List[str] + +

    +
    date_column +

    Date column.

    +

    + + TYPE: + str + +

    +
    start_date +

    start date

    +

    + + TYPE: + str + +

    +
    end_date +

    end date

    +

    + + TYPE: + str + +

    +
    mapper +

    EXAMPLE: {"column 1" : {"CR" : r"^CR", "CRH" : r"^CRH"}, "column 2" : {"code a" : r"^A", "code b" : r"^B"}}

    +

    + + TYPE: + dict + + + DEFAULT: + None + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    Formated and preprocessed table

    +
    + + + + + + + + + + + + + + +
    RAISESDESCRIPTION
    + + ValueError + + + +
    + +
    + Source code in eds_scikit/plot/table_viz.py + 
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    def preprocess_table(
+    table: DataFrame,
+    category_columns: List[str],
+    date_column: str,
+    start_date: str,
+    end_date: str,
+    mapper: dict = None,
+) -> DataFrame:
+    """
+
+    Parameters
+    ----------
+    table : DataFrame
+        Input dataframe to be reduced.
+    category_columns : List[str]
+        Columns to perform reduction on.
+    date_column : str
+        Date column.
+    start_date : str
+        start date
+    end_date : str
+        end date
+    mapper : dict
+        **EXAMPLE**: `{"column 1" : {"CR" : r"^CR", "CRH" : r"^CRH"}, "column 2" : {"code a" : r"^A", "code b" : r"^B"}}`
+
+    Returns
+    -------
+    DataFrame
+        Formated and preprocessed table
+
+    Raises
+    ------
+    ValueError
+    """
+
+    # Check and format to string category columns
+
+    remove_colums = []
+
+    for col in category_columns:
+        if not (col in table.columns):
+            logger.info(f"Column {col} not in table.")
+            remove_colums += [col]
+        else:
+            table[col] = table[col].astype(str)
+
+    for col in remove_colums:
+        category_columns.remove(col)
+
+    if category_columns == []:
+        raise Exception("No columns from category_columns in input table.")
+
+    category_columns = [*category_columns, date_column]
+
+    table = table[category_columns]
+
+    # Filter table on dates
+
+    framework = get_framework(table)
+
+    table = table[(table[date_column] >= start_date) & (table[date_column] <= end_date)]
+    table["datetime"] = framework.to_datetime(table[date_column].dt.strftime("%Y-%m"))
+    table = table.drop(columns=[date_column])
+
+    # Map category columns
+
+    if mapper:
+        for col, mapping in mapper.items():
+            if col in category_columns:
+                table = map_column(table, mapping, col, col)
+
+    return table
+
    +
    +
    + +
    + +
    + + + +

    + reduce_table + + +

    +
    reduce_table(table: DataFrame, start_date: str, end_date: str, category_columns: List[str], date_column: str, mapper: dict = None) -> DataFrame
+
    + +
    + +

    Reduce input table by counting each cartesian product values (col1, col2, ..., date_col) for each columns in category_columns and each date. +Columns values must be under 50 . Use mapper to reduce this size.

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    table +

    Input dataframe to be reduced.

    +

    + + TYPE: + DataFrame + +

    +
    start_date +

    start date

    +

    + + TYPE: + str + +

    +
    end_date +

    end date

    +

    + + TYPE: + str + +

    +
    category_columns +

    Columns to perform reduction on.

    +

    + + TYPE: + List[str] + +

    +
    date_column +

    Date column.

    +

    + + TYPE: + str + +

    +
    mapper +

    EXAMPLE: {"column 1" : {"CR" : r"^CR", "CRH" : r"^CRH"}, "column 2" : {"code a" : r"^A", "code b" : r"^B"}}

    +

    + + TYPE: + dict + + + DEFAULT: + None + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + DataFrame + + +

    Reducted DataFrame with columns category_columns, date_column and count.

    +
    + + + + + + + + + + + + + + +
    RAISESDESCRIPTION
    + + ValueError + + + +
    + +
    + Source code in eds_scikit/plot/table_viz.py + 
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    def reduce_table(
+    table: DataFrame,
+    start_date: str,
+    end_date: str,
+    category_columns: List[str],
+    date_column: str,
+    mapper: dict = None,
+) -> DataFrame:
+    """
+    Reduce input table by counting each cartesian product values (col1, col2, ..., date_col) for each columns in category_columns and each date.
+    Columns values must be under 50 . Use mapper to reduce this size.
+
+    Parameters
+    ----------
+    table : DataFrame
+        Input dataframe to be reduced.
+    start_date : str
+        start date
+    end_date : str
+        end date
+    category_columns : List[str]
+        Columns to perform reduction on.
+    date_column : str
+        Date column.
+    mapper : dict
+        **EXAMPLE**: `{"column 1" : {"CR" : r"^CR", "CRH" : r"^CRH"}, "column 2" : {"code a" : r"^A", "code b" : r"^B"}}`
+
+    Returns
+    -------
+    DataFrame
+        Reducted DataFrame with columns category_columns, date_column and count.
+
+    Raises
+    ------
+    ValueError
+    """
+
+    check_columns(
+        table,
+        required_columns=[date_column],
+    )
+    table = preprocess_table(
+        table, category_columns, date_column, start_date, end_date, mapper
+    )
+    # to prevent computation issues
+    shape = table.shape  # noqa
+
+    # raise error it too much categorical values
+    nunique = table.nunique()
+    oversized_columns = nunique[(nunique.index != "datetime") & (nunique > 50)].index
+    if len(oversized_columns) > 0:
+        raise ValueError(
+            f"Input table columns can't have more then 50 values. Consider using eds_scikit.plot.map_column. Oversized columns: {oversized_columns}",
+        )
+    # compute reducted table
+    table_count = (
+        table.fillna("NaN")
+        .groupby(["datetime", *category_columns])
+        .size()
+        .reset_index(name="count")
+    )
+
+    # to prevent computation issues
+    shape = table_count.shape  # noqa
+
+    # final formatting
+    table_count = to("pandas", table_count)
+    table_count["datetime"] = pd.to_datetime(table_count["datetime"])
+    date_dataframe = pd.DataFrame(
+        pd.date_range(start=start_date, end=end_date, freq="MS"), columns=["datetime"]
+    )
+    table_count = table_count.merge(date_dataframe, on="datetime", how="right")
+
+    table_count["count"] = table_count["count"].fillna(0)
+    table_count = table_count.fillna("NaN")
+
+    return table_count
+
    +
    +
    + +
    + +
    + + + +

    + visualize_table + + +

    +
    visualize_table(table_count: DataFrame, title: str = 'table exploration dashboard', description = True) -> alt.Chart
+
    + +
    + +

    Generate reduced table dashboard.

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    table_count +

    Output from eds_scikit.plot.table_viz.reduce_table

    +

    + + TYPE: + DataFrame + +

    +
    title +

    Chart title

    +

    + + TYPE: + str + + + DEFAULT: + 'table exploration dashboard' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + alt.Chart + + +

    reduce_table dashboard

    +
    + + + + + + + + + + + + + + +
    RAISESDESCRIPTION
    + + ValueError + + +

    description

    +
    + +
    + Source code in eds_scikit/plot/table_viz.py + 
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    def visualize_table(
+    table_count: DataFrame,
+    title: str = "table exploration dashboard",
+    description=True,
+) -> alt.Chart:
+
+    """Generate reduced table dashboard.
+
+    Parameters
+    ----------
+    table_count : DataFrame
+        Output from eds_scikit.plot.table_viz.reduce_table
+    title : str
+        Chart title
+
+    Returns
+    -------
+    alt.Chart
+        reduce_table dashboard
+
+    Raises
+    ------
+    ValueError
+        _description_
+    """
+
+    check_columns = ["count", "datetime"]
+    for check_column in check_columns:
+        if not (check_column in table_count.columns):
+            raise ValueError(f"Input table must have a {check_column} column.")
+
+    selections = {}
+    columns = [col for col in table_count.columns if not (col in ["datetime", "count"])]
+    for col in columns:
+        selections[col] = alt.selection_point(
+            fields=[col], on="click", bind="legend", clear="dblclick"
+        )
+
+    charts = []
+
+    width, height = 300, 50
+
+    # Two charts per column
+    for i, col in enumerate(columns):
+        color_scale = generate_color_map(table_count, col)
+        selection_col = [selections[s] for s in selections if (s != col)]
+        # Global volumetry chart
+        chart = (
+            alt.Chart(table_count)
+            .mark_bar()
+            .encode(
+                x=col + ":N",
+                y="sum(count):Q",
+                color=alt.Color(col + ":N", scale=color_scale),
+                opacity=alt.condition(selections[col], alt.value(1), alt.value(0.3)),
+                tooltip=[col],
+            )
+            .add_params(selections[col])
+        )
+
+        if len(selection_col) > 0:
+            chart = chart.add_params(selections[col]).transform_filter(
+                reduce(
+                    (lambda x, y: x & y),
+                    selection_col,
+                )
+            )
+
+        # Temporal volumetry chart
+        base_t = (
+            alt.Chart(table_count)
+            .mark_line()
+            .encode(
+                x=alt.X("yearmonth(datetime):T"),
+                y=alt.Y("sum(count):Q", axis=alt.Axis(format="s")),
+                color=alt.Color(col + ":N", scale=color_scale),
+                opacity=alt.condition(selections[col], alt.value(1), alt.value(0.3)),
+            )
+            .add_params(selections[col])
+        )
+        if len(selection_col) > 0:
+            base_t = base_t.transform_filter(
+                reduce(
+                    (lambda x, y: x & y),
+                    selection_col,
+                )
+            )
+        base_t = base_t.properties(width=width, height=height)
+        chart = chart.properties(width=width, height=height)
+
+        chart = (chart | base_t).properties(title=col)
+
+        charts.append(chart)
+
+    charts = alt.vconcat(*charts).resolve_scale(color="independent")
+
+    if description:
+        title = {
+            "text": [title],
+            "subtitle": [
+                "ALT + SHIFT to select multiple categories",
+                "Double-click on legend to unselect",
+                "Reduce table column and values size for better interactivity",
+            ],
+            "fontSize": 25,
+            "subtitleFontSize": 15,
+            "offset": 30,
+            "subtitlePadding": 20,
+        }
+
+        charts = charts.properties(
+            padding={"left": 50, "top": 50, "right": 50, "bottom": 50},
+            title=title,
+        ).configure_legend(columns=4, symbolLimit=0)
+
+    return charts
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/resources/index.html b/main/reference/resources/index.html new file mode 100644 index 00000000..de143e37 --- /dev/null +++ b/main/reference/resources/index.html @@ -0,0 +1,3779 @@ + + + + + + + + + + + + + + + + `eds_scikit.resources` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + +
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    + + + + + + + + +

    eds_scikit.resources

    + + +
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    +
    + + + + Back to top + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/resources/reg/index.html b/main/reference/resources/reg/index.html new file mode 100644 index 00000000..08811b99 --- /dev/null +++ b/main/reference/resources/reg/index.html @@ -0,0 +1,4101 @@ + + + + + + + + + + + + + + + + reg - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
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    +
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    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.resources.reg

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + Registry + + +

    + + +
    + + + + + + +
    + + + + + + + + + +
    + + + +

    + get + + +

    +
    get(key: str, function_name: str)
+
    + +
    + +

    Get a function from one of the registry

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    key +

    The registry's name. The function will be retrieved from self.

    +

    + + TYPE: + str + +

    +
    function_name +

    The function's name, The function will be retrieved via self..get(function_name). +Can be of the form "function_name.version"

    +

    + + TYPE: + str + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + Callable + + +

    The registered function

    +
    + +
    + Source code in eds_scikit/resources/reg.py + 
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    def get(
+    self,
+    key: str,
+    function_name: str,
+):
+    """
+    Get a function from one of the registry
+
+    Parameters
+    ----------
+    key : str
+        The registry's name. The function will be retrieved from self.<key>
+    function_name : str
+        The function's name, The function will be retrieved via self.<key>.get(function_name).
+        Can be of the form "function_name.version"
+
+    Returns
+    -------
+    Callable
+        The registered function
+    """
+
+    if not hasattr(self, key):
+        raise ValueError(f"eds-scikit's registry has no {key} key !")
+    r = getattr(self, key)
+    candidates = r.get_all().keys()
+
+    if function_name in candidates:
+        # Exact match
+        func = r.get(function_name)
+
+    else:
+        # Looking for a match excluding version string
+        candidates = [
+            func for func in candidates if function_name == func.split(".")[0]
+        ]
+        if len(candidates) > 1:
+            # Multiple versions available, a specific one should be specified
+            raise ValueError(
+                (
+                    f"Multiple functions are available under the name {function_name} :\n"
+                    f"{candidates}\n"
+                    "Please choose one of the implementation listed above."
+                )
+            )
+        if not candidates:
+            # No registered function
+            raise ValueError(
+                (
+                    f"No function registered under the name {function_name} "
+                    f"was found in eds-scikit's {key} registry.\n"
+                    "If you work in AP-HP's ecosystem, you should install "
+                    'extra resources via `pip install "eds-scikit[aphp]"'
+                    "You can define your own and decorate it as follow:\n"
+                    "from eds_scikit.resources import registry\n"
+                    f"@registry.{key}('{function_name}')"
+                    f"def your_custom_func(args, **kwargs):",
+                    "   ...",
+                )
+            )
+        func = r.get(candidates[0])
+    return func
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/resources/utils/index.html b/main/reference/resources/utils/index.html new file mode 100644 index 00000000..ffe87208 --- /dev/null +++ b/main/reference/resources/utils/index.html @@ -0,0 +1,3938 @@ + + + + + + + + + + + + + + + + utils - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
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    + + + +
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    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.resources.utils

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + versionize + + +

    +
    versionize(algo: str) -> Optional[str]
+
    + +
    + +

    Extract, if found, the version substring of an algorithm name.

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    algo +

    Of the form "" or "."

    +

    + + TYPE: + str + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + + Optional[str] + + +

    The algo version suffix

    +
    + +
    + Source code in eds_scikit/resources/utils.py + 
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    def versionize(algo: str) -> Optional[str]:
+    """
+    Extract, if found,  the version substring of an algorithm name.
+
+    Parameters
+    ----------
+    algo : str
+        Of the form "<algo_name>" or "<algo_name>.<version>"
+
+    Returns
+    -------
+    Optional[str]
+        The algo version suffix
+    """
+    splited = algo.split(".")
+    if len(splited) == 1:
+        return None
+    return splited[-1]
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/structures/attributes/index.html b/main/reference/structures/attributes/index.html new file mode 100644 index 00000000..4358071c --- /dev/null +++ b/main/reference/structures/attributes/index.html @@ -0,0 +1,4617 @@ + + + + + + + + + + + + + + + + attributes - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
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    + + + +
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    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.structures.attributes

    + + +
    + + + +
    + + + +
    + + + + + + + +
    + + + +

    + ATTRIBUTE_REGEX_PATTERNS + + + + module-attribute + + +

    +
    ATTRIBUTE_REGEX_PATTERNS = [{'attribute': 'IS_EMERGENCY', 'pattern': '\\bURG|\\bSAU\\b|\\bUHCD\\b|\\bZHTCD\\b', 'true_examples': ['URG', 'URGENCES', 'SAU'], 'false_examples': ['CHIRURGIE']}, {'attribute': 'IS_ICU', 'pattern': '\\bUSI|\\bREA[N\\s]|\\bREA\\b|\\bUSC\\b|SOINS.*INTENSIF|SURV.{0,15}CONT|\\bSI\\b|\\bSC\\b', 'true_examples': ['REA', 'REA NEURO', 'REANIMATION'], 'false_examples': ['CARREAU']}]
+
    + +
    + +

    Default argument of 🇵🇾func:~eds_scikit.structures.attributes.add_care_site_attributes.

    +

    :meta private:

    + +

    Examples:

    +

    ::

    +
    ATTRIBUTE_REGEX_PATTERNS = [
+    {
+        # required elements: name of attribute and pattern of regular expression
+        "attribute": "IS_EMERGENCY",
+        "pattern": r"URG|SAU|UHCD|ZHTCD",
+
+        # optional elements: list of test strings to validate the regular expression
+        "true_examples": ["URG", "URGENCES", "SAU"],
+        "false_examples": ["CHIRURGIE"],
+    },
+    ...
+]
+
    +
    + +
    + + + +
    + + + +

    + add_care_site_attributes + + +

    +
    add_care_site_attributes(care_site: DataFrame, only_attributes: Optional[List[str]] = None, attribute_regex_patterns: Optional[List[str]] = None) -> DataFrame
+
    + +
    + +

    Add boolean attributes as columns to care_site dataframe.

    +

    This algo applies simple regular expressions to the care_site_name +in order to compute boolean attributes of the care site. +Implemented attributes are:

    +
      +
    • IS_EMERGENCY
      • +
    • IS_ICU
      • +
    +

    In order to make the detection of attributes more robust, the +column care_site_name is first transformed to a DESCRIPTION. +This is done by 🇵🇾func:~eds_scikit.structures.description.add_care_site_description.

    + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site + +

    + + TYPE: + DataFrame + +

    +
    only_attributes +

    if only a subset of all possible attributes should be computed

    +

    + + TYPE: + list of str + + + DEFAULT: + None + +

    +
    attribute_regex_patterns +

    If None, the default value is 🇵🇾data:~eds_scikit.structures.attributes.ATTRIBUTE_REGEX_PATTERNS

    +

    + + TYPE: + list(None) + + + DEFAULT: + None + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + care_site + +

    same as input with additional columns corresponding to boolean attributes. +the column DESCRIPTION is also added : it contains of cleaner version of care_site_name.

    +

    + + TYPE: + DataFrame + +

    +
    + +

    Examples:

    + 
    >>> care_site.head(2)
+care_site_id, care_site_name
+21, HOSP ACCUEIL URG PED (UF)
+22, HOSP CHIRURGIE DIGESTIVE
+23, HOSP PEDIATRIE GEN ET SAU
+>>> care_site = add_care_site_attributes(care_site, only_attributes=["IS_EMERGENCY"])
+>>> care_site.head(2)
+care_site_id, care_site_name, DESCRIPTION, IS_EMERGENCY
+21, HOSP ACCUEIL URG PED (UF),ACCUEIL URG PED,True
+22, HOSP CHIRURGIE DIGESTIVE,CHIRURGIE DIGESTIVE,False
+23, HOSP PEDIATRIE GEN ET SAU,PEDIATRIE GEN ET SAU,True
+
    +

    Specifying custom regular expressions. It is a good idea to provide true and false examples for +each attribute. These examples will be tested against the provided regular expressions.

    + 
    >>> my_attributes = [
+    {
+        "attribute": "IS_EMERGENCY",
+        "pattern": r"URG|SAU|UHCD|ZHTCD",
+        "true_examples": ["URG", "URGENCES", "SAU"],
+        "false_examples": ["CHIRURGIE"],
+    },
+    {
+        "attribute": "IS_ICU",
+        "pattern": r"REA|REANI",
+        "true_examples": ["REA", "REA NEURO", "REANIMATION"],
+        "false_examples": ["CARREAU"],
+    },
+]
+>>> care_site = add_care_site_attributes(care_site, attribute_regex_patterns=my_attributes)
+
    + +
    + Source code in eds_scikit/structures/attributes.py + 
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    def add_care_site_attributes(
+    care_site: DataFrame,
+    only_attributes: Optional[List[str]] = None,
+    attribute_regex_patterns: Optional[List[str]] = None,
+) -> DataFrame:
+    """Add boolean attributes as columns to care_site dataframe.
+
+    This algo applies simple regular expressions to the ``care_site_name``
+    in order to compute boolean attributes of the care site.
+    Implemented attributes are:
+
+    - ``IS_EMERGENCY``
+    - ``IS_ICU``
+
+    In order to make the detection of attributes more robust, the
+    column ``care_site_name`` is first transformed to a ``DESCRIPTION``.
+    This is done by :py:func:`~eds_scikit.structures.description.add_care_site_description`.
+
+    Parameters
+    ----------
+    care_site : DataFrame
+    only_attributes : list of str
+        if only a subset of all possible attributes should be computed
+    attribute_regex_patterns : list (None)
+        If ``None``, the default value is :py:data:`~eds_scikit.structures.attributes.ATTRIBUTE_REGEX_PATTERNS`
+
+    Returns
+    -------
+    care_site: DataFrame
+        same as input with additional columns corresponding to boolean attributes.
+        the column ``DESCRIPTION`` is also added : it contains of cleaner version of ``care_site_name``.
+
+
+    Examples
+    --------
+    >>> care_site.head(2)
+    care_site_id, care_site_name
+    21, HOSP ACCUEIL URG PED (UF)
+    22, HOSP CHIRURGIE DIGESTIVE
+    23, HOSP PEDIATRIE GEN ET SAU
+    >>> care_site = add_care_site_attributes(care_site, only_attributes=["IS_EMERGENCY"])
+    >>> care_site.head(2)
+    care_site_id, care_site_name, DESCRIPTION, IS_EMERGENCY
+    21, HOSP ACCUEIL URG PED (UF),ACCUEIL URG PED,True
+    22, HOSP CHIRURGIE DIGESTIVE,CHIRURGIE DIGESTIVE,False
+    23, HOSP PEDIATRIE GEN ET SAU,PEDIATRIE GEN ET SAU,True
+
+    Specifying custom regular expressions. It is a good idea to provide true and false examples for
+    each attribute. These examples will be tested against the provided regular expressions.
+
+    >>> my_attributes = [
+        {
+            "attribute": "IS_EMERGENCY",
+            "pattern": r"\bURG|\bSAU\b|\bUHCD\b|\bZHTCD\b",
+            "true_examples": ["URG", "URGENCES", "SAU"],
+            "false_examples": ["CHIRURGIE"],
+        },
+        {
+            "attribute": "IS_ICU",
+            "pattern": r"\bREA\b|\bREANI",
+            "true_examples": ["REA", "REA NEURO", "REANIMATION"],
+            "false_examples": ["CARREAU"],
+        },
+    ]
+    >>> care_site = add_care_site_attributes(care_site, attribute_regex_patterns=my_attributes)
+
+    """
+    # validate arguments
+    if attribute_regex_patterns is None:
+        attribute_regex_patterns = ATTRIBUTE_REGEX_PATTERNS
+
+    if only_attributes:
+        impossible = set(only_attributes) - set(possible_concepts)
+        if impossible:
+            raise ValueError(f"Unknown concepts: {impossible}")
+        attribute_regex_patterns = [
+            item
+            for item in attribute_regex_patterns
+            if item["attribute"] in only_attributes
+        ]
+
+    validate_attribute_regex_patterns(attribute_regex_patterns)
+
+    if "DESCRIPTION" not in care_site.columns:
+        care_site = description.add_care_site_description(care_site)
+
+    # apply algo
+    for item in attribute_regex_patterns:
+        new_column = {
+            item["attribute"]: care_site["DESCRIPTION"].str.contains(
+                item["pattern"], regex=True
+            )
+        }
+        care_site = care_site.assign(**new_column)
+
+    if only_attributes:
+        care_site = care_site.drop(["DESCRIPTION"], axis="columns")
+
+    return care_site
+
    +
    +
    + +
    + +
    + + + +

    + get_parent_attributes + + +

    +
    get_parent_attributes(care_site: DataFrame, only_attributes: Optional[List[str]] = None, version: Optional[str] = None, parent_type: str = 'Unité Fonctionnelle (UF)') -> DataFrame
+
    + +
    + +

    Get all known attributes from parent care sites and propagates them to each child care site

    + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    required columns: ["care_site_id", "care_site_type_source_value", "care_site_name"]

    +

    + + TYPE: + DataFrame + +

    +
    only_attributes +

    same as 🇵🇾func:~eds_scikit.structures.attributes.add_care_site_attributes

    +

    + + TYPE: + list of str + + + DEFAULT: + None + +

    +
    version +

    Optional version string for the care site hierarchy

    +

    + + TYPE: + Optional[str] + + + DEFAULT: + None + +

    +
    parent_type +

    Type of care site to consider as parent, by default "Unité Fonctionnelle (UF)". +Corresponds to the "care_site_type_source_value" column

    +

    + + TYPE: + str + + + DEFAULT: + 'Unité Fonctionnelle (UF)' + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + care_site_attributes + +

    same index as input care_site. columns: care_site, is_emergency

    +

    + + TYPE: + DataFrame + +

    +
    +
    Warnings
    +

    This algo requires that the care_site dataframe contains +the parent care sites as well as the care sites +that you want to tag.

    + +

    Examples:

    + 
    >>> attributes = get_parent_attributes(care_site,
+                                       only_attributes=["IS_EMERGENCY"],
+                                       parent_type="Unité Fonctionnelle (UF)")
+>>> attributes.head()
+    care_site_id, care_site_name, care_site_type_source_value, IS_EMERGENCY
+    92829  , ... ,     False
+    29820  , ... ,     True
+
    + +
    + Source code in eds_scikit/structures/attributes.py + 
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    def get_parent_attributes(
+    care_site: DataFrame,
+    only_attributes: Optional[List[str]] = None,
+    version: Optional[str] = None,
+    parent_type: str = "Unité Fonctionnelle (UF)",
+) -> DataFrame:
+    """Get all known attributes from parent care sites and propagates them to each child care site
+
+    Parameters
+    ----------
+    care_site: DataFrame
+        required columns: ``["care_site_id", "care_site_type_source_value", "care_site_name"]``
+    only_attributes : list of str
+        same as :py:func:`~eds_scikit.structures.attributes.add_care_site_attributes`
+    version: Optional[str]
+        Optional version string for the care site hierarchy
+    parent_type: str
+        Type of care site to consider as parent, by default "Unité Fonctionnelle (UF)".
+        Corresponds to the `"care_site_type_source_value"` column
+
+    Returns
+    --------
+    care_site_attributes: DataFrame
+        same index as input care_site. columns: care_site, is_emergency
+
+
+    Warnings
+    --------
+    This algo requires that the `care_site` dataframe contains
+    the parent care sites as well as the care sites
+    that you want to tag.
+
+    Examples
+    --------
+    >>> attributes = get_parent_attributes(care_site,
+                                           only_attributes=["IS_EMERGENCY"],
+                                           parent_type="Unité Fonctionnelle (UF)")
+    >>> attributes.head()
+        care_site_id, care_site_name, care_site_type_source_value, IS_EMERGENCY
+        92829  , ... ,     False
+        29820  , ... ,     True
+
+    """
+
+    function_name = "get_care_site_hierarchy"
+    if version is not None:
+        function_name += f".{version}"
+    hierarchy = registry.get("data", function_name=function_name)()
+
+    fw = framework.get_framework(care_site)
+    hierarchy = framework.to(fw, hierarchy)
+
+    # STEP 1: get attributes of parent
+    parent_attributes = care_site.loc[
+        care_site["care_site_type_source_value"] == parent_type,
+        ["care_site_id", "care_site_name"],
+    ]
+    parent_attributes = add_care_site_attributes(
+        parent_attributes, only_attributes=only_attributes
+    )
+    boolean_columns = [
+        col for (col, dtype) in parent_attributes.dtypes.iteritems() if dtype == "bool"
+    ]
+
+    parent_attributes = parent_attributes.drop(
+        ["care_site_name"], axis="columns"
+    ).rename(columns={"care_site_id": "parent_id"})
+
+    # STEP 2: propagate attributes from parent to all children
+    hierarchy = hierarchy.loc[:, ["care_site_id", parent_type]].rename(
+        columns={parent_type: "parent_id"}
+    )
+    children_attributes = hierarchy.merge(
+        parent_attributes, how="left", on="parent_id"
+    ).drop(["parent_id"], axis="columns")
+
+    # STEP 3 : merge to input dataframe
+    old_columns = care_site.columns
+    care_site = care_site.merge(children_attributes, how="left", on="care_site_id")
+    for col in care_site.columns:
+        if col in boolean_columns and col not in old_columns:
+            care_site[col] = care_site[col].fillna(value=False)
+
+    return care_site
+
+    # NOTE: this is how to return a single column that contains
+    # EXACTLY the same index as the input dataframe.
+    # For instance koalas requires the index name to be the same
+    # for this operation to be valid:
+    # >>> df["new_column"] = compute_column(df)
+
+    # attributes = (
+    #     care_site.loc[:, ["care_site_id"]]
+    #     .reset_index()
+    #     .merge(
+    #         # drop_duplicates to ensure we keep same size as input
+    #         children_attributes.drop_duplicates(subset=["care_site_id"]),
+    #         how="left",
+    #         on="care_site_id",
+    #     )
+    #     .fillna(value=False)
+    #     # a merge "forgets" the index, we want to output the same as input
+    #     .set_index("index")
+    # )
+    # attributes.index.name = care_site.index.name
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/structures/description/index.html b/main/reference/structures/description/index.html new file mode 100644 index 00000000..c2be571a --- /dev/null +++ b/main/reference/structures/description/index.html @@ -0,0 +1,3957 @@ + + + + + + + + + + + + + + + + description - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.structures.description

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + add_care_site_description + + +

    +
    add_care_site_description(care_site: DataFrame) -> DataFrame
+
    + +
    + +

    Add column DESCRIPTION to care_site dataframe.

    +

    This algo applies simple regular expression to simplify the care site name. +This can be useful for post-processing the description, such as detecting +the care_site characteristic from the description (is it an emergency care site ?)

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    care_site +

    with column care_site_name

    +

    + + TYPE: + DataFrame + +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + care_site + +

    contains additional column DESCRIPTION

    +

    + + TYPE: + DataFrame + +

    +
    + +
    + Source code in eds_scikit/structures/description.py + 
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    @concept_checker(concepts=["DESCRIPTION"])
+def add_care_site_description(care_site: DataFrame) -> DataFrame:
+    """Add column ``DESCRIPTION`` to care_site dataframe.
+
+    This algo applies simple regular expression to simplify the care site name.
+    This can be useful for post-processing the description, such as detecting
+    the care_site characteristic from the description (is it an emergency care site ?)
+
+    Parameters
+    ----------
+    care_site : DataFrame
+        with column ``care_site_name``
+
+
+    Returns
+    -------
+    care_site : DataFrame
+        contains additional column ``DESCRIPTION``
+
+    """
+    care_site = care_site.assign(
+        DESCRIPTION=description_from_care_site_name(care_site["care_site_name"])
+    )
+    return care_site
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/structures/index.html b/main/reference/structures/index.html new file mode 100644 index 00000000..b196763a --- /dev/null +++ b/main/reference/structures/index.html @@ -0,0 +1,3779 @@ + + + + + + + + + + + + + + + + `eds_scikit.structures` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    eds_scikit.structures

    + + +
    + + + +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/utils/bunch/index.html b/main/reference/utils/bunch/index.html new file mode 100644 index 00000000..c2903814 --- /dev/null +++ b/main/reference/utils/bunch/index.html @@ -0,0 +1,3903 @@ + + + + + + + + + + + + + + + + bunch - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
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    + + + +
    +
    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.utils.bunch

    + + +
    + + + +
    + +

    Vendored Bunch class from scikit-learn.

    + + + +
    + + + + + + + + +
    + + + +

    + Bunch + + +

    +
    Bunch(**kwargs)
+
    + +
    +

    + Bases: dict

    + + +

    Container object exposing keys as attributes.

    +

    Bunch objects are sometimes used as an output for functions and methods. +They extend dictionaries by enabling values to be accessed by key, +bunch["value_key"], or by an attribute, bunch.value_key.

    + +

    Examples:

    + 
    >>> from sklearn.utils import Bunch
+>>> b = Bunch(a=1, b=2)
+>>> b['b']
+2
+>>> b.b
+2
+>>> b.a = 3
+>>> b['a']
+3
+>>> b.c = 6
+>>> b['c']
+6
+
    + + +
    + Source code in eds_scikit/utils/bunch.py + 
    29
+30
    def __init__(self, **kwargs):
+    super().__init__(kwargs)
+
    +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/utils/checks/index.html b/main/reference/utils/checks/index.html new file mode 100644 index 00000000..86eb78a8 --- /dev/null +++ b/main/reference/utils/checks/index.html @@ -0,0 +1,4301 @@ + + + + + + + + + + + + + + + + checks - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
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    + + + +
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    + + + +
    +
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    + + +
    +
    + + + + + + + + +

    eds_scikit.utils.checks

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + MissingConceptError + + +

    +
    MissingConceptError(required_concepts: Union[List[str], List[Tuple[str, str]]], df_name: str = '')
+
    + +
    +

    + Bases: Exception

    + + +

    Exception raised when a concept is missing

    + + +
    + Source code in eds_scikit/utils/checks.py + 
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    def __init__(
+    self,
+    required_concepts: Union[List[str], List[Tuple[str, str]]],
+    df_name: str = "",
+):
+
+    if all(isinstance(concept, tuple) for concept in required_concepts):
+        to_display_per_concept = [
+            f"- {concept} ({msg})" for concept, msg in required_concepts
+        ]
+    else:
+        to_display_per_concept = [f"- {concept}" for concept in required_concepts]
+    str_to_display = "\n".join(to_display_per_concept)
+
+    if df_name:
+        df_name = f" {df_name} "
+    message = (
+        f"The {df_name}DataFrame is missing some columns, "
+        "namely:\n"
+        f"{str_to_display}"
+    )
+
+    super().__init__(message)
+
    +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + +
    + + + +

    + MissingTableError + + +

    +
    MissingTableError(required_tables: Union[List[str], List[Tuple[str, str]]], data_name: str = '')
+
    + +
    +

    + Bases: Exception

    + + +

    Exception raised when a table is missing in the Data

    + + +
    + Source code in eds_scikit/utils/checks.py + 
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    def __init__(
+    self,
+    required_tables: Union[List[str], List[Tuple[str, str]]],
+    data_name: str = "",
+):
+
+    if all(isinstance(table, tuple) for table in required_tables):
+        to_display_per_table = [
+            f"- {table} ({msg})" for table, msg in required_tables
+        ]
+    else:
+        to_display_per_table = [f"- {table}" for table in required_tables]
+    str_to_display = "\n".join(to_display_per_table)
+
+    if data_name:
+        data_name = f" {data_name} "
+    message = (
+        f"The {data_name}Data is missing some tables, "
+        "namely:\n"
+        f"{str_to_display}"
+    )
+
+    super().__init__(message)
+
    +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + + +
    + + + +

    + concept_checker + + +

    +
    concept_checker(function: Callable, concepts: List[str] = None, only_adds_concepts: bool = True, *args, **kwargs) -> Any
+
    + +
    + +

    Decorator to use on functions that +- Takes a DataFrame as first argument +- Adds a concept to it

    +

    The decorator checks: +- If the first argument is a DataFrame +- If the concepts to be added aren't already in the DataFrame +- If the function correctly adds the concepts +- If no additionnal columns are added (if only_adds_concepts is True)

    +

    If one of this checks fails, raises an error

    + +
    + Source code in eds_scikit/utils/checks.py + 
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    @decorator
+def concept_checker(
+    function: Callable,
+    concepts: List[str] = None,
+    only_adds_concepts: bool = True,
+    *args,
+    **kwargs,
+) -> Any:
+    """
+    Decorator to use on functions that
+    - Takes a DataFrame as first argument
+    - Adds a concept to it
+
+    The decorator checks:
+    - If the first argument is a DataFrame
+    - If the concepts to be added aren't already in the DataFrame
+    - If the function correctly adds the concepts
+    - If no additionnal columns are added (if only_adds_concepts is True)
+
+    If one of this checks fails, raises an error
+    """
+    # Is the first argument a DataFrame
+    df = args[0]
+    if (type(df) != ks.DataFrame) & (type(df) != pd.DataFrame):
+        raise TypeError(
+            f"The first argument of '{function.__module__}.{function.__name__}' "
+            "should be a Pandas or Koalas DataFrame"
+        )
+
+    # Initial columns
+    initial_cols = set(df.columns)
+
+    # Is the concept already present
+    if type(concepts) == str:
+        concepts = [concepts]
+    present_concepts = set(concepts) & set(df.columns)
+    if present_concepts:
+        raise ValueError(
+            f"The concepts {present_concepts} are already present in the input dataframe "
+            f"of  '{function.__module__}.{function.__name__}'.\n"
+            "You can either rename the column(s) or delete them before running "
+            "the function again."
+        )
+
+    result = function(*args, **kwargs)
+
+    # Was the concept correctly added
+    missing_concepts = set(concepts) - set(result.columns)
+    if len(missing_concepts) > 0:
+        raise ValueError(
+            f"The concept(s) '{missing_concepts}' were not added to the DataFrame."
+        )
+
+    # Check that no other columns were added
+
+    if only_adds_concepts:
+        result_cols = set(result.columns)
+        additionnal_cols = result_cols - (initial_cols | set(concepts))
+        if additionnal_cols:
+            logger.warning(
+                "The columns"
+                + "".join([f"\n- {s}" for s in additionnal_cols])
+                + f"\nwere added/renamed by '{function.__module__}.{function.__name__}',"
+                + f"although it should normally only add the columns {concepts}"
+            )
+
+    return result
+
    +
    +
    + +
    + +
    + + + +

    + algo_checker + + +

    +
    algo_checker(function: Callable, algos: Optional[str] = None, *args, **kwargs) -> Any
+
    + +
    + +

    Decorator to use on wrapper that calls specific functions based on the 'algo' argument

    +

    The decorator checks if the provided algo is an implemented one.

    +

    If this checks fails, raises an error

    + +
    + Source code in eds_scikit/utils/checks.py + 
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    @decorator
+def algo_checker(
+    function: Callable,
+    algos: Optional[str] = None,
+    *args,
+    **kwargs,
+) -> Any:
+    """
+    Decorator to use on wrapper that calls specific functions based on the 'algo' argument
+
+    The decorator checks if the provided algo is an implemented one.
+
+    If this checks fails, raises an error
+    """
+
+    algo = _get_arg_value(function, "algo", args, kwargs)
+
+    # Stripping eventual version suffix
+    algo = algo.split(".")[0]
+
+    if algo not in algos:
+        raise ValueError(
+            f"Method {algo} unknown for '{function.__module__}.{function.__name__}'.\n"
+            f"Available algos are {algos}"
+        )
+    result = function(*args, **kwargs)
+    return result
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/utils/custom_implem/custom_implem/index.html b/main/reference/utils/custom_implem/custom_implem/index.html new file mode 100644 index 00000000..f578ba20 --- /dev/null +++ b/main/reference/utils/custom_implem/custom_implem/index.html @@ -0,0 +1,4097 @@ + + + + + + + + + + + + + + + + custom_implem - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
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    + + + +
    +
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    + + +
    +
    + + + + + + + + +

    eds_scikit.utils.custom_implem.custom_implem

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + CustomImplem + + +

    + + +
    + + +

    A collection of custom pandas and koalas methods.

    +

    All public facing methods must be stateless and defined as classmethods.

    + + + + + +
    + + + + + + + + + +
    + + + +

    + add_unique_id + + + + classmethod + + +

    +
    add_unique_id(obj: Any, col_name: str = 'id', backend = None) -> Any
+
    + +
    + +

    Add an ID column for koalas or pandas.

    + +
    + Source code in eds_scikit/utils/custom_implem/custom_implem.py + 
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    @classmethod
+def add_unique_id(
+    cls,
+    obj: Any,
+    col_name: str = "id",
+    backend=None,
+) -> Any:
+    """Add an ID column for koalas or pandas."""
+    if backend is pd:
+        obj[col_name] = range(obj.shape[0])
+        return obj
+    elif backend is ks:
+        return obj.koalas.attach_id_column(id_type="distributed", column=col_name)
+    else:
+        raise NotImplementedError(
+            f"No method 'add_unique_id' is available for backend '{backend}'."
+        )
+
    +
    +
    + +
    + +
    + + + +

    + cut + + + + classmethod + + +

    +
    cut(x, bins, right: bool = True, labels = None, retbins: bool = False, precision: int = 3, include_lowest: bool = False, duplicates: str = 'raise', ordered: bool = True, backend = None)
+
    + +
    + +

    koalas version of pd.cut

    +
    Notes
    +

    Simplified vendoring from: +https://github.com/pandas-dev/pandas/blob/v1.5.2/pandas/core/reshape/tile.py#L50-L305

    + +
    + Source code in eds_scikit/utils/custom_implem/custom_implem.py + 
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    @classmethod
+def cut(
+    cls,
+    x,
+    bins,
+    right: bool = True,
+    labels=None,
+    retbins: bool = False,
+    precision: int = 3,
+    include_lowest: bool = False,
+    duplicates: str = "raise",
+    ordered: bool = True,
+    backend=None,  # unused because koalas only
+):
+    """koalas version of pd.cut
+
+    Notes
+    -----
+    Simplified vendoring from:
+    https://github.com/pandas-dev/pandas/blob/v1.5.2/pandas/core/reshape/tile.py#L50-L305
+    """
+    return cut(
+        x,
+        bins,
+        right,
+        labels,
+        retbins,
+        precision,
+        include_lowest,
+        duplicates,
+        ordered,
+    )
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/utils/custom_implem/cut/index.html b/main/reference/utils/custom_implem/cut/index.html new file mode 100644 index 00000000..f475bae3 --- /dev/null +++ b/main/reference/utils/custom_implem/cut/index.html @@ -0,0 +1,4716 @@ + + + + + + + + + + + + + + + + cut - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + +
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    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.utils.custom_implem.cut

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + cut + + +

    +
    cut(x, bins, right: bool = True, labels = None, retbins: bool = False, precision: int = 3, include_lowest: bool = False, duplicates: str = 'raise', ordered: bool = True)
+
    + +
    + +

    Bin values into discrete intervals.

    +

    Use cut when you need to segment and sort data values into bins. This +function is also useful for going from a continuous variable to a +categorical variable. For example, cut could convert ages to groups of +age ranges. Supports binning into an equal number of bins, or a +pre-specified array of bins.

    +

    See original function at: https://github.com/pandas-dev/pandas/blob/v1.5.2/pandas/core/reshape/tile.py#L50-L305 # noqa

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    x +

    The input array to be binned. Must be 1-dimensional.

    +

    + + TYPE: + koalas Series. + +

    +
    bins +

    The criteria to bin by. +* int : Defines the number of equal-width bins in the range of x. The +range of x is extended by .1% on each side to include the minimum +and maximum values of x. +* sequence of scalars : Defines the bin edges allowing for non-uniform +width. No extension of the range of x is done. +* IntervalIndex : Defines the exact bins to be used. Note that +IntervalIndex for bins must be non-overlapping.

    +

    + + TYPE: + int, sequence of scalars, or IntervalIndex + +

    +
    right +

    Indicates whether bins includes the rightmost edge or not. If +right == True (the default), then the bins [1, 2, 3, 4] +indicate (1,2], (2,3], (3,4]. This argument is ignored when +bins is an IntervalIndex.

    +

    + + TYPE: + bool, default True + + + DEFAULT: + True + +

    +
    labels +

    Specifies the labels for the returned bins. Must be the same length as +the resulting bins. If False, returns only integer indicators of the +bins. This affects the type of the output container (see below). +This argument is ignored when bins is an IntervalIndex. If True, +raises an error. When ordered=False, labels must be provided.

    +

    + + TYPE: + array or False, default None + + + DEFAULT: + None + +

    +
    retbins +

    Whether to return the bins or not. Useful when bins is provided +as a scalar.

    +

    + + TYPE: + bool, default False + + + DEFAULT: + False + +

    +
    precision +

    The precision at which to store and display the bins labels.

    +

    + + TYPE: + int, default 3 + + + DEFAULT: + 3 + +

    +
    include_lowest +

    Whether the first interval should be left-inclusive or not.

    +

    + + TYPE: + bool, default False + + + DEFAULT: + False + +

    +
    duplicates +

    If bin edges are not unique, raise ValueError or drop non-uniques.

    +

    + + TYPE: + str + + + DEFAULT: + default 'raise' + +

    +
    ordered +

    Whether the labels are ordered or not. Applies to returned types +Categorical and Series (with Categorical dtype). If True, +the resulting categorical will be ordered. If False, the resulting +categorical will be unordered (labels must be provided). +.. versionadded:: 1.1.0

    +

    + + TYPE: + bool, default True + + + DEFAULT: + True + +

    +
    Returns + +

    +

    +
    out +

    An array-like object representing the respective bin for each value +of x. The type depends on the value of labels. +* None (default) : returns a Series for Series x or a +Categorical for all other inputs. The values stored within +are Interval dtype. +* sequence of scalars : returns a Series for Series x or a +Categorical for all other inputs. The values stored within +are whatever the type in the sequence is. +* False : returns an ndarray of integers.

    +

    + + TYPE: + Categorical, Series, or ndarray + +

    +
    bins +

    The computed or specified bins. Only returned when retbins=True. +For scalar or sequence bins, this is an ndarray with the computed +bins. If set duplicates=drop, bins will drop non-unique bin. For +an IntervalIndex bins, this is equal to bins.

    +

    + + TYPE: + numpy.ndarray or IntervalIndex. + +

    +
    +
    See Also
    +

    qcut : Discretize variable into equal-sized buckets based on rank + or based on sample quantiles. +Categorical : Array type for storing data that come from a + fixed set of values. +Series : One-dimensional array with axis labels (including time series). +IntervalIndex : Immutable Index implementing an ordered, sliceable set.

    +
    Notes
    +

    Any NA values will be NA in the result. Out of bounds values will be NA in +the resulting Series or Categorical object. +Reference :ref:the user guide <reshaping.tile.cut> for more examples.

    + +

    Examples:

    +

    Discretize into three equal-sized bins.

    + 
    >>> from eds_scikit.utils.framework import bd
+>>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])), 3)
+...
+[(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...
+Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ...
+
    + 
    >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])), 3, retbins=True)
+...
+([(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...
+Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ...
+array([0.994, 3.   , 5.   , 7.   ]))
+
    +

    Discovers the same bins, but assign them specific labels. Notice that +the returned Categorical's categories are labels and is ordered.

    + 
    >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])),
+...        3, labels=["bad", "medium", "good"])
+['bad', 'good', 'medium', 'medium', 'good', 'bad']
+Categories (3, object): ['bad' < 'medium' < 'good']
+
    +

    ordered=False will result in unordered categories when labels are passed. +This parameter can be used to allow non-unique labels:

    + 
    >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])), 3,
+...        labels=["B", "A", "B"], ordered=False)
+['B', 'B', 'A', 'A', 'B', 'B']
+Categories (2, object): ['A', 'B']
+
    +

    labels=False implies you just want the bins back.

    + 
    >>> bd.cut(ks.Series([0, 1, 1, 2]), bins=4, labels=False)
+array([0, 1, 1, 3])
+
    +

    Passing a Series as an input returns a Series with categorical dtype:

    + 
    >>> s = ks.Series(np.array([2, 4, 6, 8, 10]),
+...               index=['a', 'b', 'c', 'd', 'e'])
+
    + 
    >>> bd.cut(s, 3)
+...
+a    (1.992, 4.667]
+b    (1.992, 4.667]
+c    (4.667, 7.333]
+d     (7.333, 10.0]
+e     (7.333, 10.0]
+dtype: category
+Categories (3, interval[float64, right]): [(1.992, 4.667] < (4.667, ...
+
    +

    Passing a Series as an input returns a Series with mapping value. +It is used to map numerically to intervals based on bins.

    + 
    >>> s = ks.Series(np.array([2, 4, 6, 8, 10]),
+...               index=['a', 'b', 'c', 'd', 'e'])
+
    + 
    >>> bd.cut(s, [0, 2, 4, 6, 8, 10], labels=False, retbins=True, right=False)
+...
+(a    1.0
+ b    2.0
+ c    3.0
+ d    4.0
+ e    NaN
+ dtype: float64,
+ array([ 0,  2,  4,  6,  8, 10]))
+
    +

    Use drop optional when bins is not unique

    + 
    >>> bd.cut(s, [0, 2, 4, 6, 10, 10], labels=False, retbins=True,
+...        right=False, duplicates='drop')
+...
+(a    1.0
+ b    2.0
+ c    3.0
+ d    3.0
+ e    NaN
+ dtype: float64,
+ array([ 0,  2,  4,  6, 10]))
+
    +

    Passing an IntervalIndex for bins results in those categories exactly. +Notice that values not covered by the IntervalIndex are set to NaN. 0 +is to the left of the first bin (which is closed on the right), and 1.5 +falls between two bins.

    + 
    >>> bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)])
+
    + 
    >>> bd.cut(ks.Series([0, 0.5, 1.5, 2.5, 4.5]), bins)
+[NaN, (0.0, 1.0], NaN, (2.0, 3.0], (4.0, 5.0]]
+Categories (3, interval[int64, right]): [(0, 1] < (2, 3] < (4, 5]]
+
    + +
    + Source code in eds_scikit/utils/custom_implem/cut.py + 
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    def cut(
+    x,
+    bins,
+    right: bool = True,
+    labels=None,
+    retbins: bool = False,
+    precision: int = 3,
+    include_lowest: bool = False,
+    duplicates: str = "raise",
+    ordered: bool = True,
+):  # pragma: no cover
+    """
+    Bin values into discrete intervals.
+
+    Use `cut` when you need to segment and sort data values into bins. This
+    function is also useful for going from a continuous variable to a
+    categorical variable. For example, `cut` could convert ages to groups of
+    age ranges. Supports binning into an equal number of bins, or a
+    pre-specified array of bins.
+
+    See original function at: https://github.com/pandas-dev/pandas/blob/v1.5.2/pandas/core/reshape/tile.py#L50-L305 # noqa
+
+    Parameters
+    ----------
+    x : koalas Series.
+        The input array to be binned. Must be 1-dimensional.
+    bins : int, sequence of scalars, or IntervalIndex
+        The criteria to bin by.
+        * int : Defines the number of equal-width bins in the range of `x`. The
+          range of `x` is extended by .1% on each side to include the minimum
+          and maximum values of `x`.
+        * sequence of scalars : Defines the bin edges allowing for non-uniform
+          width. No extension of the range of `x` is done.
+        * IntervalIndex : Defines the exact bins to be used. Note that
+          IntervalIndex for `bins` must be non-overlapping.
+    right : bool, default True
+        Indicates whether `bins` includes the rightmost edge or not. If
+        ``right == True`` (the default), then the `bins` ``[1, 2, 3, 4]``
+        indicate (1,2], (2,3], (3,4]. This argument is ignored when
+        `bins` is an IntervalIndex.
+    labels : array or False, default None
+        Specifies the labels for the returned bins. Must be the same length as
+        the resulting bins. If False, returns only integer indicators of the
+        bins. This affects the type of the output container (see below).
+        This argument is ignored when `bins` is an IntervalIndex. If True,
+        raises an error. When `ordered=False`, labels must be provided.
+    retbins : bool, default False
+        Whether to return the bins or not. Useful when bins is provided
+        as a scalar.
+    precision : int, default 3
+        The precision at which to store and display the bins labels.
+    include_lowest : bool, default False
+        Whether the first interval should be left-inclusive or not.
+    duplicates : {default 'raise', 'drop'}, optional
+        If bin edges are not unique, raise ValueError or drop non-uniques.
+    ordered : bool, default True
+        Whether the labels are ordered or not. Applies to returned types
+        Categorical and Series (with Categorical dtype). If True,
+        the resulting categorical will be ordered. If False, the resulting
+        categorical will be unordered (labels must be provided).
+        .. versionadded:: 1.1.0
+    Returns
+    -------
+    out : Categorical, Series, or ndarray
+        An array-like object representing the respective bin for each value
+        of `x`. The type depends on the value of `labels`.
+        * None (default) : returns a Series for Series `x` or a
+          Categorical for all other inputs. The values stored within
+          are Interval dtype.
+        * sequence of scalars : returns a Series for Series `x` or a
+          Categorical for all other inputs. The values stored within
+          are whatever the type in the sequence is.
+        * False : returns an ndarray of integers.
+    bins : numpy.ndarray or IntervalIndex.
+        The computed or specified bins. Only returned when `retbins=True`.
+        For scalar or sequence `bins`, this is an ndarray with the computed
+        bins. If set `duplicates=drop`, `bins` will drop non-unique bin. For
+        an IntervalIndex `bins`, this is equal to `bins`.
+
+    See Also
+    --------
+    qcut : Discretize variable into equal-sized buckets based on rank
+        or based on sample quantiles.
+    Categorical : Array type for storing data that come from a
+        fixed set of values.
+    Series : One-dimensional array with axis labels (including time series).
+    IntervalIndex : Immutable Index implementing an ordered, sliceable set.
+
+    Notes
+    -----
+    Any NA values will be NA in the result. Out of bounds values will be NA in
+    the resulting Series or Categorical object.
+    Reference :ref:`the user guide <reshaping.tile.cut>` for more examples.
+
+    Examples
+    --------
+    Discretize into three equal-sized bins.
+
+    >>> from eds_scikit.utils.framework import bd
+    >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])), 3)
+    ... # doctest: +ELLIPSIS
+    [(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...
+    Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ...
+
+    >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])), 3, retbins=True)
+    ... # doctest: +ELLIPSIS
+    ([(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ...
+    Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ...
+    array([0.994, 3.   , 5.   , 7.   ]))
+
+    Discovers the same bins, but assign them specific labels. Notice that
+    the returned Categorical's categories are `labels` and is ordered.
+
+    >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])),
+    ...        3, labels=["bad", "medium", "good"])
+    ['bad', 'good', 'medium', 'medium', 'good', 'bad']
+    Categories (3, object): ['bad' < 'medium' < 'good']
+
+    ``ordered=False`` will result in unordered categories when labels are passed.
+    This parameter can be used to allow non-unique labels:
+
+    >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])), 3,
+    ...        labels=["B", "A", "B"], ordered=False)
+    ['B', 'B', 'A', 'A', 'B', 'B']
+    Categories (2, object): ['A', 'B']
+
+    ``labels=False`` implies you just want the bins back.
+
+    >>> bd.cut(ks.Series([0, 1, 1, 2]), bins=4, labels=False)
+    array([0, 1, 1, 3])
+
+    Passing a Series as an input returns a Series with categorical dtype:
+
+    >>> s = ks.Series(np.array([2, 4, 6, 8, 10]),
+    ...               index=['a', 'b', 'c', 'd', 'e'])
+
+    >>> bd.cut(s, 3)
+    ... # doctest: +ELLIPSIS
+    a    (1.992, 4.667]
+    b    (1.992, 4.667]
+    c    (4.667, 7.333]
+    d     (7.333, 10.0]
+    e     (7.333, 10.0]
+    dtype: category
+    Categories (3, interval[float64, right]): [(1.992, 4.667] < (4.667, ...
+
+    Passing a Series as an input returns a Series with mapping value.
+    It is used to map numerically to intervals based on bins.
+
+    >>> s = ks.Series(np.array([2, 4, 6, 8, 10]),
+    ...               index=['a', 'b', 'c', 'd', 'e'])
+
+    >>> bd.cut(s, [0, 2, 4, 6, 8, 10], labels=False, retbins=True, right=False)
+    ... # doctest: +ELLIPSIS
+    (a    1.0
+     b    2.0
+     c    3.0
+     d    4.0
+     e    NaN
+     dtype: float64,
+     array([ 0,  2,  4,  6,  8, 10]))
+
+    Use `drop` optional when bins is not unique
+
+    >>> bd.cut(s, [0, 2, 4, 6, 10, 10], labels=False, retbins=True,
+    ...        right=False, duplicates='drop')
+    ... # doctest: +ELLIPSIS
+    (a    1.0
+     b    2.0
+     c    3.0
+     d    3.0
+     e    NaN
+     dtype: float64,
+     array([ 0,  2,  4,  6, 10]))
+
+    Passing an IntervalIndex for `bins` results in those categories exactly.
+    Notice that values not covered by the IntervalIndex are set to NaN. 0
+    is to the left of the first bin (which is closed on the right), and 1.5
+    falls between two bins.
+
+    >>> bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)])
+
+    >>> bd.cut(ks.Series([0, 0.5, 1.5, 2.5, 4.5]), bins)
+    [NaN, (0.0, 1.0], NaN, (2.0, 3.0], (4.0, 5.0]]
+    Categories (3, interval[int64, right]): [(0, 1] < (2, 3] < (4, 5]]
+    """
+    if x.ndim != 1:
+        raise ValueError("x must be 1D")
+
+    x, dtype = x.astype(np.int64), x.dtype
+
+    if not np.iterable(bins):
+        if is_scalar(bins) and bins < 1:
+            raise ValueError("`bins` should be a positive integer.")
+
+        try:  # for array-like
+            sz = x.size
+        except AttributeError:
+            x = np.asarray(x)
+            sz = x.size
+
+        if sz == 0:
+            raise ValueError("Cannot cut empty array")
+
+        mn, mx = x.min(), x.max()
+
+        if np.isinf(mn) or np.isinf(mx):
+            raise ValueError(
+                "cannot specify integer `bins` when input data contains infinity"
+            )
+        elif mn == mx:  # adjust end points before binning
+            mn -= 0.001 * abs(mn) if mn != 0 else 0.001
+            mx += 0.001 * abs(mx) if mx != 0 else 0.001
+            bins = np.linspace(mn, mx, bins + 1, endpoint=True)
+        else:  # adjust end points after binning
+            bins = np.linspace(mn, mx, bins + 1, endpoint=True)
+            adj = (mx - mn) * 0.001  # 0.1% of the range
+            if right:
+                bins[0] -= adj
+            else:
+                bins[-1] += adj
+
+    elif isinstance(bins, IntervalIndex):
+        if bins.is_overlapping:
+            raise ValueError("Overlapping IntervalIndex is not accepted.")
+
+    else:
+        if is_datetime64tz_dtype(bins):
+            bins = np.asarray(bins, dtype=DT64NS_DTYPE)
+        else:
+            bins = np.asarray(bins)
+        bins = _convert_bin_to_numeric_type(bins, dtype)
+
+        # GH 26045: cast to float64 to avoid an overflow
+        if (np.diff(bins.astype("float64")) < 0).any():
+            raise ValueError("bins must increase monotonically.")
+
+    fac, bins = _bins_to_cuts(
+        x,
+        bins,
+        right=right,
+        labels=labels,
+        precision=precision,
+        include_lowest=include_lowest,
+        dtype=dtype,
+        duplicates=duplicates,
+        ordered=ordered,
+    )
+
+    if not retbins:
+        return fac
+
+    return fac, bins
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/utils/custom_implem/index.html b/main/reference/utils/custom_implem/index.html new file mode 100644 index 00000000..03737844 --- /dev/null +++ b/main/reference/utils/custom_implem/index.html @@ -0,0 +1,3781 @@ + + + + + + + + + + + + + + + + `eds_scikit.utils.custom_implem` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
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    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.utils.custom_implem

    + + +
    + + + +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/utils/datetime_helpers/index.html b/main/reference/utils/datetime_helpers/index.html new file mode 100644 index 00000000..9e9f5ce1 --- /dev/null +++ b/main/reference/utils/datetime_helpers/index.html @@ -0,0 +1,3966 @@ + + + + + + + + + + + + + + + + datetime_helpers - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
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    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.utils.datetime_helpers

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + add_timedelta + + +

    +
    add_timedelta(series: Series, **kwargs) -> Series
+
    + +
    + +

    Adds a unique timedelta to a Pandas or Koalas Series

    + +
    + Source code in eds_scikit/utils/datetime_helpers.py + 
     9
+10
+11
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    def add_timedelta(series: Series, **kwargs) -> Series:
+    """
+    Adds a unique timedelta to a Pandas or Koalas Series
+    """
+    return series.map(lambda d: d + timedelta(**kwargs))
+
    +
    +
    + +
    + +
    + + + +

    + substract_datetime + + +

    +
    substract_datetime(series_1: Series, series_2: Series, out: str = 'seconds') -> Series
+
    + +
    + +

    Substract 2 datetime series and return the number of seconds or hours +between them.

    + +
    + Source code in eds_scikit/utils/datetime_helpers.py + 
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    def substract_datetime(
+    series_1: Series,
+    series_2: Series,
+    out: str = "seconds",
+) -> Series:
+    """
+    Substract 2 datetime series and return the number of seconds or hours
+    between them.
+    """
+
+    if out not in ["seconds", "hours"]:
+        raise ValueError("the 'out' parameter should be in ['hours','seconds']")
+    if not (
+        np.issubdtype(series_1.dtype, np.datetime64)
+        and np.issubdtype(series_2.dtype, np.datetime64)
+    ):
+        raise TypeError("One of the provided Serie isn't a datetime Serie")
+
+    if is_pandas(series_1) and is_pandas(series_2):
+        diff = (series_1 - series_2).dt.total_seconds()
+
+    elif is_koalas(series_1) and is_koalas(series_2):
+        diff = series_1 - series_2
+
+    else:
+        raise TypeError("Both series should either be a Koalas or Pandas Serie")
+
+    if out == "hours":
+        return diff / 3600
+    return diff
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/utils/flowchart/flowchart/index.html b/main/reference/utils/flowchart/flowchart/index.html new file mode 100644 index 00000000..0b1bced5 --- /dev/null +++ b/main/reference/utils/flowchart/flowchart/index.html @@ -0,0 +1,4815 @@ + + + + + + + + + + + + + + + + flowchart - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + +
    +
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    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.utils.flowchart.flowchart

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + Flowchart + + +

    +
    Flowchart(initial_description: str, data: Union[DataFrame, Dict[str, Iterable]], concat_criterion_description: bool = True, to_count: str = 'person_id')
+
    + +
    + + + +

    Main class to define an flowchart (inclusion diagram)

    + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    initial_description +

    Description of the initial population

    +

    + + TYPE: + str + +

    +
    data +

    Either a Pandas/Koalas DataFrame, or a dictionary of iterables. If a dictionary, +the initial cohort should be proivided under the initial key.

    +

    + + TYPE: + Union[DataFrame, Dict[str, Iterable]] + +

    +
    concat_criterion_description +

    Whether to concatenate provided description together when adding multiple criteria

    +

    + + TYPE: + bool, optional + + + DEFAULT: + True + +

    +
    to_count +

    Only if data is a DataFrame: column of data from which the count is computed. +Usually, this will be the column containing patient or stay IDs.

    +

    + + TYPE: + str, optional + + + DEFAULT: + 'person_id' + +

    +
    + +
    + Source code in eds_scikit/utils/flowchart/flowchart.py + 
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    def __init__(
+    self,
+    initial_description: str,
+    data: Union[DataFrame, Dict[str, Iterable]],
+    concat_criterion_description: bool = True,
+    to_count: str = "person_id",
+):
+    """
+    Main class to define an flowchart (inclusion diagram)
+
+    Parameters
+    ----------
+    initial_description : str
+        Description of the initial population
+    data : Union[DataFrame, Dict[str, Iterable]]
+        Either a Pandas/Koalas DataFrame, or a dictionary of iterables. If a dictionary,
+        the initial cohort should be proivided under the **initial** key.
+    concat_criterion_description : bool, optional
+        Whether to concatenate provided description together when adding multiple criteria
+    to_count : str, optional
+        Only if `data` is a DataFrame: column of `data` from which the count is computed.
+        Usually, this will be the column containing patient or stay IDs.
+    """
+
+    self.initial_description = initial_description
+    self.data = data
+    self.to_count = to_count
+
+    self.check_data()
+
+    self.ids = self.get_unique()
+    self.criteria = []
+    self.concat_criterion_description = concat_criterion_description
+
+    self.final_split = None
+
+    self.drawing = None
+
    +
    + + + +
    + + + + + + + + + +
    + + + +

    + add_criterion + + +

    +
    add_criterion(description: str, criterion_name: str, excluded_description: str = '')
+
    + +
    + +

    Adds a criterion to the flowchart

    +

    + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    description +

    Description of the cohort passing the criterion

    +

    + + TYPE: + str + +

    +
    criterion_name +
      +
    • If data is a DataFrame, criterion_name is a +boolean column of data to split between +passing cohort (data[criterion_name] == True) and +excluded column (data[criterion_name] == False)
      • +
    • If data is a dictionary, criterion_name is a +key of data containing the passing cohort as an iterable +of IDs (list, set , Series, array, etc.)
      • +
    +

    + + TYPE: + str + +

    +
    excluded_description +

    Description of the cohort excluded by the criterion

    +

    + + TYPE: + str + + + DEFAULT: + '' + +

    +
    + +
    + Source code in eds_scikit/utils/flowchart/flowchart.py + 
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    def add_criterion(
+    self,
+    description: str,
+    criterion_name: str,
+    excluded_description: str = "",
+):
+    """
+    Adds a criterion to the flowchart
+
+    ![](../../../_static/flowchart/criterion.png)
+
+    Parameters
+    ----------
+    description : str
+        Description of the cohort passing the criterion
+    criterion_name : str
+
+        - If `data` is a DataFrame, `criterion_name` is a
+        boolean column of `data` to split between
+        passing cohort (`data[criterion_name] == True`) and
+        excluded column (`data[criterion_name] == False`)
+        - If `data` is a dictionary, `criterion_name` is a
+        key of `data` containing the passing cohort as an iterable
+        of IDs (list, set , Series, array, etc.)
+    excluded_description: str
+        Description of the cohort excluded by the criterion
+    """
+
+    input_data = (
+        Data(
+            self.ids,
+        )
+        if not self.criteria
+        else self.criteria[-1].output_data
+    )
+
+    passing_criterion_ids = self.get_unique(criterion_name=criterion_name)
+
+    output_data = Data(
+        passing_criterion_ids & input_data.ids,
+    )
+    excluded_data = Data(
+        input_data.ids - passing_criterion_ids,
+    )
+
+    description = (
+        description
+        if not self.concat_criterion_description
+        else (self.get_last_description() + description)
+    )
+    added_criterion = Criterion(
+        description=description,
+        excluded_description=excluded_description,
+        input_data=input_data,
+        output_data=output_data,
+        excluded_data=excluded_data,
+    )
+    self.criteria.append(added_criterion)
+
    +
    +
    + +
    + +
    + + + +

    + add_final_split + + +

    +
    add_final_split(left_description: str, right_description: str, criterion_name: str, left_title: str = '', right_title: str = '')
+
    + +
    + +

    Adds a final split in two distinct cohorts. +Should be called after all other critera were added.

    +

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    left_description +

    Description of the left cohort

    +

    + + TYPE: + str + +

    +
    right_description +

    Description of the right cohort

    +

    + + TYPE: + str + +

    +
    criterion_name +
      +
    • If data is a DataFrame, criterion_name is a +boolean column of data to split between +passing cohort (data[criterion_name] == True) and +excluded column (data[criterion_name] == False)
      • +
    • If data is a dictionary, criterion_name is a +key of data containing the passing cohort as an iterable +of IDs (list, set , Series, array, etc.)
      • +
    +

    + + TYPE: + str + +

    +
    left_title +

    Title of the left cohort

    +

    + + TYPE: + str, optional + + + DEFAULT: + '' + +

    +
    right_title +

    title of the right cohort

    +

    + + TYPE: + str, optional + + + DEFAULT: + '' + +

    +
    + +
    + Source code in eds_scikit/utils/flowchart/flowchart.py + 
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    def add_final_split(
+    self,
+    left_description: str,
+    right_description: str,
+    criterion_name: str,
+    left_title: str = "",
+    right_title: str = "",
+):
+    """
+    Adds a final split in two distinct cohorts.
+    Should be called after all other critera were added.
+
+    ![](../../../_static/flowchart/split.png)
+
+    Parameters
+    ----------
+    left_description : str
+        Description of the left cohort
+    right_description : str
+        Description of the right cohort
+    criterion_name : str
+
+        - If `data` is a DataFrame, `criterion_name` is a
+        boolean column of `data` to split between
+        passing cohort (`data[criterion_name] == True`) and
+        excluded column (`data[criterion_name] == False`)
+        - If `data` is a dictionary, `criterion_name` is a
+        key of `data` containing the passing cohort as an iterable
+        of IDs (list, set , Series, array, etc.)
+    left_title : str, optional
+        Title of the left cohort
+    right_title : str, optional
+        title of the right cohort
+    """
+    input_data = (
+        Data(
+            self.ids,
+        )
+        if not self.criteria
+        else self.criteria[-1].output_data
+    )
+
+    left_criterion_ids = self.get_unique(criterion_name=criterion_name)
+
+    left_data = Data(
+        left_criterion_ids & input_data.ids,
+    )
+    right_data = Data(
+        input_data.ids - left_criterion_ids,
+    )
+
+    left_description = (
+        left_description
+        if not self.concat_criterion_description
+        else (self.get_last_description() + left_description)
+    )
+
+    right_description = (
+        right_description
+        if not self.concat_criterion_description
+        else (self.get_last_description() + right_description)
+    )
+
+    added_criterion = Criterion(
+        description=left_description,
+        excluded_description=right_description,
+        input_data=input_data,
+        output_data=left_data,
+        excluded_data=right_data,
+    )
+    added_criterion.left_title = left_title
+    added_criterion.right_title = right_title
+
+    self.final_split = added_criterion
+
    +
    +
    + +
    + +
    + + + +

    + generate_flowchart + + +

    +
    generate_flowchart(alternate: bool = False, fontsize: int = 10)
+
    + +
    + +

    Generate and display the flowchart

    + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    alternate +

    Wether to alternate the excluded box positions

    +

    + + TYPE: + bool, optional + + + DEFAULT: + False + +

    +
    fontsize +

    fontsize

    +

    + + TYPE: + int, optional + + + DEFAULT: + 10 + +

    +
    + +
    + Source code in eds_scikit/utils/flowchart/flowchart.py + 
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    def generate_flowchart(
+    self,
+    alternate: bool = False,
+    fontsize: int = 10,
+):
+    """
+    Generate and display the flowchart
+
+    Parameters
+    ----------
+    alternate : bool, optional
+        Wether to alternate the excluded box positions
+    fontsize : int, optional
+        fontsize
+    """
+    max_criterion_width = max(
+        [c.get_bbox(fontsize=fontsize)["w"] for c in self.criteria]
+    )
+
+    arrow_length = 1.2 * (max_criterion_width / 2)
+
+    directions = ["right", "left"] if alternate else ["right", "right"]
+
+    d = Drawing()
+    d.config(font="dejavu sans", fontsize=fontsize, unit=1)
+
+    start_description = (
+        self.initial_description + "\n" + f"({self.criteria[0].input_data})"
+    )
+    start_bbox = Criterion.get_bbox(None, txt=start_description)
+
+    d += flow.Start(**start_bbox).label(start_description)
+    for i, c in enumerate(self.criteria):
+        d = c.draw(
+            d,
+            arrow_length=arrow_length,
+            direction=directions[i % 2],
+            fontsize=fontsize,
+        )
+    if self.final_split is not None:
+        d = self.final_split.draw(d, final_split=True, fontsize=fontsize)
+
+    self.drawing = d
+
+    return d
+
    +
    +
    + +
    + +
    + + + +

    + save + + +

    +
    save(filename: Union[str, Path], transparent: bool = False, dpi: int = 72)
+
    + +
    + +

    Save the generated flowchart

    + + + + + + + + + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    filename +

    path to the saved file (should end with svg or png)

    +

    + + TYPE: + Union[str, Path] + +

    +
    transparent +

    Wether to use a transparent background or not

    +

    + + TYPE: + bool, optional + + + DEFAULT: + False + +

    +
    dpi +

    Resolution (only when saving png)

    +

    + + TYPE: + int, optional + + + DEFAULT: + 72 + +

    +
    + +
    + Source code in eds_scikit/utils/flowchart/flowchart.py + 
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    def save(
+    self, filename: Union[str, Path], transparent: bool = False, dpi: int = 72
+):
+    """
+    Save the generated flowchart
+
+    Parameters
+    ----------
+    filename : Union[str, Path]
+        path to the saved file (should end with svg or png)
+    transparent : bool, optional
+        Wether to use a transparent background or not
+    dpi : int, optional
+        Resolution (only when saving png)
+    """
+    self.drawing.save(fname=filename, transparent=transparent, dpi=dpi)
+
    +
    +
    + +
    + + + +
    + +
    + +
    + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/utils/flowchart/index.html b/main/reference/utils/flowchart/index.html new file mode 100644 index 00000000..572b3bac --- /dev/null +++ b/main/reference/utils/flowchart/index.html @@ -0,0 +1,3781 @@ + + + + + + + + + + + + + + + + `eds_scikit.utils.flowchart` - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + +
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    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    eds_scikit.utils.flowchart

    + + +
    + + + +
    + + + +
    + + + + + + + + + + + +
    + +
    + +
    + + +
    +
    +
    + + + + Back to top + + +
    + + + +
    +
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    eds_scikit.utils.framework

    + + +
    + + + +
    + + + +
    + + + + + + + + +
    + + + +

    + BackendDispatcher + + +

    + + +
    + + +

    Dispatcher between pandas, koalas and custom methods.

    +

    In addition to the methods below, use the BackendDispatcher class +to access the custom functions defined in CustomImplem.

    + +

    Examples:

    +

    Use a dispatcher function

    + 
    >>> from eds_scikit.utils.framework import bd
+>>> bd.is_pandas(pd.DataFrame())
+True
+
    +

    Use a custom implemented function

    + 
    >>> df = pd.DataFrame({"categ": ["a", "b", "c"]})
+>>> bd.add_unique_id(df, col_name="id")
+  categ  id
+0     a   0
+1     b   1
+2     c   2
+
    + + + + + +
    + + + + + + + + + +
    + + + +

    + get_backend + + +

    +
    get_backend(obj) -> Optional[ModuleType]
+
    + +
    + +

    Return the backend of a given object.

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    obj + +

    +

    +
    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + backend + + +

    + + TYPE: + a backend among + +

    +
    + +

    Examples:

    +

    Get the backend from a DataFrame and create another DataFrame from it. +This is especially useful at runtime, when you need to infer the +backend of the input.

    + 
    >>> backend = bd.get_backend(pd.DataFrame())
+>>> backend
+<module 'pandas'>
+>>> df = backend.DataFrame()
+
    + 
    >>> bd.get_backend(ks.DataFrame())
+<module 'koalas'>
+
    +

    For demo purposes, return the backend when provided directly

    + 
    >>> bd.get_backend(ks)
+<module 'koalas'>
+>>> bd.get_backend(spark)
+None
+
    + +
    + Source code in eds_scikit/utils/framework.py + 
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    def get_backend(self, obj) -> Optional[ModuleType]:
+    """Return the backend of a given object.
+
+    Parameters
+    ----------
+    obj: DataFrame or backend module among pandas or koalas.
+
+    Returns
+    -------
+    backend: a backend among {pd, ks} or None
+
+    Examples
+    --------
+
+    Get the backend from a DataFrame and create another DataFrame from it.
+    This is especially useful at runtime, when you need to infer the
+    backend of the input.
+
+    >>> backend = bd.get_backend(pd.DataFrame())
+    >>> backend
+    <module 'pandas'>
+    >>> df = backend.DataFrame()
+
+    >>> bd.get_backend(ks.DataFrame())
+    <module 'koalas'>
+
+    For demo purposes, return the backend when provided directly
+
+    >>> bd.get_backend(ks)
+    <module 'koalas'>
+    >>> bd.get_backend(spark)
+    None
+    """
+    if isinstance(obj, str):
+        return {
+            "pd": pd,
+            "pandas": pd,
+            "ks": ks,
+            "koalas": ks,
+        }.get(obj)
+
+    for backend in VALID_FRAMEWORKS:
+        if (
+            obj.__class__.__module__.startswith(backend.__name__)  # DataFrame()
+            or getattr(obj, "__name__", None) == backend.__name__  # pd or ks
+        ):
+            return backend
+    return None
+
    +
    +
    + +
    + +
    + + + +

    + is_pandas + + +

    +
    is_pandas(obj) -> bool
+
    + +
    + +

    Return True when the obj is either a pd.DataFrame or the pandas module.

    + +
    + Source code in eds_scikit/utils/framework.py + 
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+159
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    def is_pandas(self, obj) -> bool:
+    """Return True when the obj is either a pd.DataFrame or the pandas module."""
+    return self.get_backend(obj) is pd
+
    +
    +
    + +
    + +
    + + + +

    + is_koalas + + +

    +
    is_koalas(obj: Any) -> bool
+
    + +
    + +

    Return True when the obj is either a ks.DataFrame or the koalas module.

    + +
    + Source code in eds_scikit/utils/framework.py + 
    162
+163
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    def is_koalas(self, obj: Any) -> bool:
+    """Return True when the obj is either a ks.DataFrame or the koalas module."""
+    return self.get_backend(obj) is ks
+
    +
    +
    + +
    + +
    + + + +

    + to + + +

    +
    to(obj, backend)
+
    + +
    + +

    Convert a dataframe to the provided backend.

    + + + + + + + + + + + + + + +
    PARAMETERDESCRIPTION
    obj +

    The object(s) to convert to the provided backend

    +

    +

    +
    +

    backend: str, DataFrame or pandas, koalas module + The desired output backend.

    + + + + + + + + + + + + + + +
    RETURNSDESCRIPTION
    + out + +

    The converted object, in the same format as provided in input.

    +

    + + TYPE: + DataFrame or iterabel of DataFrame (list, tuple, dict) + +

    +
    + +

    Examples:

    +

    Convert a single DataFrame

    + 
    >>> df = pd.DataFrame({"a": [1, 2]})
+>>> kdf = bd.to(df, backend="koalas")
+>>> type(kdf)
+databricks.koalas.frame.DataFrame
+
    +

    Convert a list of DataFrame

    + 
    >>> extra_kdf = ks.DataFrame({"b": [0, 1]})
+>>> another_kdf = ks.DataFrame({"c": [2, 3]})
+>>> kdf_list = [kdf, extra_kdf, another_kdf]
+>>> df_list = bd.to(kdf_list, backend="pandas")
+>>> type(df_list)
+list
+>>> len(df_list)
+3
+>>> type(df_list[0])
+pandas.core.frame.DataFrame
+
    +

    Convert a dictionnary of DataFrame

    + 
    >>> df_dict = {"df_1": pd.DataFrame({"a": [1, 2]}), "df_2": pd.DataFrame({"a": [2, 3]})}
+>>> kdf_dict = bd.to(df_dict, backend="koalas")
+>>> type(kdf_dict)
+dict
+>>> kdf_dict.keys()
+dict_keys(["df_1", "df_2"])
+>>> type(kdf_dict["df_1"])
+databricks.koalas.frame.DataFrame
+
    + +
    + Source code in eds_scikit/utils/framework.py + 
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    def to(self, obj, backend):
+    """Convert a dataframe to the provided backend.
+
+    Parameters
+    ----------
+    obj: DataFrame or iterable of DataFrame (list, tuple, dict)
+        The object(s) to convert to the provided backend
+
+    backend: str, DataFrame or pandas, koalas module
+        The desired output backend.
+
+    Returns
+    -------
+    out: DataFrame or iterabel of DataFrame (list, tuple, dict)
+      The converted object, in the same format as provided in input.
+
+    Examples
+    --------
+
+    Convert a single DataFrame
+
+    >>> df = pd.DataFrame({"a": [1, 2]})
+    >>> kdf = bd.to(df, backend="koalas")
+    >>> type(kdf)
+    databricks.koalas.frame.DataFrame
+
+    Convert a list of DataFrame
+
+    >>> extra_kdf = ks.DataFrame({"b": [0, 1]})
+    >>> another_kdf = ks.DataFrame({"c": [2, 3]})
+    >>> kdf_list = [kdf, extra_kdf, another_kdf]
+    >>> df_list = bd.to(kdf_list, backend="pandas")
+    >>> type(df_list)
+    list
+    >>> len(df_list)
+    3
+    >>> type(df_list[0])
+    pandas.core.frame.DataFrame
+
+    Convert a dictionnary of DataFrame
+
+    >>> df_dict = {"df_1": pd.DataFrame({"a": [1, 2]}), "df_2": pd.DataFrame({"a": [2, 3]})}
+    >>> kdf_dict = bd.to(df_dict, backend="koalas")
+    >>> type(kdf_dict)
+    dict
+    >>> kdf_dict.keys()
+    dict_keys(["df_1", "df_2"])
+    >>> type(kdf_dict["df_1"])
+    databricks.koalas.frame.DataFrame
+    """
+    if isinstance(obj, (list, tuple)):
+        results = []
+        for _obj in obj:
+            results.append(self.to(_obj, backend))
+        return results
+
+    if isinstance(obj, dict):
+        results = {}
+        for k, _obj in obj.items():
+            results[k] = self.to(_obj, backend)
+        return results
+
+    backend = self.get_backend(backend)
+
+    if self.is_pandas(backend):
+        return self.to_pandas(obj)
+    elif self.is_koalas(backend):
+        return self.to_koalas(obj)
+    else:
+        raise ValueError("Unknown backend")
+
    +
    +
    + +
    + + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/utils/hierarchy/index.html b/main/reference/utils/hierarchy/index.html new file mode 100644 index 00000000..e3955867 --- /dev/null +++ b/main/reference/utils/hierarchy/index.html @@ -0,0 +1,3904 @@ + + + + + + + + + + + + + + + + hierarchy - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.utils.hierarchy

    + + +
    + + + +
    + + + +
    + + + + + + + + + +
    + + + +

    + build_hierarchy + + +

    +
    build_hierarchy(categories: pd.DataFrame, relationships: pd.DataFrame) -> pd.DataFrame
+
    + +
    + +

    Build a dataframe with parent categories as columns

    + +
    + Source code in eds_scikit/utils/hierarchy.py + 
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    def build_hierarchy(
+    categories: pd.DataFrame,
+    relationships: pd.DataFrame,
+) -> pd.DataFrame:
+    """Build a dataframe with parent categories as columns"""
+    assert set(categories.columns) == {"id", "category"}
+    assert set(relationships.columns) == {"child", "parent"}
+    assert not categories["id"].duplicated().any()
+    assert not relationships.duplicated().any()
+
+    expanded_relationships = _follow_relationships(relationships)
+
+    expanded_relationships = expanded_relationships.loc[
+        expanded_relationships["child"].isin(categories["id"])
+    ]
+
+    relationships_with_category = _deduplicate_parent_category(
+        expanded_relationships, categories
+    )
+
+    categories = _finalize_parent_categories(categories, relationships_with_category)
+
+    return categories
+
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    eds_scikit.utils

    + + +
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    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/reference/utils/logging/index.html b/main/reference/utils/logging/index.html new file mode 100644 index 00000000..5dc143af --- /dev/null +++ b/main/reference/utils/logging/index.html @@ -0,0 +1,3969 @@ + + + + + + + + + + + + + + + + logging - eds-scikit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    eds_scikit.utils.logging

    + + +
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    + + + + + + + + + +
    + + + +

    + formatter + + +

    +
    formatter(record: dict)
+
    + +
    + +

    Formats the logging message by:

    +
      +
    • Adding color and bold
      • +
    • Indenting the message
      • +
    + +
    + Source code in eds_scikit/utils/logging.py + 
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    def formatter(record: dict):
+    """
+    Formats the logging message by:
+
+    - Adding color and bold
+    - Indenting the message
+    """
+
+    base_format = (
+        "<b>"  # bold
+        "<light-blue>[eds-scikit]</light-blue>"
+        "- "
+        "{name}:"  # corresponds to __name__
+        "{extra[classname]}{extra[sep]}"  # class name, if relevant
+        "</b>"
+        "{function}"  # function name
+    )
+    colored_format = Colorizer.ansify(base_format)
+    colored_message = Colorizer.ansify(str(record["message"]))
+
+    escaped_record = escape(record)
+    base = colored_format.format(**escaped_record)
+
+    lines = colored_message.splitlines()
+    new_message = "".join("\n   " + line for line in lines) + "\n"
+
+    return base + new_message
+
    +
    +
    + +
    + +
    + + + +

    + escape + + +

    +
    escape(record: dict)
+
    + +
    + +

    Escape the "<" character before markup parsing

    + +
    + Source code in eds_scikit/utils/logging.py + 
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    def escape(record: dict):
+    """
+    Escape the "<" character before markup parsing
+    """
+    return {
+        k: v if not isinstance(v, str) else v.replace("<", r"\<")
+        for k, v in record.items()
+    }
+
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    eds_scikit.utils.test_utils

    + + +
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    +
    +
    + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/main/references.bib b/main/references.bib new file mode 100644 index 00000000..ef800459 --- /dev/null +++ b/main/references.bib @@ -0,0 +1,9 @@ +@article{madmethodology, + title={Application of the MAD (Median Absolute Deviation) Methodology to Exclude +Extreme Data Values in FAIR Health Products}, + author={FAIR Health}, + journal={}, + address={530 Fifth Avenue, 18th Floor, New York, NY 10036}, + year={2017}, + url={https://s3.amazonaws.com/media2.fairhealth.org/resource/asset/FH%20Methodology%20-%20Median%20Absolute%20Deviation.pdf} +} diff --git a/main/scripts/__pycache__/plugin.cpython-37.pyc b/main/scripts/__pycache__/plugin.cpython-37.pyc new file mode 100644 index 00000000..87a63212 Binary files /dev/null and b/main/scripts/__pycache__/plugin.cpython-37.pyc differ diff --git a/main/scripts/cards.py b/main/scripts/cards.py new file mode 100644 index 00000000..8b886e19 --- /dev/null +++ b/main/scripts/cards.py @@ -0,0 +1,276 @@ +""" +Adapted from pymdownx.tabbed (https://github.com/facelessuser/pymdown-extensions/) +""" +import re +import xml.etree.ElementTree as etree + +from markdown import Extension +from markdown.blockprocessors import BlockProcessor +from markdown.extensions.attr_list import AttrListTreeprocessor, get_attrs + + +def assign_attrs(elem, attrs): + """Assign `attrs` to element.""" + for k, v in get_attrs(attrs): + if k == ".": + # add to class + cls = elem.get("class") + if cls: + elem.set("class", "{} {}".format(cls, v)) + else: + elem.set("class", v) + else: + # assign attribute `k` with `v` + elem.set(AttrListTreeprocessor.NAME_RE.sub("_", k), v) + + +class CardProcessor(BlockProcessor): + """card block processor.""" + + START = re.compile(r"(?:^|\n)={3} *(card)?(?: +({:.*?}) *(?:\n|$))?") + COMPRESS_SPACES = re.compile(r" {2,}") + + def __init__(self, parser, config): + """Initialize.""" + + super().__init__(parser) + self.card_group_count = 0 + self.current_sibling = None + self.content_indention = 0 + + def detab_by_length(self, text, length): + """Remove a card from the front of each line of the given text.""" + + newtext = [] + lines = text.split("\n") + for line in lines: + if line.startswith(" " * length): + newtext.append(line[length:]) + elif not line.strip(): + newtext.append("") # pragma: no cover + else: + break + return "\n".join(newtext), "\n".join(lines[len(newtext) :]) # noqa: E203 + + def parse_content(self, parent, block): + """ + Get sibling card. + + Retrieve the appropriate sibling element. This can get tricky when + dealing with lists. + + """ + + old_block = block + non_cards = "" + card_set = "card-set" + + # We already acquired the block via test + if self.current_sibling is not None: + sibling = self.current_sibling + block, non_cards = self.detab_by_length(block, self.content_indent) + self.current_sibling = None + self.content_indent = 0 + return sibling, block, non_cards + + sibling = self.lastChild(parent) + + if ( + sibling is None + or sibling.tag.lower() != "div" + or sibling.attrib.get("class", "") != card_set + ): + sibling = None + else: + # If the last child is a list and the content is indented sufficient + # to be under it, then the content's is sibling is in the list. + last_child = self.lastChild(sibling) + card_content = "card-content" + child_class = ( + last_child.attrib.get("class", "") if last_child is not None else "" + ) + indent = 0 + while last_child is not None: + if ( + sibling is not None + and block.startswith(" " * self.tab_length * 2) + and last_child is not None + and ( + last_child.tag in ("ul", "ol", "dl") + or (last_child.tag == "div" and child_class == card_content) + ) + ): + # Handle nested card content + if last_child.tag == "div" and child_class == card_content: + temp_child = self.lastChild(last_child) + if temp_child is None or temp_child.tag not in ( + "ul", + "ol", + "dl", + ): + break + last_child = temp_child + + # The expectation is that we'll find an `
  • `. + # We should get it's last child as well. + sibling = self.lastChild(last_child) + last_child = ( + self.lastChild(sibling) if sibling is not None else None + ) + child_class = ( + last_child.attrib.get("class", "") + if last_child is not None + else "" + ) + + # Context has been lost at this point, so we must adjust the + # text's indentation level so it will be evaluated correctly + # under the list. + block = block[self.tab_length :] # noqa: E203 + indent += self.tab_length + else: + last_child = None + + if not block.startswith(" " * self.tab_length): + sibling = None + + if sibling is not None: + indent += self.tab_length + block, non_cards = self.detab_by_length(old_block, indent) + self.current_sibling = sibling + self.content_indent = indent + + return sibling, block, non_cards + + def test(self, parent, block): + """Test block.""" + + if self.START.search(block): + return True + else: + return self.parse_content(parent, block)[0] is not None + + def run(self, parent, blocks): + """Convert to card block.""" + + block = blocks.pop(0) + m = self.START.search(block) + card_set = "card-set" + + if m: + # removes the first line + if m.start() > 0: + self.parser.parseBlocks(parent, [block[: m.start()]]) + block = block[m.end() :] # noqa: E203 + sibling = self.lastChild(parent) + block, non_cards = self.detab(block) + else: + sibling, block, non_cards = self.parse_content(parent, block) + + if m: + if ( + sibling is not None + and sibling.tag.lower() == "div" + and sibling.attrib.get("class", "") == card_set + ): + card_group = sibling + else: + self.card_group_count += 1 + card_group = etree.SubElement( + parent, + "div", + { + "class": card_set, + "data-cards": "%d:0" % self.card_group_count, + }, + ) + + data = card_group.attrib["data-cards"].split(":") + card_set = int(data[0]) + card_count = int(data[1]) + 1 + + div = etree.SubElement( + card_group, + "div", + { + "class": "card-content", + }, + ) + attributes = m.group(2) + + if attributes: + attr_m = AttrListTreeprocessor.INLINE_RE.search(attributes) + if attr_m: + assign_attrs(div, attr_m.group(1)) + if div.get("href"): + div.tag = "a" + + card_group.attrib["data-cards"] = "%d:%d" % (card_set, card_count) + else: + if sibling.tag in ("li", "dd") and sibling.text: + # Sibling is a list item, but we need to wrap it's content should be + # wrapped in

    + text = sibling.text + sibling.text = "" + p = etree.SubElement(sibling, "p") + p.text = text + div = sibling + elif sibling.tag == "div" and sibling.attrib.get("class", "") == card_set: + # Get `card-content` under `card-set` + div = self.lastChild(sibling) + else: + # Pass anything else as the parent + div = sibling + + self.parser.parseChunk(div, block) + + if non_cards: + # Insert the card content back into blocks + blocks.insert(0, non_cards) + + +class CardExtension(Extension): + """Add card extension.""" + + def __init__(self, *args, **kwargs): + """Initialize.""" + + self.config = { + "slugify": [ + 0, + "Slugify function used to create card specific IDs - Default: None", + ], + "combine_header_slug": [ + False, + "Combine the card slug with the slug of the parent header - " + "Default: False", + ], + "separator": ["-", "Slug separator - Default: '-'"], + } + + super(CardExtension, self).__init__(*args, **kwargs) + + def extendMarkdown(self, md): + """Add card to Markdown instance.""" + md.registerExtension(self) + + config = self.getConfigs() + + self.card_processor = CardProcessor(md.parser, config) + + md.parser.blockprocessors.register( + self.card_processor, + "card", + 105, + ) + + def reset(self): + """Reset.""" + + self.card_processor.card_group_count = 0 + + +def makeExtension(*args, **kwargs): + """Return extension.""" + + return CardExtension(*args, **kwargs) diff --git a/main/scripts/plugin.py b/main/scripts/plugin.py new file mode 100644 index 00000000..b881c9df --- /dev/null +++ b/main/scripts/plugin.py @@ -0,0 +1,55 @@ +import os + +import mkdocs.config +import mkdocs.plugins +import mkdocs.structure +import mkdocs.structure.files +import mkdocs.structure.nav +import mkdocs.structure.pages +import regex + + +def exclude_file(name): + return name.startswith("assets/fragments/") + + +HREF_REGEX = ( + r"(?<=<\s*(?:a[^>]*href|img[^>]*src)=)" + r'(?:"([^"]*)"|\'([^\']*)|[ ]*([^ =>]*)(?![a-z]+=))' +) + + +@mkdocs.plugins.event_priority(-1000) +def on_post_page( + output: str, + page: mkdocs.structure.pages.Page, + config: mkdocs.config.Config, +): + """ + 1. Replace absolute paths with path relative to the rendered page + This must be performed after all other plugins have run. + 2. Replace component names with links to the component reference + + Parameters + ---------- + output + page + config + + Returns + ------- + + """ + + def replace_link(match): + relative_url = url = match.group(1) or match.group(2) or match.group(3) + page_url = os.path.join("/", page.file.url) + if url and url.startswith("/"): + relative_url = os.path.relpath(url, page_url) + return f'"{relative_url}"' + + # Replace absolute paths with path relative to the rendered page + # output = regex.sub(PIPE_REGEX, replace_component, output) + output = regex.sub(HREF_REGEX, replace_link, output) + + return output diff --git a/main/search/search_index.json b/main/search/search_index.json new file mode 100644 index 00000000..3e9d5fc0 --- /dev/null +++ b/main/search/search_index.json @@ -0,0 +1 @@ +{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Getting started eds-scikit is a tool to assist data scientists working on the AP-HP\u2019s Clinical Data Warehouse. It is specifically targeted for OMOP-standardized data to: Ease access and analysis of data Allow a better transfer of knowledge between projects Improve research reproduciblity As an example, the following figure was obtained using various functionalities from eds-scikit. How was it done ? Click on the figure above to jump to the tutorial using various functionalities from eds-scikit, or continue reading the introduction! Using eds-scikit with I2B2 Although designed for OMOP databases, eds-scikit provides a connector for I2B2 databases is available. We don't guarantee its exhaustivity, but it should allow you to use functionnalities of the library seamlessly. Quick start Installation Requirements eds-scikit stands on the shoulders of Spark 2.4 which runs on Java 8 and Python ~3.7.1. If you work on AP-HP's CDW, those requirements are already fulfilled, so please disregard the following steps. Else, it is essential to: Install a version of Python \u2265 3.7.1 and < 3.8. Install OpenJDK 8 , an open-source reference implementation of Java 8 wit the following command lines: Linux (Debian, Ubunutu, etc.) Mac Windows $ sudo apt-get update $ sudo apt-get install openjdk-8-jdk ---> 100% For more details, check this installation guide $ brew tap AdoptOpenJDK/openjdk $ brew install --cask adoptopenjdk8 ---> 100% For more details, check this installation guide Follow this installation guide You can install eds-scikit via pip: $ pip install eds-scikit ---> 100% color:green Successfully installed eds_scikit ! Possible issue with pip If you get an an error during installation, please try downgrading pip via pip install -U \"pip<23\" before install eds-scikit Improving performances on distributed data It is highly recommanded (but not mandatory) to use the helper function eds_scikit.improve_performances to optimaly configure PySpark and Koalas. You can simply call import eds_scikit spark , sc , sql = eds_scikit . improve_performances () The function will return A SparkSession A SparkContext An sql function to execute SQL queries A first example: Merging visits together Let's tackle a common problem when dealing with clinical data: Merging close/consecutive visits into stays . As detailled in the dedicated section , eds-scikit is expecting to work with Pandas or Koalas DataFrames. We provide various connectors to facilitate data fetching, namely a Hive connector and a Postgres connector Using a Hive DataBase Using a Postgres DataBase Else from eds_scikit.io import HiveData data = HiveData ( DB_NAME ) visit_occurrence = data . visit_occurrence # (1) With this connector, visit_occurrence will be a Pandas DataFrame I2B2 If DB_NAME points to an I2B2 database, use data = HiveData(DB_NAME, database_type=\"I2B2\") from eds_scikit.io import PostgresData DB_NAME = \"my_db\" SCHEMA = \"my_schema\" USER = \"my_username\" data = PostgresData ( DB_NAME , schema = SCHEMA , user = USER ) # (1) visit_occurrence = data . visit_occurrence # (2) This connector expects a .pgpass file storing the connection parameters With this connector, visit_occurrence will be a Pandas DataFrame You can use eds-scikit with data from any source, as long as: - It follows the OMOP format - It is a Pandas or Koalas DataFrame import pandas as pd visit_occurrence = pd . read_csv ( \"./data/visit_occurrence.csv\" ) visit_occurrence For the sake of the example, only columns of interest are shown here. visit_occurrence_id person_id visit_start_datetime visit_end_datetime visit_source_value row_status_source_value care_site_id 0 A 999 2021-01-01 00:00:00 2021-01-05 00:00:00 hospitalis\u00e9s courant 1 1 B 999 2021-01-04 00:00:00 2021-01-08 00:00:00 hospitalis\u00e9s courant 1 2 C 999 2021-01-12 00:00:00 2021-01-18 00:00:00 hospitalis\u00e9s courant 1 3 D 999 2021-01-13 00:00:00 2021-01-14 00:00:00 urgence courant 1 4 E 999 2021-01-19 00:00:00 2021-01-21 00:00:00 hospitalis\u00e9s courant 2 5 F 999 2021-01-25 00:00:00 2021-01-27 00:00:00 hospitalis\u00e9s supprim\u00e9 1 6 G 999 2017-01-01 00:00:00 NaT hospitalis\u00e9s courant 1 # Importing the desired functions: from eds_scikit.period.stays import merge_visits , get_stays_duration # Calling the first function: computing stays visit_occurrence = merge_visits ( visit_occurrence ) As you can see, the function added a STAY_ID concept, grouping visits together visit_occurrence[[\"visit_occurrence_id\",\"STAY_ID\"]] visit_occurrence_id STAY_ID 0 A A 1 B A 2 C C 3 D C 4 E E 5 F F 6 G G # Calling the second function: computing stays duration stays = get_stays_duration ( visit_occurrence , missing_end_date_handling = \"coerce\" ) Here, each stay duration was calculated, dealing with potential overlaps and inclusions.: stays STAY_ID t_start t_end STAY_DURATION A 2021-01-01 00:00:00 2021-01-08 00:00:00 168 C 2021-01-12 00:00:00 2021-01-18 00:00:00 144 E 2021-01-19 00:00:00 2021-01-21 00:00:00 48 F 2021-01-25 00:00:00 2021-01-27 00:00:00 48 G 2017-01-01 00:00:00 NaT NaN About the code above As you noticed, the pipeline above is fairly straightforward, needing only the visit_occurrence DataFrame as input. However, it is also highly customizable, and you should always look into all the various availables options for the functions you're using. For instance, the following parameters could have been used: visit_occurrence = merge_visits ( visit_occurrence , remove_deleted_visits = True , long_stay_threshold = timedelta ( days = 365 ), long_stay_filtering = \"all\" , max_timedelta = timedelta ( hours = 24 ), merge_different_hospitals = False , merge_different_source_values = [ \"hospitalis\u00e9s\" , \"urgence\" ], ) stays = get_stays_duration ( visit_occurrence , algo = \"sum_of_visits_duration\" , missing_end_date_handling = \"coerce\" ) A word about AP-HP Specifics of AP-HP CDW eds-scikit was developped by AP-HP's Data Science team with the help of Inria's Soda team. As such, it is especially well fitted for AP-HP's Data Warehouse. In this doc, we use the following card to mention information that might be useful when using eds-scikit with AP-HP's data : Some information Here, we might for instance suggest some parameters for a function that should be used given AP-HP's data. EDS-NLP Also, a rule-based NLP library ( EDS-NLP ) designed to work on clinical texts was developped in parallel with eds-scikit. We decided not to include EDS-NLP as a dependency. Still, some functions might require an input \u00e0 la note_nlp : For instance, the current function designed to extract consultation dates from a visit_occurrence car work either on structured data only or with dates extracted in text and compiled in a DataFrame. You are free to use the method of your choice to get this DataFrame , as long as it contains the necessary columns as mentionned in the documentation. Note that we mention with the following card the availability of an EDS-NLP dedicated pipeline : A dedicated pipe For the example above, a consultation date pipeline exists. Moreover, methods are available to run an EDS-NLP pipeline on a Pandas, Spark or even Koalas DataFrame ! Contributing to eds-scikit We welcome contributions! Fork the project and create a pull request. Take a look at the dedicated page for details. Citation If you use eds-scikit , please cite us as below. @misc { eds-scikit , author = {Petit-Jean, Thomas and Remaki, Adam and Maladi\u00e8re, Vincent and Varoquaux, Ga\u00ebl and Bey, Romain} , doi = {10.5281/zenodo.7401549} , title = {eds-scikit: data analysis on OMOP databases} , url = {https://github.com/aphp/eds-scikit} }","title":"Home"},{"location":"#getting-started","text":"eds-scikit is a tool to assist data scientists working on the AP-HP\u2019s Clinical Data Warehouse. It is specifically targeted for OMOP-standardized data to: Ease access and analysis of data Allow a better transfer of knowledge between projects Improve research reproduciblity As an example, the following figure was obtained using various functionalities from eds-scikit. How was it done ? Click on the figure above to jump to the tutorial using various functionalities from eds-scikit, or continue reading the introduction! Using eds-scikit with I2B2 Although designed for OMOP databases, eds-scikit provides a connector for I2B2 databases is available. We don't guarantee its exhaustivity, but it should allow you to use functionnalities of the library seamlessly.","title":"Getting started"},{"location":"#quick-start","text":"","title":"Quick start"},{"location":"#installation","text":"Requirements eds-scikit stands on the shoulders of Spark 2.4 which runs on Java 8 and Python ~3.7.1. If you work on AP-HP's CDW, those requirements are already fulfilled, so please disregard the following steps. Else, it is essential to: Install a version of Python \u2265 3.7.1 and < 3.8. Install OpenJDK 8 , an open-source reference implementation of Java 8 wit the following command lines: Linux (Debian, Ubunutu, etc.) Mac Windows $ sudo apt-get update $ sudo apt-get install openjdk-8-jdk ---> 100% For more details, check this installation guide $ brew tap AdoptOpenJDK/openjdk $ brew install --cask adoptopenjdk8 ---> 100% For more details, check this installation guide Follow this installation guide You can install eds-scikit via pip: $ pip install eds-scikit ---> 100% color:green Successfully installed eds_scikit ! Possible issue with pip If you get an an error during installation, please try downgrading pip via pip install -U \"pip<23\" before install eds-scikit Improving performances on distributed data It is highly recommanded (but not mandatory) to use the helper function eds_scikit.improve_performances to optimaly configure PySpark and Koalas. You can simply call import eds_scikit spark , sc , sql = eds_scikit . improve_performances () The function will return A SparkSession A SparkContext An sql function to execute SQL queries","title":"Installation"},{"location":"#a-first-example-merging-visits-together","text":"Let's tackle a common problem when dealing with clinical data: Merging close/consecutive visits into stays . As detailled in the dedicated section , eds-scikit is expecting to work with Pandas or Koalas DataFrames. We provide various connectors to facilitate data fetching, namely a Hive connector and a Postgres connector Using a Hive DataBase Using a Postgres DataBase Else from eds_scikit.io import HiveData data = HiveData ( DB_NAME ) visit_occurrence = data . visit_occurrence # (1) With this connector, visit_occurrence will be a Pandas DataFrame I2B2 If DB_NAME points to an I2B2 database, use data = HiveData(DB_NAME, database_type=\"I2B2\") from eds_scikit.io import PostgresData DB_NAME = \"my_db\" SCHEMA = \"my_schema\" USER = \"my_username\" data = PostgresData ( DB_NAME , schema = SCHEMA , user = USER ) # (1) visit_occurrence = data . visit_occurrence # (2) This connector expects a .pgpass file storing the connection parameters With this connector, visit_occurrence will be a Pandas DataFrame You can use eds-scikit with data from any source, as long as: - It follows the OMOP format - It is a Pandas or Koalas DataFrame import pandas as pd visit_occurrence = pd . read_csv ( \"./data/visit_occurrence.csv\" ) visit_occurrence For the sake of the example, only columns of interest are shown here. visit_occurrence_id person_id visit_start_datetime visit_end_datetime visit_source_value row_status_source_value care_site_id 0 A 999 2021-01-01 00:00:00 2021-01-05 00:00:00 hospitalis\u00e9s courant 1 1 B 999 2021-01-04 00:00:00 2021-01-08 00:00:00 hospitalis\u00e9s courant 1 2 C 999 2021-01-12 00:00:00 2021-01-18 00:00:00 hospitalis\u00e9s courant 1 3 D 999 2021-01-13 00:00:00 2021-01-14 00:00:00 urgence courant 1 4 E 999 2021-01-19 00:00:00 2021-01-21 00:00:00 hospitalis\u00e9s courant 2 5 F 999 2021-01-25 00:00:00 2021-01-27 00:00:00 hospitalis\u00e9s supprim\u00e9 1 6 G 999 2017-01-01 00:00:00 NaT hospitalis\u00e9s courant 1 # Importing the desired functions: from eds_scikit.period.stays import merge_visits , get_stays_duration # Calling the first function: computing stays visit_occurrence = merge_visits ( visit_occurrence ) As you can see, the function added a STAY_ID concept, grouping visits together visit_occurrence[[\"visit_occurrence_id\",\"STAY_ID\"]] visit_occurrence_id STAY_ID 0 A A 1 B A 2 C C 3 D C 4 E E 5 F F 6 G G # Calling the second function: computing stays duration stays = get_stays_duration ( visit_occurrence , missing_end_date_handling = \"coerce\" ) Here, each stay duration was calculated, dealing with potential overlaps and inclusions.: stays STAY_ID t_start t_end STAY_DURATION A 2021-01-01 00:00:00 2021-01-08 00:00:00 168 C 2021-01-12 00:00:00 2021-01-18 00:00:00 144 E 2021-01-19 00:00:00 2021-01-21 00:00:00 48 F 2021-01-25 00:00:00 2021-01-27 00:00:00 48 G 2017-01-01 00:00:00 NaT NaN About the code above As you noticed, the pipeline above is fairly straightforward, needing only the visit_occurrence DataFrame as input. However, it is also highly customizable, and you should always look into all the various availables options for the functions you're using. For instance, the following parameters could have been used: visit_occurrence = merge_visits ( visit_occurrence , remove_deleted_visits = True , long_stay_threshold = timedelta ( days = 365 ), long_stay_filtering = \"all\" , max_timedelta = timedelta ( hours = 24 ), merge_different_hospitals = False , merge_different_source_values = [ \"hospitalis\u00e9s\" , \"urgence\" ], ) stays = get_stays_duration ( visit_occurrence , algo = \"sum_of_visits_duration\" , missing_end_date_handling = \"coerce\" )","title":"A first example: Merging visits together"},{"location":"#a-word-about-ap-hp","text":"","title":"A word about AP-HP"},{"location":"#specifics-of-ap-hp-cdw","text":"eds-scikit was developped by AP-HP's Data Science team with the help of Inria's Soda team. As such, it is especially well fitted for AP-HP's Data Warehouse. In this doc, we use the following card to mention information that might be useful when using eds-scikit with AP-HP's data : Some information Here, we might for instance suggest some parameters for a function that should be used given AP-HP's data.","title":"Specifics of AP-HP CDW"},{"location":"#eds-nlp","text":"Also, a rule-based NLP library ( EDS-NLP ) designed to work on clinical texts was developped in parallel with eds-scikit. We decided not to include EDS-NLP as a dependency. Still, some functions might require an input \u00e0 la note_nlp : For instance, the current function designed to extract consultation dates from a visit_occurrence car work either on structured data only or with dates extracted in text and compiled in a DataFrame. You are free to use the method of your choice to get this DataFrame , as long as it contains the necessary columns as mentionned in the documentation. Note that we mention with the following card the availability of an EDS-NLP dedicated pipeline : A dedicated pipe For the example above, a consultation date pipeline exists. Moreover, methods are available to run an EDS-NLP pipeline on a Pandas, Spark or even Koalas DataFrame !","title":"EDS-NLP"},{"location":"#contributing-to-eds-scikit","text":"We welcome contributions! Fork the project and create a pull request. Take a look at the dedicated page for details.","title":"Contributing to eds-scikit"},{"location":"#citation","text":"If you use eds-scikit , please cite us as below. @misc { eds-scikit , author = {Petit-Jean, Thomas and Remaki, Adam and Maladi\u00e8re, Vincent and Varoquaux, Ga\u00ebl and Bey, Romain} , doi = {10.5281/zenodo.7401549} , title = {eds-scikit: data analysis on OMOP databases} , url = {https://github.com/aphp/eds-scikit} }","title":"Citation"},{"location":"changelog/","text":"Changelog Unreleased Fixed Quartiles computed from plot_concepts_set does not depend on value selection anymore v0.1.8 (2024-06-13) Fixed Pyarrow fix now work on spark executors. Fix OMOP _date columns issue Added omop teva module v0.1.7 (2024-04-12) Changed Support for pyarrow > 0.17.0 Added biology module refacto load_koalas() not by default in init .py but called in the improve_performance function adding app_name in improve_performances to facilitate app monitoring Fixed Generation of an inclusion/exclusion flowchart in plotting improve_performance moved from init .py to io/improve_performance.py file Caching in spark instead of koalas to improve speed v0.1.6 (2023-09-27) Added Module event_sequences to visualize individual sequences of events. Module age_pyramid to quickly visualize the age and gender distributions in a cohort. Fixed Compatibility with EDS-TeVa and EDSNLP . v0.1.5 (2023-04-05) Added BaseData class as a parent class for HiveData, PandasData and PostgresData. Phentyping class with 4 implemented phenotyes. Custom logger to display useful information during computation. Fixed Add caching to speedup computations. Updated method to persist tables as parquet locally, with a support for ORC-stored I2B2 database. v0.1.4 (2023-02-09) Added Allow saving DB locally in client or cluster mode. Add data cleaning function to handle incorrect datetime in spark. Filter biology config on care site. Adding person-dependent datetime_ref to plot_age_pyramid . Fixed Consultations date for OMOP & I2B2 v0.1.3 (2023-02-02) Added New BackendDispatcher to handle framework-specific functions I2B2 to OMOP connector v0.1.2 (2022-12-05) Added Adding CITATION.cff Using mike as a documentation provider Fixed Correct build to PyPI Renaming from EDS-Scikit to eds-scikit v0.1.1 (2022-12-02) Added Various project metadata Full CI pipeline License checker in CI BackendDispatcher object to help with pandas / koalas manipulation Fixed Broken links in documentation and badges v0.1.0 (2022-12-01) Added Initial commit to GitHub","title":"Changelog"},{"location":"changelog/#changelog","text":"","title":"Changelog"},{"location":"changelog/#unreleased","text":"","title":"Unreleased"},{"location":"changelog/#fixed","text":"Quartiles computed from plot_concepts_set does not depend on value selection anymore","title":"Fixed"},{"location":"changelog/#v018-2024-06-13","text":"","title":"v0.1.8 (2024-06-13)"},{"location":"changelog/#fixed_1","text":"Pyarrow fix now work on spark executors. Fix OMOP _date columns issue","title":"Fixed"},{"location":"changelog/#added","text":"omop teva module","title":"Added"},{"location":"changelog/#v017-2024-04-12","text":"","title":"v0.1.7 (2024-04-12)"},{"location":"changelog/#changed","text":"Support for pyarrow > 0.17.0","title":"Changed"},{"location":"changelog/#added_1","text":"biology module refacto load_koalas() not by default in init .py but called in the improve_performance function adding app_name in improve_performances to facilitate app monitoring","title":"Added"},{"location":"changelog/#fixed_2","text":"Generation of an inclusion/exclusion flowchart in plotting improve_performance moved from init .py to io/improve_performance.py file Caching in spark instead of koalas to improve speed","title":"Fixed"},{"location":"changelog/#v016-2023-09-27","text":"","title":"v0.1.6 (2023-09-27)"},{"location":"changelog/#added_2","text":"Module event_sequences to visualize individual sequences of events. Module age_pyramid to quickly visualize the age and gender distributions in a cohort.","title":"Added"},{"location":"changelog/#fixed_3","text":"Compatibility with EDS-TeVa and EDSNLP .","title":"Fixed"},{"location":"changelog/#v015-2023-04-05","text":"","title":"v0.1.5 (2023-04-05)"},{"location":"changelog/#added_3","text":"BaseData class as a parent class for HiveData, PandasData and PostgresData. Phentyping class with 4 implemented phenotyes. Custom logger to display useful information during computation.","title":"Added"},{"location":"changelog/#fixed_4","text":"Add caching to speedup computations. Updated method to persist tables as parquet locally, with a support for ORC-stored I2B2 database.","title":"Fixed"},{"location":"changelog/#v014-2023-02-09","text":"","title":"v0.1.4 (2023-02-09)"},{"location":"changelog/#added_4","text":"Allow saving DB locally in client or cluster mode. Add data cleaning function to handle incorrect datetime in spark. Filter biology config on care site. Adding person-dependent datetime_ref to plot_age_pyramid .","title":"Added"},{"location":"changelog/#fixed_5","text":"Consultations date for OMOP & I2B2","title":"Fixed"},{"location":"changelog/#v013-2023-02-02","text":"","title":"v0.1.3 (2023-02-02)"},{"location":"changelog/#added_5","text":"New BackendDispatcher to handle framework-specific functions I2B2 to OMOP connector","title":"Added"},{"location":"changelog/#v012-2022-12-05","text":"","title":"v0.1.2 (2022-12-05)"},{"location":"changelog/#added_6","text":"Adding CITATION.cff Using mike as a documentation provider","title":"Added"},{"location":"changelog/#fixed_6","text":"Correct build to PyPI Renaming from EDS-Scikit to eds-scikit","title":"Fixed"},{"location":"changelog/#v011-2022-12-02","text":"","title":"v0.1.1 (2022-12-02)"},{"location":"changelog/#added_7","text":"Various project metadata Full CI pipeline License checker in CI BackendDispatcher object to help with pandas / koalas manipulation","title":"Added"},{"location":"changelog/#fixed_7","text":"Broken links in documentation and badges","title":"Fixed"},{"location":"changelog/#v010-2022-12-01","text":"","title":"v0.1.0 (2022-12-01)"},{"location":"changelog/#added_8","text":"Initial commit to GitHub","title":"Added"},{"location":"contributing/","text":"Contributing We welcome contributions! There are many ways to help. For example, you can: Help us track bugs by filing issues Suggest and help prioritise new functionalities Develop a new functionality! Help us make the library as straightforward as possible, by simply asking questions on whatever does not seem clear to you. Please do not hesitate to suggest functionalities you have developed and want to incorporate into eds-scikit. We will be glad to help! Also, any non-technical contribution (e.g. lists of ICD-10 codes curated for a research project) is also welcome. Development installation To be able to run the test suite, run the example notebooks and develop your own functionalities, you should clone the repo and install it locally. Spark and Java To run tests locally, you need to have Spark and Java. Whereas Spark will be installed as a dependency of PySpark, you may need to install Java yourself. Please check to installation procedure. # Clone the repository and change directory $ git clone https://github.com/aphp/eds-scikit.git ---> 100% $ cd eds-scikit # Create a virtual environment $ python -m venv venv $ source venv/bin/activate # Install dependencies and build resources $ pip install -e \".[dev, doc]\" # And switch to a new branch to begin developing $ git switch -c \"name_of_my_new_branch\" To make sure the pipeline will not fail because of formatting errors, we added pre-commit hooks using the pre-commit Python library. To use it, simply install it: $ pre-commit install The pre-commit hooks defined in the configuration will automatically run when you commit your changes, letting you know if something went wrong. The hooks only run on staged changes. To force-run it on all files, run: $ pre-commit run --all-files ---> 100% color:green All good ! Proposing a merge request At the very least, your changes should : Be well-documented ; Pass every tests, and preferably implement its own ; Follow the style guide. Testing your code We use the Pytest test suite. The following command will run the test suite. Writing your own tests is encouraged! python -m pytest ./tests Most tests are designed to run both with Pandas as Koalas DataFrames as input. However, to gain time, by default only Pandas testing is done. The above line of code is equivalent to python -m pytest ./tests -m \"not koalas\" However, you can also run tests using only Koalas input: python -m pytest ./tests -m \"koalas\" or using both inputs: python -m pytest ./tests -m \"\" Finally when developing, you might be interested to run tests for a single file, or even a single function. To do so: python -m pytest ./tests/my_file.py #(1) python -m pytest ./tests/my_file.py:my_test_function #(2) 1. Will run all tests found in this file 2. Will only run \"my_test_function\" Style Guide We use Black to reformat the code. While other formatter only enforce PEP8 compliance, Black also makes the code uniform. In short : Black reformats entire files in place. It is not configurable. Moreover, the CI/CD pipeline enforces a number of checks on the \"quality\" of the code. To wit, non black-formatted code will make the test pipeline fail. We use pre-commit to keep our codebase clean. Refer to the development install tutorial for tips on how to format your files automatically. Most modern editors propose extensions that will format files on save. On conventional commits We try to use conventional commits guidelines as much as possible. In short, prepend each commit message with one of the following prefix: fix: when patching a bug feat: when introducing a new feature If needed, you can also use one of the following: build:, chore:, ci:, docs:, style:, refactor:, perf:, test Documentation Make sure to document your improvements, both within the code with comprehensive docstrings, as well as in the documentation itself if need be. We use MkDocs for eds-scikit's documentation. You can checkout the changes you make with: # Install the requirements $ pip install \".[doc]\" ---> 100% color:green Installation successful # Run the documentation $ mkdocs serve Go to localhost:8000 to see your changes. MkDocs watches for changes in the documentation folder and automatically reloads the page. Warning MkDocs will automaticaly build code documentation by going through every .py file located in the eds_scikit directory (and sub-arborescence). It expects to find a __init__.py file in each directory, so make sure to create one if needed. Developing your own methods Even though the koalas project aim at covering most pandas functions for spark, there are some discrepancies. For instance, the pd.cut() method has no koalas alternative. To ease the development and switch gears efficiently between the two backends, we advice you to use the BackendDispatcher class and its collection of custom methods.","title":"Contributing"},{"location":"contributing/#contributing","text":"We welcome contributions! There are many ways to help. For example, you can: Help us track bugs by filing issues Suggest and help prioritise new functionalities Develop a new functionality! Help us make the library as straightforward as possible, by simply asking questions on whatever does not seem clear to you. Please do not hesitate to suggest functionalities you have developed and want to incorporate into eds-scikit. We will be glad to help! Also, any non-technical contribution (e.g. lists of ICD-10 codes curated for a research project) is also welcome.","title":"Contributing"},{"location":"contributing/#development-installation","text":"To be able to run the test suite, run the example notebooks and develop your own functionalities, you should clone the repo and install it locally. Spark and Java To run tests locally, you need to have Spark and Java. Whereas Spark will be installed as a dependency of PySpark, you may need to install Java yourself. Please check to installation procedure. # Clone the repository and change directory $ git clone https://github.com/aphp/eds-scikit.git ---> 100% $ cd eds-scikit # Create a virtual environment $ python -m venv venv $ source venv/bin/activate # Install dependencies and build resources $ pip install -e \".[dev, doc]\" # And switch to a new branch to begin developing $ git switch -c \"name_of_my_new_branch\" To make sure the pipeline will not fail because of formatting errors, we added pre-commit hooks using the pre-commit Python library. To use it, simply install it: $ pre-commit install The pre-commit hooks defined in the configuration will automatically run when you commit your changes, letting you know if something went wrong. The hooks only run on staged changes. To force-run it on all files, run: $ pre-commit run --all-files ---> 100% color:green All good !","title":"Development installation"},{"location":"contributing/#proposing-a-merge-request","text":"At the very least, your changes should : Be well-documented ; Pass every tests, and preferably implement its own ; Follow the style guide.","title":"Proposing a merge request"},{"location":"contributing/#testing-your-code","text":"We use the Pytest test suite. The following command will run the test suite. Writing your own tests is encouraged! python -m pytest ./tests Most tests are designed to run both with Pandas as Koalas DataFrames as input. However, to gain time, by default only Pandas testing is done. The above line of code is equivalent to python -m pytest ./tests -m \"not koalas\" However, you can also run tests using only Koalas input: python -m pytest ./tests -m \"koalas\" or using both inputs: python -m pytest ./tests -m \"\" Finally when developing, you might be interested to run tests for a single file, or even a single function. To do so: python -m pytest ./tests/my_file.py #(1) python -m pytest ./tests/my_file.py:my_test_function #(2) 1. Will run all tests found in this file 2. Will only run \"my_test_function\"","title":"Testing your code"},{"location":"contributing/#style-guide","text":"We use Black to reformat the code. While other formatter only enforce PEP8 compliance, Black also makes the code uniform. In short : Black reformats entire files in place. It is not configurable. Moreover, the CI/CD pipeline enforces a number of checks on the \"quality\" of the code. To wit, non black-formatted code will make the test pipeline fail. We use pre-commit to keep our codebase clean. Refer to the development install tutorial for tips on how to format your files automatically. Most modern editors propose extensions that will format files on save. On conventional commits We try to use conventional commits guidelines as much as possible. In short, prepend each commit message with one of the following prefix: fix: when patching a bug feat: when introducing a new feature If needed, you can also use one of the following: build:, chore:, ci:, docs:, style:, refactor:, perf:, test","title":"Style Guide"},{"location":"contributing/#documentation","text":"Make sure to document your improvements, both within the code with comprehensive docstrings, as well as in the documentation itself if need be. We use MkDocs for eds-scikit's documentation. You can checkout the changes you make with: # Install the requirements $ pip install \".[doc]\" ---> 100% color:green Installation successful # Run the documentation $ mkdocs serve Go to localhost:8000 to see your changes. MkDocs watches for changes in the documentation folder and automatically reloads the page. Warning MkDocs will automaticaly build code documentation by going through every .py file located in the eds_scikit directory (and sub-arborescence). It expects to find a __init__.py file in each directory, so make sure to create one if needed.","title":"Documentation"},{"location":"contributing/#developing-your-own-methods","text":"Even though the koalas project aim at covering most pandas functions for spark, there are some discrepancies. For instance, the pd.cut() method has no koalas alternative. To ease the development and switch gears efficiently between the two backends, we advice you to use the BackendDispatcher class and its collection of custom methods.","title":"Developing your own methods"},{"location":"project_description/","text":"Goal eds-scikit is a tool to assist datascientists working on the AP-HP's Clinical Data Warehouse. It is specifically targeted for OMOP-standardized data to: Ease access and analysis of data Allow a better transfer of knowledge between projects Improve research reproduciblity Main working principles Dealing with various data sizes Generally, data analysis can be done in two ways: Locally , by loading everything in RAM and working with e.g. Pandas In a distributed fashion, when dealing with a lot of data, by using e.g. Spark While working with Pandas is often more convenient, its use can be problematic once working with large cohorts. Thus, making eds-scikit a Pandas-only library wasn't conceivable. In order to allow analysis to be conducted at scale, eds-scikit integrates with Koalas . Koalas Koalas is a library implementing Pandas API on top of Spark . Basically, it allows for functions and methods developped for Pandas DataFrames to work on Spark DataFrames with close to no adjustments. Let us see a dummy example where one wants to count the number of visit occurrences per month . Using Spark (via PySpark) Using Pandas Suppose we have a Spark visit_occurrence DataFrame: type ( visit_occurrence_spark ) # Out: pyspark.sql.dataframe.DataFrame import pyspark.sql.functions as F def get_stats_spark ( visit_occurrence ): \"\"\" Computes the number of visits per month Parameters ---------- visit_occurrence : DataFrame Returns ------- stats : pd.DataFrame \"\"\" # Adding a month and year column visit_occurrence = visit_occurrence . withColumn ( \"year\" , F . year ( \"visit_start_datetime\" ) ) . withColumn ( \"month\" , F . month ( \"visit_start_datetime\" )) # Grouping and filtering stats = ( visit_occurrence . groupby ([ \"year\" , \"month\" ]) . count () . filter (( F . col ( \"year\" ) >= 2017 )) . toPandas () ) return stats stats_from_spark = get_stats_spark ( visit_occurrence_spark ) If the selected database contains few enough visits, we may have a visit_occurrence DataFrame small enough to fit in memory as a Pandas DataFrame. type ( visit_occurrence_pandas ) # Out: pandas.core.frame.DataFrame Then run the same analysis: def get_stats_pandas ( visit_occurrence ): \"\"\" Computes the number of visits per month Parameters ---------- visit_occurrence : DataFrame Returns ------- stats : pd.DataFrame \"\"\" # Adding a duration column visit_occurrence [ \"year\" ] = visit_occurrence [ \"visit_start_datetime\" ] . dt . year visit_occurrence [ \"month\" ] = visit_occurrence [ \"visit_start_datetime\" ] . dt . month # Grouping and filtering stats = ( visit_occurrence . groupby ([ \"year\" , \"month\" ]) . visit_occurrence_id . count () . reset_index () ) stats = stats [ stats [ \"year\" ] >= 2017 ] stats . columns = [ \"year\" , \"month\" , \"count\" ] return stats stats_from_pandas = get_stats_pandas ( visit_occurrence_pandas ) The two examples above clearly show the syntax differences between using Pandas and using Spark . In order for a library to work both with Pandas and Spark, one would need to developp each function twice to accomodate for those two frameworks. Another problem might occur if you are dealing with a huge cohort, forcing you to do your final analysis in a distributed manner via Spark. In that scenario, you coudn't test your code on a small Pandas DataFrame subset. The goal of Koalas is precisely to avoid this issue. It aims at allowing code to be written for Pandas DataFrames, and also run with (almost) no adjustements with Spark DataFrame: from databricks import koalas as ks # Converting the Spark DataFrame into a Koalas DataFrame visit_occurrence_koalas = visit_occurrence_spark . to_koalas () Info The code above allows the DataFrame to stay distributed \u2014as opposed to applying the .toPandas() method. We can now use the function we designed for Pandas with a Koalas DataFrame: stats_from_koalas = get_stats_pandas ( visit_occurrence_koalas ) Since we aggregated the data, its size is manageable so we can convert it back to Pandas for e.g. plotting stats_from_koalas = stats_from_koalas . to_pandas () Concept Most functions developped in the library implements a concept . For sake of clarity let us illustrate this notion with an example: The function tag_icu_care_site() can be used to tag a care site as being an ICU or not. We say that it implements the concept \"IS_ICU\" because it adds a column named \"IS_ICU\" to the input DataFrame , as it can be seen from the docstring: \"\"\" Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - 'IS_ICU' \"\"\" This follows a wide data format. However, when multiple concepts are added at once, it might be done in a long format, such as with the diabetes_from_icd10() function, which stores the diabetes type in a concept column, and the corresponding ICD-10 code in a value column: \"\"\" Returns ------- DataFrame Event DataFrame in **long** format (with a `concept` and a `value` column). The `concept` column contains one of the following: - DIABETES_TYPE_I - DIABETES_TYPE_II - DIABETES_MALNUTRITION - DIABETES_IN_PREGNANCY - OTHER_DIABETES_MELLITUS - DIABETES_INSIPIDUS The `value` column contains the corresponding ICD-10 code that was extracted \"\"\" Question Check this link for a (very) quick explanation if you aren't familiar with Long vs Wide data format. Algo Most functions also have an argument called algo , which allows you to choose how a specific concept will be implemented in a function. Let's check the docstring of the same function tag_icu_care_site() : \"\"\" Parameters ---------- care_site: DataFrame algo: str Possible values are: - `\"from_authorisation_type\"` - `\"from_regex_on_care_site_description\"` \"\"\" The function's signature shows that \"from_authorisation_type\" is the default algo , used if the algo argument isn't filled by the user. In the documentation, the different \"algo\" values will be displayed as tabs, along with a short description and optional algo-dependant parameters: Availables algorithms (values for \"algo\" ) Algo 1 (default) Algo 2 This \"algo\" is used by default. It does yadi yada. Specific parameters: This first parameter This second parameter And also this third one This second \"algo\" works differently. It has no additional parameters Please check the available algos when using a function from eds-scikit, to understand what each of them is doing and which one might fits you best.","title":"Project description"},{"location":"project_description/#goal","text":"eds-scikit is a tool to assist datascientists working on the AP-HP's Clinical Data Warehouse. It is specifically targeted for OMOP-standardized data to: Ease access and analysis of data Allow a better transfer of knowledge between projects Improve research reproduciblity","title":"Goal"},{"location":"project_description/#main-working-principles","text":"","title":"Main working principles"},{"location":"project_description/#dealing-with-various-data-sizes","text":"Generally, data analysis can be done in two ways: Locally , by loading everything in RAM and working with e.g. Pandas In a distributed fashion, when dealing with a lot of data, by using e.g. Spark While working with Pandas is often more convenient, its use can be problematic once working with large cohorts. Thus, making eds-scikit a Pandas-only library wasn't conceivable. In order to allow analysis to be conducted at scale, eds-scikit integrates with Koalas . Koalas Koalas is a library implementing Pandas API on top of Spark . Basically, it allows for functions and methods developped for Pandas DataFrames to work on Spark DataFrames with close to no adjustments. Let us see a dummy example where one wants to count the number of visit occurrences per month . Using Spark (via PySpark) Using Pandas Suppose we have a Spark visit_occurrence DataFrame: type ( visit_occurrence_spark ) # Out: pyspark.sql.dataframe.DataFrame import pyspark.sql.functions as F def get_stats_spark ( visit_occurrence ): \"\"\" Computes the number of visits per month Parameters ---------- visit_occurrence : DataFrame Returns ------- stats : pd.DataFrame \"\"\" # Adding a month and year column visit_occurrence = visit_occurrence . withColumn ( \"year\" , F . year ( \"visit_start_datetime\" ) ) . withColumn ( \"month\" , F . month ( \"visit_start_datetime\" )) # Grouping and filtering stats = ( visit_occurrence . groupby ([ \"year\" , \"month\" ]) . count () . filter (( F . col ( \"year\" ) >= 2017 )) . toPandas () ) return stats stats_from_spark = get_stats_spark ( visit_occurrence_spark ) If the selected database contains few enough visits, we may have a visit_occurrence DataFrame small enough to fit in memory as a Pandas DataFrame. type ( visit_occurrence_pandas ) # Out: pandas.core.frame.DataFrame Then run the same analysis: def get_stats_pandas ( visit_occurrence ): \"\"\" Computes the number of visits per month Parameters ---------- visit_occurrence : DataFrame Returns ------- stats : pd.DataFrame \"\"\" # Adding a duration column visit_occurrence [ \"year\" ] = visit_occurrence [ \"visit_start_datetime\" ] . dt . year visit_occurrence [ \"month\" ] = visit_occurrence [ \"visit_start_datetime\" ] . dt . month # Grouping and filtering stats = ( visit_occurrence . groupby ([ \"year\" , \"month\" ]) . visit_occurrence_id . count () . reset_index () ) stats = stats [ stats [ \"year\" ] >= 2017 ] stats . columns = [ \"year\" , \"month\" , \"count\" ] return stats stats_from_pandas = get_stats_pandas ( visit_occurrence_pandas ) The two examples above clearly show the syntax differences between using Pandas and using Spark . In order for a library to work both with Pandas and Spark, one would need to developp each function twice to accomodate for those two frameworks. Another problem might occur if you are dealing with a huge cohort, forcing you to do your final analysis in a distributed manner via Spark. In that scenario, you coudn't test your code on a small Pandas DataFrame subset. The goal of Koalas is precisely to avoid this issue. It aims at allowing code to be written for Pandas DataFrames, and also run with (almost) no adjustements with Spark DataFrame: from databricks import koalas as ks # Converting the Spark DataFrame into a Koalas DataFrame visit_occurrence_koalas = visit_occurrence_spark . to_koalas () Info The code above allows the DataFrame to stay distributed \u2014as opposed to applying the .toPandas() method. We can now use the function we designed for Pandas with a Koalas DataFrame: stats_from_koalas = get_stats_pandas ( visit_occurrence_koalas ) Since we aggregated the data, its size is manageable so we can convert it back to Pandas for e.g. plotting stats_from_koalas = stats_from_koalas . to_pandas ()","title":"Dealing with various data sizes"},{"location":"project_description/#concept","text":"Most functions developped in the library implements a concept . For sake of clarity let us illustrate this notion with an example: The function tag_icu_care_site() can be used to tag a care site as being an ICU or not. We say that it implements the concept \"IS_ICU\" because it adds a column named \"IS_ICU\" to the input DataFrame , as it can be seen from the docstring: \"\"\" Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - 'IS_ICU' \"\"\" This follows a wide data format. However, when multiple concepts are added at once, it might be done in a long format, such as with the diabetes_from_icd10() function, which stores the diabetes type in a concept column, and the corresponding ICD-10 code in a value column: \"\"\" Returns ------- DataFrame Event DataFrame in **long** format (with a `concept` and a `value` column). The `concept` column contains one of the following: - DIABETES_TYPE_I - DIABETES_TYPE_II - DIABETES_MALNUTRITION - DIABETES_IN_PREGNANCY - OTHER_DIABETES_MELLITUS - DIABETES_INSIPIDUS The `value` column contains the corresponding ICD-10 code that was extracted \"\"\" Question Check this link for a (very) quick explanation if you aren't familiar with Long vs Wide data format.","title":"Concept"},{"location":"project_description/#algo","text":"Most functions also have an argument called algo , which allows you to choose how a specific concept will be implemented in a function. Let's check the docstring of the same function tag_icu_care_site() : \"\"\" Parameters ---------- care_site: DataFrame algo: str Possible values are: - `\"from_authorisation_type\"` - `\"from_regex_on_care_site_description\"` \"\"\" The function's signature shows that \"from_authorisation_type\" is the default algo , used if the algo argument isn't filled by the user. In the documentation, the different \"algo\" values will be displayed as tabs, along with a short description and optional algo-dependant parameters: Availables algorithms (values for \"algo\" ) Algo 1 (default) Algo 2 This \"algo\" is used by default. It does yadi yada. Specific parameters: This first parameter This second parameter And also this third one This second \"algo\" works differently. It has no additional parameters Please check the available algos when using a function from eds-scikit, to understand what each of them is doing and which one might fits you best.","title":"Algo"},{"location":"datasets/care-site-emergency/","text":"Presentation Emergency This dataset is useful to extract emergency care sites from AP-HP's CDW This dataset contains care sites labelled as emergency. Those care sites were extracted and verified by Ariel COHEN, Judith LEBLANC, and an ER doctor validated them. Those emergency care sites are further divised into different categories, as defined in the concept \"EMERGENCY_TYPE\" . The different categories are: Urgences sp\u00e9cialis\u00e9es UHCD + Post-urgences Urgences p\u00e9diatriques Urgences g\u00e9n\u00e9rales adulte Consultation urgences SAMU / SMUR Warning This dataset was built in 2021. Structure and usage Internally, the dataset is returned by calling the function get_care_site_emergency_mapping() : from eds_scikit.resources import registry df = registry . get ( \"data\" , function_name = \"get_care_site_emergency_mapping\" )() It should return a Pandas Dataframe with 2 columns: care_site_source_value (OMOP column) EMERGENCY_TYPE (see above) Use your own data. It is as simple as registering a new loading function: custom_resources.py from eds_scikit.resources import registry @registry . data ( \"get_care_site_emergency_mapping\" ) def get_care_site_emergency_mapping (): \"\"\" Your code here \"\"\" return df Then simply import your custom_resources module before running eds-scikit's pipelines, and you're good to go.","title":"Emergency"},{"location":"datasets/care-site-emergency/#presentation","text":"Emergency This dataset is useful to extract emergency care sites from AP-HP's CDW This dataset contains care sites labelled as emergency. Those care sites were extracted and verified by Ariel COHEN, Judith LEBLANC, and an ER doctor validated them. Those emergency care sites are further divised into different categories, as defined in the concept \"EMERGENCY_TYPE\" . The different categories are: Urgences sp\u00e9cialis\u00e9es UHCD + Post-urgences Urgences p\u00e9diatriques Urgences g\u00e9n\u00e9rales adulte Consultation urgences SAMU / SMUR Warning This dataset was built in 2021.","title":"Presentation"},{"location":"datasets/care-site-emergency/#structure-and-usage","text":"Internally, the dataset is returned by calling the function get_care_site_emergency_mapping() : from eds_scikit.resources import registry df = registry . get ( \"data\" , function_name = \"get_care_site_emergency_mapping\" )() It should return a Pandas Dataframe with 2 columns: care_site_source_value (OMOP column) EMERGENCY_TYPE (see above)","title":"Structure and usage"},{"location":"datasets/care-site-emergency/#use-your-own-data","text":"It is as simple as registering a new loading function: custom_resources.py from eds_scikit.resources import registry @registry . data ( \"get_care_site_emergency_mapping\" ) def get_care_site_emergency_mapping (): \"\"\" Your code here \"\"\" return df Then simply import your custom_resources module before running eds-scikit's pipelines, and you're good to go.","title":"Use your own data."},{"location":"datasets/care-site-hierarchy/","text":"Presentation Care sites This dataset is useful to link AP-HP's care sites of various levels together To generate it, it uses the fact_relationship OMOP table, with the care_site domain and the A is part of B relation. Thus, it generates a wide-type table, effectively flattening out the hierarchical structure of each care site. This dataset is useful to find the parent of a care_site , e.g.: in which hospital is this UDS ( Unit\u00e9 De Soin ) ? in which UF ( Unit\u00e9 Fonctionnelle ) is this UMA ( Unit\u00e9 M\u00e9dico-Administrative ) ? Structure and usage In this dataset each row corresponds to a given care_site and the columns contain the ids of the parent care_site for several hierarchical level. Those columns are thus values contained in care_site_type_source_value . Internally, the dataset is returned by calling the function get_care_site_hierarchy() : from eds_scikit.resources import registry df = registry . get ( \"data\" , function_name = \"get_care_site_hierarchy\" )() Use your own data. It is as simple as registering a new loading function: custom_resources.py from eds_scikit.resources import registry # (1) @registry . data ( \"get_care_site_hierarchy\" ) # (2) def get_care_site_hierarchy (): \"\"\" Your code here \"\"\" return df The registry instance stores user-defined functions Using this decorator allows to register the function when importing the corresponding file Then simply import your custom_resources module before running eds-scikit's pipelines, and you're good to go. Structure and usage Internally, the dataset is returned by calling the function get_care_site_hierarchy() . It should return a Pandas Dataframe with the following columns: care_site_id (OMOP column): The identifier of the care site care_site_type_source_value (OMOP column): The type of care site Additionally, it can contains an arbitrary number of columns whose name are values from care_site_type_source_value , and whose values are care_site_id of the corresponding parent structure Generation function You can generate the dataset on your specific data using this function","title":"Hierarchy"},{"location":"datasets/care-site-hierarchy/#presentation","text":"Care sites This dataset is useful to link AP-HP's care sites of various levels together To generate it, it uses the fact_relationship OMOP table, with the care_site domain and the A is part of B relation. Thus, it generates a wide-type table, effectively flattening out the hierarchical structure of each care site. This dataset is useful to find the parent of a care_site , e.g.: in which hospital is this UDS ( Unit\u00e9 De Soin ) ? in which UF ( Unit\u00e9 Fonctionnelle ) is this UMA ( Unit\u00e9 M\u00e9dico-Administrative ) ?","title":"Presentation"},{"location":"datasets/care-site-hierarchy/#structure-and-usage","text":"In this dataset each row corresponds to a given care_site and the columns contain the ids of the parent care_site for several hierarchical level. Those columns are thus values contained in care_site_type_source_value . Internally, the dataset is returned by calling the function get_care_site_hierarchy() : from eds_scikit.resources import registry df = registry . get ( \"data\" , function_name = \"get_care_site_hierarchy\" )()","title":"Structure and usage"},{"location":"datasets/care-site-hierarchy/#use-your-own-data","text":"It is as simple as registering a new loading function: custom_resources.py from eds_scikit.resources import registry # (1) @registry . data ( \"get_care_site_hierarchy\" ) # (2) def get_care_site_hierarchy (): \"\"\" Your code here \"\"\" return df The registry instance stores user-defined functions Using this decorator allows to register the function when importing the corresponding file Then simply import your custom_resources module before running eds-scikit's pipelines, and you're good to go.","title":"Use your own data."},{"location":"datasets/care-site-hierarchy/#structure-and-usage_1","text":"Internally, the dataset is returned by calling the function get_care_site_hierarchy() . It should return a Pandas Dataframe with the following columns: care_site_id (OMOP column): The identifier of the care site care_site_type_source_value (OMOP column): The type of care site Additionally, it can contains an arbitrary number of columns whose name are values from care_site_type_source_value , and whose values are care_site_id of the corresponding parent structure","title":"Structure and usage"},{"location":"datasets/care-site-hierarchy/#generation-function","text":"You can generate the dataset on your specific data using this function","title":"Generation function"},{"location":"datasets/concepts-sets/","text":"Concepts-sets A concepts-set is a generic concept that has been deemed appropriate for most biological analyses. It is a group of several biological concepts representing the same biological entity. Concepts-sets This dataset is listing common biological entities in AP-HP's Data Warehouse. Below, one can see the list of default concepts-set provided by the library. Preview concepts_set_name GLIMS_ANABIO_concept_code concepts_set_category ALAT_Activity ['A0002', 'G1804', 'J7373', 'E2067', 'F2629'] hepatic_panel ASAT_Activity ['A0022', 'G1800', 'E2068', 'F2628'] hepatic_panel Activated_Partial_Thromboplastin_Time ['A1792', 'L7286', 'A7748'] coagulation Adenovirus ['I5915', 'I7952', 'J2229', 'J2988', 'J8817', 'K1527', 'J9968'] virology Albumine_Blood_Concentration ['D2358', 'C6841', 'C2102', 'G6616', 'L2260', 'A0006', 'E4799', 'I2013'] proteins B-HCG_Blood_Concentration ['A7426', 'F2353', 'A0164', 'L2277'] diabete B.pertussis ['I7748', 'I7968', 'K1531'] virology BNP_Concentration ['C8189', 'B5596', 'A2128'] cardiac_biomarkers BNP_and_NTProBNP_Concentration ['C8189', 'B5596', 'A2128', 'A7333', 'J7267', 'J7959'] cardiac_biomarkers Bicarbonate_Blood_Concentration ['A0422', 'H9622', 'C6408', 'F4161', 'A2136', 'J7371', 'G2031'] ionogram C.pneumoniae ['I5930', 'I7969', 'J2207', 'K1530', 'J9919'] virology CRP_Concentration ['A0248', 'E6332', 'F5581', 'J7381', 'F2631'] inflammatory_panel Calcium_Blood_Concentration ['C0543', 'D2359', 'A0038', 'H5041', 'F2625', 'L5047', 'A2512', 'A2512', 'A0607'] ionogram Chloride_Blood_Concentration ['A0079', 'J1179', 'F2619'] ionogram Coronavirus ['I5916', 'I5917', 'I5918', 'I5919', 'I7953', 'I7954', 'I7955', 'I7956', 'J2199', 'J2996', 'J2994', 'J2993', 'J2992', 'J8829', 'J8828', 'J8823', 'J8822', 'K1525', 'K1524', 'K1522', 'K1523', 'J9969'] virology Creatine Kinase ['A0090', 'G0171', 'E6330'] other D-Dimers_Concentration ['C7882', 'C7882', 'I8765', 'A0124', 'C0474', 'C0474', 'C0474', 'B4199', 'F5402'] coagulation EPP_Blood_Concentration ['A0250', 'C9874', 'A3758', 'A0004', 'F9978', 'A0005', 'H8137', 'C7087', 'A0003', 'C7088', 'B9456', 'B9455', 'A0008', 'H8138', 'C7089', 'A0007', 'C7090', 'A0010', 'H8139', 'C7091', 'A0009', 'C7092', 'C6525', 'C6524', 'A0415', 'C7093', 'A0414', 'C7094', 'B9458', 'B9457', 'A2113', 'H8140', 'E5327', 'A2112', 'E5328', 'C6536', 'C6535', 'E2398', 'E2399', 'A2115', 'H8141', 'E5329', 'A2114', 'E5330', 'C6538', 'C6537', 'E2400', 'E2401', 'A0130', 'H8142', 'C7100', 'A0129', 'C7101', 'G6942', 'G6941', 'B9460', 'B9459', 'C6596', 'C6595', 'C6598', 'C6597', 'E2402', 'E2403', 'K4483', 'A2118', 'A2117', 'E1847', 'B8047', 'A2127', 'I8076', 'A2126', 'I8077', 'X5093', 'X5094', 'X5091', 'X5092', 'A2279', 'L7258', 'A1361', 'L7259', 'C6909', 'B1727', 'B1725', 'C6924', 'I5139', 'X5097', 'X5098', 'X5095', 'X5096', 'A2278', 'L7260', 'A2277', 'L7261', 'D0265', 'B1728', 'B1726', 'D0267', 'I5140', 'X5101', 'X5102', 'X5099', 'X5100', 'H6397', 'L7262', 'H6396', 'L7263', 'D0266', 'C0616', 'D0268', 'I5141', 'A8775', 'A7816', 'A8776', 'A8777', 'A8778', 'A8779', 'A8780', 'A7330', 'F0748', 'F0749', 'H9656', 'H9657', 'H9658', 'H9659', 'H9660'] proteins Eosinophil_Polymorphonuclears_Blood_Count ['A0150', 'H6730'] blood_count_cell Ferritin_Concentration ['A0123', 'E9865'] martial_panel Fibrinogen_Concentration ['A0126'] inflammatory_panel Glomerular_Filtration_Rate_EPI_CKD ['G6921', 'F8160', 'F9613', 'F9621', 'F9621'] renal_panel Glomerular_Filtration_Rate_MDRD ['F9622', 'G7835', 'B9964', 'A7456', 'A7455', 'H5609'] renal_panel GGT_Activity ['A0131', 'F8184', 'E9771', 'J7370', 'K7045'] hepatic_panel Glucose_Blood_Concentration ['A0141', 'H7323', 'J7401', 'F2622', 'B9553', 'C7236', 'E7312', 'A7338', 'H7324', 'C0565', 'E9889', 'A8424'] diabete HCO3-_Blood_Concentration ['A0420', 'L5018'] blood_gas HIV Serology ['D2865', 'D2867', 'D2845', 'D2864', 'F4252', 'D2866', 'F3257', 'D2869', 'F1705', 'D2846', 'D2847', 'D2844', 'E8605', 'F5401', 'G0175', 'J5891', 'J2672', 'H7667'] serology HbA1c_Blood_% ['B6983', 'A2228', 'A1271', 'E6632', 'I5968'] diabete Hemoglobin_Blood_Count ['A0163', 'H6738'] blood_count_cell Hepatitis B Serology ['D2729', 'D2730', 'E1524', 'F2075', 'D2725', 'I1903', 'D2728', 'D2726', 'D2726', 'F5613', 'D2731', 'D2727', 'G1197', 'G1199', 'G1199', 'J5887', 'J5890', 'J2697', 'L6883', 'J2695', 'L7877', 'D2653', 'D2660', 'D2654', 'D2661', 'D2649', 'D2656', 'H9686', 'H9687', 'I6579', 'I6580', 'D2652', 'D2659', 'D2650', 'D2650', 'D2657', 'D2655', 'D2662', 'D2651', 'D2651', 'D2658', 'F5615', 'F5616', 'G1209', 'G1209', 'G1210', 'G1210', 'I2927', 'I2928', 'J5885', 'J5886', 'J2699', 'J2700'] serology Hepatitis C Serology ['D2780', 'H5078', 'I1846', 'D2777', 'E6503', 'D2778', 'D3088', 'D3088', 'D2774', 'J1518', 'D2776', 'D2771', 'E8372', 'F7465', 'I3151', 'G0173', 'L1237', 'J2678', 'K3833', 'E8373', 'K4228'] serology IL-1 beta_Blood_Concentration ['C9351', 'B8921', 'G4800', 'K3662', 'L2217', 'J9193', 'K3665', 'K3687', 'K3661', 'L2197'] inflammatory_biomarkers IL-10_Blood_Concentration ['B8922', 'C8763', 'K3478', 'L2210', 'J9187', 'K3481', 'K3472', 'K3475', 'L2198'] inflammatory_biomarkers IL-6_Blood_Concentration ['B8929', 'G4799', 'B1910', 'K3467', 'L2205', 'E6992', 'J9190', 'K3456', 'L2193', 'K3435', 'K3460'] inflammatory_biomarkers Quick_INR_Time ['A0269'] coagulation Influenza A ['I5922', 'I7960', 'J2198', 'J2990', 'J8825', 'K1517'] virology Influenza B ['I5923', 'I7961', 'J2203', 'J2985', 'J8819', 'K1513'] virology L.pneumophila ['J2176', 'J3006', 'J8826', 'J9899'] virology LDH ['A0170', 'H5261', 'J7400', 'C8889', 'J1161'] other Lactate_Gaz_Blood_Concentration ['C8697', 'H7748'] blood_gas Legionella Antigenuria ['D1465', 'H6694', 'J7960'] antigenury Leukocytes_Blood_Count ['A0174', 'H6740', 'C8824'] blood_count_cell Lymphocytes_Blood_Count ['A0198', 'H6743'] blood_count_cell Metapneumovirus ['I5920', 'I7958', 'J2200', 'J2991', 'J8824', 'K1519', 'J9965'] virology Monocytes_Blood_Count ['A0210', 'H6747'] blood_count_cell NTProBNP_Concentration ['A7333', 'J7267', 'J7959'] cardiac_biomarkers Neutrophil_Polymorphonuclears_Blood_Count ['A0155', 'H6732'] blood_count_cell PAL_Activity ['A0227', 'F8187', 'E6331', 'F1844'] hepatic_panel PaCO2_Blood_Concentration ['A7305', 'A0630'] blood_gas PaO2_Blood_Concentration ['A7319', 'H7747'] blood_gas Parainfluenza ['I5924', 'I5925', 'I5926', 'I5927', 'I7962', 'I7963', 'I7964', 'I7965', 'J2204', 'J2979', 'J2977', 'J2983', 'J2981', 'J8861', 'J8862', 'J8821', 'J8820', 'K1509', 'K1508', 'K1510', 'K1535', 'J9964'] virology Phosphates_Blood_Concentration ['A0226', 'F8186', 'F2626'] proteins Platelets_Blood_Count ['A0230', 'H6751', 'A1598', 'A1598', 'A2538', 'A2539', 'A2539', 'J4463'] blood_count_cell Pneumococcal Antigenuria ['D2055', 'J7962', 'A2804'] antigenury Potassium_Blood_Concentration ['A2380', 'E2073', 'F2618', 'E2337', 'J1178'] ionogram Procalcitonin_Blood_Concentration ['A1661', 'H5267', 'F2632'] inflammatory_biomarkers Proteins_Urine_24h_Concentration ['A1695', 'A1694', 'A1696', 'C9990', 'C9991', 'J7268', 'J7269', 'C3941', 'E4745', 'G4187', 'F6060'] proteins Proteins_Blood_Concentration ['A7347', 'F5122', 'F2624', 'B9417', 'A0249', 'B3990'] inflammatory_panel Quick_Prothrombin_Time ['A1805', 'E9993'] coagulation RSV ['I5928', 'I7966', 'J2201', 'J2974', 'J8859', 'K1534'] virology Rhino/Enterovirus ['I5921', 'I7959', 'J2197', 'J2973', 'J8858', 'K1515'] virology SARS-CoV-2 ['K1108', 'J9791', 'J8706', 'J8827', 'K1520'] virology SaO2_Blood_Concentration ['A7334', 'L5021'] blood_gas Sodium_Blood_Concentration ['A0262', 'J1177', 'F8162', 'F2617'] ionogram TNF alpha_Blood_Concentration ['B8931', 'G4801', 'C9393', 'K3505', 'L2203', 'E6993', 'J9194', 'K3658', 'K3502', 'K3504', 'L2191'] inflammatory_biomarkers TSH_Concentration ['A1831', 'F2150', 'I8385', 'C2666'] diabete Total_Bilirubin_Concentration ['A0029', 'H5264', 'D0189'] hepatic_panel Transferrin_Saturation_Coefficient ['A0278'] martial_panel Troponine_Concentration ['A0283', 'C5560', 'F9934', 'E6954', 'L3534', 'G7716', 'J5184', 'A3832', 'E7249'] cardiac_biomarkers Urea_Blood_Concentration ['A0286', 'G3350', 'J7372', 'F2620'] renal_panel Venous_Lactate ['A0173', 'B9146', 'A9995'] other pH_Blood ['A0221', 'L5017', 'A0219'] blood_gas Link You can see the dataset here Note The concept codes are expressed in the AnaBio and LOINC standard vocabularies (for more information about the vocabularies see the Vocabulary page).","title":"Concept sets"},{"location":"datasets/concepts-sets/#concepts-sets","text":"A concepts-set is a generic concept that has been deemed appropriate for most biological analyses. It is a group of several biological concepts representing the same biological entity. Concepts-sets This dataset is listing common biological entities in AP-HP's Data Warehouse. Below, one can see the list of default concepts-set provided by the library.","title":"Concepts-sets"},{"location":"datasets/concepts-sets/#preview","text":"concepts_set_name GLIMS_ANABIO_concept_code concepts_set_category ALAT_Activity ['A0002', 'G1804', 'J7373', 'E2067', 'F2629'] hepatic_panel ASAT_Activity ['A0022', 'G1800', 'E2068', 'F2628'] hepatic_panel Activated_Partial_Thromboplastin_Time ['A1792', 'L7286', 'A7748'] coagulation Adenovirus ['I5915', 'I7952', 'J2229', 'J2988', 'J8817', 'K1527', 'J9968'] virology Albumine_Blood_Concentration ['D2358', 'C6841', 'C2102', 'G6616', 'L2260', 'A0006', 'E4799', 'I2013'] proteins B-HCG_Blood_Concentration ['A7426', 'F2353', 'A0164', 'L2277'] diabete B.pertussis ['I7748', 'I7968', 'K1531'] virology BNP_Concentration ['C8189', 'B5596', 'A2128'] cardiac_biomarkers BNP_and_NTProBNP_Concentration ['C8189', 'B5596', 'A2128', 'A7333', 'J7267', 'J7959'] cardiac_biomarkers Bicarbonate_Blood_Concentration ['A0422', 'H9622', 'C6408', 'F4161', 'A2136', 'J7371', 'G2031'] ionogram C.pneumoniae ['I5930', 'I7969', 'J2207', 'K1530', 'J9919'] virology CRP_Concentration ['A0248', 'E6332', 'F5581', 'J7381', 'F2631'] inflammatory_panel Calcium_Blood_Concentration ['C0543', 'D2359', 'A0038', 'H5041', 'F2625', 'L5047', 'A2512', 'A2512', 'A0607'] ionogram Chloride_Blood_Concentration ['A0079', 'J1179', 'F2619'] ionogram Coronavirus ['I5916', 'I5917', 'I5918', 'I5919', 'I7953', 'I7954', 'I7955', 'I7956', 'J2199', 'J2996', 'J2994', 'J2993', 'J2992', 'J8829', 'J8828', 'J8823', 'J8822', 'K1525', 'K1524', 'K1522', 'K1523', 'J9969'] virology Creatine Kinase ['A0090', 'G0171', 'E6330'] other D-Dimers_Concentration ['C7882', 'C7882', 'I8765', 'A0124', 'C0474', 'C0474', 'C0474', 'B4199', 'F5402'] coagulation EPP_Blood_Concentration ['A0250', 'C9874', 'A3758', 'A0004', 'F9978', 'A0005', 'H8137', 'C7087', 'A0003', 'C7088', 'B9456', 'B9455', 'A0008', 'H8138', 'C7089', 'A0007', 'C7090', 'A0010', 'H8139', 'C7091', 'A0009', 'C7092', 'C6525', 'C6524', 'A0415', 'C7093', 'A0414', 'C7094', 'B9458', 'B9457', 'A2113', 'H8140', 'E5327', 'A2112', 'E5328', 'C6536', 'C6535', 'E2398', 'E2399', 'A2115', 'H8141', 'E5329', 'A2114', 'E5330', 'C6538', 'C6537', 'E2400', 'E2401', 'A0130', 'H8142', 'C7100', 'A0129', 'C7101', 'G6942', 'G6941', 'B9460', 'B9459', 'C6596', 'C6595', 'C6598', 'C6597', 'E2402', 'E2403', 'K4483', 'A2118', 'A2117', 'E1847', 'B8047', 'A2127', 'I8076', 'A2126', 'I8077', 'X5093', 'X5094', 'X5091', 'X5092', 'A2279', 'L7258', 'A1361', 'L7259', 'C6909', 'B1727', 'B1725', 'C6924', 'I5139', 'X5097', 'X5098', 'X5095', 'X5096', 'A2278', 'L7260', 'A2277', 'L7261', 'D0265', 'B1728', 'B1726', 'D0267', 'I5140', 'X5101', 'X5102', 'X5099', 'X5100', 'H6397', 'L7262', 'H6396', 'L7263', 'D0266', 'C0616', 'D0268', 'I5141', 'A8775', 'A7816', 'A8776', 'A8777', 'A8778', 'A8779', 'A8780', 'A7330', 'F0748', 'F0749', 'H9656', 'H9657', 'H9658', 'H9659', 'H9660'] proteins Eosinophil_Polymorphonuclears_Blood_Count ['A0150', 'H6730'] blood_count_cell Ferritin_Concentration ['A0123', 'E9865'] martial_panel Fibrinogen_Concentration ['A0126'] inflammatory_panel Glomerular_Filtration_Rate_EPI_CKD ['G6921', 'F8160', 'F9613', 'F9621', 'F9621'] renal_panel Glomerular_Filtration_Rate_MDRD ['F9622', 'G7835', 'B9964', 'A7456', 'A7455', 'H5609'] renal_panel GGT_Activity ['A0131', 'F8184', 'E9771', 'J7370', 'K7045'] hepatic_panel Glucose_Blood_Concentration ['A0141', 'H7323', 'J7401', 'F2622', 'B9553', 'C7236', 'E7312', 'A7338', 'H7324', 'C0565', 'E9889', 'A8424'] diabete HCO3-_Blood_Concentration ['A0420', 'L5018'] blood_gas HIV Serology ['D2865', 'D2867', 'D2845', 'D2864', 'F4252', 'D2866', 'F3257', 'D2869', 'F1705', 'D2846', 'D2847', 'D2844', 'E8605', 'F5401', 'G0175', 'J5891', 'J2672', 'H7667'] serology HbA1c_Blood_% ['B6983', 'A2228', 'A1271', 'E6632', 'I5968'] diabete Hemoglobin_Blood_Count ['A0163', 'H6738'] blood_count_cell Hepatitis B Serology ['D2729', 'D2730', 'E1524', 'F2075', 'D2725', 'I1903', 'D2728', 'D2726', 'D2726', 'F5613', 'D2731', 'D2727', 'G1197', 'G1199', 'G1199', 'J5887', 'J5890', 'J2697', 'L6883', 'J2695', 'L7877', 'D2653', 'D2660', 'D2654', 'D2661', 'D2649', 'D2656', 'H9686', 'H9687', 'I6579', 'I6580', 'D2652', 'D2659', 'D2650', 'D2650', 'D2657', 'D2655', 'D2662', 'D2651', 'D2651', 'D2658', 'F5615', 'F5616', 'G1209', 'G1209', 'G1210', 'G1210', 'I2927', 'I2928', 'J5885', 'J5886', 'J2699', 'J2700'] serology Hepatitis C Serology ['D2780', 'H5078', 'I1846', 'D2777', 'E6503', 'D2778', 'D3088', 'D3088', 'D2774', 'J1518', 'D2776', 'D2771', 'E8372', 'F7465', 'I3151', 'G0173', 'L1237', 'J2678', 'K3833', 'E8373', 'K4228'] serology IL-1 beta_Blood_Concentration ['C9351', 'B8921', 'G4800', 'K3662', 'L2217', 'J9193', 'K3665', 'K3687', 'K3661', 'L2197'] inflammatory_biomarkers IL-10_Blood_Concentration ['B8922', 'C8763', 'K3478', 'L2210', 'J9187', 'K3481', 'K3472', 'K3475', 'L2198'] inflammatory_biomarkers IL-6_Blood_Concentration ['B8929', 'G4799', 'B1910', 'K3467', 'L2205', 'E6992', 'J9190', 'K3456', 'L2193', 'K3435', 'K3460'] inflammatory_biomarkers Quick_INR_Time ['A0269'] coagulation Influenza A ['I5922', 'I7960', 'J2198', 'J2990', 'J8825', 'K1517'] virology Influenza B ['I5923', 'I7961', 'J2203', 'J2985', 'J8819', 'K1513'] virology L.pneumophila ['J2176', 'J3006', 'J8826', 'J9899'] virology LDH ['A0170', 'H5261', 'J7400', 'C8889', 'J1161'] other Lactate_Gaz_Blood_Concentration ['C8697', 'H7748'] blood_gas Legionella Antigenuria ['D1465', 'H6694', 'J7960'] antigenury Leukocytes_Blood_Count ['A0174', 'H6740', 'C8824'] blood_count_cell Lymphocytes_Blood_Count ['A0198', 'H6743'] blood_count_cell Metapneumovirus ['I5920', 'I7958', 'J2200', 'J2991', 'J8824', 'K1519', 'J9965'] virology Monocytes_Blood_Count ['A0210', 'H6747'] blood_count_cell NTProBNP_Concentration ['A7333', 'J7267', 'J7959'] cardiac_biomarkers Neutrophil_Polymorphonuclears_Blood_Count ['A0155', 'H6732'] blood_count_cell PAL_Activity ['A0227', 'F8187', 'E6331', 'F1844'] hepatic_panel PaCO2_Blood_Concentration ['A7305', 'A0630'] blood_gas PaO2_Blood_Concentration ['A7319', 'H7747'] blood_gas Parainfluenza ['I5924', 'I5925', 'I5926', 'I5927', 'I7962', 'I7963', 'I7964', 'I7965', 'J2204', 'J2979', 'J2977', 'J2983', 'J2981', 'J8861', 'J8862', 'J8821', 'J8820', 'K1509', 'K1508', 'K1510', 'K1535', 'J9964'] virology Phosphates_Blood_Concentration ['A0226', 'F8186', 'F2626'] proteins Platelets_Blood_Count ['A0230', 'H6751', 'A1598', 'A1598', 'A2538', 'A2539', 'A2539', 'J4463'] blood_count_cell Pneumococcal Antigenuria ['D2055', 'J7962', 'A2804'] antigenury Potassium_Blood_Concentration ['A2380', 'E2073', 'F2618', 'E2337', 'J1178'] ionogram Procalcitonin_Blood_Concentration ['A1661', 'H5267', 'F2632'] inflammatory_biomarkers Proteins_Urine_24h_Concentration ['A1695', 'A1694', 'A1696', 'C9990', 'C9991', 'J7268', 'J7269', 'C3941', 'E4745', 'G4187', 'F6060'] proteins Proteins_Blood_Concentration ['A7347', 'F5122', 'F2624', 'B9417', 'A0249', 'B3990'] inflammatory_panel Quick_Prothrombin_Time ['A1805', 'E9993'] coagulation RSV ['I5928', 'I7966', 'J2201', 'J2974', 'J8859', 'K1534'] virology Rhino/Enterovirus ['I5921', 'I7959', 'J2197', 'J2973', 'J8858', 'K1515'] virology SARS-CoV-2 ['K1108', 'J9791', 'J8706', 'J8827', 'K1520'] virology SaO2_Blood_Concentration ['A7334', 'L5021'] blood_gas Sodium_Blood_Concentration ['A0262', 'J1177', 'F8162', 'F2617'] ionogram TNF alpha_Blood_Concentration ['B8931', 'G4801', 'C9393', 'K3505', 'L2203', 'E6993', 'J9194', 'K3658', 'K3502', 'K3504', 'L2191'] inflammatory_biomarkers TSH_Concentration ['A1831', 'F2150', 'I8385', 'C2666'] diabete Total_Bilirubin_Concentration ['A0029', 'H5264', 'D0189'] hepatic_panel Transferrin_Saturation_Coefficient ['A0278'] martial_panel Troponine_Concentration ['A0283', 'C5560', 'F9934', 'E6954', 'L3534', 'G7716', 'J5184', 'A3832', 'E7249'] cardiac_biomarkers Urea_Blood_Concentration ['A0286', 'G3350', 'J7372', 'F2620'] renal_panel Venous_Lactate ['A0173', 'B9146', 'A9995'] other pH_Blood ['A0221', 'L5017', 'A0219'] blood_gas","title":"Preview"},{"location":"datasets/concepts-sets/#link","text":"You can see the dataset here Note The concept codes are expressed in the AnaBio and LOINC standard vocabularies (for more information about the vocabularies see the Vocabulary page).","title":"Link"},{"location":"datasets/private-resources/","text":"Resources eds-scikit contains some resources that are stored as is , either because it comes from manual work, or because its generation might be time and computationally intensive. Private data A lot of those resources are specific to AP-HP's CDW, thus are stored on a private repository. If you work on AP-HP's ecosystem, you can install those resources along with eds-scikit via pip install 'eds_scikit[aphp]' . For each resource listed bellow, you will find: A short description If relevant, a way to register your function in order to use your own data If relevant, a link to the generation function Available resources Care site hierarchy Care site emergency Default concepts-sets for Biology Default configuration for Biology","title":"A note on private resources"},{"location":"datasets/private-resources/#resources","text":"eds-scikit contains some resources that are stored as is , either because it comes from manual work, or because its generation might be time and computationally intensive. Private data A lot of those resources are specific to AP-HP's CDW, thus are stored on a private repository. If you work on AP-HP's ecosystem, you can install those resources along with eds-scikit via pip install 'eds_scikit[aphp]' . For each resource listed bellow, you will find: A short description If relevant, a way to register your function in order to use your own data If relevant, a link to the generation function","title":"Resources"},{"location":"datasets/private-resources/#available-resources","text":"Care site hierarchy Care site emergency Default concepts-sets for Biology Default configuration for Biology","title":"Available resources"},{"location":"datasets/synthetic-data/","text":"Small Datasets for testing functionalities Presentation eds-scikit was build to work seamlessly on a pre-existing OMOP database. However, the library also provides some toy datasets so that you can try out some features even without having access to a database. Usage First, you can display all availables synthetic datasets: from eds_scikit import datasets datasets . list_all_synthetics () # Out: ['load_ccam', 'load_consultation_dates', 'load_hierarchy', 'load_icd10', 'load_visit_merging', 'load_stay_duration', 'load_suicide_attempt', 'load_tagging', 'load_biology_data', 'load_event_sequences'] To load a specific dataset, simply run: data = datasets . load_icd10 () data # Out: ICD10Dataset(condition_occurrence, visit_occurrence) The data object is similar to objects available in eds_scikit.io , namely: HiveData PostgresData PandasData For instance, tables are available as attributes: data . condition_occurrence | | person_id | condition_source_value | condition_start_datetime | condition_status_source_value | visit_occurrence_id | |---|-----------|------------------------|--------------------------|-------------------------------|---------------------| | 0 | 1 | C10 | 2010-01-01 | DP | 11 | | 1 | 1 | E112 | 2010-01-01 | DAS | 12 | | 2 | 1 | D20 | 2012-01-01 | DAS | 13 | | 3 | 1 | A20 | 2020-01-01 | DP | 14 | | 4 | 1 | A21 | 2000-01-01 | DP | 15 | | 5 | 1 | X20 | 2000-01-01 | DP | 16 | | 6 | 1 | C10 | 2010-01-01 | DP | 16 | | 7 | 1 | C10 | 2010-01-01 | DP | 17 | As shown in the tutorial , you can now try out the corresponding conditions_from_icd10() function.","title":"Synthetic data"},{"location":"datasets/synthetic-data/#small-datasets-for-testing-functionalities","text":"","title":"Small Datasets for testing functionalities"},{"location":"datasets/synthetic-data/#presentation","text":"eds-scikit was build to work seamlessly on a pre-existing OMOP database. However, the library also provides some toy datasets so that you can try out some features even without having access to a database.","title":"Presentation"},{"location":"datasets/synthetic-data/#usage","text":"First, you can display all availables synthetic datasets: from eds_scikit import datasets datasets . list_all_synthetics () # Out: ['load_ccam', 'load_consultation_dates', 'load_hierarchy', 'load_icd10', 'load_visit_merging', 'load_stay_duration', 'load_suicide_attempt', 'load_tagging', 'load_biology_data', 'load_event_sequences'] To load a specific dataset, simply run: data = datasets . load_icd10 () data # Out: ICD10Dataset(condition_occurrence, visit_occurrence) The data object is similar to objects available in eds_scikit.io , namely: HiveData PostgresData PandasData For instance, tables are available as attributes: data . condition_occurrence | | person_id | condition_source_value | condition_start_datetime | condition_status_source_value | visit_occurrence_id | |---|-----------|------------------------|--------------------------|-------------------------------|---------------------| | 0 | 1 | C10 | 2010-01-01 | DP | 11 | | 1 | 1 | E112 | 2010-01-01 | DAS | 12 | | 2 | 1 | D20 | 2012-01-01 | DAS | 13 | | 3 | 1 | A20 | 2020-01-01 | DP | 14 | | 4 | 1 | A21 | 2000-01-01 | DP | 15 | | 5 | 1 | X20 | 2000-01-01 | DP | 16 | | 6 | 1 | C10 | 2010-01-01 | DP | 16 | | 7 | 1 | C10 | 2010-01-01 | DP | 17 | As shown in the tutorial , you can now try out the corresponding conditions_from_icd10() function.","title":"Usage"},{"location":"functionalities/biology/","text":"Biology The biology module of eds-scikit supports data scientists working on biological data. Its main objectives are to: Provide predefined biology concept sets based on AP-HP coding system Facilitate codes mapping between different terminologies and referentials Provide data visualization tools and statistic summary Allows automatic units conversion from heterogenous units system Quick use Simple mapping of measurement table codes to ANABIO terminology. Visualizing measurements Useful measurement visualizations. Detailed use Preparing measurements workflow : codes mapping, units conversion, outliers detection. Terminologies relationships Explore concept codes relationships between different terminologies. Predefined concept sets Explore predefined concept sets. About measurement table Knowledge about measurement table.","title":"Biology"},{"location":"functionalities/biology/#biology","text":"The biology module of eds-scikit supports data scientists working on biological data. Its main objectives are to: Provide predefined biology concept sets based on AP-HP coding system Facilitate codes mapping between different terminologies and referentials Provide data visualization tools and statistic summary Allows automatic units conversion from heterogenous units system Quick use Simple mapping of measurement table codes to ANABIO terminology. Visualizing measurements Useful measurement visualizations. Detailed use Preparing measurements workflow : codes mapping, units conversion, outliers detection. Terminologies relationships Explore concept codes relationships between different terminologies. Predefined concept sets Explore predefined concept sets. About measurement table Knowledge about measurement table.","title":"Biology"},{"location":"functionalities/biology/about_measurement/","text":"About measurements table The BioClean module focuses on three OMOP terms: Measurement is a record obtained through the standardized testing or examination of a person or person's sample. Concept is a semantic notion that uniquely identify a clinical event. It can group several measurements. Concept Relationship is a semantic relation between terminologies, allowing to map codes from different terminologies. A fourht term was created to ease the use of the two above: concepts-set is a generic concept that has been deemed appropriate for most biological analyses. It is a group of several biological concepts representing the same biological entity. Example: Let's imagine the laboratory X tests the creatinine of Mister A and Mister B in mg/dL and the laboratory Y tests the creatinine of Mister C in \u00b5mol/L. In this context, the dataset will contain: 3 measurements (one for each conducted test) 2 concepts (one concept for the creatinine tested in mg/dL and another one for the creatinine tested in \u00b5mol/L) 1 concepts-set (it groups the 2 concepts because they are the same biological entity) Vocabulary A vocabulary is a terminology system that associates a code to a specific clinical event. One may distinguish two types of vocabularies: Source vocabulary The source vocabulary is the vocabulary used in the LIMS (Laboratory Information Management System) software. It is specific to the LIMS and may be different in each laboratory. Standard vocabulary The standard vocabulary is a unified vocabulary that allows data analysis on a larger scale. It is classified in chapter. It has a bigger granularity than the source vocabulary, multiple source codes may be associated to one standard code. Vocabulary flowchart in OMOP","title":"About measurement"},{"location":"functionalities/biology/about_measurement/#about-measurements-table","text":"The BioClean module focuses on three OMOP terms: Measurement is a record obtained through the standardized testing or examination of a person or person's sample. Concept is a semantic notion that uniquely identify a clinical event. It can group several measurements. Concept Relationship is a semantic relation between terminologies, allowing to map codes from different terminologies. A fourht term was created to ease the use of the two above: concepts-set is a generic concept that has been deemed appropriate for most biological analyses. It is a group of several biological concepts representing the same biological entity. Example: Let's imagine the laboratory X tests the creatinine of Mister A and Mister B in mg/dL and the laboratory Y tests the creatinine of Mister C in \u00b5mol/L. In this context, the dataset will contain: 3 measurements (one for each conducted test) 2 concepts (one concept for the creatinine tested in mg/dL and another one for the creatinine tested in \u00b5mol/L) 1 concepts-set (it groups the 2 concepts because they are the same biological entity)","title":"About measurements table"},{"location":"functionalities/biology/about_measurement/#vocabulary","text":"A vocabulary is a terminology system that associates a code to a specific clinical event. One may distinguish two types of vocabularies:","title":"Vocabulary"},{"location":"functionalities/biology/about_measurement/#source-vocabulary","text":"The source vocabulary is the vocabulary used in the LIMS (Laboratory Information Management System) software. It is specific to the LIMS and may be different in each laboratory.","title":"Source vocabulary"},{"location":"functionalities/biology/about_measurement/#standard-vocabulary","text":"The standard vocabulary is a unified vocabulary that allows data analysis on a larger scale. It is classified in chapter. It has a bigger granularity than the source vocabulary, multiple source codes may be associated to one standard code.","title":"Standard vocabulary"},{"location":"functionalities/biology/about_measurement/#vocabulary-flowchart-in-omop","text":"","title":"Vocabulary flowchart in OMOP"},{"location":"functionalities/biology/concepts_sets/","text":"Predefined concepts sets Those concept sets can be found in eds_scikit.datasets.default_concepts_sets . How were the code selected ? Each concept set codes were selected by coding system expert and validated through statistical analysis. However new codes may appear or become outdated. Feel free to propose new concept sets or concept codes ! Blood Count Cell Ionogram Blood Gas Hepatic Panel Cardiac Biomarkers Inflammatory Panel Martial Panel Renal Panel Coagulation Proteins Diabete Inflammatory Biomarkers concepts_set_name GLIMS_ANABIO_concept_code Hemoglobin_Blood_Count ['A0163', 'H6738'] Leukocytes_Blood_Count ['A0174', 'H6740', 'C8824'] Platelets_Blood_Count ['A0230', 'H6751', 'A1598', 'A1598', 'A2538', 'A2539', 'A2539', 'J4463'] Lymphocytes_Blood_Count ['A0198', 'H6743'] Monocytes_Blood_Count ['A0210', 'H6747'] Neutrophil_Polymorphonuclears_Blood_Count ['A0155', 'H6732'] Eosinophil_Polymorphonuclears_Blood_Count ['A0150', 'H6730'] concepts_set_name GLIMS_ANABIO_concept_code Bicarbonate_Blood_Concentration ['A0422', 'H9622', 'C6408', 'F4161', 'A2136', 'J7371', 'G2031'] Calcium_Blood_Concentration ['C0543', 'D2359', 'A0038', 'H5041', 'F2625', 'L5047', 'A2512', 'A2512', 'A0607'] Chloride_Blood_Concentration ['A0079', 'J1179', 'F2619'] Potassium_Blood_Concentration ['A2380', 'E2073', 'F2618', 'E2337', 'J1178'] Sodium_Blood_Concentration ['A0262', 'J1177', 'F8162', 'F2617'] concepts_set_name GLIMS_ANABIO_concept_code HCO3-_Blood_Concentration ['A0420', 'L5018'] Lactate_Gaz_Blood_Concentration ['C8697', 'H7748'] PaCO2_Blood_Concentration ['A7305', 'A0630'] PaO2_Blood_Concentration ['A7319', 'H7747'] SaO2_Blood_Concentration ['A7334', 'L5021'] pH_Blood ['A0221', 'L5017', 'A0219'] concepts_set_name GLIMS_ANABIO_concept_code ALAT_Activity ['A0002', 'G1804', 'J7373', 'E2067', 'F2629'] ASAT_Activity ['A0022', 'G1800', 'E2068', 'F2628'] GGT_Activity ['A0131', 'F8184', 'E9771', 'J7370', 'K7045'] PAL_Activity ['A0227', 'F8187', 'E6331', 'F1844'] Total_Bilirubin_Concentration ['A0029', 'H5264', 'D0189'] concepts_set_name GLIMS_ANABIO_concept_code BNP_Concentration ['C8189', 'B5596', 'A2128'] BNP_and_NTProBNP_Concentration ['C8189', 'B5596', 'A2128', 'A7333', 'J7267', 'J7959'] NTProBNP_Concentration ['A7333', 'J7267', 'J7959'] Troponine_Concentration ['A0283', 'C5560', 'F9934', 'E6954', 'L3534', 'G7716', 'J5184', 'A3832', 'E7249'] concepts_set_name GLIMS_ANABIO_concept_code CRP_Concentration ['A0248', 'E6332', 'F5581', 'J7381', 'F2631'] Fibrinogen_Concentration ['A0126'] Proteins_Blood_Concentration ['A7347', 'F5122', 'F2624', 'B9417', 'A0249', 'B3990'] concepts_set_name GLIMS_ANABIO_concept_code Ferritin_Concentration ['A0123', 'E9865'] Transferrin_Saturation_Coefficient ['A0278'] concepts_set_name GLIMS_ANABIO_concept_code Glomerular_Filtration_Rate_EPI_CKD ['G6921', 'F8160', 'F9613', 'F9621', 'F9621'] Glomerular_Filtration_Rate_MDRD ['F9622', 'G7835', 'B9964', 'A7456', 'A7455', 'H5609'] Urea_Blood_Concentration ['A0286', 'G3350', 'J7372', 'F2620'] concepts_set_name GLIMS_ANABIO_concept_code Activated_Partial_Thromboplastin_Time ['A1792', 'L7286', 'A7748'] D-Dimers_Concentration ['C7882', 'C7882', 'I8765', 'A0124', 'C0474', 'C0474', 'C0474', 'B4199', 'F5402'] Quick_INR_Time ['A0269'] Quick_Prothrombin_Time ['A1805', 'E9993'] concepts_set_name GLIMS_ANABIO_concept_code Albumine_Blood_Concentration ['D2358', 'C6841', 'C2102', 'G6616', 'L2260', 'A0006', 'E4799', 'I2013'] EPP_Blood_Concentration ['A0250', 'C9874', 'A3758', 'A0004', 'F9978', 'A0005', 'H8137', 'C7087', 'A0003', 'C7088', 'B9456', 'B9455', 'A0008', 'H8138', 'C7089', 'A0007'... Phosphates_Blood_Concentration ['A0226', 'F8186', 'F2626'] Proteins_Urine_24h_Concentration ['A1695', 'A1694', 'A1696', 'C9990', 'C9991', 'J7268', 'J7269', 'C3941', 'E4745', 'G4187', 'F6060'] concepts_set_name GLIMS_ANABIO_concept_code B-HCG_Blood_Concentration ['A7426', 'F2353', 'A0164', 'L2277'] Glucose_Blood_Concentration ['A0141', 'H7323', 'J7401', 'F2622', 'B9553', 'C7236', 'E7312', 'A7338', 'H7324', 'C0565', 'E9889', 'A8424'] HbA1c_Blood_% ['B6983', 'A2228', 'A1271', 'E6632', 'I5968'] TSH_Concentration ['A1831', 'F2150', 'I8385', 'C2666'] concepts_set_name GLIMS_ANABIO_concept_code IL-1 beta_Blood_Concentration ['C9351', 'B8921', 'G4800', 'K3662', 'L2217', 'J9193', 'K3665', 'K3687', 'K3661', 'L2197'] IL-10_Blood_Concentration ['B8922', 'C8763', 'K3478', 'L2210', 'J9187', 'K3481', 'K3472', 'K3475', 'L2198'] IL-6_Blood_Concentration ['B8929', 'G4799', 'B1910', 'K3467', 'L2205', 'E6992', 'J9190', 'K3456', 'L2193', 'K3435', 'K3460'] Procalcitonin_Blood_Concentration ['A1661', 'H5267', 'F2632'] TNF alpha_Blood_Concentration ['B8931', 'G4801', 'C9393', 'K3505', 'L2203', 'E6993', 'J9194', 'K3658', 'K3502', 'K3504', 'L2191']","title":"Predefined concepts sets"},{"location":"functionalities/biology/concepts_sets/#predefined-concepts-sets","text":"Those concept sets can be found in eds_scikit.datasets.default_concepts_sets . How were the code selected ? Each concept set codes were selected by coding system expert and validated through statistical analysis. However new codes may appear or become outdated. Feel free to propose new concept sets or concept codes ! Blood Count Cell Ionogram Blood Gas Hepatic Panel Cardiac Biomarkers Inflammatory Panel Martial Panel Renal Panel Coagulation Proteins Diabete Inflammatory Biomarkers concepts_set_name GLIMS_ANABIO_concept_code Hemoglobin_Blood_Count ['A0163', 'H6738'] Leukocytes_Blood_Count ['A0174', 'H6740', 'C8824'] Platelets_Blood_Count ['A0230', 'H6751', 'A1598', 'A1598', 'A2538', 'A2539', 'A2539', 'J4463'] Lymphocytes_Blood_Count ['A0198', 'H6743'] Monocytes_Blood_Count ['A0210', 'H6747'] Neutrophil_Polymorphonuclears_Blood_Count ['A0155', 'H6732'] Eosinophil_Polymorphonuclears_Blood_Count ['A0150', 'H6730'] concepts_set_name GLIMS_ANABIO_concept_code Bicarbonate_Blood_Concentration ['A0422', 'H9622', 'C6408', 'F4161', 'A2136', 'J7371', 'G2031'] Calcium_Blood_Concentration ['C0543', 'D2359', 'A0038', 'H5041', 'F2625', 'L5047', 'A2512', 'A2512', 'A0607'] Chloride_Blood_Concentration ['A0079', 'J1179', 'F2619'] Potassium_Blood_Concentration ['A2380', 'E2073', 'F2618', 'E2337', 'J1178'] Sodium_Blood_Concentration ['A0262', 'J1177', 'F8162', 'F2617'] concepts_set_name GLIMS_ANABIO_concept_code HCO3-_Blood_Concentration ['A0420', 'L5018'] Lactate_Gaz_Blood_Concentration ['C8697', 'H7748'] PaCO2_Blood_Concentration ['A7305', 'A0630'] PaO2_Blood_Concentration ['A7319', 'H7747'] SaO2_Blood_Concentration ['A7334', 'L5021'] pH_Blood ['A0221', 'L5017', 'A0219'] concepts_set_name GLIMS_ANABIO_concept_code ALAT_Activity ['A0002', 'G1804', 'J7373', 'E2067', 'F2629'] ASAT_Activity ['A0022', 'G1800', 'E2068', 'F2628'] GGT_Activity ['A0131', 'F8184', 'E9771', 'J7370', 'K7045'] PAL_Activity ['A0227', 'F8187', 'E6331', 'F1844'] Total_Bilirubin_Concentration ['A0029', 'H5264', 'D0189'] concepts_set_name GLIMS_ANABIO_concept_code BNP_Concentration ['C8189', 'B5596', 'A2128'] BNP_and_NTProBNP_Concentration ['C8189', 'B5596', 'A2128', 'A7333', 'J7267', 'J7959'] NTProBNP_Concentration ['A7333', 'J7267', 'J7959'] Troponine_Concentration ['A0283', 'C5560', 'F9934', 'E6954', 'L3534', 'G7716', 'J5184', 'A3832', 'E7249'] concepts_set_name GLIMS_ANABIO_concept_code CRP_Concentration ['A0248', 'E6332', 'F5581', 'J7381', 'F2631'] Fibrinogen_Concentration ['A0126'] Proteins_Blood_Concentration ['A7347', 'F5122', 'F2624', 'B9417', 'A0249', 'B3990'] concepts_set_name GLIMS_ANABIO_concept_code Ferritin_Concentration ['A0123', 'E9865'] Transferrin_Saturation_Coefficient ['A0278'] concepts_set_name GLIMS_ANABIO_concept_code Glomerular_Filtration_Rate_EPI_CKD ['G6921', 'F8160', 'F9613', 'F9621', 'F9621'] Glomerular_Filtration_Rate_MDRD ['F9622', 'G7835', 'B9964', 'A7456', 'A7455', 'H5609'] Urea_Blood_Concentration ['A0286', 'G3350', 'J7372', 'F2620'] concepts_set_name GLIMS_ANABIO_concept_code Activated_Partial_Thromboplastin_Time ['A1792', 'L7286', 'A7748'] D-Dimers_Concentration ['C7882', 'C7882', 'I8765', 'A0124', 'C0474', 'C0474', 'C0474', 'B4199', 'F5402'] Quick_INR_Time ['A0269'] Quick_Prothrombin_Time ['A1805', 'E9993'] concepts_set_name GLIMS_ANABIO_concept_code Albumine_Blood_Concentration ['D2358', 'C6841', 'C2102', 'G6616', 'L2260', 'A0006', 'E4799', 'I2013'] EPP_Blood_Concentration ['A0250', 'C9874', 'A3758', 'A0004', 'F9978', 'A0005', 'H8137', 'C7087', 'A0003', 'C7088', 'B9456', 'B9455', 'A0008', 'H8138', 'C7089', 'A0007'... Phosphates_Blood_Concentration ['A0226', 'F8186', 'F2626'] Proteins_Urine_24h_Concentration ['A1695', 'A1694', 'A1696', 'C9990', 'C9991', 'J7268', 'J7269', 'C3941', 'E4745', 'G4187', 'F6060'] concepts_set_name GLIMS_ANABIO_concept_code B-HCG_Blood_Concentration ['A7426', 'F2353', 'A0164', 'L2277'] Glucose_Blood_Concentration ['A0141', 'H7323', 'J7401', 'F2622', 'B9553', 'C7236', 'E7312', 'A7338', 'H7324', 'C0565', 'E9889', 'A8424'] HbA1c_Blood_% ['B6983', 'A2228', 'A1271', 'E6632', 'I5968'] TSH_Concentration ['A1831', 'F2150', 'I8385', 'C2666'] concepts_set_name GLIMS_ANABIO_concept_code IL-1 beta_Blood_Concentration ['C9351', 'B8921', 'G4800', 'K3662', 'L2217', 'J9193', 'K3665', 'K3687', 'K3661', 'L2197'] IL-10_Blood_Concentration ['B8922', 'C8763', 'K3478', 'L2210', 'J9187', 'K3481', 'K3472', 'K3475', 'L2198'] IL-6_Blood_Concentration ['B8929', 'G4799', 'B1910', 'K3467', 'L2205', 'E6992', 'J9190', 'K3456', 'L2193', 'K3435', 'K3460'] Procalcitonin_Blood_Concentration ['A1661', 'H5267', 'F2632'] TNF alpha_Blood_Concentration ['B8931', 'G4801', 'C9393', 'K3505', 'L2203', 'E6993', 'J9194', 'K3658', 'K3502', 'K3504', 'L2191']","title":"Predefined concepts sets"},{"location":"functionalities/biology/exploring-relationship/","text":"Terminologies relationships Manipulating different code terminologies through OMOP concept and concept_relationship tables can be tricky. This becomes even more pronounced when working with biological measurements that may encompass multiple terminologies, including laboratory, unified, and international terminologies. Use prepare_biology_relationship_table to preprocess OMOP concept and concept_relationship into a single table and get a better insight on how terminologies are related. Relationship config Terminologies mapping from AP-HP database are used by default. See io.settings.measurement_config for mapping details or to modify it. from eds_scikit.biology import prepare_biology_relationship_table biology_relationship_table = prepare_biology_relationship_table ( data ) biology_relationship_table = biology_relationship_table . to_pandas () biology_relationship_table . head () source_concept_id source_concept_name source_concept_code standard_concept_id standard_concept_name standard_concept_code 3 xxxxxxxxxxxx CX1 4 xxxxxxxxxxxx A1 9 xxxxxxxxxxxx ZY2 5 xxxxxxxxxxxx A2 9 xxxxxxxxxxxx B3F 47 xxxxxxxxxxxx D3 7 xxxxxxxxxxxx T32 4 xxxxxxxxxxxx F82 5 xxxxxxxxxxxx S23 1 xxxxxxxxxxxx A432","title":"Terminologies relationships"},{"location":"functionalities/biology/exploring-relationship/#terminologies-relationships","text":"Manipulating different code terminologies through OMOP concept and concept_relationship tables can be tricky. This becomes even more pronounced when working with biological measurements that may encompass multiple terminologies, including laboratory, unified, and international terminologies. Use prepare_biology_relationship_table to preprocess OMOP concept and concept_relationship into a single table and get a better insight on how terminologies are related. Relationship config Terminologies mapping from AP-HP database are used by default. See io.settings.measurement_config for mapping details or to modify it. from eds_scikit.biology import prepare_biology_relationship_table biology_relationship_table = prepare_biology_relationship_table ( data ) biology_relationship_table = biology_relationship_table . to_pandas () biology_relationship_table . head () source_concept_id source_concept_name source_concept_code standard_concept_id standard_concept_name standard_concept_code 3 xxxxxxxxxxxx CX1 4 xxxxxxxxxxxx A1 9 xxxxxxxxxxxx ZY2 5 xxxxxxxxxxxx A2 9 xxxxxxxxxxxx B3F 47 xxxxxxxxxxxx D3 7 xxxxxxxxxxxx T32 4 xxxxxxxxxxxx F82 5 xxxxxxxxxxxx S23 1 xxxxxxxxxxxx A432","title":"Terminologies relationships"},{"location":"functionalities/biology/prepare_measurement/","text":"Prepare measurement Prepare measurement flowchart The pipeline is structured in 3 stages: Basic preprocessing Codes mapping Units conversion","title":"Prepare measurement"},{"location":"functionalities/biology/prepare_measurement/#prepare-measurement","text":"","title":"Prepare measurement"},{"location":"functionalities/biology/prepare_measurement/#prepare-measurement-flowchart","text":"The pipeline is structured in 3 stages: Basic preprocessing Codes mapping Units conversion","title":"Prepare measurement flowchart"},{"location":"functionalities/biology/preparing-measurement/","text":"Detailed use This tutorial demonstrates the workflow to prepare the measurement table. Big volume Measurement table can be large. Do not forget to set proper spark config before loading data. Mapping measurement table to ANABIO codes Defining Concept-Set Here we work with the Glucose pre-defined concept set . See quick-use for an example on how to create a custom concept set. from eds_scikit.biology import prepare_measurement_table , ConceptsSet glucose_blood = ConceptsSet ( \"Glucose_Blood\" ) Preparing measurement table First, we prepare measurements with convert_units = False (as we do not yet know which units are contained in the table). from eds_scikit.biology import measurement_values_summary measurement = prepare_measurement_table ( data , start_date = \"2022-01-01\" , end_date = \"2022-05-01\" , concept_sets = [ glucose_blood ], convert_units = False , get_all_terminologies = False , ) Statistical summary A statistical summary by codes allows us to gain insight into value distributions and detect possible heterogeneous units. from eds_scikit.biology import measurement_values_summary stats_summary = measurement_values_summary ( measurement , category_cols = [ \"concept_set\" , \"GLIMS_ANABIO_concept_code\" ], value_column = \"value_as_number\" , unit_column = \"unit_source_value\" , ) stats_summary concept_set ANABIO_concept_code no_units unit_source_value range_low_anomaly_count range_high_anomaly_count measurement_count value_as_number_count value_as_number_mean value_as_number_std value_as_number_min value_as_number_25% value_as_number_50% value_as_number_75% value_as_number_max Glucose_Blood XXXXX 100 mmol/l 15 5 1000 1000 5 2 0 2 5 8 9 Glucose_Blood YYYYY 50 mg/ml 20 10 5000 5000 25 10 0 20 25 37 45 Glucose_Blood ZZZZZ 10 mmol/l 5 18 1000 1000 6 1 0 4 6 7 10 Units correction To map all units to a common unit base we can use add_conversion and add_target_unit from ConceptSet class. glucose_blood . add_conversion ( \"mol\" , \"g\" , 180 ) glucose_blood . add_target_unit ( \"mmol/l\" ) We can check the new summary table after units conversion. stats_summary = measurement_values_summary ( measurement , category_cols = [ \"concept_set\" , \"GLIMS_ANABIO_concept_code\" ], value_column = \"value_as_number_normalized\" , unit_column = \"unit_source_value_normalized\" , ) stats_summary concept_set ANABIO_concept_code no_units unit_source_value range_low_anomaly_count range_high_anomaly_count measurement_count value_as_number_count value_as_number_mean value_as_number_std value_as_number_min value_as_number_25% value_as_number_50% value_as_number_75% value_as_number_max Glucose_Blood XXXXX 100 mmol/l 15 5 1000 1000 5 2 0 2 5 8 9 Glucose_Blood YYYYY 50 mmol/l 20 10 5000 5000 5 2 0 4 5 7 9 Glucose_Blood ZZZZZ 10 mmol/l 5 18 1000 1000 6 1 0 4 6 7 10 Plot summary Once all units are homogeneous, we can generate more detailed dashboard for biology investigation. from eds_scikit.biology import plot_biology_summary measurement = measurement . merge ( data . visit_occurrence [[ \"care_site_id\" , \"visit_occurrence_id\" ]], on = \"visit_occurrence_id\" , ) measurement = measurement . merge ( data . care_site [[ \"care_site_id\" , \"care_site_short_name\" ]], on = \"care_site_id\" ) plot_biology_summary ( measurement , value_column = \"value_as_number_normalized\" ) Volumetry dashboard Distribution dashboard","title":"Detailed use"},{"location":"functionalities/biology/preparing-measurement/#detailed-use","text":"This tutorial demonstrates the workflow to prepare the measurement table. Big volume Measurement table can be large. Do not forget to set proper spark config before loading data.","title":"Detailed use"},{"location":"functionalities/biology/preparing-measurement/#mapping-measurement-table-to-anabio-codes","text":"","title":"Mapping measurement table to ANABIO codes"},{"location":"functionalities/biology/preparing-measurement/#defining-concept-set","text":"Here we work with the Glucose pre-defined concept set . See quick-use for an example on how to create a custom concept set. from eds_scikit.biology import prepare_measurement_table , ConceptsSet glucose_blood = ConceptsSet ( \"Glucose_Blood\" )","title":"Defining Concept-Set"},{"location":"functionalities/biology/preparing-measurement/#preparing-measurement-table","text":"First, we prepare measurements with convert_units = False (as we do not yet know which units are contained in the table). from eds_scikit.biology import measurement_values_summary measurement = prepare_measurement_table ( data , start_date = \"2022-01-01\" , end_date = \"2022-05-01\" , concept_sets = [ glucose_blood ], convert_units = False , get_all_terminologies = False , )","title":"Preparing measurement table"},{"location":"functionalities/biology/preparing-measurement/#statistical-summary","text":"A statistical summary by codes allows us to gain insight into value distributions and detect possible heterogeneous units. from eds_scikit.biology import measurement_values_summary stats_summary = measurement_values_summary ( measurement , category_cols = [ \"concept_set\" , \"GLIMS_ANABIO_concept_code\" ], value_column = \"value_as_number\" , unit_column = \"unit_source_value\" , ) stats_summary concept_set ANABIO_concept_code no_units unit_source_value range_low_anomaly_count range_high_anomaly_count measurement_count value_as_number_count value_as_number_mean value_as_number_std value_as_number_min value_as_number_25% value_as_number_50% value_as_number_75% value_as_number_max Glucose_Blood XXXXX 100 mmol/l 15 5 1000 1000 5 2 0 2 5 8 9 Glucose_Blood YYYYY 50 mg/ml 20 10 5000 5000 25 10 0 20 25 37 45 Glucose_Blood ZZZZZ 10 mmol/l 5 18 1000 1000 6 1 0 4 6 7 10","title":"Statistical summary"},{"location":"functionalities/biology/preparing-measurement/#units-correction","text":"To map all units to a common unit base we can use add_conversion and add_target_unit from ConceptSet class. glucose_blood . add_conversion ( \"mol\" , \"g\" , 180 ) glucose_blood . add_target_unit ( \"mmol/l\" ) We can check the new summary table after units conversion. stats_summary = measurement_values_summary ( measurement , category_cols = [ \"concept_set\" , \"GLIMS_ANABIO_concept_code\" ], value_column = \"value_as_number_normalized\" , unit_column = \"unit_source_value_normalized\" , ) stats_summary concept_set ANABIO_concept_code no_units unit_source_value range_low_anomaly_count range_high_anomaly_count measurement_count value_as_number_count value_as_number_mean value_as_number_std value_as_number_min value_as_number_25% value_as_number_50% value_as_number_75% value_as_number_max Glucose_Blood XXXXX 100 mmol/l 15 5 1000 1000 5 2 0 2 5 8 9 Glucose_Blood YYYYY 50 mmol/l 20 10 5000 5000 5 2 0 4 5 7 9 Glucose_Blood ZZZZZ 10 mmol/l 5 18 1000 1000 6 1 0 4 6 7 10","title":"Units correction"},{"location":"functionalities/biology/preparing-measurement/#plot-summary","text":"Once all units are homogeneous, we can generate more detailed dashboard for biology investigation. from eds_scikit.biology import plot_biology_summary measurement = measurement . merge ( data . visit_occurrence [[ \"care_site_id\" , \"visit_occurrence_id\" ]], on = \"visit_occurrence_id\" , ) measurement = measurement . merge ( data . care_site [[ \"care_site_id\" , \"care_site_short_name\" ]], on = \"care_site_id\" ) plot_biology_summary ( measurement , value_column = \"value_as_number_normalized\" ) Volumetry dashboard Distribution dashboard","title":"Plot summary"},{"location":"functionalities/biology/quick-use/","text":"Quick use This tutorial demonstrates how the biology module can be quickly used to map measurement codes. Big volume Measurement table can be large. Do not forget to set proper spark config before loading data. Mapping measurement table to ANABIO codes Defining Concept-Set To define a concept-set variable you just need to specify a terminology and a set of codes. from eds_scikit.biology import prepare_measurement_table , ConceptsSet custom_leukocytes = ConceptsSet ( \"Custom_Leukocytes\" ) custom_leukocytes . add_concept_codes ( concept_codes = [ \"A0174\" , \"H6740\" ], terminology = \"GLIMS_ANABIO\" # (1) ) custom_leukocytes . add_concept_codes ( concept_codes = [ \"6690-2\" ], terminology = \"ITM_LOINC\" # (2) ) Codes must be given with terminology. Available terminologies can be accessed with eds_scikit.io.settings.measurement_config['source_terminologies'] . See. AP-HP biology for details on the AP-HP setting. Codes must be given with terminology. Available terminologies can be accessed with eds_scikit.io.settings.measurement_config['source_terminologies'] . See. AP-HP biology for details on the AP-HP setting. Preparing measurement table Then, simply run prepare_measurement_table to select the measurements from your concept set. measurement = prepare_measurement_table ( data , start_date = \"2022-01-01\" , end_date = \"2022-05-01\" , concept_sets = [ custom_leukocytes ], convert_units = False , get_all_terminologies = True , )","title":"Quick use"},{"location":"functionalities/biology/quick-use/#quick-use","text":"This tutorial demonstrates how the biology module can be quickly used to map measurement codes. Big volume Measurement table can be large. Do not forget to set proper spark config before loading data.","title":"Quick use"},{"location":"functionalities/biology/quick-use/#mapping-measurement-table-to-anabio-codes","text":"","title":"Mapping measurement table to ANABIO codes"},{"location":"functionalities/biology/quick-use/#defining-concept-set","text":"To define a concept-set variable you just need to specify a terminology and a set of codes. from eds_scikit.biology import prepare_measurement_table , ConceptsSet custom_leukocytes = ConceptsSet ( \"Custom_Leukocytes\" ) custom_leukocytes . add_concept_codes ( concept_codes = [ \"A0174\" , \"H6740\" ], terminology = \"GLIMS_ANABIO\" # (1) ) custom_leukocytes . add_concept_codes ( concept_codes = [ \"6690-2\" ], terminology = \"ITM_LOINC\" # (2) ) Codes must be given with terminology. Available terminologies can be accessed with eds_scikit.io.settings.measurement_config['source_terminologies'] . See. AP-HP biology for details on the AP-HP setting. Codes must be given with terminology. Available terminologies can be accessed with eds_scikit.io.settings.measurement_config['source_terminologies'] . See. AP-HP biology for details on the AP-HP setting.","title":"Defining Concept-Set"},{"location":"functionalities/biology/quick-use/#preparing-measurement-table","text":"Then, simply run prepare_measurement_table to select the measurements from your concept set. measurement = prepare_measurement_table ( data , start_date = \"2022-01-01\" , end_date = \"2022-05-01\" , concept_sets = [ custom_leukocytes ], convert_units = False , get_all_terminologies = True , )","title":"Preparing measurement table"},{"location":"functionalities/biology/tutorial/","text":"(function() { function addWidgetsRenderer() { var requireJsScript = document.createElement('script'); requireJsScript.src = 'https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js'; var mimeElement = document.querySelector('script[type=\"application/vnd.jupyter.widget-view+json\"]'); var jupyterWidgetsScript = document.createElement('script'); var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js'; var widgetState; // Fallback for older version: try { widgetState = mimeElement && JSON.parse(mimeElement.innerHTML); if (widgetState && (widgetState.version_major < 2 || !widgetState.version_major)) { widgetRendererSrc = 'jupyter-js-widgets@*/dist/embed.js'; } } catch(e) {} jupyterWidgetsScript.src = widgetRendererSrc; document.body.appendChild(requireJsScript); document.body.appendChild(jupyterWidgetsScript); } document.addEventListener('DOMContentLoaded', addWidgetsRenderer); }()); You can download this notebook directly here Tutorial - Preparing measurement table This tutorial takes you through the entire workflow of the Biology module. import eds_scikit import pandas as pd 1 - Load data Big volume Measurement table can be large. Do not forget to set proper spark config. to_add_conf = [ ( \"master\" , \"yarn\" ), ( \"deploy_mode\" , \"client\" ), ( \"spark.driver.memory\" , ... ), ( \"spark.executor.memory\" , ... ), ( \"spark.executor.cores\" , ... ), ( \"spark.executor.memoryOverhead\" , ... ), ( \"spark.driver.maxResultSize\" , ... ) ... ] spark , sc , sql = eds_scikit . improve_performances ( to_add_conf = to_add_conf ) from eds_scikit.io.hive import HiveData data = HiveData ( spark_session = spark , database_name = \"cse_xxxxxxx_xxxxxxx\" , tables_to_load = [ \"care_site\" , \"concept\" , \"visit_occurrence\" , \"measurement\" , \"concept_relationship\" , ], ) 2 - Quick use : Preparing measurement table a) Define biology concept-sets In order to work on the measurements of interest, you can extract a list of concepts-sets by: Selecting default concepts-sets provided in the library. Modifying the codes of a selected default concepts-set. Creating a concepts-set from scratch. Code selection can be tricky. See Concept codes relationships exploration section for more details on how to select them. from eds_scikit.biology import ConceptsSet # Creating Concept-Set custom_leukocytes = ConceptsSet ( \"Custom_Leukocytes\" ) custom_leukocytes . add_concept_codes ( concept_codes = [ 'A0174' , 'H6740' , 'C8824' ], terminology = 'GLIMS_ANABIO' ) custom_leukocytes . add_concept_codes ( concept_codes = [ '6690-2' ], terminology = 'ITM_LOINC' ) # Importing Concept-Set (see. 4.b for details on existing concepts sets) glucose_blood = ConceptsSet ( \"Glucose_Blood_Concentration\" ) concepts_sets = [ custom_leukocytes , glucose_blood ] b) Prepare measurements Lazy execution Execution will be lazy, except if convert_units=True . from eds_scikit.biology import prepare_measurement_table measurement = prepare_measurement_table ( data , start_date = \"2022-01-01\" , end_date = \"2022-05-01\" , concept_sets = concepts_sets , convert_units = False , get_all_terminologies = True ) Now you have your measurement table mapped with concept set terminology. Next sections are about measurement codes analysis, units and plots. 3 - Detailed use : Analysing measurement table a) Measurements statistics table from eds_scikit.biology import measurement_values_summary stats_summary = measurement_values_summary ( measurement , category_cols = [ \"concept_set\" , \"GLIMS_ANABIO_concept_code\" , \"GLIMS_LOINC_concept_code\" ], value_column = \"value_as_number\" , unit_column = \"unit_source_value\" ) stats_summary b) Measurements units correction glucose_blood . add_conversion ( \"mol\" , \"g\" , 180 ) glucose_blood . add_target_unit ( \"mmol/l\" ) concepts_sets = [ glucose_blood , custom_leukocytes ] measurement = prepare_measurement_table ( data , start_date = \"2022-01-01\" , end_date = \"2022-05-01\" , concept_sets = concepts_sets , convert_units = True , get_all_terminologies = False ) stats_summary = measurement_values_summary ( measurement , category_cols = [ \"concept_set\" , \"GLIMS_ANABIO_concept_code\" ], value_column = \"value_as_number_normalized\" , #converted unit_column = \"unit_source_value_normalized\" ) stats_summary c) Plot biology summary Applying plot_biology_summary to computed measurement dataframe, merged with care sites, allows to generate nice exploration plots such as : Interactive volumetry Interactive distribution from eds_scikit.biology import plot_biology_summary measurement = measurement . merge ( data . visit_occurrence [[ \"care_site_id\" , \"visit_occurrence_id\" ]], on = \"visit_occurrence_id\" ) measurement = measurement . merge ( data . care_site [[ \"care_site_id\" , \"care_site_short_name\" ]], on = \"care_site_id\" ) plot_biology_summary ( measurement , value_column = \"value_as_number_normalized\" ) 4 - Further : Concept Codes, Concepts Sets and Units 1 - Concept codes relationships exploration Concept codes relationships can be tricky to understand and to manipulate. Function prepare_biology_relationship_table allows to build mapping dataframe between main AP-HP biology referential . See io.settings.measurement_config[\"mapping\"] and io.settings.measurement_config[\"source_terminologies\"] configurations for mapping details. from eds_scikit.biology import prepare_biology_relationship_table biology_relationship_table = prepare_biology_relationship_table ( data ) biology_relationship_table = biology_relationship_table . to_pandas () Relationship between codes from different referentials. columns = [ col for col in biology_relationship_table . columns if \"concept_code\" in col ] biology_relationship_table [ biology_relationship_table . GLIMS_ANABIO_concept_code . isin ([ 'A0174' , 'H6740' , 'C8824' ])][ columns ] . drop_duplicates () ANALYSES_LABORATOIRE_concept_code GLIMS_ANABIO_concept_code GLIMS_LOINC_concept_code ITM_ANABIO_concept_code ITM_LOINC_concept_code 0 C8824 33256-9 Unknown Unknown 1 A0174 6690-2 A0174 6690-2 1 A0174 26464-8 A0174 6690-2 biology_relationship_table [ biology_relationship_table . GLIMS_LOINC_concept_code . isin ([ '33256-9' , '6690-2' , '26464-8' ])][ columns ] . drop_duplicates () ANALYSES_LABORATOIRE_concept_code GLIMS_ANABIO_concept_code GLIMS_LOINC_concept_code ITM_ANABIO_concept_code ITM_LOINC_concept_code 4 E4358 6690-2 Unknown Unknown 2 C9097 26464-8 Unknown Unknown 6 K3232 6690-2 Unknown Unknown 5 E6953 26464-8 Unknown Unknown 1 C8824 33256-9 Unknown Unknown 4 E4358 26464-8 Unknown Unknown 5 E6953 6690-2 Unknown Unknown 7 K6094 6690-2 Unknown Unknown 0 C9784 6690-2 C9784 6690-2 0 C9784 26464-8 C9784 6690-2 3 A0174 6690-2 A0174 6690-2 3 A0174 26464-8 A0174 6690-2 2 - Concepts-Sets To get all availables concepts sets see datasets.default_concepts_sets . More details about their definition and how they are build can be found in this section . from eds_scikit import datasets from eds_scikit.biology import ConceptsSet print ( ConceptsSet ( \"Glucose_Blood_Concentration\" ) . concept_codes ) datasets . default_concepts_sets 3 - Units Units module makes conversion between units easier. It uses configuration files datasets.units and datasets.elements . from eds_scikit import datasets from eds_scikit.biology import Units units = Units () print ( \"L to ml : \" , units . convert_unit ( \"L\" , \"ml\" )) print ( \"m/s to m/h : \" , units . convert_unit ( \"m/s\" , \"m/h\" )) print ( \"g to mol : \" , units . convert_unit ( \"g\" , \"mol\" )) units . add_conversion ( \"mol\" , \"g\" , 180 ) print ( \"g to mol : \" , units . convert_unit ( \"g\" , \"mol\" ))","title":"Tutorial"},{"location":"functionalities/biology/tutorial/#tutorial-preparing-measurement-table","text":"This tutorial takes you through the entire workflow of the Biology module. import eds_scikit import pandas as pd","title":"Tutorial - Preparing measurement table"},{"location":"functionalities/biology/tutorial/#1-load-data","text":"Big volume Measurement table can be large. Do not forget to set proper spark config. to_add_conf = [ ( \"master\" , \"yarn\" ), ( \"deploy_mode\" , \"client\" ), ( \"spark.driver.memory\" , ... ), ( \"spark.executor.memory\" , ... ), ( \"spark.executor.cores\" , ... ), ( \"spark.executor.memoryOverhead\" , ... ), ( \"spark.driver.maxResultSize\" , ... ) ... ] spark , sc , sql = eds_scikit . improve_performances ( to_add_conf = to_add_conf ) from eds_scikit.io.hive import HiveData data = HiveData ( spark_session = spark , database_name = \"cse_xxxxxxx_xxxxxxx\" , tables_to_load = [ \"care_site\" , \"concept\" , \"visit_occurrence\" , \"measurement\" , \"concept_relationship\" , ], )","title":"1 - Load data "},{"location":"functionalities/biology/tutorial/#2-quick-use-preparing-measurement-table","text":"","title":"2 - Quick use : Preparing measurement table "},{"location":"functionalities/biology/tutorial/#a-define-biology-concept-sets","text":"In order to work on the measurements of interest, you can extract a list of concepts-sets by: Selecting default concepts-sets provided in the library. Modifying the codes of a selected default concepts-set. Creating a concepts-set from scratch. Code selection can be tricky. See Concept codes relationships exploration section for more details on how to select them. from eds_scikit.biology import ConceptsSet # Creating Concept-Set custom_leukocytes = ConceptsSet ( \"Custom_Leukocytes\" ) custom_leukocytes . add_concept_codes ( concept_codes = [ 'A0174' , 'H6740' , 'C8824' ], terminology = 'GLIMS_ANABIO' ) custom_leukocytes . add_concept_codes ( concept_codes = [ '6690-2' ], terminology = 'ITM_LOINC' ) # Importing Concept-Set (see. 4.b for details on existing concepts sets) glucose_blood = ConceptsSet ( \"Glucose_Blood_Concentration\" ) concepts_sets = [ custom_leukocytes , glucose_blood ]","title":"a) Define biology concept-sets "},{"location":"functionalities/biology/tutorial/#b-prepare-measurements","text":"Lazy execution Execution will be lazy, except if convert_units=True . from eds_scikit.biology import prepare_measurement_table measurement = prepare_measurement_table ( data , start_date = \"2022-01-01\" , end_date = \"2022-05-01\" , concept_sets = concepts_sets , convert_units = False , get_all_terminologies = True ) Now you have your measurement table mapped with concept set terminology. Next sections are about measurement codes analysis, units and plots.","title":"b) Prepare measurements "},{"location":"functionalities/biology/tutorial/#3-detailed-use-analysing-measurement-table","text":"","title":"3 - Detailed use : Analysing measurement table"},{"location":"functionalities/biology/tutorial/#a-measurements-statistics-table","text":"from eds_scikit.biology import measurement_values_summary stats_summary = measurement_values_summary ( measurement , category_cols = [ \"concept_set\" , \"GLIMS_ANABIO_concept_code\" , \"GLIMS_LOINC_concept_code\" ], value_column = \"value_as_number\" , unit_column = \"unit_source_value\" ) stats_summary","title":"a) Measurements statistics table "},{"location":"functionalities/biology/tutorial/#b-measurements-units-correction","text":"glucose_blood . add_conversion ( \"mol\" , \"g\" , 180 ) glucose_blood . add_target_unit ( \"mmol/l\" ) concepts_sets = [ glucose_blood , custom_leukocytes ] measurement = prepare_measurement_table ( data , start_date = \"2022-01-01\" , end_date = \"2022-05-01\" , concept_sets = concepts_sets , convert_units = True , get_all_terminologies = False ) stats_summary = measurement_values_summary ( measurement , category_cols = [ \"concept_set\" , \"GLIMS_ANABIO_concept_code\" ], value_column = \"value_as_number_normalized\" , #converted unit_column = \"unit_source_value_normalized\" ) stats_summary","title":"b) Measurements units correction "},{"location":"functionalities/biology/tutorial/#c-plot-biology-summary","text":"Applying plot_biology_summary to computed measurement dataframe, merged with care sites, allows to generate nice exploration plots such as : Interactive volumetry Interactive distribution from eds_scikit.biology import plot_biology_summary measurement = measurement . merge ( data . visit_occurrence [[ \"care_site_id\" , \"visit_occurrence_id\" ]], on = \"visit_occurrence_id\" ) measurement = measurement . merge ( data . care_site [[ \"care_site_id\" , \"care_site_short_name\" ]], on = \"care_site_id\" ) plot_biology_summary ( measurement , value_column = \"value_as_number_normalized\" )","title":"c) Plot biology summary "},{"location":"functionalities/biology/tutorial/#4-further-concept-codes-concepts-sets-and-units","text":"","title":"4 - Further : Concept Codes, Concepts Sets and Units "},{"location":"functionalities/biology/tutorial/#1-concept-codes-relationships-exploration","text":"Concept codes relationships can be tricky to understand and to manipulate. Function prepare_biology_relationship_table allows to build mapping dataframe between main AP-HP biology referential . See io.settings.measurement_config[\"mapping\"] and io.settings.measurement_config[\"source_terminologies\"] configurations for mapping details. from eds_scikit.biology import prepare_biology_relationship_table biology_relationship_table = prepare_biology_relationship_table ( data ) biology_relationship_table = biology_relationship_table . to_pandas () Relationship between codes from different referentials. columns = [ col for col in biology_relationship_table . columns if \"concept_code\" in col ] biology_relationship_table [ biology_relationship_table . GLIMS_ANABIO_concept_code . isin ([ 'A0174' , 'H6740' , 'C8824' ])][ columns ] . drop_duplicates () ANALYSES_LABORATOIRE_concept_code GLIMS_ANABIO_concept_code GLIMS_LOINC_concept_code ITM_ANABIO_concept_code ITM_LOINC_concept_code 0 C8824 33256-9 Unknown Unknown 1 A0174 6690-2 A0174 6690-2 1 A0174 26464-8 A0174 6690-2 biology_relationship_table [ biology_relationship_table . GLIMS_LOINC_concept_code . isin ([ '33256-9' , '6690-2' , '26464-8' ])][ columns ] . drop_duplicates () ANALYSES_LABORATOIRE_concept_code GLIMS_ANABIO_concept_code GLIMS_LOINC_concept_code ITM_ANABIO_concept_code ITM_LOINC_concept_code 4 E4358 6690-2 Unknown Unknown 2 C9097 26464-8 Unknown Unknown 6 K3232 6690-2 Unknown Unknown 5 E6953 26464-8 Unknown Unknown 1 C8824 33256-9 Unknown Unknown 4 E4358 26464-8 Unknown Unknown 5 E6953 6690-2 Unknown Unknown 7 K6094 6690-2 Unknown Unknown 0 C9784 6690-2 C9784 6690-2 0 C9784 26464-8 C9784 6690-2 3 A0174 6690-2 A0174 6690-2 3 A0174 26464-8 A0174 6690-2","title":"1 - Concept codes relationships exploration "},{"location":"functionalities/biology/tutorial/#2-concepts-sets","text":"To get all availables concepts sets see datasets.default_concepts_sets . More details about their definition and how they are build can be found in this section . from eds_scikit import datasets from eds_scikit.biology import ConceptsSet print ( ConceptsSet ( \"Glucose_Blood_Concentration\" ) . concept_codes ) datasets . default_concepts_sets","title":"2 - Concepts-Sets "},{"location":"functionalities/biology/tutorial/#3-units","text":"Units module makes conversion between units easier. It uses configuration files datasets.units and datasets.elements . from eds_scikit import datasets from eds_scikit.biology import Units units = Units () print ( \"L to ml : \" , units . convert_unit ( \"L\" , \"ml\" )) print ( \"m/s to m/h : \" , units . convert_unit ( \"m/s\" , \"m/h\" )) print ( \"g to mol : \" , units . convert_unit ( \"g\" , \"mol\" )) units . add_conversion ( \"mol\" , \"g\" , 180 ) print ( \"g to mol : \" , units . convert_unit ( \"g\" , \"mol\" ))","title":"3 - Units "},{"location":"functionalities/biology/visualization/","text":"Visualizing measurements Once the measurement table has been computed, biology module provides measurement_values_summary and plot_biology_summary to gain better insights into their distribution and volumetry across codes, care sites, and time. Statistical summary measurement_values_summary generates useful statistics to identify anomalies in measurements associated with a concept set. from eds_scikit.biology import measurement_values_summary stats_summary = measurement_values_summary ( measurement , category_cols = [ \"concept_set\" , \"GLIMS_ANABIO_concept_code\" , \"GLIMS_LOINC_concept_code\" , ], value_column = \"value_as_number\" , unit_column = \"unit_source_value\" , ) stats_summary concept_set ANABIO_concept_code no_units unit_source_value range_low_anomaly_count range_high_anomaly_count measurement_count value_as_number_count value_as_number_mean value_as_number_std value_as_number_min value_as_number_25% value_as_number_50% value_as_number_75% value_as_number_max Custom_Leukocytes A0174 148 x10*9/l 813 1099 11857 11857 21 18 0 25 50 75 100 Custom_Leukocytes C8824 121 x10*9/l 1166 1196 11821 11821 20 20 0 25 50 75 100 Custom_Leukocytes C9784 83 x10*9/l 935 902 11082 11082 10 16 0 25 50 75 100 Plot summary plot_biology_summary generates a useful dashboard to better understand the volumetry and distribution of codes within the same concept set. The purpose is to identify and correct possible biases associated with sets of codes, time periods, or specific care sites. from eds_scikit.biology import plot_biology_summary # First add 'care_site_short_name' column to measurement table measurement = measurement . merge ( data . visit_occurrence [[ \"care_site_id\" , \"visit_occurrence_id\" ]], on = \"visit_occurrence_id\" , ) measurement = measurement . merge ( data . care_site [[ \"care_site_id\" , \"care_site_short_name\" ]], on = \"care_site_id\" ) plot_biology_summary ( measurement , value_column = \"value_as_number\" ) Volumetry dashboard Distribution dashboard","title":"Visualizing measurements"},{"location":"functionalities/biology/visualization/#visualizing-measurements","text":"Once the measurement table has been computed, biology module provides measurement_values_summary and plot_biology_summary to gain better insights into their distribution and volumetry across codes, care sites, and time.","title":"Visualizing measurements"},{"location":"functionalities/biology/visualization/#statistical-summary","text":"measurement_values_summary generates useful statistics to identify anomalies in measurements associated with a concept set. from eds_scikit.biology import measurement_values_summary stats_summary = measurement_values_summary ( measurement , category_cols = [ \"concept_set\" , \"GLIMS_ANABIO_concept_code\" , \"GLIMS_LOINC_concept_code\" , ], value_column = \"value_as_number\" , unit_column = \"unit_source_value\" , ) stats_summary concept_set ANABIO_concept_code no_units unit_source_value range_low_anomaly_count range_high_anomaly_count measurement_count value_as_number_count value_as_number_mean value_as_number_std value_as_number_min value_as_number_25% value_as_number_50% value_as_number_75% value_as_number_max Custom_Leukocytes A0174 148 x10*9/l 813 1099 11857 11857 21 18 0 25 50 75 100 Custom_Leukocytes C8824 121 x10*9/l 1166 1196 11821 11821 20 20 0 25 50 75 100 Custom_Leukocytes C9784 83 x10*9/l 935 902 11082 11082 10 16 0 25 50 75 100","title":"Statistical summary"},{"location":"functionalities/biology/visualization/#plot-summary","text":"plot_biology_summary generates a useful dashboard to better understand the volumetry and distribution of codes within the same concept set. The purpose is to identify and correct possible biases associated with sets of codes, time periods, or specific care sites. from eds_scikit.biology import plot_biology_summary # First add 'care_site_short_name' column to measurement table measurement = measurement . merge ( data . visit_occurrence [[ \"care_site_id\" , \"visit_occurrence_id\" ]], on = \"visit_occurrence_id\" , ) measurement = measurement . merge ( data . care_site [[ \"care_site_id\" , \"care_site_short_name\" ]], on = \"care_site_id\" ) plot_biology_summary ( measurement , value_column = \"value_as_number\" ) Volumetry dashboard Distribution dashboard","title":"Plot summary"},{"location":"functionalities/generic/introduction/","text":"(function() { function addWidgetsRenderer() { var requireJsScript = document.createElement('script'); requireJsScript.src = 'https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js'; var mimeElement = document.querySelector('script[type=\"application/vnd.jupyter.widget-view+json\"]'); var jupyterWidgetsScript = document.createElement('script'); var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js'; var widgetState; // Fallback for older version: try { widgetState = mimeElement && JSON.parse(mimeElement.innerHTML); if (widgetState && (widgetState.version_major < 2 || !widgetState.version_major)) { widgetRendererSrc = 'jupyter-js-widgets@*/dist/embed.js'; } } catch(e) {} jupyterWidgetsScript.src = widgetRendererSrc; document.body.appendChild(requireJsScript); document.body.appendChild(jupyterWidgetsScript); } document.addEventListener('DOMContentLoaded', addWidgetsRenderer); }()); You can download this notebook directly here A gentle demo import datetime import pandas as pd import eds_scikit spark , sc , sql = eds_scikit . improve_performances () # (1) See the welcome page for an explanation of this line Loading data Data loading is made easy by using the HiveData object. Simply give it the name of the database you want to use: database_name = \"MY_DATABASE_NAME\" from eds_scikit.io import HiveData data = HiveData ( database_name = \"database_name\" , ) Now your tables are available as Koalas DataFrames: Those are basically Spark DataFrames which allows for the Pandas API to be used on top (see the Project description of eds-scikit's documentation for more informations.) What we need to extract: Patients with diabetes Patients with Covid-19 Visits from those patients, and their ICU/Non-ICU status Let us import what's necessary from eds-scikit : from eds_scikit.event import conditions_from_icd10 from eds_scikit.event.diabetes import ( diabetes_from_icd10 , DEFAULT_DIABETE_FROM_ICD10_CONFIG , ) from eds_scikit.icu import tag_icu_visit DATE_MIN = datetime . datetime ( 2020 , 1 , 1 ) DATE_MAX = datetime . datetime ( 2021 , 6 , 1 ) Extracting the diabetic status Luckily, a function is available to extract diabetic patients from ICD-10: diabetes = diabetes_from_icd10 ( condition_occurrence = data . condition_occurrence , visit_occurrence = data . visit_occurrence , date_min = DATE_MIN , date_max = DATE_MAX , ) We can check the default parameters used here: DEFAULT_DIABETE_FROM_ICD10_CONFIG {'additional_filtering': {'condition_status_source_value': {'DP', 'DAS'}}, 'codes': {'DIABETES_INSIPIDUS': {'code_list': ['E232', 'N251'], 'code_type': 'exact'}, 'DIABETES_IN_PREGNANCY': {'code_list': ['O24'], 'code_type': 'prefix'}, 'DIABETES_MALNUTRITION': {'code_list': ['E12'], 'code_type': 'prefix'}, 'DIABETES_TYPE_I': {'code_list': ['E10'], 'code_type': 'prefix'}, 'DIABETES_TYPE_II': {'code_list': ['E11'], 'code_type': 'prefix'}, 'OTHER_DIABETES_MELLITUS': {'code_list': ['E13', 'E14'], 'code_type': 'prefix'}}, 'date_from_visit': True, 'default_code_type': 'prefix'} We are only interested in diabetes mellitus , although we extracted other types of diabetes: diabetes . concept . value_counts () DIABETES_TYPE_II 117843 DIABETES_TYPE_I 10597 OTHER_DIABETES_MELLITUS 6031 DIABETES_IN_PREGNANCY 2597 DIABETES_INSIPIDUS 1089 DIABETES_MALNUTRITION 199 Name: concept, dtype: int64 We will restrict the types of diabetes used here: diabetes_cohort = ( diabetes [ diabetes . concept . isin ( { \"DIABETES_TYPE_I\" , \"DIABETES_TYPE_II\" , \"OTHER_DIABETES_MELLITUS\" , } ) ] . person_id . unique () . reset_index () ) diabetes_cohort . loc [:, \"HAS_DIABETE\" ] = True Extracting the Covid status Using the conditions_from_icd10 function, we will extract visits linked to COVID-19: codes = dict ( COVID = dict ( code_list = r \"U071[0145]\" , code_type = \"regex\" , ) ) covid = conditions_from_icd10 ( condition_occurrence = data . condition_occurrence , visit_occurrence = data . visit_occurrence , codes = codes , date_min = DATE_MIN , date_max = DATE_MAX , ) Now we can go from the visit_occurrence level to the visit_detail level. visit_detail_covid = data . visit_detail . merge ( covid [[ \"visit_occurrence_id\" ]], on = \"visit_occurrence_id\" , how = \"inner\" , ) Extracting ICU visits What is left to do is to tag each visit as occurring in an ICU or not. This is achieved with the tag_icu_visit . Like many functions in eds-scikit , this function exposes an algo argument, allowing you to choose how the tagging is done. You can check the corresponding documentation to see the availables algos . visit_detail_covid = tag_icu_visit ( visit_detail = visit_detail_covid , care_site = data . care_site , algo = \"from_authorisation_type\" , ) visit_detail_covid = visit_detail_covid . merge ( diabetes_cohort , on = \"person_id\" , how = \"left\" ) visit_detail_covid [ \"HAS_DIABETE\" ] . fillna ( False , inplace = True ) visit_detail_covid [ \"IS_ICU\" ] . fillna ( False , inplace = True ) Finishing the analysis Adding patient's age We will add the patient's age at each visit_detail : from eds_scikit.utils import datetime_helpers visit_detail_covid = visit_detail_covid . merge ( data . person [[ 'person_id' , 'birth_datetime' ]], on = 'person_id' , how = 'inner' ) visit_detail_covid [ \"age\" ] = ( datetime_helpers . substract_datetime ( visit_detail_covid [ \"visit_detail_start_datetime\" ], visit_detail_covid [ \"birth_datetime\" ], out = \"hours\" , ) / ( 24 * 365.25 ) ) From distributed Koalas to local Pandas All the computing above was done using Koalas DataFrames, which are distributed. Now that we limited our cohort to a manageable size, we can switch to Pandas to finish our analysis. visit_detail_covid_pd = visit_detail_covid [ [ \"person_id\" , \"age\" , \"HAS_DIABETE\" , \"IS_ICU\" ] ] . to_pandas () Grouping by patient stats = ( visit_detail_covid_pd [[ \"person_id\" , \"age\" , \"HAS_DIABETE\" , \"IS_ICU\" ]] . groupby ( \"person_id\" ) . agg ( HAS_DIABETE = ( \"HAS_DIABETE\" , \"any\" ), IS_ICU = ( \"IS_ICU\" , \"any\" ), age = ( \"age\" , \"min\" ), ) ) Binning the age into intervals stats [ \"age\" ] = pd . cut ( stats . age , bins = [ 0 , 40 , 50 , 60 , 70 , 120 ], labels = [ \"(0, 40]\" , \"(40, 50]\" , \"(50, 60]\" , \"(60, 70]\" , \"(70, 120]\" ], ) Computing the ratio of patients that had an ICU visit stats = stats . groupby ([ \"age\" , \"HAS_DIABETE\" ], as_index = False ) . apply ( lambda x : x [ \"IS_ICU\" ] . sum () / len ( x ) ) stats . columns = [ \"age\" , \"cohorte\" , \"percent_icu\" ] stats [ \"cohorte\" ] = stats [ \"cohorte\" ] . replace ({ True : \"Diab.\" , False : \"Control\" }) Results stats .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } age cohorte percent_icu 0 (0, 40] Control 0.327988 1 (0, 40] Diab. 0.445578 2 (40, 50] Control 0.263667 3 (40, 50] Diab. 0.427203 4 (50, 60] Control 0.315931 5 (50, 60] Diab. 0.464736 6 (60, 70] Control 0.356808 7 (60, 70] Diab. 0.474766 8 (70, 120] Control 0.159337 9 (70, 120] Diab. 0.230180 We can finally plot our results using Altair : import altair as alt bars = ( alt . Chart ( stats , title = [ \"Percentage of patients who went through ICU during their COVID stay, \" , \"as a function of their age range and diabetic status\" , \" \" , ], ) . mark_bar () . encode ( x = alt . X ( \"cohorte:N\" , title = \"\" ), y = alt . Y ( \"percent_icu\" , title = \" % o f patients who went through ICU.\" , axis = alt . Axis ( format = \"%\" ), ), color = alt . Color ( \"cohorte:N\" , title = \"Cohort\" ), column = alt . Column ( \"age:N\" , title = \"Age range\" ), ) ) bars = bars . configure_title ( anchor = \"middle\" , baseline = \"bottom\" ) bars (function(spec, embedOpt){ let outputDiv = document.currentScript.previousElementSibling; if (outputDiv.id !== \"altair-viz-679a8662c76643b1ae8af86ce4171d2c\") { outputDiv = document.getElementById(\"altair-viz-679a8662c76643b1ae8af86ce4171d2c\"); } const paths = { \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\", \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\", \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\", \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\", }; function loadScript(lib) { return new Promise(function(resolve, reject) { var s = document.createElement('script'); s.src = paths[lib]; s.async = true; s.onload = () => resolve(paths[lib]); s.onerror = () => reject(`Error loading script: ${paths[lib]}`); document.getElementsByTagName(\"head\")[0].appendChild(s); }); } function showError(err) { outputDiv.innerHTML = `

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See the javascript console for the full traceback.`)); } if(typeof define === \"function\" && define.amd) { requirejs.config({paths}); require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`)); } else if (typeof vegaEmbed === \"function\") { displayChart(vegaEmbed); } else { loadScript(\"vega\") .then(() => loadScript(\"vega-lite\")) .then(() => loadScript(\"vega-embed\")) .catch(showError) .then(() => displayChart(vegaEmbed)); } })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"title\": {\"anchor\": \"middle\", \"baseline\": \"bottom\"}}, \"data\": {\"name\": \"data-55507a07f81645e51f63eaba5b403390\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"cohorte\", \"title\": \"Cohort\"}, \"column\": {\"type\": \"nominal\", \"field\": \"age\", \"title\": \"Age range\"}, \"x\": {\"type\": \"nominal\", \"field\": \"cohorte\", \"title\": \"\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"format\": \"%\"}, \"field\": \"percent_icu\", \"title\": \"% of patients who went through ICU.\"}}, \"title\": [\"Percentage of patients who went through ICU during their COVID stay, \", \"as a function of their age range and diabetic status\", \" \"], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-55507a07f81645e51f63eaba5b403390\": [{\"age\": \"(0, 40]\", \"cohorte\": \"Control\", \"percent_icu\": 0.32798833819241985}, {\"age\": \"(0, 40]\", \"cohorte\": \"Diab.\", \"percent_icu\": 0.445578231292517}, {\"age\": \"(40, 50]\", \"cohorte\": \"Control\", \"percent_icu\": 0.26366666666666666}, {\"age\": \"(40, 50]\", \"cohorte\": \"Diab.\", \"percent_icu\": 0.4272030651340996}, {\"age\": \"(50, 60]\", \"cohorte\": \"Control\", \"percent_icu\": 0.31593098812457987}, {\"age\": \"(50, 60]\", \"cohorte\": \"Diab.\", \"percent_icu\": 0.4647364513734224}, {\"age\": \"(60, 70]\", \"cohorte\": \"Control\", \"percent_icu\": 0.3568075117370892}, {\"age\": \"(60, 70]\", \"cohorte\": \"Diab.\", \"percent_icu\": 0.47476552032157215}, {\"age\": \"(70, 120]\", \"cohorte\": \"Control\", \"percent_icu\": 0.15933694181326116}, {\"age\": \"(70, 120]\", \"cohorte\": \"Diab.\", \"percent_icu\": 0.23017958826106}]}}, {\"mode\": \"vega-lite\"}); {\"state\": {}, \"version_major\": 2, \"version_minor\": 0}","title":"A gentle demo"},{"location":"functionalities/generic/introduction/#a-gentle-demo","text":"import datetime import pandas as pd import eds_scikit spark , sc , sql = eds_scikit . improve_performances () # (1) See the welcome page for an explanation of this line","title":"A gentle demo"},{"location":"functionalities/generic/introduction/#loading-data","text":"Data loading is made easy by using the HiveData object. Simply give it the name of the database you want to use: database_name = \"MY_DATABASE_NAME\" from eds_scikit.io import HiveData data = HiveData ( database_name = \"database_name\" , ) Now your tables are available as Koalas DataFrames: Those are basically Spark DataFrames which allows for the Pandas API to be used on top (see the Project description of eds-scikit's documentation for more informations.) What we need to extract: Patients with diabetes Patients with Covid-19 Visits from those patients, and their ICU/Non-ICU status Let us import what's necessary from eds-scikit : from eds_scikit.event import conditions_from_icd10 from eds_scikit.event.diabetes import ( diabetes_from_icd10 , DEFAULT_DIABETE_FROM_ICD10_CONFIG , ) from eds_scikit.icu import tag_icu_visit DATE_MIN = datetime . datetime ( 2020 , 1 , 1 ) DATE_MAX = datetime . datetime ( 2021 , 6 , 1 )","title":"Loading data"},{"location":"functionalities/generic/introduction/#extracting-the-diabetic-status","text":"Luckily, a function is available to extract diabetic patients from ICD-10: diabetes = diabetes_from_icd10 ( condition_occurrence = data . condition_occurrence , visit_occurrence = data . visit_occurrence , date_min = DATE_MIN , date_max = DATE_MAX , ) We can check the default parameters used here: DEFAULT_DIABETE_FROM_ICD10_CONFIG {'additional_filtering': {'condition_status_source_value': {'DP', 'DAS'}}, 'codes': {'DIABETES_INSIPIDUS': {'code_list': ['E232', 'N251'], 'code_type': 'exact'}, 'DIABETES_IN_PREGNANCY': {'code_list': ['O24'], 'code_type': 'prefix'}, 'DIABETES_MALNUTRITION': {'code_list': ['E12'], 'code_type': 'prefix'}, 'DIABETES_TYPE_I': {'code_list': ['E10'], 'code_type': 'prefix'}, 'DIABETES_TYPE_II': {'code_list': ['E11'], 'code_type': 'prefix'}, 'OTHER_DIABETES_MELLITUS': {'code_list': ['E13', 'E14'], 'code_type': 'prefix'}}, 'date_from_visit': True, 'default_code_type': 'prefix'} We are only interested in diabetes mellitus , although we extracted other types of diabetes: diabetes . concept . value_counts () DIABETES_TYPE_II 117843 DIABETES_TYPE_I 10597 OTHER_DIABETES_MELLITUS 6031 DIABETES_IN_PREGNANCY 2597 DIABETES_INSIPIDUS 1089 DIABETES_MALNUTRITION 199 Name: concept, dtype: int64 We will restrict the types of diabetes used here: diabetes_cohort = ( diabetes [ diabetes . concept . isin ( { \"DIABETES_TYPE_I\" , \"DIABETES_TYPE_II\" , \"OTHER_DIABETES_MELLITUS\" , } ) ] . person_id . unique () . reset_index () ) diabetes_cohort . loc [:, \"HAS_DIABETE\" ] = True","title":"Extracting the diabetic status"},{"location":"functionalities/generic/introduction/#extracting-the-covid-status","text":"Using the conditions_from_icd10 function, we will extract visits linked to COVID-19: codes = dict ( COVID = dict ( code_list = r \"U071[0145]\" , code_type = \"regex\" , ) ) covid = conditions_from_icd10 ( condition_occurrence = data . condition_occurrence , visit_occurrence = data . visit_occurrence , codes = codes , date_min = DATE_MIN , date_max = DATE_MAX , ) Now we can go from the visit_occurrence level to the visit_detail level. visit_detail_covid = data . visit_detail . merge ( covid [[ \"visit_occurrence_id\" ]], on = \"visit_occurrence_id\" , how = \"inner\" , )","title":"Extracting the Covid status"},{"location":"functionalities/generic/introduction/#extracting-icu-visits","text":"What is left to do is to tag each visit as occurring in an ICU or not. This is achieved with the tag_icu_visit . Like many functions in eds-scikit , this function exposes an algo argument, allowing you to choose how the tagging is done. You can check the corresponding documentation to see the availables algos . visit_detail_covid = tag_icu_visit ( visit_detail = visit_detail_covid , care_site = data . care_site , algo = \"from_authorisation_type\" , ) visit_detail_covid = visit_detail_covid . merge ( diabetes_cohort , on = \"person_id\" , how = \"left\" ) visit_detail_covid [ \"HAS_DIABETE\" ] . fillna ( False , inplace = True ) visit_detail_covid [ \"IS_ICU\" ] . fillna ( False , inplace = True )","title":"Extracting ICU visits"},{"location":"functionalities/generic/introduction/#finishing-the-analysis","text":"","title":"Finishing the analysis"},{"location":"functionalities/generic/introduction/#adding-patients-age","text":"We will add the patient's age at each visit_detail : from eds_scikit.utils import datetime_helpers visit_detail_covid = visit_detail_covid . merge ( data . person [[ 'person_id' , 'birth_datetime' ]], on = 'person_id' , how = 'inner' ) visit_detail_covid [ \"age\" ] = ( datetime_helpers . substract_datetime ( visit_detail_covid [ \"visit_detail_start_datetime\" ], visit_detail_covid [ \"birth_datetime\" ], out = \"hours\" , ) / ( 24 * 365.25 ) )","title":"Adding patient's age"},{"location":"functionalities/generic/introduction/#from-distributed-koalas-to-local-pandas","text":"All the computing above was done using Koalas DataFrames, which are distributed. Now that we limited our cohort to a manageable size, we can switch to Pandas to finish our analysis. visit_detail_covid_pd = visit_detail_covid [ [ \"person_id\" , \"age\" , \"HAS_DIABETE\" , \"IS_ICU\" ] ] . to_pandas ()","title":"From distributed Koalas to local Pandas"},{"location":"functionalities/generic/introduction/#grouping-by-patient","text":"stats = ( visit_detail_covid_pd [[ \"person_id\" , \"age\" , \"HAS_DIABETE\" , \"IS_ICU\" ]] . groupby ( \"person_id\" ) . agg ( HAS_DIABETE = ( \"HAS_DIABETE\" , \"any\" ), IS_ICU = ( \"IS_ICU\" , \"any\" ), age = ( \"age\" , \"min\" ), ) )","title":"Grouping by patient"},{"location":"functionalities/generic/introduction/#binning-the-age-into-intervals","text":"stats [ \"age\" ] = pd . cut ( stats . age , bins = [ 0 , 40 , 50 , 60 , 70 , 120 ], labels = [ \"(0, 40]\" , \"(40, 50]\" , \"(50, 60]\" , \"(60, 70]\" , \"(70, 120]\" ], )","title":"Binning the age into intervals"},{"location":"functionalities/generic/introduction/#computing-the-ratio-of-patients-that-had-an-icu-visit","text":"stats = stats . groupby ([ \"age\" , \"HAS_DIABETE\" ], as_index = False ) . apply ( lambda x : x [ \"IS_ICU\" ] . sum () / len ( x ) ) stats . columns = [ \"age\" , \"cohorte\" , \"percent_icu\" ] stats [ \"cohorte\" ] = stats [ \"cohorte\" ] . replace ({ True : \"Diab.\" , False : \"Control\" })","title":"Computing the ratio of patients that had an ICU visit"},{"location":"functionalities/generic/introduction/#results","text":"stats .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } age cohorte percent_icu 0 (0, 40] Control 0.327988 1 (0, 40] Diab. 0.445578 2 (40, 50] Control 0.263667 3 (40, 50] Diab. 0.427203 4 (50, 60] Control 0.315931 5 (50, 60] Diab. 0.464736 6 (60, 70] Control 0.356808 7 (60, 70] Diab. 0.474766 8 (70, 120] Control 0.159337 9 (70, 120] Diab. 0.230180 We can finally plot our results using Altair : import altair as alt bars = ( alt . Chart ( stats , title = [ \"Percentage of patients who went through ICU during their COVID stay, \" , \"as a function of their age range and diabetic status\" , \" \" , ], ) . mark_bar () . encode ( x = alt . X ( \"cohorte:N\" , title = \"\" ), y = alt . Y ( \"percent_icu\" , title = \" % o f patients who went through ICU.\" , axis = alt . Axis ( format = \"%\" ), ), color = alt . Color ( \"cohorte:N\" , title = \"Cohort\" ), column = alt . Column ( \"age:N\" , title = \"Age range\" ), ) ) bars = bars . configure_title ( anchor = \"middle\" , baseline = \"bottom\" ) bars (function(spec, embedOpt){ let outputDiv = document.currentScript.previousElementSibling; if (outputDiv.id !== \"altair-viz-679a8662c76643b1ae8af86ce4171d2c\") { outputDiv = document.getElementById(\"altair-viz-679a8662c76643b1ae8af86ce4171d2c\"); } const paths = { \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\", \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\", \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\", \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\", }; function loadScript(lib) { return new Promise(function(resolve, reject) { var s = document.createElement('script'); s.src = paths[lib]; s.async = true; s.onload = () => resolve(paths[lib]); s.onerror = () => reject(`Error loading script: ${paths[lib]}`); document.getElementsByTagName(\"head\")[0].appendChild(s); }); } function showError(err) { outputDiv.innerHTML = `
    ${err}
    `; throw err; } function displayChart(vegaEmbed) { vegaEmbed(outputDiv, spec, embedOpt) .catch(err => showError(`Javascript Error: ${err.message}
    This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`)); } if(typeof define === \"function\" && define.amd) { requirejs.config({paths}); require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`)); } else if (typeof vegaEmbed === \"function\") { displayChart(vegaEmbed); } else { loadScript(\"vega\") .then(() => loadScript(\"vega-lite\")) .then(() => loadScript(\"vega-embed\")) .catch(showError) .then(() => displayChart(vegaEmbed)); } })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"title\": {\"anchor\": \"middle\", \"baseline\": \"bottom\"}}, \"data\": {\"name\": \"data-55507a07f81645e51f63eaba5b403390\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"cohorte\", \"title\": \"Cohort\"}, \"column\": {\"type\": \"nominal\", \"field\": \"age\", \"title\": \"Age range\"}, \"x\": {\"type\": \"nominal\", \"field\": \"cohorte\", \"title\": \"\"}, \"y\": {\"type\": \"quantitative\", \"axis\": {\"format\": \"%\"}, \"field\": \"percent_icu\", \"title\": \"% of patients who went through ICU.\"}}, \"title\": [\"Percentage of patients who went through ICU during their COVID stay, \", \"as a function of their age range and diabetic status\", \" \"], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-55507a07f81645e51f63eaba5b403390\": [{\"age\": \"(0, 40]\", \"cohorte\": \"Control\", \"percent_icu\": 0.32798833819241985}, {\"age\": \"(0, 40]\", \"cohorte\": \"Diab.\", \"percent_icu\": 0.445578231292517}, {\"age\": \"(40, 50]\", \"cohorte\": \"Control\", \"percent_icu\": 0.26366666666666666}, {\"age\": \"(40, 50]\", \"cohorte\": \"Diab.\", \"percent_icu\": 0.4272030651340996}, {\"age\": \"(50, 60]\", \"cohorte\": \"Control\", \"percent_icu\": 0.31593098812457987}, {\"age\": \"(50, 60]\", \"cohorte\": \"Diab.\", \"percent_icu\": 0.4647364513734224}, {\"age\": \"(60, 70]\", \"cohorte\": \"Control\", \"percent_icu\": 0.3568075117370892}, {\"age\": \"(60, 70]\", \"cohorte\": \"Diab.\", \"percent_icu\": 0.47476552032157215}, {\"age\": \"(70, 120]\", \"cohorte\": \"Control\", \"percent_icu\": 0.15933694181326116}, {\"age\": \"(70, 120]\", \"cohorte\": \"Diab.\", \"percent_icu\": 0.23017958826106}]}}, {\"mode\": \"vega-lite\"}); {\"state\": {}, \"version_major\": 2, \"version_minor\": 0}","title":"Results"},{"location":"functionalities/generic/io/","text":"(function() { function addWidgetsRenderer() { var requireJsScript = document.createElement('script'); requireJsScript.src = 'https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js'; var mimeElement = document.querySelector('script[type=\"application/vnd.jupyter.widget-view+json\"]'); var jupyterWidgetsScript = document.createElement('script'); var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js'; var widgetState; // Fallback for older version: try { widgetState = mimeElement && JSON.parse(mimeElement.innerHTML); if (widgetState && (widgetState.version_major < 2 || !widgetState.version_major)) { widgetRendererSrc = 'jupyter-js-widgets@*/dist/embed.js'; } } catch(e) {} jupyterWidgetsScript.src = widgetRendererSrc; document.body.appendChild(requireJsScript); document.body.appendChild(jupyterWidgetsScript); } document.addEventListener('DOMContentLoaded', addWidgetsRenderer); }()); You can download this notebook directly here IO: Getting Data 3 classes are available to facilitate data access: HiveData : Getting data from a Hive cluster, returning Koalas DataFrames PandasData : Getting data from tables saved on disk, returning Pandas DataFrames PostgresData : Getting data from a PostGreSQL DB, returning Pandas DataFrames from eds_scikit.io import HiveData , PandasData , PostgresData Loading from Hive: HiveData The HiveData class expects two parameters: A SparkSession variable The name of the Database to connect to Using Spark kernels All kernels designed to use Spark are configured to expose 3 variables at startup: spark , the current SparkSession sc , the current SparkContext sql , a function to execute SQL code on the Hive Database. In this case you can just provide the spark variable to HiveData ! Working with an I2B2 database To use a built-in I2B2 to OMOP connector, specify database_type=\"I2b2\" when instantiating HiveData If needed, the following snippet allows to create the necessary variables: from pyspark import SparkConf , SparkContext from pyspark.sql.session import SparkSession conf = SparkConf () sc = SparkContext ( conf = conf ) spark = SparkSession . builder \\ . enableHiveSupport () \\ . getOrCreate () sql = spark . sql The class HiveData provides a convenient interface to OMOP data stored in Hive. The OMOP tables can be accessed as attribute and they are represented as Koalas DataFrames . You simply need to mention your Hive database name. data = HiveData ( \"cse_210038_20221219\" , #DB_NAME, spark , database_type = \"I2B2\" , ) By default, only a subset of tables are added as attributes: data . available_tables ['concept', 'visit_detail', 'note_deid', 'person', 'care_site', 'visit_occurrence', 'measurement', 'procedure_occurrence', 'condition_occurrence', 'fact_relationship', 'concept_relationship'] Koalas DataFrames, like Spark DataFrames, rely on a lazy execution plan: As long as no data needs to be specifically collected, saved or displayed, no code is executed. It is simply saved for a later execution. The main interest of Koalas DataFrames is that you can use (most of) the Pandas API: person = data . person person . drop ( columns = [ 'person_id' ]) . head () .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } birth_datetime death_datetime gender_source_value cdm_source 0 1946-06-04 NaT m ORBIS 1 1940-01-21 2018-05-07 m ORBIS 2 1979-04-25 NaT m ORBIS 3 2007-10-13 NaT f ORBIS 4 1964-12-27 NaT f ORBIS from datetime import datetime person [ 'is_over_50' ] = ( person [ 'birth_datetime' ] >= datetime ( 1971 , 1 , 1 )) stats = ( person . groupby ( 'is_over_50' ) . person_id . count () ) Once data has been sufficiently aggregated, it can be converted back to Pandas, e.g. for plotting. stats_pd = stats . to_pandas () stats_pd is_over_50 True 132794 False 66808 Name: person_id, dtype: int64 Similarily, if you want to work on the Spark DataFrame instead, a similar method is available: person_spark = person . to_spark () Persisting/Reading a sample to/from disk: PandasData Working with Pandas DataFrame is, when possible, more convenient. You have the possibility to save your database or at least a subset of it. Doing so allows you to work on it later without having to go through Spark again. Careful with cohort size Do not save it if your cohort is big : This saves all available tables on disk. For instance, let us define a dummy subset of 1000 patients: visits = data . visit_occurrence selected_visits = ( visits . loc [ visits [ \"visit_source_value\" ] == \"urgence\" ] ) sample_patients = ( selected_visits [ \"person_id\" ] . drop_duplicates () . head ( 1000 ) ) And save every table restricted to this small cohort as a parquet file: MY_FOLDER_PATH = \"./test_cohort\" import os folder = os . path . abspath ( MY_FOLDER_PATH ) tables_to_save = [ \"person\" , \"visit_detail\" , \"visit_occurrence\" ] data . persist_tables_to_folder ( folder , tables = tables_to_save , person_ids = sample_patients ) Once you saved some data to disk, a dedicated class can be used to access it: The class PandasData can be used to load OMOP data from a folder containing several parquet files. The tables are accessed as attributes and are returned as Pandas DataFrame. Warning : in this case, the whole table will be loaded into memory on a single jupyter server. Consequently it is advised to only use this for small datasets. data = PandasData ( folder ) data . available_tables ['visit_detail', 'visit_occurrence', 'person'] person = data . person print ( f \"type: { type ( person ) } \" ) print ( f \"shape: { person . shape } \" ) type: shape: (1000, 5) Loading from PostGres: PostgresData OMOP data can be stored in a PostgreSQL database. The PostgresData class provides a convinient interface to it. Note : this class relies on the file ~/.pgpass that contains your identifiers for several databases. data = PostgresData ( dbname = DB , schema = \"omop\" , user = USER , ) data . read_sql ( \"select count(*) from person\" ) {\"state\": {}, \"version_major\": 2, \"version_minor\": 0}","title":"Connectors"},{"location":"functionalities/generic/io/#io-getting-data","text":"3 classes are available to facilitate data access: HiveData : Getting data from a Hive cluster, returning Koalas DataFrames PandasData : Getting data from tables saved on disk, returning Pandas DataFrames PostgresData : Getting data from a PostGreSQL DB, returning Pandas DataFrames from eds_scikit.io import HiveData , PandasData , PostgresData","title":"IO: Getting Data"},{"location":"functionalities/generic/io/#loading-from-hive-hivedata","text":"The HiveData class expects two parameters: A SparkSession variable The name of the Database to connect to Using Spark kernels All kernels designed to use Spark are configured to expose 3 variables at startup: spark , the current SparkSession sc , the current SparkContext sql , a function to execute SQL code on the Hive Database. In this case you can just provide the spark variable to HiveData ! Working with an I2B2 database To use a built-in I2B2 to OMOP connector, specify database_type=\"I2b2\" when instantiating HiveData If needed, the following snippet allows to create the necessary variables: from pyspark import SparkConf , SparkContext from pyspark.sql.session import SparkSession conf = SparkConf () sc = SparkContext ( conf = conf ) spark = SparkSession . builder \\ . enableHiveSupport () \\ . getOrCreate () sql = spark . sql The class HiveData provides a convenient interface to OMOP data stored in Hive. The OMOP tables can be accessed as attribute and they are represented as Koalas DataFrames . You simply need to mention your Hive database name. data = HiveData ( \"cse_210038_20221219\" , #DB_NAME, spark , database_type = \"I2B2\" , ) By default, only a subset of tables are added as attributes: data . available_tables ['concept', 'visit_detail', 'note_deid', 'person', 'care_site', 'visit_occurrence', 'measurement', 'procedure_occurrence', 'condition_occurrence', 'fact_relationship', 'concept_relationship'] Koalas DataFrames, like Spark DataFrames, rely on a lazy execution plan: As long as no data needs to be specifically collected, saved or displayed, no code is executed. It is simply saved for a later execution. The main interest of Koalas DataFrames is that you can use (most of) the Pandas API: person = data . person person . drop ( columns = [ 'person_id' ]) . head () .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } birth_datetime death_datetime gender_source_value cdm_source 0 1946-06-04 NaT m ORBIS 1 1940-01-21 2018-05-07 m ORBIS 2 1979-04-25 NaT m ORBIS 3 2007-10-13 NaT f ORBIS 4 1964-12-27 NaT f ORBIS from datetime import datetime person [ 'is_over_50' ] = ( person [ 'birth_datetime' ] >= datetime ( 1971 , 1 , 1 )) stats = ( person . groupby ( 'is_over_50' ) . person_id . count () ) Once data has been sufficiently aggregated, it can be converted back to Pandas, e.g. for plotting. stats_pd = stats . to_pandas () stats_pd is_over_50 True 132794 False 66808 Name: person_id, dtype: int64 Similarily, if you want to work on the Spark DataFrame instead, a similar method is available: person_spark = person . to_spark ()","title":"Loading from Hive: HiveData"},{"location":"functionalities/generic/io/#persistingreading-a-sample-tofrom-disk-pandasdata","text":"Working with Pandas DataFrame is, when possible, more convenient. You have the possibility to save your database or at least a subset of it. Doing so allows you to work on it later without having to go through Spark again. Careful with cohort size Do not save it if your cohort is big : This saves all available tables on disk. For instance, let us define a dummy subset of 1000 patients: visits = data . visit_occurrence selected_visits = ( visits . loc [ visits [ \"visit_source_value\" ] == \"urgence\" ] ) sample_patients = ( selected_visits [ \"person_id\" ] . drop_duplicates () . head ( 1000 ) ) And save every table restricted to this small cohort as a parquet file: MY_FOLDER_PATH = \"./test_cohort\" import os folder = os . path . abspath ( MY_FOLDER_PATH ) tables_to_save = [ \"person\" , \"visit_detail\" , \"visit_occurrence\" ] data . persist_tables_to_folder ( folder , tables = tables_to_save , person_ids = sample_patients ) Once you saved some data to disk, a dedicated class can be used to access it: The class PandasData can be used to load OMOP data from a folder containing several parquet files. The tables are accessed as attributes and are returned as Pandas DataFrame. Warning : in this case, the whole table will be loaded into memory on a single jupyter server. Consequently it is advised to only use this for small datasets. data = PandasData ( folder ) data . available_tables ['visit_detail', 'visit_occurrence', 'person'] person = data . person print ( f \"type: { type ( person ) } \" ) print ( f \"shape: { person . shape } \" ) type: shape: (1000, 5)","title":"Persisting/Reading a sample to/from disk: PandasData"},{"location":"functionalities/generic/io/#loading-from-postgres-postgresdata","text":"OMOP data can be stored in a PostgreSQL database. The PostgresData class provides a convinient interface to it. Note : this class relies on the file ~/.pgpass that contains your identifiers for several databases. data = PostgresData ( dbname = DB , schema = \"omop\" , user = USER , ) data . read_sql ( \"select count(*) from person\" ) {\"state\": {}, \"version_major\": 2, \"version_minor\": 0}","title":"Loading from PostGres: PostgresData"},{"location":"functionalities/omop-teva/","text":"OMOP Teva The OMOP Teva module of eds-scikit supports data scientists working on OMOP data. OMOP Teva generates an interactive dashboard for each OMOP table, allowing timely visualization of the volumes associated with each combination of values. This provides a general overview of the possible values, their relative importance and allows to detect quickly possible bias . This module is an eds-scikit transposition of EDS-Teva EDS-Teva is a more complete library designed to handle temporal bias in EHR data. See Adjusting for the progressive digitization of health records for a better understanding about those bias. { \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.8.0.json\", \"config\": { \"view\": { \"continuousHeight\": 300, \"continuousWidth\": 300 } }, \"data\": { \"name\": \"data-54870d255b2c7e93f9c75f03839955f2\" }, \"datasets\": { \"data-54870d255b2c7e93f9c75f03839955f2\": [ { \"care_site_short_name\": \"care site 1\", \"count\": 17, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 14, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 12, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 6, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 6, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 10, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 12, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 24, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 21, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 17, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 4, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 8, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 5, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 8, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 14, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 13, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 17, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 6, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 4, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 9, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 5, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 6, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 14, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 15, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 8, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 5, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 5, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 12, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 9, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 4, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 25, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 12, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 15, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 10, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 9, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 8, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 18, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 13, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 14, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 4, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 5, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 10, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 6, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 23, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 23, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 11, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 6, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 13, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 10, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 5, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 24, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 22, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 10, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 5, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 8, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 11, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 9, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 20, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 17, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 21, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 12, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 9, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 6, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 23, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 21, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 19, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 4, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 7, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 8, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 8, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 27, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 14, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 10, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 4, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 8, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 6, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 4, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 1, \"datetime\": \"2021-12-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 1, \"datetime\": \"2021-12-01T00:00:00\", \"stay_source_value\": \"PSY\" } ] }, \"params\": [ { \"bind\": \"legend\", \"name\": \"param_7\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"care_site_short_name\" ], \"on\": \"click\", \"type\": \"point\" }, \"views\": [ \"view_7\" ] }, { \"bind\": \"legend\", \"name\": \"param_8\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"stay_source_value\" ], \"on\": \"click\", \"type\": \"point\" }, \"views\": [ \"view_8\" ] } ], \"resolve\": { \"scale\": { \"color\": \"independent\" } }, \"vconcat\": [ { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"care_site_short_name\", \"scale\": { \"domain\": [ \"care site 1\", \"care site 2\", \"care site 3\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_7\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"care_site_short_name\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_7\", \"transform\": [ { \"filter\": { \"param\": \"param_8\" } } ] }, { \"encoding\": { \"color\": { \"field\": \"care_site_short_name\", \"scale\": { \"domain\": [ \"care site 1\", \"care site 2\", \"care site 3\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_7\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"param\": \"param_8\" } } ], \"width\": 300 } ], \"title\": \"care_site_short_name\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"stay_source_value\", \"scale\": { \"domain\": [ \"MCO\", \"Other\", \"PSY\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_8\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"stay_source_value\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_8\", \"transform\": [ { \"filter\": { \"param\": \"param_7\" } } ] }, { \"encoding\": { \"color\": { \"field\": \"stay_source_value\", \"scale\": { \"domain\": [ \"MCO\", \"Other\", \"PSY\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_8\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"param\": \"param_7\" } } ], \"width\": 300 } ], \"title\": \"stay_source_value\" } ] } OMOP Teva - Quick use Quick use for OMOP dataset exploration. OMOP Teva - Config See how your can configurate your own OMOP dashboards . Custom Teva For any dataframe exploration. OMOP Teva - Example A toy example of what can be obtained with this module.","title":"OMOP Teva"},{"location":"functionalities/omop-teva/#omop-teva","text":"The OMOP Teva module of eds-scikit supports data scientists working on OMOP data. OMOP Teva generates an interactive dashboard for each OMOP table, allowing timely visualization of the volumes associated with each combination of values. This provides a general overview of the possible values, their relative importance and allows to detect quickly possible bias . This module is an eds-scikit transposition of EDS-Teva EDS-Teva is a more complete library designed to handle temporal bias in EHR data. See Adjusting for the progressive digitization of health records for a better understanding about those bias. { \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.8.0.json\", \"config\": { \"view\": { \"continuousHeight\": 300, \"continuousWidth\": 300 } }, \"data\": { \"name\": \"data-54870d255b2c7e93f9c75f03839955f2\" }, \"datasets\": { \"data-54870d255b2c7e93f9c75f03839955f2\": [ { \"care_site_short_name\": \"care site 1\", \"count\": 17, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 14, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 12, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 6, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 6, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 10, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 12, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 24, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 21, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 17, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 4, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 8, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 5, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 8, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 14, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 13, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 17, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 6, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 4, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 9, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 5, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 6, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 14, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 15, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 8, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 5, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 5, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 12, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 9, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 4, \"datetime\": \"2021-04-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 25, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 12, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 15, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 10, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 9, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 8, \"datetime\": \"2021-05-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 18, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 13, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 14, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 4, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 5, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 10, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 6, \"datetime\": \"2021-06-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 23, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 23, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 11, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 6, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 13, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 10, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 5, \"datetime\": \"2021-07-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 24, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 22, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 10, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 5, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 8, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 11, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 9, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 20, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 17, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 21, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 12, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 9, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 6, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 23, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 21, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 19, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 4, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 7, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 8, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 8, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 27, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 14, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 10, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 4, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 8, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"MCO\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 6, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 4, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"PSY\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 1, \"datetime\": \"2021-12-01T00:00:00\", \"stay_source_value\": \"Other\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 1, \"datetime\": \"2021-12-01T00:00:00\", \"stay_source_value\": \"PSY\" } ] }, \"params\": [ { \"bind\": \"legend\", \"name\": \"param_7\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"care_site_short_name\" ], \"on\": \"click\", \"type\": \"point\" }, \"views\": [ \"view_7\" ] }, { \"bind\": \"legend\", \"name\": \"param_8\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"stay_source_value\" ], \"on\": \"click\", \"type\": \"point\" }, \"views\": [ \"view_8\" ] } ], \"resolve\": { \"scale\": { \"color\": \"independent\" } }, \"vconcat\": [ { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"care_site_short_name\", \"scale\": { \"domain\": [ \"care site 1\", \"care site 2\", \"care site 3\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_7\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"care_site_short_name\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_7\", \"transform\": [ { \"filter\": { \"param\": \"param_8\" } } ] }, { \"encoding\": { \"color\": { \"field\": \"care_site_short_name\", \"scale\": { \"domain\": [ \"care site 1\", \"care site 2\", \"care site 3\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_7\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"param\": \"param_8\" } } ], \"width\": 300 } ], \"title\": \"care_site_short_name\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"stay_source_value\", \"scale\": { \"domain\": [ \"MCO\", \"Other\", \"PSY\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_8\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"stay_source_value\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_8\", \"transform\": [ { \"filter\": { \"param\": \"param_7\" } } ] }, { \"encoding\": { \"color\": { \"field\": \"stay_source_value\", \"scale\": { \"domain\": [ \"MCO\", \"Other\", \"PSY\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_8\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"param\": \"param_7\" } } ], \"width\": 300 } ], \"title\": \"stay_source_value\" } ] } OMOP Teva - Quick use Quick use for OMOP dataset exploration. OMOP Teva - Config See how your can configurate your own OMOP dashboards . Custom Teva For any dataframe exploration. OMOP Teva - Example A toy example of what can be obtained with this module.","title":"OMOP Teva"},{"location":"functionalities/omop-teva/configuration-omop/","text":"OMOP Teva - Config All plots generated by generate_omop_teva are based on the configuration file eds_scikit.plot.default_omop_teva_config . Table configuration A table configuration is defined by 3 parameters : category columns list date column category columns mapping Here is two possible configurations for OMOP condition table : Default condition teva configuration Custom diabete condition teva configuration \"condition_occurrence\" : { \"category_columns\" : [ \"visit_occurrence_id\" , \"care_site_short_name\" , \"condition_source_value\" , \"stay_source_value\" , \"visit_source_value\" , \"admission_reason_source_value\" , \"visit_type_source_value\" , \"destination_source_value\" , \"cdm_source\" , ], \"date_column\" : \"condition_start_datetime\" , \"mapper\" : { \"visit_occurrence_id\" : { \"not NaN\" : \".*\" }, \"condition_source_value\" : { \"not NaN\" : \".*\" }, }, }, \"condition_occurrence\" : { # (1) Some columns were removed . \"category_columns\" : [ \"visit_occurrence_id\" , \"care_site_short_name\" , \"condition_source_value\" , \"visit_source_value\" , \"visit_type_source_value\" , \"cdm_source\" , ], # (2) Date column remain the same . \"date_column\" : \"condition_start_datetime\" , \"mapper\" : { \"visit_occurrence_id\" : { \"not NaN\" : \".*\" }, # (3) Mapping to diabetic conditions . \"condition_source_value\" : { \"has_diabete\" : r \"^E10|^E11|^E12|^E13|^E14|O24\" }, }, }, Specifying table configuration To specify configuration, simply update default_omop_teva_config and pass it to generate_omop_teva . from eds_scikit.plot import generate_omop_teva from eds_scikit.io.omop_teva_default_config import default_omop_teva_config omop_teva_config = default_omop_teva_config condition_mapper = { \"condition_source_value\" : { \"has_diabete\" : r \"^E10|^E11|^E12|^E13|^E14|O24\" } } omop_teva_config [ \"condition_occurrence\" ][ \"mapper\" ] . update ( condition_mapper ) start_date , end_date = \"2021-01-01\" , \"2021-12-01\" generate_omop_teva ( data = data , start_date = start_date , end_date = end_date , teva_config = omop_teva_config ) Adding a new table in default_omop_teva_config Feel free to add any new table in the configuration. Just make sure it has a visit_occurrence_id column.","title":"OMOP Teva - Config"},{"location":"functionalities/omop-teva/configuration-omop/#omop-teva-config","text":"All plots generated by generate_omop_teva are based on the configuration file eds_scikit.plot.default_omop_teva_config .","title":"OMOP Teva - Config"},{"location":"functionalities/omop-teva/configuration-omop/#table-configuration","text":"A table configuration is defined by 3 parameters : category columns list date column category columns mapping Here is two possible configurations for OMOP condition table : Default condition teva configuration Custom diabete condition teva configuration \"condition_occurrence\" : { \"category_columns\" : [ \"visit_occurrence_id\" , \"care_site_short_name\" , \"condition_source_value\" , \"stay_source_value\" , \"visit_source_value\" , \"admission_reason_source_value\" , \"visit_type_source_value\" , \"destination_source_value\" , \"cdm_source\" , ], \"date_column\" : \"condition_start_datetime\" , \"mapper\" : { \"visit_occurrence_id\" : { \"not NaN\" : \".*\" }, \"condition_source_value\" : { \"not NaN\" : \".*\" }, }, }, \"condition_occurrence\" : { # (1) Some columns were removed . \"category_columns\" : [ \"visit_occurrence_id\" , \"care_site_short_name\" , \"condition_source_value\" , \"visit_source_value\" , \"visit_type_source_value\" , \"cdm_source\" , ], # (2) Date column remain the same . \"date_column\" : \"condition_start_datetime\" , \"mapper\" : { \"visit_occurrence_id\" : { \"not NaN\" : \".*\" }, # (3) Mapping to diabetic conditions . \"condition_source_value\" : { \"has_diabete\" : r \"^E10|^E11|^E12|^E13|^E14|O24\" }, }, },","title":"Table configuration"},{"location":"functionalities/omop-teva/configuration-omop/#specifying-table-configuration","text":"To specify configuration, simply update default_omop_teva_config and pass it to generate_omop_teva . from eds_scikit.plot import generate_omop_teva from eds_scikit.io.omop_teva_default_config import default_omop_teva_config omop_teva_config = default_omop_teva_config condition_mapper = { \"condition_source_value\" : { \"has_diabete\" : r \"^E10|^E11|^E12|^E13|^E14|O24\" } } omop_teva_config [ \"condition_occurrence\" ][ \"mapper\" ] . update ( condition_mapper ) start_date , end_date = \"2021-01-01\" , \"2021-12-01\" generate_omop_teva ( data = data , start_date = start_date , end_date = end_date , teva_config = omop_teva_config ) Adding a new table in default_omop_teva_config Feel free to add any new table in the configuration. Just make sure it has a visit_occurrence_id column.","title":"Specifying table configuration"},{"location":"functionalities/omop-teva/custom-teva/","text":"Custom Teva OMOP-Teva module can also be applied to any dataframe. User must use reduce_table and visualize_table from eds_scikit.plot.table_viz . Make sure to specify categorical columns with less then 50 values. Use the function eds_scikit.plot.table_viz.map_column to reduce columns volumetry. Creating synthetic dataset import numpy as np import pandas as pd data = pd . DataFrame ( { \"id\" : str ( np . arange ( 1 , 1001 )), \"category_1\" : np . random . choice ([ \"A\" , \"B\" , \"C\" ], size = 1000 , p = [ 0.4 , 0.3 , 0.3 ]), \"category_2\" : np . array ([ str ( i ) for i in range ( 500 )] * 2 ), \"location\" : np . random . choice ( [ \"location 1\" , \"location 2\" ], size = 1000 , p = [ 0.6 , 0.4 ] ), \"date\" : pd . to_datetime ( np . random . choice ( pd . date_range ( start = \"2021-01-01\" , end = \"2022-01-01\" ), size = 1000 ) ), } ) from eds_scikit.plot import reduce_table , visualize_table data_reduced = reduce_table ( data , category_columns = [ \"location\" , \"category_1\" , \"category_2\" ], date_column = \"date\" , start_date = \"2021-01-01\" , end_date = \"2021-12-01\" , mapper = { \"category_2\" : { \"even\" : r \"[02468]$\" , \"odd\" : r \"[13579]$\" }}, ) chart = visualize_table ( data_reduced , title = \"synthetic dataframe table\" , description = True ) { \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.8.0.json\", \"config\": { \"legend\": { \"columns\": 4, \"symbolLimit\": 0 }, \"view\": { \"continuousHeight\": 300, \"continuousWidth\": 300 } }, \"data\": { \"name\": \"data-6c8e9658b16d48f64ac39e6b052cf917\" }, \"datasets\": { \"data-6c8e9658b16d48f64ac39e6b052cf917\": [ { \"category_1\": \"A\", \"category_2\": \"even\", \"count\": 8, \"datetime\": \"2021-01-01T00:00:00\", \"location\": \"location 1\" }, { \"category_1\": \"A\", \"category_2\": \"odd\", \"count\": 11, \"datetime\": \"2021-01-01T00:00:00\", \"location\": \"location 1\" }, { \"category_1\": \"B\", \"category_2\": \"even\", \"count\": 11, \"datetime\": \"2021-01-01T00:00:00\", \"location\": \"location 1\" }, { \"category_1\": \"B\", \"category_2\": \"odd\", \"count\": 5, \"datetime\": \"2021-01-01T00:00:00\", \"location\": \"location 1\" }, { \"category_1\": \"C\", \"category_2\": \"even\", \"count\": 6, \"datetime\": \"2021-01-01T00:00:00\", \"location\": \"location 1\" }, { \"category_1\": \"C\", \"category_2\": \"odd\", 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User must use reduce_table and visualize_table from eds_scikit.plot.table_viz . Make sure to specify categorical columns with less then 50 values. Use the function eds_scikit.plot.table_viz.map_column to reduce columns volumetry. Creating synthetic dataset import numpy as np import pandas as pd data = pd . DataFrame ( { \"id\" : str ( np . arange ( 1 , 1001 )), \"category_1\" : np . random . choice ([ \"A\" , \"B\" , \"C\" ], size = 1000 , p = [ 0.4 , 0.3 , 0.3 ]), \"category_2\" : np . array ([ str ( i ) for i in range ( 500 )] * 2 ), \"location\" : np . random . choice ( [ \"location 1\" , \"location 2\" ], size = 1000 , p = [ 0.6 , 0.4 ] ), \"date\" : pd . to_datetime ( np . random . choice ( pd . date_range ( start = \"2021-01-01\" , end = \"2022-01-01\" ), size = 1000 ) ), } ) from eds_scikit.plot import reduce_table , visualize_table data_reduced = reduce_table ( data , category_columns = [ \"location\" , \"category_1\" , \"category_2\" ], date_column = \"date\" , start_date = \"2021-01-01\" , end_date = \"2021-12-01\" , mapper = { \"category_2\" : { \"even\" : r \"[02468]$\" , \"odd\" : r \"[13579]$\" }}, ) chart = visualize_table ( data_reduced , title = \"synthetic dataframe table\" , description = True ) { \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.8.0.json\", 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], \"width\": 300 } ], \"title\": \"location\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"category_1\", \"scale\": { \"domain\": [ \"A\", \"B\", \"C\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_42\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"category_1\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_42\", \"transform\": [ { \"filter\": { \"and\": [ { \"param\": \"param_41\" }, { \"param\": \"param_43\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"category_1\", \"scale\": { \"domain\": [ \"A\", \"B\", \"C\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_42\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": 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\"color\": { \"field\": \"category_2\", \"scale\": { \"domain\": [ \"even\", \"odd\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_43\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"param\": \"param_41\" }, { \"param\": \"param_42\" } ] } } ], \"width\": 300 } ], \"title\": \"category_2\" } ] }","title":"Custom Teva"},{"location":"functionalities/omop-teva/omop-teva-example/","text":"OMOP Teva example The dashboard below provides an example of OMOP Teva dashboard for the visit_occurrence table. Explore this dashboard to identify any abnormal data distributions that could lead to bias. Solution : The Psychological department at care site 1 appears to produce unusual NaN values after June. { \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.8.0.json\", \"config\": { \"legend\": { \"columns\": 4, \"symbolLimit\": 0 }, \"view\": { \"continuousHeight\": 300, \"continuousWidth\": 300 } }, \"data\": { \"name\": \"data-8ebd6cc9cd8e33a2c58b3179f42f592a\" }, \"datasets\": { \"data-8ebd6cc9cd8e33a2c58b3179f42f592a\": [ { \"care_site_short_name\": \"care site 1\", \"count\": 12, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 5, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 1, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 5, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 8, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 4, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 8, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 6, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 1, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 2, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 3, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 6, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 4, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 2, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 10, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 2, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 11, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 11, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 2, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 9, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 10, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 1, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 8, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 8, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 1, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 4, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 3, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 1, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 3, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 1, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 4, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 4, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 6, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 8, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 6, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 7, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 11, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 6, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 4, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 3, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 6, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 1, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 4, \"datetime\": \"2021-03-01T00:00:00\", \"stay_source_value\": \"Other\", 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\"datetime\": \"2021-12-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 1, \"datetime\": \"2021-12-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" } ] }, \"padding\": { \"bottom\": 50, \"left\": 50, \"right\": 50, \"top\": 50 }, \"params\": [ { \"bind\": \"legend\", \"name\": \"param_17\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"visit_occurrence_id\" ], \"on\": \"click\", \"type\": \"point\" }, \"views\": [ \"view_17\" ] }, { \"bind\": \"legend\", \"name\": \"param_18\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"care_site_short_name\" ], \"on\": \"click\", \"type\": \"point\" }, \"views\": [ \"view_18\" ] }, { \"bind\": \"legend\", \"name\": \"param_19\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"stay_source_value\" ], \"on\": \"click\", \"type\": 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\"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_19\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_17\" }, { \"param\": \"param_18\" } ] }, { \"param\": \"param_20\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"stay_source_value\", \"scale\": { \"domain\": [ \"MCO\", \"Other\", \"PSY\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_19\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_17\" }, { \"param\": \"param_18\" } ] }, { \"param\": \"param_20\" } ] } } ], \"width\": 300 } ], \"title\": \"stay_source_value\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"visit_source_value\", \"scale\": { \"domain\": [ \"consult\", \"hospit\", \"NaN\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"black\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_20\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"visit_source_value\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_20\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_17\" }, { \"param\": \"param_18\" } ] }, { \"param\": \"param_19\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"visit_source_value\", \"scale\": { \"domain\": [ \"consult\", \"hospit\", \"NaN\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"black\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_20\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_17\" }, { \"param\": \"param_18\" } ] }, { \"param\": \"param_19\" } ] } } ], \"width\": 300 } ], \"title\": \"visit_source_value\" } ] }","title":"OMOP Teva example"},{"location":"functionalities/omop-teva/omop-teva-example/#omop-teva-example","text":"The dashboard below provides an example of OMOP Teva dashboard for the visit_occurrence table. Explore this dashboard to identify any abnormal data distributions that could lead to bias. Solution : The Psychological department at care site 1 appears to produce unusual NaN values after June. { \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.8.0.json\", \"config\": { \"legend\": { \"columns\": 4, \"symbolLimit\": 0 }, \"view\": { \"continuousHeight\": 300, \"continuousWidth\": 300 } }, \"data\": { \"name\": \"data-8ebd6cc9cd8e33a2c58b3179f42f592a\" }, \"datasets\": { \"data-8ebd6cc9cd8e33a2c58b3179f42f592a\": [ { \"care_site_short_name\": \"care site 1\", \"count\": 12, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 5, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 1, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 5, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 8, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 4, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 8, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 6, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 1, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 2, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 3, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 6, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 4, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 2, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 10, \"datetime\": \"2021-01-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 2, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 11, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 11, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 2, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 9, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", 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\"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 3, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 1, \"datetime\": \"2021-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 4, \"datetime\": \"2021-02-01T00:00:00\", 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\"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 1, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 7, \"datetime\": \"2021-08-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 2, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 8, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 10, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 1, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 11, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 5, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 21, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-09-01T00:00:00\", 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\"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 7, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 4, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 3, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 3\", \"count\": 6, \"datetime\": \"2021-09-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { 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\"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 1, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 13, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 7, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 19, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-10-01T00:00:00\", \"stay_source_value\": \"MCO\", 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\"care_site_short_name\": \"care site 1\", \"count\": 2, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 8, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 17, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 1, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 7, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 6, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"Other\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 1\", \"count\": 10, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"PSY\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"NaN\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 2, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consult\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospit\" }, { \"care_site_short_name\": \"care site 2\", \"count\": 1, \"datetime\": \"2021-11-01T00:00:00\", \"stay_source_value\": \"Other\", 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\"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_17\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_18\" }, { \"param\": \"param_19\" } ] }, { \"param\": \"param_20\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"visit_occurrence_id\", \"scale\": { \"domain\": [ \"not NaN\" ], \"range\": [ \"#1f77b4\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_17\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_18\" }, { \"param\": \"param_19\" } ] }, { \"param\": \"param_20\" } ] } } ], \"width\": 300 } ], \"title\": \"visit_occurrence_id\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"care_site_short_name\", \"scale\": { \"domain\": [ \"care site 1\", \"care site 2\", \"care site 3\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_18\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"care_site_short_name\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_18\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_17\" }, { \"param\": \"param_19\" } ] }, { \"param\": \"param_20\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"care_site_short_name\", \"scale\": { \"domain\": [ \"care site 1\", \"care site 2\", \"care site 3\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_18\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_17\" }, { \"param\": \"param_19\" } ] }, { \"param\": \"param_20\" } ] } } ], \"width\": 300 } ], \"title\": \"care_site_short_name\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"stay_source_value\", \"scale\": { \"domain\": [ \"MCO\", \"Other\", \"PSY\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_19\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"stay_source_value\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_19\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_17\" }, { \"param\": \"param_18\" } ] }, { \"param\": \"param_20\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"stay_source_value\", \"scale\": { \"domain\": [ \"MCO\", \"Other\", \"PSY\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_19\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_17\" }, { \"param\": \"param_18\" } ] }, { \"param\": \"param_20\" } ] } } ], \"width\": 300 } ], \"title\": \"stay_source_value\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"visit_source_value\", \"scale\": { \"domain\": [ \"consult\", \"hospit\", \"NaN\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"black\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_20\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"visit_source_value\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_20\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_17\" }, { \"param\": \"param_18\" } ] }, { \"param\": \"param_19\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"visit_source_value\", \"scale\": { \"domain\": [ \"consult\", \"hospit\", \"NaN\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"black\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_20\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_17\" }, { \"param\": \"param_18\" } ] }, { \"param\": \"param_19\" } ] } } ], \"width\": 300 } ], \"title\": \"visit_source_value\" } ] }","title":"OMOP Teva example"},{"location":"functionalities/omop-teva/quick-use-omop/","text":"OMOP Teva - Quick use This tutorial demonstrates how the OMOP teva module can be quickly used to generate OMOP tables dashboard. Simply apply generate_omop_teva function after loading the data. It will create a directory with one HTML per OMOP table. Avoid Jupyter Notebook Koalas framework with high volumetry processing in Jupyter Notebook might cause computationnal delay and memory issues. Prefer spark-submit script to run OMOP Teva. Loading dataset from eds_scikit.io.hive import HiveData data = HiveData ( spark_session = spark , database_name = \"project_xxxxxxxx\" , tables_to_load = [ \"care_site\" , \"visit_occurrence\" , \"concept\" , \"concept_relationship\" , \"note\" , \"procedure_occurrence\" , \"condition_occurrence\" , \"drug_exposure_prescription\" , \"drug_exposure_administration\" , ], ) from eds_scikit.plot import generate_omop_teva start_date , end_date = \"2021-01-01\" , \"2021-12-01\" generate_omop_teva ( data = data , start_date = start_date , end_date = end_date ) Visit occurrence Note Condition occurrence Condition occurrence (diabete) { \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.8.0.json\", \"config\": { \"view\": { \"continuousHeight\": 300, \"continuousWidth\": 300 } }, \"data\": { \"name\": \"data-b12cdc97854a522ae1f1412cfb19ca93\" }, \"datasets\": { \"data-b12cdc97854a522ae1f1412cfb19ca93\": [ { \"care_site_short_name\": \"H\\u00f4pital-2\", \"count\": 1.0, \"datetime\": \"2011-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-06-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 4.0, \"datetime\": \"2011-06-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2011-06-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-06-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-06-01T00:00:00\", \"stay_source_value\": \"SLD\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-07-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-07-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-07-01T00:00:00\", \"stay_source_value\": \"SLD\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-08-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 3.0, \"datetime\": \"2011-08-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 3.0, \"datetime\": \"2011-09-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 5.0, \"datetime\": \"2011-09-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-09-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-09-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 3.0, \"datetime\": \"2011-10-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 5.0, \"datetime\": \"2011-10-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2011-10-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-10-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-10-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 5.0, \"datetime\": \"2011-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-11-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2011-12-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 6.0, \"datetime\": \"2011-12-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2011-12-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-12-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-12-01T00:00:00\", \"stay_source_value\": \"SLD\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-12-01T00:00:00\", \"stay_source_value\": \"SLD\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-12-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 4.0, \"datetime\": \"2012-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-01-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2012-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 7.0, \"datetime\": \"2012-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-02-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-02-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-02-01T00:00:00\", \"stay_source_value\": \"SLD\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2012-03-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2012-03-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 3.0, \"datetime\": \"2012-03-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2012-03-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-03-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-03-01T00:00:00\", \"stay_source_value\": \"SLD\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2012-04-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 4.0, \"datetime\": \"2012-04-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-04-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-04-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2012-04-01T00:00:00\", \"stay_source_value\": \"SLD\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-04-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-1\", \"count\": 1.0, \"datetime\": \"2012-05-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 4.0, \"datetime\": \"2012-05-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2012-05-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-05-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-05-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 3.0, \"datetime\": \"2012-06-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 4.0, \"datetime\": \"2012-06-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-06-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-06-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-07-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 4.0, \"datetime\": \"2012-07-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 5.0, \"datetime\": \"2012-07-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-07-01T00:00:00\", \"stay_source_value\": \"SLD\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-07-01T00:00:00\", \"stay_source_value\": \"SLD\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-07-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2012-08-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 4.0, \"datetime\": \"2012-08-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2012-08-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-08-01T00:00:00\", \"stay_source_value\": \"SLD\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 9.0, \"datetime\": \"2012-09-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-09-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-09-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2012-09-01T00:00:00\", \"stay_source_value\": \"SLD\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-2\", \"count\": 1.0, \"datetime\": \"2012-10-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-10-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 4.0, \"datetime\": \"2012-10-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-10-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-10-01T00:00:00\", \"stay_source_value\": \"SLD\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-10-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-1\", \"count\": 1.0, \"datetime\": \"2012-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-1\", \"count\": 1.0, \"datetime\": \"2012-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2012-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 5.0, \"datetime\": \"2012-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2012-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", 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\"2019-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"not NaN\", \"count\": 23.0, \"datetime\": \"2019-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-1\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"not NaN\", \"count\": 6.0, \"datetime\": \"2019-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-1\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"not NaN\", \"count\": 20.0, \"datetime\": \"2019-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-2\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"not NaN\", \"count\": 3.0, \"datetime\": \"2019-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-2\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"not NaN\", \"count\": 6.0, \"datetime\": \"2019-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"not NaN\", \"count\": 1.0, \"datetime\": \"2019-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"not NaN\", \"count\": 5.0, \"datetime\": \"2019-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-2\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"not NaN\", \"count\": 2.0, \"datetime\": \"2019-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"not NaN\", \"count\": 2.0, \"datetime\": \"2019-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"not NaN\", \"count\": 1.0, \"datetime\": \"2019-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" } ] }, \"params\": [ { \"bind\": \"legend\", \"name\": \"param_5\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"visit_occurrence_id\" ], \"on\": \"click\", \"type\": \"point\" }, \"views\": [ \"view_5\" ] }, { \"bind\": \"legend\", \"name\": \"param_6\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"care_site_short_name\" ], \"on\": \"click\", \"type\": \"point\" }, \"views\": [ \"view_6\" ] }, { \"bind\": \"legend\", \"name\": \"param_7\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"condition_source_value\" ], \"on\": \"click\", \"type\": \"point\" }, \"views\": [ \"view_7\" ] }, { \"bind\": \"legend\", \"name\": \"param_8\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"cdm_source\" ], \"on\": \"click\", \"type\": \"point\" }, \"views\": [ \"view_8\" ] } ], \"resolve\": { \"scale\": { \"color\": \"independent\" } }, \"vconcat\": [ { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"visit_occurrence_id\", \"scale\": { \"domain\": [ \"not NaN\" ], \"range\": [ \"#1f77b4\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_5\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"visit_occurrence_id\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_5\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_6\" }, { \"param\": \"param_7\" } ] }, { \"param\": \"param_8\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"visit_occurrence_id\", \"scale\": { \"domain\": [ \"not NaN\" ], \"range\": [ \"#1f77b4\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_5\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_6\" }, { \"param\": \"param_7\" } ] }, { \"param\": \"param_8\" } ] } } ], \"width\": 300 } ], \"title\": \"visit_occurrence_id\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"care_site_short_name\", \"scale\": { \"domain\": [ \"H\\u00f4pital-3\", \"H\\u00f4pital-2\", \"H\\u00f4pital-1\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_6\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"care_site_short_name\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_6\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_5\" }, { \"param\": \"param_7\" } ] }, { \"param\": \"param_8\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"care_site_short_name\", \"scale\": { \"domain\": [ \"H\\u00f4pital-3\", \"H\\u00f4pital-2\", \"H\\u00f4pital-1\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_6\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_5\" }, { \"param\": \"param_7\" } ] }, { \"param\": \"param_8\" } ] } } ], \"width\": 300 } ], \"title\": \"care_site_short_name\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"condition_source_value\", \"scale\": { \"domain\": [ \"not NaN\" ], \"range\": [ \"#1f77b4\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_7\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"condition_source_value\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_7\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_5\" }, { \"param\": \"param_6\" } ] }, { \"param\": \"param_8\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"condition_source_value\", \"scale\": { \"domain\": [ \"not NaN\" ], \"range\": [ \"#1f77b4\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_7\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_5\" }, { \"param\": \"param_6\" } ] }, { \"param\": \"param_8\" } ] } } ], \"width\": 300 } ], \"title\": \"condition_source_value\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"cdm_source\", \"scale\": { \"domain\": [ \"AREM\", \"ORBIS\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_8\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"cdm_source\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_8\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_5\" }, { \"param\": \"param_6\" } ] }, { \"param\": \"param_7\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"cdm_source\", \"scale\": { \"domain\": [ \"AREM\", \"ORBIS\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_8\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_5\" }, { \"param\": \"param_6\" } ] }, { \"param\": \"param_7\" } ] } } ], \"width\": 300 } ], \"title\": \"cdm_source\" } ] } In this example condition_source_value is splited between diabetic and non-diabetic conditions. You can modify dashboard configuration by importing eds_scikit.plot.default_omop_teva_config and customizing it. See next section for details on how to do it. { \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.8.0.json\", \"config\": { \"view\": { \"continuousHeight\": 300, \"continuousWidth\": 300 } }, \"data\": { \"name\": \"data-1d0fd0ace6c43f5e025c5daf44a85809\" }, \"datasets\": { \"data-1d0fd0ace6c43f5e025c5daf44a85809\": [ { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 2.0, \"datetime\": \"2011-06-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 4.0, \"datetime\": \"2011-06-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 1.0, \"datetime\": \"2011-06-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 3.0, \"datetime\": \"2011-07-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 11.0, \"datetime\": \"2011-07-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 2.0, \"datetime\": \"2011-07-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 6.0, \"datetime\": \"2011-07-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 1.0, \"datetime\": \"2011-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 9.0, \"datetime\": \"2011-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 3.0, \"datetime\": \"2011-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 2.0, \"datetime\": \"2011-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 1.0, \"datetime\": \"2011-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 8.0, \"datetime\": \"2011-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 1.0, \"datetime\": \"2011-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 2.0, \"datetime\": \"2011-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 9.0, \"datetime\": \"2011-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 13.0, \"datetime\": \"2011-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 7.0, \"datetime\": \"2011-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 8.0, \"datetime\": \"2011-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 2.0, \"datetime\": \"2011-11-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 24.0, \"datetime\": \"2011-11-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 5.0, \"datetime\": \"2011-11-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 6.0, \"datetime\": \"2011-11-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 5.0, \"datetime\": \"2011-12-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 7.0, \"datetime\": \"2011-12-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 4.0, \"datetime\": \"2011-12-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 7.0, \"datetime\": \"2011-12-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 7.0, \"datetime\": \"2012-01-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 18.0, \"datetime\": \"2012-01-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 3.0, \"datetime\": \"2012-01-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 5.0, \"datetime\": \"2012-01-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 8.0, \"datetime\": \"2012-02-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 16.0, \"datetime\": \"2012-02-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 1.0, \"datetime\": \"2012-02-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 7.0, \"datetime\": \"2012-02-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 7.0, \"datetime\": \"2012-03-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 16.0, \"datetime\": \"2012-03-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 9.0, \"datetime\": \"2012-03-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 12.0, \"datetime\": \"2012-03-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 6.0, \"datetime\": \"2012-04-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 25.0, \"datetime\": \"2012-04-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 3.0, \"datetime\": \"2012-04-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 19.0, \"datetime\": \"2012-04-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 12.0, \"datetime\": \"2012-05-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 19.0, \"datetime\": \"2012-05-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": 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\"2019-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-2\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 10.0, \"datetime\": \"2019-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 8.0, \"datetime\": \"2019-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 9.0, \"datetime\": \"2019-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 6.0, \"datetime\": \"2019-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 14.0, \"datetime\": \"2019-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-1\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 2.0, \"datetime\": \"2019-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-1\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 12.0, \"datetime\": \"2019-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-1\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 4.0, \"datetime\": \"2019-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-1\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 8.0, \"datetime\": \"2019-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-2\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 2.0, \"datetime\": \"2019-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-2\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 2.0, \"datetime\": \"2019-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-2\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 1.0, \"datetime\": \"2019-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-2\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 4.0, \"datetime\": \"2019-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 1.0, \"datetime\": 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\"condition_source_value\": \"Other\", \"count\": 1.0, \"datetime\": \"2019-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 1.0, \"datetime\": \"2019-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 1.0, \"datetime\": \"2019-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" } ] }, \"params\": [ { \"bind\": \"legend\", \"name\": \"param_29\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"visit_occurrence_id\" ], \"on\": \"click\", \"type\": \"point\" }, \"views\": [ \"view_29\" ] }, { \"bind\": \"legend\", \"name\": \"param_30\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"care_site_short_name\" ], \"on\": \"click\", \"type\": \"point\" }, \"views\": [ \"view_30\" ] }, { \"bind\": \"legend\", \"name\": \"param_31\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"condition_source_value\" ], \"on\": \"click\", \"type\": \"point\" }, \"views\": [ \"view_31\" ] }, { \"bind\": \"legend\", \"name\": \"param_32\", \"select\": { \"clear\": \"dblclick\", \"fields\": [ \"cdm_source\" ], \"on\": \"click\", \"type\": \"point\" }, \"views\": [ \"view_32\" ] } ], \"resolve\": { \"scale\": { \"color\": \"independent\" } }, \"vconcat\": [ { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"visit_occurrence_id\", \"scale\": { \"domain\": [ \"not NaN\" ], \"range\": [ \"#1f77b4\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_29\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"visit_occurrence_id\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_29\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_30\" }, { \"param\": \"param_31\" } ] }, { \"param\": \"param_32\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"visit_occurrence_id\", \"scale\": { \"domain\": [ \"not NaN\" ], \"range\": [ \"#1f77b4\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_29\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_30\" }, { \"param\": \"param_31\" } ] }, { \"param\": \"param_32\" } ] } } ], \"width\": 300 } ], \"title\": \"visit_occurrence_id\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"care_site_short_name\", \"scale\": { \"domain\": [ \"H\\u00f4pital-3\", \"H\\u00f4pital-2\", \"H\\u00f4pital-1\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_30\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"care_site_short_name\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_30\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_29\" }, { \"param\": \"param_31\" } ] }, { \"param\": \"param_32\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"care_site_short_name\", \"scale\": { \"domain\": [ \"H\\u00f4pital-3\", \"H\\u00f4pital-2\", \"H\\u00f4pital-1\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_30\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_29\" }, { \"param\": \"param_31\" } ] }, { \"param\": \"param_32\" } ] } } ], \"width\": 300 } ], \"title\": \"care_site_short_name\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"condition_source_value\", \"scale\": { \"domain\": [ \"Other\", \"has_diabete\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_31\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"condition_source_value\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_31\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_29\" }, { \"param\": \"param_30\" } ] }, { \"param\": \"param_32\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"condition_source_value\", \"scale\": { \"domain\": [ \"Other\", \"has_diabete\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_31\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_29\" }, { \"param\": \"param_30\" } ] }, { \"param\": \"param_32\" } ] } } ], \"width\": 300 } ], \"title\": \"condition_source_value\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"cdm_source\", \"scale\": { \"domain\": [ \"AREM\", \"ORBIS\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_32\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"cdm_source\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_32\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_29\" }, { \"param\": \"param_30\" } ] }, { \"param\": \"param_31\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"cdm_source\", \"scale\": { \"domain\": [ \"AREM\", \"ORBIS\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_32\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_29\" }, { \"param\": \"param_30\" } ] }, { \"param\": \"param_31\" } ] } } ], \"width\": 300 } ], \"title\": \"cdm_source\" } ] }","title":"OMOP Teva - Quick use"},{"location":"functionalities/omop-teva/quick-use-omop/#omop-teva-quick-use","text":"This tutorial demonstrates how the OMOP teva module can be quickly used to generate OMOP tables dashboard. Simply apply generate_omop_teva function after loading the data. It will create a directory with one HTML per OMOP table. Avoid Jupyter Notebook Koalas framework with high volumetry processing in Jupyter Notebook might cause computationnal delay and memory issues. Prefer spark-submit script to run OMOP Teva. Loading dataset from eds_scikit.io.hive import HiveData data = HiveData ( spark_session = spark , database_name = \"project_xxxxxxxx\" , tables_to_load = [ \"care_site\" , \"visit_occurrence\" , \"concept\" , \"concept_relationship\" , \"note\" , \"procedure_occurrence\" , \"condition_occurrence\" , \"drug_exposure_prescription\" , \"drug_exposure_administration\" , ], ) from eds_scikit.plot import generate_omop_teva start_date , end_date = \"2021-01-01\" , \"2021-12-01\" generate_omop_teva ( data = data , start_date = start_date , end_date = end_date ) Visit occurrence Note Condition occurrence Condition occurrence (diabete) { \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.8.0.json\", \"config\": { \"view\": { \"continuousHeight\": 300, \"continuousWidth\": 300 } }, \"data\": { \"name\": \"data-b12cdc97854a522ae1f1412cfb19ca93\" }, \"datasets\": { \"data-b12cdc97854a522ae1f1412cfb19ca93\": [ { \"care_site_short_name\": \"H\\u00f4pital-2\", \"count\": 1.0, \"datetime\": \"2011-01-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-06-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 4.0, \"datetime\": \"2011-06-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2011-06-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-06-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-06-01T00:00:00\", \"stay_source_value\": \"SLD\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-07-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-07-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-07-01T00:00:00\", \"stay_source_value\": \"SLD\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"urgences\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-08-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 3.0, \"datetime\": \"2011-08-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 3.0, \"datetime\": \"2011-09-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 5.0, \"datetime\": \"2011-09-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-09-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-09-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 3.0, \"datetime\": \"2011-10-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 5.0, \"datetime\": \"2011-10-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 2.0, \"datetime\": \"2011-10-01T00:00:00\", \"stay_source_value\": \"Psychiatrie\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-10-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-10-01T00:00:00\", \"stay_source_value\": \"SSR\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"consultation\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 5.0, \"datetime\": \"2011-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": \"not NaN\", \"visit_source_value\": \"hospitalis\\u00e9s\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"count\": 1.0, \"datetime\": \"2011-11-01T00:00:00\", \"stay_source_value\": \"MCO\", \"visit_occurrence_id\": 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\"type\": \"bar\" }, \"name\": \"view_6\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_5\" }, { \"param\": \"param_7\" } ] }, { \"param\": \"param_8\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"care_site_short_name\", \"scale\": { \"domain\": [ \"H\\u00f4pital-3\", \"H\\u00f4pital-2\", \"H\\u00f4pital-1\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_6\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_5\" }, { \"param\": \"param_7\" } ] }, { \"param\": \"param_8\" } ] } } ], \"width\": 300 } ], \"title\": \"care_site_short_name\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"condition_source_value\", \"scale\": { \"domain\": [ \"not NaN\" ], \"range\": [ \"#1f77b4\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_7\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"condition_source_value\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_7\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_5\" }, { \"param\": \"param_6\" } ] }, { \"param\": \"param_8\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"condition_source_value\", \"scale\": { \"domain\": [ \"not NaN\" ], \"range\": [ \"#1f77b4\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_7\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_5\" }, { \"param\": \"param_6\" } ] }, { \"param\": \"param_8\" } ] } } ], \"width\": 300 } ], \"title\": \"condition_source_value\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"cdm_source\", \"scale\": { \"domain\": [ \"AREM\", \"ORBIS\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_8\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"cdm_source\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_8\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_5\" }, { \"param\": \"param_6\" } ] }, { \"param\": \"param_7\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"cdm_source\", \"scale\": { \"domain\": [ \"AREM\", \"ORBIS\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_8\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_5\" }, { \"param\": \"param_6\" } ] }, { \"param\": \"param_7\" } ] } } ], \"width\": 300 } ], \"title\": \"cdm_source\" } ] } In this example condition_source_value is splited between diabetic and non-diabetic conditions. You can modify dashboard configuration by importing eds_scikit.plot.default_omop_teva_config and customizing it. See next section for details on how to do it. { \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.8.0.json\", \"config\": { \"view\": { \"continuousHeight\": 300, \"continuousWidth\": 300 } }, \"data\": { \"name\": \"data-1d0fd0ace6c43f5e025c5daf44a85809\" }, \"datasets\": { \"data-1d0fd0ace6c43f5e025c5daf44a85809\": [ { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 2.0, \"datetime\": \"2011-06-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 4.0, \"datetime\": \"2011-06-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 1.0, \"datetime\": \"2011-06-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 3.0, \"datetime\": \"2011-07-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 11.0, \"datetime\": \"2011-07-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 2.0, \"datetime\": \"2011-07-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 6.0, \"datetime\": \"2011-07-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 1.0, \"datetime\": \"2011-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 9.0, \"datetime\": \"2011-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 3.0, \"datetime\": \"2011-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 2.0, \"datetime\": \"2011-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 1.0, \"datetime\": \"2011-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 8.0, \"datetime\": \"2011-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 1.0, \"datetime\": \"2011-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 2.0, \"datetime\": \"2011-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 9.0, \"datetime\": \"2011-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 13.0, \"datetime\": \"2011-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 7.0, \"datetime\": \"2011-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 8.0, \"datetime\": \"2011-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 2.0, \"datetime\": \"2011-11-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 24.0, \"datetime\": \"2011-11-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 5.0, \"datetime\": \"2011-11-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 6.0, \"datetime\": \"2011-11-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 5.0, \"datetime\": \"2011-12-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 7.0, \"datetime\": \"2011-12-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 4.0, \"datetime\": \"2011-12-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 7.0, \"datetime\": \"2011-12-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 7.0, \"datetime\": \"2012-01-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 18.0, \"datetime\": \"2012-01-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 3.0, \"datetime\": \"2012-01-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 5.0, \"datetime\": \"2012-01-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 8.0, \"datetime\": \"2012-02-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 16.0, \"datetime\": \"2012-02-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 1.0, \"datetime\": \"2012-02-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 7.0, \"datetime\": \"2012-02-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 7.0, \"datetime\": \"2012-03-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 16.0, \"datetime\": \"2012-03-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 9.0, \"datetime\": \"2012-03-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 12.0, \"datetime\": \"2012-03-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 6.0, \"datetime\": \"2012-04-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 25.0, \"datetime\": \"2012-04-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 3.0, \"datetime\": \"2012-04-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 19.0, \"datetime\": \"2012-04-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 12.0, \"datetime\": \"2012-05-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 19.0, \"datetime\": \"2012-05-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 3.0, \"datetime\": \"2012-05-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 9.0, \"datetime\": \"2012-05-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 5.0, \"datetime\": \"2012-06-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 14.0, \"datetime\": \"2012-06-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 1.0, \"datetime\": \"2012-06-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 8.0, \"datetime\": \"2012-06-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 13.0, \"datetime\": \"2012-07-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 16.0, \"datetime\": \"2012-07-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 6.0, \"datetime\": \"2012-07-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 10.0, \"datetime\": \"2012-07-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 3.0, \"datetime\": \"2012-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 7.0, \"datetime\": \"2012-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 2.0, \"datetime\": \"2012-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 4.0, \"datetime\": \"2012-08-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 7.0, \"datetime\": \"2012-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 19.0, \"datetime\": \"2012-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 3.0, \"datetime\": \"2012-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 6.0, \"datetime\": \"2012-09-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-2\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 2.0, \"datetime\": \"2012-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-2\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 1.0, \"datetime\": \"2012-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 5.0, \"datetime\": \"2012-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 12.0, \"datetime\": \"2012-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 3.0, \"datetime\": \"2012-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 6.0, \"datetime\": \"2012-10-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-2\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 1.0, \"datetime\": \"2012-11-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 7.0, \"datetime\": \"2012-11-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 15.0, \"datetime\": \"2012-11-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 3.0, \"datetime\": \"2012-11-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 9.0, \"datetime\": \"2012-11-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-1\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 1.0, \"datetime\": \"2012-12-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"Other\", \"count\": 4.0, \"datetime\": \"2012-12-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 11.0, \"datetime\": \"2012-12-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 7.0, \"datetime\": \"2012-12-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 11.0, \"datetime\": \"2012-12-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-1\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"Other\", \"count\": 1.0, \"datetime\": \"2013-01-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-1\", \"cdm_source\": \"AREM\", \"condition_source_value\": \"has_diabete\", \"count\": 1.0, \"datetime\": \"2013-01-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-1\", \"cdm_source\": \"ORBIS\", \"condition_source_value\": \"has_diabete\", \"count\": 1.0, \"datetime\": \"2013-01-01T00:00:00\", \"visit_occurrence_id\": \"not NaN\" }, { \"care_site_short_name\": \"H\\u00f4pital-3\", \"cdm_source\": \"AREM\", \"condition_source_value\": 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\"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_29\" }, { \"param\": \"param_31\" } ] }, { \"param\": \"param_32\" } ] } } ], \"width\": 300 } ], \"title\": \"care_site_short_name\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"condition_source_value\", \"scale\": { \"domain\": [ \"Other\", \"has_diabete\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_31\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"condition_source_value\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_31\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_29\" }, { \"param\": \"param_30\" } ] }, { \"param\": \"param_32\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"condition_source_value\", \"scale\": { \"domain\": [ \"Other\", \"has_diabete\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_31\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_29\" }, { \"param\": \"param_30\" } ] }, { \"param\": \"param_32\" } ] } } ], \"width\": 300 } ], \"title\": \"condition_source_value\" }, { \"hconcat\": [ { \"encoding\": { \"color\": { \"field\": \"cdm_source\", \"scale\": { \"domain\": [ \"AREM\", \"ORBIS\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_32\", \"value\": 1 }, \"value\": 0.3 }, \"tooltip\": [ { \"field\": \"cdm_source\", \"type\": \"nominal\" } ], \"x\": { \"aggregate\": \"sum\", \"field\": \"count\", \"type\": \"quantitative\" } }, \"mark\": { \"type\": \"bar\" }, \"name\": \"view_32\", \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_29\" }, { \"param\": \"param_30\" } ] }, { \"param\": \"param_31\" } ] } } ] }, { \"encoding\": { \"color\": { \"field\": \"cdm_source\", \"scale\": { \"domain\": [ \"AREM\", \"ORBIS\" ], \"range\": [ \"#1f77b4\", \"#ff7f0e\" ] }, \"type\": \"nominal\" }, \"opacity\": { \"condition\": { \"param\": \"param_32\", \"value\": 1 }, \"value\": 0.3 }, \"x\": { \"field\": \"datetime\", \"timeUnit\": \"yearmonth\", \"type\": \"temporal\" }, \"y\": { \"aggregate\": \"sum\", \"axis\": { \"format\": \"s\" }, \"field\": \"count\", \"type\": \"quantitative\" } }, \"height\": 50, \"mark\": { \"type\": \"line\" }, \"transform\": [ { \"filter\": { \"and\": [ { \"and\": [ { \"param\": \"param_29\" }, { \"param\": \"param_30\" } ] }, { \"param\": \"param_31\" } ] } } ], \"width\": 300 } ], \"title\": \"cdm_source\" } ] }","title":"OMOP Teva - Quick use"},{"location":"functionalities/patients-course/consultation_dates/","text":"When a patient comes multiple times for consultations, it is often represented as a single visit_occurrence in the CDW. If a clear history of a patient's course is needed, it is then necessary to use proxies in order to access this information. An available proxy to get those consultation dates is to check for the existence of consultation reports and use the associated reports dates. To this extend, two methods are available. They can be combined or used separately: Use the note_datetime field associated to each consultation report Extract the consultation report date by using NLP An important remark Be careful when using the note_datetime field as it can represent the date of modification of a document (i.e. it can be modified if the clinician adds some information in it in the future). from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.event import get_consultation_dates get_consultation_dates ( data . visit_occurrence , note = data . note , note_nlp = note_nlp , algo = [ \"nlp\" ], ) The snippet above required us to generate a note_nlp with a consultation_date column (see below for more informations). Consultation pipe A consultation date pipeline exists and is particulary suited for this task. Moreover, methods are available to run an EDS-NLP pipeline on a Pandas, Spark or even Koalas DataFrame ! We can check the various exposed parameters if needed: Extract consultation dates. See the implementation details of the algo(s) you want to use PARAMETER DESCRIPTION vo visit_occurrence DataFrame TYPE: DataFrame note note DataFrame TYPE: DataFrame note_nlp note_nlp DataFrame, used only with the \"nlp\" algo TYPE: Optional [ DataFrame ] DEFAULT: None algo Algorithm(s) to use to determine consultation dates. Multiple algorithms can be provided as a list. Accepted values are: \"structured\" : See get_consultation_dates_structured() \"nlp\" : See get_consultation_dates_nlp() TYPE: Union [ str , List [ str ]] DEFAULT: ['nlp'] max_timedelta If two extracted consultations are spaced by less than max_timedelta , we consider that they correspond to the same event and only keep the first one. TYPE: timedelta DEFAULT: timedelta(days=7) structured_config A dictionnary of parameters when using the structured algorithm TYPE: Dict [ str , Any ] DEFAULT: dict() nlp_config A dictionnary of parameters when using the nlp algorithm TYPE: Dict [ str , Any ] DEFAULT: dict() RETURNS DESCRIPTION DataFrame Event type DataFrame with the following columns: person_id visit_occurrence_id CONSULTATION_DATE : corresponds to the note_datetime value of a consultation report coming from the considered visit. CONSULTATION_NOTE_ID : the note_id of the corresponding report. CONSULTATION_DATE_EXTRACTION : the method of extraction Availables algorithms (values for \"algo\" ) 'nlp' 'structured' Uses consultation dates extracted a priori in consultation reports to infer true consultation dates PARAMETER DESCRIPTION note_nlp A DataFrame with (at least) the following columns: note_id consultation_date end if using dates_to_keep=first : end should store the character offset of the extracted date. TYPE: DataFrame dates_to_keep How to handle multiple consultation dates found in the document: min : keep the oldest one first : keep the occurrence that appeared first in the text all : keep all date TYPE: str , optional DEFAULT: 'min' RETURNS DESCRIPTION Dataframe With 2 added columns corresponding to the following concept: CONSULTATION_DATE , containing the date CONSULTATION_DATE_EXTRACTION , containing \"NLP\" Source code in eds_scikit/event/consultations.py 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 def get_consultation_dates_nlp ( note_nlp : DataFrame , dates_to_keep : str = \"min\" , ) -> DataFrame : \"\"\" Uses consultation dates extracted *a priori* in consultation reports to infer *true* consultation dates Parameters ---------- note_nlp : DataFrame A DataFrame with (at least) the following columns: - `note_id` - `consultation_date` - `end` **if** using `dates_to_keep=first`: `end` should store the character offset of the extracted date. dates_to_keep : str, optional How to handle multiple consultation dates found in the document: - `min`: keep the oldest one - `first`: keep the occurrence that appeared first in the text - `all`: keep all date Returns ------- Dataframe With 2 added columns corresponding to the following concept: - `CONSULTATION_DATE`, containing the date - `CONSULTATION_DATE_EXTRACTION`, containing `\"NLP\"` \"\"\" if dates_to_keep == \"min\" : dates_per_note = note_nlp . groupby ( \"note_id\" ) . agg ( CONSULTATION_DATE = ( \"consultation_date\" , \"min\" ), ) elif dates_to_keep == \"first\" : dates_per_note = ( note_nlp . sort_values ( by = \"start\" ) . groupby ( \"note_id\" ) . agg ( CONSULTATION_DATE = ( \"consultation_date\" , \"first\" )) ) elif dates_to_keep == \"all\" : dates_per_note = note_nlp [[ \"consultation_date\" , \"note_id\" ]] . set_index ( \"note_id\" ) dates_per_note = dates_per_note . rename ( columns = { \"consultation_date\" : \"CONSULTATION_DATE\" } ) dates_per_note [ \"CONSULTATION_DATE_EXTRACTION\" ] = \"NLP\" return dates_per_note Uses note_datetime value to infer true consultation dates PARAMETER DESCRIPTION note A note DataFrame with at least the following columns: note_id note_datetime note_source_value if kept_note_class_source_value is not None visit_occurrence_id if kept_visit_source_value is not None TYPE: DataFrame vo A visit_occurrence DataFrame to provide if kept_visit_source_value is not None , with at least the following columns: visit_occurrence_id visit_source_value if kept_visit_source_value is not None TYPE: Optional [ DataFrame ] DEFAULT: None kept_note_class_source_value Value(s) allowed for the note_class_source_value column. TYPE: Optional [ Union [ str , List [ str ]]] DEFAULT: 'CR-CONS' kept_visit_source_value Value(s) allowed for the visit_source_value column. TYPE: Optional [ Union [ str , List [ str ]]], optional DEFAULT: 'consultation externe' RETURNS DESCRIPTION Dataframe With 2 added columns corresponding to the following concept: CONSULTATION_DATE , containing the date CONSULTATION_DATE_EXTRACTION , containing \"STRUCTURED\" Source code in eds_scikit/event/consultations.py 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 def get_consultation_dates_structured ( note : DataFrame , vo : Optional [ DataFrame ] = None , kept_note_class_source_value : Optional [ Union [ str , List [ str ]]] = \"CR-CONS\" , kept_visit_source_value : Optional [ Union [ str , List [ str ]]] = \"consultation externe\" , ) -> DataFrame : \"\"\" Uses `note_datetime` value to infer *true* consultation dates Parameters ---------- note : DataFrame A `note` DataFrame with at least the following columns: - `note_id` - `note_datetime` - `note_source_value` **if** `kept_note_class_source_value is not None` - `visit_occurrence_id` **if** `kept_visit_source_value is not None` vo : Optional[DataFrame] A visit_occurrence DataFrame to provide **if** `kept_visit_source_value is not None`, with at least the following columns: - `visit_occurrence_id` - `visit_source_value` **if** `kept_visit_source_value is not None` kept_note_class_source_value : Optional[Union[str, List[str]]] Value(s) allowed for the `note_class_source_value` column. kept_visit_source_value : Optional[Union[str, List[str]]], optional Value(s) allowed for the `visit_source_value` column. Returns ------- Dataframe With 2 added columns corresponding to the following concept: - `CONSULTATION_DATE`, containing the date - `CONSULTATION_DATE_EXTRACTION`, containing `\"STRUCTURED\"` \"\"\" kept_note = note if kept_note_class_source_value is not None : if type ( kept_note_class_source_value ) == str : kept_note_class_source_value = [ kept_note_class_source_value ] kept_note = note [ note . note_class_source_value . isin ( set ( kept_note_class_source_value )) ] if kept_visit_source_value is not None : if type ( kept_visit_source_value ) == str : kept_visit_source_value = [ kept_visit_source_value ] kept_note = kept_note . merge ( vo [ [ \"visit_occurrence_id\" , \"visit_source_value\" , ] ][ vo . visit_source_value . isin ( set ( kept_visit_source_value ))], on = \"visit_occurrence_id\" , ) dates_per_note = kept_note [[ \"note_datetime\" , \"note_id\" ]] . rename ( columns = { \"note_datetime\" : \"CONSULTATION_DATE\" , } ) dates_per_note [ \"CONSULTATION_DATE_EXTRACTION\" ] = \"STRUCTURED\" return dates_per_note . set_index ( \"note_id\" )","title":"Consultation dates"},{"location":"functionalities/patients-course/is_emergency/","text":"eds-scikit provides a function to tag care sites as being medical emergency units . It also provides a higher-level function to directly tag visits. from eds_scikit.io import HiveData data = HiveData ( DBNAME ) Tagging care sites Tagging is done using the tag_emergency_care_site function: from eds_scikit.emergency import tag_emergency_care_site Tag care sites that correspond to medical emergency units . The tagging is done by adding a \"IS_EMERGENCY\" column to the provided DataFrame. Some algos can add an additional \"EMERGENCY_TYPE\" column to the provided DataFrame, providing a more detailled classification. PARAMETER DESCRIPTION care_site TYPE: DataFrame algo Possible values are: \"from_mapping\" relies on a list of care_site_source_value extracted by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites are here further labelled to distinguish the different types of emergency \"from_regex_on_care_site_description\" : relies on a specific list of RegEx applied on the description (= simplified care site name) of each care site. \"from_regex_on_parent_UF\" : relies on a specific list of regular expressions applied on the description (= simplified care site name) of each UF (Unit\u00e9 Fonctionnelle). The obtained tag is then propagated to every UF's children. TYPE: str DEFAULT: 'from_mapping' RETURNS DESCRIPTION care_site Dataframe with 1 to 2 added columns corresponding to the following concepts: \"IS_EMERGENCY\" \"EMERGENCY_TYPE\" (if using algo \"from_mapping\" ) TYPE: DataFrame Simply call the function by providing the necessary data (see below) and by picking the algo care_site = tag_emergency_care_site ( care_site = data . care_site , algo = \"from_mapping\" , ) Availables algorithms (values for \"algo\" ) 'from_mapping' 'from_regex_on_parent_UF' 'from_regex_on_care_site_description' This algo uses a labelled list of 201 emergency care sites. Those care sites were extracted and verified by Ariel COHEN, Judith LEBLANC, and an ER doctor validated them. Those emergency care sites are further divised into different categories, as defined in the concept 'EMERGENCY_TYPE'. The different categories are: Urgences sp\u00e9cialis\u00e9es UHCD + Post-urgences Urgences p\u00e9diatriques Urgences g\u00e9n\u00e9rales adulte Consultation urgences SAMU / SMUR See the dataset here PARAMETER DESCRIPTION care_site Should at least contains the care_site_source_value column TYPE: DataFrame version Optional version string for the mapping TYPE: Optional [ str ] DEFAULT: None RETURNS DESCRIPTION care_site Dataframe with 2 added columns corresponding to the following concepts: \"IS_EMERGENCY\" \"EMERGENCY_TYPE\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 @concept_checker ( concepts = [ \"IS_EMERGENCY\" , \"EMERGENCY_TYPE\" ]) def from_mapping ( care_site : DataFrame , version : Optional [ str ] = None , ) -> DataFrame : \"\"\"This algo uses a labelled list of 201 emergency care sites. Those care sites were extracted and verified by Ariel COHEN, Judith LEBLANC, and an ER doctor validated them. Those emergency care sites are further divised into different categories, as defined in the concept 'EMERGENCY_TYPE'. The different categories are: - Urgences sp\u00e9cialis\u00e9es - UHCD + Post-urgences - Urgences p\u00e9diatriques - Urgences g\u00e9n\u00e9rales adulte - Consultation urgences - SAMU / SMUR See the dataset [here](/datasets/care-site-emergency) Parameters ---------- care_site: DataFrame Should at least contains the `care_site_source_value` column version: Optional[str] Optional version string for the mapping Returns ------- care_site: DataFrame Dataframe with 2 added columns corresponding to the following concepts: - `\"IS_EMERGENCY\"` - `\"EMERGENCY_TYPE\"` \"\"\" function_name = \"get_care_site_emergency_mapping\" if version is not None : function_name += f \". { version } \" mapping = registry . get ( \"data\" , function_name = function_name )() # Getting the right framework fw = framework . get_framework ( care_site ) mapping = framework . to ( fw , mapping ) care_site = care_site . merge ( mapping , how = \"left\" , on = \"care_site_source_value\" , ) care_site [ \"IS_EMERGENCY\" ] = care_site [ \"EMERGENCY_TYPE\" ] . notna () return care_site Use regular expressions on parent UF (Unit\u00e9 Fonctionnelle) to classify emergency care site. This relies on this function . The regular expression used to detect emergency status is r\"\bURG|\bSAU\b|\bUHCD\b|\bZHTCD\b\" PARAMETER DESCRIPTION care_site Should at least contains the care_site_name column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: 'IS_EMERGENCY' TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 @concept_checker ( concepts = [ \"IS_EMERGENCY\" ]) def from_regex_on_parent_UF ( care_site : DataFrame ) -> DataFrame : \"\"\"Use regular expressions on parent UF (Unit\u00e9 Fonctionnelle) to classify emergency care site. This relies on [this function][eds_scikit.structures.attributes.get_parent_attributes]. The regular expression used to detect emergency status is `r\"\\bURG|\\bSAU\\b|\\bUHCD\\b|\\bZHTCD\\b\"` Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - 'IS_EMERGENCY' \"\"\" return attributes . get_parent_attributes ( care_site , only_attributes = [ \"IS_EMERGENCY\" ], parent_type = \"Unit\u00e9 Fonctionnelle (UF)\" , ) Use regular expressions on care_site_name to decide if it an emergency care site. This relies on this function . The regular expression used to detect emergency status is r\"\bURG|\bSAU\b|\bUHCDb\b|\bZHTCD\b\" PARAMETER DESCRIPTION care_site Should at least contains the care_site_name column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: \"IS_EMERGENCY\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def from_regex_on_care_site_description ( care_site : DataFrame ) -> DataFrame : \"\"\"Use regular expressions on `care_site_name` to decide if it an emergency care site. This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes]. The regular expression used to detect emergency status is `r\"\\bURG|\\bSAU\\b|\\bUHCDb\\b|\\bZHTCD\\b\"` Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - `\"IS_EMERGENCY\"` \"\"\" return attributes . add_care_site_attributes ( care_site , only_attributes = [ \"IS_EMERGENCY\" ] ) Tagging visits Tagging is done using the tag_emergency_visit function: from eds_scikit.emergency import tag_emergency_visit Tag visits that correspond to medical emergency units . The tagging is done by adding a \"IS_EMERGENCY\" column to the provided DataFrame. Some algos can add an additional \"EMERGENCY_TYPE\" column to the provided DataFrame, providing a more detailled classification. It works by either tagging each visit detail's care site , or by using the visit_occurrence 's \"visit_source_value\" . PARAMETER DESCRIPTION visit_detail TYPE: DataFrame care_site Isn't necessary if the algo \"from_vo_visit_source_value\" is used TYPE: Optional [ DataFrame ] DEFAULT: None visit_occurrence Is mandatory if the algo \"from_vo_visit_source_value\" is used TYPE: Optional [ DataFrame ] DEFAULT: None algo Possible values are: \"from_mapping\" relies on a list of care_site_source_value extracted by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites are here further labelled to distinguish the different types of emergency \"from_regex_on_care_site_description\" : relies on a specific list of RegEx applied on the description (= simplified care site name) of each care site. \"from_regex_on_parent_UF\" : relies on a specific list of regular expressions applied on the description (= simplified care site name) of each UF (Unit\u00e9 Fonctionnelle). The obtained tag is then propagated to every UF's children. \"from_vo_visit_source_value\" : relies on the parent visit occurrence of each visit detail: A visit detail will be tagged as emergency if it belongs to a visit occurrence where visit_occurrence.visit_source_value=='urgence' . TYPE: str DEFAULT: 'from_mapping' RETURNS DESCRIPTION care_site Dataframe with 1 to 2 added columns corresponding to the following concepts: \"IS_EMERGENCY\" \"EMERGENCY_TYPE\" (if using algo \"from_mapping\" ) TYPE: DataFrame Simply call the function by providing the necessary data (see below) and by picking the algo visit_detail = tag_emergency_visit ( visit_detail = data . visit_detail , algo = \"from_mapping\" , ) Availables algorithms (values for \"algo\" ) 'from_mapping' 'from_regex_on_parent_UF' 'from_regex_on_care_site_description' 'from_vo_visit_source_value' This algo uses a labelled list of 201 emergency care sites. Those care sites were extracted and verified by Ariel COHEN, Judith LEBLANC, and an ER doctor validated them. Those emergency care sites are further divised into different categories, as defined in the concept 'EMERGENCY_TYPE'. The different categories are: Urgences sp\u00e9cialis\u00e9es UHCD + Post-urgences Urgences p\u00e9diatriques Urgences g\u00e9n\u00e9rales adulte Consultation urgences SAMU / SMUR See the dataset here PARAMETER DESCRIPTION care_site Should at least contains the care_site_source_value column TYPE: DataFrame version Optional version string for the mapping TYPE: Optional [ str ] DEFAULT: None RETURNS DESCRIPTION care_site Dataframe with 2 added columns corresponding to the following concepts: \"IS_EMERGENCY\" \"EMERGENCY_TYPE\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 @concept_checker ( concepts = [ \"IS_EMERGENCY\" , \"EMERGENCY_TYPE\" ]) def from_mapping ( care_site : DataFrame , version : Optional [ str ] = None , ) -> DataFrame : \"\"\"This algo uses a labelled list of 201 emergency care sites. Those care sites were extracted and verified by Ariel COHEN, Judith LEBLANC, and an ER doctor validated them. Those emergency care sites are further divised into different categories, as defined in the concept 'EMERGENCY_TYPE'. The different categories are: - Urgences sp\u00e9cialis\u00e9es - UHCD + Post-urgences - Urgences p\u00e9diatriques - Urgences g\u00e9n\u00e9rales adulte - Consultation urgences - SAMU / SMUR See the dataset [here](/datasets/care-site-emergency) Parameters ---------- care_site: DataFrame Should at least contains the `care_site_source_value` column version: Optional[str] Optional version string for the mapping Returns ------- care_site: DataFrame Dataframe with 2 added columns corresponding to the following concepts: - `\"IS_EMERGENCY\"` - `\"EMERGENCY_TYPE\"` \"\"\" function_name = \"get_care_site_emergency_mapping\" if version is not None : function_name += f \". { version } \" mapping = registry . get ( \"data\" , function_name = function_name )() # Getting the right framework fw = framework . get_framework ( care_site ) mapping = framework . to ( fw , mapping ) care_site = care_site . merge ( mapping , how = \"left\" , on = \"care_site_source_value\" , ) care_site [ \"IS_EMERGENCY\" ] = care_site [ \"EMERGENCY_TYPE\" ] . notna () return care_site Use regular expressions on parent UF (Unit\u00e9 Fonctionnelle) to classify emergency care site. This relies on this function . The regular expression used to detect emergency status is r\"\bURG|\bSAU\b|\bUHCD\b|\bZHTCD\b\" PARAMETER DESCRIPTION care_site Should at least contains the care_site_name column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: 'IS_EMERGENCY' TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 @concept_checker ( concepts = [ \"IS_EMERGENCY\" ]) def from_regex_on_parent_UF ( care_site : DataFrame ) -> DataFrame : \"\"\"Use regular expressions on parent UF (Unit\u00e9 Fonctionnelle) to classify emergency care site. This relies on [this function][eds_scikit.structures.attributes.get_parent_attributes]. The regular expression used to detect emergency status is `r\"\\bURG|\\bSAU\\b|\\bUHCD\\b|\\bZHTCD\\b\"` Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - 'IS_EMERGENCY' \"\"\" return attributes . get_parent_attributes ( care_site , only_attributes = [ \"IS_EMERGENCY\" ], parent_type = \"Unit\u00e9 Fonctionnelle (UF)\" , ) Use regular expressions on care_site_name to decide if it an emergency care site. This relies on this function . The regular expression used to detect emergency status is r\"\bURG|\bSAU\b|\bUHCDb\b|\bZHTCD\b\" PARAMETER DESCRIPTION care_site Should at least contains the care_site_name column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: \"IS_EMERGENCY\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def from_regex_on_care_site_description ( care_site : DataFrame ) -> DataFrame : \"\"\"Use regular expressions on `care_site_name` to decide if it an emergency care site. This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes]. The regular expression used to detect emergency status is `r\"\\bURG|\\bSAU\\b|\\bUHCDb\\b|\\bZHTCD\\b\"` Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - `\"IS_EMERGENCY\"` \"\"\" return attributes . add_care_site_attributes ( care_site , only_attributes = [ \"IS_EMERGENCY\" ] ) This algo uses the \"Type de dossier\" of each visit detail's parent visit occurrence. Thus, a visit_detail will be tagged with IS_EMERGENCY=True iff the visit occurrence it belongs to is an emergency-type visit (meaning that visit_occurrence.visit_source_value=='urgence' ) Admission through ICU At AP-HP, when a patient is hospitalized after coming to the ICU, its visit_source_value is set from \"urgence\" to \"hospitalisation compl\u00e8te\" . So you should keep in mind that this method doesn't tag those visits as ICU. PARAMETER DESCRIPTION visit_detail TYPE: DataFrame visit_occurrence TYPE: DataFrame RETURNS DESCRIPTION visit_detail Dataframe with added columns corresponding to the following conceps: \"IS_EMERGENCY\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_visit.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 @concept_checker ( concepts = [ \"IS_EMERGENCY\" ]) def from_vo_visit_source_value ( visit_detail : DataFrame , visit_occurrence : DataFrame , ) -> DataFrame : \"\"\" This algo uses the *\"Type de dossier\"* of each visit detail's parent visit occurrence. Thus, a visit_detail will be tagged with `IS_EMERGENCY=True` iff the visit occurrence it belongs to is an emergency-type visit (meaning that `visit_occurrence.visit_source_value=='urgence'`) !!! aphp \"Admission through ICU\" At AP-HP, when a patient is hospitalized after coming to the ICU, its `visit_source_value` is set from `\"urgence\"` to `\"hospitalisation compl\u00e8te\"`. So you should keep in mind that this method doesn't tag those visits as ICU. Parameters ---------- visit_detail: DataFrame visit_occurrence: DataFrame Returns ------- visit_detail: DataFrame Dataframe with added columns corresponding to the following conceps: - `\"IS_EMERGENCY\"` \"\"\" vo_emergency = visit_occurrence [[ \"visit_occurrence_id\" , \"visit_source_value\" ]] vo_emergency [ \"IS_EMERGENCY\" ] = visit_occurrence . visit_source_value == \"urgence\" return visit_detail . merge ( vo_emergency [[ \"visit_occurrence_id\" , \"IS_EMERGENCY\" ]], on = \"visit_occurrence_id\" , how = \"left\" , )","title":"Emergency Units"},{"location":"functionalities/patients-course/is_emergency/#tagging-care-sites","text":"Tagging is done using the tag_emergency_care_site function: from eds_scikit.emergency import tag_emergency_care_site Tag care sites that correspond to medical emergency units . The tagging is done by adding a \"IS_EMERGENCY\" column to the provided DataFrame. Some algos can add an additional \"EMERGENCY_TYPE\" column to the provided DataFrame, providing a more detailled classification. PARAMETER DESCRIPTION care_site TYPE: DataFrame algo Possible values are: \"from_mapping\" relies on a list of care_site_source_value extracted by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites are here further labelled to distinguish the different types of emergency \"from_regex_on_care_site_description\" : relies on a specific list of RegEx applied on the description (= simplified care site name) of each care site. \"from_regex_on_parent_UF\" : relies on a specific list of regular expressions applied on the description (= simplified care site name) of each UF (Unit\u00e9 Fonctionnelle). The obtained tag is then propagated to every UF's children. TYPE: str DEFAULT: 'from_mapping' RETURNS DESCRIPTION care_site Dataframe with 1 to 2 added columns corresponding to the following concepts: \"IS_EMERGENCY\" \"EMERGENCY_TYPE\" (if using algo \"from_mapping\" ) TYPE: DataFrame Simply call the function by providing the necessary data (see below) and by picking the algo care_site = tag_emergency_care_site ( care_site = data . care_site , algo = \"from_mapping\" , ) Availables algorithms (values for \"algo\" ) 'from_mapping' 'from_regex_on_parent_UF' 'from_regex_on_care_site_description' This algo uses a labelled list of 201 emergency care sites. Those care sites were extracted and verified by Ariel COHEN, Judith LEBLANC, and an ER doctor validated them. Those emergency care sites are further divised into different categories, as defined in the concept 'EMERGENCY_TYPE'. The different categories are: Urgences sp\u00e9cialis\u00e9es UHCD + Post-urgences Urgences p\u00e9diatriques Urgences g\u00e9n\u00e9rales adulte Consultation urgences SAMU / SMUR See the dataset here PARAMETER DESCRIPTION care_site Should at least contains the care_site_source_value column TYPE: DataFrame version Optional version string for the mapping TYPE: Optional [ str ] DEFAULT: None RETURNS DESCRIPTION care_site Dataframe with 2 added columns corresponding to the following concepts: \"IS_EMERGENCY\" \"EMERGENCY_TYPE\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 @concept_checker ( concepts = [ \"IS_EMERGENCY\" , \"EMERGENCY_TYPE\" ]) def from_mapping ( care_site : DataFrame , version : Optional [ str ] = None , ) -> DataFrame : \"\"\"This algo uses a labelled list of 201 emergency care sites. Those care sites were extracted and verified by Ariel COHEN, Judith LEBLANC, and an ER doctor validated them. Those emergency care sites are further divised into different categories, as defined in the concept 'EMERGENCY_TYPE'. The different categories are: - Urgences sp\u00e9cialis\u00e9es - UHCD + Post-urgences - Urgences p\u00e9diatriques - Urgences g\u00e9n\u00e9rales adulte - Consultation urgences - SAMU / SMUR See the dataset [here](/datasets/care-site-emergency) Parameters ---------- care_site: DataFrame Should at least contains the `care_site_source_value` column version: Optional[str] Optional version string for the mapping Returns ------- care_site: DataFrame Dataframe with 2 added columns corresponding to the following concepts: - `\"IS_EMERGENCY\"` - `\"EMERGENCY_TYPE\"` \"\"\" function_name = \"get_care_site_emergency_mapping\" if version is not None : function_name += f \". { version } \" mapping = registry . get ( \"data\" , function_name = function_name )() # Getting the right framework fw = framework . get_framework ( care_site ) mapping = framework . to ( fw , mapping ) care_site = care_site . merge ( mapping , how = \"left\" , on = \"care_site_source_value\" , ) care_site [ \"IS_EMERGENCY\" ] = care_site [ \"EMERGENCY_TYPE\" ] . notna () return care_site Use regular expressions on parent UF (Unit\u00e9 Fonctionnelle) to classify emergency care site. This relies on this function . The regular expression used to detect emergency status is r\"\bURG|\bSAU\b|\bUHCD\b|\bZHTCD\b\" PARAMETER DESCRIPTION care_site Should at least contains the care_site_name column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: 'IS_EMERGENCY' TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 @concept_checker ( concepts = [ \"IS_EMERGENCY\" ]) def from_regex_on_parent_UF ( care_site : DataFrame ) -> DataFrame : \"\"\"Use regular expressions on parent UF (Unit\u00e9 Fonctionnelle) to classify emergency care site. This relies on [this function][eds_scikit.structures.attributes.get_parent_attributes]. The regular expression used to detect emergency status is `r\"\\bURG|\\bSAU\\b|\\bUHCD\\b|\\bZHTCD\\b\"` Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - 'IS_EMERGENCY' \"\"\" return attributes . get_parent_attributes ( care_site , only_attributes = [ \"IS_EMERGENCY\" ], parent_type = \"Unit\u00e9 Fonctionnelle (UF)\" , ) Use regular expressions on care_site_name to decide if it an emergency care site. This relies on this function . The regular expression used to detect emergency status is r\"\bURG|\bSAU\b|\bUHCDb\b|\bZHTCD\b\" PARAMETER DESCRIPTION care_site Should at least contains the care_site_name column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: \"IS_EMERGENCY\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def from_regex_on_care_site_description ( care_site : DataFrame ) -> DataFrame : \"\"\"Use regular expressions on `care_site_name` to decide if it an emergency care site. This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes]. The regular expression used to detect emergency status is `r\"\\bURG|\\bSAU\\b|\\bUHCDb\\b|\\bZHTCD\\b\"` Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - `\"IS_EMERGENCY\"` \"\"\" return attributes . add_care_site_attributes ( care_site , only_attributes = [ \"IS_EMERGENCY\" ] )","title":"Tagging care sites"},{"location":"functionalities/patients-course/is_emergency/#tagging-visits","text":"Tagging is done using the tag_emergency_visit function: from eds_scikit.emergency import tag_emergency_visit Tag visits that correspond to medical emergency units . The tagging is done by adding a \"IS_EMERGENCY\" column to the provided DataFrame. Some algos can add an additional \"EMERGENCY_TYPE\" column to the provided DataFrame, providing a more detailled classification. It works by either tagging each visit detail's care site , or by using the visit_occurrence 's \"visit_source_value\" . PARAMETER DESCRIPTION visit_detail TYPE: DataFrame care_site Isn't necessary if the algo \"from_vo_visit_source_value\" is used TYPE: Optional [ DataFrame ] DEFAULT: None visit_occurrence Is mandatory if the algo \"from_vo_visit_source_value\" is used TYPE: Optional [ DataFrame ] DEFAULT: None algo Possible values are: \"from_mapping\" relies on a list of care_site_source_value extracted by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites are here further labelled to distinguish the different types of emergency \"from_regex_on_care_site_description\" : relies on a specific list of RegEx applied on the description (= simplified care site name) of each care site. \"from_regex_on_parent_UF\" : relies on a specific list of regular expressions applied on the description (= simplified care site name) of each UF (Unit\u00e9 Fonctionnelle). The obtained tag is then propagated to every UF's children. \"from_vo_visit_source_value\" : relies on the parent visit occurrence of each visit detail: A visit detail will be tagged as emergency if it belongs to a visit occurrence where visit_occurrence.visit_source_value=='urgence' . TYPE: str DEFAULT: 'from_mapping' RETURNS DESCRIPTION care_site Dataframe with 1 to 2 added columns corresponding to the following concepts: \"IS_EMERGENCY\" \"EMERGENCY_TYPE\" (if using algo \"from_mapping\" ) TYPE: DataFrame Simply call the function by providing the necessary data (see below) and by picking the algo visit_detail = tag_emergency_visit ( visit_detail = data . visit_detail , algo = \"from_mapping\" , ) Availables algorithms (values for \"algo\" ) 'from_mapping' 'from_regex_on_parent_UF' 'from_regex_on_care_site_description' 'from_vo_visit_source_value' This algo uses a labelled list of 201 emergency care sites. Those care sites were extracted and verified by Ariel COHEN, Judith LEBLANC, and an ER doctor validated them. Those emergency care sites are further divised into different categories, as defined in the concept 'EMERGENCY_TYPE'. The different categories are: Urgences sp\u00e9cialis\u00e9es UHCD + Post-urgences Urgences p\u00e9diatriques Urgences g\u00e9n\u00e9rales adulte Consultation urgences SAMU / SMUR See the dataset here PARAMETER DESCRIPTION care_site Should at least contains the care_site_source_value column TYPE: DataFrame version Optional version string for the mapping TYPE: Optional [ str ] DEFAULT: None RETURNS DESCRIPTION care_site Dataframe with 2 added columns corresponding to the following concepts: \"IS_EMERGENCY\" \"EMERGENCY_TYPE\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 @concept_checker ( concepts = [ \"IS_EMERGENCY\" , \"EMERGENCY_TYPE\" ]) def from_mapping ( care_site : DataFrame , version : Optional [ str ] = None , ) -> DataFrame : \"\"\"This algo uses a labelled list of 201 emergency care sites. Those care sites were extracted and verified by Ariel COHEN, Judith LEBLANC, and an ER doctor validated them. Those emergency care sites are further divised into different categories, as defined in the concept 'EMERGENCY_TYPE'. The different categories are: - Urgences sp\u00e9cialis\u00e9es - UHCD + Post-urgences - Urgences p\u00e9diatriques - Urgences g\u00e9n\u00e9rales adulte - Consultation urgences - SAMU / SMUR See the dataset [here](/datasets/care-site-emergency) Parameters ---------- care_site: DataFrame Should at least contains the `care_site_source_value` column version: Optional[str] Optional version string for the mapping Returns ------- care_site: DataFrame Dataframe with 2 added columns corresponding to the following concepts: - `\"IS_EMERGENCY\"` - `\"EMERGENCY_TYPE\"` \"\"\" function_name = \"get_care_site_emergency_mapping\" if version is not None : function_name += f \". { version } \" mapping = registry . get ( \"data\" , function_name = function_name )() # Getting the right framework fw = framework . get_framework ( care_site ) mapping = framework . to ( fw , mapping ) care_site = care_site . merge ( mapping , how = \"left\" , on = \"care_site_source_value\" , ) care_site [ \"IS_EMERGENCY\" ] = care_site [ \"EMERGENCY_TYPE\" ] . notna () return care_site Use regular expressions on parent UF (Unit\u00e9 Fonctionnelle) to classify emergency care site. This relies on this function . The regular expression used to detect emergency status is r\"\bURG|\bSAU\b|\bUHCD\b|\bZHTCD\b\" PARAMETER DESCRIPTION care_site Should at least contains the care_site_name column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: 'IS_EMERGENCY' TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 @concept_checker ( concepts = [ \"IS_EMERGENCY\" ]) def from_regex_on_parent_UF ( care_site : DataFrame ) -> DataFrame : \"\"\"Use regular expressions on parent UF (Unit\u00e9 Fonctionnelle) to classify emergency care site. This relies on [this function][eds_scikit.structures.attributes.get_parent_attributes]. The regular expression used to detect emergency status is `r\"\\bURG|\\bSAU\\b|\\bUHCD\\b|\\bZHTCD\\b\"` Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - 'IS_EMERGENCY' \"\"\" return attributes . get_parent_attributes ( care_site , only_attributes = [ \"IS_EMERGENCY\" ], parent_type = \"Unit\u00e9 Fonctionnelle (UF)\" , ) Use regular expressions on care_site_name to decide if it an emergency care site. This relies on this function . The regular expression used to detect emergency status is r\"\bURG|\bSAU\b|\bUHCDb\b|\bZHTCD\b\" PARAMETER DESCRIPTION care_site Should at least contains the care_site_name column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: \"IS_EMERGENCY\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def from_regex_on_care_site_description ( care_site : DataFrame ) -> DataFrame : \"\"\"Use regular expressions on `care_site_name` to decide if it an emergency care site. This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes]. The regular expression used to detect emergency status is `r\"\\bURG|\\bSAU\\b|\\bUHCDb\\b|\\bZHTCD\\b\"` Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - `\"IS_EMERGENCY\"` \"\"\" return attributes . add_care_site_attributes ( care_site , only_attributes = [ \"IS_EMERGENCY\" ] ) This algo uses the \"Type de dossier\" of each visit detail's parent visit occurrence. Thus, a visit_detail will be tagged with IS_EMERGENCY=True iff the visit occurrence it belongs to is an emergency-type visit (meaning that visit_occurrence.visit_source_value=='urgence' ) Admission through ICU At AP-HP, when a patient is hospitalized after coming to the ICU, its visit_source_value is set from \"urgence\" to \"hospitalisation compl\u00e8te\" . So you should keep in mind that this method doesn't tag those visits as ICU. PARAMETER DESCRIPTION visit_detail TYPE: DataFrame visit_occurrence TYPE: DataFrame RETURNS DESCRIPTION visit_detail Dataframe with added columns corresponding to the following conceps: \"IS_EMERGENCY\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_visit.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 @concept_checker ( concepts = [ \"IS_EMERGENCY\" ]) def from_vo_visit_source_value ( visit_detail : DataFrame , visit_occurrence : DataFrame , ) -> DataFrame : \"\"\" This algo uses the *\"Type de dossier\"* of each visit detail's parent visit occurrence. Thus, a visit_detail will be tagged with `IS_EMERGENCY=True` iff the visit occurrence it belongs to is an emergency-type visit (meaning that `visit_occurrence.visit_source_value=='urgence'`) !!! aphp \"Admission through ICU\" At AP-HP, when a patient is hospitalized after coming to the ICU, its `visit_source_value` is set from `\"urgence\"` to `\"hospitalisation compl\u00e8te\"`. So you should keep in mind that this method doesn't tag those visits as ICU. Parameters ---------- visit_detail: DataFrame visit_occurrence: DataFrame Returns ------- visit_detail: DataFrame Dataframe with added columns corresponding to the following conceps: - `\"IS_EMERGENCY\"` \"\"\" vo_emergency = visit_occurrence [[ \"visit_occurrence_id\" , \"visit_source_value\" ]] vo_emergency [ \"IS_EMERGENCY\" ] = visit_occurrence . visit_source_value == \"urgence\" return visit_detail . merge ( vo_emergency [[ \"visit_occurrence_id\" , \"IS_EMERGENCY\" ]], on = \"visit_occurrence_id\" , how = \"left\" , )","title":"Tagging visits"},{"location":"functionalities/patients-course/is_icu/","text":"eds-scikit provides a function to tag care sites as being Intensive Care Units . It also provides a higher-level function to directly tag visits. from eds_scikit.io import HiveData data = HiveData ( DBNAME ) Tagging care sites Tagging is done using the tag_icu_care_site function: from eds_scikit.icu import tag_icu_care_site Tag care sites that correspond to ICU units . The tagging is done by adding a \"IS_ICU\" column to the provided DataFrame. PARAMETER DESCRIPTION care_site TYPE: DataFrame algo Possible values are: \"from_authorisation_type\" \"from_regex_on_care_site_description\" TYPE: str DEFAULT: 'from_mapping' RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: \"IS_ICU\" TYPE: DataFrame Simply call the function by providing the necessary data (see below) and by picking the algo care_site = tag_icu_care_site ( care_site = data . care_site , algo = \"from_authorisation_type\" , ) Availables algorithms (values for \"algo\" ) 'from_authorisation_type' 'from_regex_on_care_site_description' This algo uses the care_site.place_of_service_source_value columns to retrieve Intensive Care Units. The following values are used to tag a care site as ICU: \"REA PED\" \"REA\" \"REA ADULTE\" \"REA NEONAT\" \"USI\" \"USI ADULTE\" \"USI NEONAT\" \"SC PED\" \"SC\" \"SC ADULTE\" PARAMETER DESCRIPTION care_site Should at least contains the place_of_service_source_value column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concepts: \"IS_ICU\" TYPE: DataFrame Source code in eds_scikit/icu/icu_care_site.py 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 @concept_checker ( concepts = [ \"IS_ICU\" ]) def from_authorisation_type ( care_site : DataFrame ) -> DataFrame : \"\"\"This algo uses the `care_site.place_of_service_source_value` columns to retrieve Intensive Care Units. The following values are used to tag a care site as ICU: - `\"REA PED\"` - `\"REA\"` - `\"REA ADULTE\"` - `\"REA NEONAT\"` - `\"USI\"` - `\"USI ADULTE\"` - `\"USI NEONAT\"` - `\"SC PED\"` - `\"SC\"` - `\"SC ADULTE\"` Parameters ---------- care_site: DataFrame Should at least contains the `place_of_service_source_value` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concepts: - `\"IS_ICU\"` \"\"\" icu_units = set ( [ \"REA PED\" , \"USI\" , \"SC PED\" , \"SC\" , \"REA\" , \"SC ADULTE\" , \"USI ADULTE\" , \"REA ADULTE\" , \"USI NEONAT\" , \"REA NEONAT\" , ] ) care_site [ \"IS_ICU\" ] = care_site [ \"place_of_service_source_value\" ] . isin ( icu_units ) return care_site Use regular expressions on care_site_name to decide if it an ICU care site. This relies on this function . The regular expression used to detect ICU is r\"\bUSI|\bREA[N\\s]|\bREA\b|\bUSC\b|SOINS.*INTENSIF|SURV.{0,15}CONT|\bSI\b|\bSC\b\" . Keeping only 'UDS' At AP-HP, all ICU are UDS ( Unit\u00e9 De Soins ). Therefore, this function filters care sites by default to only keep UDS. PARAMETER DESCRIPTION care_site Should at least contains the care_site_name and care_site_type_source_value columns TYPE: DataFrame subset_care_site_type_source_value Acceptable values for care_site_type_source_value TYPE: Union [ list , set ] DEFAULT: {'UDS'} RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: \"IS_ICU\" TYPE: DataFrame Source code in eds_scikit/icu/icu_care_site.py 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 def from_regex_on_care_site_description ( care_site : DataFrame , subset_care_site_type_source_value : Union [ list , set ] = { \"UDS\" } ) -> DataFrame : \"\"\"Use regular expressions on `care_site_name` to decide if it an ICU care site. This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes]. The regular expression used to detect ICU is `r\"\\bUSI|\\bREA[N\\s]|\\bREA\\b|\\bUSC\\b|SOINS.*INTENSIF|SURV.{0,15}CONT|\\bSI\\b|\\bSC\\b\"`. !!! aphp \"Keeping only 'UDS'\" At AP-HP, all ICU are **UDS** (*Unit\u00e9 De Soins*). Therefore, this function filters care sites by default to only keep UDS. Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` and `care_site_type_source_value` columns subset_care_site_type_source_value: Union[list, set] Acceptable values for `care_site_type_source_value` Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - `\"IS_ICU\"` \"\"\" # noqa care_site = attributes . add_care_site_attributes ( care_site , only_attributes = [ \"IS_ICU\" ] ) # Filtering matches if subset_care_site_type_source_value : care_site [ \"IS_ICU\" ] = care_site [ \"IS_ICU\" ] & ( care_site . care_site_type_source_value . isin ( subset_care_site_type_source_value ) ) return care_site Tagging visits Tagging is done using the tag_icu_visit function: from eds_scikit.icu import tag_icu_visit Tag care_sites that correspond to ICU units . The tagging is done by adding a \"IS_ICU\" column to the provided DataFrame. It works by tagging each visit detail's care site . PARAMETER DESCRIPTION visit_detail TYPE: DataFrame care_site TYPE: DataFrame algo Possible values are: \"from_authorisation_type\" \"from_regex_on_care_site_description\" TYPE: str DEFAULT: 'from_authorisation_type' RETURNS DESCRIPTION visit_detail Dataframe with 1 added column corresponding to the following concept: \"IS_ICU\" TYPE: DataFrame Simply call the function by providing the necessary data (see below) and by picking the algo visit_detail = tag_icu_visit ( visit_detail = data . visit_detail , algo = \"from_mapping\" , ) Availables algorithms (values for \"algo\" ) Those are the same as tag_icu_care_site","title":"ICU"},{"location":"functionalities/patients-course/is_icu/#tagging-care-sites","text":"Tagging is done using the tag_icu_care_site function: from eds_scikit.icu import tag_icu_care_site Tag care sites that correspond to ICU units . The tagging is done by adding a \"IS_ICU\" column to the provided DataFrame. PARAMETER DESCRIPTION care_site TYPE: DataFrame algo Possible values are: \"from_authorisation_type\" \"from_regex_on_care_site_description\" TYPE: str DEFAULT: 'from_mapping' RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: \"IS_ICU\" TYPE: DataFrame Simply call the function by providing the necessary data (see below) and by picking the algo care_site = tag_icu_care_site ( care_site = data . care_site , algo = \"from_authorisation_type\" , ) Availables algorithms (values for \"algo\" ) 'from_authorisation_type' 'from_regex_on_care_site_description' This algo uses the care_site.place_of_service_source_value columns to retrieve Intensive Care Units. The following values are used to tag a care site as ICU: \"REA PED\" \"REA\" \"REA ADULTE\" \"REA NEONAT\" \"USI\" \"USI ADULTE\" \"USI NEONAT\" \"SC PED\" \"SC\" \"SC ADULTE\" PARAMETER DESCRIPTION care_site Should at least contains the place_of_service_source_value column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concepts: \"IS_ICU\" TYPE: DataFrame Source code in eds_scikit/icu/icu_care_site.py 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 @concept_checker ( concepts = [ \"IS_ICU\" ]) def from_authorisation_type ( care_site : DataFrame ) -> DataFrame : \"\"\"This algo uses the `care_site.place_of_service_source_value` columns to retrieve Intensive Care Units. The following values are used to tag a care site as ICU: - `\"REA PED\"` - `\"REA\"` - `\"REA ADULTE\"` - `\"REA NEONAT\"` - `\"USI\"` - `\"USI ADULTE\"` - `\"USI NEONAT\"` - `\"SC PED\"` - `\"SC\"` - `\"SC ADULTE\"` Parameters ---------- care_site: DataFrame Should at least contains the `place_of_service_source_value` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concepts: - `\"IS_ICU\"` \"\"\" icu_units = set ( [ \"REA PED\" , \"USI\" , \"SC PED\" , \"SC\" , \"REA\" , \"SC ADULTE\" , \"USI ADULTE\" , \"REA ADULTE\" , \"USI NEONAT\" , \"REA NEONAT\" , ] ) care_site [ \"IS_ICU\" ] = care_site [ \"place_of_service_source_value\" ] . isin ( icu_units ) return care_site Use regular expressions on care_site_name to decide if it an ICU care site. This relies on this function . The regular expression used to detect ICU is r\"\bUSI|\bREA[N\\s]|\bREA\b|\bUSC\b|SOINS.*INTENSIF|SURV.{0,15}CONT|\bSI\b|\bSC\b\" . Keeping only 'UDS' At AP-HP, all ICU are UDS ( Unit\u00e9 De Soins ). Therefore, this function filters care sites by default to only keep UDS. PARAMETER DESCRIPTION care_site Should at least contains the care_site_name and care_site_type_source_value columns TYPE: DataFrame subset_care_site_type_source_value Acceptable values for care_site_type_source_value TYPE: Union [ list , set ] DEFAULT: {'UDS'} RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: \"IS_ICU\" TYPE: DataFrame Source code in eds_scikit/icu/icu_care_site.py 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 def from_regex_on_care_site_description ( care_site : DataFrame , subset_care_site_type_source_value : Union [ list , set ] = { \"UDS\" } ) -> DataFrame : \"\"\"Use regular expressions on `care_site_name` to decide if it an ICU care site. This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes]. The regular expression used to detect ICU is `r\"\\bUSI|\\bREA[N\\s]|\\bREA\\b|\\bUSC\\b|SOINS.*INTENSIF|SURV.{0,15}CONT|\\bSI\\b|\\bSC\\b\"`. !!! aphp \"Keeping only 'UDS'\" At AP-HP, all ICU are **UDS** (*Unit\u00e9 De Soins*). Therefore, this function filters care sites by default to only keep UDS. Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` and `care_site_type_source_value` columns subset_care_site_type_source_value: Union[list, set] Acceptable values for `care_site_type_source_value` Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - `\"IS_ICU\"` \"\"\" # noqa care_site = attributes . add_care_site_attributes ( care_site , only_attributes = [ \"IS_ICU\" ] ) # Filtering matches if subset_care_site_type_source_value : care_site [ \"IS_ICU\" ] = care_site [ \"IS_ICU\" ] & ( care_site . care_site_type_source_value . isin ( subset_care_site_type_source_value ) ) return care_site","title":"Tagging care sites"},{"location":"functionalities/patients-course/is_icu/#tagging-visits","text":"Tagging is done using the tag_icu_visit function: from eds_scikit.icu import tag_icu_visit Tag care_sites that correspond to ICU units . The tagging is done by adding a \"IS_ICU\" column to the provided DataFrame. It works by tagging each visit detail's care site . PARAMETER DESCRIPTION visit_detail TYPE: DataFrame care_site TYPE: DataFrame algo Possible values are: \"from_authorisation_type\" \"from_regex_on_care_site_description\" TYPE: str DEFAULT: 'from_authorisation_type' RETURNS DESCRIPTION visit_detail Dataframe with 1 added column corresponding to the following concept: \"IS_ICU\" TYPE: DataFrame Simply call the function by providing the necessary data (see below) and by picking the algo visit_detail = tag_icu_visit ( visit_detail = data . visit_detail , algo = \"from_mapping\" , ) Availables algorithms (values for \"algo\" ) Those are the same as tag_icu_care_site","title":"Tagging visits"},{"location":"functionalities/patients-course/visit_merging/","text":"(function() { function addWidgetsRenderer() { var requireJsScript = document.createElement('script'); requireJsScript.src = 'https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js'; var mimeElement = document.querySelector('script[type=\"application/vnd.jupyter.widget-view+json\"]'); var jupyterWidgetsScript = document.createElement('script'); var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js'; var widgetState; // Fallback for older version: try { widgetState = mimeElement && JSON.parse(mimeElement.innerHTML); if (widgetState && (widgetState.version_major < 2 || !widgetState.version_major)) { widgetRendererSrc = 'jupyter-js-widgets@*/dist/embed.js'; } } catch(e) {} jupyterWidgetsScript.src = widgetRendererSrc; document.body.appendChild(requireJsScript); document.body.appendChild(jupyterWidgetsScript); } document.addEventListener('DOMContentLoaded', addWidgetsRenderer); }()); You can download this notebook directly here Merging visits into stays In order to have a precise view of each patient's course of care, it can be useful to merge together visit occurrences into stays. A crude way of doing so is by using the preceding_visit_occurrence_id column in the visit_occurrence table. However, this columns isn't always filled, and a lot of visits would be missed by using only this method. The method proposed here relies on how close two visits are in order to put them in the same stay. This is the role of the merge_visits() functions: import eds_scikit spark , sc , sql = eds_scikit . improve_performances () from eds_scikit.period.stays import merge_visits Another function, get_stays_duration() , can then be used to extract a stay DataFrame with useful informations: from eds_scikit.period.stays import get_stays_duration % config Completer . use_jedi = False % load_ext autoreload % autoreload 2 Loading data import databricks.koalas as ks import pandas as pd import altair as alt from datetime import datetime , timedelta from eds_scikit.io import HiveData from eds_scikit.utils.datetime_helpers import substract_datetime data = HiveData ( spark , database_name = 'eds_lib_poc' ) vo = data . visit_occurrence This cohort is of reasonnable size, so we can work with Pandas in this case: vo_pd = vo . to_pandas () Getting stays We can now merge visits into stays. Check the corresponding documentation for more informations about each individual parameter vo_pd = merge_visits ( vo_pd , remove_deleted_visits = True , long_stay_threshold = timedelta ( days = 365 ), long_stay_filtering = 'all' , max_timedelta = timedelta ( days = 2 ), merge_different_hospitals = False , merge_different_source_values = [ 'hospitalis\u00e9s' , 'urgence' ], ) This functions will add a 'STAY_ID' column, corresponding to the visit_occurrence_id of the first visit of the stay. We can check that indeed, most stays are composed of a single visit (notice that the Y-axis is in log scale): stats = vo_pd . groupby ( 'STAY_ID' ) . visit_occurrence_id . count () _ = stats . hist ( bins = 20 , log = True ) We can finally display the number of merged visits: stats [ stats > 1 ] . sum () 42738 or in % of the total number of visits: round ( 100 * stats [ stats > 1 ] . sum () / len ( vo_pd ), 2 ) 8.69 Getting stays durations This second function generates an easy-to-use stay DataFrame : We will only focus on emergency and hospitalisation for this part, which is no problem since we only allowed merging those two types of stay (via merge_different_source_values=['hospitalis\u00e9s', 'urgence'] ) vo_pd = vo_pd [ vo_pd . visit_source_value . isin ([ 'hospitalis\u00e9s' , 'urgence' ])] stays = get_stays_duration ( vo_pd , algo = 'visits_date_difference' , missing_end_date_handling = 'coerce' ) stays . head () .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } person_id t_start t_end STAY_DURATION STAY_ID -2.147468e+09 -793380275 2017-08-22 18:45:00 2017-08-25 14:24:00 67.650000 -2.147465e+09 -1632607976 2019-12-03 12:20:00 2019-12-03 13:42:00 1.366667 -2.147450e+09 56307107 2019-04-23 17:18:00 2019-04-25 16:21:00 47.050000 -2.147433e+09 -916176344 2021-04-23 10:40:00 2021-04-23 11:50:00 1.166667 -2.147431e+09 1814301591 2018-06-06 14:25:00 2018-06-06 15:24:00 0.983333 Let us compare the distribution of stay/visit durations. # Extracting visit duration (in hours) vo_pd [ 'VISIT_DURATION' ] = substract_datetime ( vo_pd [ 'visit_end_datetime' ], vo_pd [ 'visit_start_datetime' ], out = 'hours' ) # COnverting to days stays [ 'STAY_DURATION' ] = stays [ 'STAY_DURATION' ] / 24 vo_pd [ 'VISIT_DURATION' ] = vo_pd [ 'VISIT_DURATION' ] / 24 # Keeping only visits/stays less than a month long vo_pd = vo_pd [ vo_pd [ 'VISIT_DURATION' ] <= 31 ] stays = stays [ stays [ 'STAY_DURATION' ] <= 31 ] stays . STAY_DURATION . mean () 2.306892174348691 vo_pd . VISIT_DURATION . mean () 2.2188916112474866 We will aggregate the data into bins of 1 week days = list ( range ( 1 , 32 )) stays_distribution = pd . cut ( stays [ 'STAY_DURATION' ], 31 , labels = days ) . value_counts ( normalize = True ) . sort_index () visits_distribution = pd . cut ( vo_pd [ 'VISIT_DURATION' ], 31 , labels = days ) . value_counts ( normalize = True ) . sort_index () data = pd . concat ([ pd . DataFrame ( data = { 'density' : stays_distribution . values , 'day' : stays_distribution . index , 'type' : 'STAY' }), pd . DataFrame ( data = { 'density' : visits_distribution . values , 'day' : visits_distribution . index , 'type' : 'VISIT' }) ]) diff_distribution = ( stays_distribution - visits_distribution ) . to_frame () . reset_index () diff_distribution . columns = [ 'day' , 'difference' ] alt . Chart ( data ) . mark_bar ( opacity = 0.5 ) . encode ( x = \"day:N\" , y = alt . Y ( \"density:Q\" , stack = None , scale = alt . Scale ( type = 'log' )), color = alt . Color ( \"type:N\" , title = 'Type' ) ) . properties ( width = 800 ) (function(spec, embedOpt){ let outputDiv = document.currentScript.previousElementSibling; if (outputDiv.id !== \"altair-viz-06979841a60142e78d60594f1d1b0722\") { outputDiv = document.getElementById(\"altair-viz-06979841a60142e78d60594f1d1b0722\"); } const paths = { \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\", \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\", \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\", \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\", }; function loadScript(lib) { return new Promise(function(resolve, reject) { var s = document.createElement('script'); s.src = paths[lib]; s.async = true; s.onload = () => resolve(paths[lib]); s.onerror = () => reject(`Error loading script: ${paths[lib]}`); document.getElementsByTagName(\"head\")[0].appendChild(s); }); } function showError(err) { outputDiv.innerHTML = `
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{\"density\": 0.0014926964495148736, \"day\": 28, \"type\": \"STAY\"}, {\"density\": 0.0012233376917076785, \"day\": 29, \"type\": \"STAY\"}, {\"density\": 0.001015706982564632, \"day\": 30, \"type\": \"STAY\"}, {\"density\": 0.0009539789339004832, \"day\": 31, \"type\": \"STAY\"}, {\"density\": 0.65274397841082, \"day\": 1, \"type\": \"VISIT\"}, {\"density\": 0.07284227492272537, \"day\": 2, \"type\": \"VISIT\"}, {\"density\": 0.05349578236918982, \"day\": 3, \"type\": \"VISIT\"}, {\"density\": 0.04853854188205479, \"day\": 4, \"type\": \"VISIT\"}, {\"density\": 0.038677079522620395, \"day\": 5, \"type\": \"VISIT\"}, {\"density\": 0.02326456818989147, \"day\": 6, \"type\": \"VISIT\"}, {\"density\": 0.018222497919019368, \"day\": 7, \"type\": \"VISIT\"}, {\"density\": 0.015560963454268793, \"day\": 8, \"type\": \"VISIT\"}, {\"density\": 0.010550704352298091, \"day\": 9, \"type\": \"VISIT\"}, {\"density\": 0.008281507637331468, \"day\": 10, \"type\": \"VISIT\"}, {\"density\": 0.006993155296824715, \"day\": 11, \"type\": \"VISIT\"}, {\"density\": 0.005810840185989301, \"day\": 12, \"type\": \"VISIT\"}, {\"density\": 0.005100390747191339, \"day\": 13, \"type\": \"VISIT\"}, {\"density\": 0.005206427976862676, \"day\": 14, \"type\": \"VISIT\"}, {\"density\": 0.0046550343825717206, \"day\": 15, \"type\": \"VISIT\"}, {\"density\": 0.0038226421296517206, \"day\": 16, \"type\": \"VISIT\"}, {\"density\": 0.0030750796604687904, \"day\": 17, \"type\": \"VISIT\"}, {\"density\": 0.0026297232958491725, \"day\": 18, \"type\": \"VISIT\"}, {\"density\": 0.0024812711743093, \"day\": 19, \"type\": \"VISIT\"}, {\"density\": 0.0022373855460652236, \"day\": 20, \"type\": \"VISIT\"}, {\"density\": 0.0023169134683187266, \"day\": 21, \"type\": \"VISIT\"}, {\"density\": 0.0021419520393610196, \"day\": 22, \"type\": \"VISIT\"}, {\"density\": 0.0015852565835864972, \"day\": 23, \"type\": \"VISIT\"}, {\"density\": 0.0014951249383658603, \"day\": 24, \"type\": \"VISIT\"}, {\"density\": 0.001245937448638217, \"day\": 25, \"type\": \"VISIT\"}, {\"density\": 0.0011345983574833124, \"day\": 26, \"type\": \"VISIT\"}, {\"density\": 0.0012194281412203824, \"day\": 27, \"type\": \"VISIT\"}, {\"density\": 0.0015481435532015292, \"day\": 28, \"type\": \"VISIT\"}, {\"density\": 0.001245937448638217, \"day\": 29, \"type\": \"VISIT\"}, {\"density\": 0.0009808443744598729, \"day\": 30, \"type\": \"VISIT\"}, {\"density\": 0.0008960145907228028, \"day\": 31, \"type\": \"VISIT\"}]}}, {\"mode\": \"vega-lite\"}); Plotting the difference between those two distributions shows that - Some short visits ( < 1 week ) seems to be merged into longer stays - This cause stays of duration ~ N weeks to be more frequent than visits of duration ~ N weeks alt . Chart ( diff_distribution ) . mark_bar () . encode ( x = alt . X ( \"day:N\" , title = \"Day\" ), y = alt . Y ( \"difference:Q\" , title = 'Difference between stay and visit distribution' ) ) . properties ( width = 800 ) (function(spec, embedOpt){ let outputDiv = document.currentScript.previousElementSibling; if (outputDiv.id !== \"altair-viz-bfc21174f79b41678f31cbc2a2bdb70d\") { outputDiv = document.getElementById(\"altair-viz-bfc21174f79b41678f31cbc2a2bdb70d\"); } const paths = { \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\", \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\", \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\", \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\", }; function loadScript(lib) { return new Promise(function(resolve, reject) { var s = document.createElement('script'); s.src = paths[lib]; s.async = true; s.onload = () => resolve(paths[lib]); s.onerror = () => reject(`Error loading script: ${paths[lib]}`); document.getElementsByTagName(\"head\")[0].appendChild(s); }); } function showError(err) { outputDiv.innerHTML = `
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A crude way of doing so is by using the preceding_visit_occurrence_id column in the visit_occurrence table. However, this columns isn't always filled, and a lot of visits would be missed by using only this method. The method proposed here relies on how close two visits are in order to put them in the same stay. This is the role of the merge_visits() functions: import eds_scikit spark , sc , sql = eds_scikit . improve_performances () from eds_scikit.period.stays import merge_visits Another function, get_stays_duration() , can then be used to extract a stay DataFrame with useful informations: from eds_scikit.period.stays import get_stays_duration % config Completer . use_jedi = False % load_ext autoreload % autoreload 2","title":"Merging visits into stays"},{"location":"functionalities/patients-course/visit_merging/#loading-data","text":"import databricks.koalas as ks import pandas as pd import altair as alt from datetime import datetime , timedelta from eds_scikit.io import HiveData from eds_scikit.utils.datetime_helpers import substract_datetime data = HiveData ( spark , database_name = 'eds_lib_poc' ) vo = data . visit_occurrence This cohort is of reasonnable size, so we can work with Pandas in this case: vo_pd = vo . to_pandas ()","title":"Loading data"},{"location":"functionalities/patients-course/visit_merging/#getting-stays","text":"We can now merge visits into stays. Check the corresponding documentation for more informations about each individual parameter vo_pd = merge_visits ( vo_pd , remove_deleted_visits = True , long_stay_threshold = timedelta ( days = 365 ), long_stay_filtering = 'all' , max_timedelta = timedelta ( days = 2 ), merge_different_hospitals = False , merge_different_source_values = [ 'hospitalis\u00e9s' , 'urgence' ], ) This functions will add a 'STAY_ID' column, corresponding to the visit_occurrence_id of the first visit of the stay. We can check that indeed, most stays are composed of a single visit (notice that the Y-axis is in log scale): stats = vo_pd . groupby ( 'STAY_ID' ) . visit_occurrence_id . count () _ = stats . hist ( bins = 20 , log = True ) We can finally display the number of merged visits: stats [ stats > 1 ] . sum () 42738 or in % of the total number of visits: round ( 100 * stats [ stats > 1 ] . sum () / len ( vo_pd ), 2 ) 8.69","title":"Getting stays"},{"location":"functionalities/patients-course/visit_merging/#getting-stays-durations","text":"This second function generates an easy-to-use stay DataFrame : We will only focus on emergency and hospitalisation for this part, which is no problem since we only allowed merging those two types of stay (via merge_different_source_values=['hospitalis\u00e9s', 'urgence'] ) vo_pd = vo_pd [ vo_pd . visit_source_value . isin ([ 'hospitalis\u00e9s' , 'urgence' ])] stays = get_stays_duration ( vo_pd , algo = 'visits_date_difference' , missing_end_date_handling = 'coerce' ) stays . head () .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } person_id t_start t_end STAY_DURATION STAY_ID -2.147468e+09 -793380275 2017-08-22 18:45:00 2017-08-25 14:24:00 67.650000 -2.147465e+09 -1632607976 2019-12-03 12:20:00 2019-12-03 13:42:00 1.366667 -2.147450e+09 56307107 2019-04-23 17:18:00 2019-04-25 16:21:00 47.050000 -2.147433e+09 -916176344 2021-04-23 10:40:00 2021-04-23 11:50:00 1.166667 -2.147431e+09 1814301591 2018-06-06 14:25:00 2018-06-06 15:24:00 0.983333 Let us compare the distribution of stay/visit durations. # Extracting visit duration (in hours) vo_pd [ 'VISIT_DURATION' ] = substract_datetime ( vo_pd [ 'visit_end_datetime' ], vo_pd [ 'visit_start_datetime' ], out = 'hours' ) # COnverting to days stays [ 'STAY_DURATION' ] = stays [ 'STAY_DURATION' ] / 24 vo_pd [ 'VISIT_DURATION' ] = vo_pd [ 'VISIT_DURATION' ] / 24 # Keeping only visits/stays less than a month long vo_pd = vo_pd [ vo_pd [ 'VISIT_DURATION' ] <= 31 ] stays = stays [ stays [ 'STAY_DURATION' ] <= 31 ] stays . STAY_DURATION . mean () 2.306892174348691 vo_pd . VISIT_DURATION . mean () 2.2188916112474866 We will aggregate the data into bins of 1 week days = list ( range ( 1 , 32 )) stays_distribution = pd . cut ( stays [ 'STAY_DURATION' ], 31 , labels = days ) . value_counts ( normalize = True ) . sort_index () visits_distribution = pd . cut ( vo_pd [ 'VISIT_DURATION' ], 31 , labels = days ) . value_counts ( normalize = True ) . sort_index () data = pd . concat ([ pd . DataFrame ( data = { 'density' : stays_distribution . values , 'day' : stays_distribution . index , 'type' : 'STAY' }), pd . DataFrame ( data = { 'density' : visits_distribution . values , 'day' : visits_distribution . index , 'type' : 'VISIT' }) ]) diff_distribution = ( stays_distribution - visits_distribution ) . to_frame () . reset_index () diff_distribution . columns = [ 'day' , 'difference' ] alt . Chart ( data ) . mark_bar ( opacity = 0.5 ) . encode ( x = \"day:N\" , y = alt . 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A crude way of doing so is by using the preceding_visit_occurrence_id column in the visit_occurrence table. However, this column isn't always filled, and a lot of visits would be missed by using only this method. The method proposed here relies on how close two visits are in order to put them in the same stay. This is the role of the merge_visits() functions. The figure below shows how the merging of visits into stays would occurs The merge_visits() function from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.period.stays import merge_visits visit_occurrence = merge_visits ( visit_occurrence ) Warning The snippet above should run as is , however the merge_visits() function provides a lot of parameters that you should check in order to use it properly. Those parameters are described below or in the corresponding code reference Merge \"close\" visit occurrences to consider them as a single stay by adding a STAY_ID and CONTIGUOUS_STAY_ID columns to the DataFrame. The value of these columns will be the visit_occurrence_id of the first (meaning the oldest) visit of the stay. From a temporal point of view, we consider that two visits belong to the same stay if either: They intersect The time difference between the end of the most recent and the start of the oldest is lower than max_timedelta (for STAY_ID ) or 0 (for CONTIGUOUS_STAY_ID ) Additionally, other parameters are available to further adjust the merging rules. See below. PARAMETER DESCRIPTION vo The visit_occurrence DataFrame, with at least the following columns: - visit_occurrence_id - person_id - visit_start_datetime_calc (from preprocessing) - visit_end_datetime (from preprocessing) Depending on the input parameters, additional columns may be required: - care_site_id (if merge_different_hospitals == True ) - visit_source_value (if merge_different_source_values != False ) - row_status_source_value (if remove_deleted_visits= True ) TYPE: DataFrame remove_deleted_visits Wether to remove deleted visits from the merging procedure. Deleted visits are extracted via the row_status_source_value column TYPE: bool DEFAULT: True long_stay_filtering Filtering method for long and/or non-closed visits. First of all, visits with no starting date won't be merged with any other visit, and visits with no ending date will have a temporary \"theoretical\" ending date set by datetime.now() . That being said, some visits are sometimes years long because they weren't closed at time. If other visits occurred during this timespan, they could be all merged into the same stay. To avoid this issue, filtering can be done depending on the long_stay_filtering value: all : All long stays (closed and open) are removed from the merging procedure open : Only long open stays are removed from the merging procedure None : No filtering is done on long visits Long stays are determined by the long_stay_threshold value. TYPE: Optional [ str ] DEFAULT: 'all' long_stay_threshold Minimum visit duration value to consider a visit as candidate for \"long visits filtering\" TYPE: timedelta DEFAULT: timedelta(days=365) open_stay_end_datetime Datetime to use in order to fill the visit_end_datetime of open visits. This is necessary in order to compute stay duration and to filter long stays. If not provided datetime.now() will be used. You might provide the extraction date of your data here. TYPE: Optional [ datetime ] DEFAULT: None max_timedelta Maximum time difference between the end of a visit and the start of another to consider them as belonging to the same stay. This duration is internally converted in seconds before comparing. Thus, if you want e.g. to merge visits happening in two consecutive days, you should use timedelta(days=2) and NOT timedelta(days=1) in order to take into account extreme cases such as an first visit ending on Monday at 00h01 AM and another one starting at 23h59 PM on Tuesday TYPE: timedelta DEFAULT: timedelta(days=2) merge_different_hospitals Wether to allow visits occurring in different hospitals to be merged into a same stay TYPE: bool DEFAULT: False merge_different_source_values Wether to allow visits with different visit_source_value to be merged into a same stay. Values can be: True : the visit_source_value isn't taken into account for the merging False : only visits with the same visit_source_value can be merged into a same stay List[str] : only visits which visit_source_value is in the provided list can be merged together. Warning : You should avoid merging visits where visit_source_value == \"hospitalisation incompl\u00e8te\" , because those stays are often never closed. TYPE: Union [ bool , List [ str ]] DEFAULT: ['hospitalis\u00e9s', 'urgence'] RETURNS DESCRIPTION vo Visit occurrence DataFrame with additional STAY_ID column TYPE: DataFrame Examples: >>> import pandas as pd >>> from datetime import datetime , timedelta >>> data = { 1 : ['A', 999, datetime(2021,1,1), datetime(2021,1,5), 'hospitalis\u00e9s'], 2 : ['B', 999, datetime(2021,1,4), datetime(2021,1,8), 'hospitalis\u00e9s'], 3 : ['C', 999, datetime(2021,1,12), datetime(2021,1,18), 'hospitalis\u00e9s'], 4 : ['D', 999, datetime(2021,1,13), datetime(2021,1,14), 'urgence'], 5 : ['E', 999, datetime(2021,1,19), datetime(2021,1,21), 'hospitalis\u00e9s'], 6 : ['F', 999, datetime(2021,1,25), datetime(2021,1,27), 'hospitalis\u00e9s'], 7 : ['G', 999, datetime(2017,1,1), None, \"hospitalis\u00e9s\"] } >>> vo = pd . DataFrame . from_dict ( data, orient=\"index\", columns=[ \"visit_occurrence_id\", \"person_id\", \"visit_start_datetime\", \"visit_end_datetime\", \"visit_source_value\", ], ) >>> vo visit_occurrence_id person_id visit_start_datetime visit_end_datetime visit_source_value 1 A 999 2021-01-01 2021-01-05 hospitalis\u00e9s 2 B 999 2021-01-04 2021-01-08 hospitalis\u00e9s 3 C 999 2021-01-12 2021-01-18 hospitalis\u00e9s 4 D 999 2021-01-13 2021-01-14 urgence 5 E 999 2021-01-19 2021-01-21 hospitalis\u00e9s 6 F 999 2021-01-25 2021-01-27 hospitalis\u00e9s 7 G 999 2017-01-01 NaT hospitalis\u00e9s >>> vo = merge_visits ( vo, remove_deleted_visits=True, long_stay_threshold=timedelta(days=365), long_stay_filtering=\"all\", max_timedelta=timedelta(hours=24), merge_different_hospitals=False, merge_different_source_values=[\"hospitalis\u00e9s\", \"urgence\"], ) >>> vo visit_occurrence_id person_id visit_start_datetime visit_end_datetime visit_source_value STAY_ID CONTIGUOUS_STAY_ID 1 A 999 2021-01-01 2021-01-05 hospitalis\u00e9s A A 2 B 999 2021-01-04 2021-01-08 hospitalis\u00e9s A A 3 C 999 2021-01-12 2021-01-18 hospitalis\u00e9s C C 4 D 999 2021-01-13 2021-01-14 urgence C C 5 E 999 2021-01-19 2021-01-21 hospitalis\u00e9s C E 6 F 999 2021-01-25 2021-01-27 hospitalis\u00e9s F F 7 G 999 2017-01-01 NaT hospitalis\u00e9s G G Computing stay duration Presentation of the problem Once that visits are grouped into stays, you might want to compute stays duration. The get_stays_duration() function from eds_scikit.period.stays import get_stays_duration This function should be used once you called the merge_visits() functions. It adds a STAY_DURATION column. vo = get_stays_duration ( vo , algo = \"visits_date_difference\" , missing_end_date_handling = \"fill\" , ) There are actually two ways to compute those stays durations. Pick the \"algo\" value that suits your needs. Availables algorithms (values for \"algo\" ) 'visits_date_difference' 'sum_of_visits_duration' The stay duration corresponds to the difference between the end datetime of the stay's last visit and the start datetime of the stay's first visit . The stay duration corresponds to the sum of the duration of all visits of the stay (and by handling overlapping) Please check the documentation for additional parameters.","title":"Visit merging"},{"location":"functionalities/patients-course/visit_merging/#merging-visits-into-stays","text":"","title":"Merging visits into stays"},{"location":"functionalities/patients-course/visit_merging/#presentation-of-the-problem","text":"In order to have a precise view of each patient's course of care, it can be useful to merge together visit occurrences into stays. A crude way of doing so is by using the preceding_visit_occurrence_id column in the visit_occurrence table. However, this column isn't always filled, and a lot of visits would be missed by using only this method. The method proposed here relies on how close two visits are in order to put them in the same stay. This is the role of the merge_visits() functions. The figure below shows how the merging of visits into stays would occurs","title":"Presentation of the problem"},{"location":"functionalities/patients-course/visit_merging/#the-merge_visits-function","text":"from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.period.stays import merge_visits visit_occurrence = merge_visits ( visit_occurrence ) Warning The snippet above should run as is , however the merge_visits() function provides a lot of parameters that you should check in order to use it properly. Those parameters are described below or in the corresponding code reference Merge \"close\" visit occurrences to consider them as a single stay by adding a STAY_ID and CONTIGUOUS_STAY_ID columns to the DataFrame. The value of these columns will be the visit_occurrence_id of the first (meaning the oldest) visit of the stay. From a temporal point of view, we consider that two visits belong to the same stay if either: They intersect The time difference between the end of the most recent and the start of the oldest is lower than max_timedelta (for STAY_ID ) or 0 (for CONTIGUOUS_STAY_ID ) Additionally, other parameters are available to further adjust the merging rules. See below. PARAMETER DESCRIPTION vo The visit_occurrence DataFrame, with at least the following columns: - visit_occurrence_id - person_id - visit_start_datetime_calc (from preprocessing) - visit_end_datetime (from preprocessing) Depending on the input parameters, additional columns may be required: - care_site_id (if merge_different_hospitals == True ) - visit_source_value (if merge_different_source_values != False ) - row_status_source_value (if remove_deleted_visits= True ) TYPE: DataFrame remove_deleted_visits Wether to remove deleted visits from the merging procedure. Deleted visits are extracted via the row_status_source_value column TYPE: bool DEFAULT: True long_stay_filtering Filtering method for long and/or non-closed visits. First of all, visits with no starting date won't be merged with any other visit, and visits with no ending date will have a temporary \"theoretical\" ending date set by datetime.now() . That being said, some visits are sometimes years long because they weren't closed at time. If other visits occurred during this timespan, they could be all merged into the same stay. To avoid this issue, filtering can be done depending on the long_stay_filtering value: all : All long stays (closed and open) are removed from the merging procedure open : Only long open stays are removed from the merging procedure None : No filtering is done on long visits Long stays are determined by the long_stay_threshold value. TYPE: Optional [ str ] DEFAULT: 'all' long_stay_threshold Minimum visit duration value to consider a visit as candidate for \"long visits filtering\" TYPE: timedelta DEFAULT: timedelta(days=365) open_stay_end_datetime Datetime to use in order to fill the visit_end_datetime of open visits. This is necessary in order to compute stay duration and to filter long stays. If not provided datetime.now() will be used. You might provide the extraction date of your data here. TYPE: Optional [ datetime ] DEFAULT: None max_timedelta Maximum time difference between the end of a visit and the start of another to consider them as belonging to the same stay. This duration is internally converted in seconds before comparing. Thus, if you want e.g. to merge visits happening in two consecutive days, you should use timedelta(days=2) and NOT timedelta(days=1) in order to take into account extreme cases such as an first visit ending on Monday at 00h01 AM and another one starting at 23h59 PM on Tuesday TYPE: timedelta DEFAULT: timedelta(days=2) merge_different_hospitals Wether to allow visits occurring in different hospitals to be merged into a same stay TYPE: bool DEFAULT: False merge_different_source_values Wether to allow visits with different visit_source_value to be merged into a same stay. Values can be: True : the visit_source_value isn't taken into account for the merging False : only visits with the same visit_source_value can be merged into a same stay List[str] : only visits which visit_source_value is in the provided list can be merged together. Warning : You should avoid merging visits where visit_source_value == \"hospitalisation incompl\u00e8te\" , because those stays are often never closed. TYPE: Union [ bool , List [ str ]] DEFAULT: ['hospitalis\u00e9s', 'urgence'] RETURNS DESCRIPTION vo Visit occurrence DataFrame with additional STAY_ID column TYPE: DataFrame Examples: >>> import pandas as pd >>> from datetime import datetime , timedelta >>> data = { 1 : ['A', 999, datetime(2021,1,1), datetime(2021,1,5), 'hospitalis\u00e9s'], 2 : ['B', 999, datetime(2021,1,4), datetime(2021,1,8), 'hospitalis\u00e9s'], 3 : ['C', 999, datetime(2021,1,12), datetime(2021,1,18), 'hospitalis\u00e9s'], 4 : ['D', 999, datetime(2021,1,13), datetime(2021,1,14), 'urgence'], 5 : ['E', 999, datetime(2021,1,19), datetime(2021,1,21), 'hospitalis\u00e9s'], 6 : ['F', 999, datetime(2021,1,25), datetime(2021,1,27), 'hospitalis\u00e9s'], 7 : ['G', 999, datetime(2017,1,1), None, \"hospitalis\u00e9s\"] } >>> vo = pd . DataFrame . from_dict ( data, orient=\"index\", columns=[ \"visit_occurrence_id\", \"person_id\", \"visit_start_datetime\", \"visit_end_datetime\", \"visit_source_value\", ], ) >>> vo visit_occurrence_id person_id visit_start_datetime visit_end_datetime visit_source_value 1 A 999 2021-01-01 2021-01-05 hospitalis\u00e9s 2 B 999 2021-01-04 2021-01-08 hospitalis\u00e9s 3 C 999 2021-01-12 2021-01-18 hospitalis\u00e9s 4 D 999 2021-01-13 2021-01-14 urgence 5 E 999 2021-01-19 2021-01-21 hospitalis\u00e9s 6 F 999 2021-01-25 2021-01-27 hospitalis\u00e9s 7 G 999 2017-01-01 NaT hospitalis\u00e9s >>> vo = merge_visits ( vo, remove_deleted_visits=True, long_stay_threshold=timedelta(days=365), long_stay_filtering=\"all\", max_timedelta=timedelta(hours=24), merge_different_hospitals=False, merge_different_source_values=[\"hospitalis\u00e9s\", \"urgence\"], ) >>> vo visit_occurrence_id person_id visit_start_datetime visit_end_datetime visit_source_value STAY_ID CONTIGUOUS_STAY_ID 1 A 999 2021-01-01 2021-01-05 hospitalis\u00e9s A A 2 B 999 2021-01-04 2021-01-08 hospitalis\u00e9s A A 3 C 999 2021-01-12 2021-01-18 hospitalis\u00e9s C C 4 D 999 2021-01-13 2021-01-14 urgence C C 5 E 999 2021-01-19 2021-01-21 hospitalis\u00e9s C E 6 F 999 2021-01-25 2021-01-27 hospitalis\u00e9s F F 7 G 999 2017-01-01 NaT hospitalis\u00e9s G G","title":"The merge_visits() function"},{"location":"functionalities/patients-course/visit_merging/#computing-stay-duration","text":"","title":"Computing stay duration"},{"location":"functionalities/patients-course/visit_merging/#presentation-of-the-problem_1","text":"Once that visits are grouped into stays, you might want to compute stays duration.","title":"Presentation of the problem"},{"location":"functionalities/patients-course/visit_merging/#the-get_stays_duration-function","text":"from eds_scikit.period.stays import get_stays_duration This function should be used once you called the merge_visits() functions. It adds a STAY_DURATION column. vo = get_stays_duration ( vo , algo = \"visits_date_difference\" , missing_end_date_handling = \"fill\" , ) There are actually two ways to compute those stays durations. Pick the \"algo\" value that suits your needs. Availables algorithms (values for \"algo\" ) 'visits_date_difference' 'sum_of_visits_duration' The stay duration corresponds to the difference between the end datetime of the stay's last visit and the start datetime of the stay's first visit . The stay duration corresponds to the sum of the duration of all visits of the stay (and by handling overlapping) Please check the documentation for additional parameters.","title":"The get_stays_duration() function"},{"location":"functionalities/phenotyping/base/","text":"How to use and developp phenotyping algorithms in eds-scikit The Phenotype class Phenotyping is done via the Phenotype class. Using this class, we can add features that will be stored in the features attribute. Features are DataFrames containing at least a person_id and a phenotype column. Additionaly: If phenotyping at the visit level, features contains a visit_occurrence_id column If using sub-phenotypes (e.g. types of diabetes, or various cancer localiizations), features contains a subphenotype column. We distinguish 2 main ways of adding features to a Phenotype instance: By querying the database to extract raw features By aggregating one or multiple existing features Available phenotypes eds-scikit is shipped with various phenotyping algorithms. For instance, the CancerFromICD10 class can be used to extract visits or patients with a cancer-related ICD10 code. All those phenotyping algorithms share the same API. We will demonstrate it using the CancerFromICD10 class from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.phenotype import CancerFromICD10 cancer = CancerFromICD10 ( data ) To run the phenotyping algorithm, simply run: data = cancer . to_data () This will put the resulting phenotype DataFrame in data.computed[\"CancerFromICD10\"] Most available phenotypes share the same parameters: PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData cancer_types Optional list of cancer types to use for phenotyping TYPE: Optional[List[str]] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int DEFAULT: 1 Please look into each algorithm's documentation for further specific details. Implement your own phenotyping algorithm TO help you implement your own phenotyping algorithm, the Phenotype class exposes method to Easily featch features based on ICD10 and CCAM codes Easily aggregate feature(s) using simple threshold rules The following paragraph will show how to implement a dummy phenotyping algorithm for moderate to terminal Chronic Kidney Disease (CKD). In short, it will: - Extract patients with ICD10 code for CKD - Extract patients with CCAM code for dialysis or kidney transplant - Aggregate those two feature by keeping patients with both features We will start by creating an instance of the Phenotype class: from eds_scikit.phenotype import Phenotype ckd = Phenotype ( data , name = \"DummyCKD\" ) Next we define the ICD10 and CCAM codes Codes formatting Under the hood, Phenotype will use the conditions_from_icd10 and procedures_from_ccam functions. Check their documentation for details on how to format the provided codes icd10_codes = { \"CKD\" : { \"regex\" : [ \"N18[345]\" ]}, } ccam_codes = { \"dialysis\" : { \"regex\" : [ \"JVJB001\" ]}, \"transplant\" : { \"exact\" : [ \"JAEA003\" ]}, } Finally, we can start designing the phenotyping algorithm: Get ICD10 features ckd = ckd.add_code_feature( output_feature=\"icd10\", source=\"icd10\", codes=icd10_codes, ) Get CCAM features ckd = ckd.add_code_feature( output_feature=\"ccam\", source=\"ccam\", codes=ccam_codes, ) Aggregate those 2 features ckd = ckd.agg_two_features( input_feature_1=\"icd10\", input_feature_2=\"ccam\", output_feature=\"CKD\", how=\"AND\", level=\"patient\", subphenotype=False, thresholds=(1, 1), ) The final phenotype DataFrame can now be added to the data object: data = ckd . to_data () It will be available under data.computed.CKD Available methods on Phenotype : Base class for phenotyping PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData name Name of the phenotype. If left to None, the name of the class will be used instead TYPE: Optional [ str ] DEFAULT: None add_code_feature add_code_feature ( output_feature : str , codes : dict , source : str = 'icd10' , additional_filtering : Optional [ dict ] = None ) Adds a feature from either ICD10 or CCAM codes PARAMETER DESCRIPTION output_feature Name of the feature TYPE: str codes Dictionary of codes to provide to the from_codes function TYPE: dict source Either 'icd10' or 'ccam', by default 'icd10' TYPE: str DEFAULT: 'icd10' additional_filtering Dictionary passed to the from_codes functions for filtering TYPE: Optional [ dict ] DEFAULT: None RETURNS DESCRIPTION Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] agg_single_feature agg_single_feature ( input_feature : str , output_feature : Optional [ str ] = None , level : str = 'patient' , subphenotype : bool = True , threshold : int = 1 ) -> Phenotype Simple aggregation rule on a feature: If level=\"patient\", keeps patients with at least threshold visits showing the (sub)phenotype If level=\"visit\", keeps visits with at least threshold events (could be ICD10 codes, NLP features, biology, etc) showing the (sub)phenotype PARAMETER DESCRIPTION input_feature Name of the input feature TYPE: str output_feature Name of the input feature. If None, will be set to input_feature + \"_agg\" TYPE: Optional [ str ] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int , optional DEFAULT: 1 RETURNS DESCRIPTION Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] agg_two_features agg_two_features ( input_feature_1 : str , input_feature_2 : str , output_feature : str = None , how : str = 'AND' , level : str = 'patient' , subphenotype : bool = True , thresholds : Tuple [ int , int ] = ( 1 , 1 )) -> Phenotype If level='patient', keeps a specific patient if At least thresholds[0] visits are found in feature_1 AND/OR At least thresholds[1] visits are found in feature_2 If level='visit', keeps a specific visit if At least thresholds[0] events are found in feature_1 AND/OR At least thresholds[1] events are found in feature_2 PARAMETER DESCRIPTION input_feature_1 Name of the first input feature TYPE: str input_feature_2 Name of the second input feature TYPE: str output_feature Name of the input feature. If None, will be set to input_feature + \"_agg\" TYPE: str DEFAULT: None how Whether to perform a boolean \"AND\" or \"OR\" aggregation TYPE: str , optional DEFAULT: 'AND' level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True thresholds Repsective threshold for the first and second feature TYPE: Tuple [ int , int ], optional DEFAULT: (1, 1) RETURNS DESCRIPTION Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] compute compute ( ** kwargs ) Fetch all necessary features and perform aggregation to_data to_data ( key : Optional [ str ] = None ) -> BaseData Appends the feature found in self.features[key] to the data object. If no key is provided, uses the last added feature PARAMETER DESCRIPTION key Key of the self.feature dictionary TYPE: Optional [ str ] DEFAULT: None RETURNS DESCRIPTION BaseData The data object with phenotype added to data.computed Citation Most available phenotypes implement an algorithm described in an academic paper. When using this algorithm, you can get the BibTex citation of the corrresponding paper by calling the cite method. For instance: cancer . cite () @article { kempf2022impact , title = {Impact of two waves of Sars-Cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: A French multicentric cohort study from a large group of University hospitals} , author = {Kempf, Emmanuelle and Priou, Sonia and Lam{\\'e}, Guillaume and Daniel, Christel and Bellamine, Ali and Sommacale, Daniele and Belkacemi, Yazid and Bey, Romain and Galula, Gilles and Taright, Namik and others} , journal = {International Journal of Cancer} , volume = {150} , number = {10} , pages = {1609--1618} , year = {2022} , publisher = {Wiley Online Library} }","title":"The `Phenotype` class"},{"location":"functionalities/phenotyping/base/#how-to-use-and-developp-phenotyping-algorithms-in-eds-scikit","text":"","title":"How to use and developp phenotyping algorithms in eds-scikit"},{"location":"functionalities/phenotyping/base/#the-phenotype-class","text":"Phenotyping is done via the Phenotype class. Using this class, we can add features that will be stored in the features attribute. Features are DataFrames containing at least a person_id and a phenotype column. Additionaly: If phenotyping at the visit level, features contains a visit_occurrence_id column If using sub-phenotypes (e.g. types of diabetes, or various cancer localiizations), features contains a subphenotype column. We distinguish 2 main ways of adding features to a Phenotype instance: By querying the database to extract raw features By aggregating one or multiple existing features","title":"The Phenotype class"},{"location":"functionalities/phenotyping/base/#available-phenotypes","text":"eds-scikit is shipped with various phenotyping algorithms. For instance, the CancerFromICD10 class can be used to extract visits or patients with a cancer-related ICD10 code. All those phenotyping algorithms share the same API. We will demonstrate it using the CancerFromICD10 class from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.phenotype import CancerFromICD10 cancer = CancerFromICD10 ( data ) To run the phenotyping algorithm, simply run: data = cancer . to_data () This will put the resulting phenotype DataFrame in data.computed[\"CancerFromICD10\"] Most available phenotypes share the same parameters: PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData cancer_types Optional list of cancer types to use for phenotyping TYPE: Optional[List[str]] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int DEFAULT: 1 Please look into each algorithm's documentation for further specific details.","title":"Available phenotypes"},{"location":"functionalities/phenotyping/base/#implement-your-own-phenotyping-algorithm","text":"TO help you implement your own phenotyping algorithm, the Phenotype class exposes method to Easily featch features based on ICD10 and CCAM codes Easily aggregate feature(s) using simple threshold rules The following paragraph will show how to implement a dummy phenotyping algorithm for moderate to terminal Chronic Kidney Disease (CKD). In short, it will: - Extract patients with ICD10 code for CKD - Extract patients with CCAM code for dialysis or kidney transplant - Aggregate those two feature by keeping patients with both features We will start by creating an instance of the Phenotype class: from eds_scikit.phenotype import Phenotype ckd = Phenotype ( data , name = \"DummyCKD\" ) Next we define the ICD10 and CCAM codes Codes formatting Under the hood, Phenotype will use the conditions_from_icd10 and procedures_from_ccam functions. Check their documentation for details on how to format the provided codes icd10_codes = { \"CKD\" : { \"regex\" : [ \"N18[345]\" ]}, } ccam_codes = { \"dialysis\" : { \"regex\" : [ \"JVJB001\" ]}, \"transplant\" : { \"exact\" : [ \"JAEA003\" ]}, } Finally, we can start designing the phenotyping algorithm:","title":"Implement your own phenotyping algorithm"},{"location":"functionalities/phenotyping/base/#get-icd10-features","text":"ckd = ckd.add_code_feature( output_feature=\"icd10\", source=\"icd10\", codes=icd10_codes, )","title":"Get ICD10 features"},{"location":"functionalities/phenotyping/base/#get-ccam-features","text":"ckd = ckd.add_code_feature( output_feature=\"ccam\", source=\"ccam\", codes=ccam_codes, )","title":"Get CCAM features"},{"location":"functionalities/phenotyping/base/#aggregate-those-2-features","text":"ckd = ckd.agg_two_features( input_feature_1=\"icd10\", input_feature_2=\"ccam\", output_feature=\"CKD\", how=\"AND\", level=\"patient\", subphenotype=False, thresholds=(1, 1), ) The final phenotype DataFrame can now be added to the data object: data = ckd . to_data () It will be available under data.computed.CKD","title":"Aggregate those 2 features"},{"location":"functionalities/phenotyping/base/#available-methods-on-phenotype","text":"Base class for phenotyping PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData name Name of the phenotype. If left to None, the name of the class will be used instead TYPE: Optional [ str ] DEFAULT: None","title":"Available methods on Phenotype:"},{"location":"functionalities/phenotyping/base/#eds_scikit.phenotype.base.Phenotype.add_code_feature","text":"add_code_feature ( output_feature : str , codes : dict , source : str = 'icd10' , additional_filtering : Optional [ dict ] = None ) Adds a feature from either ICD10 or CCAM codes PARAMETER DESCRIPTION output_feature Name of the feature TYPE: str codes Dictionary of codes to provide to the from_codes function TYPE: dict source Either 'icd10' or 'ccam', by default 'icd10' TYPE: str DEFAULT: 'icd10' additional_filtering Dictionary passed to the from_codes functions for filtering TYPE: Optional [ dict ] DEFAULT: None RETURNS DESCRIPTION Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature]","title":"add_code_feature()"},{"location":"functionalities/phenotyping/base/#eds_scikit.phenotype.base.Phenotype.agg_single_feature","text":"agg_single_feature ( input_feature : str , output_feature : Optional [ str ] = None , level : str = 'patient' , subphenotype : bool = True , threshold : int = 1 ) -> Phenotype Simple aggregation rule on a feature: If level=\"patient\", keeps patients with at least threshold visits showing the (sub)phenotype If level=\"visit\", keeps visits with at least threshold events (could be ICD10 codes, NLP features, biology, etc) showing the (sub)phenotype PARAMETER DESCRIPTION input_feature Name of the input feature TYPE: str output_feature Name of the input feature. If None, will be set to input_feature + \"_agg\" TYPE: Optional [ str ] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int , optional DEFAULT: 1 RETURNS DESCRIPTION Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature]","title":"agg_single_feature()"},{"location":"functionalities/phenotyping/base/#eds_scikit.phenotype.base.Phenotype.agg_two_features","text":"agg_two_features ( input_feature_1 : str , input_feature_2 : str , output_feature : str = None , how : str = 'AND' , level : str = 'patient' , subphenotype : bool = True , thresholds : Tuple [ int , int ] = ( 1 , 1 )) -> Phenotype If level='patient', keeps a specific patient if At least thresholds[0] visits are found in feature_1 AND/OR At least thresholds[1] visits are found in feature_2 If level='visit', keeps a specific visit if At least thresholds[0] events are found in feature_1 AND/OR At least thresholds[1] events are found in feature_2 PARAMETER DESCRIPTION input_feature_1 Name of the first input feature TYPE: str input_feature_2 Name of the second input feature TYPE: str output_feature Name of the input feature. If None, will be set to input_feature + \"_agg\" TYPE: str DEFAULT: None how Whether to perform a boolean \"AND\" or \"OR\" aggregation TYPE: str , optional DEFAULT: 'AND' level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True thresholds Repsective threshold for the first and second feature TYPE: Tuple [ int , int ], optional DEFAULT: (1, 1) RETURNS DESCRIPTION Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature]","title":"agg_two_features()"},{"location":"functionalities/phenotyping/base/#eds_scikit.phenotype.base.Phenotype.compute","text":"compute ( ** kwargs ) Fetch all necessary features and perform aggregation","title":"compute()"},{"location":"functionalities/phenotyping/base/#eds_scikit.phenotype.base.Phenotype.to_data","text":"to_data ( key : Optional [ str ] = None ) -> BaseData Appends the feature found in self.features[key] to the data object. If no key is provided, uses the last added feature PARAMETER DESCRIPTION key Key of the self.feature dictionary TYPE: Optional [ str ] DEFAULT: None RETURNS DESCRIPTION BaseData The data object with phenotype added to data.computed","title":"to_data()"},{"location":"functionalities/phenotyping/base/#citation","text":"Most available phenotypes implement an algorithm described in an academic paper. When using this algorithm, you can get the BibTex citation of the corrresponding paper by calling the cite method. For instance: cancer . cite () @article { kempf2022impact , title = {Impact of two waves of Sars-Cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: A French multicentric cohort study from a large group of University hospitals} , author = {Kempf, Emmanuelle and Priou, Sonia and Lam{\\'e}, Guillaume and Daniel, Christel and Bellamine, Ali and Sommacale, Daniele and Belkacemi, Yazid and Bey, Romain and Galula, Gilles and Taright, Namik and others} , journal = {International Journal of Cancer} , volume = {150} , number = {10} , pages = {1609--1618} , year = {2022} , publisher = {Wiley Online Library} }","title":"Citation"},{"location":"functionalities/phenotyping/diabetes/","text":"Diabetes Presentation For the moment, we provide a diabetes phenotyping function based solely on ICD-10 codes. The diabetes_from_icd10() function from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.event import diabetes_from_icd10 visit_occurrence = diabetes_from_icd10 ( data . condition_occurrence , data . visit_occurrence , ) The snippet above will run as is and add two columns to the condition_occurrence DataFrame: A \"concept\" column, containing the \"DIABETES_FROM_ICD10\" value A \"value\" column, containing the type of diabetes extracted Please check the code reference for a complete explanation of the function.","title":"Diabetes"},{"location":"functionalities/phenotyping/diabetes/#diabetes","text":"","title":"Diabetes"},{"location":"functionalities/phenotyping/diabetes/#presentation","text":"For the moment, we provide a diabetes phenotyping function based solely on ICD-10 codes.","title":"Presentation"},{"location":"functionalities/phenotyping/diabetes/#the-diabetes_from_icd10-function","text":"from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.event import diabetes_from_icd10 visit_occurrence = diabetes_from_icd10 ( data . condition_occurrence , data . visit_occurrence , ) The snippet above will run as is and add two columns to the condition_occurrence DataFrame: A \"concept\" column, containing the \"DIABETES_FROM_ICD10\" value A \"value\" column, containing the type of diabetes extracted Please check the code reference for a complete explanation of the function.","title":"The diabetes_from_icd10() function"},{"location":"functionalities/phenotyping/suicide_attempts/","text":"Suicide attempt Presentation We provide the tag_suicide_attempt() function to extract suicide attempt from ICD-10 codes. The tag_suicide_attempt() function from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.event import tag_suicide_attempt visit_occurrence = tag_suicide_attempt ( data . visit_occurrence , data . condition_occurrence , algo = \"X60-X84\" , ) Availables algorithms (values for \"algo\" ) 'X60-X84' 'Haguenoer2008' Returns the visits that have at least one ICD code that belongs to the range X60 to X84. Returns the visits that follow the definiton of \"Haguenoer, Ken, Agn\u00e8s Caille, Marc Fillatre, Anne Isabelle Lecuyer, et Emmanuel Rusch. \u00ab Tentatives de Suicide \u00bb, 2008, 4.\". This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84. You can check the documentation of the function for additional parameters: Function to return visits that fulfill different definitions of suicide attempt by ICD10. PARAMETER DESCRIPTION visit_occurrence TYPE: DataFrame condition_occurrence TYPE: DataFrame date_min Minimal starting date (on visit_start_datetime ) TYPE: Optional [ datetime ] DEFAULT: None date_max Maximal starting date (on visit_start_datetime ) TYPE: Optional [ datetime ] DEFAULT: None algo Method to use. Available values are: \"X60-X84\" : Will return a the visits that have at least one ICD code that belongs to the range X60 to X84. \"Haguenoer2008\" : Will return a the visits that follow the definiton of \" Haguenoer, Ken, Agn\u00e8s Caille, Marc Fillatre, Anne Isabelle Lecuyer, et Emmanuel Rusch. \u00ab Tentatives de Suicide \u00bb, 2008, 4. \". This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84. TYPE: str DEFAULT: 'X60-X84' RETURNS DESCRIPTION visit_occurrence Tagged with an additional column SUICIDE_ATTEMPT TYPE: DataFrame Tip These rules were implemented in the CSE project n\u00b0210013","title":"Suicide attempt"},{"location":"functionalities/phenotyping/suicide_attempts/#suicide-attempt","text":"","title":"Suicide attempt"},{"location":"functionalities/phenotyping/suicide_attempts/#presentation","text":"We provide the tag_suicide_attempt() function to extract suicide attempt from ICD-10 codes.","title":"Presentation"},{"location":"functionalities/phenotyping/suicide_attempts/#the-tag_suicide_attempt-function","text":"from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.event import tag_suicide_attempt visit_occurrence = tag_suicide_attempt ( data . visit_occurrence , data . condition_occurrence , algo = \"X60-X84\" , ) Availables algorithms (values for \"algo\" ) 'X60-X84' 'Haguenoer2008' Returns the visits that have at least one ICD code that belongs to the range X60 to X84. Returns the visits that follow the definiton of \"Haguenoer, Ken, Agn\u00e8s Caille, Marc Fillatre, Anne Isabelle Lecuyer, et Emmanuel Rusch. \u00ab Tentatives de Suicide \u00bb, 2008, 4.\". This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84. You can check the documentation of the function for additional parameters: Function to return visits that fulfill different definitions of suicide attempt by ICD10. PARAMETER DESCRIPTION visit_occurrence TYPE: DataFrame condition_occurrence TYPE: DataFrame date_min Minimal starting date (on visit_start_datetime ) TYPE: Optional [ datetime ] DEFAULT: None date_max Maximal starting date (on visit_start_datetime ) TYPE: Optional [ datetime ] DEFAULT: None algo Method to use. Available values are: \"X60-X84\" : Will return a the visits that have at least one ICD code that belongs to the range X60 to X84. \"Haguenoer2008\" : Will return a the visits that follow the definiton of \" Haguenoer, Ken, Agn\u00e8s Caille, Marc Fillatre, Anne Isabelle Lecuyer, et Emmanuel Rusch. \u00ab Tentatives de Suicide \u00bb, 2008, 4. \". This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84. TYPE: str DEFAULT: 'X60-X84' RETURNS DESCRIPTION visit_occurrence Tagged with an additional column SUICIDE_ATTEMPT TYPE: DataFrame Tip These rules were implemented in the CSE project n\u00b0210013","title":"The tag_suicide_attempt() function"},{"location":"functionalities/phenotyping/working-with-codes/","text":"Using ICD-10 and CCAM eds-scikit provides two functions to ease the extraction of occurrrences of ICD-10 codes : eds_scikit.event.icd10.conditions_from_icd10 CCAM codes : eds_scikit.event.ccam.procedures_from_ccam These two functions are by design similar. In fact, they call under the hood the same base function . Let us see a minimal working example that would allow us to select patients with Deep Vein Thrombosis based on the presence of specific ICD-10 codes. from eds_scikit.io import HiveData data = HiveData ( DBNAME ) codes = dict ( DVT = dict ( exact = [ \"I81\" , \"O223\" , \"O082\" , \"O871\" ], regex = [ \"I82[02389]\" , \"I80[12]\" ] # (1) ) ) from eds_scikit.event.icd10 import conditions_from_icd10 DVTs = conditions_from_icd10 ( condition_occurrence = data . condition_occurrence , visit_occurrence = data . visit_occurrence , codes = codes , date_from_visit = True , additional_filtering = dict ( condition_status_source_value = { \"DP\" , \"DAS\" }, # (1) ), ) Here you can provide either exact , regex or prefix codes With this syntax we will keep only DP ( Diagnostic Principal ) or DAS ( Diagnostic Associ\u00e9 ) diagnoses Of course, you are encouraged to check the documentation of those functions as they provide additional parameters that might be useful depending on your needs.","title":"Using ICD-10 and CCAM"},{"location":"functionalities/phenotyping/working-with-codes/#using-icd-10-and-ccam","text":"eds-scikit provides two functions to ease the extraction of occurrrences of ICD-10 codes : eds_scikit.event.icd10.conditions_from_icd10 CCAM codes : eds_scikit.event.ccam.procedures_from_ccam These two functions are by design similar. In fact, they call under the hood the same base function . Let us see a minimal working example that would allow us to select patients with Deep Vein Thrombosis based on the presence of specific ICD-10 codes. from eds_scikit.io import HiveData data = HiveData ( DBNAME ) codes = dict ( DVT = dict ( exact = [ \"I81\" , \"O223\" , \"O082\" , \"O871\" ], regex = [ \"I82[02389]\" , \"I80[12]\" ] # (1) ) ) from eds_scikit.event.icd10 import conditions_from_icd10 DVTs = conditions_from_icd10 ( condition_occurrence = data . condition_occurrence , visit_occurrence = data . visit_occurrence , codes = codes , date_from_visit = True , additional_filtering = dict ( condition_status_source_value = { \"DP\" , \"DAS\" }, # (1) ), ) Here you can provide either exact , regex or prefix codes With this syntax we will keep only DP ( Diagnostic Principal ) or DAS ( Diagnostic Associ\u00e9 ) diagnoses Of course, you are encouraged to check the documentation of those functions as they provide additional parameters that might be useful depending on your needs.","title":"Using ICD-10 and CCAM"},{"location":"functionalities/phenotyping/phenotypes/cancer/","text":"Cancer Presentation We provide the CancerFromICD10 class to extract visits or patients with cancer related ICD10 code Available cancer types Anus Biliary_duct Bladder Bowel Breast CNS CUP Cervix Colon Endometrium Eye Gastric Head_neck Hodgkin_lymphoma Kidney Leukemia Liver Lung Melanoma Mesothelioma Myeloma Nonhodgkin_lymphoma Oesophagus Osteosarcoma Other_digestive Other_endocrinial Other_gynecology Other_hematologic_malignancies Other_pneumology Other_skin Other_urothelial Ovary PNS Pancreas Prostate Rectum Soft_tissue Testis Thyroid How it works The algorithm works by looking for either DP ou DR ICD10 codes associated with cancer. The codes terminology comes from this article 1 and is available under CancerFromICD10.ICD10_CODES Usage By default, all cancer types mentionned above are extracted from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.phenotype import CancerFromICD10 cancer = CancerFromICD10 ( data ) data = cancer . to_data () To choose a subset of cancer types, use the cancer_types argument: cancer = CancerFromICD10 ( data , cancer_types = [ \"Eye\" , \"Liver\" , \"Leukemia\" , ], ) The final phenotype DataFrame is then available at data.computed[\"CancerFromICD10\"] Optional parameters PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData cancer_types Optional list of cancer types to use for phenotyping TYPE: Optional[List[str]] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int DEFAULT: 1 Citation You can get the BibTex of the corresponding article 1 by calling cancer . cite () @article { kempf2022impact , title = {Impact of two waves of Sars-Cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: A French multicentric cohort study from a large group of University hospitals} , author = {Kempf, Emmanuelle and Priou, Sonia and Lam{\\'e}, Guillaume and Daniel, Christel and Bellamine, Ali and Sommacale, Daniele and Belkacemi, Yazid and Bey, Romain and Galula, Gilles and Taright, Namik and others} , journal = {International Journal of Cancer} , volume = {150} , number = {10} , pages = {1609--1618} , year = {2022} , publisher = {Wiley Online Library} } Reference Check the code reference here for a more detailled look. Emmanuelle Kempf, Sonia Priou, Guillaume Lam\u00e9, Christel Daniel, Ali Bellamine, Daniele Sommacale, Yazid Belkacemi, Romain Bey, Gilles Galula, Namik Taright, and others. Impact of two waves of sars-cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: a french multicentric cohort study from a large group of university hospitals. International Journal of Cancer , 150(10):1609\u20131618, 2022. \u21a9 \u21a9","title":"Cancer"},{"location":"functionalities/phenotyping/phenotypes/cancer/#cancer","text":"","title":"Cancer"},{"location":"functionalities/phenotyping/phenotypes/cancer/#presentation","text":"We provide the CancerFromICD10 class to extract visits or patients with cancer related ICD10 code Available cancer types Anus Biliary_duct Bladder Bowel Breast CNS CUP Cervix Colon Endometrium Eye Gastric Head_neck Hodgkin_lymphoma Kidney Leukemia Liver Lung Melanoma Mesothelioma Myeloma Nonhodgkin_lymphoma Oesophagus Osteosarcoma Other_digestive Other_endocrinial Other_gynecology Other_hematologic_malignancies Other_pneumology Other_skin Other_urothelial Ovary PNS Pancreas Prostate Rectum Soft_tissue Testis Thyroid How it works The algorithm works by looking for either DP ou DR ICD10 codes associated with cancer. The codes terminology comes from this article 1 and is available under CancerFromICD10.ICD10_CODES","title":"Presentation"},{"location":"functionalities/phenotyping/phenotypes/cancer/#usage","text":"By default, all cancer types mentionned above are extracted from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.phenotype import CancerFromICD10 cancer = CancerFromICD10 ( data ) data = cancer . to_data () To choose a subset of cancer types, use the cancer_types argument: cancer = CancerFromICD10 ( data , cancer_types = [ \"Eye\" , \"Liver\" , \"Leukemia\" , ], ) The final phenotype DataFrame is then available at data.computed[\"CancerFromICD10\"]","title":"Usage"},{"location":"functionalities/phenotyping/phenotypes/cancer/#optional-parameters","text":"PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData cancer_types Optional list of cancer types to use for phenotyping TYPE: Optional[List[str]] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int DEFAULT: 1","title":"Optional parameters"},{"location":"functionalities/phenotyping/phenotypes/cancer/#citation","text":"You can get the BibTex of the corresponding article 1 by calling cancer . cite () @article { kempf2022impact , title = {Impact of two waves of Sars-Cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: A French multicentric cohort study from a large group of University hospitals} , author = {Kempf, Emmanuelle and Priou, Sonia and Lam{\\'e}, Guillaume and Daniel, Christel and Bellamine, Ali and Sommacale, Daniele and Belkacemi, Yazid and Bey, Romain and Galula, Gilles and Taright, Namik and others} , journal = {International Journal of Cancer} , volume = {150} , number = {10} , pages = {1609--1618} , year = {2022} , publisher = {Wiley Online Library} }","title":"Citation"},{"location":"functionalities/phenotyping/phenotypes/cancer/#reference","text":"Check the code reference here for a more detailled look. Emmanuelle Kempf, Sonia Priou, Guillaume Lam\u00e9, Christel Daniel, Ali Bellamine, Daniele Sommacale, Yazid Belkacemi, Romain Bey, Gilles Galula, Namik Taright, and others. Impact of two waves of sars-cov2 outbreak on the number, clinical presentation, care trajectories and survival of patients newly referred for a colorectal cancer: a french multicentric cohort study from a large group of university hospitals. International Journal of Cancer , 150(10):1609\u20131618, 2022. \u21a9 \u21a9","title":"Reference"},{"location":"functionalities/phenotyping/phenotypes/diabetes/","text":"Diabetes Presentation We provide the DiabetesFromICD10 class to extract visits or patients with ICD10 codes related to diabetes Available diabetes types DIABETES_IN_PREGNANCY DIABETES_MALNUTRITION DIABETES_TYPE_I DIABETES_TYPE_II OTHER_DIABETES_MELLITUS How it works The algorithm works by looking for either DP, DR or DAS ICD10 codes associated with cancer. Those codes are available under DiabetesFromICD10.ICD10_CODES Usage By default, all diabetes types mentionned above are extracted from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.phenotype import DiabetesFromICD10 diabetes = DiabetesFromICD10 ( data ) data = diabetes . to_data () To choose a subset of disorders, use the diabetes_types argument: diabetes = DiabetesFromICD10 ( data , diabetes_types = [ \"DIABETES_TYPE_I\" , \"DIABETES_IN_PREGNANCY\" , ], ) The final phenotype DataFrame is then available at data.computed[\"DiabetesFromICD10\"] Optional parameters PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData diabetes_types Optional list of diabetes types to use for phenotyping TYPE: Optional[List[str]] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'visit' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int DEFAULT: 1 Reference Check the code reference here for a more detailled look.","title":"Diabetes"},{"location":"functionalities/phenotyping/phenotypes/diabetes/#diabetes","text":"","title":"Diabetes"},{"location":"functionalities/phenotyping/phenotypes/diabetes/#presentation","text":"We provide the DiabetesFromICD10 class to extract visits or patients with ICD10 codes related to diabetes Available diabetes types DIABETES_IN_PREGNANCY DIABETES_MALNUTRITION DIABETES_TYPE_I DIABETES_TYPE_II OTHER_DIABETES_MELLITUS How it works The algorithm works by looking for either DP, DR or DAS ICD10 codes associated with cancer. Those codes are available under DiabetesFromICD10.ICD10_CODES","title":"Presentation"},{"location":"functionalities/phenotyping/phenotypes/diabetes/#usage","text":"By default, all diabetes types mentionned above are extracted from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.phenotype import DiabetesFromICD10 diabetes = DiabetesFromICD10 ( data ) data = diabetes . to_data () To choose a subset of disorders, use the diabetes_types argument: diabetes = DiabetesFromICD10 ( data , diabetes_types = [ \"DIABETES_TYPE_I\" , \"DIABETES_IN_PREGNANCY\" , ], ) The final phenotype DataFrame is then available at data.computed[\"DiabetesFromICD10\"]","title":"Usage"},{"location":"functionalities/phenotyping/phenotypes/diabetes/#optional-parameters","text":"PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData diabetes_types Optional list of diabetes types to use for phenotyping TYPE: Optional[List[str]] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'visit' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int DEFAULT: 1","title":"Optional parameters"},{"location":"functionalities/phenotyping/phenotypes/diabetes/#reference","text":"Check the code reference here for a more detailled look.","title":"Reference"},{"location":"functionalities/phenotyping/phenotypes/psychiatric_disorder/","text":"Psychiatric disorder Presentation We provide the PsychiatricDisorderFromICD10 class to extract visits or patients with ICD10 codes related to psychiatric disorders Available disorders Anxiety Disorders Bipolar and Related Disorders Depressive Disorders Disruptive, Impulse Control and Conduct Disorders Dissociative Disorders Feeding and Eating Disorders Mental Health Symptom Miscellaneous Obsessive-Compulsive and Related Disorders Personality Disorders Schizophrenia Spectrum and Other Psychotic Disorders Sleep-Wake Disorders Somatic Symptom and Related Disorders Substance-Related and Addictive Disorders Suicide or Self-Injury Trauma and Stressor-Related Disorders How it works The algorithm works by looking for either DP, DR or DAS ICD10 codes associated with psychiatric disorder. The codes terminology comes from this article 1 and is available under PsychiatricDisorderFromICD10.ICD10_CODES Usage By default, all cancer types mentionned above are extracted from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.phenotype import PsychiatricDisorderFromICD10 psy = PsychiatricDisorderFromICD10 ( data ) data = psy . to_data () To choose a subset of disorders, use the disorder_types argument: psy = PsychiatricDisorderFromICD10 ( data , disorder_types = [ \"Anxiety Disorders\" , \"Trauma and Stressor-Related Disorders\" , ], ) The final phenotype DataFrame is then available at data.computed[\"PsychiatricDisorderFromICD10\"] Optional parameters PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData disorder_types Optional list of disorder types to use for phenotyping TYPE: Optional[List[str]] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int DEFAULT: 1 Citation You can get the BibTex of the corresponding article 1 by calling cancer . cite () @article { 2022_covid_4CE , author = {Guti\u00e9rrez-Sacrist\u00e1n, Alba and Serret-Larmande, Arnaud and Hutch, Meghan R. and S\u00e1ez, Carlos and Aronow, Bruce J. and Bhatnagar, Surbhi and Bonzel, Clara-Lea and Cai, Tianxi and Devkota, Batsal and Hanauer, David A. and Loh, Ne Hooi Will and Luo, Yuan and Moal, Bertrand and Ahooyi, Taha Mohseni and Njoroge, Wanjik\u0169 F. M. and Omenn, Gilbert S. and Sanchez-Pinto, L. Nelson and South, Andrew M. and Sperotto, Francesca and Tan, Amelia L. M. and Taylor, Deanne M. and Verdy, Guillaume and Visweswaran, Shyam and Xia, Zongqi and Zahner, Janet and Avillach, Paul and Bourgeois, Florence T. and Consortium for Clinical Characterization of COVID-19 by EHR (4CE)} , title = \"{Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic}\" , journal = {JAMA Network Open} , volume = {5} , number = {12} , pages = {e2246548-e2246548} , year = {2022} , month = {12} , abstract = \"{The COVID-19 pandemic has been associated with an increase in mental health diagnoses among adolescents, though the extent of the increase, particularly for severe cases requiring hospitalization, has not been well characterized. Large-scale federated informatics approaches provide the ability to efficiently and securely query health care data sets to assess and monitor hospitalization patterns for mental health conditions among adolescents.To estimate changes in the proportion of hospitalizations associated with mental health conditions among adolescents following onset of the COVID-19 pandemic.This retrospective, multisite cohort study of adolescents 11 to 17 years of age who were hospitalized with at least 1 mental health condition diagnosis between February 1, 2019, and April 30, 2021, used patient-level data from electronic health records of 8 children\u2019s hospitals in the US and France.Change in the monthly proportion of mental health condition\u2013associated hospitalizations between the prepandemic (February 1, 2019, to March 31, 2020) and pandemic (April 1, 2020, to April 30, 2021) periods using interrupted time series analysis.There were 9696 adolescents hospitalized with a mental health condition during the prepandemic period (5966 [61.5\\\\%] female) and 11\u202f101 during the pandemic period (7603 [68.5\\\\%] female). The mean (SD) age in the prepandemic cohort was 14.6 (1.9) years and in the pandemic cohort, 14.7 (1.8) years. The most prevalent diagnoses during the pandemic were anxiety (6066 [57.4\\\\%]), depression (5065 [48.0\\\\%]), and suicidality or self-injury (4673 [44.2\\\\%]). There was an increase in the proportions of monthly hospitalizations during the pandemic for anxiety (0.55\\\\%; 95\\\\% CI, 0.26\\\\%-0.84\\\\%), depression (0.50\\\\%; 95\\\\% CI, 0.19\\\\%-0.79\\\\%), and suicidality or self-injury (0.38\\\\%; 95\\\\% CI, 0.08\\\\%-0.68\\\\%). There was an estimated 0.60\\\\% increase (95\\\\% CI, 0.31\\\\%-0.89\\\\%) overall in the monthly proportion of mental health\u2013associated hospitalizations following onset of the pandemic compared with the prepandemic period.In this cohort study, onset of the COVID-19 pandemic was associated with increased hospitalizations with mental health diagnoses among adolescents. These findings support the need for greater resources within children\u2019s hospitals to care for adolescents with mental health conditions during the pandemic and beyond.}\" , issn = {2574-3805} , doi = {10.1001/jamanetworkopen.2022.46548} , url = {https://doi.org/10.1001/jamanetworkopen.2022.46548} , eprint = {https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2799437/gutirrezsacristn\\_2022\\_oi\\_221314\\_1670339179.72376.pdf} , } Reference Check the code reference here for a more detailled look. Alba Guti\u00e9rrez-Sacrist\u00e1n, Arnaud Serret-Larmande, Meghan R. Hutch, Carlos S\u00e1ez, Bruce J. Aronow, Surbhi Bhatnagar, Clara-Lea Bonzel, Tianxi Cai, Batsal Devkota, David A. Hanauer, Ne Hooi Will Loh, Yuan Luo, Bertrand Moal, Taha Mohseni Ahooyi, Wanjik\u0169 F. M. Njoroge, Gilbert S. Omenn, L. Nelson Sanchez-Pinto, Andrew M. South, Francesca Sperotto, Amelia L. M. Tan, Deanne M. Taylor, Guillaume Verdy, Shyam Visweswaran, Zongqi Xia, Janet Zahner, Paul Avillach, Florence T. Bourgeois, and Consortium for Clinical Characterization of COVID-19 by EHR (4CE). Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic. JAMA Network Open , 5(12):e2246548\u2013e2246548, 12 2022. URL: https://doi.org/10.1001/jamanetworkopen.2022.46548 , arXiv:https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2799437/gutirrezsacristn\\_2022\\_oi\\_221314\\_1670339179.72376.pdf , doi:10.1001/jamanetworkopen.2022.46548 . \u21a9 \u21a9","title":"Psychiatric disorder"},{"location":"functionalities/phenotyping/phenotypes/psychiatric_disorder/#psychiatric-disorder","text":"","title":"Psychiatric disorder"},{"location":"functionalities/phenotyping/phenotypes/psychiatric_disorder/#presentation","text":"We provide the PsychiatricDisorderFromICD10 class to extract visits or patients with ICD10 codes related to psychiatric disorders Available disorders Anxiety Disorders Bipolar and Related Disorders Depressive Disorders Disruptive, Impulse Control and Conduct Disorders Dissociative Disorders Feeding and Eating Disorders Mental Health Symptom Miscellaneous Obsessive-Compulsive and Related Disorders Personality Disorders Schizophrenia Spectrum and Other Psychotic Disorders Sleep-Wake Disorders Somatic Symptom and Related Disorders Substance-Related and Addictive Disorders Suicide or Self-Injury Trauma and Stressor-Related Disorders How it works The algorithm works by looking for either DP, DR or DAS ICD10 codes associated with psychiatric disorder. The codes terminology comes from this article 1 and is available under PsychiatricDisorderFromICD10.ICD10_CODES","title":"Presentation"},{"location":"functionalities/phenotyping/phenotypes/psychiatric_disorder/#usage","text":"By default, all cancer types mentionned above are extracted from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.phenotype import PsychiatricDisorderFromICD10 psy = PsychiatricDisorderFromICD10 ( data ) data = psy . to_data () To choose a subset of disorders, use the disorder_types argument: psy = PsychiatricDisorderFromICD10 ( data , disorder_types = [ \"Anxiety Disorders\" , \"Trauma and Stressor-Related Disorders\" , ], ) The final phenotype DataFrame is then available at data.computed[\"PsychiatricDisorderFromICD10\"]","title":"Usage"},{"location":"functionalities/phenotyping/phenotypes/psychiatric_disorder/#optional-parameters","text":"PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData disorder_types Optional list of disorder types to use for phenotyping TYPE: Optional[List[str]] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int DEFAULT: 1","title":"Optional parameters"},{"location":"functionalities/phenotyping/phenotypes/psychiatric_disorder/#citation","text":"You can get the BibTex of the corresponding article 1 by calling cancer . cite () @article { 2022_covid_4CE , author = {Guti\u00e9rrez-Sacrist\u00e1n, Alba and Serret-Larmande, Arnaud and Hutch, Meghan R. and S\u00e1ez, Carlos and Aronow, Bruce J. and Bhatnagar, Surbhi and Bonzel, Clara-Lea and Cai, Tianxi and Devkota, Batsal and Hanauer, David A. and Loh, Ne Hooi Will and Luo, Yuan and Moal, Bertrand and Ahooyi, Taha Mohseni and Njoroge, Wanjik\u0169 F. M. and Omenn, Gilbert S. and Sanchez-Pinto, L. Nelson and South, Andrew M. and Sperotto, Francesca and Tan, Amelia L. M. and Taylor, Deanne M. and Verdy, Guillaume and Visweswaran, Shyam and Xia, Zongqi and Zahner, Janet and Avillach, Paul and Bourgeois, Florence T. and Consortium for Clinical Characterization of COVID-19 by EHR (4CE)} , title = \"{Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic}\" , journal = {JAMA Network Open} , volume = {5} , number = {12} , pages = {e2246548-e2246548} , year = {2022} , month = {12} , abstract = \"{The COVID-19 pandemic has been associated with an increase in mental health diagnoses among adolescents, though the extent of the increase, particularly for severe cases requiring hospitalization, has not been well characterized. Large-scale federated informatics approaches provide the ability to efficiently and securely query health care data sets to assess and monitor hospitalization patterns for mental health conditions among adolescents.To estimate changes in the proportion of hospitalizations associated with mental health conditions among adolescents following onset of the COVID-19 pandemic.This retrospective, multisite cohort study of adolescents 11 to 17 years of age who were hospitalized with at least 1 mental health condition diagnosis between February 1, 2019, and April 30, 2021, used patient-level data from electronic health records of 8 children\u2019s hospitals in the US and France.Change in the monthly proportion of mental health condition\u2013associated hospitalizations between the prepandemic (February 1, 2019, to March 31, 2020) and pandemic (April 1, 2020, to April 30, 2021) periods using interrupted time series analysis.There were 9696 adolescents hospitalized with a mental health condition during the prepandemic period (5966 [61.5\\\\%] female) and 11\u202f101 during the pandemic period (7603 [68.5\\\\%] female). The mean (SD) age in the prepandemic cohort was 14.6 (1.9) years and in the pandemic cohort, 14.7 (1.8) years. The most prevalent diagnoses during the pandemic were anxiety (6066 [57.4\\\\%]), depression (5065 [48.0\\\\%]), and suicidality or self-injury (4673 [44.2\\\\%]). There was an increase in the proportions of monthly hospitalizations during the pandemic for anxiety (0.55\\\\%; 95\\\\% CI, 0.26\\\\%-0.84\\\\%), depression (0.50\\\\%; 95\\\\% CI, 0.19\\\\%-0.79\\\\%), and suicidality or self-injury (0.38\\\\%; 95\\\\% CI, 0.08\\\\%-0.68\\\\%). There was an estimated 0.60\\\\% increase (95\\\\% CI, 0.31\\\\%-0.89\\\\%) overall in the monthly proportion of mental health\u2013associated hospitalizations following onset of the pandemic compared with the prepandemic period.In this cohort study, onset of the COVID-19 pandemic was associated with increased hospitalizations with mental health diagnoses among adolescents. These findings support the need for greater resources within children\u2019s hospitals to care for adolescents with mental health conditions during the pandemic and beyond.}\" , issn = {2574-3805} , doi = {10.1001/jamanetworkopen.2022.46548} , url = {https://doi.org/10.1001/jamanetworkopen.2022.46548} , eprint = {https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2799437/gutirrezsacristn\\_2022\\_oi\\_221314\\_1670339179.72376.pdf} , }","title":"Citation"},{"location":"functionalities/phenotyping/phenotypes/psychiatric_disorder/#reference","text":"Check the code reference here for a more detailled look. Alba Guti\u00e9rrez-Sacrist\u00e1n, Arnaud Serret-Larmande, Meghan R. Hutch, Carlos S\u00e1ez, Bruce J. Aronow, Surbhi Bhatnagar, Clara-Lea Bonzel, Tianxi Cai, Batsal Devkota, David A. Hanauer, Ne Hooi Will Loh, Yuan Luo, Bertrand Moal, Taha Mohseni Ahooyi, Wanjik\u0169 F. M. Njoroge, Gilbert S. Omenn, L. Nelson Sanchez-Pinto, Andrew M. South, Francesca Sperotto, Amelia L. M. Tan, Deanne M. Taylor, Guillaume Verdy, Shyam Visweswaran, Zongqi Xia, Janet Zahner, Paul Avillach, Florence T. Bourgeois, and Consortium for Clinical Characterization of COVID-19 by EHR (4CE). Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic. JAMA Network Open , 5(12):e2246548\u2013e2246548, 12 2022. URL: https://doi.org/10.1001/jamanetworkopen.2022.46548 , arXiv:https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2799437/gutirrezsacristn\\_2022\\_oi\\_221314\\_1670339179.72376.pdf , doi:10.1001/jamanetworkopen.2022.46548 . \u21a9 \u21a9","title":"Reference"},{"location":"functionalities/phenotyping/phenotypes/suicide_attempt/","text":"Suicide Attempt Presentation We provide the SuicideAttemptFromICD10 class to extract visits linked to suicide attempt from ICD-10 codes. Usage As mentionned below, two algorithms ( \"Haguenoer2008\" (default) and \"X60-X84\" ) are available from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.phenotype import SuicideAttemptFromICD10 sa = SuicideAttemptFromICD10 ( data ) data = sa . to_data () The final phenotype DataFrame is then available at data.computed[\"SuicideAttemptFromICD10_Haguenoer2008\"] or data.computed[\"SuicideAttemptFromICD10_X60_X84\"] depending on the used algorithm Availables algorithms (values for \"algo\" ) The ICD10 codes are available under SuicideAttemptFromICD10.ICD10_CODES 'X60-X84' 'Haguenoer2008' Returns the visits that have at least one ICD code that belongs to the range X60 to X84. Returns the visits that follow the definiton of Haguenoer2008 1 . This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84. Citation When using algo=\"Haguenoer2008\" , you can get the BibTex of the corresponding article 1 by calling sa . cite () @misc { haguenoer_tentatives_2008 , title = {\u00c9pid\u00e9miologie des tentatives de suicide en r\u00e9gion Centre} , language = {fr} , author = {Haguenoer, Ken and Caille, Agn\u00e8s and Fillatre, Marc and Lecuyer, Anne Isabelle and Rusch, Emmanuel} , year = {2008} , pages = {4} , howpublished = {\\url{https://www.esante-centre.fr/portail_pro/minisite_25/media-files/56393/plaquette-2006-2009.pdf}} } Reference Check the code reference here for a more detailled look. Ken Haguenoer, Agn\u00e8s Caille, Marc Fillatre, Anne Isabelle Lecuyer, and Emmanuel Rusch. \u00c9pid\u00e9miologie des tentatives de suicide en r\u00e9gion centre. \\url https://www.esante-centre.fr/portail_pro/minisite_25/media-files/56393/plaquette-2006-2009.pdf, 2008. \u21a9 \u21a9","title":"Suicide Attempt"},{"location":"functionalities/phenotyping/phenotypes/suicide_attempt/#suicide-attempt","text":"","title":"Suicide Attempt"},{"location":"functionalities/phenotyping/phenotypes/suicide_attempt/#presentation","text":"We provide the SuicideAttemptFromICD10 class to extract visits linked to suicide attempt from ICD-10 codes.","title":"Presentation"},{"location":"functionalities/phenotyping/phenotypes/suicide_attempt/#usage","text":"As mentionned below, two algorithms ( \"Haguenoer2008\" (default) and \"X60-X84\" ) are available from eds_scikit.io import HiveData data = HiveData ( DBNAME ) from eds_scikit.phenotype import SuicideAttemptFromICD10 sa = SuicideAttemptFromICD10 ( data ) data = sa . to_data () The final phenotype DataFrame is then available at data.computed[\"SuicideAttemptFromICD10_Haguenoer2008\"] or data.computed[\"SuicideAttemptFromICD10_X60_X84\"] depending on the used algorithm Availables algorithms (values for \"algo\" ) The ICD10 codes are available under SuicideAttemptFromICD10.ICD10_CODES 'X60-X84' 'Haguenoer2008' Returns the visits that have at least one ICD code that belongs to the range X60 to X84. Returns the visits that follow the definiton of Haguenoer2008 1 . This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84.","title":"Usage"},{"location":"functionalities/phenotyping/phenotypes/suicide_attempt/#citation","text":"When using algo=\"Haguenoer2008\" , you can get the BibTex of the corresponding article 1 by calling sa . cite () @misc { haguenoer_tentatives_2008 , title = {\u00c9pid\u00e9miologie des tentatives de suicide en r\u00e9gion Centre} , language = {fr} , author = {Haguenoer, Ken and Caille, Agn\u00e8s and Fillatre, Marc and Lecuyer, Anne Isabelle and Rusch, Emmanuel} , year = {2008} , pages = {4} , howpublished = {\\url{https://www.esante-centre.fr/portail_pro/minisite_25/media-files/56393/plaquette-2006-2009.pdf}} }","title":"Citation"},{"location":"functionalities/phenotyping/phenotypes/suicide_attempt/#reference","text":"Check the code reference here for a more detailled look. Ken Haguenoer, Agn\u00e8s Caille, Marc Fillatre, Anne Isabelle Lecuyer, and Emmanuel Rusch. \u00c9pid\u00e9miologie des tentatives de suicide en r\u00e9gion centre. \\url https://www.esante-centre.fr/portail_pro/minisite_25/media-files/56393/plaquette-2006-2009.pdf, 2008. \u21a9 \u21a9","title":"Reference"},{"location":"functionalities/plotting/age_pyramid/","text":"Visualizing age pyramid The age pyramid is helpful to quickly visualize the age and gender distributions in a cohort. Load a synthetic dataset plot_age_pyramid uses the \"person\" table: from eds_scikit.datasets.synthetic.person import load_person df_person = load_person () df_person . head () | | person_id | gender_source_value | birth_datetime | |---|-----------|---------------------|----------------| | 0 | 0 | m | 2010-01-01 | | 1 | 1 | m | 1938-01-01 | | 2 | 2 | f | 1994-01-01 | | 3 | 3 | m | 1994-01-01 | | 4 | 4 | m | 2004-01-01 | Visualize age pyramid Basic usage By default, plot_age_pyramid will compute age as the difference between today and the date of birth: from eds_scikit.plot.age_pyramid import plot_age_pyramid plot_age_pyramid ( df_person ) Advanced parameters Further configuration can be provided, including : datetime_ref : Choose the reference to compute the age from. It can be either a single datetime (string or datetime type), an array of datetime (one reference for each patient) or a string representing a column of the input dataframe return_array : If set to True, return a dataframe instead of a chart. import pandas as pd from datetime import datetime from eds_scikit.plot.age_pyramid import plot_age_pyramid dates_of_first_visit = pd . Series ([ datetime ( 2020 , 1 , 1 )] * df_person . shape [ 0 ]) plot_age_pyramid ( df_person , datetime_ref = dates_of_first_visit ) Please check the documentation for further details on the function's parameters.","title":"Age pyramid"},{"location":"functionalities/plotting/age_pyramid/#visualizing-age-pyramid","text":"The age pyramid is helpful to quickly visualize the age and gender distributions in a cohort.","title":"Visualizing age pyramid"},{"location":"functionalities/plotting/age_pyramid/#load-a-synthetic-dataset","text":"plot_age_pyramid uses the \"person\" table: from eds_scikit.datasets.synthetic.person import load_person df_person = load_person () df_person . head () | | person_id | gender_source_value | birth_datetime | |---|-----------|---------------------|----------------| | 0 | 0 | m | 2010-01-01 | | 1 | 1 | m | 1938-01-01 | | 2 | 2 | f | 1994-01-01 | | 3 | 3 | m | 1994-01-01 | | 4 | 4 | m | 2004-01-01 |","title":"Load a synthetic dataset"},{"location":"functionalities/plotting/age_pyramid/#visualize-age-pyramid","text":"","title":"Visualize age pyramid"},{"location":"functionalities/plotting/age_pyramid/#basic-usage","text":"By default, plot_age_pyramid will compute age as the difference between today and the date of birth: from eds_scikit.plot.age_pyramid import plot_age_pyramid plot_age_pyramid ( df_person )","title":"Basic usage"},{"location":"functionalities/plotting/age_pyramid/#advanced-parameters","text":"Further configuration can be provided, including : datetime_ref : Choose the reference to compute the age from. It can be either a single datetime (string or datetime type), an array of datetime (one reference for each patient) or a string representing a column of the input dataframe return_array : If set to True, return a dataframe instead of a chart. import pandas as pd from datetime import datetime from eds_scikit.plot.age_pyramid import plot_age_pyramid dates_of_first_visit = pd . Series ([ datetime ( 2020 , 1 , 1 )] * df_person . shape [ 0 ]) plot_age_pyramid ( df_person , datetime_ref = dates_of_first_visit ) Please check the documentation for further details on the function's parameters.","title":"Advanced parameters"},{"location":"functionalities/plotting/event_sequences/","text":"Visualizing event sequences When studying sequences of events (e.g care trajectories, drug sequences, ...), it might be useful to visualize individual sequences. To that end, we provide the plot_event_sequences function to plot individual sequences given an events dataframe. Load a synthetic dataset An events dataset has been created to illustrate the visualization function. It can be load as follows : from eds_scikit.datasets.synthetic.event_sequences import load_event_sequences df_events = load_event_sequences () The df_events dataset contains occurrences of 12 events, derived from 7 events' families (\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G). Events can be both one-time and continuous. An index_date is also provided and refers to the inclusion date of each patient in the cohort. The first raws of the dataframe are as follows : df_events . head () | | person_id | event_family | event | event_start_datetime | event_end_datetime | index_date | |---|-----------|--------------|-------|----------------------|--------------------|------------| | 0 | 1 | A | a1 | 2020-01-01 | 2020-01-02 | 2020-01-01 | | 1 | 1 | A | a2 | 2020-01-03 | 2020-01-04 | 2020-01-01 | | 2 | 1 | B | b1 | 2020-01-03 | 2020-01-06 | 2020-01-01 | | 3 | 1 | C | c1 | 2020-01-05 | NaT | 2020-01-01 | | 4 | 1 | C | c2 | 2020-01-06 | 2020-01-08 | 2020-01-01 | Visualize individual sequences Basic usage Individual sequences of events can be plotted using the plot_event_sequences function : from eds_scikit.plot.event_sequences import plot_event_sequences chart = plot_event_sequences ( df_events ) chart Advanced parameters Further configuration can be provided, including : - dim_mapping : dictionary to set colors and labels for each event type. - family_col : column name of events' families. - list_person_ids : List of specific person_id - same_x_axis_scale : boolean to set all individual charts to the same scale Here we provide an exemple of dim_mapping , and we plot sequences aggregated following the event_family classification. dim_mapping = { \"a1\" : { \"color\" : ( 255 , 200 , 150 ), \"label\" : \"eventA1\" }, \"a2\" : { \"color\" : ( 255 , 150 , 150 ), \"label\" : \"eventA2\" }, \"a3\" : { \"color\" : ( 255 , 100 , 150 ), \"label\" : \"eventA3\" }, \"b1\" : { \"color\" : ( 100 , 200 , 150 ), \"label\" : \"eventB1\" }, \"c1\" : { \"color\" : ( 50 , 255 , 255 ), \"label\" : \"eventC1\" }, \"c2\" : { \"color\" : ( 50 , 200 , 255 ), \"label\" : \"eventC2\" }, \"c3\" : { \"color\" : ( 50 , 100 , 255 ), \"label\" : \"eventC3\" }, \"d1\" : { \"color\" : ( 180 , 200 , 100 ), \"label\" : \"eventD1\" }, \"d2\" : { \"color\" : ( 180 , 150 , 100 ), \"label\" : \"eventD2\" }, \"e1\" : { \"color\" : ( 130 , 60 , 10 ), \"label\" : \"eventE1\" }, \"f1\" : { \"color\" : ( 255 , 0 , 0 ), \"label\" : \"eventF1\" }, \"g1\" : { \"color\" : ( 100 , 0 , 200 ), \"label\" : \"eventG1\" }, } plot_event_sequences ( df_events , family_col = \"event_family\" , dim_mapping = dim_mapping , same_x_axis_scale = True , title = \"Event sequences\" , ) Please check the documentation for further details on the function's parameters.","title":"Event sequence"},{"location":"functionalities/plotting/event_sequences/#visualizing-event-sequences","text":"When studying sequences of events (e.g care trajectories, drug sequences, ...), it might be useful to visualize individual sequences. To that end, we provide the plot_event_sequences function to plot individual sequences given an events dataframe.","title":"Visualizing event sequences"},{"location":"functionalities/plotting/event_sequences/#load-a-synthetic-dataset","text":"An events dataset has been created to illustrate the visualization function. It can be load as follows : from eds_scikit.datasets.synthetic.event_sequences import load_event_sequences df_events = load_event_sequences () The df_events dataset contains occurrences of 12 events, derived from 7 events' families (\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G). Events can be both one-time and continuous. An index_date is also provided and refers to the inclusion date of each patient in the cohort. The first raws of the dataframe are as follows : df_events . head () | | person_id | event_family | event | event_start_datetime | event_end_datetime | index_date | |---|-----------|--------------|-------|----------------------|--------------------|------------| | 0 | 1 | A | a1 | 2020-01-01 | 2020-01-02 | 2020-01-01 | | 1 | 1 | A | a2 | 2020-01-03 | 2020-01-04 | 2020-01-01 | | 2 | 1 | B | b1 | 2020-01-03 | 2020-01-06 | 2020-01-01 | | 3 | 1 | C | c1 | 2020-01-05 | NaT | 2020-01-01 | | 4 | 1 | C | c2 | 2020-01-06 | 2020-01-08 | 2020-01-01 |","title":"Load a synthetic dataset"},{"location":"functionalities/plotting/event_sequences/#visualize-individual-sequences","text":"","title":"Visualize individual sequences"},{"location":"functionalities/plotting/event_sequences/#basic-usage","text":"Individual sequences of events can be plotted using the plot_event_sequences function : from eds_scikit.plot.event_sequences import plot_event_sequences chart = plot_event_sequences ( df_events ) chart","title":"Basic usage"},{"location":"functionalities/plotting/event_sequences/#advanced-parameters","text":"Further configuration can be provided, including : - dim_mapping : dictionary to set colors and labels for each event type. - family_col : column name of events' families. - list_person_ids : List of specific person_id - same_x_axis_scale : boolean to set all individual charts to the same scale Here we provide an exemple of dim_mapping , and we plot sequences aggregated following the event_family classification. dim_mapping = { \"a1\" : { \"color\" : ( 255 , 200 , 150 ), \"label\" : \"eventA1\" }, \"a2\" : { \"color\" : ( 255 , 150 , 150 ), \"label\" : \"eventA2\" }, \"a3\" : { \"color\" : ( 255 , 100 , 150 ), \"label\" : \"eventA3\" }, \"b1\" : { \"color\" : ( 100 , 200 , 150 ), \"label\" : \"eventB1\" }, \"c1\" : { \"color\" : ( 50 , 255 , 255 ), \"label\" : \"eventC1\" }, \"c2\" : { \"color\" : ( 50 , 200 , 255 ), \"label\" : \"eventC2\" }, \"c3\" : { \"color\" : ( 50 , 100 , 255 ), \"label\" : \"eventC3\" }, \"d1\" : { \"color\" : ( 180 , 200 , 100 ), \"label\" : \"eventD1\" }, \"d2\" : { \"color\" : ( 180 , 150 , 100 ), \"label\" : \"eventD2\" }, \"e1\" : { \"color\" : ( 130 , 60 , 10 ), \"label\" : \"eventE1\" }, \"f1\" : { \"color\" : ( 255 , 0 , 0 ), \"label\" : \"eventF1\" }, \"g1\" : { \"color\" : ( 100 , 0 , 200 ), \"label\" : \"eventG1\" }, } plot_event_sequences ( df_events , family_col = \"event_family\" , dim_mapping = dim_mapping , same_x_axis_scale = True , title = \"Event sequences\" , ) Please check the documentation for further details on the function's parameters.","title":"Advanced parameters"},{"location":"functionalities/plotting/flowchart/","text":"(function() { function addWidgetsRenderer() { var requireJsScript = document.createElement('script'); requireJsScript.src = 'https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js'; var mimeElement = document.querySelector('script[type=\"application/vnd.jupyter.widget-view+json\"]'); var jupyterWidgetsScript = document.createElement('script'); var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js'; var widgetState; // Fallback for older version: try { widgetState = mimeElement && JSON.parse(mimeElement.innerHTML); if (widgetState && (widgetState.version_major < 2 || !widgetState.version_major)) { widgetRendererSrc = 'jupyter-js-widgets@*/dist/embed.js'; } } catch(e) {} jupyterWidgetsScript.src = widgetRendererSrc; document.body.appendChild(requireJsScript); document.body.appendChild(jupyterWidgetsScript); } document.addEventListener('DOMContentLoaded', addWidgetsRenderer); }()); You can download this notebook directly here Generation of an inclusion/exclusion flowchart Inclusion and exclusion flowcharts are one of the key figure to generate when doing medical research. We provide a class to help you in this task. To summarize, you can sequentialy add criteria to the flowchart, by providing A description of the criterion Which patients check the criterion To make the use of this class easier, each criterion can be build independently. The order will be determined by how you add the criteria to the flowchart. At this step, criteria will be combined to output a corrrect flowchart. Counting ( n=... ) is done automatically ! The input data Data can be provided in two forms: DataFrame form or Dictionary form. DataFrame form Dictionary form In this case, data is provided as a unique DataFrame . One column (by default person_id ) stores the ids that constitutes the initial cohort. A criterion, it this case, will be defined as a boolean column, where each row is either accepted or rejected. For instance: data = pd . DataFrame ( dict ( person_id = list ( range ( 10 )), over_18 = 5 * [ True ] + 5 * [ False ], diabetes = [ True , False , True , False , True , False , True , False , True , False ], infarction = [ True , True , False , False , True , True , False , False , True , True ], final_split = [ True ] + 9 * [ False ], ) ) In this case, data is provided as dictionary. Keys represent criteria names, and values contains the ids constituting the passing cohort. Those ids can be in the form of any iterable (list, set, Series, ...). The initial cohort should be provided under the initial key . For instance: data = dict ( initial = list ( range ( 10 )), over_18 = [ 0 , 1 , 2 , 3 , 4 ], diabetes = [ 0 , 2 , 4 , 6 , 8 ], infarction = [ 0 , 1 , 4 , 5 , 8 , 9 ], final_split = [ 0 ], ) A step-by-step example Let us suppose we have a small cohort of 10 patients. From this cohort, we want to select patients with three consecutive criteria: Patients should be at least 18 years old. Patients should have Type I or Type II diabetes. Patients should've had at least one infarction event. On having multiple criteria On advantage of this flowchart generation is that it will handle multiple criteria by itself, by computing intersection iteratively import pandas as pd from eds_scikit.utils.flowchart import Flowchart So let us describe our initial cohort in the DataFrame form from eds_scikit.utils.flowchart import Flowchart data = pd . DataFrame ( dict ( person_id = list ( range ( 10 )), over_18 = 5 * [ True ] + 5 * [ False ], diabetes = [ True , False , True , False , True , False , True , False , True , False ], infarction = [ True , True , False , False , True , True , False , False , True , True ], final_split = [ True ] + 9 * [ False ], ) ) We added an extra final_split column that can, for instance, occur when splitting a cohort into a training and a testing subcohorts. This will result in a split in the flowchart (see below). Here we instantiate the Flowchart with the initial cohort: F = Flowchart ( data = data , initial_description = \"Initial population\" , ) And we add each criterion with the add_criterion method: F . add_criterion ( description = \"Patients over 18 y.o.\" , excluded_description = \"\" , criterion_name = \"over_18\" , ) F . add_criterion ( description = \"With Type I or II diabetes\" , excluded_description = \"\" , criterion_name = \"diabetes\" , ) F . add_criterion ( description = \"With infarction\" , excluded_description = \"\" , criterion_name = \"infarction\" , ) This add_criterion method expects 3 parameters: description : The description to add in the corresponding flowchart's box excluded_description : The description to add in the excluded box of the flowchart criterion_name : DataFrame form : The column name of the data object that contains boolean values to discriminate between rows that checks the criterion and rows that doesn't Dictionary form : The key of the dictionary containing the ids of the passing cohort If you need to do a final split in your flowchart, you can via the dedicated method: F . add_final_split ( left_description = \"\" , right_description = \"\" , criterion_name = \"final_split\" , left_title = \"Cohort 1\" , right_title = \"Cohort 2\" , ) At this point, we are ready to generate the flowchart. Just run the following snippet: F . generate_flowchart ( alternate = True ) 2022-11-15T13:25:23.976033 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/ *{stroke-linejoin: round; stroke-linecap: butt} Finally, you can save your flowchart (in \".png\" or \".svg\" ): F . save ( \"my_flowchart.png\" ) For more details, you can check the code reference of the Flowchart object.","title":"Generating inclusion/exclusion flowchart"},{"location":"functionalities/plotting/flowchart/#generation-of-an-inclusionexclusion-flowchart","text":"Inclusion and exclusion flowcharts are one of the key figure to generate when doing medical research. We provide a class to help you in this task. To summarize, you can sequentialy add criteria to the flowchart, by providing A description of the criterion Which patients check the criterion To make the use of this class easier, each criterion can be build independently. The order will be determined by how you add the criteria to the flowchart. At this step, criteria will be combined to output a corrrect flowchart. Counting ( n=... ) is done automatically !","title":"Generation of an inclusion/exclusion flowchart"},{"location":"functionalities/plotting/flowchart/#the-input-data","text":"Data can be provided in two forms: DataFrame form or Dictionary form. DataFrame form Dictionary form In this case, data is provided as a unique DataFrame . One column (by default person_id ) stores the ids that constitutes the initial cohort. A criterion, it this case, will be defined as a boolean column, where each row is either accepted or rejected. For instance: data = pd . DataFrame ( dict ( person_id = list ( range ( 10 )), over_18 = 5 * [ True ] + 5 * [ False ], diabetes = [ True , False , True , False , True , False , True , False , True , False ], infarction = [ True , True , False , False , True , True , False , False , True , True ], final_split = [ True ] + 9 * [ False ], ) ) In this case, data is provided as dictionary. Keys represent criteria names, and values contains the ids constituting the passing cohort. Those ids can be in the form of any iterable (list, set, Series, ...). The initial cohort should be provided under the initial key . For instance: data = dict ( initial = list ( range ( 10 )), over_18 = [ 0 , 1 , 2 , 3 , 4 ], diabetes = [ 0 , 2 , 4 , 6 , 8 ], infarction = [ 0 , 1 , 4 , 5 , 8 , 9 ], final_split = [ 0 ], )","title":"The input data"},{"location":"functionalities/plotting/flowchart/#a-step-by-step-example","text":"Let us suppose we have a small cohort of 10 patients. From this cohort, we want to select patients with three consecutive criteria: Patients should be at least 18 years old. Patients should have Type I or Type II diabetes. Patients should've had at least one infarction event. On having multiple criteria On advantage of this flowchart generation is that it will handle multiple criteria by itself, by computing intersection iteratively import pandas as pd from eds_scikit.utils.flowchart import Flowchart So let us describe our initial cohort in the DataFrame form from eds_scikit.utils.flowchart import Flowchart data = pd . DataFrame ( dict ( person_id = list ( range ( 10 )), over_18 = 5 * [ True ] + 5 * [ False ], diabetes = [ True , False , True , False , True , False , True , False , True , False ], infarction = [ True , True , False , False , True , True , False , False , True , True ], final_split = [ True ] + 9 * [ False ], ) ) We added an extra final_split column that can, for instance, occur when splitting a cohort into a training and a testing subcohorts. This will result in a split in the flowchart (see below). Here we instantiate the Flowchart with the initial cohort: F = Flowchart ( data = data , initial_description = \"Initial population\" , ) And we add each criterion with the add_criterion method: F . add_criterion ( description = \"Patients over 18 y.o.\" , excluded_description = \"\" , criterion_name = \"over_18\" , ) F . add_criterion ( description = \"With Type I or II diabetes\" , excluded_description = \"\" , criterion_name = \"diabetes\" , ) F . add_criterion ( description = \"With infarction\" , excluded_description = \"\" , criterion_name = \"infarction\" , ) This add_criterion method expects 3 parameters: description : The description to add in the corresponding flowchart's box excluded_description : The description to add in the excluded box of the flowchart criterion_name : DataFrame form : The column name of the data object that contains boolean values to discriminate between rows that checks the criterion and rows that doesn't Dictionary form : The key of the dictionary containing the ids of the passing cohort If you need to do a final split in your flowchart, you can via the dedicated method: F . add_final_split ( left_description = \"\" , right_description = \"\" , criterion_name = \"final_split\" , left_title = \"Cohort 1\" , right_title = \"Cohort 2\" , ) At this point, we are ready to generate the flowchart. Just run the following snippet: F . generate_flowchart ( alternate = True ) 2022-11-15T13:25:23.976033 image/svg+xml Matplotlib v3.5.3, https://matplotlib.org/ *{stroke-linejoin: round; stroke-linecap: butt} Finally, you can save your flowchart (in \".png\" or \".svg\" ): F . save ( \"my_flowchart.png\" ) For more details, you can check the code reference of the Flowchart object.","title":"A step-by-step example"},{"location":"recipes/small-cohorts/","text":"(function() { function addWidgetsRenderer() { var requireJsScript = document.createElement('script'); requireJsScript.src = 'https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js'; var mimeElement = document.querySelector('script[type=\"application/vnd.jupyter.widget-view+json\"]'); var jupyterWidgetsScript = document.createElement('script'); var widgetRendererSrc = 'https://unpkg.com/@jupyter-widgets/html-manager@*/dist/embed-amd.js'; var widgetState; // Fallback for older version: try { widgetState = mimeElement && JSON.parse(mimeElement.innerHTML); if (widgetState && (widgetState.version_major < 2 || !widgetState.version_major)) { widgetRendererSrc = 'jupyter-js-widgets@*/dist/embed.js'; } } catch(e) {} jupyterWidgetsScript.src = widgetRendererSrc; document.body.appendChild(requireJsScript); document.body.appendChild(jupyterWidgetsScript); } document.addEventListener('DOMContentLoaded', addWidgetsRenderer); }()); You can download this notebook directly here Introduction The goal of this small notebook is to show you how to: Work on a big cohort by staying distributed Do some phenotyping to select a small subcohort Save this subcohort locally to work on it later As a dummy example, we will select patients that underwent a cardiac transplantation. The selection will be performed by using both ICD-10 and by CCAM terminologies. Data Loading import eds_scikit spark , sc , sql = eds_scikit . improve_performances () DBNAME = \"YOUR_DATABASE_NAME\" from eds_scikit.io.hive import HiveData # Data from Hive data = HiveData ( DBNAME ) Phenotyping from eds_scikit.event.ccam import procedures_from_ccam from eds_scikit.event.icd10 import conditions_from_icd10 CCAM = dict ( HEART_TRANSPLANT = dict ( prefix = \"DZEA00\" , # ) ) ICD10 = dict ( HEART_TRANSPLANT = dict ( exact = \"Z941\" , # ) ) procedure_occurrence = procedures_from_ccam ( procedure_occurrence = data . procedure_occurrence , visit_occurrence = data . visit_occurrence , codes = CCAM , date_from_visit = True , ) condition_occurrence = conditions_from_icd10 ( condition_occurrence = data . condition_occurrence , visit_occurrence = data . visit_occurrence , codes = ICD10 , date_from_visit = True , additional_filtering = dict ( condition_status_source_value = { \"DP\" , \"DAS\" }, # ) ) procedure_occurrence . groupby ([ \"concept\" , \"value\" ]) . size () concept value HEART_TRANSPLANT DZEA002 39 dtype: int64 condition_occurrence . groupby ([ \"concept\" , \"value\" ]) . size () concept value HEART_TRANSPLANT Z941 602 dtype: int64 Saving to disk cohort = set ( procedure_occurrence . person_id . to_list () + condition_occurrence . person_id . to_list () ) We can check that our cohort is indeed small and can be stored locally without any concerns: len ( cohort ) 53 And we can also compute a very crude prevalence of heart transplant in our database: f \" { 100 * len ( cohort ) / len ( set ( data . procedure_occurrence . person_id . to_list () + data . condition_occurrence . person_id . to_list ())) : .5f } %\" '0.06849 %' Finally let us save the tables we need locally. Under the hood, eds-scikit will only keep data corresponding to the provided cohort. import os folder = os . path . abspath ( \"./heart_transplant_cohort\" ) tables_to_save = [ \"person\" , \"visit_detail\" , \"visit_occurrence\" , \"procedure_occurrence\" , \"condition_occurrence\" , ] data . persist_tables_to_folder ( folder , tables = tables_to_save , person_ids = cohort , ) Number of unique patients: 53 writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/person.parquet writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/visit_detail.parquet writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/visit_occurrence.parquet writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/procedure_occurrence.parquet writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/condition_occurrence.parquet Using the saved cohort Now that our cohort is saved locally, it can be accessed directly by using the PandasData class. Its akin to the HiveData class, except that the loaded tables will be stored directly as Pandas DataFrames, allowing for faster and easier analysis from eds_scikit.io.files import PandasData data = PandasData ( folder ) As a sanity check, let us display the number of patient in our saved cohort (we are expecting 30) cohort = data . person . person_id . to_list () len ( cohort ) 53 And the crude prevalence that should now be 100% ! f \" { 100 * len ( cohort ) / len ( set ( data . procedure_occurrence . person_id . to_list () + data . condition_occurrence . person_id . to_list ())) : .5f } %\" '100.00000 %'","title":"Saving small cohorts locally"},{"location":"recipes/small-cohorts/#introduction","text":"The goal of this small notebook is to show you how to: Work on a big cohort by staying distributed Do some phenotyping to select a small subcohort Save this subcohort locally to work on it later As a dummy example, we will select patients that underwent a cardiac transplantation. The selection will be performed by using both ICD-10 and by CCAM terminologies.","title":"Introduction"},{"location":"recipes/small-cohorts/#data-loading","text":"import eds_scikit spark , sc , sql = eds_scikit . improve_performances () DBNAME = \"YOUR_DATABASE_NAME\" from eds_scikit.io.hive import HiveData # Data from Hive data = HiveData ( DBNAME )","title":"Data Loading"},{"location":"recipes/small-cohorts/#phenotyping","text":"from eds_scikit.event.ccam import procedures_from_ccam from eds_scikit.event.icd10 import conditions_from_icd10 CCAM = dict ( HEART_TRANSPLANT = dict ( prefix = \"DZEA00\" , # ) ) ICD10 = dict ( HEART_TRANSPLANT = dict ( exact = \"Z941\" , # ) ) procedure_occurrence = procedures_from_ccam ( procedure_occurrence = data . procedure_occurrence , visit_occurrence = data . visit_occurrence , codes = CCAM , date_from_visit = True , ) condition_occurrence = conditions_from_icd10 ( condition_occurrence = data . condition_occurrence , visit_occurrence = data . visit_occurrence , codes = ICD10 , date_from_visit = True , additional_filtering = dict ( condition_status_source_value = { \"DP\" , \"DAS\" }, # ) ) procedure_occurrence . groupby ([ \"concept\" , \"value\" ]) . size () concept value HEART_TRANSPLANT DZEA002 39 dtype: int64 condition_occurrence . groupby ([ \"concept\" , \"value\" ]) . size () concept value HEART_TRANSPLANT Z941 602 dtype: int64","title":"Phenotyping"},{"location":"recipes/small-cohorts/#saving-to-disk","text":"cohort = set ( procedure_occurrence . person_id . to_list () + condition_occurrence . person_id . to_list () ) We can check that our cohort is indeed small and can be stored locally without any concerns: len ( cohort ) 53 And we can also compute a very crude prevalence of heart transplant in our database: f \" { 100 * len ( cohort ) / len ( set ( data . procedure_occurrence . person_id . to_list () + data . condition_occurrence . person_id . to_list ())) : .5f } %\" '0.06849 %' Finally let us save the tables we need locally. Under the hood, eds-scikit will only keep data corresponding to the provided cohort. import os folder = os . path . abspath ( \"./heart_transplant_cohort\" ) tables_to_save = [ \"person\" , \"visit_detail\" , \"visit_occurrence\" , \"procedure_occurrence\" , \"condition_occurrence\" , ] data . persist_tables_to_folder ( folder , tables = tables_to_save , person_ids = cohort , ) Number of unique patients: 53 writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/person.parquet writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/visit_detail.parquet writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/visit_occurrence.parquet writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/procedure_occurrence.parquet writing /export/home/cse210038/Thomas/eds-scikit/docs/recipes/heart_transplant_cohort/condition_occurrence.parquet","title":"Saving to disk"},{"location":"recipes/small-cohorts/#using-the-saved-cohort","text":"Now that our cohort is saved locally, it can be accessed directly by using the PandasData class. Its akin to the HiveData class, except that the loaded tables will be stored directly as Pandas DataFrames, allowing for faster and easier analysis from eds_scikit.io.files import PandasData data = PandasData ( folder ) As a sanity check, let us display the number of patient in our saved cohort (we are expecting 30) cohort = data . person . person_id . to_list () len ( cohort ) 53 And the crude prevalence that should now be 100% ! f \" { 100 * len ( cohort ) / len ( set ( data . procedure_occurrence . person_id . to_list () + data . condition_occurrence . person_id . to_list ())) : .5f } %\" '100.00000 %'","title":"Using the saved cohort"},{"location":"reference/","text":"eds_scikit Top-level package for eds_scikit.","title":"`eds_scikit`"},{"location":"reference/#eds_scikit","text":"Top-level package for eds_scikit.","title":"eds_scikit"},{"location":"reference/SUMMARY/","text":"eds_scikit biology cleaning cohort main utils check_data config prepare_measurement prepare_relationship process_concepts process_measurement process_units viz aggregate plot stats_summary wrapper datasets generation_scripts care_site_hierarchy synthetic base_dataset biology ccam consultation_dates event_sequences hierarchy icd10 person stay_duration suicide_attempt tagging visit_merging emergency emergency_care_site emergency_visit event ccam consultations diabetes from_code icd10 suicide_attempt icu icu_care_site icu_visit io base data_quality files hive i2b2_mapping improve_performance omop_teva_default_config postgres settings period stays tagging_functions phenotype base cancer cancer diabetes diabetes psychiatric_disorder psychiatric_disorder suicide_attempt suicide_attempt plot age_pyramid altair_utils event_sequences omop_teva table_viz resources reg utils structures attributes description utils bunch checks custom_implem custom_implem cut datetime_helpers flowchart flowchart framework hierarchy logging test_utils typing","title":"SUMMARY"},{"location":"reference/biology/","text":"eds_scikit.biology","title":"`eds_scikit.biology`"},{"location":"reference/biology/#eds_scikitbiology","text":"","title":"eds_scikit.biology"},{"location":"reference/biology/cleaning/","text":"eds_scikit.biology.cleaning","title":"`eds_scikit.biology.cleaning`"},{"location":"reference/biology/cleaning/#eds_scikitbiologycleaning","text":"","title":"eds_scikit.biology.cleaning"},{"location":"reference/biology/cleaning/cohort/","text":"eds_scikit.biology.cleaning.cohort select_cohort select_cohort ( measurement : DataFrame , studied_pop : Union [ DataFrame , List [ int ]]) -> DataFrame Select the patient_ids PARAMETER DESCRIPTION measurement Target DataFrame TYPE: DataFrame studied_pop List of patient_ids to select TYPE: Union [ DataFrame , List [ int ]] RETURNS DESCRIPTION DataFrame Filtered DataFrame with selected patients Source code in eds_scikit/biology/cleaning/cohort.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def select_cohort ( measurement : DataFrame , studied_pop : Union [ DataFrame , List [ int ]], ) -> DataFrame : \"\"\"Select the patient_ids Parameters ---------- measurement : DataFrame Target DataFrame studied_pop : Union[DataFrame, List[int]] List of patient_ids to select Returns ------- DataFrame Filtered DataFrame with selected patients \"\"\" logger . info ( \"Selecting cohort...\" ) if isinstance ( studied_pop , DataFrame . __args__ ): filtered_measures = measurement . merge ( studied_pop , on = \"person_id\" , ) else : filtered_measures = measurement [ measurement . person_id . isin ( studied_pop )] return filtered_measures","title":"cohort"},{"location":"reference/biology/cleaning/cohort/#eds_scikitbiologycleaningcohort","text":"","title":"eds_scikit.biology.cleaning.cohort"},{"location":"reference/biology/cleaning/cohort/#eds_scikit.biology.cleaning.cohort.select_cohort","text":"select_cohort ( measurement : DataFrame , studied_pop : Union [ DataFrame , List [ int ]]) -> DataFrame Select the patient_ids PARAMETER DESCRIPTION measurement Target DataFrame TYPE: DataFrame studied_pop List of patient_ids to select TYPE: Union [ DataFrame , List [ int ]] RETURNS DESCRIPTION DataFrame Filtered DataFrame with selected patients Source code in eds_scikit/biology/cleaning/cohort.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 def select_cohort ( measurement : DataFrame , studied_pop : Union [ DataFrame , List [ int ]], ) -> DataFrame : \"\"\"Select the patient_ids Parameters ---------- measurement : DataFrame Target DataFrame studied_pop : Union[DataFrame, List[int]] List of patient_ids to select Returns ------- DataFrame Filtered DataFrame with selected patients \"\"\" logger . info ( \"Selecting cohort...\" ) if isinstance ( studied_pop , DataFrame . __args__ ): filtered_measures = measurement . merge ( studied_pop , on = \"person_id\" , ) else : filtered_measures = measurement [ measurement . person_id . isin ( studied_pop )] return filtered_measures","title":"select_cohort()"},{"location":"reference/biology/cleaning/main/","text":"eds_scikit.biology.cleaning.main bioclean bioclean ( data : Data , concepts_sets : List [ ConceptsSet ] = None , start_date : datetime = None , end_date : datetime = None , convert_units : bool = False , studied_cohort : Union [ DataFrame , List [ int ]] = None ) -> Data It follows the pipeline explained here : PARAMETER DESCRIPTION data Instantiated HiveData , PostgresData or PandasData TYPE: Data concepts_sets List of concepts-sets to select TYPE: List [ ConceptsSet ], optional DEFAULT: None start_date EXAMPLE : \"2019-05-01\" TYPE: datetime , optional DEFAULT: None end_date EXAMPLE : \"2022-05-01\" TYPE: datetime , optional DEFAULT: None convert_units If True, convert units based on ConceptsSets Units object. Eager execution., by default False TYPE: bool , optional DEFAULT: False studied_cohort List of patient_ids to select TYPE: Union [ DataFrame , np . iterable , set ], optional DEFAULT: None RETURNS DESCRIPTION Data Same as the input with the transformed bioclean table Source code in eds_scikit/biology/cleaning/main.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 def bioclean ( data : Data , concepts_sets : List [ ConceptsSet ] = None , start_date : datetime = None , end_date : datetime = None , convert_units : bool = False , studied_cohort : Union [ DataFrame , List [ int ]] = None , ) -> Data : \"\"\"It follows the pipeline explained [here][cleaning]: Parameters ---------- data : Data Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData] concepts_sets : List[ConceptsSet], optional List of concepts-sets to select start_date : datetime, optional **EXAMPLE**: `\"2019-05-01\"` end_date : datetime, optional **EXAMPLE**: `\"2022-05-01\"` convert_units : bool, optional If True, convert units based on ConceptsSets Units object. Eager execution., by default False studied_cohort : Union[DataFrame, np.iterable, set], optional List of patient_ids to select Returns ------- Data Same as the input with the transformed `bioclean` table \"\"\" if concepts_sets is None : logger . info ( \"No concepts sets provided. Loading default concepts sets.\" ) concepts_sets = fetch_all_concepts_set () measurements = prepare_measurement_table ( data , start_date , end_date , concepts_sets , False , convert_units ) # Filter Measurement. if studied_cohort : measurements = select_cohort ( measurements , studied_cohort ) # Transform values data . bioclean = measurements measurements = measurements . merge ( data . visit_occurrence [[ \"care_site_id\" , \"visit_occurrence_id\" ]], on = \"visit_occurrence_id\" , ) measurements = measurements . merge ( data . care_site [[ \"care_site_id\" , \"care_site_short_name\" ]], on = \"care_site_id\" ) # Plot values value_column = \"value_as_number_normalized\" if convert_units else \"value_as_number\" unit_column = ( \"unit_source_value_normalized\" if convert_units else \"unit_source_value\" ) plot_biology_summary ( measurements , value_column , unit_column )","title":"main"},{"location":"reference/biology/cleaning/main/#eds_scikitbiologycleaningmain","text":"","title":"eds_scikit.biology.cleaning.main"},{"location":"reference/biology/cleaning/main/#eds_scikit.biology.cleaning.main.bioclean","text":"bioclean ( data : Data , concepts_sets : List [ ConceptsSet ] = None , start_date : datetime = None , end_date : datetime = None , convert_units : bool = False , studied_cohort : Union [ DataFrame , List [ int ]] = None ) -> Data It follows the pipeline explained here : PARAMETER DESCRIPTION data Instantiated HiveData , PostgresData or PandasData TYPE: Data concepts_sets List of concepts-sets to select TYPE: List [ ConceptsSet ], optional DEFAULT: None start_date EXAMPLE : \"2019-05-01\" TYPE: datetime , optional DEFAULT: None end_date EXAMPLE : \"2022-05-01\" TYPE: datetime , optional DEFAULT: None convert_units If True, convert units based on ConceptsSets Units object. Eager execution., by default False TYPE: bool , optional DEFAULT: False studied_cohort List of patient_ids to select TYPE: Union [ DataFrame , np . iterable , set ], optional DEFAULT: None RETURNS DESCRIPTION Data Same as the input with the transformed bioclean table Source code in eds_scikit/biology/cleaning/main.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 def bioclean ( data : Data , concepts_sets : List [ ConceptsSet ] = None , start_date : datetime = None , end_date : datetime = None , convert_units : bool = False , studied_cohort : Union [ DataFrame , List [ int ]] = None , ) -> Data : \"\"\"It follows the pipeline explained [here][cleaning]: Parameters ---------- data : Data Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData] concepts_sets : List[ConceptsSet], optional List of concepts-sets to select start_date : datetime, optional **EXAMPLE**: `\"2019-05-01\"` end_date : datetime, optional **EXAMPLE**: `\"2022-05-01\"` convert_units : bool, optional If True, convert units based on ConceptsSets Units object. Eager execution., by default False studied_cohort : Union[DataFrame, np.iterable, set], optional List of patient_ids to select Returns ------- Data Same as the input with the transformed `bioclean` table \"\"\" if concepts_sets is None : logger . info ( \"No concepts sets provided. Loading default concepts sets.\" ) concepts_sets = fetch_all_concepts_set () measurements = prepare_measurement_table ( data , start_date , end_date , concepts_sets , False , convert_units ) # Filter Measurement. if studied_cohort : measurements = select_cohort ( measurements , studied_cohort ) # Transform values data . bioclean = measurements measurements = measurements . merge ( data . visit_occurrence [[ \"care_site_id\" , \"visit_occurrence_id\" ]], on = \"visit_occurrence_id\" , ) measurements = measurements . merge ( data . care_site [[ \"care_site_id\" , \"care_site_short_name\" ]], on = \"care_site_id\" ) # Plot values value_column = \"value_as_number_normalized\" if convert_units else \"value_as_number\" unit_column = ( \"unit_source_value_normalized\" if convert_units else \"unit_source_value\" ) plot_biology_summary ( measurements , value_column , unit_column )","title":"bioclean()"},{"location":"reference/biology/utils/","text":"eds_scikit.biology.utils","title":"`eds_scikit.biology.utils`"},{"location":"reference/biology/utils/#eds_scikitbiologyutils","text":"","title":"eds_scikit.biology.utils"},{"location":"reference/biology/utils/check_data/","text":"eds_scikit.biology.utils.check_data check_data_and_select_columns_measurement check_data_and_select_columns_measurement ( data : Data ) Check the required tables and columns in the Data and extract them. PARAMETER DESCRIPTION data Instantiated HiveData , PostgresData or PandasData TYPE: Data Source code in eds_scikit/biology/utils/check_data.py 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 def check_data_and_select_columns_measurement ( data : Data ): \"\"\"Check the required tables and columns in the Data and extract them. Parameters ---------- data : Data Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData] \"\"\" check_tables ( data , required_tables = [ \"measurement\" , \"concept\" , \"concept_relationship\" , ], ) _measurement_required_columns = [ \"measurement_id\" , \"person_id\" , \"visit_occurrence_id\" , \"measurement_date\" , \"measurement_datetime\" , \"value_source_value\" , \"value_as_number\" , \"unit_source_value\" , \"row_status_source_value\" , \"measurement_source_concept_id\" , \"range_high\" , \"range_low\" , ] _concept_required_columns = [ \"concept_id\" , \"concept_name\" , \"concept_code\" , \"vocabulary_id\" , ] _relationship_required_columns = [ \"concept_id_1\" , \"concept_id_2\" , \"relationship_id\" ] check_columns ( data . measurement , required_columns = _measurement_required_columns , ) check_columns ( data . concept , required_columns = _concept_required_columns ) check_columns ( data . concept_relationship , required_columns = _relationship_required_columns , ) measurement = data . measurement concept = data . concept [ _concept_required_columns ] concept_relationship = data . concept_relationship [ _relationship_required_columns ] return measurement , concept , concept_relationship check_data_and_select_columns_relationship check_data_and_select_columns_relationship ( data : Data ) Check the required tables and columns in the Data and extract them. PARAMETER DESCRIPTION data Instantiated HiveData , PostgresData or PandasData TYPE: Data Source code in eds_scikit/biology/utils/check_data.py 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 def check_data_and_select_columns_relationship ( data : Data ): \"\"\"Check the required tables and columns in the Data and extract them. Parameters ---------- data : Data Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData] \"\"\" check_tables ( data , required_tables = [ \"concept\" , \"concept_relationship\" , ], ) _concept_required_columns = [ \"concept_id\" , \"concept_name\" , \"concept_code\" , \"vocabulary_id\" , ] _relationship_required_columns = [ \"concept_id_1\" , \"concept_id_2\" , \"relationship_id\" , ] check_columns ( data . concept , required_columns = _concept_required_columns ) check_columns ( data . concept_relationship , required_columns = _relationship_required_columns , ) concept = data . concept [ _concept_required_columns ] concept_relationship = data . concept_relationship [ _relationship_required_columns ] return concept , concept_relationship","title":"check_data"},{"location":"reference/biology/utils/check_data/#eds_scikitbiologyutilscheck_data","text":"","title":"eds_scikit.biology.utils.check_data"},{"location":"reference/biology/utils/check_data/#eds_scikit.biology.utils.check_data.check_data_and_select_columns_measurement","text":"check_data_and_select_columns_measurement ( data : Data ) Check the required tables and columns in the Data and extract them. PARAMETER DESCRIPTION data Instantiated HiveData , PostgresData or PandasData TYPE: Data Source code in eds_scikit/biology/utils/check_data.py 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 def check_data_and_select_columns_measurement ( data : Data ): \"\"\"Check the required tables and columns in the Data and extract them. Parameters ---------- data : Data Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData] \"\"\" check_tables ( data , required_tables = [ \"measurement\" , \"concept\" , \"concept_relationship\" , ], ) _measurement_required_columns = [ \"measurement_id\" , \"person_id\" , \"visit_occurrence_id\" , \"measurement_date\" , \"measurement_datetime\" , \"value_source_value\" , \"value_as_number\" , \"unit_source_value\" , \"row_status_source_value\" , \"measurement_source_concept_id\" , \"range_high\" , \"range_low\" , ] _concept_required_columns = [ \"concept_id\" , \"concept_name\" , \"concept_code\" , \"vocabulary_id\" , ] _relationship_required_columns = [ \"concept_id_1\" , \"concept_id_2\" , \"relationship_id\" ] check_columns ( data . measurement , required_columns = _measurement_required_columns , ) check_columns ( data . concept , required_columns = _concept_required_columns ) check_columns ( data . concept_relationship , required_columns = _relationship_required_columns , ) measurement = data . measurement concept = data . concept [ _concept_required_columns ] concept_relationship = data . concept_relationship [ _relationship_required_columns ] return measurement , concept , concept_relationship","title":"check_data_and_select_columns_measurement()"},{"location":"reference/biology/utils/check_data/#eds_scikit.biology.utils.check_data.check_data_and_select_columns_relationship","text":"check_data_and_select_columns_relationship ( data : Data ) Check the required tables and columns in the Data and extract them. PARAMETER DESCRIPTION data Instantiated HiveData , PostgresData or PandasData TYPE: Data Source code in eds_scikit/biology/utils/check_data.py 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 def check_data_and_select_columns_relationship ( data : Data ): \"\"\"Check the required tables and columns in the Data and extract them. Parameters ---------- data : Data Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData] \"\"\" check_tables ( data , required_tables = [ \"concept\" , \"concept_relationship\" , ], ) _concept_required_columns = [ \"concept_id\" , \"concept_name\" , \"concept_code\" , \"vocabulary_id\" , ] _relationship_required_columns = [ \"concept_id_1\" , \"concept_id_2\" , \"relationship_id\" , ] check_columns ( data . concept , required_columns = _concept_required_columns ) check_columns ( data . concept_relationship , required_columns = _relationship_required_columns , ) concept = data . concept [ _concept_required_columns ] concept_relationship = data . concept_relationship [ _relationship_required_columns ] return concept , concept_relationship","title":"check_data_and_select_columns_relationship()"},{"location":"reference/biology/utils/config/","text":"eds_scikit.biology.utils.config create_config_from_stats create_config_from_stats ( concepts_sets : List [ ConceptsSet ], config_name : str , stats_folder : str = 'Biology_summary' ) Generate the configuration file from a statistical summary. It is needed here PARAMETER DESCRIPTION concepts_sets List of concepts-sets to select TYPE: List [ ConceptsSet ] config_name Name of the folder where the configuration will be saved. TYPE: str stats_folder Name of the statistical summary folder TYPE: str DEFAULT: 'Biology_summary' Source code in eds_scikit/biology/utils/config.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 def create_config_from_stats ( concepts_sets : List [ ConceptsSet ], config_name : str , stats_folder : str = \"Biology_summary\" , ): \"\"\"Generate the configuration file from a statistical summary. It is needed [here][eds_scikit.biology.cleaning.transform.transform_measurement] Parameters ---------- concepts_sets : List[ConceptsSet] List of concepts-sets to select config_name : str Name of the folder where the configuration will be saved. stats_folder : str Name of the statistical summary folder \"\"\" my_custom_config = pd . DataFrame () for concepts_set in concepts_sets : try : stats = pd . read_pickle ( \" {} / {} /measurement_stats.pkl\" . format ( stats_folder , concepts_set . name ) ) stats [ \"transformed_unit\" ] = ( stats . groupby ( \"unit_source_value\" )[ \"count\" ] . sum ( \"count\" ) . sort_values ( ascending = False ) . index [ 0 ] ) stats [ \"concepts_set\" ] = concepts_set . name stats [ \"Action\" ] = None stats [ \"Coefficient\" ] = None my_custom_config = pd . concat ([ my_custom_config , stats ]) except OSError : logger . error ( \" {} has no statistical summary saved in {} \" , concepts_set . name , stats_folder , ) pass if \"care_site_short_name\" in my_custom_config . columns : # Keep only the row computed from every care site my_custom_config = my_custom_config [ my_custom_config . care_site_short_name == \"ALL\" ] os . makedirs ( CONFIGS_PATH , exist_ok = True ) my_custom_config . to_csv ( \" {} / {} .csv\" . format ( CONFIGS_PATH , config_name ), index = False ) register_configs () list_all_configs list_all_configs () -> List [ str ] Helper to get the names of all saved biology configurations RETURNS DESCRIPTION List [ str ] The configurations names Source code in eds_scikit/biology/utils/config.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 def list_all_configs () -> List [ str ]: \"\"\" Helper to get the names of all saved biology configurations Returns ------- List[str] The configurations names \"\"\" registered = list ( registry . data . get_all () . keys ()) configs = [ r . split ( \".\" )[ - 1 ] for r in registered if r . startswith ( \"get_biology_config\" ) ] return configs","title":"config"},{"location":"reference/biology/utils/config/#eds_scikitbiologyutilsconfig","text":"","title":"eds_scikit.biology.utils.config"},{"location":"reference/biology/utils/config/#eds_scikit.biology.utils.config.create_config_from_stats","text":"create_config_from_stats ( concepts_sets : List [ ConceptsSet ], config_name : str , stats_folder : str = 'Biology_summary' ) Generate the configuration file from a statistical summary. It is needed here PARAMETER DESCRIPTION concepts_sets List of concepts-sets to select TYPE: List [ ConceptsSet ] config_name Name of the folder where the configuration will be saved. TYPE: str stats_folder Name of the statistical summary folder TYPE: str DEFAULT: 'Biology_summary' Source code in eds_scikit/biology/utils/config.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 def create_config_from_stats ( concepts_sets : List [ ConceptsSet ], config_name : str , stats_folder : str = \"Biology_summary\" , ): \"\"\"Generate the configuration file from a statistical summary. It is needed [here][eds_scikit.biology.cleaning.transform.transform_measurement] Parameters ---------- concepts_sets : List[ConceptsSet] List of concepts-sets to select config_name : str Name of the folder where the configuration will be saved. stats_folder : str Name of the statistical summary folder \"\"\" my_custom_config = pd . DataFrame () for concepts_set in concepts_sets : try : stats = pd . read_pickle ( \" {} / {} /measurement_stats.pkl\" . format ( stats_folder , concepts_set . name ) ) stats [ \"transformed_unit\" ] = ( stats . groupby ( \"unit_source_value\" )[ \"count\" ] . sum ( \"count\" ) . sort_values ( ascending = False ) . index [ 0 ] ) stats [ \"concepts_set\" ] = concepts_set . name stats [ \"Action\" ] = None stats [ \"Coefficient\" ] = None my_custom_config = pd . concat ([ my_custom_config , stats ]) except OSError : logger . error ( \" {} has no statistical summary saved in {} \" , concepts_set . name , stats_folder , ) pass if \"care_site_short_name\" in my_custom_config . columns : # Keep only the row computed from every care site my_custom_config = my_custom_config [ my_custom_config . care_site_short_name == \"ALL\" ] os . makedirs ( CONFIGS_PATH , exist_ok = True ) my_custom_config . to_csv ( \" {} / {} .csv\" . format ( CONFIGS_PATH , config_name ), index = False ) register_configs ()","title":"create_config_from_stats()"},{"location":"reference/biology/utils/config/#eds_scikit.biology.utils.config.list_all_configs","text":"list_all_configs () -> List [ str ] Helper to get the names of all saved biology configurations RETURNS DESCRIPTION List [ str ] The configurations names Source code in eds_scikit/biology/utils/config.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 def list_all_configs () -> List [ str ]: \"\"\" Helper to get the names of all saved biology configurations Returns ------- List[str] The configurations names \"\"\" registered = list ( registry . data . get_all () . keys ()) configs = [ r . split ( \".\" )[ - 1 ] for r in registered if r . startswith ( \"get_biology_config\" ) ] return configs","title":"list_all_configs()"},{"location":"reference/biology/utils/prepare_measurement/","text":"eds_scikit.biology.utils.prepare_measurement prepare_measurement_table prepare_measurement_table ( data : Data , start_date : datetime = None , end_date : datetime = None , concept_sets : List [ ConceptsSet ] = None , get_all_terminologies = True , convert_units = False , compute_table = False ) -> DataFrame Returns filtered measurement table based on validity, date and concept_sets. The output format is identical to data.measurement but adding following columns : - range_high_anomaly, range_low_anomaly - {terminology}_code based on concept_sets terminologies - concept_sets - normalized_units and normalized_values if convert_units==True PARAMETER DESCRIPTION data Instantiated HiveData , PostgresData or PandasData TYPE: Data start_date EXAMPLE : \"2019-05-01\" TYPE: datetime , optional DEFAULT: None end_date EXAMPLE : \"2022-05-01\" TYPE: datetime , optional DEFAULT: None concept_sets List of concepts-sets to select TYPE: List [ ConceptsSet ], optional DEFAULT: None get_all_terminologies If True, all terminologies from settings terminologies will be added, by default True TYPE: bool , optional DEFAULT: True convert_units If True, convert units based on ConceptsSets Units object. Eager execution., by default False TYPE: bool , optional DEFAULT: False compute_table If True, compute table then cache it. Useful to prevent spark issues, especially when running in notebooks. TYPE: bool , optional DEFAULT: False RETURNS DESCRIPTION DataFrame Preprocessed measurement dataframe Source code in eds_scikit/biology/utils/prepare_measurement.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 def prepare_measurement_table ( data : Data , start_date : datetime = None , end_date : datetime = None , concept_sets : List [ ConceptsSet ] = None , get_all_terminologies = True , convert_units = False , compute_table = False , ) -> DataFrame : \"\"\"Returns filtered measurement table based on validity, date and concept_sets. The output format is identical to data.measurement but adding following columns : - range_high_anomaly, range_low_anomaly - {terminology}_code based on concept_sets terminologies - concept_sets - normalized_units and normalized_values if convert_units==True Parameters ---------- data : Data Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData] start_date : datetime, optional **EXAMPLE**: `\"2019-05-01\"` end_date : datetime, optional **EXAMPLE**: `\"2022-05-01\"` concept_sets : List[ConceptsSet], optional List of concepts-sets to select get_all_terminologies : bool, optional If True, all terminologies from settings terminologies will be added, by default True convert_units : bool, optional If True, convert units based on ConceptsSets Units object. Eager execution., by default False compute_table : bool, optional If True, compute table then cache it. Useful to prevent spark issues, especially when running in notebooks. Returns ------- DataFrame Preprocessed measurement dataframe \"\"\" measurement , _ , _ = check_data_and_select_columns_measurement ( data ) # measurement preprocessing measurement = filter_measurement_valid ( measurement ) measurement = filter_measurement_by_date ( measurement , start_date , end_date ) measurement = normalize_unit ( measurement ) measurement = tag_measurement_anomaly ( measurement ) # measurement codes mapping biology_relationship_table = prepare_biology_relationship_table ( data , concept_sets , get_all_terminologies ) measurement = measurement . merge ( biology_relationship_table , left_on = \"measurement_source_concept_id\" , right_on = f \" { mapping [ 0 ][ 0 ] } _concept_id\" , ) if convert_units : logger . info ( \"Lazy preparation not available if convert_units=True. Table will be computed then cached.\" ) measurement = convert_measurement_units ( measurement , concept_sets ) measurement = cache ( measurement ) if compute_table or convert_units : measurement . shape if is_koalas ( measurement ): logger . info ( \"Done. Once computed, measurement will be cached.\" ) return measurement","title":"prepare_measurement"},{"location":"reference/biology/utils/prepare_measurement/#eds_scikitbiologyutilsprepare_measurement","text":"","title":"eds_scikit.biology.utils.prepare_measurement"},{"location":"reference/biology/utils/prepare_measurement/#eds_scikit.biology.utils.prepare_measurement.prepare_measurement_table","text":"prepare_measurement_table ( data : Data , start_date : datetime = None , end_date : datetime = None , concept_sets : List [ ConceptsSet ] = None , get_all_terminologies = True , convert_units = False , compute_table = False ) -> DataFrame Returns filtered measurement table based on validity, date and concept_sets. The output format is identical to data.measurement but adding following columns : - range_high_anomaly, range_low_anomaly - {terminology}_code based on concept_sets terminologies - concept_sets - normalized_units and normalized_values if convert_units==True PARAMETER DESCRIPTION data Instantiated HiveData , PostgresData or PandasData TYPE: Data start_date EXAMPLE : \"2019-05-01\" TYPE: datetime , optional DEFAULT: None end_date EXAMPLE : \"2022-05-01\" TYPE: datetime , optional DEFAULT: None concept_sets List of concepts-sets to select TYPE: List [ ConceptsSet ], optional DEFAULT: None get_all_terminologies If True, all terminologies from settings terminologies will be added, by default True TYPE: bool , optional DEFAULT: True convert_units If True, convert units based on ConceptsSets Units object. Eager execution., by default False TYPE: bool , optional DEFAULT: False compute_table If True, compute table then cache it. Useful to prevent spark issues, especially when running in notebooks. TYPE: bool , optional DEFAULT: False RETURNS DESCRIPTION DataFrame Preprocessed measurement dataframe Source code in eds_scikit/biology/utils/prepare_measurement.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 def prepare_measurement_table ( data : Data , start_date : datetime = None , end_date : datetime = None , concept_sets : List [ ConceptsSet ] = None , get_all_terminologies = True , convert_units = False , compute_table = False , ) -> DataFrame : \"\"\"Returns filtered measurement table based on validity, date and concept_sets. The output format is identical to data.measurement but adding following columns : - range_high_anomaly, range_low_anomaly - {terminology}_code based on concept_sets terminologies - concept_sets - normalized_units and normalized_values if convert_units==True Parameters ---------- data : Data Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData] start_date : datetime, optional **EXAMPLE**: `\"2019-05-01\"` end_date : datetime, optional **EXAMPLE**: `\"2022-05-01\"` concept_sets : List[ConceptsSet], optional List of concepts-sets to select get_all_terminologies : bool, optional If True, all terminologies from settings terminologies will be added, by default True convert_units : bool, optional If True, convert units based on ConceptsSets Units object. Eager execution., by default False compute_table : bool, optional If True, compute table then cache it. Useful to prevent spark issues, especially when running in notebooks. Returns ------- DataFrame Preprocessed measurement dataframe \"\"\" measurement , _ , _ = check_data_and_select_columns_measurement ( data ) # measurement preprocessing measurement = filter_measurement_valid ( measurement ) measurement = filter_measurement_by_date ( measurement , start_date , end_date ) measurement = normalize_unit ( measurement ) measurement = tag_measurement_anomaly ( measurement ) # measurement codes mapping biology_relationship_table = prepare_biology_relationship_table ( data , concept_sets , get_all_terminologies ) measurement = measurement . merge ( biology_relationship_table , left_on = \"measurement_source_concept_id\" , right_on = f \" { mapping [ 0 ][ 0 ] } _concept_id\" , ) if convert_units : logger . info ( \"Lazy preparation not available if convert_units=True. Table will be computed then cached.\" ) measurement = convert_measurement_units ( measurement , concept_sets ) measurement = cache ( measurement ) if compute_table or convert_units : measurement . shape if is_koalas ( measurement ): logger . info ( \"Done. Once computed, measurement will be cached.\" ) return measurement","title":"prepare_measurement_table()"},{"location":"reference/biology/utils/prepare_relationship/","text":"eds_scikit.biology.utils.prepare_relationship prepare_relationship_table prepare_relationship_table ( data : Data , source_terminologies : Dict [ str , str ], mapping : List [ Tuple [ str , str , str ]]) -> ks . DataFrame Create easy-to-use relationship table based on given terminologies and mapping between them. PARAMETER DESCRIPTION data Instantiated HiveData , PostgresData or LocalData TYPE: Data source_terminologies Dictionary of concepts terminologies with their associated regex. TYPE: Dict [ str , str ] **EXAMPLE mapping Ordered mapping of terminologies based on concept_relationship table TYPE: List [ Tuple [ str , str , str ]] **EXAMPLE Output source_concept_id source_concept_name source_concept_code standard_concept_id standard_concept_name standard_concept_code 3 xxxxxxxxxxxx CX1 4 xxxxxxxxxxxx A1 9 xxxxxxxxxxxx ZY2 5 xxxxxxxxxxxx A2 9 xxxxxxxxxxxx B3F 47 xxxxxxxxxxxx D3 7 xxxxxxxxxxxx T32 4 xxxxxxxxxxxx F82 5 xxxxxxxxxxxx S23 1 xxxxxxxxxxxx A432 Source code in eds_scikit/biology/utils/prepare_relationship.py 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 def prepare_relationship_table ( data : Data , source_terminologies : Dict [ str , str ], mapping : List [ Tuple [ str , str , str ]], ) -> ks . DataFrame : # ks or pandas \"\"\" Create easy-to-use relationship table based on given terminologies and mapping between them. Parameters ---------- data : Data Instantiated [``HiveData``][edsteva.io.hive.HiveData], [``PostgresData``][edsteva.io.postgres.PostgresData] or [``LocalData``][edsteva.io.files.LocalData] source_terminologies : Dict[str, str] Dictionary of concepts terminologies with their associated regex. **EXAMPLE**: `{'source_concept' : r'src_.{0, 10}_lab', 'standard_concept' : r'std_concept'}` mapping : List[Tuple[str, str, str]] Ordered mapping of terminologies based on concept_relationship table **EXAMPLE**: `[(\"source_concept\", \"standard_concept\", \"Maps to\")]` Output ------- | source_concept_id | source_concept_name | source_concept_code | standard_concept_id | standard_concept_name | standard_concept_code | |--------------------:|:---------------------:|:---------------------:|:-------------------------:|:-------------------------:|:---------------------------:| | 3 | xxxxxxxxxxxx | CX1 | 4 | xxxxxxxxxxxx | A1 | | 9 | xxxxxxxxxxxx | ZY2 | 5 | xxxxxxxxxxxx | A2 | | 9 | xxxxxxxxxxxx | B3F | 47 | xxxxxxxxxxxx | D3 | | 7 | xxxxxxxxxxxx | T32 | 4 | xxxxxxxxxxxx | F82 | | 5 | xxxxxxxxxxxx | S23 | 1 | xxxxxxxxxxxx | A432 | \"\"\" concept , concept_relationship = check_data_and_select_columns_relationship ( data ) concept_by_terminology = {} for terminology , regex in source_terminologies . items (): concept_by_terminology [ terminology ] = ( concept [ concept . vocabulary_id . str . contains ( regex )] . rename ( columns = { \"concept_id\" : \" {} _concept_id\" . format ( terminology ), \"concept_name\" : \" {} _concept_name\" . format ( terminology ), \"concept_code\" : \" {} _concept_code\" . format ( terminology ), } ) . drop ( columns = \"vocabulary_id\" ) ) root_terminology = mapping [ 0 ][ 0 ] relationship_table = concept_by_terminology [ root_terminology ] # Look over all predefined structured mapping for source , target , relationship_id in mapping : relationship = concept_relationship . rename ( columns = { \"concept_id_1\" : \" {} _concept_id\" . format ( source ), \"concept_id_2\" : \" {} _concept_id\" . format ( target ), } )[ concept_relationship . relationship_id == relationship_id ] . drop ( columns = \"relationship_id\" ) relationship = relationship . merge ( concept_by_terminology [ target ], on = \" {} _concept_id\" . format ( target ) ) relationship_table = relationship_table . merge ( relationship , on = \" {} _concept_id\" . format ( source ), how = \"left\" ) relationship_table = relationship_table . fillna ( \"Unknown\" ) return relationship_table filter_concept_sets_relationship_table filter_concept_sets_relationship_table ( relationship_table , concept_sets ) Filter relationship table using concept_sets concept codes. PARAMETER DESCRIPTION relationship_table Biology relationship table TYPE: DataFrame concept_sets List of concepts-sets to select TYPE: List [ ConceptsSet ] RETURNS DESCRIPTION DataFrame Filtered biology relationship table Source code in eds_scikit/biology/utils/prepare_relationship.py 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 def filter_concept_sets_relationship_table ( relationship_table , concept_sets ): \"\"\"Filter relationship table using concept_sets concept codes. Parameters ---------- relationship_table : DataFrame Biology relationship table concept_sets : List[ConceptsSet] List of concepts-sets to select Returns ------- DataFrame Filtered biology relationship table \"\"\" framework = get_framework ( relationship_table ) concept_sets_tables = pd . DataFrame ({}) for concept_set in concept_sets : concept_set_table = concept_set . get_concept_codes_table () concept_sets_tables = pd . concat ( ( concept_set_table , concept_sets_tables ), axis = 0 ) terminologies = concept_sets_tables . terminology . unique () concept_sets_tables = to ( framework , concept_sets_tables ) filtered_terminology_table = framework . DataFrame ({}) for terminology in terminologies : if f \" { terminology } _concept_code\" in relationship_table . columns : filtered_terminology_table_ = concept_sets_tables [ concept_sets_tables . terminology == terminology ] . merge ( relationship_table , on = f \" { terminology } _concept_code\" , how = \"left\" , suffixes = ( \"_x\" , \"\" ), ) filtered_terminology_table_ = filtered_terminology_table_ [ [ column for column in filtered_terminology_table_ . columns if not ( \"_x\" in column ) ] ] filtered_terminology_table = framework . concat ( ( filtered_terminology_table_ , filtered_terminology_table ) ) . drop_duplicates () return filtered_terminology_table concept_sets_columns concept_sets_columns ( relationship_table : DataFrame , concept_sets : List [ ConceptsSet ], extra_terminologies : List = List [ str ]) -> List [ str ] Filter relationship_table keeping concepts_sets terminologies columns. PARAMETER DESCRIPTION relationship_table TYPE: DataFrame concept_sets TYPE: List [ ConceptsSet ] extra_terminologies TYPE: List , optional DEFAULT: List[str] RETURNS DESCRIPTION List [ str ] Source code in eds_scikit/biology/utils/prepare_relationship.py 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 def concept_sets_columns ( relationship_table : DataFrame , concept_sets : List [ ConceptsSet ], extra_terminologies : List = List [ str ], ) -> List [ str ]: \"\"\"Filter relationship_table keeping concepts_sets terminologies columns. Parameters ---------- relationship_table : DataFrame concept_sets : List[ConceptsSet] extra_terminologies : List, optional Returns ------- List[str] \"\"\" keep_terminologies = extra_terminologies for concept_set in concept_sets : keep_terminologies += concept_set . concept_codes . keys () keep_columns = [] for col in relationship_table . columns : if any ([ terminology in col for terminology in keep_terminologies ]): keep_columns . append ( col ) return keep_columns prepare_biology_relationship_table prepare_biology_relationship_table ( data : Data , concept_sets : List [ ConceptsSet ] = None , get_all_terminologies : bool = True ) -> DataFrame Prepare biology relationship table to map concept codes based on settings.source_terminologies and settings.mapping. PARAMETER DESCRIPTION data Instantiated HiveData , PostgresData or PandasData TYPE: Data concept_sets List of concepts-sets to select TYPE: List [ ConceptsSet ], optional DEFAULT: None get_all_terminologies If True, all terminologies from settings terminologies will be added, by default True TYPE: bool , optional DEFAULT: True Returns DataFrame biology_relationship_table to be merged with measurement Source code in eds_scikit/biology/utils/prepare_relationship.py 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 def prepare_biology_relationship_table ( data : Data , concept_sets : List [ ConceptsSet ] = None , get_all_terminologies : bool = True , ) -> DataFrame : \"\"\"Prepare biology relationship table to map concept codes based on settings.source_terminologies and settings.mapping. Parameters ---------- data : Data Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData] concept_sets : List[ConceptsSet], optional List of concepts-sets to select get_all_terminologies : bool, optional If True, all terminologies from settings terminologies will be added, by default True Returns ------- DataFrame biology_relationship_table to be merged with measurement \"\"\" if concept_sets is None and not get_all_terminologies : raise Exception ( \"get_all_terminologies must be True if no concept_sets provided.\" ) biology_relationship_table = prepare_relationship_table ( data , source_terminologies , mapping ) biology_relationship_table = ( filter_concept_sets_relationship_table ( biology_relationship_table , concept_sets ) if concept_sets else biology_relationship_table ) keep_columns = ( biology_relationship_table . columns if get_all_terminologies else concept_sets_columns ( biology_relationship_table , concept_sets , [ mapping [ 0 ][ 0 ], \"concept_set\" ], ) ) biology_relationship_table = biology_relationship_table [ keep_columns ] return biology_relationship_table","title":"prepare_relationship"},{"location":"reference/biology/utils/prepare_relationship/#eds_scikitbiologyutilsprepare_relationship","text":"","title":"eds_scikit.biology.utils.prepare_relationship"},{"location":"reference/biology/utils/prepare_relationship/#eds_scikit.biology.utils.prepare_relationship.prepare_relationship_table","text":"prepare_relationship_table ( data : Data , source_terminologies : Dict [ str , str ], mapping : List [ Tuple [ str , str , str ]]) -> ks . DataFrame Create easy-to-use relationship table based on given terminologies and mapping between them. PARAMETER DESCRIPTION data Instantiated HiveData , PostgresData or LocalData TYPE: Data source_terminologies Dictionary of concepts terminologies with their associated regex. TYPE: Dict [ str , str ] **EXAMPLE mapping Ordered mapping of terminologies based on concept_relationship table TYPE: List [ Tuple [ str , str , str ]] **EXAMPLE","title":"prepare_relationship_table()"},{"location":"reference/biology/utils/prepare_relationship/#eds_scikit.biology.utils.prepare_relationship.prepare_relationship_table--output","text":"source_concept_id source_concept_name source_concept_code standard_concept_id standard_concept_name standard_concept_code 3 xxxxxxxxxxxx CX1 4 xxxxxxxxxxxx A1 9 xxxxxxxxxxxx ZY2 5 xxxxxxxxxxxx A2 9 xxxxxxxxxxxx B3F 47 xxxxxxxxxxxx D3 7 xxxxxxxxxxxx T32 4 xxxxxxxxxxxx F82 5 xxxxxxxxxxxx S23 1 xxxxxxxxxxxx A432 Source code in eds_scikit/biology/utils/prepare_relationship.py 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 def prepare_relationship_table ( data : Data , source_terminologies : Dict [ str , str ], mapping : List [ Tuple [ str , str , str ]], ) -> ks . DataFrame : # ks or pandas \"\"\" Create easy-to-use relationship table based on given terminologies and mapping between them. Parameters ---------- data : Data Instantiated [``HiveData``][edsteva.io.hive.HiveData], [``PostgresData``][edsteva.io.postgres.PostgresData] or [``LocalData``][edsteva.io.files.LocalData] source_terminologies : Dict[str, str] Dictionary of concepts terminologies with their associated regex. **EXAMPLE**: `{'source_concept' : r'src_.{0, 10}_lab', 'standard_concept' : r'std_concept'}` mapping : List[Tuple[str, str, str]] Ordered mapping of terminologies based on concept_relationship table **EXAMPLE**: `[(\"source_concept\", \"standard_concept\", \"Maps to\")]` Output ------- | source_concept_id | source_concept_name | source_concept_code | standard_concept_id | standard_concept_name | standard_concept_code | |--------------------:|:---------------------:|:---------------------:|:-------------------------:|:-------------------------:|:---------------------------:| | 3 | xxxxxxxxxxxx | CX1 | 4 | xxxxxxxxxxxx | A1 | | 9 | xxxxxxxxxxxx | ZY2 | 5 | xxxxxxxxxxxx | A2 | | 9 | xxxxxxxxxxxx | B3F | 47 | xxxxxxxxxxxx | D3 | | 7 | xxxxxxxxxxxx | T32 | 4 | xxxxxxxxxxxx | F82 | | 5 | xxxxxxxxxxxx | S23 | 1 | xxxxxxxxxxxx | A432 | \"\"\" concept , concept_relationship = check_data_and_select_columns_relationship ( data ) concept_by_terminology = {} for terminology , regex in source_terminologies . items (): concept_by_terminology [ terminology ] = ( concept [ concept . vocabulary_id . str . contains ( regex )] . rename ( columns = { \"concept_id\" : \" {} _concept_id\" . format ( terminology ), \"concept_name\" : \" {} _concept_name\" . format ( terminology ), \"concept_code\" : \" {} _concept_code\" . format ( terminology ), } ) . drop ( columns = \"vocabulary_id\" ) ) root_terminology = mapping [ 0 ][ 0 ] relationship_table = concept_by_terminology [ root_terminology ] # Look over all predefined structured mapping for source , target , relationship_id in mapping : relationship = concept_relationship . rename ( columns = { \"concept_id_1\" : \" {} _concept_id\" . format ( source ), \"concept_id_2\" : \" {} _concept_id\" . format ( target ), } )[ concept_relationship . relationship_id == relationship_id ] . drop ( columns = \"relationship_id\" ) relationship = relationship . merge ( concept_by_terminology [ target ], on = \" {} _concept_id\" . format ( target ) ) relationship_table = relationship_table . merge ( relationship , on = \" {} _concept_id\" . format ( source ), how = \"left\" ) relationship_table = relationship_table . fillna ( \"Unknown\" ) return relationship_table","title":"Output"},{"location":"reference/biology/utils/prepare_relationship/#eds_scikit.biology.utils.prepare_relationship.filter_concept_sets_relationship_table","text":"filter_concept_sets_relationship_table ( relationship_table , concept_sets ) Filter relationship table using concept_sets concept codes. PARAMETER DESCRIPTION relationship_table Biology relationship table TYPE: DataFrame concept_sets List of concepts-sets to select TYPE: List [ ConceptsSet ] RETURNS DESCRIPTION DataFrame Filtered biology relationship table Source code in eds_scikit/biology/utils/prepare_relationship.py 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 def filter_concept_sets_relationship_table ( relationship_table , concept_sets ): \"\"\"Filter relationship table using concept_sets concept codes. Parameters ---------- relationship_table : DataFrame Biology relationship table concept_sets : List[ConceptsSet] List of concepts-sets to select Returns ------- DataFrame Filtered biology relationship table \"\"\" framework = get_framework ( relationship_table ) concept_sets_tables = pd . DataFrame ({}) for concept_set in concept_sets : concept_set_table = concept_set . get_concept_codes_table () concept_sets_tables = pd . concat ( ( concept_set_table , concept_sets_tables ), axis = 0 ) terminologies = concept_sets_tables . terminology . unique () concept_sets_tables = to ( framework , concept_sets_tables ) filtered_terminology_table = framework . DataFrame ({}) for terminology in terminologies : if f \" { terminology } _concept_code\" in relationship_table . columns : filtered_terminology_table_ = concept_sets_tables [ concept_sets_tables . terminology == terminology ] . merge ( relationship_table , on = f \" { terminology } _concept_code\" , how = \"left\" , suffixes = ( \"_x\" , \"\" ), ) filtered_terminology_table_ = filtered_terminology_table_ [ [ column for column in filtered_terminology_table_ . columns if not ( \"_x\" in column ) ] ] filtered_terminology_table = framework . concat ( ( filtered_terminology_table_ , filtered_terminology_table ) ) . drop_duplicates () return filtered_terminology_table","title":"filter_concept_sets_relationship_table()"},{"location":"reference/biology/utils/prepare_relationship/#eds_scikit.biology.utils.prepare_relationship.concept_sets_columns","text":"concept_sets_columns ( relationship_table : DataFrame , concept_sets : List [ ConceptsSet ], extra_terminologies : List = List [ str ]) -> List [ str ] Filter relationship_table keeping concepts_sets terminologies columns. PARAMETER DESCRIPTION relationship_table TYPE: DataFrame concept_sets TYPE: List [ ConceptsSet ] extra_terminologies TYPE: List , optional DEFAULT: List[str] RETURNS DESCRIPTION List [ str ] Source code in eds_scikit/biology/utils/prepare_relationship.py 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 def concept_sets_columns ( relationship_table : DataFrame , concept_sets : List [ ConceptsSet ], extra_terminologies : List = List [ str ], ) -> List [ str ]: \"\"\"Filter relationship_table keeping concepts_sets terminologies columns. Parameters ---------- relationship_table : DataFrame concept_sets : List[ConceptsSet] extra_terminologies : List, optional Returns ------- List[str] \"\"\" keep_terminologies = extra_terminologies for concept_set in concept_sets : keep_terminologies += concept_set . concept_codes . keys () keep_columns = [] for col in relationship_table . columns : if any ([ terminology in col for terminology in keep_terminologies ]): keep_columns . append ( col ) return keep_columns","title":"concept_sets_columns()"},{"location":"reference/biology/utils/prepare_relationship/#eds_scikit.biology.utils.prepare_relationship.prepare_biology_relationship_table","text":"prepare_biology_relationship_table ( data : Data , concept_sets : List [ ConceptsSet ] = None , get_all_terminologies : bool = True ) -> DataFrame Prepare biology relationship table to map concept codes based on settings.source_terminologies and settings.mapping. PARAMETER DESCRIPTION data Instantiated HiveData , PostgresData or PandasData TYPE: Data concept_sets List of concepts-sets to select TYPE: List [ ConceptsSet ], optional DEFAULT: None get_all_terminologies If True, all terminologies from settings terminologies will be added, by default True TYPE: bool , optional DEFAULT: True Returns DataFrame biology_relationship_table to be merged with measurement Source code in eds_scikit/biology/utils/prepare_relationship.py 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 def prepare_biology_relationship_table ( data : Data , concept_sets : List [ ConceptsSet ] = None , get_all_terminologies : bool = True , ) -> DataFrame : \"\"\"Prepare biology relationship table to map concept codes based on settings.source_terminologies and settings.mapping. Parameters ---------- data : Data Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData] concept_sets : List[ConceptsSet], optional List of concepts-sets to select get_all_terminologies : bool, optional If True, all terminologies from settings terminologies will be added, by default True Returns ------- DataFrame biology_relationship_table to be merged with measurement \"\"\" if concept_sets is None and not get_all_terminologies : raise Exception ( \"get_all_terminologies must be True if no concept_sets provided.\" ) biology_relationship_table = prepare_relationship_table ( data , source_terminologies , mapping ) biology_relationship_table = ( filter_concept_sets_relationship_table ( biology_relationship_table , concept_sets ) if concept_sets else biology_relationship_table ) keep_columns = ( biology_relationship_table . columns if get_all_terminologies else concept_sets_columns ( biology_relationship_table , concept_sets , [ mapping [ 0 ][ 0 ], \"concept_set\" ], ) ) biology_relationship_table = biology_relationship_table [ keep_columns ] return biology_relationship_table","title":"prepare_biology_relationship_table()"},{"location":"reference/biology/utils/process_concepts/","text":"eds_scikit.biology.utils.process_concepts ConceptsSet ConceptsSet ( name : str ) Class defining the concepts-sets with 2 attributes: name : the name of the concepts-set concept_codes : the list of concepts codes included in the concepts-set Source code in eds_scikit/biology/utils/process_concepts.py 25 26 27 28 29 30 31 32 33 def __init__ ( self , name : str ): self . name = name self . units = Units () fetched_codes = fetch_concept_codes_from_name ( name ) if fetched_codes : self . concept_codes = { \"GLIMS_ANABIO\" : fetch_concept_codes_from_name ( name )} else : self . concept_codes = {} fetch_all_concepts_set fetch_all_concepts_set ( concepts_sets_table_name : str = 'default_concepts_sets' ) -> List [ ConceptsSet ] Returns a list of all the concepts-sets of the chosen tables. By default, the table is here . PARAMETER DESCRIPTION concepts_sets_table_name Name of the table to extract concepts-sets from TYPE: str , optional DEFAULT: 'default_concepts_sets' RETURNS DESCRIPTION List [ ConceptsSet ] The list of all concepts-sets in the selected table Source code in eds_scikit/biology/utils/process_concepts.py 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 def fetch_all_concepts_set ( concepts_sets_table_name : str = \"default_concepts_sets\" , ) -> List [ ConceptsSet ]: \"\"\"Returns a list of all the concepts-sets of the chosen tables. By default, the table is [here][concepts-sets]. Parameters ---------- concepts_sets_table_name : str, optional Name of the table to extract concepts-sets from Returns ------- List[ConceptsSet] The list of all concepts-sets in the selected table \"\"\" concepts_sets = [] default_concepts_sets = getattr ( datasets , concepts_sets_table_name ) for concepts_set_name in default_concepts_sets . concepts_set_name : concepts_sets . append ( ConceptsSet ( concepts_set_name )) logger . info ( \"Fetch all concepts-sets from table {} \" , concepts_sets_table_name ) return concepts_sets","title":"process_concepts"},{"location":"reference/biology/utils/process_concepts/#eds_scikitbiologyutilsprocess_concepts","text":"","title":"eds_scikit.biology.utils.process_concepts"},{"location":"reference/biology/utils/process_concepts/#eds_scikit.biology.utils.process_concepts.ConceptsSet","text":"ConceptsSet ( name : str ) Class defining the concepts-sets with 2 attributes: name : the name of the concepts-set concept_codes : the list of concepts codes included in the concepts-set Source code in eds_scikit/biology/utils/process_concepts.py 25 26 27 28 29 30 31 32 33 def __init__ ( self , name : str ): self . name = name self . units = Units () fetched_codes = fetch_concept_codes_from_name ( name ) if fetched_codes : self . concept_codes = { \"GLIMS_ANABIO\" : fetch_concept_codes_from_name ( name )} else : self . concept_codes = {}","title":"ConceptsSet"},{"location":"reference/biology/utils/process_concepts/#eds_scikit.biology.utils.process_concepts.fetch_all_concepts_set","text":"fetch_all_concepts_set ( concepts_sets_table_name : str = 'default_concepts_sets' ) -> List [ ConceptsSet ] Returns a list of all the concepts-sets of the chosen tables. By default, the table is here . PARAMETER DESCRIPTION concepts_sets_table_name Name of the table to extract concepts-sets from TYPE: str , optional DEFAULT: 'default_concepts_sets' RETURNS DESCRIPTION List [ ConceptsSet ] The list of all concepts-sets in the selected table Source code in eds_scikit/biology/utils/process_concepts.py 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 def fetch_all_concepts_set ( concepts_sets_table_name : str = \"default_concepts_sets\" , ) -> List [ ConceptsSet ]: \"\"\"Returns a list of all the concepts-sets of the chosen tables. By default, the table is [here][concepts-sets]. Parameters ---------- concepts_sets_table_name : str, optional Name of the table to extract concepts-sets from Returns ------- List[ConceptsSet] The list of all concepts-sets in the selected table \"\"\" concepts_sets = [] default_concepts_sets = getattr ( datasets , concepts_sets_table_name ) for concepts_set_name in default_concepts_sets . concepts_set_name : concepts_sets . append ( ConceptsSet ( concepts_set_name )) logger . info ( \"Fetch all concepts-sets from table {} \" , concepts_sets_table_name ) return concepts_sets","title":"fetch_all_concepts_set()"},{"location":"reference/biology/utils/process_measurement/","text":"eds_scikit.biology.utils.process_measurement filter_measurement_valid filter_measurement_valid ( measurement : DataFrame ) -> DataFrame Filter valid observations based on the row_status_source_value column PARAMETER DESCRIPTION measurement DataFrame to filter TYPE: DataFrame RETURNS DESCRIPTION DataFrame DataFrame with valid observations only Source code in eds_scikit/biology/utils/process_measurement.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def filter_measurement_valid ( measurement : DataFrame ) -> DataFrame : \"\"\"Filter valid observations based on the `row_status_source_value` column Parameters ---------- measurement : DataFrame DataFrame to filter Returns ------- DataFrame DataFrame with valid observations only \"\"\" check_columns ( df = measurement , required_columns = [ \"row_status_source_value\" ], df_name = \"measurment\" , ) measurement_valid = measurement [ measurement [ \"row_status_source_value\" ] == \"Valid\u00e9\" ] measurement_valid = measurement_valid . drop ( columns = [ \"row_status_source_value\" ]) return measurement_valid filter_measurement_by_date filter_measurement_by_date ( measurement : DataFrame , start_date : datetime = None , end_date : datetime = None ) -> DataFrame Filter observations that are inside the selected time window PARAMETER DESCRIPTION measurement DataFrame to filter TYPE: DataFrame start_date EXAMPLE : \"2019-05-01\" TYPE: datetime , optional DEFAULT: None end_date EXAMPLE : \"2022-05-01\" TYPE: datetime , optional DEFAULT: None RETURNS DESCRIPTION DataFrame DataFrame with observations inside the selected time window only Source code in eds_scikit/biology/utils/process_measurement.py 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 def filter_measurement_by_date ( measurement : DataFrame , start_date : datetime = None , end_date : datetime = None ) -> DataFrame : \"\"\"Filter observations that are inside the selected time window Parameters ---------- measurement : DataFrame DataFrame to filter start_date : datetime, optional **EXAMPLE**: `\"2019-05-01\"` end_date : datetime, optional **EXAMPLE**: `\"2022-05-01\"` Returns ------- DataFrame DataFrame with observations inside the selected time window only \"\"\" check_columns ( df = measurement , required_columns = [ \"measurement_date\" ], df_name = \"measurment\" ) measurement . measurement_date = measurement . measurement_date . astype ( \"datetime64[ns]\" ) measurement . dropna ( subset = [ \"measurement_date\" ], inplace = True ) if start_date : measurement = measurement [ measurement [ \"measurement_date\" ] >= start_date ] if end_date : measurement = measurement [ measurement [ \"measurement_date\" ] <= end_date ] return measurement tag_measurement_anomaly tag_measurement_anomaly ( measurement : DataFrame ) -> DataFrame PARAMETER DESCRIPTION measurement DataFrame to filter TYPE: DataFrame start_date EXAMPLE : \"2019-05-01\" TYPE: datetime , optional end_date EXAMPLE : \"2022-05-01\" TYPE: datetime , optional Source code in eds_scikit/biology/utils/process_measurement.py 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 def tag_measurement_anomaly ( measurement : DataFrame ) -> DataFrame : \"\"\" Parameters ---------- measurement : DataFrame DataFrame to filter start_date : datetime, optional **EXAMPLE**: `\"2019-05-01\"` end_date : datetime, optional **EXAMPLE**: `\"2022-05-01\"` Returns ------- \"\"\" measurement [ \"range_high_anomaly\" ] = ( ~ measurement . range_high . isna ()) & ( measurement [ \"value_as_number\" ] > measurement [ \"range_high\" ] ) measurement [ \"range_low_anomaly\" ] = ( ~ measurement . range_low . isna ()) & ( measurement [ \"value_as_number\" ] < measurement [ \"range_low\" ] ) return measurement convert_measurement_units convert_measurement_units ( measurement : DataFrame , concepts_sets : List [ ConceptsSet ]) -> DataFrame Add value_as_number_normalized, unit_source_value_normalized and factor columns to measurement dataframe based on concepts_sets and units. PARAMETER DESCRIPTION measurement TYPE: DataFrame concepts_sets TYPE: List [ ConceptsSet ] RETURNS DESCRIPTION DataFrame Measurement with added columns value_as_number_normalized, unit_source_value_normalized and factor. Source code in eds_scikit/biology/utils/process_measurement.py 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 def convert_measurement_units ( measurement : DataFrame , concepts_sets : List [ ConceptsSet ] ) -> DataFrame : \"\"\"Add value_as_number_normalized, unit_source_value_normalized and factor columns to measurement dataframe based on concepts_sets and units. Parameters ---------- measurement : DataFrame concepts_sets : List[ConceptsSet] Returns ------- DataFrame Measurement with added columns value_as_number_normalized, unit_source_value_normalized and factor. \"\"\" if is_koalas ( measurement ): measurement = cache ( measurement ) measurement . shape conversion_table = to ( \"koalas\" , get_conversion_table ( measurement , concepts_sets ) ) else : conversion_table = get_conversion_table ( measurement , concepts_sets ) measurement = measurement . merge ( conversion_table , on = [ \"concept_set\" , \"unit_source_value\" ] ) measurement [ \"value_as_number_normalized\" ] = ( measurement [ \"value_as_number\" ] * measurement [ \"factor\" ] ) return measurement get_conversion_table get_conversion_table ( measurement : DataFrame , concepts_sets : List [ ConceptsSet ]) -> DataFrame Given measurement dataframe and list of concepts_sets output conversion table to be merged with measurement. PARAMETER DESCRIPTION measurement TYPE: DataFrame concepts_sets TYPE: List [ ConceptsSet ] RETURNS DESCRIPTION DataFrame Conversion table to be merged with measurement Source code in eds_scikit/biology/utils/process_measurement.py 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 def get_conversion_table ( measurement : DataFrame , concepts_sets : List [ ConceptsSet ] ) -> DataFrame : \"\"\"Given measurement dataframe and list of concepts_sets output conversion table to be merged with measurement. Parameters ---------- measurement : DataFrame concepts_sets : List[ConceptsSet] Returns ------- DataFrame Conversion table to be merged with measurement \"\"\" conversion_table = ( measurement . groupby ( \"concept_set\" )[ \"unit_source_value\" ] . unique () . explode () . to_frame () . reset_index () ) conversion_table = to ( \"pandas\" , conversion_table ) conversion_table [ \"unit_source_value_normalized\" ] = conversion_table [ \"unit_source_value\" ] conversion_table [ \"factor\" ] = conversion_table . apply ( lambda x : 1 if x . unit_source_value_normalized else 0 , axis = 1 ) for concept_set in concepts_sets : unit_source_value_normalized = concept_set . units . target_unit conversion_table . loc [ conversion_table . concept_set == concept_set . name , \"unit_source_value_normalized\" , ] = conversion_table . apply ( lambda x : unit_source_value_normalized if concept_set . units . can_be_converted ( x . unit_source_value , unit_source_value_normalized ) else concept_set . units . get_unit_base ( x . unit_source_value ), axis = 1 , ) conversion_table . loc [ conversion_table . concept_set == concept_set . name , \"factor\" ] = conversion_table . apply ( lambda x : concept_set . units . convert_unit ( x . unit_source_value , x . unit_source_value_normalized ), axis = 1 , ) conversion_table = conversion_table . fillna ( 1 ) return conversion_table","title":"process_measurement"},{"location":"reference/biology/utils/process_measurement/#eds_scikitbiologyutilsprocess_measurement","text":"","title":"eds_scikit.biology.utils.process_measurement"},{"location":"reference/biology/utils/process_measurement/#eds_scikit.biology.utils.process_measurement.filter_measurement_valid","text":"filter_measurement_valid ( measurement : DataFrame ) -> DataFrame Filter valid observations based on the row_status_source_value column PARAMETER DESCRIPTION measurement DataFrame to filter TYPE: DataFrame RETURNS DESCRIPTION DataFrame DataFrame with valid observations only Source code in eds_scikit/biology/utils/process_measurement.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 def filter_measurement_valid ( measurement : DataFrame ) -> DataFrame : \"\"\"Filter valid observations based on the `row_status_source_value` column Parameters ---------- measurement : DataFrame DataFrame to filter Returns ------- DataFrame DataFrame with valid observations only \"\"\" check_columns ( df = measurement , required_columns = [ \"row_status_source_value\" ], df_name = \"measurment\" , ) measurement_valid = measurement [ measurement [ \"row_status_source_value\" ] == \"Valid\u00e9\" ] measurement_valid = measurement_valid . drop ( columns = [ \"row_status_source_value\" ]) return measurement_valid","title":"filter_measurement_valid()"},{"location":"reference/biology/utils/process_measurement/#eds_scikit.biology.utils.process_measurement.filter_measurement_by_date","text":"filter_measurement_by_date ( measurement : DataFrame , start_date : datetime = None , end_date : datetime = None ) -> DataFrame Filter observations that are inside the selected time window PARAMETER DESCRIPTION measurement DataFrame to filter TYPE: DataFrame start_date EXAMPLE : \"2019-05-01\" TYPE: datetime , optional DEFAULT: None end_date EXAMPLE : \"2022-05-01\" TYPE: datetime , optional DEFAULT: None RETURNS DESCRIPTION DataFrame DataFrame with observations inside the selected time window only Source code in eds_scikit/biology/utils/process_measurement.py 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 def filter_measurement_by_date ( measurement : DataFrame , start_date : datetime = None , end_date : datetime = None ) -> DataFrame : \"\"\"Filter observations that are inside the selected time window Parameters ---------- measurement : DataFrame DataFrame to filter start_date : datetime, optional **EXAMPLE**: `\"2019-05-01\"` end_date : datetime, optional **EXAMPLE**: `\"2022-05-01\"` Returns ------- DataFrame DataFrame with observations inside the selected time window only \"\"\" check_columns ( df = measurement , required_columns = [ \"measurement_date\" ], df_name = \"measurment\" ) measurement . measurement_date = measurement . measurement_date . astype ( \"datetime64[ns]\" ) measurement . dropna ( subset = [ \"measurement_date\" ], inplace = True ) if start_date : measurement = measurement [ measurement [ \"measurement_date\" ] >= start_date ] if end_date : measurement = measurement [ measurement [ \"measurement_date\" ] <= end_date ] return measurement","title":"filter_measurement_by_date()"},{"location":"reference/biology/utils/process_measurement/#eds_scikit.biology.utils.process_measurement.tag_measurement_anomaly","text":"tag_measurement_anomaly ( measurement : DataFrame ) -> DataFrame PARAMETER DESCRIPTION measurement DataFrame to filter TYPE: DataFrame start_date EXAMPLE : \"2019-05-01\" TYPE: datetime , optional end_date EXAMPLE : \"2022-05-01\" TYPE: datetime , optional Source code in eds_scikit/biology/utils/process_measurement.py 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 def tag_measurement_anomaly ( measurement : DataFrame ) -> DataFrame : \"\"\" Parameters ---------- measurement : DataFrame DataFrame to filter start_date : datetime, optional **EXAMPLE**: `\"2019-05-01\"` end_date : datetime, optional **EXAMPLE**: `\"2022-05-01\"` Returns ------- \"\"\" measurement [ \"range_high_anomaly\" ] = ( ~ measurement . range_high . isna ()) & ( measurement [ \"value_as_number\" ] > measurement [ \"range_high\" ] ) measurement [ \"range_low_anomaly\" ] = ( ~ measurement . range_low . isna ()) & ( measurement [ \"value_as_number\" ] < measurement [ \"range_low\" ] ) return measurement","title":"tag_measurement_anomaly()"},{"location":"reference/biology/utils/process_measurement/#eds_scikit.biology.utils.process_measurement.convert_measurement_units","text":"convert_measurement_units ( measurement : DataFrame , concepts_sets : List [ ConceptsSet ]) -> DataFrame Add value_as_number_normalized, unit_source_value_normalized and factor columns to measurement dataframe based on concepts_sets and units. PARAMETER DESCRIPTION measurement TYPE: DataFrame concepts_sets TYPE: List [ ConceptsSet ] RETURNS DESCRIPTION DataFrame Measurement with added columns value_as_number_normalized, unit_source_value_normalized and factor. Source code in eds_scikit/biology/utils/process_measurement.py 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 def convert_measurement_units ( measurement : DataFrame , concepts_sets : List [ ConceptsSet ] ) -> DataFrame : \"\"\"Add value_as_number_normalized, unit_source_value_normalized and factor columns to measurement dataframe based on concepts_sets and units. Parameters ---------- measurement : DataFrame concepts_sets : List[ConceptsSet] Returns ------- DataFrame Measurement with added columns value_as_number_normalized, unit_source_value_normalized and factor. \"\"\" if is_koalas ( measurement ): measurement = cache ( measurement ) measurement . shape conversion_table = to ( \"koalas\" , get_conversion_table ( measurement , concepts_sets ) ) else : conversion_table = get_conversion_table ( measurement , concepts_sets ) measurement = measurement . merge ( conversion_table , on = [ \"concept_set\" , \"unit_source_value\" ] ) measurement [ \"value_as_number_normalized\" ] = ( measurement [ \"value_as_number\" ] * measurement [ \"factor\" ] ) return measurement","title":"convert_measurement_units()"},{"location":"reference/biology/utils/process_measurement/#eds_scikit.biology.utils.process_measurement.get_conversion_table","text":"get_conversion_table ( measurement : DataFrame , concepts_sets : List [ ConceptsSet ]) -> DataFrame Given measurement dataframe and list of concepts_sets output conversion table to be merged with measurement. PARAMETER DESCRIPTION measurement TYPE: DataFrame concepts_sets TYPE: List [ ConceptsSet ] RETURNS DESCRIPTION DataFrame Conversion table to be merged with measurement Source code in eds_scikit/biology/utils/process_measurement.py 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 def get_conversion_table ( measurement : DataFrame , concepts_sets : List [ ConceptsSet ] ) -> DataFrame : \"\"\"Given measurement dataframe and list of concepts_sets output conversion table to be merged with measurement. Parameters ---------- measurement : DataFrame concepts_sets : List[ConceptsSet] Returns ------- DataFrame Conversion table to be merged with measurement \"\"\" conversion_table = ( measurement . groupby ( \"concept_set\" )[ \"unit_source_value\" ] . unique () . explode () . to_frame () . reset_index () ) conversion_table = to ( \"pandas\" , conversion_table ) conversion_table [ \"unit_source_value_normalized\" ] = conversion_table [ \"unit_source_value\" ] conversion_table [ \"factor\" ] = conversion_table . apply ( lambda x : 1 if x . unit_source_value_normalized else 0 , axis = 1 ) for concept_set in concepts_sets : unit_source_value_normalized = concept_set . units . target_unit conversion_table . loc [ conversion_table . concept_set == concept_set . name , \"unit_source_value_normalized\" , ] = conversion_table . apply ( lambda x : unit_source_value_normalized if concept_set . units . can_be_converted ( x . unit_source_value , unit_source_value_normalized ) else concept_set . units . get_unit_base ( x . unit_source_value ), axis = 1 , ) conversion_table . loc [ conversion_table . concept_set == concept_set . name , \"factor\" ] = conversion_table . apply ( lambda x : concept_set . units . convert_unit ( x . unit_source_value , x . unit_source_value_normalized ), axis = 1 , ) conversion_table = conversion_table . fillna ( 1 ) return conversion_table","title":"get_conversion_table()"},{"location":"reference/biology/utils/process_units/","text":"eds_scikit.biology.utils.process_units","title":"process_units"},{"location":"reference/biology/utils/process_units/#eds_scikitbiologyutilsprocess_units","text":"","title":"eds_scikit.biology.utils.process_units"},{"location":"reference/biology/viz/","text":"eds_scikit.biology.viz","title":"`eds_scikit.biology.viz`"},{"location":"reference/biology/viz/#eds_scikitbiologyviz","text":"","title":"eds_scikit.biology.viz"},{"location":"reference/biology/viz/aggregate/","text":"eds_scikit.biology.viz.aggregate aggregate_measurement aggregate_measurement ( measurement : DataFrame , stats_only : bool , overall_only : bool , value_column : str , unit_column : str , category_columns = [], debug = False ) Aggregates measurement dataframe in three descriptive and synthetic dataframe : - measurement_stats - measurement_volumetry - measurement_distribution Useful function before plotting. PARAMETER DESCRIPTION measurement description TYPE: DataFrame stats_only description TYPE: bool overall_only description TYPE: bool category_columns description , by default [] TYPE: list , optional DEFAULT: [] RETURNS DESCRIPTION _type_ description Source code in eds_scikit/biology/viz/aggregate.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def aggregate_measurement ( measurement : DataFrame , stats_only : bool , overall_only : bool , value_column : str , unit_column : str , category_columns = [], debug = False , ): \"\"\"Aggregates measurement dataframe in three descriptive and synthetic dataframe : - measurement_stats - measurement_volumetry - measurement_distribution Useful function before plotting. Parameters ---------- measurement : DataFrame _description_ stats_only : bool _description_ overall_only : bool _description_ category_columns : list, optional _description_, by default [] Returns ------- _type_ _description_ \"\"\" check_columns ( df = measurement , required_columns = [ \"measurement_id\" , unit_column , \"measurement_date\" , value_column , ] + category_columns , df_name = \"measurement\" , ) measurement . shape # Truncate date measurement [ \"measurement_month\" ] = ( measurement [ \"measurement_date\" ] . astype ( \"datetime64\" ) . dt . strftime ( \"%Y-%m\" ) ) measurement = measurement . drop ( columns = [ \"measurement_date\" ]) # Filter measurement with missing values filtered_measurement , missing_value = filter_missing_values ( measurement ) # Compute measurement statistics by code measurement_stats = _describe_measurement_by_code ( filtered_measurement , overall_only , value_column , unit_column , category_columns , debug , ) if stats_only : return { \"measurement_stats\" : measurement_stats } # Count measurement by care_site and by code per each month measurement_volumetry = _count_measurement_by_category_and_code_per_month ( filtered_measurement , missing_value , value_column , unit_column , category_columns , debug , ) # Bin measurement values by care_site and by code measurement_distribution = _bin_measurement_value_by_category_and_code ( filtered_measurement , value_column , unit_column , category_columns , debug ) return { \"measurement_stats\" : measurement_stats , \"measurement_volumetry\" : measurement_volumetry , \"measurement_distribution\" : measurement_distribution , } add_mad_minmax add_mad_minmax ( measurement : DataFrame , category_cols : List [ str ], value_column : str = 'value_as_number' , unit_column : str = 'unit_source_value' ) -> DataFrame Add min_value, max_value column to measurement based on MAD criteria. PARAMETER DESCRIPTION measurement measurement dataframe TYPE: DataFrame category_cols measurement category columns to perform the groupby on when computing MAD TYPE: List [ str ] value_column measurement value column on which MAD will be computed TYPE: str DEFAULT: 'value_as_number' RETURNS DESCRIPTION DataFrame measurement dataframe with added columns min_value, max_value Source code in eds_scikit/biology/viz/aggregate.py 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 def add_mad_minmax ( measurement : DataFrame , category_cols : List [ str ], value_column : str = \"value_as_number\" , unit_column : str = \"unit_source_value\" , ) -> DataFrame : \"\"\"Add min_value, max_value column to measurement based on MAD criteria. Parameters ---------- measurement : DataFrame measurement dataframe category_cols : List[str] measurement category columns to perform the groupby on when computing MAD value_column : str measurement value column on which MAD will be computed Returns ------- DataFrame measurement dataframe with added columns min_value, max_value \"\"\" measurement_median = ( measurement [ category_cols + [ value_column ]] . groupby ( category_cols , as_index = False , dropna = False , ) . median () . rename ( columns = { value_column : \"median\" }) ) # Add median column to the measurement table measurement_median = measurement_median . merge ( measurement [ category_cols + [ value_column , ] ], on = category_cols , ) # Compute median deviation for each measurement measurement_median [ \"median_deviation\" ] = abs ( measurement_median [ \"median\" ] - measurement_median [ value_column ] ) # Compute MAD per care site and code measurement_mad = ( measurement_median [ category_cols + [ \"median\" , \"median_deviation\" , ] ] . groupby ( category_cols + [ \"median\" , ], as_index = False , dropna = False , ) . median () . rename ( columns = { \"median_deviation\" : \"MAD\" }) ) measurement_mad [ \"MAD\" ] = 1.48 * measurement_mad [ \"MAD\" ] # Add MAD column to the measurement table measurement = measurement_mad . merge ( measurement , on = category_cols , ) # Compute binned value measurement [ \"max_value\" ] = measurement [ \"median\" ] + 4 * measurement [ \"MAD\" ] measurement [ \"min_value\" ] = measurement [ \"median\" ] - 4 * measurement [ \"MAD\" ] return measurement","title":"aggregate"},{"location":"reference/biology/viz/aggregate/#eds_scikitbiologyvizaggregate","text":"","title":"eds_scikit.biology.viz.aggregate"},{"location":"reference/biology/viz/aggregate/#eds_scikit.biology.viz.aggregate.aggregate_measurement","text":"aggregate_measurement ( measurement : DataFrame , stats_only : bool , overall_only : bool , value_column : str , unit_column : str , category_columns = [], debug = False ) Aggregates measurement dataframe in three descriptive and synthetic dataframe : - measurement_stats - measurement_volumetry - measurement_distribution Useful function before plotting. PARAMETER DESCRIPTION measurement description TYPE: DataFrame stats_only description TYPE: bool overall_only description TYPE: bool category_columns description , by default [] TYPE: list , optional DEFAULT: [] RETURNS DESCRIPTION _type_ description Source code in eds_scikit/biology/viz/aggregate.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def aggregate_measurement ( measurement : DataFrame , stats_only : bool , overall_only : bool , value_column : str , unit_column : str , category_columns = [], debug = False , ): \"\"\"Aggregates measurement dataframe in three descriptive and synthetic dataframe : - measurement_stats - measurement_volumetry - measurement_distribution Useful function before plotting. Parameters ---------- measurement : DataFrame _description_ stats_only : bool _description_ overall_only : bool _description_ category_columns : list, optional _description_, by default [] Returns ------- _type_ _description_ \"\"\" check_columns ( df = measurement , required_columns = [ \"measurement_id\" , unit_column , \"measurement_date\" , value_column , ] + category_columns , df_name = \"measurement\" , ) measurement . shape # Truncate date measurement [ \"measurement_month\" ] = ( measurement [ \"measurement_date\" ] . astype ( \"datetime64\" ) . dt . strftime ( \"%Y-%m\" ) ) measurement = measurement . drop ( columns = [ \"measurement_date\" ]) # Filter measurement with missing values filtered_measurement , missing_value = filter_missing_values ( measurement ) # Compute measurement statistics by code measurement_stats = _describe_measurement_by_code ( filtered_measurement , overall_only , value_column , unit_column , category_columns , debug , ) if stats_only : return { \"measurement_stats\" : measurement_stats } # Count measurement by care_site and by code per each month measurement_volumetry = _count_measurement_by_category_and_code_per_month ( filtered_measurement , missing_value , value_column , unit_column , category_columns , debug , ) # Bin measurement values by care_site and by code measurement_distribution = _bin_measurement_value_by_category_and_code ( filtered_measurement , value_column , unit_column , category_columns , debug ) return { \"measurement_stats\" : measurement_stats , \"measurement_volumetry\" : measurement_volumetry , \"measurement_distribution\" : measurement_distribution , }","title":"aggregate_measurement()"},{"location":"reference/biology/viz/aggregate/#eds_scikit.biology.viz.aggregate.add_mad_minmax","text":"add_mad_minmax ( measurement : DataFrame , category_cols : List [ str ], value_column : str = 'value_as_number' , unit_column : str = 'unit_source_value' ) -> DataFrame Add min_value, max_value column to measurement based on MAD criteria. PARAMETER DESCRIPTION measurement measurement dataframe TYPE: DataFrame category_cols measurement category columns to perform the groupby on when computing MAD TYPE: List [ str ] value_column measurement value column on which MAD will be computed TYPE: str DEFAULT: 'value_as_number' RETURNS DESCRIPTION DataFrame measurement dataframe with added columns min_value, max_value Source code in eds_scikit/biology/viz/aggregate.py 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 def add_mad_minmax ( measurement : DataFrame , category_cols : List [ str ], value_column : str = \"value_as_number\" , unit_column : str = \"unit_source_value\" , ) -> DataFrame : \"\"\"Add min_value, max_value column to measurement based on MAD criteria. Parameters ---------- measurement : DataFrame measurement dataframe category_cols : List[str] measurement category columns to perform the groupby on when computing MAD value_column : str measurement value column on which MAD will be computed Returns ------- DataFrame measurement dataframe with added columns min_value, max_value \"\"\" measurement_median = ( measurement [ category_cols + [ value_column ]] . groupby ( category_cols , as_index = False , dropna = False , ) . median () . rename ( columns = { value_column : \"median\" }) ) # Add median column to the measurement table measurement_median = measurement_median . merge ( measurement [ category_cols + [ value_column , ] ], on = category_cols , ) # Compute median deviation for each measurement measurement_median [ \"median_deviation\" ] = abs ( measurement_median [ \"median\" ] - measurement_median [ value_column ] ) # Compute MAD per care site and code measurement_mad = ( measurement_median [ category_cols + [ \"median\" , \"median_deviation\" , ] ] . groupby ( category_cols + [ \"median\" , ], as_index = False , dropna = False , ) . median () . rename ( columns = { \"median_deviation\" : \"MAD\" }) ) measurement_mad [ \"MAD\" ] = 1.48 * measurement_mad [ \"MAD\" ] # Add MAD column to the measurement table measurement = measurement_mad . merge ( measurement , on = category_cols , ) # Compute binned value measurement [ \"max_value\" ] = measurement [ \"median\" ] + 4 * measurement [ \"MAD\" ] measurement [ \"min_value\" ] = measurement [ \"median\" ] - 4 * measurement [ \"MAD\" ] return measurement","title":"add_mad_minmax()"},{"location":"reference/biology/viz/plot/","text":"eds_scikit.biology.viz.plot plot_concepts_set plot_concepts_set ( concepts_set_name : str , source_path : str = 'Biology_summary' ) -> Union [ alt . ConcatChart , pd . DataFrame ] Plot and save a summary table and 2 interactive dashboards. For more details, have a look on the visualization section PARAMETER DESCRIPTION concepts_set_name Name of the concepts-set to plot TYPE: str source_path Name of the folder with aggregated data where the plots will be saved TYPE: str , optional DEFAULT: 'Biology_summary' RETURNS DESCRIPTION List [ alt . ConcatChart , pd . DataFrame ] Altair plots describing the volumetric and the distribution properties of your biological data along with a pandas DataFrame with a statistical summary Source code in eds_scikit/biology/viz/plot.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 def plot_concepts_set ( concepts_set_name : str , source_path : str = \"Biology_summary\" , ) -> Union [ alt . ConcatChart , pd . DataFrame ]: \"\"\"Plot and save a summary table and 2 interactive dashboards. For more details, have a look on the [visualization section][visualization] Parameters ---------- concepts_set_name : str Name of the concepts-set to plot source_path : str, optional Name of the folder with aggregated data where the plots will be saved Returns ------- List[alt.ConcatChart, pd.DataFrame] Altair plots describing the volumetric and the distribution properties of your biological data along with a pandas DataFrame with a statistical summary \"\"\" if os . path . isdir ( \" {} / {} \" . format ( source_path , concepts_set_name )): if os . path . isfile ( \" {} / {} /measurement_stats.pkl\" . format ( source_path , concepts_set_name ) ): measurement_stats = pd . read_pickle ( \" {} / {} /measurement_stats.pkl\" . format ( source_path , concepts_set_name ) ) _save_and_display_table ( measurement_stats , source_path , concepts_set_name ) if os . path . isfile ( \" {} / {} /measurement_volumetry.pkl\" . format ( source_path , concepts_set_name ) ): measurement_volumetry = pd . read_pickle ( \" {} / {} /measurement_volumetry.pkl\" . format ( source_path , concepts_set_name ) ) interactive_volumetry = plot_interactive_volumetry ( measurement_volumetry , ) _save_and_display_chart ( interactive_volumetry , source_path , concepts_set_name , \"interactive_volumetry\" , ) if os . path . isfile ( \" {} / {} /measurement_distribution.pkl\" . format ( source_path , concepts_set_name ) ): measurement_distribution = pd . read_pickle ( \" {} / {} /measurement_distribution.pkl\" . format ( source_path , concepts_set_name ) ) interactive_distribution = plot_interactive_distribution ( measurement_distribution , ) _save_and_display_chart ( interactive_distribution , source_path , concepts_set_name , \"interactive_distribution\" , ) else : logger . error ( \"The folder {} has not been found\" , source_path , ) raise FileNotFoundError","title":"plot"},{"location":"reference/biology/viz/plot/#eds_scikitbiologyvizplot","text":"","title":"eds_scikit.biology.viz.plot"},{"location":"reference/biology/viz/plot/#eds_scikit.biology.viz.plot.plot_concepts_set","text":"plot_concepts_set ( concepts_set_name : str , source_path : str = 'Biology_summary' ) -> Union [ alt . ConcatChart , pd . DataFrame ] Plot and save a summary table and 2 interactive dashboards. For more details, have a look on the visualization section PARAMETER DESCRIPTION concepts_set_name Name of the concepts-set to plot TYPE: str source_path Name of the folder with aggregated data where the plots will be saved TYPE: str , optional DEFAULT: 'Biology_summary' RETURNS DESCRIPTION List [ alt . ConcatChart , pd . DataFrame ] Altair plots describing the volumetric and the distribution properties of your biological data along with a pandas DataFrame with a statistical summary Source code in eds_scikit/biology/viz/plot.py 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 def plot_concepts_set ( concepts_set_name : str , source_path : str = \"Biology_summary\" , ) -> Union [ alt . ConcatChart , pd . DataFrame ]: \"\"\"Plot and save a summary table and 2 interactive dashboards. For more details, have a look on the [visualization section][visualization] Parameters ---------- concepts_set_name : str Name of the concepts-set to plot source_path : str, optional Name of the folder with aggregated data where the plots will be saved Returns ------- List[alt.ConcatChart, pd.DataFrame] Altair plots describing the volumetric and the distribution properties of your biological data along with a pandas DataFrame with a statistical summary \"\"\" if os . path . isdir ( \" {} / {} \" . format ( source_path , concepts_set_name )): if os . path . isfile ( \" {} / {} /measurement_stats.pkl\" . format ( source_path , concepts_set_name ) ): measurement_stats = pd . read_pickle ( \" {} / {} /measurement_stats.pkl\" . format ( source_path , concepts_set_name ) ) _save_and_display_table ( measurement_stats , source_path , concepts_set_name ) if os . path . isfile ( \" {} / {} /measurement_volumetry.pkl\" . format ( source_path , concepts_set_name ) ): measurement_volumetry = pd . read_pickle ( \" {} / {} /measurement_volumetry.pkl\" . format ( source_path , concepts_set_name ) ) interactive_volumetry = plot_interactive_volumetry ( measurement_volumetry , ) _save_and_display_chart ( interactive_volumetry , source_path , concepts_set_name , \"interactive_volumetry\" , ) if os . path . isfile ( \" {} / {} /measurement_distribution.pkl\" . format ( source_path , concepts_set_name ) ): measurement_distribution = pd . read_pickle ( \" {} / {} /measurement_distribution.pkl\" . format ( source_path , concepts_set_name ) ) interactive_distribution = plot_interactive_distribution ( measurement_distribution , ) _save_and_display_chart ( interactive_distribution , source_path , concepts_set_name , \"interactive_distribution\" , ) else : logger . error ( \"The folder {} has not been found\" , source_path , ) raise FileNotFoundError","title":"plot_concepts_set()"},{"location":"reference/biology/viz/stats_summary/","text":"eds_scikit.biology.viz.stats_summary measurement_values_summary measurement_values_summary ( measurement : DataFrame , category_cols : List [ str ] = [ 'concept_set' , 'GLIMS_ANABIO_concept_code' ], value_column : str = 'value_as_number' , unit_column : str = 'unit_source_value' ) -> DataFrame Compute measurement values and units summary by category_cols. PARAMETER DESCRIPTION measurement measurement dataframe TYPE: DataFrame category_cols columns on which to groupby the summary, by default [\"concept_set\", \"GLIMS_ANABIO_concept_code\",] TYPE: List [ str ], optional DEFAULT: ['concept_set', 'GLIMS_ANABIO_concept_code'] value_column value column to summarize, by default \"value_as_number\" but can be value_as_number_normalized if units conversion is applied. TYPE: str , optional DEFAULT: 'value_as_number' unit_column units column to summarize, by default \"unit_source_value\" but can be unit_source_value_normalized if units conversion is applied. TYPE: str , optional DEFAULT: 'unit_source_value' RETURNS DESCRIPTION DataFrame statistic summary dataframe Source code in eds_scikit/biology/viz/stats_summary.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 def measurement_values_summary ( measurement : DataFrame , category_cols : List [ str ] = [ \"concept_set\" , \"GLIMS_ANABIO_concept_code\" , ], value_column : str = \"value_as_number\" , unit_column : str = \"unit_source_value\" , ) -> DataFrame : \"\"\"Compute measurement values and units summary by category_cols. Parameters ---------- measurement : DataFrame measurement dataframe category_cols : List[str], optional columns on which to groupby the summary, by default [\"concept_set\", \"GLIMS_ANABIO_concept_code\",] value_column : str, optional value column to summarize, by default \"value_as_number\" but can be value_as_number_normalized if units conversion is applied. unit_column : str, optional units column to summarize, by default \"unit_source_value\" but can be unit_source_value_normalized if units conversion is applied. Returns ------- DataFrame statistic summary dataframe \"\"\" measurement . shape no_units = ( measurement [ unit_column ] == \"non renseigne\" ) | ( measurement [ unit_column ] == \"Unkown\" ) stats_summary = ( measurement [ no_units ] . groupby ( category_cols ) . agg ( no_units = ( \"measurement_id\" , \"count\" )) . reset_index () ) # Count measurements measurement_count = ( measurement . groupby ([ * category_cols , unit_column ]) . agg ( measurement_count = ( \"measurement_id\" , \"count\" )) . reset_index () ) stats_summary = stats_summary . merge ( measurement_count , how = \"right\" , on = category_cols ) # Describe stats measurements measurement_stats = ( measurement [ ~ no_units ] . groupby ([ * category_cols , unit_column ])[[ value_column ]] . describe () ) measurement_stats . columns = [ \"_\" . join ( map ( str , col )) for col in measurement_stats . columns ] measurement_stats = measurement_stats . reset_index () stats_summary = measurement_stats . merge ( stats_summary , how = \"left\" , on = ([ * category_cols , unit_column ]) ) # Count anomalies occurrences_to_count = { \"range_high_anomaly_count\" : measurement [ ~ no_units ] . range_high_anomaly , \"range_low_anomaly_count\" : measurement [ ~ no_units ] . range_low_anomaly , } for key , to_count in occurrences_to_count . items (): additional_summary = ( measurement [ ~ no_units ][ to_count ] . groupby ([ * category_cols , unit_column ])[[ \"measurement_id\" ]] . count () . rename ( columns = { \"measurement_id\" : key }) . reset_index () ) stats_summary = stats_summary . merge ( additional_summary , how = \"left\" , on = [ * category_cols , unit_column ] ) stats_summary = stats_summary . fillna ( 0 ) stats_summary = stats_summary . set_index ( [ * category_cols , \"no_units\" , unit_column ] ) . sort_index () stats_summary = stats_summary [ [ * stats_summary . columns [:: - 1 ][: 3 ], * stats_summary . columns [: - 3 ]] ] stats_summary = to ( \"pandas\" , stats_summary ) return stats_summary","title":"stats_summary"},{"location":"reference/biology/viz/stats_summary/#eds_scikitbiologyvizstats_summary","text":"","title":"eds_scikit.biology.viz.stats_summary"},{"location":"reference/biology/viz/stats_summary/#eds_scikit.biology.viz.stats_summary.measurement_values_summary","text":"measurement_values_summary ( measurement : DataFrame , category_cols : List [ str ] = [ 'concept_set' , 'GLIMS_ANABIO_concept_code' ], value_column : str = 'value_as_number' , unit_column : str = 'unit_source_value' ) -> DataFrame Compute measurement values and units summary by category_cols. PARAMETER DESCRIPTION measurement measurement dataframe TYPE: DataFrame category_cols columns on which to groupby the summary, by default [\"concept_set\", \"GLIMS_ANABIO_concept_code\",] TYPE: List [ str ], optional DEFAULT: ['concept_set', 'GLIMS_ANABIO_concept_code'] value_column value column to summarize, by default \"value_as_number\" but can be value_as_number_normalized if units conversion is applied. TYPE: str , optional DEFAULT: 'value_as_number' unit_column units column to summarize, by default \"unit_source_value\" but can be unit_source_value_normalized if units conversion is applied. TYPE: str , optional DEFAULT: 'unit_source_value' RETURNS DESCRIPTION DataFrame statistic summary dataframe Source code in eds_scikit/biology/viz/stats_summary.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 def measurement_values_summary ( measurement : DataFrame , category_cols : List [ str ] = [ \"concept_set\" , \"GLIMS_ANABIO_concept_code\" , ], value_column : str = \"value_as_number\" , unit_column : str = \"unit_source_value\" , ) -> DataFrame : \"\"\"Compute measurement values and units summary by category_cols. Parameters ---------- measurement : DataFrame measurement dataframe category_cols : List[str], optional columns on which to groupby the summary, by default [\"concept_set\", \"GLIMS_ANABIO_concept_code\",] value_column : str, optional value column to summarize, by default \"value_as_number\" but can be value_as_number_normalized if units conversion is applied. unit_column : str, optional units column to summarize, by default \"unit_source_value\" but can be unit_source_value_normalized if units conversion is applied. Returns ------- DataFrame statistic summary dataframe \"\"\" measurement . shape no_units = ( measurement [ unit_column ] == \"non renseigne\" ) | ( measurement [ unit_column ] == \"Unkown\" ) stats_summary = ( measurement [ no_units ] . groupby ( category_cols ) . agg ( no_units = ( \"measurement_id\" , \"count\" )) . reset_index () ) # Count measurements measurement_count = ( measurement . groupby ([ * category_cols , unit_column ]) . agg ( measurement_count = ( \"measurement_id\" , \"count\" )) . reset_index () ) stats_summary = stats_summary . merge ( measurement_count , how = \"right\" , on = category_cols ) # Describe stats measurements measurement_stats = ( measurement [ ~ no_units ] . groupby ([ * category_cols , unit_column ])[[ value_column ]] . describe () ) measurement_stats . columns = [ \"_\" . join ( map ( str , col )) for col in measurement_stats . columns ] measurement_stats = measurement_stats . reset_index () stats_summary = measurement_stats . merge ( stats_summary , how = \"left\" , on = ([ * category_cols , unit_column ]) ) # Count anomalies occurrences_to_count = { \"range_high_anomaly_count\" : measurement [ ~ no_units ] . range_high_anomaly , \"range_low_anomaly_count\" : measurement [ ~ no_units ] . range_low_anomaly , } for key , to_count in occurrences_to_count . items (): additional_summary = ( measurement [ ~ no_units ][ to_count ] . groupby ([ * category_cols , unit_column ])[[ \"measurement_id\" ]] . count () . rename ( columns = { \"measurement_id\" : key }) . reset_index () ) stats_summary = stats_summary . merge ( additional_summary , how = \"left\" , on = [ * category_cols , unit_column ] ) stats_summary = stats_summary . fillna ( 0 ) stats_summary = stats_summary . set_index ( [ * category_cols , \"no_units\" , unit_column ] ) . sort_index () stats_summary = stats_summary [ [ * stats_summary . columns [:: - 1 ][: 3 ], * stats_summary . columns [: - 3 ]] ] stats_summary = to ( \"pandas\" , stats_summary ) return stats_summary","title":"measurement_values_summary()"},{"location":"reference/biology/viz/wrapper/","text":"eds_scikit.biology.viz.wrapper plot_biology_summary plot_biology_summary ( measurement : DataFrame , value_column : str = 'value_as_number' , unit_column : str = 'unit_source_value' , save_folder_path : str = 'Biology_summary' , stats_only : bool = False , terminologies : List [ str ] = None , debug : bool = False ) -> Union [ alt . ConcatChart , pd . DataFrame ] Aggregate measurements, create plots and saves all the concepts-sets in folder. PARAMETER DESCRIPTION data Instantiated HiveData , PostgresData or PandasData TYPE: Data save_folder_path Name of the folder where the plots will be saved TYPE: str , optional DEFAULT: 'Biology_summary' stats_only If True , it will only aggregate the data for the summary table . TYPE: bool , optional DEFAULT: False terminologies biology summary only on terminologies codes columns TYPE: List [ str ], optional DEFAULT: None value_column value column for distribution summary plot TYPE: str , optional DEFAULT: 'value_as_number' debug If True , info log will de displayed to follow aggregation steps TYPE: bool , optional DEFAULT: False RETURNS DESCRIPTION List [ alt . ConcatChart , pd . DataFrame ] Altair plots describing the volumetric and the distribution properties of your biological data along with a pandas DataFrame with a statistical summary Source code in eds_scikit/biology/viz/wrapper.py 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 def plot_biology_summary ( measurement : DataFrame , value_column : str = \"value_as_number\" , unit_column : str = \"unit_source_value\" , save_folder_path : str = \"Biology_summary\" , stats_only : bool = False , terminologies : List [ str ] = None , debug : bool = False , ) -> Union [ alt . ConcatChart , pd . DataFrame ]: \"\"\" Aggregate measurements, create plots and saves all the concepts-sets in folder. Parameters ---------- data : Data Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData] save_folder_path : str, optional Name of the folder where the plots will be saved stats_only : bool, optional If ``True``, it will only aggregate the data for the [summary table][summary-table]. terminologies : List[str], optional biology summary only on terminologies codes columns value_column : str, optional value column for distribution summary plot debug : bool, optional If ``True``, info log will de displayed to follow aggregation steps Returns ------- List[alt.ConcatChart, pd.DataFrame] Altair plots describing the volumetric and the distribution properties of your biological data along with a pandas DataFrame with a statistical summary \"\"\" if not value_column : raise ValueError ( \"Must give a 'value_column' parameter. By default, use value_as_number. Or value_as_number_normalized if exists.\" ) if not unit_column : raise ValueError ( \"Must give a 'unit_column' parameter. By default, use unit_source_value. Or unit_source_value_normalized if exists.\" ) if not os . path . isdir ( save_folder_path ): os . mkdir ( save_folder_path ) logger . info ( \" {} folder has been created.\" , save_folder_path ) if terminologies : measurement = measurement . drop ( columns = [ f \" { col } _concept_code\" for col in terminologies ] ) tables_agg = aggregate_measurement ( measurement = measurement , value_column = value_column , unit_column = unit_column , stats_only = stats_only , overall_only = stats_only , category_columns = [ \"concept_set\" , \"care_site_short_name\" ], debug = debug , ) table_names = list ( tables_agg . keys ()) concept_sets_names = tables_agg [ table_names [ 0 ]] . concept_set . unique () for concept_set_name in concept_sets_names : concepts_set_path = \" {} / {} \" . format ( save_folder_path , concept_set_name ) rmtree ( concepts_set_path , ignore_errors = True ) os . mkdir ( concepts_set_path ) logger . info ( \" {} / {} folder has been created.\" , save_folder_path , concept_set_name , ) for table_name in table_names : table = tables_agg [ table_name ] . query ( \"concept_set == @concept_set_name\" ) table . to_pickle ( \" {} / {} / {} .pkl\" . format ( save_folder_path , concept_set_name , table_name ) ) logger . info ( \" {} has been processed and saved in {} / {} folder.\" , concept_set_name , save_folder_path , concept_set_name , ) plot_concepts_set ( concepts_set_name = concept_set_name , source_path = save_folder_path )","title":"wrapper"},{"location":"reference/biology/viz/wrapper/#eds_scikitbiologyvizwrapper","text":"","title":"eds_scikit.biology.viz.wrapper"},{"location":"reference/biology/viz/wrapper/#eds_scikit.biology.viz.wrapper.plot_biology_summary","text":"plot_biology_summary ( measurement : DataFrame , value_column : str = 'value_as_number' , unit_column : str = 'unit_source_value' , save_folder_path : str = 'Biology_summary' , stats_only : bool = False , terminologies : List [ str ] = None , debug : bool = False ) -> Union [ alt . ConcatChart , pd . DataFrame ] Aggregate measurements, create plots and saves all the concepts-sets in folder. PARAMETER DESCRIPTION data Instantiated HiveData , PostgresData or PandasData TYPE: Data save_folder_path Name of the folder where the plots will be saved TYPE: str , optional DEFAULT: 'Biology_summary' stats_only If True , it will only aggregate the data for the summary table . TYPE: bool , optional DEFAULT: False terminologies biology summary only on terminologies codes columns TYPE: List [ str ], optional DEFAULT: None value_column value column for distribution summary plot TYPE: str , optional DEFAULT: 'value_as_number' debug If True , info log will de displayed to follow aggregation steps TYPE: bool , optional DEFAULT: False RETURNS DESCRIPTION List [ alt . ConcatChart , pd . DataFrame ] Altair plots describing the volumetric and the distribution properties of your biological data along with a pandas DataFrame with a statistical summary Source code in eds_scikit/biology/viz/wrapper.py 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 def plot_biology_summary ( measurement : DataFrame , value_column : str = \"value_as_number\" , unit_column : str = \"unit_source_value\" , save_folder_path : str = \"Biology_summary\" , stats_only : bool = False , terminologies : List [ str ] = None , debug : bool = False , ) -> Union [ alt . ConcatChart , pd . DataFrame ]: \"\"\" Aggregate measurements, create plots and saves all the concepts-sets in folder. Parameters ---------- data : Data Instantiated [``HiveData``][eds_scikit.io.hive.HiveData], [``PostgresData``][eds_scikit.io.postgres.PostgresData] or [``PandasData``][eds_scikit.io.files.PandasData] save_folder_path : str, optional Name of the folder where the plots will be saved stats_only : bool, optional If ``True``, it will only aggregate the data for the [summary table][summary-table]. terminologies : List[str], optional biology summary only on terminologies codes columns value_column : str, optional value column for distribution summary plot debug : bool, optional If ``True``, info log will de displayed to follow aggregation steps Returns ------- List[alt.ConcatChart, pd.DataFrame] Altair plots describing the volumetric and the distribution properties of your biological data along with a pandas DataFrame with a statistical summary \"\"\" if not value_column : raise ValueError ( \"Must give a 'value_column' parameter. By default, use value_as_number. Or value_as_number_normalized if exists.\" ) if not unit_column : raise ValueError ( \"Must give a 'unit_column' parameter. By default, use unit_source_value. Or unit_source_value_normalized if exists.\" ) if not os . path . isdir ( save_folder_path ): os . mkdir ( save_folder_path ) logger . info ( \" {} folder has been created.\" , save_folder_path ) if terminologies : measurement = measurement . drop ( columns = [ f \" { col } _concept_code\" for col in terminologies ] ) tables_agg = aggregate_measurement ( measurement = measurement , value_column = value_column , unit_column = unit_column , stats_only = stats_only , overall_only = stats_only , category_columns = [ \"concept_set\" , \"care_site_short_name\" ], debug = debug , ) table_names = list ( tables_agg . keys ()) concept_sets_names = tables_agg [ table_names [ 0 ]] . concept_set . unique () for concept_set_name in concept_sets_names : concepts_set_path = \" {} / {} \" . format ( save_folder_path , concept_set_name ) rmtree ( concepts_set_path , ignore_errors = True ) os . mkdir ( concepts_set_path ) logger . info ( \" {} / {} folder has been created.\" , save_folder_path , concept_set_name , ) for table_name in table_names : table = tables_agg [ table_name ] . query ( \"concept_set == @concept_set_name\" ) table . to_pickle ( \" {} / {} / {} .pkl\" . format ( save_folder_path , concept_set_name , table_name ) ) logger . info ( \" {} has been processed and saved in {} / {} folder.\" , concept_set_name , save_folder_path , concept_set_name , ) plot_concepts_set ( concepts_set_name = concept_set_name , source_path = save_folder_path )","title":"plot_biology_summary()"},{"location":"reference/datasets/","text":"eds_scikit.datasets list_all_synthetics list_all_synthetics () -> List [ str ] Helper to list all available synthetic datasets RETURNS DESCRIPTION List [ str ] List of datasets names Source code in eds_scikit/datasets/__init__.py 59 60 61 62 63 64 65 66 67 68 def list_all_synthetics () -> List [ str ]: \"\"\" Helper to list all available synthetic datasets Returns ------- List[str] List of datasets names \"\"\" return [ func . __name__ for func in __all__ ]","title":"`eds_scikit.datasets`"},{"location":"reference/datasets/#eds_scikitdatasets","text":"","title":"eds_scikit.datasets"},{"location":"reference/datasets/#eds_scikit.datasets.list_all_synthetics","text":"list_all_synthetics () -> List [ str ] Helper to list all available synthetic datasets RETURNS DESCRIPTION List [ str ] List of datasets names Source code in eds_scikit/datasets/__init__.py 59 60 61 62 63 64 65 66 67 68 def list_all_synthetics () -> List [ str ]: \"\"\" Helper to list all available synthetic datasets Returns ------- List[str] List of datasets names \"\"\" return [ func . __name__ for func in __all__ ]","title":"list_all_synthetics()"},{"location":"reference/datasets/generation_scripts/","text":"eds_scikit.datasets.generation_scripts","title":"`eds_scikit.datasets.generation_scripts`"},{"location":"reference/datasets/generation_scripts/#eds_scikitdatasetsgeneration_scripts","text":"","title":"eds_scikit.datasets.generation_scripts"},{"location":"reference/datasets/generation_scripts/care_site_hierarchy/","text":"eds_scikit.datasets.generation_scripts.care_site_hierarchy generate_care_site_hierarchy generate_care_site_hierarchy ( care_site : framework . DataFrame , fact_relationship : framework . DataFrame , care_site_categories : List [ str ]) -> None Generate the care site hierarchy dataset. PARAMETER DESCRIPTION care_site The care_site DataFrame TYPE: framework . DataFrame fact_relationship The fact_relationship DataFrame TYPE: framework . DataFrame care_site_categories A list of care_site_type_source_value to use as categories TYPE: List [ str ] Source code in eds_scikit/datasets/generation_scripts/care_site_hierarchy.py 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 def generate_care_site_hierarchy ( care_site : framework . DataFrame , fact_relationship : framework . DataFrame , care_site_categories : List [ str ], ) -> None : # pragma: no cover \"\"\" Generate the care site hierarchy dataset. Parameters ---------- care_site : framework.DataFrame The `care_site` DataFrame fact_relationship : framework.DataFrame The `fact_relationship` DataFrame care_site_categories : List[str] A list of `care_site_type_source_value` to use as categories \"\"\" care_site = _load_care_site_categories ( care_site , care_site_categories ) relationships = _load_care_site_relationships ( fact_relationship ) care_site = _simplify_care_site_categories ( care_site , relationships ) care_site_hierarchy = hierarchy . build_hierarchy ( care_site , relationships ) care_site_hierarchy = _simplify_care_site_hierarchy ( care_site_hierarchy ) _save_care_site_hierarchy ( care_site_hierarchy , DATASET_FOLDER )","title":"care_site_hierarchy"},{"location":"reference/datasets/generation_scripts/care_site_hierarchy/#eds_scikitdatasetsgeneration_scriptscare_site_hierarchy","text":"","title":"eds_scikit.datasets.generation_scripts.care_site_hierarchy"},{"location":"reference/datasets/generation_scripts/care_site_hierarchy/#eds_scikit.datasets.generation_scripts.care_site_hierarchy.generate_care_site_hierarchy","text":"generate_care_site_hierarchy ( care_site : framework . DataFrame , fact_relationship : framework . DataFrame , care_site_categories : List [ str ]) -> None Generate the care site hierarchy dataset. PARAMETER DESCRIPTION care_site The care_site DataFrame TYPE: framework . DataFrame fact_relationship The fact_relationship DataFrame TYPE: framework . DataFrame care_site_categories A list of care_site_type_source_value to use as categories TYPE: List [ str ] Source code in eds_scikit/datasets/generation_scripts/care_site_hierarchy.py 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 def generate_care_site_hierarchy ( care_site : framework . DataFrame , fact_relationship : framework . DataFrame , care_site_categories : List [ str ], ) -> None : # pragma: no cover \"\"\" Generate the care site hierarchy dataset. Parameters ---------- care_site : framework.DataFrame The `care_site` DataFrame fact_relationship : framework.DataFrame The `fact_relationship` DataFrame care_site_categories : List[str] A list of `care_site_type_source_value` to use as categories \"\"\" care_site = _load_care_site_categories ( care_site , care_site_categories ) relationships = _load_care_site_relationships ( fact_relationship ) care_site = _simplify_care_site_categories ( care_site , relationships ) care_site_hierarchy = hierarchy . build_hierarchy ( care_site , relationships ) care_site_hierarchy = _simplify_care_site_hierarchy ( care_site_hierarchy ) _save_care_site_hierarchy ( care_site_hierarchy , DATASET_FOLDER )","title":"generate_care_site_hierarchy()"},{"location":"reference/datasets/synthetic/","text":"eds_scikit.datasets.synthetic","title":"`eds_scikit.datasets.synthetic`"},{"location":"reference/datasets/synthetic/#eds_scikitdatasetssynthetic","text":"","title":"eds_scikit.datasets.synthetic"},{"location":"reference/datasets/synthetic/base_dataset/","text":"eds_scikit.datasets.synthetic.base_dataset","title":"base_dataset"},{"location":"reference/datasets/synthetic/base_dataset/#eds_scikitdatasetssyntheticbase_dataset","text":"","title":"eds_scikit.datasets.synthetic.base_dataset"},{"location":"reference/datasets/synthetic/biology/","text":"eds_scikit.datasets.synthetic.biology load_biology_data load_biology_data ( n_entity : int = 5 , mean_measurement : int = 10000 , n_care_site : int = 5 , n_person : int = 5 , n_visit_occurrence : int = 5 , units : List [ str ] = [ 'g' , 'g/l' , 'mol' , 's' ], row_status_source_values : List [ str ] = [ 'Valid\u00e9' , 'Discontinu\u00e9' , 'Disponible' , 'Attendu' , 'Confirm\u00e9' , 'Initial' ], t_start : datetime = datetime ( 2017 , 1 , 1 ), t_end : datetime = datetime ( 2022 , 1 , 1 ), seed : int = None ) Create a minimalistic dataset for the bioclean function. RETURNS DESCRIPTION biology_dataset measurement, concept and concept_relationship. TYPE: BiologyDataset, a dataclass comprised of Source code in eds_scikit/datasets/synthetic/biology.py 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 def load_biology_data ( n_entity : int = 5 , mean_measurement : int = 10000 , n_care_site : int = 5 , n_person : int = 5 , n_visit_occurrence : int = 5 , units : List [ str ] = [ \"g\" , \"g/l\" , \"mol\" , \"s\" ], row_status_source_values : List [ str ] = [ \"Valid\u00e9\" , \"Discontinu\u00e9\" , \"Disponible\" , \"Attendu\" , \"Confirm\u00e9\" , \"Initial\" , ], t_start : datetime = datetime ( 2017 , 1 , 1 ), t_end : datetime = datetime ( 2022 , 1 , 1 ), seed : int = None , ): \"\"\" Create a minimalistic dataset for the `bioclean` function. Returns ------- biology_dataset: BiologyDataset, a dataclass comprised of measurement, concept and concept_relationship. \"\"\" if seed : np . random . seed ( seed = seed ) concept , concept_relationship , src_concept_name = _generate_concept ( n_entity = n_entity , units = units ) measurement = _generate_measurement ( t_start = t_start , t_end = t_end , mean_measurement = mean_measurement , units = units , src_concept_name = src_concept_name , n_visit_occurrence = n_visit_occurrence , n_person = n_person , row_status_source_values = row_status_source_values , ) care_site = _generate_care_site ( n_care_site = n_care_site ) visit_occurrence = _generate_visit_occurrence ( n_visit_occurrence = n_visit_occurrence , n_care_site = n_care_site ) return BiologyDataset ( measurement = measurement , concept = concept , concept_relationship = concept_relationship , visit_occurrence = visit_occurrence , care_site = care_site , available_tables = [ \"measurement\" , \"concept\" , \"concept_relationship\" , \"visit_occurrence\" , \"care_site\" , ], t_start = t_start , t_end = t_end , module = \"pandas\" , )","title":"biology"},{"location":"reference/datasets/synthetic/biology/#eds_scikitdatasetssyntheticbiology","text":"","title":"eds_scikit.datasets.synthetic.biology"},{"location":"reference/datasets/synthetic/biology/#eds_scikit.datasets.synthetic.biology.load_biology_data","text":"load_biology_data ( n_entity : int = 5 , mean_measurement : int = 10000 , n_care_site : int = 5 , n_person : int = 5 , n_visit_occurrence : int = 5 , units : List [ str ] = [ 'g' , 'g/l' , 'mol' , 's' ], row_status_source_values : List [ str ] = [ 'Valid\u00e9' , 'Discontinu\u00e9' , 'Disponible' , 'Attendu' , 'Confirm\u00e9' , 'Initial' ], t_start : datetime = datetime ( 2017 , 1 , 1 ), t_end : datetime = datetime ( 2022 , 1 , 1 ), seed : int = None ) Create a minimalistic dataset for the bioclean function. RETURNS DESCRIPTION biology_dataset measurement, concept and concept_relationship. TYPE: BiologyDataset, a dataclass comprised of Source code in eds_scikit/datasets/synthetic/biology.py 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 def load_biology_data ( n_entity : int = 5 , mean_measurement : int = 10000 , n_care_site : int = 5 , n_person : int = 5 , n_visit_occurrence : int = 5 , units : List [ str ] = [ \"g\" , \"g/l\" , \"mol\" , \"s\" ], row_status_source_values : List [ str ] = [ \"Valid\u00e9\" , \"Discontinu\u00e9\" , \"Disponible\" , \"Attendu\" , \"Confirm\u00e9\" , \"Initial\" , ], t_start : datetime = datetime ( 2017 , 1 , 1 ), t_end : datetime = datetime ( 2022 , 1 , 1 ), seed : int = None , ): \"\"\" Create a minimalistic dataset for the `bioclean` function. Returns ------- biology_dataset: BiologyDataset, a dataclass comprised of measurement, concept and concept_relationship. \"\"\" if seed : np . random . seed ( seed = seed ) concept , concept_relationship , src_concept_name = _generate_concept ( n_entity = n_entity , units = units ) measurement = _generate_measurement ( t_start = t_start , t_end = t_end , mean_measurement = mean_measurement , units = units , src_concept_name = src_concept_name , n_visit_occurrence = n_visit_occurrence , n_person = n_person , row_status_source_values = row_status_source_values , ) care_site = _generate_care_site ( n_care_site = n_care_site ) visit_occurrence = _generate_visit_occurrence ( n_visit_occurrence = n_visit_occurrence , n_care_site = n_care_site ) return BiologyDataset ( measurement = measurement , concept = concept , concept_relationship = concept_relationship , visit_occurrence = visit_occurrence , care_site = care_site , available_tables = [ \"measurement\" , \"concept\" , \"concept_relationship\" , \"visit_occurrence\" , \"care_site\" , ], t_start = t_start , t_end = t_end , module = \"pandas\" , )","title":"load_biology_data()"},{"location":"reference/datasets/synthetic/ccam/","text":"eds_scikit.datasets.synthetic.ccam load_ccam load_ccam () Create a minimalistic dataset for the procedures_from_ccam function. RETURNS DESCRIPTION ccam_dataset procedure_occurrence and visit_occurrence. TYPE: CCAMDataset, a dataclass comprised of Source code in eds_scikit/datasets/synthetic/ccam.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 def load_ccam (): \"\"\" Create a minimalistic dataset for the `procedures_from_ccam` function. Returns ------- ccam_dataset: CCAMDataset, a dataclass comprised of procedure_occurrence and visit_occurrence. \"\"\" person_ids = [ 1 , 1 , 2 , 3 , 4 , 5 ] procedure_source_values = [ \"DZEA001\" , \"DZEA003\" , \"GFEA004\" , \"EQQF006\" , \"DZEA001\" , \"DZEA001\" , ] procedure_datetimes = pd . to_datetime ( [ \"2010-01-01\" , \"2010-01-01\" , \"2012-01-01\" , \"2012-01-01\" , \"2012-01-01\" , \"2012-01-01\" , ] ) visit_occurrence_ids = [ 11 , 12 , 13 , 14 , 98 , 99 ] procedure_occurrence = pd . DataFrame ( { \"person_id\" : person_ids , \"procedure_source_value\" : procedure_source_values , \"procedure_datetime\" : procedure_datetimes , \"visit_occurrence_id\" : visit_occurrence_ids , } ) person_ids = [ 1 ] * 6 visit_occurrence_ids = [ 11 , 12 , 13 , 14 , 98 , 99 ] visit_start_datetimes = pd . to_datetime ( [ \"2010-01-01\" , \"2010-01-01\" , \"2012-01-01\" , \"2020-01-01\" , \"2000-01-01\" , \"2050-01-01\" , ] ) visit_end_datetimes = pd . to_datetime ( [ \"2010-01-01\" , \"2010-01-01\" , \"2012-01-01\" , \"2020-01-01\" , \"2020-01-01\" , \"1900-01-01\" , ] ) visit_occurrence = pd . DataFrame ( { \"person_id\" : person_ids , \"visit_occurrence_id\" : visit_occurrence_ids , \"visit_start_datetime\" : visit_start_datetimes , \"visit_end_datetime\" : visit_end_datetimes , } ) return CCAMDataset ( procedure_occurrence = procedure_occurrence , visit_occurrence = visit_occurrence , )","title":"ccam"},{"location":"reference/datasets/synthetic/ccam/#eds_scikitdatasetssyntheticccam","text":"","title":"eds_scikit.datasets.synthetic.ccam"},{"location":"reference/datasets/synthetic/ccam/#eds_scikit.datasets.synthetic.ccam.load_ccam","text":"load_ccam () Create a minimalistic dataset for the procedures_from_ccam function. RETURNS DESCRIPTION ccam_dataset procedure_occurrence and visit_occurrence. TYPE: CCAMDataset, a dataclass comprised of Source code in eds_scikit/datasets/synthetic/ccam.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 def load_ccam (): \"\"\" Create a minimalistic dataset for the `procedures_from_ccam` function. Returns ------- ccam_dataset: CCAMDataset, a dataclass comprised of procedure_occurrence and visit_occurrence. \"\"\" person_ids = [ 1 , 1 , 2 , 3 , 4 , 5 ] procedure_source_values = [ \"DZEA001\" , \"DZEA003\" , \"GFEA004\" , \"EQQF006\" , \"DZEA001\" , \"DZEA001\" , ] procedure_datetimes = pd . to_datetime ( [ \"2010-01-01\" , \"2010-01-01\" , \"2012-01-01\" , \"2012-01-01\" , \"2012-01-01\" , \"2012-01-01\" , ] ) visit_occurrence_ids = [ 11 , 12 , 13 , 14 , 98 , 99 ] procedure_occurrence = pd . DataFrame ( { \"person_id\" : person_ids , \"procedure_source_value\" : procedure_source_values , \"procedure_datetime\" : procedure_datetimes , \"visit_occurrence_id\" : visit_occurrence_ids , } ) person_ids = [ 1 ] * 6 visit_occurrence_ids = [ 11 , 12 , 13 , 14 , 98 , 99 ] visit_start_datetimes = pd . to_datetime ( [ \"2010-01-01\" , \"2010-01-01\" , \"2012-01-01\" , \"2020-01-01\" , \"2000-01-01\" , \"2050-01-01\" , ] ) visit_end_datetimes = pd . to_datetime ( [ \"2010-01-01\" , \"2010-01-01\" , \"2012-01-01\" , \"2020-01-01\" , \"2020-01-01\" , \"1900-01-01\" , ] ) visit_occurrence = pd . DataFrame ( { \"person_id\" : person_ids , \"visit_occurrence_id\" : visit_occurrence_ids , \"visit_start_datetime\" : visit_start_datetimes , \"visit_end_datetime\" : visit_end_datetimes , } ) return CCAMDataset ( procedure_occurrence = procedure_occurrence , visit_occurrence = visit_occurrence , )","title":"load_ccam()"},{"location":"reference/datasets/synthetic/consultation_dates/","text":"eds_scikit.datasets.synthetic.consultation_dates load_consultation_dates load_consultation_dates () Create a minimalistic dataset for the get_consultation_dates function. RETURNS DESCRIPTION consultation_dataset visit_occurence, note and note_nlp. TYPE: ConsultationDataset, a dataclass comprised of Source code in eds_scikit/datasets/synthetic/consultation_dates.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 def load_consultation_dates (): \"\"\" Create a minimalistic dataset for the `get_consultation_dates` function. Returns ------- consultation_dataset: ConsultationDataset, a dataclass comprised of visit_occurence, note and note_nlp. \"\"\" n_visits = 4 visit_occurrence_ids = list ( range ( n_visits )) visit_source_value = [ \"consultation externe\" , \"consultation externe\" , \"hospitalisation\" , \"consultation externe\" , ] visit_occurrence = pd . DataFrame ( { \"visit_occurrence_id\" : visit_occurrence_ids , \"visit_source_value\" : visit_source_value , } ) n_notes = 10 visit_occurrence_ids = [ n_visits * idx // n_notes for idx in range ( n_notes )] note_ids = list ( range ( n_notes )) note_datetimes = [ 1 , 1 , 5 , 6 , 7 , 1 , 1 , 2 , 3 , 8 ] note_datetimes = [ datetime ( 2020 , 1 , day ) for day in note_datetimes ] note_class_source_value = ( n_notes // 2 ) * [ \"CR-CONS\" ] + ( n_notes // 2 ) * [ \"CR-HOSP\" ] note = pd . DataFrame ( { \"visit_occurrence_id\" : visit_occurrence_ids , \"note_id\" : note_ids , \"note_datetime\" : note_datetimes , \"note_class_source_value\" : note_class_source_value , } ) n_note_nlp = 20 starts = [ 4 , 14 , 0 , 7 , 5 , 11 , 8 , 18 , 6 , 19 , 15 , 9 , 17 , 1 , 12 , 2 , 3 , 16 , 10 , 13 , ] note_ids = [ n_notes * idx // n_note_nlp for idx in range ( n_note_nlp )] consultation_dates = 2 * [ 1 , 1 , 5 , 6 , 7 , 1 , 2 , 3 , 9 , 12 ] consultation_dates = [ datetime ( 2020 , 1 , day ) for day in consultation_dates ] note_nlp = pd . DataFrame ( { \"note_id\" : note_ids , \"consultation_date\" : consultation_dates , \"start\" : starts , } ) return ConsultationDataset ( visit_occurrence = visit_occurrence , note = note , note_nlp = note_nlp , )","title":"consultation_dates"},{"location":"reference/datasets/synthetic/consultation_dates/#eds_scikitdatasetssyntheticconsultation_dates","text":"","title":"eds_scikit.datasets.synthetic.consultation_dates"},{"location":"reference/datasets/synthetic/consultation_dates/#eds_scikit.datasets.synthetic.consultation_dates.load_consultation_dates","text":"load_consultation_dates () Create a minimalistic dataset for the get_consultation_dates function. RETURNS DESCRIPTION consultation_dataset visit_occurence, note and note_nlp. TYPE: ConsultationDataset, a dataclass comprised of Source code in eds_scikit/datasets/synthetic/consultation_dates.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 def load_consultation_dates (): \"\"\" Create a minimalistic dataset for the `get_consultation_dates` function. Returns ------- consultation_dataset: ConsultationDataset, a dataclass comprised of visit_occurence, note and note_nlp. \"\"\" n_visits = 4 visit_occurrence_ids = list ( range ( n_visits )) visit_source_value = [ \"consultation externe\" , \"consultation externe\" , \"hospitalisation\" , \"consultation externe\" , ] visit_occurrence = pd . DataFrame ( { \"visit_occurrence_id\" : visit_occurrence_ids , \"visit_source_value\" : visit_source_value , } ) n_notes = 10 visit_occurrence_ids = [ n_visits * idx // n_notes for idx in range ( n_notes )] note_ids = list ( range ( n_notes )) note_datetimes = [ 1 , 1 , 5 , 6 , 7 , 1 , 1 , 2 , 3 , 8 ] note_datetimes = [ datetime ( 2020 , 1 , day ) for day in note_datetimes ] note_class_source_value = ( n_notes // 2 ) * [ \"CR-CONS\" ] + ( n_notes // 2 ) * [ \"CR-HOSP\" ] note = pd . DataFrame ( { \"visit_occurrence_id\" : visit_occurrence_ids , \"note_id\" : note_ids , \"note_datetime\" : note_datetimes , \"note_class_source_value\" : note_class_source_value , } ) n_note_nlp = 20 starts = [ 4 , 14 , 0 , 7 , 5 , 11 , 8 , 18 , 6 , 19 , 15 , 9 , 17 , 1 , 12 , 2 , 3 , 16 , 10 , 13 , ] note_ids = [ n_notes * idx // n_note_nlp for idx in range ( n_note_nlp )] consultation_dates = 2 * [ 1 , 1 , 5 , 6 , 7 , 1 , 2 , 3 , 9 , 12 ] consultation_dates = [ datetime ( 2020 , 1 , day ) for day in consultation_dates ] note_nlp = pd . DataFrame ( { \"note_id\" : note_ids , \"consultation_date\" : consultation_dates , \"start\" : starts , } ) return ConsultationDataset ( visit_occurrence = visit_occurrence , note = note , note_nlp = note_nlp , )","title":"load_consultation_dates()"},{"location":"reference/datasets/synthetic/event_sequences/","text":"eds_scikit.datasets.synthetic.event_sequences","title":"event_sequences"},{"location":"reference/datasets/synthetic/event_sequences/#eds_scikitdatasetssyntheticevent_sequences","text":"","title":"eds_scikit.datasets.synthetic.event_sequences"},{"location":"reference/datasets/synthetic/hierarchy/","text":"eds_scikit.datasets.synthetic.hierarchy","title":"hierarchy"},{"location":"reference/datasets/synthetic/hierarchy/#eds_scikitdatasetssynthetichierarchy","text":"","title":"eds_scikit.datasets.synthetic.hierarchy"},{"location":"reference/datasets/synthetic/icd10/","text":"eds_scikit.datasets.synthetic.icd10","title":"icd10"},{"location":"reference/datasets/synthetic/icd10/#eds_scikitdatasetssyntheticicd10","text":"","title":"eds_scikit.datasets.synthetic.icd10"},{"location":"reference/datasets/synthetic/person/","text":"eds_scikit.datasets.synthetic.person","title":"person"},{"location":"reference/datasets/synthetic/person/#eds_scikitdatasetssyntheticperson","text":"","title":"eds_scikit.datasets.synthetic.person"},{"location":"reference/datasets/synthetic/stay_duration/","text":"eds_scikit.datasets.synthetic.stay_duration","title":"stay_duration"},{"location":"reference/datasets/synthetic/stay_duration/#eds_scikitdatasetssyntheticstay_duration","text":"","title":"eds_scikit.datasets.synthetic.stay_duration"},{"location":"reference/datasets/synthetic/suicide_attempt/","text":"eds_scikit.datasets.synthetic.suicide_attempt","title":"suicide_attempt"},{"location":"reference/datasets/synthetic/suicide_attempt/#eds_scikitdatasetssyntheticsuicide_attempt","text":"","title":"eds_scikit.datasets.synthetic.suicide_attempt"},{"location":"reference/datasets/synthetic/tagging/","text":"eds_scikit.datasets.synthetic.tagging","title":"tagging"},{"location":"reference/datasets/synthetic/tagging/#eds_scikitdatasetssynthetictagging","text":"","title":"eds_scikit.datasets.synthetic.tagging"},{"location":"reference/datasets/synthetic/visit_merging/","text":"eds_scikit.datasets.synthetic.visit_merging load_visit_merging load_visit_merging () Create a minimalistic dataset for the visit_merging function. RETURNS DESCRIPTION visit_dataset TYPE: VisitDataset, a dataclass comprised of visit_occurence. Source code in eds_scikit/datasets/synthetic/visit_merging.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 def load_visit_merging (): \"\"\" Create a minimalistic dataset for the `visit_merging` function. Returns ------- visit_dataset : VisitDataset, a dataclass comprised of visit_occurence. \"\"\" visit_occurrence = pd . DataFrame ( { \"visit_occurrence_id\" : [ \"A\" , \"B\" , \"C\" , \"D\" , \"E\" , \"F\" , \"G\" ], \"person_id\" : [ \"999\" ] * 7 , \"visit_start_datetime\" : [ \"2021-01-01\" , \"2021-01-04\" , \"2021-01-12\" , \"2021-01-13\" , \"2021-01-19\" , \"2021-01-25\" , \"2017-01-01\" , ], \"visit_end_datetime\" : [ \"2021-01-05\" , \"2021-01-08\" , \"2021-01-18\" , \"2021-01-14\" , \"2021-01-21\" , \"2021-01-27\" , None , ], \"visit_source_value\" : [ \"hospitalis\u00e9s\" , \"hospitalis\u00e9s\" , \"hospitalis\u00e9s\" , \"urgence\" , \"hospitalis\u00e9s\" , \"hospitalis\u00e9s\" , \"hospitalis\u00e9s\" , ], \"row_status_source_value\" : [ \"supprim\u00e9\" , \"courant\" , \"courant\" , \"courant\" , \"courant\" , \"courant\" , \"courant\" , ], \"care_site_id\" : [ \"1\" , \"1\" , \"1\" , \"1\" , \"2\" , \"1\" , \"1\" ], } ) for col in [ \"visit_start_datetime\" , \"visit_end_datetime\" ]: visit_occurrence [ col ] = pd . to_datetime ( visit_occurrence [ col ]) return VisitDataset ( visit_occurrence = visit_occurrence )","title":"visit_merging"},{"location":"reference/datasets/synthetic/visit_merging/#eds_scikitdatasetssyntheticvisit_merging","text":"","title":"eds_scikit.datasets.synthetic.visit_merging"},{"location":"reference/datasets/synthetic/visit_merging/#eds_scikit.datasets.synthetic.visit_merging.load_visit_merging","text":"load_visit_merging () Create a minimalistic dataset for the visit_merging function. RETURNS DESCRIPTION visit_dataset TYPE: VisitDataset, a dataclass comprised of visit_occurence. Source code in eds_scikit/datasets/synthetic/visit_merging.py 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 def load_visit_merging (): \"\"\" Create a minimalistic dataset for the `visit_merging` function. Returns ------- visit_dataset : VisitDataset, a dataclass comprised of visit_occurence. \"\"\" visit_occurrence = pd . DataFrame ( { \"visit_occurrence_id\" : [ \"A\" , \"B\" , \"C\" , \"D\" , \"E\" , \"F\" , \"G\" ], \"person_id\" : [ \"999\" ] * 7 , \"visit_start_datetime\" : [ \"2021-01-01\" , \"2021-01-04\" , \"2021-01-12\" , \"2021-01-13\" , \"2021-01-19\" , \"2021-01-25\" , \"2017-01-01\" , ], \"visit_end_datetime\" : [ \"2021-01-05\" , \"2021-01-08\" , \"2021-01-18\" , \"2021-01-14\" , \"2021-01-21\" , \"2021-01-27\" , None , ], \"visit_source_value\" : [ \"hospitalis\u00e9s\" , \"hospitalis\u00e9s\" , \"hospitalis\u00e9s\" , \"urgence\" , \"hospitalis\u00e9s\" , \"hospitalis\u00e9s\" , \"hospitalis\u00e9s\" , ], \"row_status_source_value\" : [ \"supprim\u00e9\" , \"courant\" , \"courant\" , \"courant\" , \"courant\" , \"courant\" , \"courant\" , ], \"care_site_id\" : [ \"1\" , \"1\" , \"1\" , \"1\" , \"2\" , \"1\" , \"1\" ], } ) for col in [ \"visit_start_datetime\" , \"visit_end_datetime\" ]: visit_occurrence [ col ] = pd . to_datetime ( visit_occurrence [ col ]) return VisitDataset ( visit_occurrence = visit_occurrence )","title":"load_visit_merging()"},{"location":"reference/emergency/","text":"eds_scikit.emergency","title":"`eds_scikit.emergency`"},{"location":"reference/emergency/#eds_scikitemergency","text":"","title":"eds_scikit.emergency"},{"location":"reference/emergency/emergency_care_site/","text":"eds_scikit.emergency.emergency_care_site tag_emergency_care_site tag_emergency_care_site ( care_site : DataFrame , algo : str = 'from_mapping' ) -> DataFrame Tag care sites that correspond to medical emergency units . The tagging is done by adding a \"IS_EMERGENCY\" column to the provided DataFrame. Some algos can add an additional \"EMERGENCY_TYPE\" column to the provided DataFrame, providing a more detailled classification. PARAMETER DESCRIPTION care_site TYPE: DataFrame algo Possible values are: \"from_mapping\" relies on a list of care_site_source_value extracted by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites are here further labelled to distinguish the different types of emergency \"from_regex_on_care_site_description\" : relies on a specific list of RegEx applied on the description (= simplified care site name) of each care site. \"from_regex_on_parent_UF\" : relies on a specific list of regular expressions applied on the description (= simplified care site name) of each UF (Unit\u00e9 Fonctionnelle). The obtained tag is then propagated to every UF's children. TYPE: str DEFAULT: 'from_mapping' RETURNS DESCRIPTION care_site Dataframe with 1 to 2 added columns corresponding to the following concepts: \"IS_EMERGENCY\" \"EMERGENCY_TYPE\" (if using algo \"from_mapping\" ) TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 @algo_checker ( algos = ALGOS ) def tag_emergency_care_site ( care_site : DataFrame , algo : str = \"from_mapping\" , ) -> DataFrame : \"\"\"Tag care sites that correspond to **medical emergency units**. The tagging is done by adding a `\"IS_EMERGENCY\"` column to the provided DataFrame. Some algos can add an additional `\"EMERGENCY_TYPE\"` column to the provided DataFrame, providing a more detailled classification. Parameters ---------- care_site: DataFrame algo: str Possible values are: - [`\"from_mapping\"`][eds_scikit.emergency.emergency_care_site.from_mapping] relies on a list of `care_site_source_value` extracted by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites are here further labelled to distinguish the different types of emergency - [`\"from_regex_on_care_site_description\"`][eds_scikit.emergency.emergency_care_site.from_regex_on_care_site_description]: relies on a specific list of RegEx applied on the description (= simplified care site name) of each care site. - [`\"from_regex_on_parent_UF\"`][eds_scikit.emergency.emergency_care_site.from_regex_on_parent_UF]: relies on a specific list of regular expressions applied on the description (= simplified care site name) of each UF (Unit\u00e9 Fonctionnelle). The obtained tag is then propagated to every UF's children. Returns ------- care_site: DataFrame Dataframe with 1 to 2 added columns corresponding to the following concepts: - `\"IS_EMERGENCY\"` - `\"EMERGENCY_TYPE\"` (if using algo `\"from_mapping\"`) \"\"\" if algo == \"from_regex_on_parent_UF\" : return from_regex_on_parent_UF ( care_site ) elif algo == \"from_regex_on_care_site_description\" : return from_regex_on_care_site_description ( care_site ) elif algo . startswith ( \"from_mapping\" ): return from_mapping ( care_site , version = versionize ( algo )) from_mapping from_mapping ( care_site : DataFrame , version : Optional [ str ] = None ) -> DataFrame This algo uses a labelled list of 201 emergency care sites. Those care sites were extracted and verified by Ariel COHEN, Judith LEBLANC, and an ER doctor validated them. Those emergency care sites are further divised into different categories, as defined in the concept 'EMERGENCY_TYPE'. The different categories are: Urgences sp\u00e9cialis\u00e9es UHCD + Post-urgences Urgences p\u00e9diatriques Urgences g\u00e9n\u00e9rales adulte Consultation urgences SAMU / SMUR See the dataset here PARAMETER DESCRIPTION care_site Should at least contains the care_site_source_value column TYPE: DataFrame version Optional version string for the mapping TYPE: Optional [ str ] DEFAULT: None RETURNS DESCRIPTION care_site Dataframe with 2 added columns corresponding to the following concepts: \"IS_EMERGENCY\" \"EMERGENCY_TYPE\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 @concept_checker ( concepts = [ \"IS_EMERGENCY\" , \"EMERGENCY_TYPE\" ]) def from_mapping ( care_site : DataFrame , version : Optional [ str ] = None , ) -> DataFrame : \"\"\"This algo uses a labelled list of 201 emergency care sites. Those care sites were extracted and verified by Ariel COHEN, Judith LEBLANC, and an ER doctor validated them. Those emergency care sites are further divised into different categories, as defined in the concept 'EMERGENCY_TYPE'. The different categories are: - Urgences sp\u00e9cialis\u00e9es - UHCD + Post-urgences - Urgences p\u00e9diatriques - Urgences g\u00e9n\u00e9rales adulte - Consultation urgences - SAMU / SMUR See the dataset [here](/datasets/care-site-emergency) Parameters ---------- care_site: DataFrame Should at least contains the `care_site_source_value` column version: Optional[str] Optional version string for the mapping Returns ------- care_site: DataFrame Dataframe with 2 added columns corresponding to the following concepts: - `\"IS_EMERGENCY\"` - `\"EMERGENCY_TYPE\"` \"\"\" function_name = \"get_care_site_emergency_mapping\" if version is not None : function_name += f \". { version } \" mapping = registry . get ( \"data\" , function_name = function_name )() # Getting the right framework fw = framework . get_framework ( care_site ) mapping = framework . to ( fw , mapping ) care_site = care_site . merge ( mapping , how = \"left\" , on = \"care_site_source_value\" , ) care_site [ \"IS_EMERGENCY\" ] = care_site [ \"EMERGENCY_TYPE\" ] . notna () return care_site from_regex_on_care_site_description from_regex_on_care_site_description ( care_site : DataFrame ) -> DataFrame Use regular expressions on care_site_name to decide if it an emergency care site. This relies on this function . The regular expression used to detect emergency status is r\"\bURG|\bSAU\b|\bUHCDb\b|\bZHTCD\b\" PARAMETER DESCRIPTION care_site Should at least contains the care_site_name column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: \"IS_EMERGENCY\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def from_regex_on_care_site_description ( care_site : DataFrame ) -> DataFrame : \"\"\"Use regular expressions on `care_site_name` to decide if it an emergency care site. This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes]. The regular expression used to detect emergency status is `r\"\\bURG|\\bSAU\\b|\\bUHCDb\\b|\\bZHTCD\\b\"` Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - `\"IS_EMERGENCY\"` \"\"\" return attributes . add_care_site_attributes ( care_site , only_attributes = [ \"IS_EMERGENCY\" ] ) from_regex_on_parent_UF from_regex_on_parent_UF ( care_site : DataFrame ) -> DataFrame Use regular expressions on parent UF (Unit\u00e9 Fonctionnelle) to classify emergency care site. This relies on this function . The regular expression used to detect emergency status is r\"\bURG|\bSAU\b|\bUHCD\b|\bZHTCD\b\" PARAMETER DESCRIPTION care_site Should at least contains the care_site_name column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: 'IS_EMERGENCY' TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 @concept_checker ( concepts = [ \"IS_EMERGENCY\" ]) def from_regex_on_parent_UF ( care_site : DataFrame ) -> DataFrame : \"\"\"Use regular expressions on parent UF (Unit\u00e9 Fonctionnelle) to classify emergency care site. This relies on [this function][eds_scikit.structures.attributes.get_parent_attributes]. The regular expression used to detect emergency status is `r\"\\bURG|\\bSAU\\b|\\bUHCD\\b|\\bZHTCD\\b\"` Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - 'IS_EMERGENCY' \"\"\" return attributes . get_parent_attributes ( care_site , only_attributes = [ \"IS_EMERGENCY\" ], parent_type = \"Unit\u00e9 Fonctionnelle (UF)\" , )","title":"emergency_care_site"},{"location":"reference/emergency/emergency_care_site/#eds_scikitemergencyemergency_care_site","text":"","title":"eds_scikit.emergency.emergency_care_site"},{"location":"reference/emergency/emergency_care_site/#eds_scikit.emergency.emergency_care_site.tag_emergency_care_site","text":"tag_emergency_care_site ( care_site : DataFrame , algo : str = 'from_mapping' ) -> DataFrame Tag care sites that correspond to medical emergency units . The tagging is done by adding a \"IS_EMERGENCY\" column to the provided DataFrame. Some algos can add an additional \"EMERGENCY_TYPE\" column to the provided DataFrame, providing a more detailled classification. PARAMETER DESCRIPTION care_site TYPE: DataFrame algo Possible values are: \"from_mapping\" relies on a list of care_site_source_value extracted by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites are here further labelled to distinguish the different types of emergency \"from_regex_on_care_site_description\" : relies on a specific list of RegEx applied on the description (= simplified care site name) of each care site. \"from_regex_on_parent_UF\" : relies on a specific list of regular expressions applied on the description (= simplified care site name) of each UF (Unit\u00e9 Fonctionnelle). The obtained tag is then propagated to every UF's children. TYPE: str DEFAULT: 'from_mapping' RETURNS DESCRIPTION care_site Dataframe with 1 to 2 added columns corresponding to the following concepts: \"IS_EMERGENCY\" \"EMERGENCY_TYPE\" (if using algo \"from_mapping\" ) TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 @algo_checker ( algos = ALGOS ) def tag_emergency_care_site ( care_site : DataFrame , algo : str = \"from_mapping\" , ) -> DataFrame : \"\"\"Tag care sites that correspond to **medical emergency units**. The tagging is done by adding a `\"IS_EMERGENCY\"` column to the provided DataFrame. Some algos can add an additional `\"EMERGENCY_TYPE\"` column to the provided DataFrame, providing a more detailled classification. Parameters ---------- care_site: DataFrame algo: str Possible values are: - [`\"from_mapping\"`][eds_scikit.emergency.emergency_care_site.from_mapping] relies on a list of `care_site_source_value` extracted by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites are here further labelled to distinguish the different types of emergency - [`\"from_regex_on_care_site_description\"`][eds_scikit.emergency.emergency_care_site.from_regex_on_care_site_description]: relies on a specific list of RegEx applied on the description (= simplified care site name) of each care site. - [`\"from_regex_on_parent_UF\"`][eds_scikit.emergency.emergency_care_site.from_regex_on_parent_UF]: relies on a specific list of regular expressions applied on the description (= simplified care site name) of each UF (Unit\u00e9 Fonctionnelle). The obtained tag is then propagated to every UF's children. Returns ------- care_site: DataFrame Dataframe with 1 to 2 added columns corresponding to the following concepts: - `\"IS_EMERGENCY\"` - `\"EMERGENCY_TYPE\"` (if using algo `\"from_mapping\"`) \"\"\" if algo == \"from_regex_on_parent_UF\" : return from_regex_on_parent_UF ( care_site ) elif algo == \"from_regex_on_care_site_description\" : return from_regex_on_care_site_description ( care_site ) elif algo . startswith ( \"from_mapping\" ): return from_mapping ( care_site , version = versionize ( algo ))","title":"tag_emergency_care_site()"},{"location":"reference/emergency/emergency_care_site/#eds_scikit.emergency.emergency_care_site.from_mapping","text":"from_mapping ( care_site : DataFrame , version : Optional [ str ] = None ) -> DataFrame This algo uses a labelled list of 201 emergency care sites. Those care sites were extracted and verified by Ariel COHEN, Judith LEBLANC, and an ER doctor validated them. Those emergency care sites are further divised into different categories, as defined in the concept 'EMERGENCY_TYPE'. The different categories are: Urgences sp\u00e9cialis\u00e9es UHCD + Post-urgences Urgences p\u00e9diatriques Urgences g\u00e9n\u00e9rales adulte Consultation urgences SAMU / SMUR See the dataset here PARAMETER DESCRIPTION care_site Should at least contains the care_site_source_value column TYPE: DataFrame version Optional version string for the mapping TYPE: Optional [ str ] DEFAULT: None RETURNS DESCRIPTION care_site Dataframe with 2 added columns corresponding to the following concepts: \"IS_EMERGENCY\" \"EMERGENCY_TYPE\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 @concept_checker ( concepts = [ \"IS_EMERGENCY\" , \"EMERGENCY_TYPE\" ]) def from_mapping ( care_site : DataFrame , version : Optional [ str ] = None , ) -> DataFrame : \"\"\"This algo uses a labelled list of 201 emergency care sites. Those care sites were extracted and verified by Ariel COHEN, Judith LEBLANC, and an ER doctor validated them. Those emergency care sites are further divised into different categories, as defined in the concept 'EMERGENCY_TYPE'. The different categories are: - Urgences sp\u00e9cialis\u00e9es - UHCD + Post-urgences - Urgences p\u00e9diatriques - Urgences g\u00e9n\u00e9rales adulte - Consultation urgences - SAMU / SMUR See the dataset [here](/datasets/care-site-emergency) Parameters ---------- care_site: DataFrame Should at least contains the `care_site_source_value` column version: Optional[str] Optional version string for the mapping Returns ------- care_site: DataFrame Dataframe with 2 added columns corresponding to the following concepts: - `\"IS_EMERGENCY\"` - `\"EMERGENCY_TYPE\"` \"\"\" function_name = \"get_care_site_emergency_mapping\" if version is not None : function_name += f \". { version } \" mapping = registry . get ( \"data\" , function_name = function_name )() # Getting the right framework fw = framework . get_framework ( care_site ) mapping = framework . to ( fw , mapping ) care_site = care_site . merge ( mapping , how = \"left\" , on = \"care_site_source_value\" , ) care_site [ \"IS_EMERGENCY\" ] = care_site [ \"EMERGENCY_TYPE\" ] . notna () return care_site","title":"from_mapping()"},{"location":"reference/emergency/emergency_care_site/#eds_scikit.emergency.emergency_care_site.from_regex_on_care_site_description","text":"from_regex_on_care_site_description ( care_site : DataFrame ) -> DataFrame Use regular expressions on care_site_name to decide if it an emergency care site. This relies on this function . The regular expression used to detect emergency status is r\"\bURG|\bSAU\b|\bUHCDb\b|\bZHTCD\b\" PARAMETER DESCRIPTION care_site Should at least contains the care_site_name column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: \"IS_EMERGENCY\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def from_regex_on_care_site_description ( care_site : DataFrame ) -> DataFrame : \"\"\"Use regular expressions on `care_site_name` to decide if it an emergency care site. This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes]. The regular expression used to detect emergency status is `r\"\\bURG|\\bSAU\\b|\\bUHCDb\\b|\\bZHTCD\\b\"` Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - `\"IS_EMERGENCY\"` \"\"\" return attributes . add_care_site_attributes ( care_site , only_attributes = [ \"IS_EMERGENCY\" ] )","title":"from_regex_on_care_site_description()"},{"location":"reference/emergency/emergency_care_site/#eds_scikit.emergency.emergency_care_site.from_regex_on_parent_UF","text":"from_regex_on_parent_UF ( care_site : DataFrame ) -> DataFrame Use regular expressions on parent UF (Unit\u00e9 Fonctionnelle) to classify emergency care site. This relies on this function . The regular expression used to detect emergency status is r\"\bURG|\bSAU\b|\bUHCD\b|\bZHTCD\b\" PARAMETER DESCRIPTION care_site Should at least contains the care_site_name column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: 'IS_EMERGENCY' TYPE: DataFrame Source code in eds_scikit/emergency/emergency_care_site.py 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 @concept_checker ( concepts = [ \"IS_EMERGENCY\" ]) def from_regex_on_parent_UF ( care_site : DataFrame ) -> DataFrame : \"\"\"Use regular expressions on parent UF (Unit\u00e9 Fonctionnelle) to classify emergency care site. This relies on [this function][eds_scikit.structures.attributes.get_parent_attributes]. The regular expression used to detect emergency status is `r\"\\bURG|\\bSAU\\b|\\bUHCD\\b|\\bZHTCD\\b\"` Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - 'IS_EMERGENCY' \"\"\" return attributes . get_parent_attributes ( care_site , only_attributes = [ \"IS_EMERGENCY\" ], parent_type = \"Unit\u00e9 Fonctionnelle (UF)\" , )","title":"from_regex_on_parent_UF()"},{"location":"reference/emergency/emergency_visit/","text":"eds_scikit.emergency.emergency_visit tag_emergency_visit tag_emergency_visit ( visit_detail : DataFrame , care_site : Optional [ DataFrame ] = None , visit_occurrence : Optional [ DataFrame ] = None , algo : str = 'from_mapping' ) -> DataFrame Tag visits that correspond to medical emergency units . The tagging is done by adding a \"IS_EMERGENCY\" column to the provided DataFrame. Some algos can add an additional \"EMERGENCY_TYPE\" column to the provided DataFrame, providing a more detailled classification. It works by either tagging each visit detail's care site , or by using the visit_occurrence 's \"visit_source_value\" . PARAMETER DESCRIPTION visit_detail TYPE: DataFrame care_site Isn't necessary if the algo \"from_vo_visit_source_value\" is used TYPE: Optional [ DataFrame ] DEFAULT: None visit_occurrence Is mandatory if the algo \"from_vo_visit_source_value\" is used TYPE: Optional [ DataFrame ] DEFAULT: None algo Possible values are: \"from_mapping\" relies on a list of care_site_source_value extracted by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites are here further labelled to distinguish the different types of emergency \"from_regex_on_care_site_description\" : relies on a specific list of RegEx applied on the description (= simplified care site name) of each care site. \"from_regex_on_parent_UF\" : relies on a specific list of regular expressions applied on the description (= simplified care site name) of each UF (Unit\u00e9 Fonctionnelle). The obtained tag is then propagated to every UF's children. \"from_vo_visit_source_value\" : relies on the parent visit occurrence of each visit detail: A visit detail will be tagged as emergency if it belongs to a visit occurrence where visit_occurrence.visit_source_value=='urgence' . TYPE: str DEFAULT: 'from_mapping' RETURNS DESCRIPTION care_site Dataframe with 1 to 2 added columns corresponding to the following concepts: \"IS_EMERGENCY\" \"EMERGENCY_TYPE\" (if using algo \"from_mapping\" ) TYPE: DataFrame Source code in eds_scikit/emergency/emergency_visit.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 @algo_checker ( algos = ALGOS ) def tag_emergency_visit ( visit_detail : DataFrame , care_site : Optional [ DataFrame ] = None , visit_occurrence : Optional [ DataFrame ] = None , algo : str = \"from_mapping\" , ) -> DataFrame : \"\"\"Tag visits that correspond to **medical emergency units**. The tagging is done by adding a `\"IS_EMERGENCY\"` column to the provided DataFrame. Some algos can add an additional `\"EMERGENCY_TYPE\"` column to the provided DataFrame, providing a more detailled classification. It works by either [tagging each visit detail's care site][eds_scikit.emergency.emergency_care_site.tag_emergency_care_site], or by using the *visit_occurrence*'s `\"visit_source_value\"`. Parameters ---------- visit_detail: DataFrame care_site: DataFrame Isn't necessary if the algo `\"from_vo_visit_source_value\"` is used visit_occurrence: DataFrame, optional. Is mandatory if the algo `\"from_vo_visit_source_value\"` is used algo: str Possible values are: - [`\"from_mapping\"`][eds_scikit.emergency.emergency_care_site.from_mapping] relies on a list of `care_site_source_value` extracted by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites are here further labelled to distinguish the different types of emergency - [`\"from_regex_on_care_site_description\"`][eds_scikit.emergency.emergency_care_site.from_regex_on_care_site_description]: relies on a specific list of RegEx applied on the description (= simplified care site name) of each care site. - [`\"from_regex_on_parent_UF\"`][eds_scikit.emergency.emergency_care_site.from_regex_on_parent_UF]: relies on a specific list of regular expressions applied on the description (= simplified care site name) of each UF (Unit\u00e9 Fonctionnelle). The obtained tag is then propagated to every UF's children. - [`\"from_vo_visit_source_value\"`][eds_scikit.emergency.emergency_visit.from_vo_visit_source_value]: relies on the parent visit occurrence of each visit detail: A visit detail will be tagged as emergency if it belongs to a visit occurrence where `visit_occurrence.visit_source_value=='urgence'`. Returns ------- care_site: DataFrame Dataframe with 1 to 2 added columns corresponding to the following concepts: - `\"IS_EMERGENCY\"` - `\"EMERGENCY_TYPE\"` (if using algo `\"from_mapping\"`) \"\"\" if algo == \"from_vo_visit_source_value\" : return from_vo_visit_source_value ( visit_detail , visit_occurrence ) else : initial_care_site_columns = set ( care_site . columns ) tagged_care_site = tag_emergency_care_site ( care_site , algo = algo ) to_add_columns = list ( set ( tagged_care_site ) - initial_care_site_columns | set ([ \"care_site_id\" ]) ) return visit_detail . merge ( tagged_care_site [ to_add_columns ], on = \"care_site_id\" , how = \"left\" ) from_vo_visit_source_value from_vo_visit_source_value ( visit_detail : DataFrame , visit_occurrence : DataFrame ) -> DataFrame This algo uses the \"Type de dossier\" of each visit detail's parent visit occurrence. Thus, a visit_detail will be tagged with IS_EMERGENCY=True iff the visit occurrence it belongs to is an emergency-type visit (meaning that visit_occurrence.visit_source_value=='urgence' ) Admission through ICU At AP-HP, when a patient is hospitalized after coming to the ICU, its visit_source_value is set from \"urgence\" to \"hospitalisation compl\u00e8te\" . So you should keep in mind that this method doesn't tag those visits as ICU. PARAMETER DESCRIPTION visit_detail TYPE: DataFrame visit_occurrence TYPE: DataFrame RETURNS DESCRIPTION visit_detail Dataframe with added columns corresponding to the following conceps: \"IS_EMERGENCY\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_visit.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 @concept_checker ( concepts = [ \"IS_EMERGENCY\" ]) def from_vo_visit_source_value ( visit_detail : DataFrame , visit_occurrence : DataFrame , ) -> DataFrame : \"\"\" This algo uses the *\"Type de dossier\"* of each visit detail's parent visit occurrence. Thus, a visit_detail will be tagged with `IS_EMERGENCY=True` iff the visit occurrence it belongs to is an emergency-type visit (meaning that `visit_occurrence.visit_source_value=='urgence'`) !!! aphp \"Admission through ICU\" At AP-HP, when a patient is hospitalized after coming to the ICU, its `visit_source_value` is set from `\"urgence\"` to `\"hospitalisation compl\u00e8te\"`. So you should keep in mind that this method doesn't tag those visits as ICU. Parameters ---------- visit_detail: DataFrame visit_occurrence: DataFrame Returns ------- visit_detail: DataFrame Dataframe with added columns corresponding to the following conceps: - `\"IS_EMERGENCY\"` \"\"\" vo_emergency = visit_occurrence [[ \"visit_occurrence_id\" , \"visit_source_value\" ]] vo_emergency [ \"IS_EMERGENCY\" ] = visit_occurrence . visit_source_value == \"urgence\" return visit_detail . merge ( vo_emergency [[ \"visit_occurrence_id\" , \"IS_EMERGENCY\" ]], on = \"visit_occurrence_id\" , how = \"left\" , )","title":"emergency_visit"},{"location":"reference/emergency/emergency_visit/#eds_scikitemergencyemergency_visit","text":"","title":"eds_scikit.emergency.emergency_visit"},{"location":"reference/emergency/emergency_visit/#eds_scikit.emergency.emergency_visit.tag_emergency_visit","text":"tag_emergency_visit ( visit_detail : DataFrame , care_site : Optional [ DataFrame ] = None , visit_occurrence : Optional [ DataFrame ] = None , algo : str = 'from_mapping' ) -> DataFrame Tag visits that correspond to medical emergency units . The tagging is done by adding a \"IS_EMERGENCY\" column to the provided DataFrame. Some algos can add an additional \"EMERGENCY_TYPE\" column to the provided DataFrame, providing a more detailled classification. It works by either tagging each visit detail's care site , or by using the visit_occurrence 's \"visit_source_value\" . PARAMETER DESCRIPTION visit_detail TYPE: DataFrame care_site Isn't necessary if the algo \"from_vo_visit_source_value\" is used TYPE: Optional [ DataFrame ] DEFAULT: None visit_occurrence Is mandatory if the algo \"from_vo_visit_source_value\" is used TYPE: Optional [ DataFrame ] DEFAULT: None algo Possible values are: \"from_mapping\" relies on a list of care_site_source_value extracted by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites are here further labelled to distinguish the different types of emergency \"from_regex_on_care_site_description\" : relies on a specific list of RegEx applied on the description (= simplified care site name) of each care site. \"from_regex_on_parent_UF\" : relies on a specific list of regular expressions applied on the description (= simplified care site name) of each UF (Unit\u00e9 Fonctionnelle). The obtained tag is then propagated to every UF's children. \"from_vo_visit_source_value\" : relies on the parent visit occurrence of each visit detail: A visit detail will be tagged as emergency if it belongs to a visit occurrence where visit_occurrence.visit_source_value=='urgence' . TYPE: str DEFAULT: 'from_mapping' RETURNS DESCRIPTION care_site Dataframe with 1 to 2 added columns corresponding to the following concepts: \"IS_EMERGENCY\" \"EMERGENCY_TYPE\" (if using algo \"from_mapping\" ) TYPE: DataFrame Source code in eds_scikit/emergency/emergency_visit.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 @algo_checker ( algos = ALGOS ) def tag_emergency_visit ( visit_detail : DataFrame , care_site : Optional [ DataFrame ] = None , visit_occurrence : Optional [ DataFrame ] = None , algo : str = \"from_mapping\" , ) -> DataFrame : \"\"\"Tag visits that correspond to **medical emergency units**. The tagging is done by adding a `\"IS_EMERGENCY\"` column to the provided DataFrame. Some algos can add an additional `\"EMERGENCY_TYPE\"` column to the provided DataFrame, providing a more detailled classification. It works by either [tagging each visit detail's care site][eds_scikit.emergency.emergency_care_site.tag_emergency_care_site], or by using the *visit_occurrence*'s `\"visit_source_value\"`. Parameters ---------- visit_detail: DataFrame care_site: DataFrame Isn't necessary if the algo `\"from_vo_visit_source_value\"` is used visit_occurrence: DataFrame, optional. Is mandatory if the algo `\"from_vo_visit_source_value\"` is used algo: str Possible values are: - [`\"from_mapping\"`][eds_scikit.emergency.emergency_care_site.from_mapping] relies on a list of `care_site_source_value` extracted by Judith LEBLANC, Ariel COHEN and validated by an ER doctor. The emergency care sites are here further labelled to distinguish the different types of emergency - [`\"from_regex_on_care_site_description\"`][eds_scikit.emergency.emergency_care_site.from_regex_on_care_site_description]: relies on a specific list of RegEx applied on the description (= simplified care site name) of each care site. - [`\"from_regex_on_parent_UF\"`][eds_scikit.emergency.emergency_care_site.from_regex_on_parent_UF]: relies on a specific list of regular expressions applied on the description (= simplified care site name) of each UF (Unit\u00e9 Fonctionnelle). The obtained tag is then propagated to every UF's children. - [`\"from_vo_visit_source_value\"`][eds_scikit.emergency.emergency_visit.from_vo_visit_source_value]: relies on the parent visit occurrence of each visit detail: A visit detail will be tagged as emergency if it belongs to a visit occurrence where `visit_occurrence.visit_source_value=='urgence'`. Returns ------- care_site: DataFrame Dataframe with 1 to 2 added columns corresponding to the following concepts: - `\"IS_EMERGENCY\"` - `\"EMERGENCY_TYPE\"` (if using algo `\"from_mapping\"`) \"\"\" if algo == \"from_vo_visit_source_value\" : return from_vo_visit_source_value ( visit_detail , visit_occurrence ) else : initial_care_site_columns = set ( care_site . columns ) tagged_care_site = tag_emergency_care_site ( care_site , algo = algo ) to_add_columns = list ( set ( tagged_care_site ) - initial_care_site_columns | set ([ \"care_site_id\" ]) ) return visit_detail . merge ( tagged_care_site [ to_add_columns ], on = \"care_site_id\" , how = \"left\" )","title":"tag_emergency_visit()"},{"location":"reference/emergency/emergency_visit/#eds_scikit.emergency.emergency_visit.from_vo_visit_source_value","text":"from_vo_visit_source_value ( visit_detail : DataFrame , visit_occurrence : DataFrame ) -> DataFrame This algo uses the \"Type de dossier\" of each visit detail's parent visit occurrence. Thus, a visit_detail will be tagged with IS_EMERGENCY=True iff the visit occurrence it belongs to is an emergency-type visit (meaning that visit_occurrence.visit_source_value=='urgence' ) Admission through ICU At AP-HP, when a patient is hospitalized after coming to the ICU, its visit_source_value is set from \"urgence\" to \"hospitalisation compl\u00e8te\" . So you should keep in mind that this method doesn't tag those visits as ICU. PARAMETER DESCRIPTION visit_detail TYPE: DataFrame visit_occurrence TYPE: DataFrame RETURNS DESCRIPTION visit_detail Dataframe with added columns corresponding to the following conceps: \"IS_EMERGENCY\" TYPE: DataFrame Source code in eds_scikit/emergency/emergency_visit.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 @concept_checker ( concepts = [ \"IS_EMERGENCY\" ]) def from_vo_visit_source_value ( visit_detail : DataFrame , visit_occurrence : DataFrame , ) -> DataFrame : \"\"\" This algo uses the *\"Type de dossier\"* of each visit detail's parent visit occurrence. Thus, a visit_detail will be tagged with `IS_EMERGENCY=True` iff the visit occurrence it belongs to is an emergency-type visit (meaning that `visit_occurrence.visit_source_value=='urgence'`) !!! aphp \"Admission through ICU\" At AP-HP, when a patient is hospitalized after coming to the ICU, its `visit_source_value` is set from `\"urgence\"` to `\"hospitalisation compl\u00e8te\"`. So you should keep in mind that this method doesn't tag those visits as ICU. Parameters ---------- visit_detail: DataFrame visit_occurrence: DataFrame Returns ------- visit_detail: DataFrame Dataframe with added columns corresponding to the following conceps: - `\"IS_EMERGENCY\"` \"\"\" vo_emergency = visit_occurrence [[ \"visit_occurrence_id\" , \"visit_source_value\" ]] vo_emergency [ \"IS_EMERGENCY\" ] = visit_occurrence . visit_source_value == \"urgence\" return visit_detail . merge ( vo_emergency [[ \"visit_occurrence_id\" , \"IS_EMERGENCY\" ]], on = \"visit_occurrence_id\" , how = \"left\" , )","title":"from_vo_visit_source_value()"},{"location":"reference/event/","text":"eds_scikit.event","title":"`eds_scikit.event`"},{"location":"reference/event/#eds_scikitevent","text":"","title":"eds_scikit.event"},{"location":"reference/event/ccam/","text":"eds_scikit.event.ccam procedures_from_ccam procedures_from_ccam ( procedure_occurrence : DataFrame , visit_occurrence : Optional [ DataFrame ] = None , codes : Optional [ Dict [ str , Union [ str , List [ str ]]]] = None , date_from_visit : bool = True , additional_filtering = dict (), date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None ) -> DataFrame Phenotyping based on CCAM codes. PARAMETER DESCRIPTION procedure_occurrence procedure_occurrence OMOP DataFrame. TYPE: DataFrame visit_occurrence visit_occurrence OMOP DataFrame, only necessary if date_from_visit is set to True . TYPE: Optional [ DataFrame ] DEFAULT: None codes Dictionary which values are CCAM codes (as a unique string or as a list) and which keys are at least one of the following: exact : To match the codes in codes[\"exact\"] exactly prefix : To match the codes in codes[\"prefix\"] as prefixes regex : To match the codes in codes[\"regex\"] as regexes You can combine any of those keys. TYPE: Dict [ str , Union [ str , List [ str ]]] DEFAULT: None date_from_visit If set to True , uses visit_start_datetime as the code datetime TYPE: bool DEFAULT: True additional_filtering An optional dictionary to filter the resulting DataFrame. Keys should be column names on which to filter, and values should be either A single value A list or set of values. TYPE: Dict [ str , Any ] DEFAULT: dict() date_min The minimum code datetime to keep. Depends on the date_from_visit flag TYPE: Optional [ datetime ] DEFAULT: None date_max The minimum code datetime to keep. Depends on the date_from_visit flag TYPE: Optional [ datetime ] DEFAULT: None RETURNS DESCRIPTION DataFrame \"event\" DataFrame including the following columns: t_start : If date_from_visit is set to False , contains procedure_datetime , else contains visit_start_datetime t_end : If date_from_visit is set to False , contains procedure_datetime , else contains visit_end_datetime concept : contaning values from codes.keys() value : The extracted CCAM code. visit_occurrence_id : the visit_occurrence_id from the visit which contains the CCAM code. Source code in eds_scikit/event/ccam.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 def procedures_from_ccam ( procedure_occurrence : DataFrame , visit_occurrence : Optional [ DataFrame ] = None , codes : Optional [ Dict [ str , Union [ str , List [ str ]]]] = None , date_from_visit : bool = True , additional_filtering = dict (), date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None , ) -> DataFrame : \"\"\" Phenotyping based on CCAM codes. Parameters ---------- procedure_occurrence : DataFrame `procedure_occurrence` OMOP DataFrame. visit_occurrence : Optional[DataFrame] `visit_occurrence` OMOP DataFrame, only necessary if `date_from_visit` is set to `True`. codes : Dict[str, Union[str, List[str]]] Dictionary which values are CCAM codes (as a unique string or as a list) and which keys are at least one of the following: - `exact`: To match the codes in `codes[\"exact\"]` **exactly** - `prefix`: To match the codes in `codes[\"prefix\"]` **as prefixes** - `regex`: To match the codes in `codes[\"regex\"]` **as regexes** You can combine any of those keys. date_from_visit : bool If set to `True`, uses `visit_start_datetime` as the code datetime additional_filtering : Dict[str, Any] An optional dictionary to filter the resulting DataFrame. Keys should be column names on which to filter, and values should be either - A single value - A list or set of values. date_min : Optional[datetime] The minimum code datetime to keep. **Depends on the `date_from_visit` flag** date_max : Optional[datetime] The minimum code datetime to keep. **Depends on the `date_from_visit` flag** Returns ------- DataFrame \"event\" DataFrame including the following columns: - `t_start`: If `date_from_visit` is set to `False`, contains `procedure_datetime`, else contains `visit_start_datetime` - `t_end`: If `date_from_visit` is set to `False`, contains `procedure_datetime`, else contains `visit_end_datetime` - `concept` : contaning values from `codes.keys()` - `value` : The extracted CCAM code. - `visit_occurrence_id` : the `visit_occurrence_id` from the visit which contains the CCAM code. \"\"\" # noqa: E501 procedure_columns = dict ( code_source_value = \"procedure_source_value\" , code_start_datetime = \"procedure_datetime\" , code_end_datetime = \"procedure_datetime\" , ) events = [] for concept , code_dict in codes . items (): tmp_df = event_from_code ( df = procedure_occurrence , columns = procedure_columns , visit_occurrence = visit_occurrence , concept = concept , codes = code_dict , date_from_visit = date_from_visit , additional_filtering = additional_filtering , date_min = date_min , date_max = date_max , ) events . append ( tmp_df ) framework = get_framework ( procedure_occurrence ) return framework . concat ( events )","title":"ccam"},{"location":"reference/event/ccam/#eds_scikiteventccam","text":"","title":"eds_scikit.event.ccam"},{"location":"reference/event/ccam/#eds_scikit.event.ccam.procedures_from_ccam","text":"procedures_from_ccam ( procedure_occurrence : DataFrame , visit_occurrence : Optional [ DataFrame ] = None , codes : Optional [ Dict [ str , Union [ str , List [ str ]]]] = None , date_from_visit : bool = True , additional_filtering = dict (), date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None ) -> DataFrame Phenotyping based on CCAM codes. PARAMETER DESCRIPTION procedure_occurrence procedure_occurrence OMOP DataFrame. TYPE: DataFrame visit_occurrence visit_occurrence OMOP DataFrame, only necessary if date_from_visit is set to True . TYPE: Optional [ DataFrame ] DEFAULT: None codes Dictionary which values are CCAM codes (as a unique string or as a list) and which keys are at least one of the following: exact : To match the codes in codes[\"exact\"] exactly prefix : To match the codes in codes[\"prefix\"] as prefixes regex : To match the codes in codes[\"regex\"] as regexes You can combine any of those keys. TYPE: Dict [ str , Union [ str , List [ str ]]] DEFAULT: None date_from_visit If set to True , uses visit_start_datetime as the code datetime TYPE: bool DEFAULT: True additional_filtering An optional dictionary to filter the resulting DataFrame. Keys should be column names on which to filter, and values should be either A single value A list or set of values. TYPE: Dict [ str , Any ] DEFAULT: dict() date_min The minimum code datetime to keep. Depends on the date_from_visit flag TYPE: Optional [ datetime ] DEFAULT: None date_max The minimum code datetime to keep. Depends on the date_from_visit flag TYPE: Optional [ datetime ] DEFAULT: None RETURNS DESCRIPTION DataFrame \"event\" DataFrame including the following columns: t_start : If date_from_visit is set to False , contains procedure_datetime , else contains visit_start_datetime t_end : If date_from_visit is set to False , contains procedure_datetime , else contains visit_end_datetime concept : contaning values from codes.keys() value : The extracted CCAM code. visit_occurrence_id : the visit_occurrence_id from the visit which contains the CCAM code. Source code in eds_scikit/event/ccam.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 def procedures_from_ccam ( procedure_occurrence : DataFrame , visit_occurrence : Optional [ DataFrame ] = None , codes : Optional [ Dict [ str , Union [ str , List [ str ]]]] = None , date_from_visit : bool = True , additional_filtering = dict (), date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None , ) -> DataFrame : \"\"\" Phenotyping based on CCAM codes. Parameters ---------- procedure_occurrence : DataFrame `procedure_occurrence` OMOP DataFrame. visit_occurrence : Optional[DataFrame] `visit_occurrence` OMOP DataFrame, only necessary if `date_from_visit` is set to `True`. codes : Dict[str, Union[str, List[str]]] Dictionary which values are CCAM codes (as a unique string or as a list) and which keys are at least one of the following: - `exact`: To match the codes in `codes[\"exact\"]` **exactly** - `prefix`: To match the codes in `codes[\"prefix\"]` **as prefixes** - `regex`: To match the codes in `codes[\"regex\"]` **as regexes** You can combine any of those keys. date_from_visit : bool If set to `True`, uses `visit_start_datetime` as the code datetime additional_filtering : Dict[str, Any] An optional dictionary to filter the resulting DataFrame. Keys should be column names on which to filter, and values should be either - A single value - A list or set of values. date_min : Optional[datetime] The minimum code datetime to keep. **Depends on the `date_from_visit` flag** date_max : Optional[datetime] The minimum code datetime to keep. **Depends on the `date_from_visit` flag** Returns ------- DataFrame \"event\" DataFrame including the following columns: - `t_start`: If `date_from_visit` is set to `False`, contains `procedure_datetime`, else contains `visit_start_datetime` - `t_end`: If `date_from_visit` is set to `False`, contains `procedure_datetime`, else contains `visit_end_datetime` - `concept` : contaning values from `codes.keys()` - `value` : The extracted CCAM code. - `visit_occurrence_id` : the `visit_occurrence_id` from the visit which contains the CCAM code. \"\"\" # noqa: E501 procedure_columns = dict ( code_source_value = \"procedure_source_value\" , code_start_datetime = \"procedure_datetime\" , code_end_datetime = \"procedure_datetime\" , ) events = [] for concept , code_dict in codes . items (): tmp_df = event_from_code ( df = procedure_occurrence , columns = procedure_columns , visit_occurrence = visit_occurrence , concept = concept , codes = code_dict , date_from_visit = date_from_visit , additional_filtering = additional_filtering , date_min = date_min , date_max = date_max , ) events . append ( tmp_df ) framework = get_framework ( procedure_occurrence ) return framework . concat ( events )","title":"procedures_from_ccam()"},{"location":"reference/event/consultations/","text":"eds_scikit.event.consultations get_consultation_dates get_consultation_dates ( vo : DataFrame , note : DataFrame , note_nlp : Optional [ DataFrame ] = None , algo : Union [ str , List [ str ]] = [ 'nlp' ], max_timedelta : timedelta = timedelta ( days = 7 ), structured_config : Dict [ str , Any ] = dict (), nlp_config : Dict [ str , Any ] = dict ()) -> DataFrame Extract consultation dates. See the implementation details of the algo(s) you want to use PARAMETER DESCRIPTION vo visit_occurrence DataFrame TYPE: DataFrame note note DataFrame TYPE: DataFrame note_nlp note_nlp DataFrame, used only with the \"nlp\" algo TYPE: Optional [ DataFrame ] DEFAULT: None algo Algorithm(s) to use to determine consultation dates. Multiple algorithms can be provided as a list. Accepted values are: \"structured\" : See get_consultation_dates_structured() \"nlp\" : See get_consultation_dates_nlp() TYPE: Union [ str , List [ str ]] DEFAULT: ['nlp'] max_timedelta If two extracted consultations are spaced by less than max_timedelta , we consider that they correspond to the same event and only keep the first one. TYPE: timedelta DEFAULT: timedelta(days=7) structured_config A dictionnary of parameters when using the structured algorithm TYPE: Dict [ str , Any ] DEFAULT: dict() nlp_config A dictionnary of parameters when using the nlp algorithm TYPE: Dict [ str , Any ] DEFAULT: dict() RETURNS DESCRIPTION DataFrame Event type DataFrame with the following columns: person_id visit_occurrence_id CONSULTATION_DATE : corresponds to the note_datetime value of a consultation report coming from the considered visit. CONSULTATION_NOTE_ID : the note_id of the corresponding report. CONSULTATION_DATE_EXTRACTION : the method of extraction Source code in eds_scikit/event/consultations.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 @concept_checker ( concepts = [ \"CONSULTATION_DATE\" , \"CONSULTATION_ID\" , \"CONSULTATION_DATE_EXTRACTION\" , ] ) def get_consultation_dates ( vo : DataFrame , note : DataFrame , note_nlp : Optional [ DataFrame ] = None , algo : Union [ str , List [ str ]] = [ \"nlp\" ], max_timedelta : timedelta = timedelta ( days = 7 ), structured_config : Dict [ str , Any ] = dict (), nlp_config : Dict [ str , Any ] = dict (), ) -> DataFrame : \"\"\" Extract consultation dates. See the implementation details of the algo(s) you want to use Parameters ---------- vo : DataFrame `visit_occurrence` DataFrame note : DataFrame `note` DataFrame note_nlp : Optional[DataFrame] `note_nlp` DataFrame, used only with the `\"nlp\"` algo algo: Union[str, List[str]] = [\"nlp\"] Algorithm(s) to use to determine consultation dates. Multiple algorithms can be provided as a list. Accepted values are: - `\"structured\"`: See [get_consultation_dates_structured()][eds_scikit.event.consultations.get_consultation_dates_structured] - `\"nlp\"`: See [get_consultation_dates_nlp()][eds_scikit.event.consultations.get_consultation_dates_nlp] max_timedelta: timedelta = timedelta(days=7) If two extracted consultations are spaced by less than `max_timedelta`, we consider that they correspond to the same event and only keep the first one. structured_config : Dict[str, Any] = dict() A dictionnary of parameters when using the [`structured`][eds_scikit.event.consultations.get_consultation_dates_structured] algorithm nlp_config : Dict[str, Any] = dict() A dictionnary of parameters when using the [`nlp`][eds_scikit.event.consultations.get_consultation_dates_nlp] algorithm Returns ------- DataFrame Event type DataFrame with the following columns: - `person_id` - `visit_occurrence_id` - `CONSULTATION_DATE`: corresponds to the `note_datetime` value of a consultation report coming from the considered visit. - `CONSULTATION_NOTE_ID`: the `note_id` of the corresponding report. - `CONSULTATION_DATE_EXTRACTION`: the method of extraction \"\"\" fw = get_framework ( vo ) if type ( algo ) == str : algo = [ algo ] dates = [] for a in algo : if a == \"structured\" : dates . append ( get_consultation_dates_structured ( vo = vo , note = note , ** structured_config , ) ) if a == \"nlp\" : dates . append ( get_consultation_dates_nlp ( note_nlp = note_nlp , ** nlp_config , ) ) dates_per_note = ( fw . concat ( dates ) . reset_index () . merge ( note [[ \"note_id\" , \"visit_occurrence_id\" ]], on = \"note_id\" , how = \"inner\" ) ) # Remove timezone errors from spark dates_per_note [ \"CONSULTATION_DATE\" ] = dates_per_note [ \"CONSULTATION_DATE\" ] . astype ( str ) dates_per_visit = ( dates_per_note . groupby ([ \"visit_occurrence_id\" , \"CONSULTATION_DATE\" ])[ \"CONSULTATION_DATE_EXTRACTION\" ] . unique () . apply ( sorted ) . str . join ( \"+\" ) ) dates_per_visit . name = \"CONSULTATION_DATE_EXTRACTION\" dates_per_visit = bd . add_unique_id ( dates_per_visit . reset_index (), col_name = \"TMP_CONSULTATION_ID\" ) # Convert back to datetime format dates_per_visit [ \"CONSULTATION_DATE\" ] = bd . to_datetime ( dates_per_visit [ \"CONSULTATION_DATE\" ], errors = \"coerce\" ) dates_per_visit = clean_consultations ( dates_per_visit , max_timedelta , ) # Equivalent to df.spark.cache() for ks.DataFrame bd . cache ( dates_per_visit ) return dates_per_visit get_consultation_dates_structured get_consultation_dates_structured ( note : DataFrame , vo : Optional [ DataFrame ] = None , kept_note_class_source_value : Optional [ Union [ str , List [ str ]]] = 'CR-CONS' , kept_visit_source_value : Optional [ Union [ str , List [ str ]]] = 'consultation externe' ) -> DataFrame Uses note_datetime value to infer true consultation dates PARAMETER DESCRIPTION note A note DataFrame with at least the following columns: note_id note_datetime note_source_value if kept_note_class_source_value is not None visit_occurrence_id if kept_visit_source_value is not None TYPE: DataFrame vo A visit_occurrence DataFrame to provide if kept_visit_source_value is not None , with at least the following columns: visit_occurrence_id visit_source_value if kept_visit_source_value is not None TYPE: Optional [ DataFrame ] DEFAULT: None kept_note_class_source_value Value(s) allowed for the note_class_source_value column. TYPE: Optional [ Union [ str , List [ str ]]] DEFAULT: 'CR-CONS' kept_visit_source_value Value(s) allowed for the visit_source_value column. TYPE: Optional [ Union [ str , List [ str ]]], optional DEFAULT: 'consultation externe' RETURNS DESCRIPTION Dataframe With 2 added columns corresponding to the following concept: CONSULTATION_DATE , containing the date CONSULTATION_DATE_EXTRACTION , containing \"STRUCTURED\" Source code in eds_scikit/event/consultations.py 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 def get_consultation_dates_structured ( note : DataFrame , vo : Optional [ DataFrame ] = None , kept_note_class_source_value : Optional [ Union [ str , List [ str ]]] = \"CR-CONS\" , kept_visit_source_value : Optional [ Union [ str , List [ str ]]] = \"consultation externe\" , ) -> DataFrame : \"\"\" Uses `note_datetime` value to infer *true* consultation dates Parameters ---------- note : DataFrame A `note` DataFrame with at least the following columns: - `note_id` - `note_datetime` - `note_source_value` **if** `kept_note_class_source_value is not None` - `visit_occurrence_id` **if** `kept_visit_source_value is not None` vo : Optional[DataFrame] A visit_occurrence DataFrame to provide **if** `kept_visit_source_value is not None`, with at least the following columns: - `visit_occurrence_id` - `visit_source_value` **if** `kept_visit_source_value is not None` kept_note_class_source_value : Optional[Union[str, List[str]]] Value(s) allowed for the `note_class_source_value` column. kept_visit_source_value : Optional[Union[str, List[str]]], optional Value(s) allowed for the `visit_source_value` column. Returns ------- Dataframe With 2 added columns corresponding to the following concept: - `CONSULTATION_DATE`, containing the date - `CONSULTATION_DATE_EXTRACTION`, containing `\"STRUCTURED\"` \"\"\" kept_note = note if kept_note_class_source_value is not None : if type ( kept_note_class_source_value ) == str : kept_note_class_source_value = [ kept_note_class_source_value ] kept_note = note [ note . note_class_source_value . isin ( set ( kept_note_class_source_value )) ] if kept_visit_source_value is not None : if type ( kept_visit_source_value ) == str : kept_visit_source_value = [ kept_visit_source_value ] kept_note = kept_note . merge ( vo [ [ \"visit_occurrence_id\" , \"visit_source_value\" , ] ][ vo . visit_source_value . isin ( set ( kept_visit_source_value ))], on = \"visit_occurrence_id\" , ) dates_per_note = kept_note [[ \"note_datetime\" , \"note_id\" ]] . rename ( columns = { \"note_datetime\" : \"CONSULTATION_DATE\" , } ) dates_per_note [ \"CONSULTATION_DATE_EXTRACTION\" ] = \"STRUCTURED\" return dates_per_note . set_index ( \"note_id\" ) get_consultation_dates_nlp get_consultation_dates_nlp ( note_nlp : DataFrame , dates_to_keep : str = 'min' ) -> DataFrame Uses consultation dates extracted a priori in consultation reports to infer true consultation dates PARAMETER DESCRIPTION note_nlp A DataFrame with (at least) the following columns: note_id consultation_date end if using dates_to_keep=first : end should store the character offset of the extracted date. TYPE: DataFrame dates_to_keep How to handle multiple consultation dates found in the document: min : keep the oldest one first : keep the occurrence that appeared first in the text all : keep all date TYPE: str , optional DEFAULT: 'min' RETURNS DESCRIPTION Dataframe With 2 added columns corresponding to the following concept: CONSULTATION_DATE , containing the date CONSULTATION_DATE_EXTRACTION , containing \"NLP\" Source code in eds_scikit/event/consultations.py 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 def get_consultation_dates_nlp ( note_nlp : DataFrame , dates_to_keep : str = \"min\" , ) -> DataFrame : \"\"\" Uses consultation dates extracted *a priori* in consultation reports to infer *true* consultation dates Parameters ---------- note_nlp : DataFrame A DataFrame with (at least) the following columns: - `note_id` - `consultation_date` - `end` **if** using `dates_to_keep=first`: `end` should store the character offset of the extracted date. dates_to_keep : str, optional How to handle multiple consultation dates found in the document: - `min`: keep the oldest one - `first`: keep the occurrence that appeared first in the text - `all`: keep all date Returns ------- Dataframe With 2 added columns corresponding to the following concept: - `CONSULTATION_DATE`, containing the date - `CONSULTATION_DATE_EXTRACTION`, containing `\"NLP\"` \"\"\" if dates_to_keep == \"min\" : dates_per_note = note_nlp . groupby ( \"note_id\" ) . agg ( CONSULTATION_DATE = ( \"consultation_date\" , \"min\" ), ) elif dates_to_keep == \"first\" : dates_per_note = ( note_nlp . sort_values ( by = \"start\" ) . groupby ( \"note_id\" ) . agg ( CONSULTATION_DATE = ( \"consultation_date\" , \"first\" )) ) elif dates_to_keep == \"all\" : dates_per_note = note_nlp [[ \"consultation_date\" , \"note_id\" ]] . set_index ( \"note_id\" ) dates_per_note = dates_per_note . rename ( columns = { \"consultation_date\" : \"CONSULTATION_DATE\" } ) dates_per_note [ \"CONSULTATION_DATE_EXTRACTION\" ] = \"NLP\" return dates_per_note","title":"consultations"},{"location":"reference/event/consultations/#eds_scikiteventconsultations","text":"","title":"eds_scikit.event.consultations"},{"location":"reference/event/consultations/#eds_scikit.event.consultations.get_consultation_dates","text":"get_consultation_dates ( vo : DataFrame , note : DataFrame , note_nlp : Optional [ DataFrame ] = None , algo : Union [ str , List [ str ]] = [ 'nlp' ], max_timedelta : timedelta = timedelta ( days = 7 ), structured_config : Dict [ str , Any ] = dict (), nlp_config : Dict [ str , Any ] = dict ()) -> DataFrame Extract consultation dates. See the implementation details of the algo(s) you want to use PARAMETER DESCRIPTION vo visit_occurrence DataFrame TYPE: DataFrame note note DataFrame TYPE: DataFrame note_nlp note_nlp DataFrame, used only with the \"nlp\" algo TYPE: Optional [ DataFrame ] DEFAULT: None algo Algorithm(s) to use to determine consultation dates. Multiple algorithms can be provided as a list. Accepted values are: \"structured\" : See get_consultation_dates_structured() \"nlp\" : See get_consultation_dates_nlp() TYPE: Union [ str , List [ str ]] DEFAULT: ['nlp'] max_timedelta If two extracted consultations are spaced by less than max_timedelta , we consider that they correspond to the same event and only keep the first one. TYPE: timedelta DEFAULT: timedelta(days=7) structured_config A dictionnary of parameters when using the structured algorithm TYPE: Dict [ str , Any ] DEFAULT: dict() nlp_config A dictionnary of parameters when using the nlp algorithm TYPE: Dict [ str , Any ] DEFAULT: dict() RETURNS DESCRIPTION DataFrame Event type DataFrame with the following columns: person_id visit_occurrence_id CONSULTATION_DATE : corresponds to the note_datetime value of a consultation report coming from the considered visit. CONSULTATION_NOTE_ID : the note_id of the corresponding report. CONSULTATION_DATE_EXTRACTION : the method of extraction Source code in eds_scikit/event/consultations.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 @concept_checker ( concepts = [ \"CONSULTATION_DATE\" , \"CONSULTATION_ID\" , \"CONSULTATION_DATE_EXTRACTION\" , ] ) def get_consultation_dates ( vo : DataFrame , note : DataFrame , note_nlp : Optional [ DataFrame ] = None , algo : Union [ str , List [ str ]] = [ \"nlp\" ], max_timedelta : timedelta = timedelta ( days = 7 ), structured_config : Dict [ str , Any ] = dict (), nlp_config : Dict [ str , Any ] = dict (), ) -> DataFrame : \"\"\" Extract consultation dates. See the implementation details of the algo(s) you want to use Parameters ---------- vo : DataFrame `visit_occurrence` DataFrame note : DataFrame `note` DataFrame note_nlp : Optional[DataFrame] `note_nlp` DataFrame, used only with the `\"nlp\"` algo algo: Union[str, List[str]] = [\"nlp\"] Algorithm(s) to use to determine consultation dates. Multiple algorithms can be provided as a list. Accepted values are: - `\"structured\"`: See [get_consultation_dates_structured()][eds_scikit.event.consultations.get_consultation_dates_structured] - `\"nlp\"`: See [get_consultation_dates_nlp()][eds_scikit.event.consultations.get_consultation_dates_nlp] max_timedelta: timedelta = timedelta(days=7) If two extracted consultations are spaced by less than `max_timedelta`, we consider that they correspond to the same event and only keep the first one. structured_config : Dict[str, Any] = dict() A dictionnary of parameters when using the [`structured`][eds_scikit.event.consultations.get_consultation_dates_structured] algorithm nlp_config : Dict[str, Any] = dict() A dictionnary of parameters when using the [`nlp`][eds_scikit.event.consultations.get_consultation_dates_nlp] algorithm Returns ------- DataFrame Event type DataFrame with the following columns: - `person_id` - `visit_occurrence_id` - `CONSULTATION_DATE`: corresponds to the `note_datetime` value of a consultation report coming from the considered visit. - `CONSULTATION_NOTE_ID`: the `note_id` of the corresponding report. - `CONSULTATION_DATE_EXTRACTION`: the method of extraction \"\"\" fw = get_framework ( vo ) if type ( algo ) == str : algo = [ algo ] dates = [] for a in algo : if a == \"structured\" : dates . append ( get_consultation_dates_structured ( vo = vo , note = note , ** structured_config , ) ) if a == \"nlp\" : dates . append ( get_consultation_dates_nlp ( note_nlp = note_nlp , ** nlp_config , ) ) dates_per_note = ( fw . concat ( dates ) . reset_index () . merge ( note [[ \"note_id\" , \"visit_occurrence_id\" ]], on = \"note_id\" , how = \"inner\" ) ) # Remove timezone errors from spark dates_per_note [ \"CONSULTATION_DATE\" ] = dates_per_note [ \"CONSULTATION_DATE\" ] . astype ( str ) dates_per_visit = ( dates_per_note . groupby ([ \"visit_occurrence_id\" , \"CONSULTATION_DATE\" ])[ \"CONSULTATION_DATE_EXTRACTION\" ] . unique () . apply ( sorted ) . str . join ( \"+\" ) ) dates_per_visit . name = \"CONSULTATION_DATE_EXTRACTION\" dates_per_visit = bd . add_unique_id ( dates_per_visit . reset_index (), col_name = \"TMP_CONSULTATION_ID\" ) # Convert back to datetime format dates_per_visit [ \"CONSULTATION_DATE\" ] = bd . to_datetime ( dates_per_visit [ \"CONSULTATION_DATE\" ], errors = \"coerce\" ) dates_per_visit = clean_consultations ( dates_per_visit , max_timedelta , ) # Equivalent to df.spark.cache() for ks.DataFrame bd . cache ( dates_per_visit ) return dates_per_visit","title":"get_consultation_dates()"},{"location":"reference/event/consultations/#eds_scikit.event.consultations.get_consultation_dates_structured","text":"get_consultation_dates_structured ( note : DataFrame , vo : Optional [ DataFrame ] = None , kept_note_class_source_value : Optional [ Union [ str , List [ str ]]] = 'CR-CONS' , kept_visit_source_value : Optional [ Union [ str , List [ str ]]] = 'consultation externe' ) -> DataFrame Uses note_datetime value to infer true consultation dates PARAMETER DESCRIPTION note A note DataFrame with at least the following columns: note_id note_datetime note_source_value if kept_note_class_source_value is not None visit_occurrence_id if kept_visit_source_value is not None TYPE: DataFrame vo A visit_occurrence DataFrame to provide if kept_visit_source_value is not None , with at least the following columns: visit_occurrence_id visit_source_value if kept_visit_source_value is not None TYPE: Optional [ DataFrame ] DEFAULT: None kept_note_class_source_value Value(s) allowed for the note_class_source_value column. TYPE: Optional [ Union [ str , List [ str ]]] DEFAULT: 'CR-CONS' kept_visit_source_value Value(s) allowed for the visit_source_value column. TYPE: Optional [ Union [ str , List [ str ]]], optional DEFAULT: 'consultation externe' RETURNS DESCRIPTION Dataframe With 2 added columns corresponding to the following concept: CONSULTATION_DATE , containing the date CONSULTATION_DATE_EXTRACTION , containing \"STRUCTURED\" Source code in eds_scikit/event/consultations.py 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 def get_consultation_dates_structured ( note : DataFrame , vo : Optional [ DataFrame ] = None , kept_note_class_source_value : Optional [ Union [ str , List [ str ]]] = \"CR-CONS\" , kept_visit_source_value : Optional [ Union [ str , List [ str ]]] = \"consultation externe\" , ) -> DataFrame : \"\"\" Uses `note_datetime` value to infer *true* consultation dates Parameters ---------- note : DataFrame A `note` DataFrame with at least the following columns: - `note_id` - `note_datetime` - `note_source_value` **if** `kept_note_class_source_value is not None` - `visit_occurrence_id` **if** `kept_visit_source_value is not None` vo : Optional[DataFrame] A visit_occurrence DataFrame to provide **if** `kept_visit_source_value is not None`, with at least the following columns: - `visit_occurrence_id` - `visit_source_value` **if** `kept_visit_source_value is not None` kept_note_class_source_value : Optional[Union[str, List[str]]] Value(s) allowed for the `note_class_source_value` column. kept_visit_source_value : Optional[Union[str, List[str]]], optional Value(s) allowed for the `visit_source_value` column. Returns ------- Dataframe With 2 added columns corresponding to the following concept: - `CONSULTATION_DATE`, containing the date - `CONSULTATION_DATE_EXTRACTION`, containing `\"STRUCTURED\"` \"\"\" kept_note = note if kept_note_class_source_value is not None : if type ( kept_note_class_source_value ) == str : kept_note_class_source_value = [ kept_note_class_source_value ] kept_note = note [ note . note_class_source_value . isin ( set ( kept_note_class_source_value )) ] if kept_visit_source_value is not None : if type ( kept_visit_source_value ) == str : kept_visit_source_value = [ kept_visit_source_value ] kept_note = kept_note . merge ( vo [ [ \"visit_occurrence_id\" , \"visit_source_value\" , ] ][ vo . visit_source_value . isin ( set ( kept_visit_source_value ))], on = \"visit_occurrence_id\" , ) dates_per_note = kept_note [[ \"note_datetime\" , \"note_id\" ]] . rename ( columns = { \"note_datetime\" : \"CONSULTATION_DATE\" , } ) dates_per_note [ \"CONSULTATION_DATE_EXTRACTION\" ] = \"STRUCTURED\" return dates_per_note . set_index ( \"note_id\" )","title":"get_consultation_dates_structured()"},{"location":"reference/event/consultations/#eds_scikit.event.consultations.get_consultation_dates_nlp","text":"get_consultation_dates_nlp ( note_nlp : DataFrame , dates_to_keep : str = 'min' ) -> DataFrame Uses consultation dates extracted a priori in consultation reports to infer true consultation dates PARAMETER DESCRIPTION note_nlp A DataFrame with (at least) the following columns: note_id consultation_date end if using dates_to_keep=first : end should store the character offset of the extracted date. TYPE: DataFrame dates_to_keep How to handle multiple consultation dates found in the document: min : keep the oldest one first : keep the occurrence that appeared first in the text all : keep all date TYPE: str , optional DEFAULT: 'min' RETURNS DESCRIPTION Dataframe With 2 added columns corresponding to the following concept: CONSULTATION_DATE , containing the date CONSULTATION_DATE_EXTRACTION , containing \"NLP\" Source code in eds_scikit/event/consultations.py 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 def get_consultation_dates_nlp ( note_nlp : DataFrame , dates_to_keep : str = \"min\" , ) -> DataFrame : \"\"\" Uses consultation dates extracted *a priori* in consultation reports to infer *true* consultation dates Parameters ---------- note_nlp : DataFrame A DataFrame with (at least) the following columns: - `note_id` - `consultation_date` - `end` **if** using `dates_to_keep=first`: `end` should store the character offset of the extracted date. dates_to_keep : str, optional How to handle multiple consultation dates found in the document: - `min`: keep the oldest one - `first`: keep the occurrence that appeared first in the text - `all`: keep all date Returns ------- Dataframe With 2 added columns corresponding to the following concept: - `CONSULTATION_DATE`, containing the date - `CONSULTATION_DATE_EXTRACTION`, containing `\"NLP\"` \"\"\" if dates_to_keep == \"min\" : dates_per_note = note_nlp . groupby ( \"note_id\" ) . agg ( CONSULTATION_DATE = ( \"consultation_date\" , \"min\" ), ) elif dates_to_keep == \"first\" : dates_per_note = ( note_nlp . sort_values ( by = \"start\" ) . groupby ( \"note_id\" ) . agg ( CONSULTATION_DATE = ( \"consultation_date\" , \"first\" )) ) elif dates_to_keep == \"all\" : dates_per_note = note_nlp [[ \"consultation_date\" , \"note_id\" ]] . set_index ( \"note_id\" ) dates_per_note = dates_per_note . rename ( columns = { \"consultation_date\" : \"CONSULTATION_DATE\" } ) dates_per_note [ \"CONSULTATION_DATE_EXTRACTION\" ] = \"NLP\" return dates_per_note","title":"get_consultation_dates_nlp()"},{"location":"reference/event/diabetes/","text":"eds_scikit.event.diabetes DEFAULT_DIABETE_FROM_ICD10_CONFIG module-attribute DEFAULT_DIABETE_FROM_ICD10_CONFIG = dict ( codes = dict ( DIABETES_TYPE_I = dict ( prefix = 'E10' ), DIABETES_TYPE_II = dict ( prefix = 'E11' ), DIABETES_MALNUTRITION = dict ( prefix = 'E12' ), DIABETES_IN_PREGNANCY = dict ( prefix = 'O24' ), OTHER_DIABETES_MELLITUS = dict ( prefix = [ 'E13' , 'E14' ]), DIABETES_INSIPIDUS = dict ( exact = [ 'E232' , 'N251' ])), date_from_visit = True , additional_filtering = dict ( condition_status_source_value = { 'DP' , 'DAS' })) Default parameters feeded to conditions_from_icd10() diabetes_from_icd10 diabetes_from_icd10 ( condition_occurrence : DataFrame , visit_occurrence : DataFrame , date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None , codes : Dict [ str , Union [ str , List [ str ]]] = DEFAULT_DIABETE_FROM_ICD10_CONFIG [ 'codes' ], date_from_visit : bool = DEFAULT_DIABETE_FROM_ICD10_CONFIG [ 'date_from_visit' ], additional_filtering : Dict [ str , Any ] = DEFAULT_DIABETE_FROM_ICD10_CONFIG [ 'additional_filtering' ]) -> DataFrame Wrapper around the conditions_from_icd10() function. Check the default configuration to see the used parameters PARAMETER DESCRIPTION condition_occurrence OMOP-like condition occurrence DataFrame TYPE: DataFrame visit_occurrence OMOP-like visit_occurrence DataFrame TYPE: Optional [ DataFrame ] date_min Lower temporal bound TYPE: Optional [ datetime ] DEFAULT: None date_max Upper temporal bound TYPE: Optional [ datetime ] DEFAULT: None codes Dictionary of ICD-10 used for phenotyping TYPE: Optional [ Dict [ str , Union [ str , List [ str ]]]] DEFAULT: DEFAULT_DIABETE_FROM_ICD10_CONFIG['codes'] date_from_visit If true, use the visit_[start/end]_datetime for filtering. Else, use condition_start_datetime TYPE: bool, by default True DEFAULT: DEFAULT_DIABETE_FROM_ICD10_CONFIG['date_from_visit'] additional_filtering A dictionary to perform additional filtering. Each key should be a valid column name from condition_occurrence Each value should be a value / set of values / list of values For each pair (key, value), filtering is done as condition_occurrence[condition_occurrence[k].isin(v)] TYPE: Dict [ str , Any ] DEFAULT: DEFAULT_DIABETE_FROM_ICD10_CONFIG['additional_filtering'] RETURNS DESCRIPTION DataFrame Event DataFrame in long format (with a concept and a value column). The concept column contains one of the following: DIABETES_TYPE_I DIABETES_TYPE_II DIABETES_MALNUTRITION DIABETES_IN_PREGNANCY OTHER_DIABETES_MELLITUS DIABETES_INSIPIDUS The value column contains the corresponding ICD-10 code that was extracted Source code in eds_scikit/event/diabetes.py 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 def diabetes_from_icd10 ( condition_occurrence : DataFrame , visit_occurrence : DataFrame , date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None , codes : Dict [ str , Union [ str , List [ str ]]] = DEFAULT_DIABETE_FROM_ICD10_CONFIG [ \"codes\" ], date_from_visit : bool = DEFAULT_DIABETE_FROM_ICD10_CONFIG [ \"date_from_visit\" ], additional_filtering : Dict [ str , Any ] = DEFAULT_DIABETE_FROM_ICD10_CONFIG [ \"additional_filtering\" ], ) -> DataFrame : \"\"\" Wrapper around the [conditions_from_icd10()][eds_scikit.event.icd10.conditions_from_icd10] function. Check the [default configuration][eds_scikit.event.diabetes.DEFAULT_DIABETE_FROM_ICD10_CONFIG] to see the used parameters Parameters ---------- condition_occurrence OMOP-like condition occurrence DataFrame visit_occurrence : Optional[DataFrame] OMOP-like visit_occurrence DataFrame date_min : Optional[datetime] Lower temporal bound date_max : Optional[datetime] Upper temporal bound codes : Optional[Dict[str, Union[str, List[str]]]] Dictionary of ICD-10 used for phenotyping date_from_visit : bool, by default True If true, use the `visit_[start/end]_datetime` for filtering. Else, use `condition_start_datetime` additional_filtering : Dict[str, Any] A dictionary to perform additional filtering. - **Each key** should be a valid column name from `condition_occurrence` - **Each value** should be a value / set of values / list of values For each pair (key, value), filtering is done as `condition_occurrence[condition_occurrence[k].isin(v)]` Returns ------- DataFrame Event DataFrame in **long** format (with a `concept` and a `value` column). The `concept` column contains one of the following: - DIABETES_TYPE_I - DIABETES_TYPE_II - DIABETES_MALNUTRITION - DIABETES_IN_PREGNANCY - OTHER_DIABETES_MELLITUS - DIABETES_INSIPIDUS The `value` column contains the corresponding ICD-10 code that was extracted \"\"\" diabetes = conditions_from_icd10 ( condition_occurrence = condition_occurrence , visit_occurrence = visit_occurrence , date_min = date_min , date_max = date_max , codes = codes , date_from_visit = date_from_visit , additional_filtering = additional_filtering , ) diabetes [ \"value\" ] = diabetes [ \"concept\" ] diabetes [ \"concept\" ] = \"DIABETES_FROM_ICD10\" return diabetes","title":"diabetes"},{"location":"reference/event/diabetes/#eds_scikiteventdiabetes","text":"","title":"eds_scikit.event.diabetes"},{"location":"reference/event/diabetes/#eds_scikit.event.diabetes.DEFAULT_DIABETE_FROM_ICD10_CONFIG","text":"DEFAULT_DIABETE_FROM_ICD10_CONFIG = dict ( codes = dict ( DIABETES_TYPE_I = dict ( prefix = 'E10' ), DIABETES_TYPE_II = dict ( prefix = 'E11' ), DIABETES_MALNUTRITION = dict ( prefix = 'E12' ), DIABETES_IN_PREGNANCY = dict ( prefix = 'O24' ), OTHER_DIABETES_MELLITUS = dict ( prefix = [ 'E13' , 'E14' ]), DIABETES_INSIPIDUS = dict ( exact = [ 'E232' , 'N251' ])), date_from_visit = True , additional_filtering = dict ( condition_status_source_value = { 'DP' , 'DAS' })) Default parameters feeded to conditions_from_icd10()","title":"DEFAULT_DIABETE_FROM_ICD10_CONFIG"},{"location":"reference/event/diabetes/#eds_scikit.event.diabetes.diabetes_from_icd10","text":"diabetes_from_icd10 ( condition_occurrence : DataFrame , visit_occurrence : DataFrame , date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None , codes : Dict [ str , Union [ str , List [ str ]]] = DEFAULT_DIABETE_FROM_ICD10_CONFIG [ 'codes' ], date_from_visit : bool = DEFAULT_DIABETE_FROM_ICD10_CONFIG [ 'date_from_visit' ], additional_filtering : Dict [ str , Any ] = DEFAULT_DIABETE_FROM_ICD10_CONFIG [ 'additional_filtering' ]) -> DataFrame Wrapper around the conditions_from_icd10() function. Check the default configuration to see the used parameters PARAMETER DESCRIPTION condition_occurrence OMOP-like condition occurrence DataFrame TYPE: DataFrame visit_occurrence OMOP-like visit_occurrence DataFrame TYPE: Optional [ DataFrame ] date_min Lower temporal bound TYPE: Optional [ datetime ] DEFAULT: None date_max Upper temporal bound TYPE: Optional [ datetime ] DEFAULT: None codes Dictionary of ICD-10 used for phenotyping TYPE: Optional [ Dict [ str , Union [ str , List [ str ]]]] DEFAULT: DEFAULT_DIABETE_FROM_ICD10_CONFIG['codes'] date_from_visit If true, use the visit_[start/end]_datetime for filtering. Else, use condition_start_datetime TYPE: bool, by default True DEFAULT: DEFAULT_DIABETE_FROM_ICD10_CONFIG['date_from_visit'] additional_filtering A dictionary to perform additional filtering. Each key should be a valid column name from condition_occurrence Each value should be a value / set of values / list of values For each pair (key, value), filtering is done as condition_occurrence[condition_occurrence[k].isin(v)] TYPE: Dict [ str , Any ] DEFAULT: DEFAULT_DIABETE_FROM_ICD10_CONFIG['additional_filtering'] RETURNS DESCRIPTION DataFrame Event DataFrame in long format (with a concept and a value column). The concept column contains one of the following: DIABETES_TYPE_I DIABETES_TYPE_II DIABETES_MALNUTRITION DIABETES_IN_PREGNANCY OTHER_DIABETES_MELLITUS DIABETES_INSIPIDUS The value column contains the corresponding ICD-10 code that was extracted Source code in eds_scikit/event/diabetes.py 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 def diabetes_from_icd10 ( condition_occurrence : DataFrame , visit_occurrence : DataFrame , date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None , codes : Dict [ str , Union [ str , List [ str ]]] = DEFAULT_DIABETE_FROM_ICD10_CONFIG [ \"codes\" ], date_from_visit : bool = DEFAULT_DIABETE_FROM_ICD10_CONFIG [ \"date_from_visit\" ], additional_filtering : Dict [ str , Any ] = DEFAULT_DIABETE_FROM_ICD10_CONFIG [ \"additional_filtering\" ], ) -> DataFrame : \"\"\" Wrapper around the [conditions_from_icd10()][eds_scikit.event.icd10.conditions_from_icd10] function. Check the [default configuration][eds_scikit.event.diabetes.DEFAULT_DIABETE_FROM_ICD10_CONFIG] to see the used parameters Parameters ---------- condition_occurrence OMOP-like condition occurrence DataFrame visit_occurrence : Optional[DataFrame] OMOP-like visit_occurrence DataFrame date_min : Optional[datetime] Lower temporal bound date_max : Optional[datetime] Upper temporal bound codes : Optional[Dict[str, Union[str, List[str]]]] Dictionary of ICD-10 used for phenotyping date_from_visit : bool, by default True If true, use the `visit_[start/end]_datetime` for filtering. Else, use `condition_start_datetime` additional_filtering : Dict[str, Any] A dictionary to perform additional filtering. - **Each key** should be a valid column name from `condition_occurrence` - **Each value** should be a value / set of values / list of values For each pair (key, value), filtering is done as `condition_occurrence[condition_occurrence[k].isin(v)]` Returns ------- DataFrame Event DataFrame in **long** format (with a `concept` and a `value` column). The `concept` column contains one of the following: - DIABETES_TYPE_I - DIABETES_TYPE_II - DIABETES_MALNUTRITION - DIABETES_IN_PREGNANCY - OTHER_DIABETES_MELLITUS - DIABETES_INSIPIDUS The `value` column contains the corresponding ICD-10 code that was extracted \"\"\" diabetes = conditions_from_icd10 ( condition_occurrence = condition_occurrence , visit_occurrence = visit_occurrence , date_min = date_min , date_max = date_max , codes = codes , date_from_visit = date_from_visit , additional_filtering = additional_filtering , ) diabetes [ \"value\" ] = diabetes [ \"concept\" ] diabetes [ \"concept\" ] = \"DIABETES_FROM_ICD10\" return diabetes","title":"diabetes_from_icd10()"},{"location":"reference/event/from_code/","text":"eds_scikit.event.from_code event_from_code event_from_code ( df : DataFrame , columns : Dict [ str , str ], visit_occurrence : Optional [ DataFrame ] = None , concept : str = 'ICD10' , codes : Optional [ Dict [ str , Union [ str , List [ str ]]]] = None , date_from_visit : bool = True , additional_filtering : Dict [ str , Any ] = dict (), date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None ) -> DataFrame Generic function to filter a DataFrame based on one of its column and an ensemble of codes to select from. For instance, this function is called when phenotyping via ICD-10 or CCAM. PARAMETER DESCRIPTION df The DataFrame to filter. TYPE: DataFrame columns Dictionary with the following keys: code_source_value : The column name containing the code to filter code_start_datetime : The column name containing the starting date code_end_datetime : The column name containing the ending date TYPE: Dict [ str , str ] visit_occurrence The visit_occurrence DataFrame, only necessary if date_from_visit is set to True . TYPE: Optional [ DataFrame ] DEFAULT: None concept The name of the extracted concept TYPE: str DEFAULT: 'ICD10' codes Dictionary which values are codes (as a unique string or as a list) and which keys are at least one of the following: exact : To match the codes in codes[\"exact\"] exactly prefix : To match the codes in codes[\"prefix\"] as prefixes regex : To match the codes in codes[\"regex\"] as regexes You can combine any of those keys. TYPE: Dict [ str , Union [ str , List [ str ]]] DEFAULT: None date_from_visit If set to True , uses visit_start_datetime as the code datetime TYPE: bool DEFAULT: True additional_filtering An optional dictionary to filter the resulting DataFrame. Keys should be column names on which too filter, and values should be either A single value A list or set of values. TYPE: Dict [ str , Any ] DEFAULT: dict() date_min The minimum code datetime to keep. Depends on the date_from_visit flag TYPE: Optional [ datetime ] DEFAULT: None date_max The minimum code datetime to keep. Depends on the date_from_visit flag TYPE: Optional [ datetime ] DEFAULT: None RETURNS DESCRIPTION DataFrame A DataFrame containing especially the following columns: t_start t_end concept : The provided concept string value : The matched code Source code in eds_scikit/event/from_code.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 def event_from_code ( df : DataFrame , columns : Dict [ str , str ], visit_occurrence : Optional [ DataFrame ] = None , concept : str = \"ICD10\" , codes : Optional [ Dict [ str , Union [ str , List [ str ]]]] = None , date_from_visit : bool = True , additional_filtering : Dict [ str , Any ] = dict (), date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None , ) -> DataFrame : \"\"\" Generic function to filter a DataFrame based on one of its column and an ensemble of codes to select from. For instance, this function is called when phenotyping via ICD-10 or CCAM. Parameters ---------- df : DataFrame The DataFrame to filter. columns : Dict[str, str] Dictionary with the following keys: - `code_source_value` : The column name containing the code to filter - `code_start_datetime` : The column name containing the starting date - `code_end_datetime` : The column name containing the ending date visit_occurrence : Optional[DataFrame] The `visit_occurrence` DataFrame, only necessary if `date_from_visit` is set to `True`. concept : str The name of the extracted concept codes : Dict[str, Union[str, List[str]]] Dictionary which values are codes (as a unique string or as a list) and which keys are at least one of the following: - `exact`: To match the codes in `codes[\"exact\"]` **exactly** - `prefix`: To match the codes in `codes[\"prefix\"]` **as prefixes** - `regex`: To match the codes in `codes[\"regex\"]` **as regexes** You can combine any of those keys. date_from_visit : bool If set to `True`, uses `visit_start_datetime` as the code datetime additional_filtering : Dict[str, Any] An optional dictionary to filter the resulting DataFrame. Keys should be column names on which too filter, and values should be either - A single value - A list or set of values. date_min : Optional[datetime] The minimum code datetime to keep. **Depends on the `date_from_visit` flag** date_max : Optional[datetime] The minimum code datetime to keep. **Depends on the `date_from_visit` flag** Returns ------- DataFrame A DataFrame containing especially the following columns: - `t_start` - `t_end` - `concept` : The provided `concept` string - `value` : The matched code \"\"\" required_columns = list ( columns . values ()) + [ \"visit_occurrence_id\" , \"person_id\" ] check_columns ( df , required_columns = required_columns ) d_format = { \"exact\" : r \" {code} \\b\" , \"regex\" : r \" {code} \" , \"prefix\" : r \"\\b {code} \" } regexes = [] for code_type , code_list in codes . items (): if type ( code_list ) == str : code_list = [ code_list ] codes_formated = [ d_format [ code_type ] . format ( code = code ) for code in code_list ] regexes . append ( r \"(?:\" + \"|\" . join ( codes_formated ) + \")\" ) final_regex = \"|\" . join ( regexes ) mask = df [ columns [ \"code_source_value\" ]] . str . contains ( final_regex ) . fillna ( False ) event = df [ mask ] if date_from_visit : if visit_occurrence is None : raise ValueError ( \"With 'date_from_visit=True', you should provide a 'visit_occurrence' DataFrame.\" ) event = event . merge ( visit_occurrence [ [ \"visit_occurrence_id\" , \"visit_start_datetime\" , \"visit_end_datetime\" ] ], on = \"visit_occurrence_id\" , how = \"inner\" , ) . rename ( columns = { \"visit_start_datetime\" : \"t_start\" , \"visit_end_datetime\" : \"t_end\" , } ) else : event . loc [:, \"t_start\" ] = event . loc [:, columns [ \"code_start_datetime\" ]] event . loc [:, \"t_end\" ] = event . loc [:, columns [ \"code_end_datetime\" ]] event = event . drop ( columns = [ columns [ \"code_start_datetime\" ], columns [ \"code_end_datetime\" ]] ) event = _column_filtering ( event , filtering_dict = additional_filtering ) mask = True # Resetting the mask if date_min is not None : mask = mask & ( event . t_start >= date_min ) if date_max is not None : mask = mask & ( event . t_start <= date_max ) if type ( mask ) != bool : # We have a Series mask event = event [ mask ] event [ \"concept\" ] = concept return event . rename ( columns = { columns [ \"code_source_value\" ]: \"value\" })[ [ \"person_id\" , \"t_start\" , \"t_end\" , \"concept\" , \"value\" , \"visit_occurrence_id\" , ] + list ( additional_filtering . keys ()) ] . reset_index ( drop = True )","title":"from_code"},{"location":"reference/event/from_code/#eds_scikiteventfrom_code","text":"","title":"eds_scikit.event.from_code"},{"location":"reference/event/from_code/#eds_scikit.event.from_code.event_from_code","text":"event_from_code ( df : DataFrame , columns : Dict [ str , str ], visit_occurrence : Optional [ DataFrame ] = None , concept : str = 'ICD10' , codes : Optional [ Dict [ str , Union [ str , List [ str ]]]] = None , date_from_visit : bool = True , additional_filtering : Dict [ str , Any ] = dict (), date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None ) -> DataFrame Generic function to filter a DataFrame based on one of its column and an ensemble of codes to select from. For instance, this function is called when phenotyping via ICD-10 or CCAM. PARAMETER DESCRIPTION df The DataFrame to filter. TYPE: DataFrame columns Dictionary with the following keys: code_source_value : The column name containing the code to filter code_start_datetime : The column name containing the starting date code_end_datetime : The column name containing the ending date TYPE: Dict [ str , str ] visit_occurrence The visit_occurrence DataFrame, only necessary if date_from_visit is set to True . TYPE: Optional [ DataFrame ] DEFAULT: None concept The name of the extracted concept TYPE: str DEFAULT: 'ICD10' codes Dictionary which values are codes (as a unique string or as a list) and which keys are at least one of the following: exact : To match the codes in codes[\"exact\"] exactly prefix : To match the codes in codes[\"prefix\"] as prefixes regex : To match the codes in codes[\"regex\"] as regexes You can combine any of those keys. TYPE: Dict [ str , Union [ str , List [ str ]]] DEFAULT: None date_from_visit If set to True , uses visit_start_datetime as the code datetime TYPE: bool DEFAULT: True additional_filtering An optional dictionary to filter the resulting DataFrame. Keys should be column names on which too filter, and values should be either A single value A list or set of values. TYPE: Dict [ str , Any ] DEFAULT: dict() date_min The minimum code datetime to keep. Depends on the date_from_visit flag TYPE: Optional [ datetime ] DEFAULT: None date_max The minimum code datetime to keep. Depends on the date_from_visit flag TYPE: Optional [ datetime ] DEFAULT: None RETURNS DESCRIPTION DataFrame A DataFrame containing especially the following columns: t_start t_end concept : The provided concept string value : The matched code Source code in eds_scikit/event/from_code.py 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 def event_from_code ( df : DataFrame , columns : Dict [ str , str ], visit_occurrence : Optional [ DataFrame ] = None , concept : str = \"ICD10\" , codes : Optional [ Dict [ str , Union [ str , List [ str ]]]] = None , date_from_visit : bool = True , additional_filtering : Dict [ str , Any ] = dict (), date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None , ) -> DataFrame : \"\"\" Generic function to filter a DataFrame based on one of its column and an ensemble of codes to select from. For instance, this function is called when phenotyping via ICD-10 or CCAM. Parameters ---------- df : DataFrame The DataFrame to filter. columns : Dict[str, str] Dictionary with the following keys: - `code_source_value` : The column name containing the code to filter - `code_start_datetime` : The column name containing the starting date - `code_end_datetime` : The column name containing the ending date visit_occurrence : Optional[DataFrame] The `visit_occurrence` DataFrame, only necessary if `date_from_visit` is set to `True`. concept : str The name of the extracted concept codes : Dict[str, Union[str, List[str]]] Dictionary which values are codes (as a unique string or as a list) and which keys are at least one of the following: - `exact`: To match the codes in `codes[\"exact\"]` **exactly** - `prefix`: To match the codes in `codes[\"prefix\"]` **as prefixes** - `regex`: To match the codes in `codes[\"regex\"]` **as regexes** You can combine any of those keys. date_from_visit : bool If set to `True`, uses `visit_start_datetime` as the code datetime additional_filtering : Dict[str, Any] An optional dictionary to filter the resulting DataFrame. Keys should be column names on which too filter, and values should be either - A single value - A list or set of values. date_min : Optional[datetime] The minimum code datetime to keep. **Depends on the `date_from_visit` flag** date_max : Optional[datetime] The minimum code datetime to keep. **Depends on the `date_from_visit` flag** Returns ------- DataFrame A DataFrame containing especially the following columns: - `t_start` - `t_end` - `concept` : The provided `concept` string - `value` : The matched code \"\"\" required_columns = list ( columns . values ()) + [ \"visit_occurrence_id\" , \"person_id\" ] check_columns ( df , required_columns = required_columns ) d_format = { \"exact\" : r \" {code} \\b\" , \"regex\" : r \" {code} \" , \"prefix\" : r \"\\b {code} \" } regexes = [] for code_type , code_list in codes . items (): if type ( code_list ) == str : code_list = [ code_list ] codes_formated = [ d_format [ code_type ] . format ( code = code ) for code in code_list ] regexes . append ( r \"(?:\" + \"|\" . join ( codes_formated ) + \")\" ) final_regex = \"|\" . join ( regexes ) mask = df [ columns [ \"code_source_value\" ]] . str . contains ( final_regex ) . fillna ( False ) event = df [ mask ] if date_from_visit : if visit_occurrence is None : raise ValueError ( \"With 'date_from_visit=True', you should provide a 'visit_occurrence' DataFrame.\" ) event = event . merge ( visit_occurrence [ [ \"visit_occurrence_id\" , \"visit_start_datetime\" , \"visit_end_datetime\" ] ], on = \"visit_occurrence_id\" , how = \"inner\" , ) . rename ( columns = { \"visit_start_datetime\" : \"t_start\" , \"visit_end_datetime\" : \"t_end\" , } ) else : event . loc [:, \"t_start\" ] = event . loc [:, columns [ \"code_start_datetime\" ]] event . loc [:, \"t_end\" ] = event . loc [:, columns [ \"code_end_datetime\" ]] event = event . drop ( columns = [ columns [ \"code_start_datetime\" ], columns [ \"code_end_datetime\" ]] ) event = _column_filtering ( event , filtering_dict = additional_filtering ) mask = True # Resetting the mask if date_min is not None : mask = mask & ( event . t_start >= date_min ) if date_max is not None : mask = mask & ( event . t_start <= date_max ) if type ( mask ) != bool : # We have a Series mask event = event [ mask ] event [ \"concept\" ] = concept return event . rename ( columns = { columns [ \"code_source_value\" ]: \"value\" })[ [ \"person_id\" , \"t_start\" , \"t_end\" , \"concept\" , \"value\" , \"visit_occurrence_id\" , ] + list ( additional_filtering . keys ()) ] . reset_index ( drop = True )","title":"event_from_code()"},{"location":"reference/event/icd10/","text":"eds_scikit.event.icd10 conditions_from_icd10 conditions_from_icd10 ( condition_occurrence : DataFrame , visit_occurrence : Optional [ DataFrame ] = None , codes : Optional [ Dict [ str , Union [ str , List [ str ]]]] = None , date_from_visit : bool = True , additional_filtering : Dict [ str , Any ] = None , date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None ) -> DataFrame Phenotyping based on ICD-10 codes. PARAMETER DESCRIPTION condition_occurrence condition_occurrence OMOP DataFrame. TYPE: DataFrame visit_occurrence visit_occurrence OMOP DataFrame, only necessary if date_from_visit is set to True . TYPE: Optional [ DataFrame ] DEFAULT: None codes Dictionary which values are ICD-10 codes (as a unique string or as a list) and which keys are at least one of the following: exact : To match the codes in codes[\"exact\"] exactly prefix : To match the codes in codes[\"prefix\"] as prefixes regex : To match the codes in codes[\"regex\"] as regexes You can combine any of those keys. TYPE: Dict [ str , Union [ str , List [ str ]]] DEFAULT: None date_from_visit If set to True , uses visit_start_datetime as the code datetime TYPE: bool DEFAULT: True additional_filtering An optional dictionary to filter the resulting DataFrame. Keys should be column names on which to filter, and values should be either A single value A list or set of values. Default filetring is condition_status_source_value in {\"DP\", \"DAS\", \"DR\"} TYPE: Dict [ str , Any ] DEFAULT: None date_min The minimum code datetime to keep. Depends on the date_from_visit flag TYPE: Optional [ datetime ] DEFAULT: None date_max The minimum code datetime to keep. Depends on the date_from_visit flag TYPE: Optional [ datetime ] DEFAULT: None RETURNS DESCRIPTION DataFrame \"event\" DataFrame including the following columns: t_start : If date_from_visit is set to False , contains condition_start_datetime , else contains visit_start_datetime t_end : If date_from_visit is set to False , contains condition_start_datetime , else contains visit_end_datetime concept : contaning values from codes.keys() value : The extracted ICD-10 code. visit_occurrence_id : the visit_occurrence_id from the visit which contains the ICD-10 code. Source code in eds_scikit/event/icd10.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 def conditions_from_icd10 ( condition_occurrence : DataFrame , visit_occurrence : Optional [ DataFrame ] = None , codes : Optional [ Dict [ str , Union [ str , List [ str ]]]] = None , date_from_visit : bool = True , additional_filtering : Dict [ str , Any ] = None , date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None , ) -> DataFrame : \"\"\" Phenotyping based on ICD-10 codes. Parameters ---------- condition_occurrence : DataFrame `condition_occurrence` OMOP DataFrame. visit_occurrence : Optional[DataFrame] `visit_occurrence` OMOP DataFrame, only necessary if `date_from_visit` is set to `True`. codes : Dict[str, Union[str, List[str]]] Dictionary which values are ICD-10 codes (as a unique string or as a list) and which keys are at least one of the following: - `exact`: To match the codes in `codes[\"exact\"]` **exactly** - `prefix`: To match the codes in `codes[\"prefix\"]` **as prefixes** - `regex`: To match the codes in `codes[\"regex\"]` **as regexes** You can combine any of those keys. date_from_visit : bool If set to `True`, uses `visit_start_datetime` as the code datetime additional_filtering : Dict[str, Any] An optional dictionary to filter the resulting DataFrame. Keys should be column names on which to filter, and values should be either - A single value - A list or set of values. Default filetring is condition_status_source_value in {\"DP\", \"DAS\", \"DR\"} date_min : Optional[datetime] The minimum code datetime to keep. **Depends on the `date_from_visit` flag** date_max : Optional[datetime] The minimum code datetime to keep. **Depends on the `date_from_visit` flag** Returns ------- DataFrame \"event\" DataFrame including the following columns: - `t_start`: If `date_from_visit` is set to `False`, contains `condition_start_datetime`, else contains `visit_start_datetime` - `t_end`: If `date_from_visit` is set to `False`, contains `condition_start_datetime`, else contains `visit_end_datetime` - `concept` : contaning values from `codes.keys()` - `value` : The extracted ICD-10 code. - `visit_occurrence_id` : the `visit_occurrence_id` from the visit which contains the ICD-10 code. \"\"\" # noqa: E501 if additional_filtering is None : additional_filtering = dict () DEFAULT_FILTERING = dict ( condition_status_source_value = { \"DP\" , \"DAS\" , \"DR\" }) DEFAULT_FILTERING . update ( additional_filtering ) condition_columns = dict ( code_source_value = \"condition_source_value\" , code_start_datetime = \"condition_start_datetime\" , code_end_datetime = \"condition_start_datetime\" , ) events = [] for concept , code_dict in codes . items (): tmp_df = event_from_code ( df = condition_occurrence , columns = condition_columns , visit_occurrence = visit_occurrence , concept = concept , codes = code_dict , date_from_visit = date_from_visit , additional_filtering = DEFAULT_FILTERING , date_min = date_min , date_max = date_max , ) events . append ( tmp_df ) framework = get_framework ( condition_occurrence ) return framework . concat ( events )","title":"icd10"},{"location":"reference/event/icd10/#eds_scikiteventicd10","text":"","title":"eds_scikit.event.icd10"},{"location":"reference/event/icd10/#eds_scikit.event.icd10.conditions_from_icd10","text":"conditions_from_icd10 ( condition_occurrence : DataFrame , visit_occurrence : Optional [ DataFrame ] = None , codes : Optional [ Dict [ str , Union [ str , List [ str ]]]] = None , date_from_visit : bool = True , additional_filtering : Dict [ str , Any ] = None , date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None ) -> DataFrame Phenotyping based on ICD-10 codes. PARAMETER DESCRIPTION condition_occurrence condition_occurrence OMOP DataFrame. TYPE: DataFrame visit_occurrence visit_occurrence OMOP DataFrame, only necessary if date_from_visit is set to True . TYPE: Optional [ DataFrame ] DEFAULT: None codes Dictionary which values are ICD-10 codes (as a unique string or as a list) and which keys are at least one of the following: exact : To match the codes in codes[\"exact\"] exactly prefix : To match the codes in codes[\"prefix\"] as prefixes regex : To match the codes in codes[\"regex\"] as regexes You can combine any of those keys. TYPE: Dict [ str , Union [ str , List [ str ]]] DEFAULT: None date_from_visit If set to True , uses visit_start_datetime as the code datetime TYPE: bool DEFAULT: True additional_filtering An optional dictionary to filter the resulting DataFrame. Keys should be column names on which to filter, and values should be either A single value A list or set of values. Default filetring is condition_status_source_value in {\"DP\", \"DAS\", \"DR\"} TYPE: Dict [ str , Any ] DEFAULT: None date_min The minimum code datetime to keep. Depends on the date_from_visit flag TYPE: Optional [ datetime ] DEFAULT: None date_max The minimum code datetime to keep. Depends on the date_from_visit flag TYPE: Optional [ datetime ] DEFAULT: None RETURNS DESCRIPTION DataFrame \"event\" DataFrame including the following columns: t_start : If date_from_visit is set to False , contains condition_start_datetime , else contains visit_start_datetime t_end : If date_from_visit is set to False , contains condition_start_datetime , else contains visit_end_datetime concept : contaning values from codes.keys() value : The extracted ICD-10 code. visit_occurrence_id : the visit_occurrence_id from the visit which contains the ICD-10 code. Source code in eds_scikit/event/icd10.py 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 def conditions_from_icd10 ( condition_occurrence : DataFrame , visit_occurrence : Optional [ DataFrame ] = None , codes : Optional [ Dict [ str , Union [ str , List [ str ]]]] = None , date_from_visit : bool = True , additional_filtering : Dict [ str , Any ] = None , date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None , ) -> DataFrame : \"\"\" Phenotyping based on ICD-10 codes. Parameters ---------- condition_occurrence : DataFrame `condition_occurrence` OMOP DataFrame. visit_occurrence : Optional[DataFrame] `visit_occurrence` OMOP DataFrame, only necessary if `date_from_visit` is set to `True`. codes : Dict[str, Union[str, List[str]]] Dictionary which values are ICD-10 codes (as a unique string or as a list) and which keys are at least one of the following: - `exact`: To match the codes in `codes[\"exact\"]` **exactly** - `prefix`: To match the codes in `codes[\"prefix\"]` **as prefixes** - `regex`: To match the codes in `codes[\"regex\"]` **as regexes** You can combine any of those keys. date_from_visit : bool If set to `True`, uses `visit_start_datetime` as the code datetime additional_filtering : Dict[str, Any] An optional dictionary to filter the resulting DataFrame. Keys should be column names on which to filter, and values should be either - A single value - A list or set of values. Default filetring is condition_status_source_value in {\"DP\", \"DAS\", \"DR\"} date_min : Optional[datetime] The minimum code datetime to keep. **Depends on the `date_from_visit` flag** date_max : Optional[datetime] The minimum code datetime to keep. **Depends on the `date_from_visit` flag** Returns ------- DataFrame \"event\" DataFrame including the following columns: - `t_start`: If `date_from_visit` is set to `False`, contains `condition_start_datetime`, else contains `visit_start_datetime` - `t_end`: If `date_from_visit` is set to `False`, contains `condition_start_datetime`, else contains `visit_end_datetime` - `concept` : contaning values from `codes.keys()` - `value` : The extracted ICD-10 code. - `visit_occurrence_id` : the `visit_occurrence_id` from the visit which contains the ICD-10 code. \"\"\" # noqa: E501 if additional_filtering is None : additional_filtering = dict () DEFAULT_FILTERING = dict ( condition_status_source_value = { \"DP\" , \"DAS\" , \"DR\" }) DEFAULT_FILTERING . update ( additional_filtering ) condition_columns = dict ( code_source_value = \"condition_source_value\" , code_start_datetime = \"condition_start_datetime\" , code_end_datetime = \"condition_start_datetime\" , ) events = [] for concept , code_dict in codes . items (): tmp_df = event_from_code ( df = condition_occurrence , columns = condition_columns , visit_occurrence = visit_occurrence , concept = concept , codes = code_dict , date_from_visit = date_from_visit , additional_filtering = DEFAULT_FILTERING , date_min = date_min , date_max = date_max , ) events . append ( tmp_df ) framework = get_framework ( condition_occurrence ) return framework . concat ( events )","title":"conditions_from_icd10()"},{"location":"reference/event/suicide_attempt/","text":"eds_scikit.event.suicide_attempt tag_suicide_attempt tag_suicide_attempt ( visit_occurrence : DataFrame , condition_occurrence : DataFrame , date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None , algo : str = 'X60-X84' ) -> DataFrame Function to return visits that fulfill different definitions of suicide attempt by ICD10. PARAMETER DESCRIPTION visit_occurrence TYPE: DataFrame condition_occurrence TYPE: DataFrame date_min Minimal starting date (on visit_start_datetime ) TYPE: Optional [ datetime ] DEFAULT: None date_max Maximal starting date (on visit_start_datetime ) TYPE: Optional [ datetime ] DEFAULT: None algo Method to use. Available values are: \"X60-X84\" : Will return a the visits that have at least one ICD code that belongs to the range X60 to X84. \"Haguenoer2008\" : Will return a the visits that follow the definiton of \" Haguenoer, Ken, Agn\u00e8s Caille, Marc Fillatre, Anne Isabelle Lecuyer, et Emmanuel Rusch. \u00ab Tentatives de Suicide \u00bb, 2008, 4. \". This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84. TYPE: str DEFAULT: 'X60-X84' RETURNS DESCRIPTION visit_occurrence Tagged with an additional column SUICIDE_ATTEMPT TYPE: DataFrame Tip These rules were implemented in the CSE project n\u00b0210013 Source code in eds_scikit/event/suicide_attempt.py 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 @concept_checker ( concepts = [ \"SUICIDE_ATTEMPT\" ]) @algo_checker ( algos = ALGOS ) def tag_suicide_attempt ( visit_occurrence : DataFrame , condition_occurrence : DataFrame , date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None , algo : str = \"X60-X84\" , ) -> DataFrame : \"\"\" Function to return visits that fulfill different definitions of suicide attempt by ICD10. Parameters ---------- visit_occurrence: DataFrame condition_occurrence: DataFrame date_min: datetime Minimal starting date (on `visit_start_datetime`) date_max: datetime Maximal starting date (on `visit_start_datetime`) algo: str Method to use. Available values are: - `\"X60-X84\"`: Will return a the visits that have at least one ICD code that belongs to the range X60 to X84. - `\"Haguenoer2008\"`: Will return a the visits that follow the definiton of \"*Haguenoer, Ken, Agn\u00e8s Caille, Marc Fillatre, Anne Isabelle Lecuyer, et Emmanuel Rusch. \u00ab Tentatives de Suicide \u00bb, 2008, 4.*\". This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84. Returns ------- visit_occurrence: DataFrame Tagged with an additional column `SUICIDE_ATTEMPT` !!! tip These rules were implemented in the CSE project n\u00b0210013 \"\"\" events_1 = conditions_from_icd10 ( condition_occurrence , visit_occurrence = visit_occurrence , date_min = date_min , date_max = date_max , ** DEFAULT_CONFIG [ \"X60-X84\" ], ) events_1 = events_1 [ [ \"visit_occurrence_id\" , \"condition_status_source_value\" ] ] . drop_duplicates ( subset = \"visit_occurrence_id\" ) events_1 [ CONCEPT ] = True if algo == \"X60-X84\" : visit_occurrence_tagged = visit_occurrence . merge ( events_1 [[ \"visit_occurrence_id\" , CONCEPT ]], on = \"visit_occurrence_id\" , how = \"left\" , ) visit_occurrence_tagged [ CONCEPT ] . fillna ( False , inplace = True ) return visit_occurrence_tagged if algo == \"Haguenoer2008\" : events_1 = events_1 [ events_1 . condition_status_source_value == \"DAS\" ] events_2 = conditions_from_icd10 ( condition_occurrence , visit_occurrence = visit_occurrence , date_min = date_min , date_max = date_max , ** DEFAULT_CONFIG [ algo ], ) events_2 = events_2 [[ \"visit_occurrence_id\" ]] . drop_duplicates () events_2 [ f \" { CONCEPT } _BIS\" ] = True visit_occurrence_tagged = visit_occurrence . merge ( events_1 [[ \"visit_occurrence_id\" , CONCEPT ]], on = \"visit_occurrence_id\" , how = \"left\" , ) . merge ( events_2 [[ \"visit_occurrence_id\" , f \" { CONCEPT } _BIS\" ]], on = \"visit_occurrence_id\" , how = \"left\" , ) visit_occurrence_tagged [ CONCEPT ] = ( visit_occurrence_tagged [ CONCEPT ] & visit_occurrence_tagged [ f \" { CONCEPT } _BIS\" ] ) visit_occurrence_tagged = visit_occurrence_tagged . drop ( columns = [ f \" { CONCEPT } _BIS\" ] ) return visit_occurrence_tagged","title":"suicide_attempt"},{"location":"reference/event/suicide_attempt/#eds_scikiteventsuicide_attempt","text":"","title":"eds_scikit.event.suicide_attempt"},{"location":"reference/event/suicide_attempt/#eds_scikit.event.suicide_attempt.tag_suicide_attempt","text":"tag_suicide_attempt ( visit_occurrence : DataFrame , condition_occurrence : DataFrame , date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None , algo : str = 'X60-X84' ) -> DataFrame Function to return visits that fulfill different definitions of suicide attempt by ICD10. PARAMETER DESCRIPTION visit_occurrence TYPE: DataFrame condition_occurrence TYPE: DataFrame date_min Minimal starting date (on visit_start_datetime ) TYPE: Optional [ datetime ] DEFAULT: None date_max Maximal starting date (on visit_start_datetime ) TYPE: Optional [ datetime ] DEFAULT: None algo Method to use. Available values are: \"X60-X84\" : Will return a the visits that have at least one ICD code that belongs to the range X60 to X84. \"Haguenoer2008\" : Will return a the visits that follow the definiton of \" Haguenoer, Ken, Agn\u00e8s Caille, Marc Fillatre, Anne Isabelle Lecuyer, et Emmanuel Rusch. \u00ab Tentatives de Suicide \u00bb, 2008, 4. \". This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84. TYPE: str DEFAULT: 'X60-X84' RETURNS DESCRIPTION visit_occurrence Tagged with an additional column SUICIDE_ATTEMPT TYPE: DataFrame Tip These rules were implemented in the CSE project n\u00b0210013 Source code in eds_scikit/event/suicide_attempt.py 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 @concept_checker ( concepts = [ \"SUICIDE_ATTEMPT\" ]) @algo_checker ( algos = ALGOS ) def tag_suicide_attempt ( visit_occurrence : DataFrame , condition_occurrence : DataFrame , date_min : Optional [ datetime ] = None , date_max : Optional [ datetime ] = None , algo : str = \"X60-X84\" , ) -> DataFrame : \"\"\" Function to return visits that fulfill different definitions of suicide attempt by ICD10. Parameters ---------- visit_occurrence: DataFrame condition_occurrence: DataFrame date_min: datetime Minimal starting date (on `visit_start_datetime`) date_max: datetime Maximal starting date (on `visit_start_datetime`) algo: str Method to use. Available values are: - `\"X60-X84\"`: Will return a the visits that have at least one ICD code that belongs to the range X60 to X84. - `\"Haguenoer2008\"`: Will return a the visits that follow the definiton of \"*Haguenoer, Ken, Agn\u00e8s Caille, Marc Fillatre, Anne Isabelle Lecuyer, et Emmanuel Rusch. \u00ab Tentatives de Suicide \u00bb, 2008, 4.*\". This rule requires at least one Main Diagnostic (DP) belonging to S00 to T98, and at least one Associated Diagnostic (DAS) that belongs to the range X60 to X84. Returns ------- visit_occurrence: DataFrame Tagged with an additional column `SUICIDE_ATTEMPT` !!! tip These rules were implemented in the CSE project n\u00b0210013 \"\"\" events_1 = conditions_from_icd10 ( condition_occurrence , visit_occurrence = visit_occurrence , date_min = date_min , date_max = date_max , ** DEFAULT_CONFIG [ \"X60-X84\" ], ) events_1 = events_1 [ [ \"visit_occurrence_id\" , \"condition_status_source_value\" ] ] . drop_duplicates ( subset = \"visit_occurrence_id\" ) events_1 [ CONCEPT ] = True if algo == \"X60-X84\" : visit_occurrence_tagged = visit_occurrence . merge ( events_1 [[ \"visit_occurrence_id\" , CONCEPT ]], on = \"visit_occurrence_id\" , how = \"left\" , ) visit_occurrence_tagged [ CONCEPT ] . fillna ( False , inplace = True ) return visit_occurrence_tagged if algo == \"Haguenoer2008\" : events_1 = events_1 [ events_1 . condition_status_source_value == \"DAS\" ] events_2 = conditions_from_icd10 ( condition_occurrence , visit_occurrence = visit_occurrence , date_min = date_min , date_max = date_max , ** DEFAULT_CONFIG [ algo ], ) events_2 = events_2 [[ \"visit_occurrence_id\" ]] . drop_duplicates () events_2 [ f \" { CONCEPT } _BIS\" ] = True visit_occurrence_tagged = visit_occurrence . merge ( events_1 [[ \"visit_occurrence_id\" , CONCEPT ]], on = \"visit_occurrence_id\" , how = \"left\" , ) . merge ( events_2 [[ \"visit_occurrence_id\" , f \" { CONCEPT } _BIS\" ]], on = \"visit_occurrence_id\" , how = \"left\" , ) visit_occurrence_tagged [ CONCEPT ] = ( visit_occurrence_tagged [ CONCEPT ] & visit_occurrence_tagged [ f \" { CONCEPT } _BIS\" ] ) visit_occurrence_tagged = visit_occurrence_tagged . drop ( columns = [ f \" { CONCEPT } _BIS\" ] ) return visit_occurrence_tagged","title":"tag_suicide_attempt()"},{"location":"reference/icu/","text":"eds_scikit.icu","title":"`eds_scikit.icu`"},{"location":"reference/icu/#eds_scikiticu","text":"","title":"eds_scikit.icu"},{"location":"reference/icu/icu_care_site/","text":"eds_scikit.icu.icu_care_site tag_icu_care_site tag_icu_care_site ( care_site : DataFrame , algo : str = 'from_mapping' ) -> DataFrame Tag care sites that correspond to ICU units . The tagging is done by adding a \"IS_ICU\" column to the provided DataFrame. PARAMETER DESCRIPTION care_site TYPE: DataFrame algo Possible values are: \"from_authorisation_type\" \"from_regex_on_care_site_description\" TYPE: str DEFAULT: 'from_mapping' RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: \"IS_ICU\" TYPE: DataFrame Source code in eds_scikit/icu/icu_care_site.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 @algo_checker ( algos = ALGOS ) def tag_icu_care_site ( care_site : DataFrame , algo : str = \"from_mapping\" , ) -> DataFrame : \"\"\"Tag care sites that correspond to **ICU units**. The tagging is done by adding a `\"IS_ICU\"` column to the provided DataFrame. Parameters ---------- care_site: DataFrame algo: str Possible values are: - [`\"from_authorisation_type\"`][eds_scikit.icu.icu_care_site.from_authorisation_type] - [`\"from_regex_on_care_site_description\"`][eds_scikit.icu.icu_care_site.from_regex_on_care_site_description] Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - `\"IS_ICU\"` \"\"\" if algo == \"from_authorisation_type\" : return from_authorisation_type ( care_site ) elif algo == \"from_regex_on_care_site_description\" : return from_regex_on_care_site_description ( care_site ) from_authorisation_type from_authorisation_type ( care_site : DataFrame ) -> DataFrame This algo uses the care_site.place_of_service_source_value columns to retrieve Intensive Care Units. The following values are used to tag a care site as ICU: \"REA PED\" \"REA\" \"REA ADULTE\" \"REA NEONAT\" \"USI\" \"USI ADULTE\" \"USI NEONAT\" \"SC PED\" \"SC\" \"SC ADULTE\" PARAMETER DESCRIPTION care_site Should at least contains the place_of_service_source_value column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concepts: \"IS_ICU\" TYPE: DataFrame Source code in eds_scikit/icu/icu_care_site.py 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 @concept_checker ( concepts = [ \"IS_ICU\" ]) def from_authorisation_type ( care_site : DataFrame ) -> DataFrame : \"\"\"This algo uses the `care_site.place_of_service_source_value` columns to retrieve Intensive Care Units. The following values are used to tag a care site as ICU: - `\"REA PED\"` - `\"REA\"` - `\"REA ADULTE\"` - `\"REA NEONAT\"` - `\"USI\"` - `\"USI ADULTE\"` - `\"USI NEONAT\"` - `\"SC PED\"` - `\"SC\"` - `\"SC ADULTE\"` Parameters ---------- care_site: DataFrame Should at least contains the `place_of_service_source_value` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concepts: - `\"IS_ICU\"` \"\"\" icu_units = set ( [ \"REA PED\" , \"USI\" , \"SC PED\" , \"SC\" , \"REA\" , \"SC ADULTE\" , \"USI ADULTE\" , \"REA ADULTE\" , \"USI NEONAT\" , \"REA NEONAT\" , ] ) care_site [ \"IS_ICU\" ] = care_site [ \"place_of_service_source_value\" ] . isin ( icu_units ) return care_site from_regex_on_care_site_description from_regex_on_care_site_description ( care_site : DataFrame , subset_care_site_type_source_value : Union [ list , set ] = { 'UDS' }) -> DataFrame Use regular expressions on care_site_name to decide if it an ICU care site. This relies on this function . The regular expression used to detect ICU is r\"\bUSI|\bREA[N\\s]|\bREA\b|\bUSC\b|SOINS.*INTENSIF|SURV.{0,15}CONT|\bSI\b|\bSC\b\" . Keeping only 'UDS' At AP-HP, all ICU are UDS ( Unit\u00e9 De Soins ). Therefore, this function filters care sites by default to only keep UDS. PARAMETER DESCRIPTION care_site Should at least contains the care_site_name and care_site_type_source_value columns TYPE: DataFrame subset_care_site_type_source_value Acceptable values for care_site_type_source_value TYPE: Union [ list , set ] DEFAULT: {'UDS'} RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: \"IS_ICU\" TYPE: DataFrame Source code in eds_scikit/icu/icu_care_site.py 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 def from_regex_on_care_site_description ( care_site : DataFrame , subset_care_site_type_source_value : Union [ list , set ] = { \"UDS\" } ) -> DataFrame : \"\"\"Use regular expressions on `care_site_name` to decide if it an ICU care site. This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes]. The regular expression used to detect ICU is `r\"\\bUSI|\\bREA[N\\s]|\\bREA\\b|\\bUSC\\b|SOINS.*INTENSIF|SURV.{0,15}CONT|\\bSI\\b|\\bSC\\b\"`. !!! aphp \"Keeping only 'UDS'\" At AP-HP, all ICU are **UDS** (*Unit\u00e9 De Soins*). Therefore, this function filters care sites by default to only keep UDS. Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` and `care_site_type_source_value` columns subset_care_site_type_source_value: Union[list, set] Acceptable values for `care_site_type_source_value` Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - `\"IS_ICU\"` \"\"\" # noqa care_site = attributes . add_care_site_attributes ( care_site , only_attributes = [ \"IS_ICU\" ] ) # Filtering matches if subset_care_site_type_source_value : care_site [ \"IS_ICU\" ] = care_site [ \"IS_ICU\" ] & ( care_site . care_site_type_source_value . isin ( subset_care_site_type_source_value ) ) return care_site","title":"icu_care_site"},{"location":"reference/icu/icu_care_site/#eds_scikiticuicu_care_site","text":"","title":"eds_scikit.icu.icu_care_site"},{"location":"reference/icu/icu_care_site/#eds_scikit.icu.icu_care_site.tag_icu_care_site","text":"tag_icu_care_site ( care_site : DataFrame , algo : str = 'from_mapping' ) -> DataFrame Tag care sites that correspond to ICU units . The tagging is done by adding a \"IS_ICU\" column to the provided DataFrame. PARAMETER DESCRIPTION care_site TYPE: DataFrame algo Possible values are: \"from_authorisation_type\" \"from_regex_on_care_site_description\" TYPE: str DEFAULT: 'from_mapping' RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: \"IS_ICU\" TYPE: DataFrame Source code in eds_scikit/icu/icu_care_site.py 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 @algo_checker ( algos = ALGOS ) def tag_icu_care_site ( care_site : DataFrame , algo : str = \"from_mapping\" , ) -> DataFrame : \"\"\"Tag care sites that correspond to **ICU units**. The tagging is done by adding a `\"IS_ICU\"` column to the provided DataFrame. Parameters ---------- care_site: DataFrame algo: str Possible values are: - [`\"from_authorisation_type\"`][eds_scikit.icu.icu_care_site.from_authorisation_type] - [`\"from_regex_on_care_site_description\"`][eds_scikit.icu.icu_care_site.from_regex_on_care_site_description] Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - `\"IS_ICU\"` \"\"\" if algo == \"from_authorisation_type\" : return from_authorisation_type ( care_site ) elif algo == \"from_regex_on_care_site_description\" : return from_regex_on_care_site_description ( care_site )","title":"tag_icu_care_site()"},{"location":"reference/icu/icu_care_site/#eds_scikit.icu.icu_care_site.from_authorisation_type","text":"from_authorisation_type ( care_site : DataFrame ) -> DataFrame This algo uses the care_site.place_of_service_source_value columns to retrieve Intensive Care Units. The following values are used to tag a care site as ICU: \"REA PED\" \"REA\" \"REA ADULTE\" \"REA NEONAT\" \"USI\" \"USI ADULTE\" \"USI NEONAT\" \"SC PED\" \"SC\" \"SC ADULTE\" PARAMETER DESCRIPTION care_site Should at least contains the place_of_service_source_value column TYPE: DataFrame RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concepts: \"IS_ICU\" TYPE: DataFrame Source code in eds_scikit/icu/icu_care_site.py 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 @concept_checker ( concepts = [ \"IS_ICU\" ]) def from_authorisation_type ( care_site : DataFrame ) -> DataFrame : \"\"\"This algo uses the `care_site.place_of_service_source_value` columns to retrieve Intensive Care Units. The following values are used to tag a care site as ICU: - `\"REA PED\"` - `\"REA\"` - `\"REA ADULTE\"` - `\"REA NEONAT\"` - `\"USI\"` - `\"USI ADULTE\"` - `\"USI NEONAT\"` - `\"SC PED\"` - `\"SC\"` - `\"SC ADULTE\"` Parameters ---------- care_site: DataFrame Should at least contains the `place_of_service_source_value` column Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concepts: - `\"IS_ICU\"` \"\"\" icu_units = set ( [ \"REA PED\" , \"USI\" , \"SC PED\" , \"SC\" , \"REA\" , \"SC ADULTE\" , \"USI ADULTE\" , \"REA ADULTE\" , \"USI NEONAT\" , \"REA NEONAT\" , ] ) care_site [ \"IS_ICU\" ] = care_site [ \"place_of_service_source_value\" ] . isin ( icu_units ) return care_site","title":"from_authorisation_type()"},{"location":"reference/icu/icu_care_site/#eds_scikit.icu.icu_care_site.from_regex_on_care_site_description","text":"from_regex_on_care_site_description ( care_site : DataFrame , subset_care_site_type_source_value : Union [ list , set ] = { 'UDS' }) -> DataFrame Use regular expressions on care_site_name to decide if it an ICU care site. This relies on this function . The regular expression used to detect ICU is r\"\bUSI|\bREA[N\\s]|\bREA\b|\bUSC\b|SOINS.*INTENSIF|SURV.{0,15}CONT|\bSI\b|\bSC\b\" . Keeping only 'UDS' At AP-HP, all ICU are UDS ( Unit\u00e9 De Soins ). Therefore, this function filters care sites by default to only keep UDS. PARAMETER DESCRIPTION care_site Should at least contains the care_site_name and care_site_type_source_value columns TYPE: DataFrame subset_care_site_type_source_value Acceptable values for care_site_type_source_value TYPE: Union [ list , set ] DEFAULT: {'UDS'} RETURNS DESCRIPTION care_site Dataframe with 1 added column corresponding to the following concept: \"IS_ICU\" TYPE: DataFrame Source code in eds_scikit/icu/icu_care_site.py 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 def from_regex_on_care_site_description ( care_site : DataFrame , subset_care_site_type_source_value : Union [ list , set ] = { \"UDS\" } ) -> DataFrame : \"\"\"Use regular expressions on `care_site_name` to decide if it an ICU care site. This relies on [this function][eds_scikit.structures.attributes.add_care_site_attributes]. The regular expression used to detect ICU is `r\"\\bUSI|\\bREA[N\\s]|\\bREA\\b|\\bUSC\\b|SOINS.*INTENSIF|SURV.{0,15}CONT|\\bSI\\b|\\bSC\\b\"`. !!! aphp \"Keeping only 'UDS'\" At AP-HP, all ICU are **UDS** (*Unit\u00e9 De Soins*). Therefore, this function filters care sites by default to only keep UDS. Parameters ---------- care_site: DataFrame Should at least contains the `care_site_name` and `care_site_type_source_value` columns subset_care_site_type_source_value: Union[list, set] Acceptable values for `care_site_type_source_value` Returns ------- care_site: DataFrame Dataframe with 1 added column corresponding to the following concept: - `\"IS_ICU\"` \"\"\" # noqa care_site = attributes . add_care_site_attributes ( care_site , only_attributes = [ \"IS_ICU\" ] ) # Filtering matches if subset_care_site_type_source_value : care_site [ \"IS_ICU\" ] = care_site [ \"IS_ICU\" ] & ( care_site . care_site_type_source_value . isin ( subset_care_site_type_source_value ) ) return care_site","title":"from_regex_on_care_site_description()"},{"location":"reference/icu/icu_visit/","text":"eds_scikit.icu.icu_visit tag_icu_visit tag_icu_visit ( visit_detail : DataFrame , care_site : DataFrame , algo : str = 'from_authorisation_type' ) -> DataFrame Tag care_sites that correspond to ICU units . The tagging is done by adding a \"IS_ICU\" column to the provided DataFrame. It works by tagging each visit detail's care site . PARAMETER DESCRIPTION visit_detail TYPE: DataFrame care_site TYPE: DataFrame algo Possible values are: \"from_authorisation_type\" \"from_regex_on_care_site_description\" TYPE: str DEFAULT: 'from_authorisation_type' RETURNS DESCRIPTION visit_detail Dataframe with 1 added column corresponding to the following concept: \"IS_ICU\" TYPE: DataFrame Source code in eds_scikit/icu/icu_visit.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 @algo_checker ( algos = ALGOS ) def tag_icu_visit ( visit_detail : DataFrame , care_site : DataFrame , algo : str = \"from_authorisation_type\" , ) -> DataFrame : \"\"\"Tag care_sites that correspond to **ICU units**. The tagging is done by adding a `\"IS_ICU\"` column to the provided DataFrame. It works by [tagging each visit detail's care site][eds_scikit.icu.icu_care_site.tag_icu_care_site]. Parameters ---------- visit_detail: DataFrame care_site: DataFrame algo: str Possible values are: - [`\"from_authorisation_type\"`][eds_scikit.icu.icu_care_site.from_authorisation_type] - [`\"from_regex_on_care_site_description\"`][eds_scikit.icu.icu_care_site.from_regex_on_care_site_description] Returns ------- visit_detail: DataFrame Dataframe with 1 added column corresponding to the following concept: - `\"IS_ICU\"` \"\"\" tagged_care_site = tag_icu_care_site ( care_site , algo = algo ) return visit_detail . merge ( tagged_care_site [[ \"care_site_id\" , \"IS_ICU\" ]], on = \"care_site_id\" , how = \"left\" )","title":"icu_visit"},{"location":"reference/icu/icu_visit/#eds_scikiticuicu_visit","text":"","title":"eds_scikit.icu.icu_visit"},{"location":"reference/icu/icu_visit/#eds_scikit.icu.icu_visit.tag_icu_visit","text":"tag_icu_visit ( visit_detail : DataFrame , care_site : DataFrame , algo : str = 'from_authorisation_type' ) -> DataFrame Tag care_sites that correspond to ICU units . The tagging is done by adding a \"IS_ICU\" column to the provided DataFrame. It works by tagging each visit detail's care site . PARAMETER DESCRIPTION visit_detail TYPE: DataFrame care_site TYPE: DataFrame algo Possible values are: \"from_authorisation_type\" \"from_regex_on_care_site_description\" TYPE: str DEFAULT: 'from_authorisation_type' RETURNS DESCRIPTION visit_detail Dataframe with 1 added column corresponding to the following concept: \"IS_ICU\" TYPE: DataFrame Source code in eds_scikit/icu/icu_visit.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 @algo_checker ( algos = ALGOS ) def tag_icu_visit ( visit_detail : DataFrame , care_site : DataFrame , algo : str = \"from_authorisation_type\" , ) -> DataFrame : \"\"\"Tag care_sites that correspond to **ICU units**. The tagging is done by adding a `\"IS_ICU\"` column to the provided DataFrame. It works by [tagging each visit detail's care site][eds_scikit.icu.icu_care_site.tag_icu_care_site]. Parameters ---------- visit_detail: DataFrame care_site: DataFrame algo: str Possible values are: - [`\"from_authorisation_type\"`][eds_scikit.icu.icu_care_site.from_authorisation_type] - [`\"from_regex_on_care_site_description\"`][eds_scikit.icu.icu_care_site.from_regex_on_care_site_description] Returns ------- visit_detail: DataFrame Dataframe with 1 added column corresponding to the following concept: - `\"IS_ICU\"` \"\"\" tagged_care_site = tag_icu_care_site ( care_site , algo = algo ) return visit_detail . merge ( tagged_care_site [[ \"care_site_id\" , \"IS_ICU\" ]], on = \"care_site_id\" , how = \"left\" )","title":"tag_icu_visit()"},{"location":"reference/io/","text":"eds_scikit.io PandasData PandasData ( folder : str ) Bases: BaseData Pandas interface to OMOP data stored as local parquet files/folders. PARAMETER DESCRIPTION folder absolute path to a folder containing several parquet files with OMOP data TYPE: str Examples: >>> data = PandasData ( folder = \"/export/home/USER/my_data/\" ) >>> person = data . person >>> person . shape (100, 10) Source code in eds_scikit/io/files.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def __init__ ( self , folder : str , ): \"\"\"Pandas interface to OMOP data stored as local parquet files/folders. Parameters ---------- folder: str absolute path to a folder containing several parquet files with OMOP data Examples -------- >>> data = PandasData(folder=\"/export/home/USER/my_data/\") >>> person = data.person >>> person.shape (100, 10) \"\"\" super () . __init__ () self . folder = folder self . available_tables = self . list_available_tables () self . tables_paths = self . get_table_path () if not self . available_tables : raise ValueError ( f \"Folder { folder } does not contain any parquet omop data.\" ) PostgresData PostgresData ( dbname : Optional [ str ] = None , schema : Optional [ str ] = None , user : Optional [ str ] = None , host : Optional [ str ] = None , port : Optional [ int ] = None ) Bases: BaseData Source code in eds_scikit/io/postgres.py 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def __init__ ( self , dbname : Optional [ str ] = None , schema : Optional [ str ] = None , user : Optional [ str ] = None , host : Optional [ str ] = None , port : Optional [ int ] = None , ): ( self . host , self . port , self . dbname , self . user , ) = self . _find_matching_pgpass_params ( host , port , dbname , user ) self . schema = schema read_sql read_sql ( sql_query : str , ** kwargs ) -> pd . DataFrame Execute pandas.read_sql() on the database. PARAMETER DESCRIPTION sql_query SQL query (postgres flavor) TYPE: str **kwargs additional arguments passed to pandas.read_sql() DEFAULT: {} RETURNS DESCRIPTION df TYPE: pandas . DataFrame Source code in eds_scikit/io/postgres.py 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 def read_sql ( self , sql_query : str , ** kwargs ) -> pd . DataFrame : \"\"\"Execute pandas.read_sql() on the database. Parameters ---------- sql_query : str SQL query (postgres flavor) **kwargs additional arguments passed to pandas.read_sql() Returns ------- df : pandas.DataFrame \"\"\" connection_infos = { param : getattr ( self , param ) for param in [ \"host\" , \"port\" , \"dbname\" , \"user\" ] } connection_infos [ \"password\" ] = pgpasslib . getpass ( ** connection_infos ) connection = pg . connect ( ** connection_infos ) if self . schema : connection . cursor () . execute ( f \"SET SCHEMA ' { self . schema } '\" ) df = pd . read_sql ( sql_query , con = connection , ** kwargs ) connection . close () return df HiveData HiveData ( database_name : str , spark_session : Optional [ SparkSession ] = None , person_ids : Optional [ Iterable [ int ]] = None , tables_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]]] = None , columns_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]]] = None , database_type : Optional [ str ] = 'OMOP' , prune_omop_date_columns : bool = True , cache : bool = True ) Bases: BaseData Spark interface for OMOP data stored in a Hive database. This class provides a simple access to data stored in Hive. Data is returned as koalas dataframes that match the tables stored in Hive. PARAMETER DESCRIPTION database_name The name of you database in Hive. Ex: \"cse_82727572\" TYPE: str spark_session If None, a SparkSession will be retrieved or created via SparkSession.builder.enableHiveSupport().getOrCreate() TYPE: pyspark . sql . SparkSession DEFAULT: None person_ids An iterable of person_id that is used to define a subset of the database. TYPE: Optional [ Iterable [ int ]] DEFAULT: None tables_to_load deprecated TYPE: dict , default DEFAULT: None columns_to_load deprecated TYPE: dict , default DEFAULT: None database_type Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP. TYPE: Optional [ str ] DEFAULT: 'OMOP' prune_omop_date_columns In OMOP, most date values are stored both in a _date and _datetime column Koalas has trouble handling the date time, so we only keep the datetime column TYPE: bool DEFAULT: True cache Whether to cache each table after preprocessing or not. Will speed-up subsequent calculations, but can be long/infeasable for very large tables TYPE: bool DEFAULT: True ATTRIBUTE DESCRIPTION person Hive data for table person as a koalas dataframe. Other OMOP tables can also be accessed as attributes TYPE: koalas dataframe available_tables names of OMOP tables that can be accessed as attributes with this HiveData object. TYPE: list of str Examples: data = HiveData ( database_name = \"edsomop_prod_a\" ) data . available_tables # Out: [\"person\", \"care_site\", \"condition_occurrence\", ... ] person = data . person type ( person ) # Out: databricks.koalas.frame.DataFrame person [ \"person_id\" ] . count () # Out: 12670874 This class can be used to create a subset of data for a given list of person_id . This is useful because the smaller dataset can then be used to prototype more rapidly. my_person_ids = [ 9226726 , 2092082 , ... ] data = HiveData ( spark_session = spark , database_name = \"edsomop_prod_a\" , person_ids = my_person_ids ) data . person [ \"person_id\" ] . count () # Out: 1000 tables_to_save = [ \"person\" , \"visit_occurrence\" ] data . persist_tables_to_folder ( \"./cohort_sample_1000\" , table_names = tables_to_save ) # Out: writing /export/home/USER/cohort_sample_1000/person.parquet # Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet # Out: ... Source code in eds_scikit/io/hive.py 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def __init__ ( self , database_name : str , spark_session : Optional [ SparkSession ] = None , person_ids : Optional [ Iterable [ int ]] = None , tables_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]] ] = None , columns_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]] ] = None , database_type : Optional [ str ] = \"OMOP\" , prune_omop_date_columns : bool = True , cache : bool = True , ): \"\"\"Spark interface for OMOP data stored in a Hive database. This class provides a simple access to data stored in Hive. Data is returned as koalas dataframes that match the tables stored in Hive. Parameters ---------- database_name : str The name of you database in Hive. Ex: \"cse_82727572\" spark_session : pyspark.sql.SparkSession If None, a SparkSession will be retrieved or created via `SparkSession.builder.enableHiveSupport().getOrCreate()` person_ids : Optional[Iterable[int]] An iterable of `person_id` that is used to define a subset of the database. tables_to_load : dict, default=None *deprecated* columns_to_load : dict, default=None *deprecated* database_type: Optional[str] = 'OMOP'. Must be 'OMOP' or 'I2B2' Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP. prune_omop_date_columns: bool, default=True In OMOP, most date values are stored both in a `_date` and `_datetime` column Koalas has trouble handling the `date` time, so we only keep the `datetime` column cache: bool, default=True Whether to cache each table after preprocessing or not. Will speed-up subsequent calculations, but can be long/infeasable for very large tables Attributes ---------- person : koalas dataframe Hive data for table `person` as a koalas dataframe. Other OMOP tables can also be accessed as attributes available_tables : list of str names of OMOP tables that can be accessed as attributes with this HiveData object. Examples -------- ```python data = HiveData(database_name=\"edsomop_prod_a\") data.available_tables # Out: [\"person\", \"care_site\", \"condition_occurrence\", ... ] person = data.person type(person) # Out: databricks.koalas.frame.DataFrame person[\"person_id\"].count() # Out: 12670874 ``` This class can be used to create a subset of data for a given list of `person_id`. This is useful because the smaller dataset can then be used to prototype more rapidly. ```python my_person_ids = [9226726, 2092082, ...] data = HiveData( spark_session=spark, database_name=\"edsomop_prod_a\", person_ids=my_person_ids ) data.person[\"person_id\"].count() # Out: 1000 tables_to_save = [\"person\", \"visit_occurrence\"] data.persist_tables_to_folder(\"./cohort_sample_1000\", table_names=tables_to_save) # Out: writing /export/home/USER/cohort_sample_1000/person.parquet # Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet # Out: ... ``` \"\"\" super () . __init__ () if columns_to_load is not None : logger . warning ( \"'columns_to_load' is deprecated and won't be used\" ) if tables_to_load is not None : logger . warning ( \"'tables_to_load' is deprecated and won't be used\" ) self . spark_session = ( spark_session or SparkSession . builder . enableHiveSupport () . getOrCreate () ) self . database_name = database_name if database_type not in [ \"I2B2\" , \"OMOP\" ]: raise ValueError ( f \"`database_type` must be either 'I2B2' or 'OMOP'. Got { database_type } \" ) self . database_type = database_type if self . database_type == \"I2B2\" : self . database_source = \"cse\" if \"cse\" in self . database_name else \"edsprod\" self . omop_to_i2b2 = settings . i2b2_tables [ self . database_source ] self . i2b2_to_omop = defaultdict ( list ) for omop_table , i2b2_table in self . omop_to_i2b2 . items (): self . i2b2_to_omop [ i2b2_table ] . append ( omop_table ) self . prune_omop_date_columns = prune_omop_date_columns self . cache = cache self . user = os . environ [ \"USER\" ] self . person_ids , self . person_ids_df = self . _prepare_person_ids ( person_ids ) self . available_tables = self . list_available_tables () self . _tables = {} persist_tables_to_folder persist_tables_to_folder ( folder : str , person_ids : Optional [ Iterable [ int ]] = None , tables : List [ str ] = None , overwrite : bool = False ) -> None Save OMOP tables as parquet files in a given folder. PARAMETER DESCRIPTION folder path to folder where the tables will be written. TYPE: str person_ids : iterable person_ids to keep in the subcohort. tables : list of str, default None list of table names to save. Default value is data: ~eds_scikit.io.settings.default_tables_to_save . overwrite : bool, default=False whether to overwrite files if 'folder' already exists. Source code in eds_scikit/io/hive.py 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 def persist_tables_to_folder ( self , folder : str , person_ids : Optional [ Iterable [ int ]] = None , tables : List [ str ] = None , overwrite : bool = False , ) -> None : \"\"\"Save OMOP tables as parquet files in a given folder. Parameters ---------- folder : str path to folder where the tables will be written. person_ids : iterable person_ids to keep in the subcohort. tables : list of str, default None list of table names to save. Default value is :py:data:`~eds_scikit.io.settings.default_tables_to_save`. overwrite : bool, default=False whether to overwrite files if 'folder' already exists. \"\"\" # Manage tables if tables is None : tables = settings . default_tables_to_save unknown_tables = [ table for table in tables if table not in self . available_tables ] if unknown_tables : raise ValueError ( f \"The following tables are not available : { str ( unknown_tables ) } \" ) # Create folder folder = Path ( folder ) . absolute () if folder . exists () and overwrite : shutil . rmtree ( folder ) folder . mkdir ( parents = True , mode = 0o766 ) assert os . path . exists ( folder ) and os . path . isdir ( folder ), f \"Folder { folder } not found.\" # TODO: remove everything in this folder that is a valid # omop table. This prevents a user from having a # folder containing datasets generated from different # patient subsets. # TODO: maybe check how much the user wants to persist # to disk. Set a limit on the number of patients in the cohort ? if person_ids is not None : person_ids = self . _prepare_person_ids ( person_ids , return_df = False ) database_path = self . get_db_path () for idx , table in enumerate ( tables ): if self . database_type == \"I2B2\" : table_path = self . _hdfs_write_orc_to_parquet ( table , person_ids , overwrite ) else : table_path = os . path . join ( database_path , table ) df = self . get_table_from_parquet ( table_path , person_ids = person_ids ) local_file_path = os . path . join ( folder , f \" { table } .parquet\" ) df . to_parquet ( local_file_path , allow_truncated_timestamps = True , coerce_timestamps = \"ms\" , ) logger . info ( f \"( { idx + 1 } / { len ( tables ) } ) Table { table } saved at \" f \" { local_file_path } (N= { len ( df ) } ).\" ) get_db_path get_db_path () Get the HDFS path of the database Source code in eds_scikit/io/hive.py 374 375 376 377 378 379 380 381 def get_db_path ( self ): \"\"\"Get the HDFS path of the database\"\"\" return ( self . spark_session . sql ( f \"DESCRIBE DATABASE EXTENDED { self . database_name } \" ) . filter ( \"database_description_item=='Location'\" ) . collect ()[ 0 ] . database_description_value )","title":"`eds_scikit.io`"},{"location":"reference/io/#eds_scikitio","text":"","title":"eds_scikit.io"},{"location":"reference/io/#eds_scikit.io.PandasData","text":"PandasData ( folder : str ) Bases: BaseData Pandas interface to OMOP data stored as local parquet files/folders. PARAMETER DESCRIPTION folder absolute path to a folder containing several parquet files with OMOP data TYPE: str Examples: >>> data = PandasData ( folder = \"/export/home/USER/my_data/\" ) >>> person = data . person >>> person . shape (100, 10) Source code in eds_scikit/io/files.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def __init__ ( self , folder : str , ): \"\"\"Pandas interface to OMOP data stored as local parquet files/folders. Parameters ---------- folder: str absolute path to a folder containing several parquet files with OMOP data Examples -------- >>> data = PandasData(folder=\"/export/home/USER/my_data/\") >>> person = data.person >>> person.shape (100, 10) \"\"\" super () . __init__ () self . folder = folder self . available_tables = self . list_available_tables () self . tables_paths = self . get_table_path () if not self . available_tables : raise ValueError ( f \"Folder { folder } does not contain any parquet omop data.\" )","title":"PandasData"},{"location":"reference/io/#eds_scikit.io.PostgresData","text":"PostgresData ( dbname : Optional [ str ] = None , schema : Optional [ str ] = None , user : Optional [ str ] = None , host : Optional [ str ] = None , port : Optional [ int ] = None ) Bases: BaseData Source code in eds_scikit/io/postgres.py 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def __init__ ( self , dbname : Optional [ str ] = None , schema : Optional [ str ] = None , user : Optional [ str ] = None , host : Optional [ str ] = None , port : Optional [ int ] = None , ): ( self . host , self . port , self . dbname , self . user , ) = self . _find_matching_pgpass_params ( host , port , dbname , user ) self . schema = schema","title":"PostgresData"},{"location":"reference/io/#eds_scikit.io.postgres.PostgresData.read_sql","text":"read_sql ( sql_query : str , ** kwargs ) -> pd . DataFrame Execute pandas.read_sql() on the database. PARAMETER DESCRIPTION sql_query SQL query (postgres flavor) TYPE: str **kwargs additional arguments passed to pandas.read_sql() DEFAULT: {} RETURNS DESCRIPTION df TYPE: pandas . DataFrame Source code in eds_scikit/io/postgres.py 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 def read_sql ( self , sql_query : str , ** kwargs ) -> pd . DataFrame : \"\"\"Execute pandas.read_sql() on the database. Parameters ---------- sql_query : str SQL query (postgres flavor) **kwargs additional arguments passed to pandas.read_sql() Returns ------- df : pandas.DataFrame \"\"\" connection_infos = { param : getattr ( self , param ) for param in [ \"host\" , \"port\" , \"dbname\" , \"user\" ] } connection_infos [ \"password\" ] = pgpasslib . getpass ( ** connection_infos ) connection = pg . connect ( ** connection_infos ) if self . schema : connection . cursor () . execute ( f \"SET SCHEMA ' { self . schema } '\" ) df = pd . read_sql ( sql_query , con = connection , ** kwargs ) connection . close () return df","title":"read_sql()"},{"location":"reference/io/#eds_scikit.io.HiveData","text":"HiveData ( database_name : str , spark_session : Optional [ SparkSession ] = None , person_ids : Optional [ Iterable [ int ]] = None , tables_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]]] = None , columns_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]]] = None , database_type : Optional [ str ] = 'OMOP' , prune_omop_date_columns : bool = True , cache : bool = True ) Bases: BaseData Spark interface for OMOP data stored in a Hive database. This class provides a simple access to data stored in Hive. Data is returned as koalas dataframes that match the tables stored in Hive. PARAMETER DESCRIPTION database_name The name of you database in Hive. Ex: \"cse_82727572\" TYPE: str spark_session If None, a SparkSession will be retrieved or created via SparkSession.builder.enableHiveSupport().getOrCreate() TYPE: pyspark . sql . SparkSession DEFAULT: None person_ids An iterable of person_id that is used to define a subset of the database. TYPE: Optional [ Iterable [ int ]] DEFAULT: None tables_to_load deprecated TYPE: dict , default DEFAULT: None columns_to_load deprecated TYPE: dict , default DEFAULT: None database_type Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP. TYPE: Optional [ str ] DEFAULT: 'OMOP' prune_omop_date_columns In OMOP, most date values are stored both in a _date and _datetime column Koalas has trouble handling the date time, so we only keep the datetime column TYPE: bool DEFAULT: True cache Whether to cache each table after preprocessing or not. Will speed-up subsequent calculations, but can be long/infeasable for very large tables TYPE: bool DEFAULT: True ATTRIBUTE DESCRIPTION person Hive data for table person as a koalas dataframe. Other OMOP tables can also be accessed as attributes TYPE: koalas dataframe available_tables names of OMOP tables that can be accessed as attributes with this HiveData object. TYPE: list of str Examples: data = HiveData ( database_name = \"edsomop_prod_a\" ) data . available_tables # Out: [\"person\", \"care_site\", \"condition_occurrence\", ... ] person = data . person type ( person ) # Out: databricks.koalas.frame.DataFrame person [ \"person_id\" ] . count () # Out: 12670874 This class can be used to create a subset of data for a given list of person_id . This is useful because the smaller dataset can then be used to prototype more rapidly. my_person_ids = [ 9226726 , 2092082 , ... ] data = HiveData ( spark_session = spark , database_name = \"edsomop_prod_a\" , person_ids = my_person_ids ) data . person [ \"person_id\" ] . count () # Out: 1000 tables_to_save = [ \"person\" , \"visit_occurrence\" ] data . persist_tables_to_folder ( \"./cohort_sample_1000\" , table_names = tables_to_save ) # Out: writing /export/home/USER/cohort_sample_1000/person.parquet # Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet # Out: ... Source code in eds_scikit/io/hive.py 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def __init__ ( self , database_name : str , spark_session : Optional [ SparkSession ] = None , person_ids : Optional [ Iterable [ int ]] = None , tables_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]] ] = None , columns_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]] ] = None , database_type : Optional [ str ] = \"OMOP\" , prune_omop_date_columns : bool = True , cache : bool = True , ): \"\"\"Spark interface for OMOP data stored in a Hive database. This class provides a simple access to data stored in Hive. Data is returned as koalas dataframes that match the tables stored in Hive. Parameters ---------- database_name : str The name of you database in Hive. Ex: \"cse_82727572\" spark_session : pyspark.sql.SparkSession If None, a SparkSession will be retrieved or created via `SparkSession.builder.enableHiveSupport().getOrCreate()` person_ids : Optional[Iterable[int]] An iterable of `person_id` that is used to define a subset of the database. tables_to_load : dict, default=None *deprecated* columns_to_load : dict, default=None *deprecated* database_type: Optional[str] = 'OMOP'. Must be 'OMOP' or 'I2B2' Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP. prune_omop_date_columns: bool, default=True In OMOP, most date values are stored both in a `_date` and `_datetime` column Koalas has trouble handling the `date` time, so we only keep the `datetime` column cache: bool, default=True Whether to cache each table after preprocessing or not. Will speed-up subsequent calculations, but can be long/infeasable for very large tables Attributes ---------- person : koalas dataframe Hive data for table `person` as a koalas dataframe. Other OMOP tables can also be accessed as attributes available_tables : list of str names of OMOP tables that can be accessed as attributes with this HiveData object. Examples -------- ```python data = HiveData(database_name=\"edsomop_prod_a\") data.available_tables # Out: [\"person\", \"care_site\", \"condition_occurrence\", ... ] person = data.person type(person) # Out: databricks.koalas.frame.DataFrame person[\"person_id\"].count() # Out: 12670874 ``` This class can be used to create a subset of data for a given list of `person_id`. This is useful because the smaller dataset can then be used to prototype more rapidly. ```python my_person_ids = [9226726, 2092082, ...] data = HiveData( spark_session=spark, database_name=\"edsomop_prod_a\", person_ids=my_person_ids ) data.person[\"person_id\"].count() # Out: 1000 tables_to_save = [\"person\", \"visit_occurrence\"] data.persist_tables_to_folder(\"./cohort_sample_1000\", table_names=tables_to_save) # Out: writing /export/home/USER/cohort_sample_1000/person.parquet # Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet # Out: ... ``` \"\"\" super () . __init__ () if columns_to_load is not None : logger . warning ( \"'columns_to_load' is deprecated and won't be used\" ) if tables_to_load is not None : logger . warning ( \"'tables_to_load' is deprecated and won't be used\" ) self . spark_session = ( spark_session or SparkSession . builder . enableHiveSupport () . getOrCreate () ) self . database_name = database_name if database_type not in [ \"I2B2\" , \"OMOP\" ]: raise ValueError ( f \"`database_type` must be either 'I2B2' or 'OMOP'. Got { database_type } \" ) self . database_type = database_type if self . database_type == \"I2B2\" : self . database_source = \"cse\" if \"cse\" in self . database_name else \"edsprod\" self . omop_to_i2b2 = settings . i2b2_tables [ self . database_source ] self . i2b2_to_omop = defaultdict ( list ) for omop_table , i2b2_table in self . omop_to_i2b2 . items (): self . i2b2_to_omop [ i2b2_table ] . append ( omop_table ) self . prune_omop_date_columns = prune_omop_date_columns self . cache = cache self . user = os . environ [ \"USER\" ] self . person_ids , self . person_ids_df = self . _prepare_person_ids ( person_ids ) self . available_tables = self . list_available_tables () self . _tables = {}","title":"HiveData"},{"location":"reference/io/#eds_scikit.io.hive.HiveData.persist_tables_to_folder","text":"persist_tables_to_folder ( folder : str , person_ids : Optional [ Iterable [ int ]] = None , tables : List [ str ] = None , overwrite : bool = False ) -> None Save OMOP tables as parquet files in a given folder. PARAMETER DESCRIPTION folder path to folder where the tables will be written. TYPE: str person_ids : iterable person_ids to keep in the subcohort. tables : list of str, default None list of table names to save. Default value is data: ~eds_scikit.io.settings.default_tables_to_save . overwrite : bool, default=False whether to overwrite files if 'folder' already exists. Source code in eds_scikit/io/hive.py 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 def persist_tables_to_folder ( self , folder : str , person_ids : Optional [ Iterable [ int ]] = None , tables : List [ str ] = None , overwrite : bool = False , ) -> None : \"\"\"Save OMOP tables as parquet files in a given folder. Parameters ---------- folder : str path to folder where the tables will be written. person_ids : iterable person_ids to keep in the subcohort. tables : list of str, default None list of table names to save. Default value is :py:data:`~eds_scikit.io.settings.default_tables_to_save`. overwrite : bool, default=False whether to overwrite files if 'folder' already exists. \"\"\" # Manage tables if tables is None : tables = settings . default_tables_to_save unknown_tables = [ table for table in tables if table not in self . available_tables ] if unknown_tables : raise ValueError ( f \"The following tables are not available : { str ( unknown_tables ) } \" ) # Create folder folder = Path ( folder ) . absolute () if folder . exists () and overwrite : shutil . rmtree ( folder ) folder . mkdir ( parents = True , mode = 0o766 ) assert os . path . exists ( folder ) and os . path . isdir ( folder ), f \"Folder { folder } not found.\" # TODO: remove everything in this folder that is a valid # omop table. This prevents a user from having a # folder containing datasets generated from different # patient subsets. # TODO: maybe check how much the user wants to persist # to disk. Set a limit on the number of patients in the cohort ? if person_ids is not None : person_ids = self . _prepare_person_ids ( person_ids , return_df = False ) database_path = self . get_db_path () for idx , table in enumerate ( tables ): if self . database_type == \"I2B2\" : table_path = self . _hdfs_write_orc_to_parquet ( table , person_ids , overwrite ) else : table_path = os . path . join ( database_path , table ) df = self . get_table_from_parquet ( table_path , person_ids = person_ids ) local_file_path = os . path . join ( folder , f \" { table } .parquet\" ) df . to_parquet ( local_file_path , allow_truncated_timestamps = True , coerce_timestamps = \"ms\" , ) logger . info ( f \"( { idx + 1 } / { len ( tables ) } ) Table { table } saved at \" f \" { local_file_path } (N= { len ( df ) } ).\" )","title":"persist_tables_to_folder()"},{"location":"reference/io/#eds_scikit.io.hive.HiveData.get_db_path","text":"get_db_path () Get the HDFS path of the database Source code in eds_scikit/io/hive.py 374 375 376 377 378 379 380 381 def get_db_path ( self ): \"\"\"Get the HDFS path of the database\"\"\" return ( self . spark_session . sql ( f \"DESCRIBE DATABASE EXTENDED { self . database_name } \" ) . filter ( \"database_description_item=='Location'\" ) . collect ()[ 0 ] . database_description_value )","title":"get_db_path()"},{"location":"reference/io/base/","text":"eds_scikit.io.base","title":"base"},{"location":"reference/io/base/#eds_scikitiobase","text":"","title":"eds_scikit.io.base"},{"location":"reference/io/data_quality/","text":"eds_scikit.io.data_quality","title":"data_quality"},{"location":"reference/io/data_quality/#eds_scikitiodata_quality","text":"","title":"eds_scikit.io.data_quality"},{"location":"reference/io/files/","text":"eds_scikit.io.files PandasData PandasData ( folder : str ) Bases: BaseData Pandas interface to OMOP data stored as local parquet files/folders. PARAMETER DESCRIPTION folder absolute path to a folder containing several parquet files with OMOP data TYPE: str Examples: >>> data = PandasData ( folder = \"/export/home/USER/my_data/\" ) >>> person = data . person >>> person . shape (100, 10) Source code in eds_scikit/io/files.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def __init__ ( self , folder : str , ): \"\"\"Pandas interface to OMOP data stored as local parquet files/folders. Parameters ---------- folder: str absolute path to a folder containing several parquet files with OMOP data Examples -------- >>> data = PandasData(folder=\"/export/home/USER/my_data/\") >>> person = data.person >>> person.shape (100, 10) \"\"\" super () . __init__ () self . folder = folder self . available_tables = self . list_available_tables () self . tables_paths = self . get_table_path () if not self . available_tables : raise ValueError ( f \"Folder { folder } does not contain any parquet omop data.\" )","title":"files"},{"location":"reference/io/files/#eds_scikitiofiles","text":"","title":"eds_scikit.io.files"},{"location":"reference/io/files/#eds_scikit.io.files.PandasData","text":"PandasData ( folder : str ) Bases: BaseData Pandas interface to OMOP data stored as local parquet files/folders. PARAMETER DESCRIPTION folder absolute path to a folder containing several parquet files with OMOP data TYPE: str Examples: >>> data = PandasData ( folder = \"/export/home/USER/my_data/\" ) >>> person = data . person >>> person . shape (100, 10) Source code in eds_scikit/io/files.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 def __init__ ( self , folder : str , ): \"\"\"Pandas interface to OMOP data stored as local parquet files/folders. Parameters ---------- folder: str absolute path to a folder containing several parquet files with OMOP data Examples -------- >>> data = PandasData(folder=\"/export/home/USER/my_data/\") >>> person = data.person >>> person.shape (100, 10) \"\"\" super () . __init__ () self . folder = folder self . available_tables = self . list_available_tables () self . tables_paths = self . get_table_path () if not self . available_tables : raise ValueError ( f \"Folder { folder } does not contain any parquet omop data.\" )","title":"PandasData"},{"location":"reference/io/hive/","text":"eds_scikit.io.hive HiveData HiveData ( database_name : str , spark_session : Optional [ SparkSession ] = None , person_ids : Optional [ Iterable [ int ]] = None , tables_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]]] = None , columns_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]]] = None , database_type : Optional [ str ] = 'OMOP' , prune_omop_date_columns : bool = True , cache : bool = True ) Bases: BaseData Spark interface for OMOP data stored in a Hive database. This class provides a simple access to data stored in Hive. Data is returned as koalas dataframes that match the tables stored in Hive. PARAMETER DESCRIPTION database_name The name of you database in Hive. Ex: \"cse_82727572\" TYPE: str spark_session If None, a SparkSession will be retrieved or created via SparkSession.builder.enableHiveSupport().getOrCreate() TYPE: pyspark . sql . SparkSession DEFAULT: None person_ids An iterable of person_id that is used to define a subset of the database. TYPE: Optional [ Iterable [ int ]] DEFAULT: None tables_to_load deprecated TYPE: dict , default DEFAULT: None columns_to_load deprecated TYPE: dict , default DEFAULT: None database_type Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP. TYPE: Optional [ str ] DEFAULT: 'OMOP' prune_omop_date_columns In OMOP, most date values are stored both in a _date and _datetime column Koalas has trouble handling the date time, so we only keep the datetime column TYPE: bool DEFAULT: True cache Whether to cache each table after preprocessing or not. Will speed-up subsequent calculations, but can be long/infeasable for very large tables TYPE: bool DEFAULT: True ATTRIBUTE DESCRIPTION person Hive data for table person as a koalas dataframe. Other OMOP tables can also be accessed as attributes TYPE: koalas dataframe available_tables names of OMOP tables that can be accessed as attributes with this HiveData object. TYPE: list of str Examples: data = HiveData ( database_name = \"edsomop_prod_a\" ) data . available_tables # Out: [\"person\", \"care_site\", \"condition_occurrence\", ... ] person = data . person type ( person ) # Out: databricks.koalas.frame.DataFrame person [ \"person_id\" ] . count () # Out: 12670874 This class can be used to create a subset of data for a given list of person_id . This is useful because the smaller dataset can then be used to prototype more rapidly. my_person_ids = [ 9226726 , 2092082 , ... ] data = HiveData ( spark_session = spark , database_name = \"edsomop_prod_a\" , person_ids = my_person_ids ) data . person [ \"person_id\" ] . count () # Out: 1000 tables_to_save = [ \"person\" , \"visit_occurrence\" ] data . persist_tables_to_folder ( \"./cohort_sample_1000\" , table_names = tables_to_save ) # Out: writing /export/home/USER/cohort_sample_1000/person.parquet # Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet # Out: ... Source code in eds_scikit/io/hive.py 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def __init__ ( self , database_name : str , spark_session : Optional [ SparkSession ] = None , person_ids : Optional [ Iterable [ int ]] = None , tables_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]] ] = None , columns_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]] ] = None , database_type : Optional [ str ] = \"OMOP\" , prune_omop_date_columns : bool = True , cache : bool = True , ): \"\"\"Spark interface for OMOP data stored in a Hive database. This class provides a simple access to data stored in Hive. Data is returned as koalas dataframes that match the tables stored in Hive. Parameters ---------- database_name : str The name of you database in Hive. Ex: \"cse_82727572\" spark_session : pyspark.sql.SparkSession If None, a SparkSession will be retrieved or created via `SparkSession.builder.enableHiveSupport().getOrCreate()` person_ids : Optional[Iterable[int]] An iterable of `person_id` that is used to define a subset of the database. tables_to_load : dict, default=None *deprecated* columns_to_load : dict, default=None *deprecated* database_type: Optional[str] = 'OMOP'. Must be 'OMOP' or 'I2B2' Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP. prune_omop_date_columns: bool, default=True In OMOP, most date values are stored both in a `_date` and `_datetime` column Koalas has trouble handling the `date` time, so we only keep the `datetime` column cache: bool, default=True Whether to cache each table after preprocessing or not. Will speed-up subsequent calculations, but can be long/infeasable for very large tables Attributes ---------- person : koalas dataframe Hive data for table `person` as a koalas dataframe. Other OMOP tables can also be accessed as attributes available_tables : list of str names of OMOP tables that can be accessed as attributes with this HiveData object. Examples -------- ```python data = HiveData(database_name=\"edsomop_prod_a\") data.available_tables # Out: [\"person\", \"care_site\", \"condition_occurrence\", ... ] person = data.person type(person) # Out: databricks.koalas.frame.DataFrame person[\"person_id\"].count() # Out: 12670874 ``` This class can be used to create a subset of data for a given list of `person_id`. This is useful because the smaller dataset can then be used to prototype more rapidly. ```python my_person_ids = [9226726, 2092082, ...] data = HiveData( spark_session=spark, database_name=\"edsomop_prod_a\", person_ids=my_person_ids ) data.person[\"person_id\"].count() # Out: 1000 tables_to_save = [\"person\", \"visit_occurrence\"] data.persist_tables_to_folder(\"./cohort_sample_1000\", table_names=tables_to_save) # Out: writing /export/home/USER/cohort_sample_1000/person.parquet # Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet # Out: ... ``` \"\"\" super () . __init__ () if columns_to_load is not None : logger . warning ( \"'columns_to_load' is deprecated and won't be used\" ) if tables_to_load is not None : logger . warning ( \"'tables_to_load' is deprecated and won't be used\" ) self . spark_session = ( spark_session or SparkSession . builder . enableHiveSupport () . getOrCreate () ) self . database_name = database_name if database_type not in [ \"I2B2\" , \"OMOP\" ]: raise ValueError ( f \"`database_type` must be either 'I2B2' or 'OMOP'. Got { database_type } \" ) self . database_type = database_type if self . database_type == \"I2B2\" : self . database_source = \"cse\" if \"cse\" in self . database_name else \"edsprod\" self . omop_to_i2b2 = settings . i2b2_tables [ self . database_source ] self . i2b2_to_omop = defaultdict ( list ) for omop_table , i2b2_table in self . omop_to_i2b2 . items (): self . i2b2_to_omop [ i2b2_table ] . append ( omop_table ) self . prune_omop_date_columns = prune_omop_date_columns self . cache = cache self . user = os . environ [ \"USER\" ] self . person_ids , self . person_ids_df = self . _prepare_person_ids ( person_ids ) self . available_tables = self . list_available_tables () self . _tables = {} persist_tables_to_folder persist_tables_to_folder ( folder : str , person_ids : Optional [ Iterable [ int ]] = None , tables : List [ str ] = None , overwrite : bool = False ) -> None Save OMOP tables as parquet files in a given folder. PARAMETER DESCRIPTION folder path to folder where the tables will be written. TYPE: str person_ids : iterable person_ids to keep in the subcohort. tables : list of str, default None list of table names to save. Default value is data: ~eds_scikit.io.settings.default_tables_to_save . overwrite : bool, default=False whether to overwrite files if 'folder' already exists. Source code in eds_scikit/io/hive.py 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 def persist_tables_to_folder ( self , folder : str , person_ids : Optional [ Iterable [ int ]] = None , tables : List [ str ] = None , overwrite : bool = False , ) -> None : \"\"\"Save OMOP tables as parquet files in a given folder. Parameters ---------- folder : str path to folder where the tables will be written. person_ids : iterable person_ids to keep in the subcohort. tables : list of str, default None list of table names to save. Default value is :py:data:`~eds_scikit.io.settings.default_tables_to_save`. overwrite : bool, default=False whether to overwrite files if 'folder' already exists. \"\"\" # Manage tables if tables is None : tables = settings . default_tables_to_save unknown_tables = [ table for table in tables if table not in self . available_tables ] if unknown_tables : raise ValueError ( f \"The following tables are not available : { str ( unknown_tables ) } \" ) # Create folder folder = Path ( folder ) . absolute () if folder . exists () and overwrite : shutil . rmtree ( folder ) folder . mkdir ( parents = True , mode = 0o766 ) assert os . path . exists ( folder ) and os . path . isdir ( folder ), f \"Folder { folder } not found.\" # TODO: remove everything in this folder that is a valid # omop table. This prevents a user from having a # folder containing datasets generated from different # patient subsets. # TODO: maybe check how much the user wants to persist # to disk. Set a limit on the number of patients in the cohort ? if person_ids is not None : person_ids = self . _prepare_person_ids ( person_ids , return_df = False ) database_path = self . get_db_path () for idx , table in enumerate ( tables ): if self . database_type == \"I2B2\" : table_path = self . _hdfs_write_orc_to_parquet ( table , person_ids , overwrite ) else : table_path = os . path . join ( database_path , table ) df = self . get_table_from_parquet ( table_path , person_ids = person_ids ) local_file_path = os . path . join ( folder , f \" { table } .parquet\" ) df . to_parquet ( local_file_path , allow_truncated_timestamps = True , coerce_timestamps = \"ms\" , ) logger . info ( f \"( { idx + 1 } / { len ( tables ) } ) Table { table } saved at \" f \" { local_file_path } (N= { len ( df ) } ).\" ) get_db_path get_db_path () Get the HDFS path of the database Source code in eds_scikit/io/hive.py 374 375 376 377 378 379 380 381 def get_db_path ( self ): \"\"\"Get the HDFS path of the database\"\"\" return ( self . spark_session . sql ( f \"DESCRIBE DATABASE EXTENDED { self . database_name } \" ) . filter ( \"database_description_item=='Location'\" ) . collect ()[ 0 ] . database_description_value )","title":"hive"},{"location":"reference/io/hive/#eds_scikitiohive","text":"","title":"eds_scikit.io.hive"},{"location":"reference/io/hive/#eds_scikit.io.hive.HiveData","text":"HiveData ( database_name : str , spark_session : Optional [ SparkSession ] = None , person_ids : Optional [ Iterable [ int ]] = None , tables_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]]] = None , columns_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]]] = None , database_type : Optional [ str ] = 'OMOP' , prune_omop_date_columns : bool = True , cache : bool = True ) Bases: BaseData Spark interface for OMOP data stored in a Hive database. This class provides a simple access to data stored in Hive. Data is returned as koalas dataframes that match the tables stored in Hive. PARAMETER DESCRIPTION database_name The name of you database in Hive. Ex: \"cse_82727572\" TYPE: str spark_session If None, a SparkSession will be retrieved or created via SparkSession.builder.enableHiveSupport().getOrCreate() TYPE: pyspark . sql . SparkSession DEFAULT: None person_ids An iterable of person_id that is used to define a subset of the database. TYPE: Optional [ Iterable [ int ]] DEFAULT: None tables_to_load deprecated TYPE: dict , default DEFAULT: None columns_to_load deprecated TYPE: dict , default DEFAULT: None database_type Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP. TYPE: Optional [ str ] DEFAULT: 'OMOP' prune_omop_date_columns In OMOP, most date values are stored both in a _date and _datetime column Koalas has trouble handling the date time, so we only keep the datetime column TYPE: bool DEFAULT: True cache Whether to cache each table after preprocessing or not. Will speed-up subsequent calculations, but can be long/infeasable for very large tables TYPE: bool DEFAULT: True ATTRIBUTE DESCRIPTION person Hive data for table person as a koalas dataframe. Other OMOP tables can also be accessed as attributes TYPE: koalas dataframe available_tables names of OMOP tables that can be accessed as attributes with this HiveData object. TYPE: list of str Examples: data = HiveData ( database_name = \"edsomop_prod_a\" ) data . available_tables # Out: [\"person\", \"care_site\", \"condition_occurrence\", ... ] person = data . person type ( person ) # Out: databricks.koalas.frame.DataFrame person [ \"person_id\" ] . count () # Out: 12670874 This class can be used to create a subset of data for a given list of person_id . This is useful because the smaller dataset can then be used to prototype more rapidly. my_person_ids = [ 9226726 , 2092082 , ... ] data = HiveData ( spark_session = spark , database_name = \"edsomop_prod_a\" , person_ids = my_person_ids ) data . person [ \"person_id\" ] . count () # Out: 1000 tables_to_save = [ \"person\" , \"visit_occurrence\" ] data . persist_tables_to_folder ( \"./cohort_sample_1000\" , table_names = tables_to_save ) # Out: writing /export/home/USER/cohort_sample_1000/person.parquet # Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet # Out: ... Source code in eds_scikit/io/hive.py 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 def __init__ ( self , database_name : str , spark_session : Optional [ SparkSession ] = None , person_ids : Optional [ Iterable [ int ]] = None , tables_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]] ] = None , columns_to_load : Optional [ Union [ Dict [ str , Optional [ List [ str ]]], List [ str ]] ] = None , database_type : Optional [ str ] = \"OMOP\" , prune_omop_date_columns : bool = True , cache : bool = True , ): \"\"\"Spark interface for OMOP data stored in a Hive database. This class provides a simple access to data stored in Hive. Data is returned as koalas dataframes that match the tables stored in Hive. Parameters ---------- database_name : str The name of you database in Hive. Ex: \"cse_82727572\" spark_session : pyspark.sql.SparkSession If None, a SparkSession will be retrieved or created via `SparkSession.builder.enableHiveSupport().getOrCreate()` person_ids : Optional[Iterable[int]] An iterable of `person_id` that is used to define a subset of the database. tables_to_load : dict, default=None *deprecated* columns_to_load : dict, default=None *deprecated* database_type: Optional[str] = 'OMOP'. Must be 'OMOP' or 'I2B2' Whether to use the native OMOP schema or to convert I2B2 inputs to OMOP. prune_omop_date_columns: bool, default=True In OMOP, most date values are stored both in a `_date` and `_datetime` column Koalas has trouble handling the `date` time, so we only keep the `datetime` column cache: bool, default=True Whether to cache each table after preprocessing or not. Will speed-up subsequent calculations, but can be long/infeasable for very large tables Attributes ---------- person : koalas dataframe Hive data for table `person` as a koalas dataframe. Other OMOP tables can also be accessed as attributes available_tables : list of str names of OMOP tables that can be accessed as attributes with this HiveData object. Examples -------- ```python data = HiveData(database_name=\"edsomop_prod_a\") data.available_tables # Out: [\"person\", \"care_site\", \"condition_occurrence\", ... ] person = data.person type(person) # Out: databricks.koalas.frame.DataFrame person[\"person_id\"].count() # Out: 12670874 ``` This class can be used to create a subset of data for a given list of `person_id`. This is useful because the smaller dataset can then be used to prototype more rapidly. ```python my_person_ids = [9226726, 2092082, ...] data = HiveData( spark_session=spark, database_name=\"edsomop_prod_a\", person_ids=my_person_ids ) data.person[\"person_id\"].count() # Out: 1000 tables_to_save = [\"person\", \"visit_occurrence\"] data.persist_tables_to_folder(\"./cohort_sample_1000\", table_names=tables_to_save) # Out: writing /export/home/USER/cohort_sample_1000/person.parquet # Out: writing /export/home/USER/cohort_sample_1000/visit_occurrence.parquet # Out: ... ``` \"\"\" super () . __init__ () if columns_to_load is not None : logger . warning ( \"'columns_to_load' is deprecated and won't be used\" ) if tables_to_load is not None : logger . warning ( \"'tables_to_load' is deprecated and won't be used\" ) self . spark_session = ( spark_session or SparkSession . builder . enableHiveSupport () . getOrCreate () ) self . database_name = database_name if database_type not in [ \"I2B2\" , \"OMOP\" ]: raise ValueError ( f \"`database_type` must be either 'I2B2' or 'OMOP'. Got { database_type } \" ) self . database_type = database_type if self . database_type == \"I2B2\" : self . database_source = \"cse\" if \"cse\" in self . database_name else \"edsprod\" self . omop_to_i2b2 = settings . i2b2_tables [ self . database_source ] self . i2b2_to_omop = defaultdict ( list ) for omop_table , i2b2_table in self . omop_to_i2b2 . items (): self . i2b2_to_omop [ i2b2_table ] . append ( omop_table ) self . prune_omop_date_columns = prune_omop_date_columns self . cache = cache self . user = os . environ [ \"USER\" ] self . person_ids , self . person_ids_df = self . _prepare_person_ids ( person_ids ) self . available_tables = self . list_available_tables () self . _tables = {}","title":"HiveData"},{"location":"reference/io/hive/#eds_scikit.io.hive.HiveData.persist_tables_to_folder","text":"persist_tables_to_folder ( folder : str , person_ids : Optional [ Iterable [ int ]] = None , tables : List [ str ] = None , overwrite : bool = False ) -> None Save OMOP tables as parquet files in a given folder. PARAMETER DESCRIPTION folder path to folder where the tables will be written. TYPE: str person_ids : iterable person_ids to keep in the subcohort. tables : list of str, default None list of table names to save. Default value is data: ~eds_scikit.io.settings.default_tables_to_save . overwrite : bool, default=False whether to overwrite files if 'folder' already exists. Source code in eds_scikit/io/hive.py 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 def persist_tables_to_folder ( self , folder : str , person_ids : Optional [ Iterable [ int ]] = None , tables : List [ str ] = None , overwrite : bool = False , ) -> None : \"\"\"Save OMOP tables as parquet files in a given folder. Parameters ---------- folder : str path to folder where the tables will be written. person_ids : iterable person_ids to keep in the subcohort. tables : list of str, default None list of table names to save. Default value is :py:data:`~eds_scikit.io.settings.default_tables_to_save`. overwrite : bool, default=False whether to overwrite files if 'folder' already exists. \"\"\" # Manage tables if tables is None : tables = settings . default_tables_to_save unknown_tables = [ table for table in tables if table not in self . available_tables ] if unknown_tables : raise ValueError ( f \"The following tables are not available : { str ( unknown_tables ) } \" ) # Create folder folder = Path ( folder ) . absolute () if folder . exists () and overwrite : shutil . rmtree ( folder ) folder . mkdir ( parents = True , mode = 0o766 ) assert os . path . exists ( folder ) and os . path . isdir ( folder ), f \"Folder { folder } not found.\" # TODO: remove everything in this folder that is a valid # omop table. This prevents a user from having a # folder containing datasets generated from different # patient subsets. # TODO: maybe check how much the user wants to persist # to disk. Set a limit on the number of patients in the cohort ? if person_ids is not None : person_ids = self . _prepare_person_ids ( person_ids , return_df = False ) database_path = self . get_db_path () for idx , table in enumerate ( tables ): if self . database_type == \"I2B2\" : table_path = self . _hdfs_write_orc_to_parquet ( table , person_ids , overwrite ) else : table_path = os . path . join ( database_path , table ) df = self . get_table_from_parquet ( table_path , person_ids = person_ids ) local_file_path = os . path . join ( folder , f \" { table } .parquet\" ) df . to_parquet ( local_file_path , allow_truncated_timestamps = True , coerce_timestamps = \"ms\" , ) logger . info ( f \"( { idx + 1 } / { len ( tables ) } ) Table { table } saved at \" f \" { local_file_path } (N= { len ( df ) } ).\" )","title":"persist_tables_to_folder()"},{"location":"reference/io/hive/#eds_scikit.io.hive.HiveData.get_db_path","text":"get_db_path () Get the HDFS path of the database Source code in eds_scikit/io/hive.py 374 375 376 377 378 379 380 381 def get_db_path ( self ): \"\"\"Get the HDFS path of the database\"\"\" return ( self . spark_session . sql ( f \"DESCRIBE DATABASE EXTENDED { self . database_name } \" ) . filter ( \"database_description_item=='Location'\" ) . collect ()[ 0 ] . database_description_value )","title":"get_db_path()"},{"location":"reference/io/i2b2_mapping/","text":"eds_scikit.io.i2b2_mapping get_i2b2_table get_i2b2_table ( spark_session : SparkSession , db_name : str , db_source : str , table : str ) -> SparkDataFrame Convert a Spark table from i2b2 to OMOP format. PARAMETER DESCRIPTION db_name Name of the database where the data is stored. TYPE: str table Name of the table to extract. TYPE: str RETURNS DESCRIPTION df Spark DataFrame extracted from the i2b2 database given and converted to OMOP standard. TYPE: Spark DataFrame Source code in eds_scikit/io/i2b2_mapping.py 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 def get_i2b2_table ( spark_session : SparkSession , db_name : str , db_source : str , table : str ) -> SparkDataFrame : \"\"\" Convert a Spark table from i2b2 to OMOP format. Parameters ---------- db_name: str Name of the database where the data is stored. table: str Name of the table to extract. Returns ------- df: Spark DataFrame Spark DataFrame extracted from the i2b2 database given and converted to OMOP standard. \"\"\" i2b2_table_name = i2b2_tables [ db_source ][ table ] # Dictionary of omop_col -> i2b2_col columns = i2b2_renaming . get ( table ) # Can be None if creating a table from scratch (e.g. concept_relationship if columns is not None : query = f \"describe { db_name } . { i2b2_table_name } \" available_columns = set ( spark_session . sql ( query ) . toPandas () . col_name . tolist ()) if db_source == \"cse\" : columns . pop ( \"i2b2_action\" , None ) cols = \", \" . join ( [ f \" { i2b2 } AS { omop } \" for omop , i2b2 in columns . items () if i2b2 in available_columns ] ) query = f \"SELECT { cols } FROM { db_name } . { i2b2_table_name } \" df = spark_session . sql ( query ) # Special mapping for i2b2 : # CIM10 if table == \"condition_occurrence\" : df = df . withColumn ( \"condition_source_value\" , F . substring ( F . col ( \"condition_source_value\" ), 7 , 20 ), ) # CCAM elif table == \"procedure_occurrence\" : df = df . withColumn ( \"procedure_source_value\" , F . substring ( F . col ( \"procedure_source_value\" ), 6 , 20 ), ) # Visits elif table == \"visit_occurrence\" : df = df . withColumn ( \"visit_source_value\" , mapping_dict ( visit_type_mapping , \"Non Renseign\u00e9\" )( F . col ( \"visit_source_value\" ) ), ) if db_source == \"cse\" : df = df . withColumn ( \"row_status_source_value\" , F . lit ( \"Actif\" )) df = df . withColumn ( \"visit_occurrence_source_value\" , df [ \"visit_occurrence_id\" ] ) else : df = df . withColumn ( \"row_status_source_value\" , F . when ( F . col ( \"row_status_source_value\" ) . isin ([ - 1 , - 2 ]), \"supprim\u00e9\" ) . otherwise ( \"Actif\" ), ) # Retrieve Hospital trigram ufr = spark_session . sql ( f \"SELECT * FROM { db_name } . { i2b2_tables [ db_source ][ 'visit_detail' ] } \" ) ufr = ufr . withColumn ( \"care_site_id\" , F . substring ( F . split ( F . col ( \"concept_cd\" ), \":\" ) . getItem ( 1 ), 1 , 3 ), ) ufr = ufr . withColumnRenamed ( \"encounter_num\" , \"visit_occurrence_id\" ) ufr = ufr . drop_duplicates ( subset = [ \"visit_occurrence_id\" ]) ufr = ufr . select ([ \"visit_occurrence_id\" , \"care_site_id\" ]) df = df . join ( ufr , how = \"inner\" , on = [ \"visit_occurrence_id\" ]) # Patients elif table == \"person\" : df = df . withColumn ( \"gender_source_value\" , mapping_dict ( sex_cd_mapping , \"Non Renseign\u00e9\" )( F . col ( \"gender_source_value\" )), ) # Documents elif table . startswith ( \"note\" ): df = df . withColumn ( \"note_class_source_value\" , F . substring ( F . col ( \"note_class_source_value\" ), 4 , 100 ), ) if db_source == \"cse\" : df = df . withColumn ( \"row_status_source_value\" , F . lit ( \"Actif\" )) else : df = df . withColumn ( \"row_status_source_value\" , F . when ( F . col ( \"row_status_source_value\" ) < 0 , \"SUPP\" ) . otherwise ( \"Actif\" ), ) # Hospital trigrams elif table == \"care_site\" : df = df . withColumn ( \"care_site_type_source_value\" , F . lit ( \"H\u00f4pital\" )) df = df . withColumn ( \"care_site_source_value\" , F . split ( F . col ( \"care_site_source_value\" ), \":\" ) . getItem ( 1 ), ) df = df . withColumn ( \"care_site_id\" , F . substring ( F . col ( \"care_site_source_value\" ), 1 , 3 ) ) df = df . drop_duplicates ( subset = [ \"care_site_id\" ]) df = df . withColumn ( \"care_site_short_name\" , mapping_dict ( dict_code_UFR , \"Non Renseign\u00e9\" )( F . col ( \"care_site_id\" )), ) # UFR elif table == \"visit_detail\" : df = df . withColumn ( \"care_site_id\" , F . split ( F . col ( \"care_site_id\" ), \":\" ) . getItem ( 1 ) ) df = df . withColumn ( \"visit_detail_type_source_value\" , F . lit ( \"PASS\" )) df = df . withColumn ( \"row_status_source_value\" , F . lit ( \"Actif\" )) # measurement elif table == \"measurement\" : df = df . withColumn ( \"measurement_source_concept_id\" , F . substring ( F . col ( \"measurement_source_concept_id\" ), 5 , 20 ), ) . withColumn ( \"row_status_source_value\" , F . lit ( \"Valid\u00e9\" )) # concept elif table == \"concept\" : df = ( df . withColumn ( \"concept_source_value\" , F . substring ( F . col ( \"concept_source_value\" ), 5 , 20 ), # TODO: use regexp_extract to take substring after ':' ) . withColumn ( \"concept_id\" , F . col ( \"concept_source_value\" )) . withColumn ( \"concept_code\" , F . col ( \"concept_id\" )) . withColumn ( \"vocabulary_id\" , F . lit ( \"ANABIO\" )) ) # Adding LOINC if \"get_additional_i2b2_concept\" in registry . data . get_all (): loinc_pd = registry . get ( \"data\" , \"get_additional_i2b2_concept\" )() assert len ( loinc_pd . columns ) == len ( df . columns ) loinc_pd = loinc_pd [ df . columns ] # for columns ordering df = df . union ( spark_session . createDataFrame ( loinc_pd , df . schema , verifySchema = False ) ) . cache () # fact_relationship elif table == \"fact_relationship\" : # Retrieve UF information df = df . withColumn ( \"fact_id_1\" , F . split ( F . col ( \"care_site_source_value\" ), \":\" ) . getItem ( 1 ), ) df = df . withColumn ( \"domain_concept_id_1\" , F . lit ( 57 )) # Care_site domain # Retrieve hospital information df = df . withColumn ( \"fact_id_2\" , F . substring ( F . col ( \"fact_id_1\" ), 1 , 3 )) df = df . withColumn ( \"domain_concept_id_2\" , F . lit ( 57 )) # Care_site domain df = df . drop_duplicates ( subset = [ \"fact_id_1\" , \"fact_id_2\" ]) # Only UF-Hospital relationships in i2b2 df = df . withColumn ( \"relationship_concept_id\" , F . lit ( 46233688 )) # Included in elif table == \"concept_relationship\" : data = [] schema = T . StructType ( [ T . StructField ( \"concept_id_1\" , T . StringType (), True ), T . StructField ( \"concept_id_2\" , T . StringType (), True ), T . StructField ( \"relationship_id\" , T . StringType (), True ), ] ) if \"get_additional_i2b2_concept_relationship\" in registry . data . get_all (): data = registry . get ( \"data\" , \"get_additional_i2b2_concept_relationship\" )() df = spark_session . createDataFrame ( data , schema ) . cache () return df mapping_dict mapping_dict ( mapping : Dict [ str , str ], default : str ) -> FunctionUDF Returns a function that maps data according to a mapping dictionnary in a Spark DataFrame. PARAMETER DESCRIPTION mapping Mapping dictionnary TYPE: Dict [ str , str ] default Value to return if the function input is not find in the mapping dictionnary. TYPE: str RETURNS DESCRIPTION Callable Function that maps the values of Spark DataFrame column. Source code in eds_scikit/io/i2b2_mapping.py 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 def mapping_dict ( mapping : Dict [ str , str ], default : str ) -> FunctionUDF : \"\"\" Returns a function that maps data according to a mapping dictionnary in a Spark DataFrame. Parameters ---------- mapping: Dict Mapping dictionnary default: str Value to return if the function input is not find in the mapping dictionnary. Returns ------- Callable Function that maps the values of Spark DataFrame column. \"\"\" def f ( x ): return mapping . get ( x , default ) return F . udf ( f )","title":"i2b2_mapping"},{"location":"reference/io/i2b2_mapping/#eds_scikitioi2b2_mapping","text":"","title":"eds_scikit.io.i2b2_mapping"},{"location":"reference/io/i2b2_mapping/#eds_scikit.io.i2b2_mapping.get_i2b2_table","text":"get_i2b2_table ( spark_session : SparkSession , db_name : str , db_source : str , table : str ) -> SparkDataFrame Convert a Spark table from i2b2 to OMOP format. PARAMETER DESCRIPTION db_name Name of the database where the data is stored. TYPE: str table Name of the table to extract. TYPE: str RETURNS DESCRIPTION df Spark DataFrame extracted from the i2b2 database given and converted to OMOP standard. TYPE: Spark DataFrame Source code in eds_scikit/io/i2b2_mapping.py 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 def get_i2b2_table ( spark_session : SparkSession , db_name : str , db_source : str , table : str ) -> SparkDataFrame : \"\"\" Convert a Spark table from i2b2 to OMOP format. Parameters ---------- db_name: str Name of the database where the data is stored. table: str Name of the table to extract. Returns ------- df: Spark DataFrame Spark DataFrame extracted from the i2b2 database given and converted to OMOP standard. \"\"\" i2b2_table_name = i2b2_tables [ db_source ][ table ] # Dictionary of omop_col -> i2b2_col columns = i2b2_renaming . get ( table ) # Can be None if creating a table from scratch (e.g. concept_relationship if columns is not None : query = f \"describe { db_name } . { i2b2_table_name } \" available_columns = set ( spark_session . sql ( query ) . toPandas () . col_name . tolist ()) if db_source == \"cse\" : columns . pop ( \"i2b2_action\" , None ) cols = \", \" . join ( [ f \" { i2b2 } AS { omop } \" for omop , i2b2 in columns . items () if i2b2 in available_columns ] ) query = f \"SELECT { cols } FROM { db_name } . { i2b2_table_name } \" df = spark_session . sql ( query ) # Special mapping for i2b2 : # CIM10 if table == \"condition_occurrence\" : df = df . withColumn ( \"condition_source_value\" , F . substring ( F . col ( \"condition_source_value\" ), 7 , 20 ), ) # CCAM elif table == \"procedure_occurrence\" : df = df . withColumn ( \"procedure_source_value\" , F . substring ( F . col ( \"procedure_source_value\" ), 6 , 20 ), ) # Visits elif table == \"visit_occurrence\" : df = df . withColumn ( \"visit_source_value\" , mapping_dict ( visit_type_mapping , \"Non Renseign\u00e9\" )( F . col ( \"visit_source_value\" ) ), ) if db_source == \"cse\" : df = df . withColumn ( \"row_status_source_value\" , F . lit ( \"Actif\" )) df = df . withColumn ( \"visit_occurrence_source_value\" , df [ \"visit_occurrence_id\" ] ) else : df = df . withColumn ( \"row_status_source_value\" , F . when ( F . col ( \"row_status_source_value\" ) . isin ([ - 1 , - 2 ]), \"supprim\u00e9\" ) . otherwise ( \"Actif\" ), ) # Retrieve Hospital trigram ufr = spark_session . sql ( f \"SELECT * FROM { db_name } . { i2b2_tables [ db_source ][ 'visit_detail' ] } \" ) ufr = ufr . withColumn ( \"care_site_id\" , F . substring ( F . split ( F . col ( \"concept_cd\" ), \":\" ) . getItem ( 1 ), 1 , 3 ), ) ufr = ufr . withColumnRenamed ( \"encounter_num\" , \"visit_occurrence_id\" ) ufr = ufr . drop_duplicates ( subset = [ \"visit_occurrence_id\" ]) ufr = ufr . select ([ \"visit_occurrence_id\" , \"care_site_id\" ]) df = df . join ( ufr , how = \"inner\" , on = [ \"visit_occurrence_id\" ]) # Patients elif table == \"person\" : df = df . withColumn ( \"gender_source_value\" , mapping_dict ( sex_cd_mapping , \"Non Renseign\u00e9\" )( F . col ( \"gender_source_value\" )), ) # Documents elif table . startswith ( \"note\" ): df = df . withColumn ( \"note_class_source_value\" , F . substring ( F . col ( \"note_class_source_value\" ), 4 , 100 ), ) if db_source == \"cse\" : df = df . withColumn ( \"row_status_source_value\" , F . lit ( \"Actif\" )) else : df = df . withColumn ( \"row_status_source_value\" , F . when ( F . col ( \"row_status_source_value\" ) < 0 , \"SUPP\" ) . otherwise ( \"Actif\" ), ) # Hospital trigrams elif table == \"care_site\" : df = df . withColumn ( \"care_site_type_source_value\" , F . lit ( \"H\u00f4pital\" )) df = df . withColumn ( \"care_site_source_value\" , F . split ( F . col ( \"care_site_source_value\" ), \":\" ) . getItem ( 1 ), ) df = df . withColumn ( \"care_site_id\" , F . substring ( F . col ( \"care_site_source_value\" ), 1 , 3 ) ) df = df . drop_duplicates ( subset = [ \"care_site_id\" ]) df = df . withColumn ( \"care_site_short_name\" , mapping_dict ( dict_code_UFR , \"Non Renseign\u00e9\" )( F . col ( \"care_site_id\" )), ) # UFR elif table == \"visit_detail\" : df = df . withColumn ( \"care_site_id\" , F . split ( F . col ( \"care_site_id\" ), \":\" ) . getItem ( 1 ) ) df = df . withColumn ( \"visit_detail_type_source_value\" , F . lit ( \"PASS\" )) df = df . withColumn ( \"row_status_source_value\" , F . lit ( \"Actif\" )) # measurement elif table == \"measurement\" : df = df . withColumn ( \"measurement_source_concept_id\" , F . substring ( F . col ( \"measurement_source_concept_id\" ), 5 , 20 ), ) . withColumn ( \"row_status_source_value\" , F . lit ( \"Valid\u00e9\" )) # concept elif table == \"concept\" : df = ( df . withColumn ( \"concept_source_value\" , F . substring ( F . col ( \"concept_source_value\" ), 5 , 20 ), # TODO: use regexp_extract to take substring after ':' ) . withColumn ( \"concept_id\" , F . col ( \"concept_source_value\" )) . withColumn ( \"concept_code\" , F . col ( \"concept_id\" )) . withColumn ( \"vocabulary_id\" , F . lit ( \"ANABIO\" )) ) # Adding LOINC if \"get_additional_i2b2_concept\" in registry . data . get_all (): loinc_pd = registry . get ( \"data\" , \"get_additional_i2b2_concept\" )() assert len ( loinc_pd . columns ) == len ( df . columns ) loinc_pd = loinc_pd [ df . columns ] # for columns ordering df = df . union ( spark_session . createDataFrame ( loinc_pd , df . schema , verifySchema = False ) ) . cache () # fact_relationship elif table == \"fact_relationship\" : # Retrieve UF information df = df . withColumn ( \"fact_id_1\" , F . split ( F . col ( \"care_site_source_value\" ), \":\" ) . getItem ( 1 ), ) df = df . withColumn ( \"domain_concept_id_1\" , F . lit ( 57 )) # Care_site domain # Retrieve hospital information df = df . withColumn ( \"fact_id_2\" , F . substring ( F . col ( \"fact_id_1\" ), 1 , 3 )) df = df . withColumn ( \"domain_concept_id_2\" , F . lit ( 57 )) # Care_site domain df = df . drop_duplicates ( subset = [ \"fact_id_1\" , \"fact_id_2\" ]) # Only UF-Hospital relationships in i2b2 df = df . withColumn ( \"relationship_concept_id\" , F . lit ( 46233688 )) # Included in elif table == \"concept_relationship\" : data = [] schema = T . StructType ( [ T . StructField ( \"concept_id_1\" , T . StringType (), True ), T . StructField ( \"concept_id_2\" , T . StringType (), True ), T . StructField ( \"relationship_id\" , T . StringType (), True ), ] ) if \"get_additional_i2b2_concept_relationship\" in registry . data . get_all (): data = registry . get ( \"data\" , \"get_additional_i2b2_concept_relationship\" )() df = spark_session . createDataFrame ( data , schema ) . cache () return df","title":"get_i2b2_table()"},{"location":"reference/io/i2b2_mapping/#eds_scikit.io.i2b2_mapping.mapping_dict","text":"mapping_dict ( mapping : Dict [ str , str ], default : str ) -> FunctionUDF Returns a function that maps data according to a mapping dictionnary in a Spark DataFrame. PARAMETER DESCRIPTION mapping Mapping dictionnary TYPE: Dict [ str , str ] default Value to return if the function input is not find in the mapping dictionnary. TYPE: str RETURNS DESCRIPTION Callable Function that maps the values of Spark DataFrame column. Source code in eds_scikit/io/i2b2_mapping.py 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 def mapping_dict ( mapping : Dict [ str , str ], default : str ) -> FunctionUDF : \"\"\" Returns a function that maps data according to a mapping dictionnary in a Spark DataFrame. Parameters ---------- mapping: Dict Mapping dictionnary default: str Value to return if the function input is not find in the mapping dictionnary. Returns ------- Callable Function that maps the values of Spark DataFrame column. \"\"\" def f ( x ): return mapping . get ( x , default ) return F . udf ( f )","title":"mapping_dict()"},{"location":"reference/io/improve_performance/","text":"eds_scikit.io.improve_performance koalas_options koalas_options () -> None Set necessary options to optimise Koalas Source code in eds_scikit/io/improve_performance.py 31 32 33 34 35 36 37 38 39 40 41 def koalas_options () -> None : \"\"\" Set necessary options to optimise Koalas \"\"\" # Reloading Koalas to use the new configuration ks = load_koalas () ks . set_option ( \"compute.default_index_type\" , \"distributed\" ) ks . set_option ( \"compute.ops_on_diff_frames\" , True ) ks . set_option ( \"display.max_rows\" , 50 ) pyarrow_fix pyarrow_fix () Fixing error 'pyarrow has no attributes open_stream' due to PySpark 2 incompatibility with pyarrow > 0.17 Source code in eds_scikit/io/improve_performance.py 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def pyarrow_fix (): \"\"\" Fixing error 'pyarrow has no attributes open_stream' due to PySpark 2 incompatibility with pyarrow > 0.17 \"\"\" # Setting path to our patched pyarrow module pyarrow . open_stream = pyarrow . ipc . open_stream sys . path . insert ( 0 , ( Path ( __file__ ) . parent / \"package-override\" ) . absolute () . as_posix () ) os . environ [ \"PYTHONPATH\" ] = \":\" . join ( sys . path ) # Setting this path for Pyspark executors global spark , sc , sql spark = SparkSession . builder . getOrCreate () conf = spark . sparkContext . getConf () conf . set ( \"spark.executorEnv.PYTHONPATH\" , f \" { Path ( __file__ ) . parent . parent } /package-override: { conf . get ( 'spark.executorEnv.PYTHONPATH' ) } \" , ) spark = SparkSession . builder . enableHiveSupport () . config ( conf = conf ) . getOrCreate () sc = spark . sparkContext sql = spark . sql improve_performances improve_performances ( to_add_conf : List [ Tuple [ str , str ]] = [], quiet_spark : bool = True , app_name : str = '' ) -> Tuple [ SparkSession , SparkContext , SparkSession . sql ] (Re)defines various Spark variable with some configuration changes to improve performances by enabling Arrow This has to be done - Before launching a SparkCOntext - Before importing Koalas Those two points are being taken care on this function. If a SparkSession already exists, it will copy its configuration before creating a new one RETURNS DESCRIPTION Tuple of - A SparkSession - The associated SparkContext - The associated Source code in eds_scikit/io/improve_performance.py 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 def improve_performances ( to_add_conf : List [ Tuple [ str , str ]] = [], quiet_spark : bool = True , app_name : str = \"\" , ) -> Tuple [ SparkSession , SparkContext , SparkSession . sql ]: \"\"\" (Re)defines various Spark variable with some configuration changes to improve performances by enabling Arrow This has to be done - Before launching a SparkCOntext - Before importing Koalas Those two points are being taken care on this function. If a SparkSession already exists, it will copy its configuration before creating a new one Returns ------- Tuple of - A SparkSession - The associated SparkContext - The associated ``sql`` object to run SQL queries \"\"\" # Check if a spark Session is up global spark , sc , sql spark = SparkSession . builder . getOrCreate () sc = spark . sparkContext if quiet_spark : sc . setLogLevel ( \"ERROR\" ) conf = sc . getConf () # Synchronizing TimeZone tz = os . environ . get ( \"TZ\" , \"UTC\" ) os . environ [ \"TZ\" ] = tz time . tzset () to_add_conf . extend ( [ ( \"spark.app.name\" , f \" { os . environ . get ( 'USER' ) } _ { app_name } _scikit\" ), ( \"spark.sql.session.timeZone\" , tz ), ( \"spark.sql.execution.arrow.enabled\" , \"true\" ), ( \"spark.sql.execution.arrow.pyspark.enabled\" , \"true\" ), ] ) for key , value in to_add_conf : conf . set ( key , value ) # Stopping context to add necessary env variables sc . stop () spark . stop () set_env_variables () spark = SparkSession . builder . enableHiveSupport () . config ( conf = conf ) . getOrCreate () sc = spark . sparkContext if quiet_spark : sc . setLogLevel ( \"ERROR\" ) sql = spark . sql koalas_options () return spark , sc , sql","title":"improve_performance"},{"location":"reference/io/improve_performance/#eds_scikitioimprove_performance","text":"","title":"eds_scikit.io.improve_performance"},{"location":"reference/io/improve_performance/#eds_scikit.io.improve_performance.koalas_options","text":"koalas_options () -> None Set necessary options to optimise Koalas Source code in eds_scikit/io/improve_performance.py 31 32 33 34 35 36 37 38 39 40 41 def koalas_options () -> None : \"\"\" Set necessary options to optimise Koalas \"\"\" # Reloading Koalas to use the new configuration ks = load_koalas () ks . set_option ( \"compute.default_index_type\" , \"distributed\" ) ks . set_option ( \"compute.ops_on_diff_frames\" , True ) ks . set_option ( \"display.max_rows\" , 50 )","title":"koalas_options()"},{"location":"reference/io/improve_performance/#eds_scikit.io.improve_performance.pyarrow_fix","text":"pyarrow_fix () Fixing error 'pyarrow has no attributes open_stream' due to PySpark 2 incompatibility with pyarrow > 0.17 Source code in eds_scikit/io/improve_performance.py 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 def pyarrow_fix (): \"\"\" Fixing error 'pyarrow has no attributes open_stream' due to PySpark 2 incompatibility with pyarrow > 0.17 \"\"\" # Setting path to our patched pyarrow module pyarrow . open_stream = pyarrow . ipc . open_stream sys . path . insert ( 0 , ( Path ( __file__ ) . parent / \"package-override\" ) . absolute () . as_posix () ) os . environ [ \"PYTHONPATH\" ] = \":\" . join ( sys . path ) # Setting this path for Pyspark executors global spark , sc , sql spark = SparkSession . builder . getOrCreate () conf = spark . sparkContext . getConf () conf . set ( \"spark.executorEnv.PYTHONPATH\" , f \" { Path ( __file__ ) . parent . parent } /package-override: { conf . get ( 'spark.executorEnv.PYTHONPATH' ) } \" , ) spark = SparkSession . builder . enableHiveSupport () . config ( conf = conf ) . getOrCreate () sc = spark . sparkContext sql = spark . sql","title":"pyarrow_fix()"},{"location":"reference/io/improve_performance/#eds_scikit.io.improve_performance.improve_performances","text":"improve_performances ( to_add_conf : List [ Tuple [ str , str ]] = [], quiet_spark : bool = True , app_name : str = '' ) -> Tuple [ SparkSession , SparkContext , SparkSession . sql ] (Re)defines various Spark variable with some configuration changes to improve performances by enabling Arrow This has to be done - Before launching a SparkCOntext - Before importing Koalas Those two points are being taken care on this function. If a SparkSession already exists, it will copy its configuration before creating a new one RETURNS DESCRIPTION Tuple of - A SparkSession - The associated SparkContext - The associated Source code in eds_scikit/io/improve_performance.py 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 def improve_performances ( to_add_conf : List [ Tuple [ str , str ]] = [], quiet_spark : bool = True , app_name : str = \"\" , ) -> Tuple [ SparkSession , SparkContext , SparkSession . sql ]: \"\"\" (Re)defines various Spark variable with some configuration changes to improve performances by enabling Arrow This has to be done - Before launching a SparkCOntext - Before importing Koalas Those two points are being taken care on this function. If a SparkSession already exists, it will copy its configuration before creating a new one Returns ------- Tuple of - A SparkSession - The associated SparkContext - The associated ``sql`` object to run SQL queries \"\"\" # Check if a spark Session is up global spark , sc , sql spark = SparkSession . builder . getOrCreate () sc = spark . sparkContext if quiet_spark : sc . setLogLevel ( \"ERROR\" ) conf = sc . getConf () # Synchronizing TimeZone tz = os . environ . get ( \"TZ\" , \"UTC\" ) os . environ [ \"TZ\" ] = tz time . tzset () to_add_conf . extend ( [ ( \"spark.app.name\" , f \" { os . environ . get ( 'USER' ) } _ { app_name } _scikit\" ), ( \"spark.sql.session.timeZone\" , tz ), ( \"spark.sql.execution.arrow.enabled\" , \"true\" ), ( \"spark.sql.execution.arrow.pyspark.enabled\" , \"true\" ), ] ) for key , value in to_add_conf : conf . set ( key , value ) # Stopping context to add necessary env variables sc . stop () spark . stop () set_env_variables () spark = SparkSession . builder . enableHiveSupport () . config ( conf = conf ) . getOrCreate () sc = spark . sparkContext if quiet_spark : sc . setLogLevel ( \"ERROR\" ) sql = spark . sql koalas_options () return spark , sc , sql","title":"improve_performances()"},{"location":"reference/io/omop_teva_default_config/","text":"eds_scikit.io.omop_teva_default_config","title":"omop_teva_default_config"},{"location":"reference/io/omop_teva_default_config/#eds_scikitioomop_teva_default_config","text":"","title":"eds_scikit.io.omop_teva_default_config"},{"location":"reference/io/postgres/","text":"eds_scikit.io.postgres PostgresData PostgresData ( dbname : Optional [ str ] = None , schema : Optional [ str ] = None , user : Optional [ str ] = None , host : Optional [ str ] = None , port : Optional [ int ] = None ) Bases: BaseData Source code in eds_scikit/io/postgres.py 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def __init__ ( self , dbname : Optional [ str ] = None , schema : Optional [ str ] = None , user : Optional [ str ] = None , host : Optional [ str ] = None , port : Optional [ int ] = None , ): ( self . host , self . port , self . dbname , self . user , ) = self . _find_matching_pgpass_params ( host , port , dbname , user ) self . schema = schema read_sql read_sql ( sql_query : str , ** kwargs ) -> pd . DataFrame Execute pandas.read_sql() on the database. PARAMETER DESCRIPTION sql_query SQL query (postgres flavor) TYPE: str **kwargs additional arguments passed to pandas.read_sql() DEFAULT: {} RETURNS DESCRIPTION df TYPE: pandas . DataFrame Source code in eds_scikit/io/postgres.py 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 def read_sql ( self , sql_query : str , ** kwargs ) -> pd . DataFrame : \"\"\"Execute pandas.read_sql() on the database. Parameters ---------- sql_query : str SQL query (postgres flavor) **kwargs additional arguments passed to pandas.read_sql() Returns ------- df : pandas.DataFrame \"\"\" connection_infos = { param : getattr ( self , param ) for param in [ \"host\" , \"port\" , \"dbname\" , \"user\" ] } connection_infos [ \"password\" ] = pgpasslib . getpass ( ** connection_infos ) connection = pg . connect ( ** connection_infos ) if self . schema : connection . cursor () . execute ( f \"SET SCHEMA ' { self . schema } '\" ) df = pd . read_sql ( sql_query , con = connection , ** kwargs ) connection . close () return df","title":"postgres"},{"location":"reference/io/postgres/#eds_scikitiopostgres","text":"","title":"eds_scikit.io.postgres"},{"location":"reference/io/postgres/#eds_scikit.io.postgres.PostgresData","text":"PostgresData ( dbname : Optional [ str ] = None , schema : Optional [ str ] = None , user : Optional [ str ] = None , host : Optional [ str ] = None , port : Optional [ int ] = None ) Bases: BaseData Source code in eds_scikit/io/postgres.py 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 def __init__ ( self , dbname : Optional [ str ] = None , schema : Optional [ str ] = None , user : Optional [ str ] = None , host : Optional [ str ] = None , port : Optional [ int ] = None , ): ( self . host , self . port , self . dbname , self . user , ) = self . _find_matching_pgpass_params ( host , port , dbname , user ) self . schema = schema","title":"PostgresData"},{"location":"reference/io/postgres/#eds_scikit.io.postgres.PostgresData.read_sql","text":"read_sql ( sql_query : str , ** kwargs ) -> pd . DataFrame Execute pandas.read_sql() on the database. PARAMETER DESCRIPTION sql_query SQL query (postgres flavor) TYPE: str **kwargs additional arguments passed to pandas.read_sql() DEFAULT: {} RETURNS DESCRIPTION df TYPE: pandas . DataFrame Source code in eds_scikit/io/postgres.py 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 def read_sql ( self , sql_query : str , ** kwargs ) -> pd . DataFrame : \"\"\"Execute pandas.read_sql() on the database. Parameters ---------- sql_query : str SQL query (postgres flavor) **kwargs additional arguments passed to pandas.read_sql() Returns ------- df : pandas.DataFrame \"\"\" connection_infos = { param : getattr ( self , param ) for param in [ \"host\" , \"port\" , \"dbname\" , \"user\" ] } connection_infos [ \"password\" ] = pgpasslib . getpass ( ** connection_infos ) connection = pg . connect ( ** connection_infos ) if self . schema : connection . cursor () . execute ( f \"SET SCHEMA ' { self . schema } '\" ) df = pd . read_sql ( sql_query , con = connection , ** kwargs ) connection . close () return df","title":"read_sql()"},{"location":"reference/io/settings/","text":"eds_scikit.io.settings default_tables_to_save module-attribute default_tables_to_save = [ 'person' , 'visit_occurrence' , 'visit_detail' , 'condition_occurrence' , 'procedure_occurrence' , 'care_site' , 'concept' ] The default tables loaded when instanciating a HiveData or a PostgresData tables_to_load module-attribute tables_to_load = { 'person' : [ 'person_id' , 'location_id' , 'year_of_birth' , 'month_of_birth' , 'day_of_birth' , 'birth_datetime' , 'death_datetime' , 'gender_source_value' , 'gender_source_concept_id' , 'cdm_source' ], 'visit_occurrence' : [ 'visit_occurrence_id' , 'person_id' , 'visit_occurrence_source_value' , 'preceding_visit_occurrence_id' , 'care_site_id' , 'visit_start_datetime' , 'visit_end_datetime' , 'visit_source_value' , 'visit_source_concept_id' , 'visit_type_source_value' , 'visit_type_source_concept_id' , 'admitted_from_source_value' , 'admitted_from_source_concept_id' , 'discharge_to_source_value' , 'discharge_to_source_concept_id' , 'row_status_source_value' , 'stay_source_value' , 'stay_source_concept_id' , 'cdm_source' ], 'care_site' : [ 'care_site_id' , 'care_site_source_value' , 'care_site_name' , 'care_site_short_name' , 'place_of_service_source_value' , 'care_site_type_source_value' , 'valid_start_date' , 'valid_end_date' ], 'visit_detail' : [ 'visit_detail_id' , 'visit_occurrence_id' , 'person_id' , 'preceding_visit_detail_id' , 'visit_detail_parent_id' , 'care_site_id' , 'visit_detail_start_date' , 'visit_detail_start_datetime' , 'visit_detail_end_date' , 'visit_detail_end_datetime' , 'visit_detail_source_value' , 'visit_detail_source_concept_id' , 'visit_detail_type_source_value' , 'visit_detail_type_source_concept_id' , 'admitted_from_source_value' , 'admitted_from_source_concept_id' , 'discharge_to_source_value' , 'discharge_to_source_concept_id' , 'cdm_source' ], 'condition_occurrence' : [ 'condition_occurrence_id' , 'person_id' , 'visit_occurrence_id' , 'visit_detail_id' , 'condition_start_datetime' , 'condition_source_value' , 'condition_source_concept_id' , 'condition_status_source_value' , 'condition_status_source_concept_id' , 'cdm_source' ], 'procedure_occurrence' : [ 'procedure_occurrence_id' , 'person_id' , 'visit_occurrence_id' , 'visit_detail_id' , 'procedure_datetime' , 'procedure_source_value' , 'procedure_source_concept_id' , 'cdm_source' ], 'concept' : [ 'concept_id' , 'concept_name' , 'domain_id' , 'vocabulary_id' , 'concept_class_id' , 'standard_concept' , 'concept_code' , 'valid_start_date' , 'valid_end_date' , 'invalid_reason' ]} The default columns loaded when instanciating a HiveData or a PostgresData measurement_config module-attribute measurement_config = dict ( standard_terminologies = [ 'LOINC' , 'AnaBio' , 'ANABIO' , 'ANALYSES_LABORATOIRE' ], standard_concept_regex = { 'LOINC' : '[0-9]{2,5}[-][0-9]' , 'AnaBio' : '[A-Z][0-9] {4} ' , 'ANABIO' : '[A-Z][0-9] {4} ' }, source_terminologies = { 'ANALYSES_LABORATOIRE' : 'Analyses Laboratoire' , 'GLIMS_ANABIO' : 'GLIMS.{0,20}Anabio' , 'GLIMS_LOINC' : 'GLIMS.{0,20}LOINC' , 'ITM_ANABIO' : 'ITM - ANABIO' , 'ITM_LOINC' : 'ITM - LOINC' }, mapping = [( 'ANALYSES_LABORATOIRE' , 'GLIMS_ANABIO' , 'Maps to' ), ( 'ANALYSES_LABORATOIRE' , 'GLIMS_LOINC' , 'Maps to' ), ( 'GLIMS_ANABIO' , 'ITM_ANABIO' , 'Mapped from' ), ( 'ITM_ANABIO' , 'ITM_LOINC' , 'Maps to' )]) AP-HP specific configuration. ITM and GLIMS do not share the same ANABIO-to-LOINC mapping. ITM referential is more reliable but covers less ANABIO codes the GLIMS referential.","title":"settings"},{"location":"reference/io/settings/#eds_scikitiosettings","text":"","title":"eds_scikit.io.settings"},{"location":"reference/io/settings/#eds_scikit.io.settings.default_tables_to_save","text":"default_tables_to_save = [ 'person' , 'visit_occurrence' , 'visit_detail' , 'condition_occurrence' , 'procedure_occurrence' , 'care_site' , 'concept' ] The default tables loaded when instanciating a HiveData or a PostgresData","title":"default_tables_to_save"},{"location":"reference/io/settings/#eds_scikit.io.settings.tables_to_load","text":"tables_to_load = { 'person' : [ 'person_id' , 'location_id' , 'year_of_birth' , 'month_of_birth' , 'day_of_birth' , 'birth_datetime' , 'death_datetime' , 'gender_source_value' , 'gender_source_concept_id' , 'cdm_source' ], 'visit_occurrence' : [ 'visit_occurrence_id' , 'person_id' , 'visit_occurrence_source_value' , 'preceding_visit_occurrence_id' , 'care_site_id' , 'visit_start_datetime' , 'visit_end_datetime' , 'visit_source_value' , 'visit_source_concept_id' , 'visit_type_source_value' , 'visit_type_source_concept_id' , 'admitted_from_source_value' , 'admitted_from_source_concept_id' , 'discharge_to_source_value' , 'discharge_to_source_concept_id' , 'row_status_source_value' , 'stay_source_value' , 'stay_source_concept_id' , 'cdm_source' ], 'care_site' : [ 'care_site_id' , 'care_site_source_value' , 'care_site_name' , 'care_site_short_name' , 'place_of_service_source_value' , 'care_site_type_source_value' , 'valid_start_date' , 'valid_end_date' ], 'visit_detail' : [ 'visit_detail_id' , 'visit_occurrence_id' , 'person_id' , 'preceding_visit_detail_id' , 'visit_detail_parent_id' , 'care_site_id' , 'visit_detail_start_date' , 'visit_detail_start_datetime' , 'visit_detail_end_date' , 'visit_detail_end_datetime' , 'visit_detail_source_value' , 'visit_detail_source_concept_id' , 'visit_detail_type_source_value' , 'visit_detail_type_source_concept_id' , 'admitted_from_source_value' , 'admitted_from_source_concept_id' , 'discharge_to_source_value' , 'discharge_to_source_concept_id' , 'cdm_source' ], 'condition_occurrence' : [ 'condition_occurrence_id' , 'person_id' , 'visit_occurrence_id' , 'visit_detail_id' , 'condition_start_datetime' , 'condition_source_value' , 'condition_source_concept_id' , 'condition_status_source_value' , 'condition_status_source_concept_id' , 'cdm_source' ], 'procedure_occurrence' : [ 'procedure_occurrence_id' , 'person_id' , 'visit_occurrence_id' , 'visit_detail_id' , 'procedure_datetime' , 'procedure_source_value' , 'procedure_source_concept_id' , 'cdm_source' ], 'concept' : [ 'concept_id' , 'concept_name' , 'domain_id' , 'vocabulary_id' , 'concept_class_id' , 'standard_concept' , 'concept_code' , 'valid_start_date' , 'valid_end_date' , 'invalid_reason' ]} The default columns loaded when instanciating a HiveData or a PostgresData","title":"tables_to_load"},{"location":"reference/io/settings/#eds_scikit.io.settings.measurement_config","text":"measurement_config = dict ( standard_terminologies = [ 'LOINC' , 'AnaBio' , 'ANABIO' , 'ANALYSES_LABORATOIRE' ], standard_concept_regex = { 'LOINC' : '[0-9]{2,5}[-][0-9]' , 'AnaBio' : '[A-Z][0-9] {4} ' , 'ANABIO' : '[A-Z][0-9] {4} ' }, source_terminologies = { 'ANALYSES_LABORATOIRE' : 'Analyses Laboratoire' , 'GLIMS_ANABIO' : 'GLIMS.{0,20}Anabio' , 'GLIMS_LOINC' : 'GLIMS.{0,20}LOINC' , 'ITM_ANABIO' : 'ITM - ANABIO' , 'ITM_LOINC' : 'ITM - LOINC' }, mapping = [( 'ANALYSES_LABORATOIRE' , 'GLIMS_ANABIO' , 'Maps to' ), ( 'ANALYSES_LABORATOIRE' , 'GLIMS_LOINC' , 'Maps to' ), ( 'GLIMS_ANABIO' , 'ITM_ANABIO' , 'Mapped from' ), ( 'ITM_ANABIO' , 'ITM_LOINC' , 'Maps to' )]) AP-HP specific configuration. ITM and GLIMS do not share the same ANABIO-to-LOINC mapping. ITM referential is more reliable but covers less ANABIO codes the GLIMS referential.","title":"measurement_config"},{"location":"reference/period/","text":"eds_scikit.period tagging tagging ( tag_to_df : DataFrame , tag_from_df : DataFrame , concept_to_tag : str , tag_to_date_cols : List [ str ] = [ 't_start' , 't_end' ], tag_from_date_cols : List [ str ] = [ 't_start' , 't_end' ], algo : str = 'intersection' ) -> DataFrame PARAMETER DESCRIPTION tag_to_df TYPE: DataFrame tag_from_df TYPE: DataFrame concept_to_tag TYPE: str tag_to_date_cols TYPE: List [ str ], optional DEFAULT: ['t_start', 't_end'] tag_from_date_cols TYPE: List [ str ], optional DEFAULT: ['t_start', 't_end'] algo TYPE: str , optional DEFAULT: 'intersection' RETURNS DESCRIPTION DataFrame Source code in eds_scikit/period/tagging_functions.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 def tagging ( tag_to_df : DataFrame , tag_from_df : DataFrame , concept_to_tag : str , tag_to_date_cols : List [ str ] = [ \"t_start\" , \"t_end\" ], tag_from_date_cols : List [ str ] = [ \"t_start\" , \"t_end\" ], algo : str = \"intersection\" , ) -> DataFrame : \"\"\" Parameters ---------- tag_to_df : DataFrame tag_from_df : DataFrame concept_to_tag : str tag_to_date_cols : List[str], optional tag_from_date_cols : List[str], optional algo : str, optional Returns ------- DataFrame \"\"\" framework = get_framework ( tag_to_df ) tag_to_df = tag_to_df . assign ( event_id = tag_to_df . index ) tag_from = tag_from_df . loc [ tag_from_df . concept == concept_to_tag , [ \"person_id\" , \"value\" ] + [ \"t_start\" , \"t_end\" ], ] tmp = ( tag_to_df . rename ( columns = { tag_to_date_cols [ 0 ]: \"t_start_x\" , tag_to_date_cols [ 1 ]: \"t_end_x\" } ) . merge ( tag_from . rename ( columns = { tag_from_date_cols [ 0 ]: \"t_start_y\" , tag_from_date_cols [ 1 ]: \"t_end_y\" , } ), on = \"person_id\" , how = \"left\" , ) . dropna ( subset = [ \"t_start_x\" , \"t_end_x\" , \"t_start_y\" , \"t_end_y\" ]) ) if len ( tmp ) == 0 : # TODO: is this necessary ? logger . warning ( \"No matching were found between the 2 DataFrames\" ) return framework . DataFrame ( columns = [ \"person_id\" , \"t_start\" , \"t_end\" , \"concept\" , \"value\" ] ) tmp [ \"tag\" ] = compare_intervals ( tmp [ \"t_start_x\" ], tmp [ \"t_end_x\" ], tmp [ \"t_start_y\" ], tmp [ \"t_end_y\" ], algo = algo , ) value_col = ( \"value_y\" if (( \"value\" in tag_to_df . columns ) and ( \"value\" in tag_from_df . columns )) else \"value\" ) tags = ( tmp . groupby ([ \"event_id\" , value_col ]) . tag . any () . unstack () . fillna ( False ) . reset_index () ) tags = tag_to_df [[ \"event_id\" ]] . merge ( tags , on = \"event_id\" , how = \"left\" ) . fillna ( False ) tags = tag_to_df . merge ( tags , on = \"event_id\" , how = \"left\" ) . drop ( columns = \"event_id\" ) return tags","title":"`eds_scikit.period`"},{"location":"reference/period/#eds_scikitperiod","text":"","title":"eds_scikit.period"},{"location":"reference/period/#eds_scikit.period.tagging","text":"tagging ( tag_to_df : DataFrame , tag_from_df : DataFrame , concept_to_tag : str , tag_to_date_cols : List [ str ] = [ 't_start' , 't_end' ], tag_from_date_cols : List [ str ] = [ 't_start' , 't_end' ], algo : str = 'intersection' ) -> DataFrame PARAMETER DESCRIPTION tag_to_df TYPE: DataFrame tag_from_df TYPE: DataFrame concept_to_tag TYPE: str tag_to_date_cols TYPE: List [ str ], optional DEFAULT: ['t_start', 't_end'] tag_from_date_cols TYPE: List [ str ], optional DEFAULT: ['t_start', 't_end'] algo TYPE: str , optional DEFAULT: 'intersection' RETURNS DESCRIPTION DataFrame Source code in eds_scikit/period/tagging_functions.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 def tagging ( tag_to_df : DataFrame , tag_from_df : DataFrame , concept_to_tag : str , tag_to_date_cols : List [ str ] = [ \"t_start\" , \"t_end\" ], tag_from_date_cols : List [ str ] = [ \"t_start\" , \"t_end\" ], algo : str = \"intersection\" , ) -> DataFrame : \"\"\" Parameters ---------- tag_to_df : DataFrame tag_from_df : DataFrame concept_to_tag : str tag_to_date_cols : List[str], optional tag_from_date_cols : List[str], optional algo : str, optional Returns ------- DataFrame \"\"\" framework = get_framework ( tag_to_df ) tag_to_df = tag_to_df . assign ( event_id = tag_to_df . index ) tag_from = tag_from_df . loc [ tag_from_df . concept == concept_to_tag , [ \"person_id\" , \"value\" ] + [ \"t_start\" , \"t_end\" ], ] tmp = ( tag_to_df . rename ( columns = { tag_to_date_cols [ 0 ]: \"t_start_x\" , tag_to_date_cols [ 1 ]: \"t_end_x\" } ) . merge ( tag_from . rename ( columns = { tag_from_date_cols [ 0 ]: \"t_start_y\" , tag_from_date_cols [ 1 ]: \"t_end_y\" , } ), on = \"person_id\" , how = \"left\" , ) . dropna ( subset = [ \"t_start_x\" , \"t_end_x\" , \"t_start_y\" , \"t_end_y\" ]) ) if len ( tmp ) == 0 : # TODO: is this necessary ? logger . warning ( \"No matching were found between the 2 DataFrames\" ) return framework . DataFrame ( columns = [ \"person_id\" , \"t_start\" , \"t_end\" , \"concept\" , \"value\" ] ) tmp [ \"tag\" ] = compare_intervals ( tmp [ \"t_start_x\" ], tmp [ \"t_end_x\" ], tmp [ \"t_start_y\" ], tmp [ \"t_end_y\" ], algo = algo , ) value_col = ( \"value_y\" if (( \"value\" in tag_to_df . columns ) and ( \"value\" in tag_from_df . columns )) else \"value\" ) tags = ( tmp . groupby ([ \"event_id\" , value_col ]) . tag . any () . unstack () . fillna ( False ) . reset_index () ) tags = tag_to_df [[ \"event_id\" ]] . merge ( tags , on = \"event_id\" , how = \"left\" ) . fillna ( False ) tags = tag_to_df . merge ( tags , on = \"event_id\" , how = \"left\" ) . drop ( columns = \"event_id\" ) return tags","title":"tagging()"},{"location":"reference/period/stays/","text":"eds_scikit.period.stays cleaning cleaning ( vo , long_stay_threshold : timedelta , long_stay_filtering : Union [ str , None ], remove_deleted_visits : bool , open_stay_end_datetime : datetime ) -> Tuple [ DataFrame , DataFrame ] Preprocessing of visits before merging them in stays. The function will split the input vo DataFrame into 2, one that should undergo the merging procedure, and one that shouldn't. Depending on the input parameters, 3 type of visits can be prevented to undergo the merging procedure: Too long visits Too long AND unclosed visits Removed visits See the merge_visits() function for details of the parameters Source code in eds_scikit/period/stays.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 def cleaning ( vo , long_stay_threshold : timedelta , long_stay_filtering : Union [ str , None ], remove_deleted_visits : bool , open_stay_end_datetime : datetime , ) -> Tuple [ DataFrame , DataFrame ]: \"\"\" Preprocessing of visits before merging them in stays. The function will split the input `vo` DataFrame into 2, one that should undergo the merging procedure, and one that shouldn't. Depending on the input parameters, 3 type of visits can be prevented to undergo the merging procedure: - Too long visits - Too long AND unclosed visits - Removed visits See the [merge_visits()][eds_scikit.period.stays.merge_visits] function for details of the parameters \"\"\" LONG_STAY_FILTERING_VALUES = [ \"all\" , \"open\" , None ] DELETED_ROW_VALUE = \"supprim\u00e9\" if long_stay_filtering not in LONG_STAY_FILTERING_VALUES : raise ValueError ( f \"\"\"Unknown value for `long_stay_filtering`. Accepted values are { LONG_STAY_FILTERING_VALUES } \"\"\" ) if remove_deleted_visits : deleted_visit_mask = vo [ \"row_status_source_value\" ] == DELETED_ROW_VALUE no_starting_date_mask = vo [ \"visit_start_datetime\" ] . isna () no_ending_date_mask = vo [ \"visit_end_datetime\" ] . isna () vo [ \"visit_end_datetime_calc\" ] = open_stay_end_datetime # Cannot use fillna() with datetime in Koalas vo [ \"visit_end_datetime_calc\" ] = vo [ \"visit_end_datetime\" ] . combine_first ( vo [ \"visit_end_datetime_calc\" ] ) too_long_stays_mask = ( substract_datetime ( vo [ \"visit_end_datetime_calc\" ], vo [ \"visit_start_datetime\" ]) >= long_stay_threshold . total_seconds () ) mask = no_starting_date_mask if long_stay_filtering == \"all\" : mask = mask | too_long_stays_mask elif long_stay_filtering == \"open\" : mask = mask | ( too_long_stays_mask & no_ending_date_mask ) if remove_deleted_visits : mask = ( mask ) | deleted_visit_mask return vo [ ~ mask ], vo [ mask ] merge_visits merge_visits ( vo : DataFrame , remove_deleted_visits : bool = True , long_stay_threshold : timedelta = timedelta ( days = 365 ), long_stay_filtering : Optional [ str ] = 'all' , open_stay_end_datetime : Optional [ datetime ] = None , max_timedelta : timedelta = timedelta ( days = 2 ), merge_different_hospitals : bool = False , merge_different_source_values : Union [ bool , List [ str ]] = [ 'hospitalis\u00e9s' , 'urgence' ]) -> DataFrame Merge \"close\" visit occurrences to consider them as a single stay by adding a STAY_ID and CONTIGUOUS_STAY_ID columns to the DataFrame. The value of these columns will be the visit_occurrence_id of the first (meaning the oldest) visit of the stay. From a temporal point of view, we consider that two visits belong to the same stay if either: They intersect The time difference between the end of the most recent and the start of the oldest is lower than max_timedelta (for STAY_ID ) or 0 (for CONTIGUOUS_STAY_ID ) Additionally, other parameters are available to further adjust the merging rules. See below. PARAMETER DESCRIPTION vo The visit_occurrence DataFrame, with at least the following columns: - visit_occurrence_id - person_id - visit_start_datetime_calc (from preprocessing) - visit_end_datetime (from preprocessing) Depending on the input parameters, additional columns may be required: - care_site_id (if merge_different_hospitals == True ) - visit_source_value (if merge_different_source_values != False ) - row_status_source_value (if remove_deleted_visits= True ) TYPE: DataFrame remove_deleted_visits Wether to remove deleted visits from the merging procedure. Deleted visits are extracted via the row_status_source_value column TYPE: bool DEFAULT: True long_stay_filtering Filtering method for long and/or non-closed visits. First of all, visits with no starting date won't be merged with any other visit, and visits with no ending date will have a temporary \"theoretical\" ending date set by datetime.now() . That being said, some visits are sometimes years long because they weren't closed at time. If other visits occurred during this timespan, they could be all merged into the same stay. To avoid this issue, filtering can be done depending on the long_stay_filtering value: all : All long stays (closed and open) are removed from the merging procedure open : Only long open stays are removed from the merging procedure None : No filtering is done on long visits Long stays are determined by the long_stay_threshold value. TYPE: Optional [ str ] DEFAULT: 'all' long_stay_threshold Minimum visit duration value to consider a visit as candidate for \"long visits filtering\" TYPE: timedelta DEFAULT: timedelta(days=365) open_stay_end_datetime Datetime to use in order to fill the visit_end_datetime of open visits. This is necessary in order to compute stay duration and to filter long stays. If not provided datetime.now() will be used. You might provide the extraction date of your data here. TYPE: Optional [ datetime ] DEFAULT: None max_timedelta Maximum time difference between the end of a visit and the start of another to consider them as belonging to the same stay. This duration is internally converted in seconds before comparing. Thus, if you want e.g. to merge visits happening in two consecutive days, you should use timedelta(days=2) and NOT timedelta(days=1) in order to take into account extreme cases such as an first visit ending on Monday at 00h01 AM and another one starting at 23h59 PM on Tuesday TYPE: timedelta DEFAULT: timedelta(days=2) merge_different_hospitals Wether to allow visits occurring in different hospitals to be merged into a same stay TYPE: bool DEFAULT: False merge_different_source_values Wether to allow visits with different visit_source_value to be merged into a same stay. Values can be: True : the visit_source_value isn't taken into account for the merging False : only visits with the same visit_source_value can be merged into a same stay List[str] : only visits which visit_source_value is in the provided list can be merged together. Warning : You should avoid merging visits where visit_source_value == \"hospitalisation incompl\u00e8te\" , because those stays are often never closed. TYPE: Union [ bool , List [ str ]] DEFAULT: ['hospitalis\u00e9s', 'urgence'] RETURNS DESCRIPTION vo Visit occurrence DataFrame with additional STAY_ID column TYPE: DataFrame Examples: >>> import pandas as pd >>> from datetime import datetime , timedelta >>> data = { 1 : ['A', 999, datetime(2021,1,1), datetime(2021,1,5), 'hospitalis\u00e9s'], 2 : ['B', 999, datetime(2021,1,4), datetime(2021,1,8), 'hospitalis\u00e9s'], 3 : ['C', 999, datetime(2021,1,12), datetime(2021,1,18), 'hospitalis\u00e9s'], 4 : ['D', 999, datetime(2021,1,13), datetime(2021,1,14), 'urgence'], 5 : ['E', 999, datetime(2021,1,19), datetime(2021,1,21), 'hospitalis\u00e9s'], 6 : ['F', 999, datetime(2021,1,25), datetime(2021,1,27), 'hospitalis\u00e9s'], 7 : ['G', 999, datetime(2017,1,1), None, \"hospitalis\u00e9s\"] } >>> vo = pd . DataFrame . from_dict ( data, orient=\"index\", columns=[ \"visit_occurrence_id\", \"person_id\", \"visit_start_datetime\", \"visit_end_datetime\", \"visit_source_value\", ], ) >>> vo visit_occurrence_id person_id visit_start_datetime visit_end_datetime visit_source_value 1 A 999 2021-01-01 2021-01-05 hospitalis\u00e9s 2 B 999 2021-01-04 2021-01-08 hospitalis\u00e9s 3 C 999 2021-01-12 2021-01-18 hospitalis\u00e9s 4 D 999 2021-01-13 2021-01-14 urgence 5 E 999 2021-01-19 2021-01-21 hospitalis\u00e9s 6 F 999 2021-01-25 2021-01-27 hospitalis\u00e9s 7 G 999 2017-01-01 NaT hospitalis\u00e9s >>> vo = merge_visits ( vo, remove_deleted_visits=True, long_stay_threshold=timedelta(days=365), long_stay_filtering=\"all\", max_timedelta=timedelta(hours=24), merge_different_hospitals=False, merge_different_source_values=[\"hospitalis\u00e9s\", \"urgence\"], ) >>> vo visit_occurrence_id person_id visit_start_datetime visit_end_datetime visit_source_value STAY_ID CONTIGUOUS_STAY_ID 1 A 999 2021-01-01 2021-01-05 hospitalis\u00e9s A A 2 B 999 2021-01-04 2021-01-08 hospitalis\u00e9s A A 3 C 999 2021-01-12 2021-01-18 hospitalis\u00e9s C C 4 D 999 2021-01-13 2021-01-14 urgence C C 5 E 999 2021-01-19 2021-01-21 hospitalis\u00e9s C E 6 F 999 2021-01-25 2021-01-27 hospitalis\u00e9s F F 7 G 999 2017-01-01 NaT hospitalis\u00e9s G G Source code in eds_scikit/period/stays.py 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 @concept_checker ( concepts = [ \"STAY_ID\" , \"CONTIGUOUS_STAY_ID\" ]) def merge_visits ( vo : DataFrame , remove_deleted_visits : bool = True , long_stay_threshold : timedelta = timedelta ( days = 365 ), long_stay_filtering : Optional [ str ] = \"all\" , open_stay_end_datetime : Optional [ datetime ] = None , max_timedelta : timedelta = timedelta ( days = 2 ), merge_different_hospitals : bool = False , merge_different_source_values : Union [ bool , List [ str ]] = [ \"hospitalis\u00e9s\" , \"urgence\" ], ) -> DataFrame : \"\"\" Merge \"close\" visit occurrences to consider them as a single stay by adding a ``STAY_ID`` and ``CONTIGUOUS_STAY_ID`` columns to the DataFrame. The value of these columns will be the `visit_occurrence_id` of the first (meaning the oldest) visit of the stay. From a temporal point of view, we consider that two visits belong to the same stay if either: - They intersect - The time difference between the end of the most recent and the start of the oldest is lower than ``max_timedelta`` (for ``STAY_ID``) or 0 (for ``CONTIGUOUS_STAY_ID``) Additionally, other parameters are available to further adjust the merging rules. See below. Parameters ---------- vo : DataFrame The ``visit_occurrence`` DataFrame, with at least the following columns: - visit_occurrence_id - person_id - visit_start_datetime_calc (from preprocessing) - visit_end_datetime (from preprocessing) Depending on the input parameters, additional columns may be required: - care_site_id (if ``merge_different_hospitals == True``) - visit_source_value (if ``merge_different_source_values != False``) - row_status_source_value (if ``remove_deleted_visits= True``) remove_deleted_visits: bool Wether to remove deleted visits from the merging procedure. Deleted visits are extracted via the `row_status_source_value` column long_stay_filtering : Optional[str] Filtering method for long and/or non-closed visits. First of all, visits with no starting date won't be merged with any other visit, and visits with no ending date will have a temporary \"theoretical\" ending date set by ``datetime.now()``. That being said, some visits are sometimes years long because they weren't closed at time. If other visits occurred during this timespan, they could be all merged into the same stay. To avoid this issue, filtering can be done depending on the ``long_stay_filtering`` value: - ``all``: All long stays (closed and open) are removed from the merging procedure - ``open``: Only long open stays are removed from the merging procedure - ``None``: No filtering is done on long visits Long stays are determined by the ``long_stay_threshold`` value. long_stay_threshold : timedelta Minimum visit duration value to consider a visit as candidate for \"long visits filtering\" open_stay_end_datetime: Optional[datetime] Datetime to use in order to fill the `visit_end_datetime` of open visits. This is necessary in order to compute stay duration and to filter long stays. If not provided `datetime.now()` will be used. You might provide the extraction date of your data here. max_timedelta : timedelta Maximum time difference between the end of a visit and the start of another to consider them as belonging to the same stay. This duration is internally converted in seconds before comparing. Thus, if you want e.g. to merge visits happening in two consecutive days, you should use `timedelta(days=2)` and NOT `timedelta(days=1)` in order to take into account extreme cases such as an first visit ending on Monday at 00h01 AM and another one starting at 23h59 PM on Tuesday merge_different_hospitals : bool Wether to allow visits occurring in different hospitals to be merged into a same stay merge_different_source_values : Union[bool, List[str]] Wether to allow visits with different `visit_source_value` to be merged into a same stay. Values can be: - `True`: the `visit_source_value` isn't taken into account for the merging - `False`: only visits with the same `visit_source_value` can be merged into a same stay - `List[str]`: only visits which `visit_source_value` is in the provided list can be merged together. **Warning**: You should avoid merging visits where `visit_source_value == \"hospitalisation incompl\u00e8te\"`, because those stays are often never closed. Returns ------- vo : DataFrame Visit occurrence DataFrame with additional `STAY_ID` column Examples -------- >>> import pandas as pd >>> from datetime import datetime, timedelta >>> data = { 1 : ['A', 999, datetime(2021,1,1), datetime(2021,1,5), 'hospitalis\u00e9s'], 2 : ['B', 999, datetime(2021,1,4), datetime(2021,1,8), 'hospitalis\u00e9s'], 3 : ['C', 999, datetime(2021,1,12), datetime(2021,1,18), 'hospitalis\u00e9s'], 4 : ['D', 999, datetime(2021,1,13), datetime(2021,1,14), 'urgence'], 5 : ['E', 999, datetime(2021,1,19), datetime(2021,1,21), 'hospitalis\u00e9s'], 6 : ['F', 999, datetime(2021,1,25), datetime(2021,1,27), 'hospitalis\u00e9s'], 7 : ['G', 999, datetime(2017,1,1), None, \"hospitalis\u00e9s\"] } >>> vo = pd.DataFrame.from_dict( data, orient=\"index\", columns=[ \"visit_occurrence_id\", \"person_id\", \"visit_start_datetime\", \"visit_end_datetime\", \"visit_source_value\", ], ) >>> vo visit_occurrence_id person_id visit_start_datetime visit_end_datetime visit_source_value 1 A 999 2021-01-01 2021-01-05 hospitalis\u00e9s 2 B 999 2021-01-04 2021-01-08 hospitalis\u00e9s 3 C 999 2021-01-12 2021-01-18 hospitalis\u00e9s 4 D 999 2021-01-13 2021-01-14 urgence 5 E 999 2021-01-19 2021-01-21 hospitalis\u00e9s 6 F 999 2021-01-25 2021-01-27 hospitalis\u00e9s 7 G 999 2017-01-01 NaT hospitalis\u00e9s >>> vo = merge_visits( vo, remove_deleted_visits=True, long_stay_threshold=timedelta(days=365), long_stay_filtering=\"all\", max_timedelta=timedelta(hours=24), merge_different_hospitals=False, merge_different_source_values=[\"hospitalis\u00e9s\", \"urgence\"], ) >>> vo visit_occurrence_id person_id visit_start_datetime visit_end_datetime visit_source_value STAY_ID CONTIGUOUS_STAY_ID 1 A 999 2021-01-01 2021-01-05 hospitalis\u00e9s A A 2 B 999 2021-01-04 2021-01-08 hospitalis\u00e9s A A 3 C 999 2021-01-12 2021-01-18 hospitalis\u00e9s C C 4 D 999 2021-01-13 2021-01-14 urgence C C 5 E 999 2021-01-19 2021-01-21 hospitalis\u00e9s C E 6 F 999 2021-01-25 2021-01-27 hospitalis\u00e9s F F 7 G 999 2017-01-01 NaT hospitalis\u00e9s G G \"\"\" # Preprocessing vo_to_merge , vo_to_not_merge = cleaning ( vo , remove_deleted_visits = remove_deleted_visits , long_stay_threshold = long_stay_threshold , long_stay_filtering = long_stay_filtering , open_stay_end_datetime = open_stay_end_datetime if open_stay_end_datetime is not None else datetime . now (), ) fw = get_framework ( vo_to_merge ) grouping_keys = [ \"person_id\" ] if not merge_different_hospitals : grouping_keys . append ( \"care_site_id\" ) if not merge_different_source_values : grouping_keys . append ( \"visit_source_value\" ) elif type ( merge_different_source_values ) == list : tmp = fw . DataFrame ( data = dict ( visit_source_value = merge_different_source_values , grouped_visit_source_value = True , ) ) vo_to_merge = vo_to_merge . merge ( tmp , on = \"visit_source_value\" , how = \"left\" ) vo_to_merge [ \"grouped_visit_source_value\" ] = vo_to_merge [ \"grouped_visit_source_value\" ] . fillna ( value = False ) grouping_keys . append ( \"grouped_visit_source_value\" ) # Cartesian product merged = vo_to_merge . merge ( vo_to_merge , on = grouping_keys , how = \"inner\" , suffixes = ( \"_1\" , \"_2\" ), ) # Keeping only visits where 1 occurs before 2 merged = merged [ merged [ \"visit_start_datetime_1\" ] <= merged [ \"visit_start_datetime_2\" ] ] # Checking correct overlap th = max_timedelta . total_seconds () merged [ \"overlap\" ] = substract_datetime ( merged [ \"visit_start_datetime_2\" ], merged [ \"visit_end_datetime_calc_1\" ] ) merged [ \"to_merge\" ] = ( merged [ \"overlap\" ] <= th ) . astype ( int ) merged [ \"contiguous\" ] = ( merged [ \"overlap\" ] <= 0 ) . astype ( int ) def get_first ( merged : DataFrame , contiguous_only : bool = False , ) -> Tuple [ DataFrame , DataFrame ]: \"\"\" Returns a boolean flag for each visit, telling if the visit if the first of a stay. The ``contiguous_only`` parameter controls if the visits have to be contiguous in the stay \"\"\" flag_col = \"contiguous\" if contiguous_only else \"to_merge\" flag_name = \"1_is_first_contiguous\" if contiguous_only else \"1_is_first\" concept_prefix = \"CONTIGUOUS_\" if contiguous_only else \"\" # If the only previous visit to be merged with is itself, we found our first visit ! first_visits = merged . groupby ( \"visit_occurrence_id_2\" )[ flag_col ] . sum () == 1 first_visits . name = flag_name # Adding this boolean flag to the merged DataFrame merged = merged . merge ( first_visits , left_on = \"visit_occurrence_id_1\" , right_index = True , how = \"inner\" , ) # Getting the corresponding first visit first_visit = ( merged . sort_values ( by = [ flag_name , \"visit_start_datetime_1\" ], ascending = [ False , False ] ) . groupby ( \"visit_occurrence_id_2\" ) . first ()[ \"visit_occurrence_id_1\" ] . reset_index () . rename ( columns = { \"visit_occurrence_id_1\" : f \" { concept_prefix } STAY_ID\" , \"visit_occurrence_id_2\" : \"visit_occurrence_id\" , } ) ) return merged , first_visit merged , first_contiguous_visit = get_first ( merged , contiguous_only = True ) merged , first_visit = get_first ( merged , contiguous_only = False ) # Concatenating merge visits with previously discarded ones results = fw . concat ( [ vo_to_merge . merge ( first_visit , on = \"visit_occurrence_id\" , how = \"inner\" , ) . merge ( first_contiguous_visit , on = \"visit_occurrence_id\" , how = \"inner\" , ), vo_to_not_merge , ] ) # Adding visit_occurrence_id as STAY_ID and CONTIGUOUS_STAY_ID to discarded visits results [ \"STAY_ID\" ] = results [ \"STAY_ID\" ] . combine_first ( results [ \"visit_occurrence_id\" ] ) results [ \"CONTIGUOUS_STAY_ID\" ] = results [ \"CONTIGUOUS_STAY_ID\" ] . combine_first ( results [ \"visit_occurrence_id\" ] ) # Removing tmp columns vo = vo . drop ( columns = [ \"visit_end_datetime_calc\" ]) return results . drop ( columns = ( set ( results . columns ) & set ([ \"visit_end_datetime_calc\" , \"grouped_visit_source_value\" ]) ) ) get_stays_duration get_stays_duration ( vo : DataFrame , algo : str = 'sum_of_visits_duration' , missing_end_date_handling : str = 'fill' , open_stay_end_datetime : Optional [ datetime ] = None ) -> DataFrame Computes stay duration. The input DataFrame should contain the STAY_ID and CONTIGUOUS_STAY_ID columns, that can be computed via the merge_visits() function. PARAMETER DESCRIPTION vo visit occurrence DataFrame with the STAY_ID and CONTIGUOUS_STAY_ID columns TYPE: DataFrame algo Which algo to use for computing stay durations. Available values are: \"sum_of_visits_duration\" : The stay duration will correspond to the sum of each visit duration in the stay. \"visits_date_difference\" : The stay duration will correspond to the difference between the end date of the last visit and the start date of the first visit. TYPE: str DEFAULT: 'sum_of_visits_duration' missing_end_date_handling How to handle visits with no end date. Available values are: \"fill\" : Missing values are filled with datetime.now() \"coerce\" : Missing values are handled as such, so duration of stays with open visits will be NaN. TYPE: str DEFAULT: 'fill' open_stay_end_datetime Used if missing_end_date_handling == \"fill\" . Provide the datetime with which open stays should be ended. Leave to None in order to used datetime.now() TYPE: Optional [ datetime ] DEFAULT: None RETURNS DESCRIPTION DataFrame stay DataFrame with STAY_ID as index, and the following columns: \"person_id\" \"t_start\" : The start date of the first visit of the stay \"t_end\" : The end date of the last visit of the stay \"STAY_DURATION\" : The duration (in hours) of the stay RAISES DESCRIPTION MissingConceptError If STAY_ID and CONTIGUOUS_STAY_ID are not in the input columns. Source code in eds_scikit/period/stays.py 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 @algo_checker ( algos = [ \"sum_of_visits_duration\" , \"visits_date_difference\" ]) @concept_checker ( concepts = [ \"STAY_DURATION\" ], only_adds_concepts = False ) def get_stays_duration ( vo : DataFrame , algo : str = \"sum_of_visits_duration\" , missing_end_date_handling : str = \"fill\" , open_stay_end_datetime : Optional [ datetime ] = None , ) -> DataFrame : \"\"\" Computes stay duration. The input DataFrame should contain the `STAY_ID` and `CONTIGUOUS_STAY_ID` columns, that can be computed via the `merge_visits()` function. Parameters ---------- vo : DataFrame visit occurrence DataFrame with the `STAY_ID` and `CONTIGUOUS_STAY_ID` columns algo : str Which algo to use for computing stay durations. Available values are: - `\"sum_of_visits_duration\"`: The stay duration will correspond to the sum of each visit duration in the stay. - `\"visits_date_difference\"`: The stay duration will correspond to the difference between the end date of the last visit and the start date of the first visit. missing_end_date_handling : str How to handle visits with no end date. Available values are: - `\"fill\"`: Missing values are filled with `datetime.now()` - `\"coerce\"`: Missing values are handled as such, so duration of stays with open visits will be NaN. open_stay_end_datetime: Optional[datetime] Used if `missing_end_date_handling == \"fill\"`. Provide the `datetime` with which open stays should be ended. Leave to `None` in order to used `datetime.now()` Returns ------- DataFrame *stay* DataFrame with `STAY_ID` as index, and the following columns: - `\"person_id\"` - `\"t_start\"`: The start date of the first visit of the stay - `\"t_end\"`: The end date of the last visit of the stay - `\"STAY_DURATION\"`: The duration (in hours) of the stay Raises ------ MissingConceptError If `STAY_ID` and `CONTIGUOUS_STAY_ID` are not in the input columns. \"\"\" if set (( \"STAY_ID\" , \"CONTIGUOUS_STAY_ID\" )) - set ( vo . columns ): raise MissingConceptError ( df_name = \"visit_occurence\" , required_concepts = [ ( \"STAY_ID\" , \"should be computed via 'merge_visits'\" ), ( \"CONTIGUOUS_STAY_ID\" , \"should be computed via 'merge_visits'\" ), ], ) if missing_end_date_handling == \"fill\" : # Cannot use fillna() with datetime in Koalas if open_stay_end_datetime is None : open_stay_end_datetime = datetime . now () vo [ \"visit_end_datetime_calc\" ] = open_stay_end_datetime vo [ \"visit_end_datetime_calc\" ] = vo [ \"visit_end_datetime\" ] . combine_first ( vo [ \"visit_end_datetime_calc\" ] ) elif missing_end_date_handling == \"coerce\" : vo [ \"visit_end_datetime_calc\" ] = vo [ \"visit_end_datetime\" ] agg_dict = dict ( person_id = ( \"person_id\" , \"first\" ), t_start = ( \"visit_start_datetime\" , \"min\" ), t_end = ( \"visit_end_datetime_calc\" , \"max\" ), ) if algo == \"sum_of_visits_duration\" : agg_dict [ \"STAY_ID\" ] = ( \"STAY_ID\" , \"first\" ) contiguous_stays = vo . groupby ( \"CONTIGUOUS_STAY_ID\" ) . agg ( ** agg_dict ) contiguous_stays [ \"CONTIGUOUS_STAY_DURATION\" ] = substract_datetime ( contiguous_stays [ \"t_end\" ], contiguous_stays [ \"t_start\" ], out = \"hours\" ) agg_dict = dict ( person_id = ( \"person_id\" , \"first\" ), t_start = ( \"t_start\" , \"min\" ), t_end = ( \"t_end\" , \"max\" ), STAY_DURATION = ( \"CONTIGUOUS_STAY_DURATION\" , \"sum\" ), ) stays = contiguous_stays . groupby ( \"STAY_ID\" ) . agg ( ** agg_dict ) elif algo == \"visits_date_difference\" : stays = vo . groupby ( \"STAY_ID\" ) . agg ( ** agg_dict ) stays [ \"STAY_DURATION\" ] = substract_datetime ( stays [ \"t_end\" ], stays [ \"t_start\" ], out = \"hours\" ) if missing_end_date_handling == \"coerce\" : stays . loc [ stays [ \"t_end\" ] . isna (), \"STAY_DURATION\" ] = NaN return stays","title":"stays"},{"location":"reference/period/stays/#eds_scikitperiodstays","text":"","title":"eds_scikit.period.stays"},{"location":"reference/period/stays/#eds_scikit.period.stays.cleaning","text":"cleaning ( vo , long_stay_threshold : timedelta , long_stay_filtering : Union [ str , None ], remove_deleted_visits : bool , open_stay_end_datetime : datetime ) -> Tuple [ DataFrame , DataFrame ] Preprocessing of visits before merging them in stays. The function will split the input vo DataFrame into 2, one that should undergo the merging procedure, and one that shouldn't. Depending on the input parameters, 3 type of visits can be prevented to undergo the merging procedure: Too long visits Too long AND unclosed visits Removed visits See the merge_visits() function for details of the parameters Source code in eds_scikit/period/stays.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 def cleaning ( vo , long_stay_threshold : timedelta , long_stay_filtering : Union [ str , None ], remove_deleted_visits : bool , open_stay_end_datetime : datetime , ) -> Tuple [ DataFrame , DataFrame ]: \"\"\" Preprocessing of visits before merging them in stays. The function will split the input `vo` DataFrame into 2, one that should undergo the merging procedure, and one that shouldn't. Depending on the input parameters, 3 type of visits can be prevented to undergo the merging procedure: - Too long visits - Too long AND unclosed visits - Removed visits See the [merge_visits()][eds_scikit.period.stays.merge_visits] function for details of the parameters \"\"\" LONG_STAY_FILTERING_VALUES = [ \"all\" , \"open\" , None ] DELETED_ROW_VALUE = \"supprim\u00e9\" if long_stay_filtering not in LONG_STAY_FILTERING_VALUES : raise ValueError ( f \"\"\"Unknown value for `long_stay_filtering`. Accepted values are { LONG_STAY_FILTERING_VALUES } \"\"\" ) if remove_deleted_visits : deleted_visit_mask = vo [ \"row_status_source_value\" ] == DELETED_ROW_VALUE no_starting_date_mask = vo [ \"visit_start_datetime\" ] . isna () no_ending_date_mask = vo [ \"visit_end_datetime\" ] . isna () vo [ \"visit_end_datetime_calc\" ] = open_stay_end_datetime # Cannot use fillna() with datetime in Koalas vo [ \"visit_end_datetime_calc\" ] = vo [ \"visit_end_datetime\" ] . combine_first ( vo [ \"visit_end_datetime_calc\" ] ) too_long_stays_mask = ( substract_datetime ( vo [ \"visit_end_datetime_calc\" ], vo [ \"visit_start_datetime\" ]) >= long_stay_threshold . total_seconds () ) mask = no_starting_date_mask if long_stay_filtering == \"all\" : mask = mask | too_long_stays_mask elif long_stay_filtering == \"open\" : mask = mask | ( too_long_stays_mask & no_ending_date_mask ) if remove_deleted_visits : mask = ( mask ) | deleted_visit_mask return vo [ ~ mask ], vo [ mask ]","title":"cleaning()"},{"location":"reference/period/stays/#eds_scikit.period.stays.merge_visits","text":"merge_visits ( vo : DataFrame , remove_deleted_visits : bool = True , long_stay_threshold : timedelta = timedelta ( days = 365 ), long_stay_filtering : Optional [ str ] = 'all' , open_stay_end_datetime : Optional [ datetime ] = None , max_timedelta : timedelta = timedelta ( days = 2 ), merge_different_hospitals : bool = False , merge_different_source_values : Union [ bool , List [ str ]] = [ 'hospitalis\u00e9s' , 'urgence' ]) -> DataFrame Merge \"close\" visit occurrences to consider them as a single stay by adding a STAY_ID and CONTIGUOUS_STAY_ID columns to the DataFrame. The value of these columns will be the visit_occurrence_id of the first (meaning the oldest) visit of the stay. From a temporal point of view, we consider that two visits belong to the same stay if either: They intersect The time difference between the end of the most recent and the start of the oldest is lower than max_timedelta (for STAY_ID ) or 0 (for CONTIGUOUS_STAY_ID ) Additionally, other parameters are available to further adjust the merging rules. See below. PARAMETER DESCRIPTION vo The visit_occurrence DataFrame, with at least the following columns: - visit_occurrence_id - person_id - visit_start_datetime_calc (from preprocessing) - visit_end_datetime (from preprocessing) Depending on the input parameters, additional columns may be required: - care_site_id (if merge_different_hospitals == True ) - visit_source_value (if merge_different_source_values != False ) - row_status_source_value (if remove_deleted_visits= True ) TYPE: DataFrame remove_deleted_visits Wether to remove deleted visits from the merging procedure. Deleted visits are extracted via the row_status_source_value column TYPE: bool DEFAULT: True long_stay_filtering Filtering method for long and/or non-closed visits. First of all, visits with no starting date won't be merged with any other visit, and visits with no ending date will have a temporary \"theoretical\" ending date set by datetime.now() . That being said, some visits are sometimes years long because they weren't closed at time. If other visits occurred during this timespan, they could be all merged into the same stay. To avoid this issue, filtering can be done depending on the long_stay_filtering value: all : All long stays (closed and open) are removed from the merging procedure open : Only long open stays are removed from the merging procedure None : No filtering is done on long visits Long stays are determined by the long_stay_threshold value. TYPE: Optional [ str ] DEFAULT: 'all' long_stay_threshold Minimum visit duration value to consider a visit as candidate for \"long visits filtering\" TYPE: timedelta DEFAULT: timedelta(days=365) open_stay_end_datetime Datetime to use in order to fill the visit_end_datetime of open visits. This is necessary in order to compute stay duration and to filter long stays. If not provided datetime.now() will be used. You might provide the extraction date of your data here. TYPE: Optional [ datetime ] DEFAULT: None max_timedelta Maximum time difference between the end of a visit and the start of another to consider them as belonging to the same stay. This duration is internally converted in seconds before comparing. Thus, if you want e.g. to merge visits happening in two consecutive days, you should use timedelta(days=2) and NOT timedelta(days=1) in order to take into account extreme cases such as an first visit ending on Monday at 00h01 AM and another one starting at 23h59 PM on Tuesday TYPE: timedelta DEFAULT: timedelta(days=2) merge_different_hospitals Wether to allow visits occurring in different hospitals to be merged into a same stay TYPE: bool DEFAULT: False merge_different_source_values Wether to allow visits with different visit_source_value to be merged into a same stay. Values can be: True : the visit_source_value isn't taken into account for the merging False : only visits with the same visit_source_value can be merged into a same stay List[str] : only visits which visit_source_value is in the provided list can be merged together. Warning : You should avoid merging visits where visit_source_value == \"hospitalisation incompl\u00e8te\" , because those stays are often never closed. TYPE: Union [ bool , List [ str ]] DEFAULT: ['hospitalis\u00e9s', 'urgence'] RETURNS DESCRIPTION vo Visit occurrence DataFrame with additional STAY_ID column TYPE: DataFrame Examples: >>> import pandas as pd >>> from datetime import datetime , timedelta >>> data = { 1 : ['A', 999, datetime(2021,1,1), datetime(2021,1,5), 'hospitalis\u00e9s'], 2 : ['B', 999, datetime(2021,1,4), datetime(2021,1,8), 'hospitalis\u00e9s'], 3 : ['C', 999, datetime(2021,1,12), datetime(2021,1,18), 'hospitalis\u00e9s'], 4 : ['D', 999, datetime(2021,1,13), datetime(2021,1,14), 'urgence'], 5 : ['E', 999, datetime(2021,1,19), datetime(2021,1,21), 'hospitalis\u00e9s'], 6 : ['F', 999, datetime(2021,1,25), datetime(2021,1,27), 'hospitalis\u00e9s'], 7 : ['G', 999, datetime(2017,1,1), None, \"hospitalis\u00e9s\"] } >>> vo = pd . DataFrame . from_dict ( data, orient=\"index\", columns=[ \"visit_occurrence_id\", \"person_id\", \"visit_start_datetime\", \"visit_end_datetime\", \"visit_source_value\", ], ) >>> vo visit_occurrence_id person_id visit_start_datetime visit_end_datetime visit_source_value 1 A 999 2021-01-01 2021-01-05 hospitalis\u00e9s 2 B 999 2021-01-04 2021-01-08 hospitalis\u00e9s 3 C 999 2021-01-12 2021-01-18 hospitalis\u00e9s 4 D 999 2021-01-13 2021-01-14 urgence 5 E 999 2021-01-19 2021-01-21 hospitalis\u00e9s 6 F 999 2021-01-25 2021-01-27 hospitalis\u00e9s 7 G 999 2017-01-01 NaT hospitalis\u00e9s >>> vo = merge_visits ( vo, remove_deleted_visits=True, long_stay_threshold=timedelta(days=365), long_stay_filtering=\"all\", max_timedelta=timedelta(hours=24), merge_different_hospitals=False, merge_different_source_values=[\"hospitalis\u00e9s\", \"urgence\"], ) >>> vo visit_occurrence_id person_id visit_start_datetime visit_end_datetime visit_source_value STAY_ID CONTIGUOUS_STAY_ID 1 A 999 2021-01-01 2021-01-05 hospitalis\u00e9s A A 2 B 999 2021-01-04 2021-01-08 hospitalis\u00e9s A A 3 C 999 2021-01-12 2021-01-18 hospitalis\u00e9s C C 4 D 999 2021-01-13 2021-01-14 urgence C C 5 E 999 2021-01-19 2021-01-21 hospitalis\u00e9s C E 6 F 999 2021-01-25 2021-01-27 hospitalis\u00e9s F F 7 G 999 2017-01-01 NaT hospitalis\u00e9s G G Source code in eds_scikit/period/stays.py 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 @concept_checker ( concepts = [ \"STAY_ID\" , \"CONTIGUOUS_STAY_ID\" ]) def merge_visits ( vo : DataFrame , remove_deleted_visits : bool = True , long_stay_threshold : timedelta = timedelta ( days = 365 ), long_stay_filtering : Optional [ str ] = \"all\" , open_stay_end_datetime : Optional [ datetime ] = None , max_timedelta : timedelta = timedelta ( days = 2 ), merge_different_hospitals : bool = False , merge_different_source_values : Union [ bool , List [ str ]] = [ \"hospitalis\u00e9s\" , \"urgence\" ], ) -> DataFrame : \"\"\" Merge \"close\" visit occurrences to consider them as a single stay by adding a ``STAY_ID`` and ``CONTIGUOUS_STAY_ID`` columns to the DataFrame. The value of these columns will be the `visit_occurrence_id` of the first (meaning the oldest) visit of the stay. From a temporal point of view, we consider that two visits belong to the same stay if either: - They intersect - The time difference between the end of the most recent and the start of the oldest is lower than ``max_timedelta`` (for ``STAY_ID``) or 0 (for ``CONTIGUOUS_STAY_ID``) Additionally, other parameters are available to further adjust the merging rules. See below. Parameters ---------- vo : DataFrame The ``visit_occurrence`` DataFrame, with at least the following columns: - visit_occurrence_id - person_id - visit_start_datetime_calc (from preprocessing) - visit_end_datetime (from preprocessing) Depending on the input parameters, additional columns may be required: - care_site_id (if ``merge_different_hospitals == True``) - visit_source_value (if ``merge_different_source_values != False``) - row_status_source_value (if ``remove_deleted_visits= True``) remove_deleted_visits: bool Wether to remove deleted visits from the merging procedure. Deleted visits are extracted via the `row_status_source_value` column long_stay_filtering : Optional[str] Filtering method for long and/or non-closed visits. First of all, visits with no starting date won't be merged with any other visit, and visits with no ending date will have a temporary \"theoretical\" ending date set by ``datetime.now()``. That being said, some visits are sometimes years long because they weren't closed at time. If other visits occurred during this timespan, they could be all merged into the same stay. To avoid this issue, filtering can be done depending on the ``long_stay_filtering`` value: - ``all``: All long stays (closed and open) are removed from the merging procedure - ``open``: Only long open stays are removed from the merging procedure - ``None``: No filtering is done on long visits Long stays are determined by the ``long_stay_threshold`` value. long_stay_threshold : timedelta Minimum visit duration value to consider a visit as candidate for \"long visits filtering\" open_stay_end_datetime: Optional[datetime] Datetime to use in order to fill the `visit_end_datetime` of open visits. This is necessary in order to compute stay duration and to filter long stays. If not provided `datetime.now()` will be used. You might provide the extraction date of your data here. max_timedelta : timedelta Maximum time difference between the end of a visit and the start of another to consider them as belonging to the same stay. This duration is internally converted in seconds before comparing. Thus, if you want e.g. to merge visits happening in two consecutive days, you should use `timedelta(days=2)` and NOT `timedelta(days=1)` in order to take into account extreme cases such as an first visit ending on Monday at 00h01 AM and another one starting at 23h59 PM on Tuesday merge_different_hospitals : bool Wether to allow visits occurring in different hospitals to be merged into a same stay merge_different_source_values : Union[bool, List[str]] Wether to allow visits with different `visit_source_value` to be merged into a same stay. Values can be: - `True`: the `visit_source_value` isn't taken into account for the merging - `False`: only visits with the same `visit_source_value` can be merged into a same stay - `List[str]`: only visits which `visit_source_value` is in the provided list can be merged together. **Warning**: You should avoid merging visits where `visit_source_value == \"hospitalisation incompl\u00e8te\"`, because those stays are often never closed. Returns ------- vo : DataFrame Visit occurrence DataFrame with additional `STAY_ID` column Examples -------- >>> import pandas as pd >>> from datetime import datetime, timedelta >>> data = { 1 : ['A', 999, datetime(2021,1,1), datetime(2021,1,5), 'hospitalis\u00e9s'], 2 : ['B', 999, datetime(2021,1,4), datetime(2021,1,8), 'hospitalis\u00e9s'], 3 : ['C', 999, datetime(2021,1,12), datetime(2021,1,18), 'hospitalis\u00e9s'], 4 : ['D', 999, datetime(2021,1,13), datetime(2021,1,14), 'urgence'], 5 : ['E', 999, datetime(2021,1,19), datetime(2021,1,21), 'hospitalis\u00e9s'], 6 : ['F', 999, datetime(2021,1,25), datetime(2021,1,27), 'hospitalis\u00e9s'], 7 : ['G', 999, datetime(2017,1,1), None, \"hospitalis\u00e9s\"] } >>> vo = pd.DataFrame.from_dict( data, orient=\"index\", columns=[ \"visit_occurrence_id\", \"person_id\", \"visit_start_datetime\", \"visit_end_datetime\", \"visit_source_value\", ], ) >>> vo visit_occurrence_id person_id visit_start_datetime visit_end_datetime visit_source_value 1 A 999 2021-01-01 2021-01-05 hospitalis\u00e9s 2 B 999 2021-01-04 2021-01-08 hospitalis\u00e9s 3 C 999 2021-01-12 2021-01-18 hospitalis\u00e9s 4 D 999 2021-01-13 2021-01-14 urgence 5 E 999 2021-01-19 2021-01-21 hospitalis\u00e9s 6 F 999 2021-01-25 2021-01-27 hospitalis\u00e9s 7 G 999 2017-01-01 NaT hospitalis\u00e9s >>> vo = merge_visits( vo, remove_deleted_visits=True, long_stay_threshold=timedelta(days=365), long_stay_filtering=\"all\", max_timedelta=timedelta(hours=24), merge_different_hospitals=False, merge_different_source_values=[\"hospitalis\u00e9s\", \"urgence\"], ) >>> vo visit_occurrence_id person_id visit_start_datetime visit_end_datetime visit_source_value STAY_ID CONTIGUOUS_STAY_ID 1 A 999 2021-01-01 2021-01-05 hospitalis\u00e9s A A 2 B 999 2021-01-04 2021-01-08 hospitalis\u00e9s A A 3 C 999 2021-01-12 2021-01-18 hospitalis\u00e9s C C 4 D 999 2021-01-13 2021-01-14 urgence C C 5 E 999 2021-01-19 2021-01-21 hospitalis\u00e9s C E 6 F 999 2021-01-25 2021-01-27 hospitalis\u00e9s F F 7 G 999 2017-01-01 NaT hospitalis\u00e9s G G \"\"\" # Preprocessing vo_to_merge , vo_to_not_merge = cleaning ( vo , remove_deleted_visits = remove_deleted_visits , long_stay_threshold = long_stay_threshold , long_stay_filtering = long_stay_filtering , open_stay_end_datetime = open_stay_end_datetime if open_stay_end_datetime is not None else datetime . now (), ) fw = get_framework ( vo_to_merge ) grouping_keys = [ \"person_id\" ] if not merge_different_hospitals : grouping_keys . append ( \"care_site_id\" ) if not merge_different_source_values : grouping_keys . append ( \"visit_source_value\" ) elif type ( merge_different_source_values ) == list : tmp = fw . DataFrame ( data = dict ( visit_source_value = merge_different_source_values , grouped_visit_source_value = True , ) ) vo_to_merge = vo_to_merge . merge ( tmp , on = \"visit_source_value\" , how = \"left\" ) vo_to_merge [ \"grouped_visit_source_value\" ] = vo_to_merge [ \"grouped_visit_source_value\" ] . fillna ( value = False ) grouping_keys . append ( \"grouped_visit_source_value\" ) # Cartesian product merged = vo_to_merge . merge ( vo_to_merge , on = grouping_keys , how = \"inner\" , suffixes = ( \"_1\" , \"_2\" ), ) # Keeping only visits where 1 occurs before 2 merged = merged [ merged [ \"visit_start_datetime_1\" ] <= merged [ \"visit_start_datetime_2\" ] ] # Checking correct overlap th = max_timedelta . total_seconds () merged [ \"overlap\" ] = substract_datetime ( merged [ \"visit_start_datetime_2\" ], merged [ \"visit_end_datetime_calc_1\" ] ) merged [ \"to_merge\" ] = ( merged [ \"overlap\" ] <= th ) . astype ( int ) merged [ \"contiguous\" ] = ( merged [ \"overlap\" ] <= 0 ) . astype ( int ) def get_first ( merged : DataFrame , contiguous_only : bool = False , ) -> Tuple [ DataFrame , DataFrame ]: \"\"\" Returns a boolean flag for each visit, telling if the visit if the first of a stay. The ``contiguous_only`` parameter controls if the visits have to be contiguous in the stay \"\"\" flag_col = \"contiguous\" if contiguous_only else \"to_merge\" flag_name = \"1_is_first_contiguous\" if contiguous_only else \"1_is_first\" concept_prefix = \"CONTIGUOUS_\" if contiguous_only else \"\" # If the only previous visit to be merged with is itself, we found our first visit ! first_visits = merged . groupby ( \"visit_occurrence_id_2\" )[ flag_col ] . sum () == 1 first_visits . name = flag_name # Adding this boolean flag to the merged DataFrame merged = merged . merge ( first_visits , left_on = \"visit_occurrence_id_1\" , right_index = True , how = \"inner\" , ) # Getting the corresponding first visit first_visit = ( merged . sort_values ( by = [ flag_name , \"visit_start_datetime_1\" ], ascending = [ False , False ] ) . groupby ( \"visit_occurrence_id_2\" ) . first ()[ \"visit_occurrence_id_1\" ] . reset_index () . rename ( columns = { \"visit_occurrence_id_1\" : f \" { concept_prefix } STAY_ID\" , \"visit_occurrence_id_2\" : \"visit_occurrence_id\" , } ) ) return merged , first_visit merged , first_contiguous_visit = get_first ( merged , contiguous_only = True ) merged , first_visit = get_first ( merged , contiguous_only = False ) # Concatenating merge visits with previously discarded ones results = fw . concat ( [ vo_to_merge . merge ( first_visit , on = \"visit_occurrence_id\" , how = \"inner\" , ) . merge ( first_contiguous_visit , on = \"visit_occurrence_id\" , how = \"inner\" , ), vo_to_not_merge , ] ) # Adding visit_occurrence_id as STAY_ID and CONTIGUOUS_STAY_ID to discarded visits results [ \"STAY_ID\" ] = results [ \"STAY_ID\" ] . combine_first ( results [ \"visit_occurrence_id\" ] ) results [ \"CONTIGUOUS_STAY_ID\" ] = results [ \"CONTIGUOUS_STAY_ID\" ] . combine_first ( results [ \"visit_occurrence_id\" ] ) # Removing tmp columns vo = vo . drop ( columns = [ \"visit_end_datetime_calc\" ]) return results . drop ( columns = ( set ( results . columns ) & set ([ \"visit_end_datetime_calc\" , \"grouped_visit_source_value\" ]) ) )","title":"merge_visits()"},{"location":"reference/period/stays/#eds_scikit.period.stays.get_stays_duration","text":"get_stays_duration ( vo : DataFrame , algo : str = 'sum_of_visits_duration' , missing_end_date_handling : str = 'fill' , open_stay_end_datetime : Optional [ datetime ] = None ) -> DataFrame Computes stay duration. The input DataFrame should contain the STAY_ID and CONTIGUOUS_STAY_ID columns, that can be computed via the merge_visits() function. PARAMETER DESCRIPTION vo visit occurrence DataFrame with the STAY_ID and CONTIGUOUS_STAY_ID columns TYPE: DataFrame algo Which algo to use for computing stay durations. Available values are: \"sum_of_visits_duration\" : The stay duration will correspond to the sum of each visit duration in the stay. \"visits_date_difference\" : The stay duration will correspond to the difference between the end date of the last visit and the start date of the first visit. TYPE: str DEFAULT: 'sum_of_visits_duration' missing_end_date_handling How to handle visits with no end date. Available values are: \"fill\" : Missing values are filled with datetime.now() \"coerce\" : Missing values are handled as such, so duration of stays with open visits will be NaN. TYPE: str DEFAULT: 'fill' open_stay_end_datetime Used if missing_end_date_handling == \"fill\" . Provide the datetime with which open stays should be ended. Leave to None in order to used datetime.now() TYPE: Optional [ datetime ] DEFAULT: None RETURNS DESCRIPTION DataFrame stay DataFrame with STAY_ID as index, and the following columns: \"person_id\" \"t_start\" : The start date of the first visit of the stay \"t_end\" : The end date of the last visit of the stay \"STAY_DURATION\" : The duration (in hours) of the stay RAISES DESCRIPTION MissingConceptError If STAY_ID and CONTIGUOUS_STAY_ID are not in the input columns. Source code in eds_scikit/period/stays.py 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 @algo_checker ( algos = [ \"sum_of_visits_duration\" , \"visits_date_difference\" ]) @concept_checker ( concepts = [ \"STAY_DURATION\" ], only_adds_concepts = False ) def get_stays_duration ( vo : DataFrame , algo : str = \"sum_of_visits_duration\" , missing_end_date_handling : str = \"fill\" , open_stay_end_datetime : Optional [ datetime ] = None , ) -> DataFrame : \"\"\" Computes stay duration. The input DataFrame should contain the `STAY_ID` and `CONTIGUOUS_STAY_ID` columns, that can be computed via the `merge_visits()` function. Parameters ---------- vo : DataFrame visit occurrence DataFrame with the `STAY_ID` and `CONTIGUOUS_STAY_ID` columns algo : str Which algo to use for computing stay durations. Available values are: - `\"sum_of_visits_duration\"`: The stay duration will correspond to the sum of each visit duration in the stay. - `\"visits_date_difference\"`: The stay duration will correspond to the difference between the end date of the last visit and the start date of the first visit. missing_end_date_handling : str How to handle visits with no end date. Available values are: - `\"fill\"`: Missing values are filled with `datetime.now()` - `\"coerce\"`: Missing values are handled as such, so duration of stays with open visits will be NaN. open_stay_end_datetime: Optional[datetime] Used if `missing_end_date_handling == \"fill\"`. Provide the `datetime` with which open stays should be ended. Leave to `None` in order to used `datetime.now()` Returns ------- DataFrame *stay* DataFrame with `STAY_ID` as index, and the following columns: - `\"person_id\"` - `\"t_start\"`: The start date of the first visit of the stay - `\"t_end\"`: The end date of the last visit of the stay - `\"STAY_DURATION\"`: The duration (in hours) of the stay Raises ------ MissingConceptError If `STAY_ID` and `CONTIGUOUS_STAY_ID` are not in the input columns. \"\"\" if set (( \"STAY_ID\" , \"CONTIGUOUS_STAY_ID\" )) - set ( vo . columns ): raise MissingConceptError ( df_name = \"visit_occurence\" , required_concepts = [ ( \"STAY_ID\" , \"should be computed via 'merge_visits'\" ), ( \"CONTIGUOUS_STAY_ID\" , \"should be computed via 'merge_visits'\" ), ], ) if missing_end_date_handling == \"fill\" : # Cannot use fillna() with datetime in Koalas if open_stay_end_datetime is None : open_stay_end_datetime = datetime . now () vo [ \"visit_end_datetime_calc\" ] = open_stay_end_datetime vo [ \"visit_end_datetime_calc\" ] = vo [ \"visit_end_datetime\" ] . combine_first ( vo [ \"visit_end_datetime_calc\" ] ) elif missing_end_date_handling == \"coerce\" : vo [ \"visit_end_datetime_calc\" ] = vo [ \"visit_end_datetime\" ] agg_dict = dict ( person_id = ( \"person_id\" , \"first\" ), t_start = ( \"visit_start_datetime\" , \"min\" ), t_end = ( \"visit_end_datetime_calc\" , \"max\" ), ) if algo == \"sum_of_visits_duration\" : agg_dict [ \"STAY_ID\" ] = ( \"STAY_ID\" , \"first\" ) contiguous_stays = vo . groupby ( \"CONTIGUOUS_STAY_ID\" ) . agg ( ** agg_dict ) contiguous_stays [ \"CONTIGUOUS_STAY_DURATION\" ] = substract_datetime ( contiguous_stays [ \"t_end\" ], contiguous_stays [ \"t_start\" ], out = \"hours\" ) agg_dict = dict ( person_id = ( \"person_id\" , \"first\" ), t_start = ( \"t_start\" , \"min\" ), t_end = ( \"t_end\" , \"max\" ), STAY_DURATION = ( \"CONTIGUOUS_STAY_DURATION\" , \"sum\" ), ) stays = contiguous_stays . groupby ( \"STAY_ID\" ) . agg ( ** agg_dict ) elif algo == \"visits_date_difference\" : stays = vo . groupby ( \"STAY_ID\" ) . agg ( ** agg_dict ) stays [ \"STAY_DURATION\" ] = substract_datetime ( stays [ \"t_end\" ], stays [ \"t_start\" ], out = \"hours\" ) if missing_end_date_handling == \"coerce\" : stays . loc [ stays [ \"t_end\" ] . isna (), \"STAY_DURATION\" ] = NaN return stays","title":"get_stays_duration()"},{"location":"reference/period/tagging_functions/","text":"eds_scikit.period.tagging_functions tagging tagging ( tag_to_df : DataFrame , tag_from_df : DataFrame , concept_to_tag : str , tag_to_date_cols : List [ str ] = [ 't_start' , 't_end' ], tag_from_date_cols : List [ str ] = [ 't_start' , 't_end' ], algo : str = 'intersection' ) -> DataFrame PARAMETER DESCRIPTION tag_to_df TYPE: DataFrame tag_from_df TYPE: DataFrame concept_to_tag TYPE: str tag_to_date_cols TYPE: List [ str ], optional DEFAULT: ['t_start', 't_end'] tag_from_date_cols TYPE: List [ str ], optional DEFAULT: ['t_start', 't_end'] algo TYPE: str , optional DEFAULT: 'intersection' RETURNS DESCRIPTION DataFrame Source code in eds_scikit/period/tagging_functions.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 def tagging ( tag_to_df : DataFrame , tag_from_df : DataFrame , concept_to_tag : str , tag_to_date_cols : List [ str ] = [ \"t_start\" , \"t_end\" ], tag_from_date_cols : List [ str ] = [ \"t_start\" , \"t_end\" ], algo : str = \"intersection\" , ) -> DataFrame : \"\"\" Parameters ---------- tag_to_df : DataFrame tag_from_df : DataFrame concept_to_tag : str tag_to_date_cols : List[str], optional tag_from_date_cols : List[str], optional algo : str, optional Returns ------- DataFrame \"\"\" framework = get_framework ( tag_to_df ) tag_to_df = tag_to_df . assign ( event_id = tag_to_df . index ) tag_from = tag_from_df . loc [ tag_from_df . concept == concept_to_tag , [ \"person_id\" , \"value\" ] + [ \"t_start\" , \"t_end\" ], ] tmp = ( tag_to_df . rename ( columns = { tag_to_date_cols [ 0 ]: \"t_start_x\" , tag_to_date_cols [ 1 ]: \"t_end_x\" } ) . merge ( tag_from . rename ( columns = { tag_from_date_cols [ 0 ]: \"t_start_y\" , tag_from_date_cols [ 1 ]: \"t_end_y\" , } ), on = \"person_id\" , how = \"left\" , ) . dropna ( subset = [ \"t_start_x\" , \"t_end_x\" , \"t_start_y\" , \"t_end_y\" ]) ) if len ( tmp ) == 0 : # TODO: is this necessary ? logger . warning ( \"No matching were found between the 2 DataFrames\" ) return framework . DataFrame ( columns = [ \"person_id\" , \"t_start\" , \"t_end\" , \"concept\" , \"value\" ] ) tmp [ \"tag\" ] = compare_intervals ( tmp [ \"t_start_x\" ], tmp [ \"t_end_x\" ], tmp [ \"t_start_y\" ], tmp [ \"t_end_y\" ], algo = algo , ) value_col = ( \"value_y\" if (( \"value\" in tag_to_df . columns ) and ( \"value\" in tag_from_df . columns )) else \"value\" ) tags = ( tmp . groupby ([ \"event_id\" , value_col ]) . tag . any () . unstack () . fillna ( False ) . reset_index () ) tags = tag_to_df [[ \"event_id\" ]] . merge ( tags , on = \"event_id\" , how = \"left\" ) . fillna ( False ) tags = tag_to_df . merge ( tags , on = \"event_id\" , how = \"left\" ) . drop ( columns = \"event_id\" ) return tags","title":"tagging_functions"},{"location":"reference/period/tagging_functions/#eds_scikitperiodtagging_functions","text":"","title":"eds_scikit.period.tagging_functions"},{"location":"reference/period/tagging_functions/#eds_scikit.period.tagging_functions.tagging","text":"tagging ( tag_to_df : DataFrame , tag_from_df : DataFrame , concept_to_tag : str , tag_to_date_cols : List [ str ] = [ 't_start' , 't_end' ], tag_from_date_cols : List [ str ] = [ 't_start' , 't_end' ], algo : str = 'intersection' ) -> DataFrame PARAMETER DESCRIPTION tag_to_df TYPE: DataFrame tag_from_df TYPE: DataFrame concept_to_tag TYPE: str tag_to_date_cols TYPE: List [ str ], optional DEFAULT: ['t_start', 't_end'] tag_from_date_cols TYPE: List [ str ], optional DEFAULT: ['t_start', 't_end'] algo TYPE: str , optional DEFAULT: 'intersection' RETURNS DESCRIPTION DataFrame Source code in eds_scikit/period/tagging_functions.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 def tagging ( tag_to_df : DataFrame , tag_from_df : DataFrame , concept_to_tag : str , tag_to_date_cols : List [ str ] = [ \"t_start\" , \"t_end\" ], tag_from_date_cols : List [ str ] = [ \"t_start\" , \"t_end\" ], algo : str = \"intersection\" , ) -> DataFrame : \"\"\" Parameters ---------- tag_to_df : DataFrame tag_from_df : DataFrame concept_to_tag : str tag_to_date_cols : List[str], optional tag_from_date_cols : List[str], optional algo : str, optional Returns ------- DataFrame \"\"\" framework = get_framework ( tag_to_df ) tag_to_df = tag_to_df . assign ( event_id = tag_to_df . index ) tag_from = tag_from_df . loc [ tag_from_df . concept == concept_to_tag , [ \"person_id\" , \"value\" ] + [ \"t_start\" , \"t_end\" ], ] tmp = ( tag_to_df . rename ( columns = { tag_to_date_cols [ 0 ]: \"t_start_x\" , tag_to_date_cols [ 1 ]: \"t_end_x\" } ) . merge ( tag_from . rename ( columns = { tag_from_date_cols [ 0 ]: \"t_start_y\" , tag_from_date_cols [ 1 ]: \"t_end_y\" , } ), on = \"person_id\" , how = \"left\" , ) . dropna ( subset = [ \"t_start_x\" , \"t_end_x\" , \"t_start_y\" , \"t_end_y\" ]) ) if len ( tmp ) == 0 : # TODO: is this necessary ? logger . warning ( \"No matching were found between the 2 DataFrames\" ) return framework . DataFrame ( columns = [ \"person_id\" , \"t_start\" , \"t_end\" , \"concept\" , \"value\" ] ) tmp [ \"tag\" ] = compare_intervals ( tmp [ \"t_start_x\" ], tmp [ \"t_end_x\" ], tmp [ \"t_start_y\" ], tmp [ \"t_end_y\" ], algo = algo , ) value_col = ( \"value_y\" if (( \"value\" in tag_to_df . columns ) and ( \"value\" in tag_from_df . columns )) else \"value\" ) tags = ( tmp . groupby ([ \"event_id\" , value_col ]) . tag . any () . unstack () . fillna ( False ) . reset_index () ) tags = tag_to_df [[ \"event_id\" ]] . merge ( tags , on = \"event_id\" , how = \"left\" ) . fillna ( False ) tags = tag_to_df . merge ( tags , on = \"event_id\" , how = \"left\" ) . drop ( columns = \"event_id\" ) return tags","title":"tagging()"},{"location":"reference/phenotype/","text":"eds_scikit.phenotype","title":"`eds_scikit.phenotype`"},{"location":"reference/phenotype/#eds_scikitphenotype","text":"","title":"eds_scikit.phenotype"},{"location":"reference/phenotype/base/","text":"eds_scikit.phenotype.base Features Features () Class used to store features (i.e. DataFrames). Features are stored in the self._features dictionary. Source code in eds_scikit/phenotype/base.py 22 23 24 def __init__ ( self ): self . _features = {} self . last_feature = None Phenotype Phenotype ( data : BaseData , name : Optional [ str ] = None , ** kwargs ) Base class for phenotyping PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData name Name of the phenotype. If left to None, the name of the class will be used instead TYPE: Optional [ str ] DEFAULT: None Source code in eds_scikit/phenotype/base.py 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 def __init__ ( self , data : BaseData , name : Optional [ str ] = None , ** kwargs , ): \"\"\" Parameters ---------- data : BaseData A BaseData object name : Optional[str] Name of the phenotype. If left to None, the name of the class will be used instead \"\"\" self . data = data self . features = Features () self . name = ( to_valid_variable_name ( name ) if name is not None else self . __class__ . __name__ ) self . logger = logger . bind ( classname = self . name , sep = \".\" ) add_code_feature add_code_feature ( output_feature : str , codes : dict , source : str = 'icd10' , additional_filtering : Optional [ dict ] = None ) Adds a feature from either ICD10 or CCAM codes PARAMETER DESCRIPTION output_feature Name of the feature TYPE: str codes Dictionary of codes to provide to the from_codes function TYPE: dict source Either 'icd10' or 'ccam', by default 'icd10' TYPE: str DEFAULT: 'icd10' additional_filtering Dictionary passed to the from_codes functions for filtering TYPE: Optional [ dict ] DEFAULT: None RETURNS DESCRIPTION Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] Source code in eds_scikit/phenotype/base.py 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 def add_code_feature ( self , output_feature : str , codes : dict , source : str = \"icd10\" , additional_filtering : Optional [ dict ] = None , ): \"\"\" Adds a feature from either ICD10 or CCAM codes Parameters ---------- output_feature : str Name of the feature codes : dict Dictionary of codes to provide to the `from_codes` function source : str, Either 'icd10' or 'ccam', by default 'icd10' additional_filtering : Optional[dict] Dictionary passed to the `from_codes` functions for filtering Returns ------- Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] \"\"\" additional_filtering = additional_filtering or dict () if source not in [ \"icd10\" , \"ccam\" ]: raise ValueError ( f \"source should be either 'icd10' or 'ccam', got { source } \" ) self . logger . info ( f \"Getting { source . upper () } features...\" ) from_code_func = ( conditions_from_icd10 if ( source == \"icd10\" ) else procedures_from_ccam ) codes_df = ( self . data . condition_occurrence if ( source == \"icd10\" ) else self . data . procedure_occurrence ) df = from_code_func ( codes_df , codes = codes , additional_filtering = additional_filtering , date_from_visit = False , ) df [ \"phenotype\" ] = self . name df = df . rename ( columns = { \"concept\" : \"subphenotype\" }) bd . cache ( df ) self . features [ output_feature ] = df self . logger . info ( f \" { source . upper () } features stored in self.features[' { output_feature } '] (N = { len ( df ) } )\" ) return self agg_single_feature agg_single_feature ( input_feature : str , output_feature : Optional [ str ] = None , level : str = 'patient' , subphenotype : bool = True , threshold : int = 1 ) -> Phenotype Simple aggregation rule on a feature: If level=\"patient\", keeps patients with at least threshold visits showing the (sub)phenotype If level=\"visit\", keeps visits with at least threshold events (could be ICD10 codes, NLP features, biology, etc) showing the (sub)phenotype PARAMETER DESCRIPTION input_feature Name of the input feature TYPE: str output_feature Name of the input feature. If None, will be set to input_feature + \"_agg\" TYPE: Optional [ str ] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int , optional DEFAULT: 1 RETURNS DESCRIPTION Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] Source code in eds_scikit/phenotype/base.py 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 def agg_single_feature ( self , input_feature : str , output_feature : Optional [ str ] = None , level : str = \"patient\" , subphenotype : bool = True , threshold : int = 1 , ) -> \"Phenotype\" : \"\"\" Simple aggregation rule on a feature: - If level=\"patient\", keeps patients with at least `threshold` visits showing the (sub)phenotype - If level=\"visit\", keeps visits with at least `threshold` events (could be ICD10 codes, NLP features, biology, etc) showing the (sub)phenotype Parameters ---------- input_feature : str Name of the input feature output_feature : Optional[str] Name of the input feature. If None, will be set to input_feature + \"_agg\" level : str On which level to do the aggregation, either \"patient\" or \"visit\" subphenotype : bool Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) threshold : int, optional Minimal number of *events* (which definition depends on the `level` value) Returns ------- Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] \"\"\" assert level in { \"patient\" , \"visit\" } output_feature = output_feature or f \" { input_feature } _agg\" if input_feature not in self . features : raise ValueError ( f \"Input feature { input_feature } not found in self.features. \" \"Maybe you forgot to call self.get_features() ?\" ) # We use `size` below for two reasons # 1) to use it with the `threshold` parameter directly if level == 'visit' # 2) to drop duplicates on the group_cols + [\"visit_occurrence_id\"] subset phenotype_type = \"subphenotype\" if subphenotype else \"phenotype\" group_cols = [ \"person_id\" , phenotype_type ] group_visit = ( self . features [ input_feature ] . groupby ( group_cols + [ \"visit_occurrence_id\" ]) . size () . rename ( \"N\" ) # number of events per visit_occurrence . reset_index () ) if level == \"patient\" : group_visit = ( group_visit . groupby ( group_cols ) . size () . rename ( \"N\" ) # number of visits per person . reset_index () ) group_visit = group_visit [ group_visit [ \"N\" ] >= threshold ] . drop ( columns = \"N\" ) group_visit [ \"phenotype\" ] = self . name bd . cache ( group_visit ) self . features [ output_feature ] = group_visit self . logger . info ( f \"Aggregation from { input_feature } stored in self.features[' { output_feature } '] \" f \"(N = { len ( group_visit ) } )\" ) return self agg_two_features agg_two_features ( input_feature_1 : str , input_feature_2 : str , output_feature : str = None , how : str = 'AND' , level : str = 'patient' , subphenotype : bool = True , thresholds : Tuple [ int , int ] = ( 1 , 1 )) -> Phenotype If level='patient', keeps a specific patient if At least thresholds[0] visits are found in feature_1 AND/OR At least thresholds[1] visits are found in feature_2 If level='visit', keeps a specific visit if At least thresholds[0] events are found in feature_1 AND/OR At least thresholds[1] events are found in feature_2 PARAMETER DESCRIPTION input_feature_1 Name of the first input feature TYPE: str input_feature_2 Name of the second input feature TYPE: str output_feature Name of the input feature. If None, will be set to input_feature + \"_agg\" TYPE: str DEFAULT: None how Whether to perform a boolean \"AND\" or \"OR\" aggregation TYPE: str , optional DEFAULT: 'AND' level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True thresholds Repsective threshold for the first and second feature TYPE: Tuple [ int , int ], optional DEFAULT: (1, 1) RETURNS DESCRIPTION Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] Source code in eds_scikit/phenotype/base.py 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 def agg_two_features ( self , input_feature_1 : str , input_feature_2 : str , output_feature : str = None , how : str = \"AND\" , level : str = \"patient\" , subphenotype : bool = True , thresholds : Tuple [ int , int ] = ( 1 , 1 ), ) -> \"Phenotype\" : \"\"\" - If level='patient', keeps a specific patient if - At least `thresholds[0]` visits are found in feature_1 AND/OR - At least `thresholds[1]` visits are found in feature_2 - If level='visit', keeps a specific visit if - At least `thresholds[0]` events are found in feature_1 AND/OR - At least `thresholds[1]` events are found in feature_2 Parameters ---------- input_feature_1 : str Name of the first input feature input_feature_2 : str Name of the second input feature output_feature : str Name of the input feature. If None, will be set to input_feature + \"_agg\" how : str, optional Whether to perform a boolean \"AND\" or \"OR\" aggregation level : str On which level to do the aggregation, either \"patient\" or \"visit\" subphenotype : bool Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) thresholds : Tuple[int, int], optional Repsective threshold for the first and second feature Returns ------- Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] \"\"\" self . agg_single_feature ( input_feature = input_feature_1 , level = level , subphenotype = subphenotype , threshold = thresholds [ 0 ], ) self . agg_single_feature ( input_feature = input_feature_2 , level = level , subphenotype = subphenotype , threshold = thresholds [ 1 ], ) results_1 = self . features [ f \" { input_feature_1 } _agg\" ] results_2 = self . features [ f \" { input_feature_2 } _agg\" ] assert set ( results_1 . columns ) == set ( results_2 . columns ) if how == \"AND\" : result = results_1 . merge ( results_2 , on = list ( results_1 . columns ), how = \"inner\" ) elif how == \"OR\" : result = bd . concat ( [ results_1 , results_2 , ] ) . drop_duplicates () else : raise ValueError ( f \"'how' options are ('AND', 'OR'), got { how } .\" ) bd . cache ( result ) output_feature = output_feature or f \" { input_feature_1 } _ { how } _ { input_feature_2 } \" self . features [ output_feature ] = result self . logger . info ( f \"Aggregation from { input_feature_1 } { how } { input_feature_1 } stored in self.features[' { output_feature } '] \" f \"(N = { len ( result ) } )\" ) return self compute compute ( ** kwargs ) Fetch all necessary features and perform aggregation Source code in eds_scikit/phenotype/base.py 325 326 327 328 329 def compute ( self , ** kwargs ): \"\"\" Fetch all necessary features and perform aggregation \"\"\" raise NotImplementedError () to_data to_data ( key : Optional [ str ] = None ) -> BaseData Appends the feature found in self.features[key] to the data object. If no key is provided, uses the last added feature PARAMETER DESCRIPTION key Key of the self.feature dictionary TYPE: Optional [ str ] DEFAULT: None RETURNS DESCRIPTION BaseData The data object with phenotype added to data.computed Source code in eds_scikit/phenotype/base.py 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 def to_data ( self , key : Optional [ str ] = None ) -> BaseData : \"\"\" Appends the feature found in self.features[key] to the data object. If no key is provided, uses the last added feature Parameters ---------- key : Optional[str] Key of the self.feature dictionary Returns ------- BaseData The data object with phenotype added to `data.computed` \"\"\" if not self . features : self . compute () if key is None : self . logger . info ( \"No key provided: Using last added feature.\" ) return self . _set ( self . features . last ()) else : assert ( key in self . features ), f \"Key { key } not found in features. Available { self . features } \" self . logger . info ( \"Using feature {key} \" ) return self . _set ( self . features [ key ]) to_valid_variable_name to_valid_variable_name ( s : str ) Converts a string to a valid variable name Source code in eds_scikit/phenotype/base.py 415 416 417 418 419 420 421 422 423 424 425 426 def to_valid_variable_name ( s : str ): \"\"\" Converts a string to a valid variable name \"\"\" # Replace non-alphanumeric characters with underscores s = re . sub ( r \"\\W+\" , \"_\" , s ) # Remove leading underscores s = re . sub ( r \"^_+\" , \"\" , s ) # If the string is empty or starts with a number, prepend an underscore if not s or s [ 0 ] . isdigit (): s = \"_\" + s return s","title":"base"},{"location":"reference/phenotype/base/#eds_scikitphenotypebase","text":"","title":"eds_scikit.phenotype.base"},{"location":"reference/phenotype/base/#eds_scikit.phenotype.base.Features","text":"Features () Class used to store features (i.e. DataFrames). Features are stored in the self._features dictionary. Source code in eds_scikit/phenotype/base.py 22 23 24 def __init__ ( self ): self . _features = {} self . last_feature = None","title":"Features"},{"location":"reference/phenotype/base/#eds_scikit.phenotype.base.Phenotype","text":"Phenotype ( data : BaseData , name : Optional [ str ] = None , ** kwargs ) Base class for phenotyping PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData name Name of the phenotype. If left to None, the name of the class will be used instead TYPE: Optional [ str ] DEFAULT: None Source code in eds_scikit/phenotype/base.py 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 def __init__ ( self , data : BaseData , name : Optional [ str ] = None , ** kwargs , ): \"\"\" Parameters ---------- data : BaseData A BaseData object name : Optional[str] Name of the phenotype. If left to None, the name of the class will be used instead \"\"\" self . data = data self . features = Features () self . name = ( to_valid_variable_name ( name ) if name is not None else self . __class__ . __name__ ) self . logger = logger . bind ( classname = self . name , sep = \".\" )","title":"Phenotype"},{"location":"reference/phenotype/base/#eds_scikit.phenotype.base.Phenotype.add_code_feature","text":"add_code_feature ( output_feature : str , codes : dict , source : str = 'icd10' , additional_filtering : Optional [ dict ] = None ) Adds a feature from either ICD10 or CCAM codes PARAMETER DESCRIPTION output_feature Name of the feature TYPE: str codes Dictionary of codes to provide to the from_codes function TYPE: dict source Either 'icd10' or 'ccam', by default 'icd10' TYPE: str DEFAULT: 'icd10' additional_filtering Dictionary passed to the from_codes functions for filtering TYPE: Optional [ dict ] DEFAULT: None RETURNS DESCRIPTION Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] Source code in eds_scikit/phenotype/base.py 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 def add_code_feature ( self , output_feature : str , codes : dict , source : str = \"icd10\" , additional_filtering : Optional [ dict ] = None , ): \"\"\" Adds a feature from either ICD10 or CCAM codes Parameters ---------- output_feature : str Name of the feature codes : dict Dictionary of codes to provide to the `from_codes` function source : str, Either 'icd10' or 'ccam', by default 'icd10' additional_filtering : Optional[dict] Dictionary passed to the `from_codes` functions for filtering Returns ------- Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] \"\"\" additional_filtering = additional_filtering or dict () if source not in [ \"icd10\" , \"ccam\" ]: raise ValueError ( f \"source should be either 'icd10' or 'ccam', got { source } \" ) self . logger . info ( f \"Getting { source . upper () } features...\" ) from_code_func = ( conditions_from_icd10 if ( source == \"icd10\" ) else procedures_from_ccam ) codes_df = ( self . data . condition_occurrence if ( source == \"icd10\" ) else self . data . procedure_occurrence ) df = from_code_func ( codes_df , codes = codes , additional_filtering = additional_filtering , date_from_visit = False , ) df [ \"phenotype\" ] = self . name df = df . rename ( columns = { \"concept\" : \"subphenotype\" }) bd . cache ( df ) self . features [ output_feature ] = df self . logger . info ( f \" { source . upper () } features stored in self.features[' { output_feature } '] (N = { len ( df ) } )\" ) return self","title":"add_code_feature()"},{"location":"reference/phenotype/base/#eds_scikit.phenotype.base.Phenotype.agg_single_feature","text":"agg_single_feature ( input_feature : str , output_feature : Optional [ str ] = None , level : str = 'patient' , subphenotype : bool = True , threshold : int = 1 ) -> Phenotype Simple aggregation rule on a feature: If level=\"patient\", keeps patients with at least threshold visits showing the (sub)phenotype If level=\"visit\", keeps visits with at least threshold events (could be ICD10 codes, NLP features, biology, etc) showing the (sub)phenotype PARAMETER DESCRIPTION input_feature Name of the input feature TYPE: str output_feature Name of the input feature. If None, will be set to input_feature + \"_agg\" TYPE: Optional [ str ] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int , optional DEFAULT: 1 RETURNS DESCRIPTION Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] Source code in eds_scikit/phenotype/base.py 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 def agg_single_feature ( self , input_feature : str , output_feature : Optional [ str ] = None , level : str = \"patient\" , subphenotype : bool = True , threshold : int = 1 , ) -> \"Phenotype\" : \"\"\" Simple aggregation rule on a feature: - If level=\"patient\", keeps patients with at least `threshold` visits showing the (sub)phenotype - If level=\"visit\", keeps visits with at least `threshold` events (could be ICD10 codes, NLP features, biology, etc) showing the (sub)phenotype Parameters ---------- input_feature : str Name of the input feature output_feature : Optional[str] Name of the input feature. If None, will be set to input_feature + \"_agg\" level : str On which level to do the aggregation, either \"patient\" or \"visit\" subphenotype : bool Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) threshold : int, optional Minimal number of *events* (which definition depends on the `level` value) Returns ------- Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] \"\"\" assert level in { \"patient\" , \"visit\" } output_feature = output_feature or f \" { input_feature } _agg\" if input_feature not in self . features : raise ValueError ( f \"Input feature { input_feature } not found in self.features. \" \"Maybe you forgot to call self.get_features() ?\" ) # We use `size` below for two reasons # 1) to use it with the `threshold` parameter directly if level == 'visit' # 2) to drop duplicates on the group_cols + [\"visit_occurrence_id\"] subset phenotype_type = \"subphenotype\" if subphenotype else \"phenotype\" group_cols = [ \"person_id\" , phenotype_type ] group_visit = ( self . features [ input_feature ] . groupby ( group_cols + [ \"visit_occurrence_id\" ]) . size () . rename ( \"N\" ) # number of events per visit_occurrence . reset_index () ) if level == \"patient\" : group_visit = ( group_visit . groupby ( group_cols ) . size () . rename ( \"N\" ) # number of visits per person . reset_index () ) group_visit = group_visit [ group_visit [ \"N\" ] >= threshold ] . drop ( columns = \"N\" ) group_visit [ \"phenotype\" ] = self . name bd . cache ( group_visit ) self . features [ output_feature ] = group_visit self . logger . info ( f \"Aggregation from { input_feature } stored in self.features[' { output_feature } '] \" f \"(N = { len ( group_visit ) } )\" ) return self","title":"agg_single_feature()"},{"location":"reference/phenotype/base/#eds_scikit.phenotype.base.Phenotype.agg_two_features","text":"agg_two_features ( input_feature_1 : str , input_feature_2 : str , output_feature : str = None , how : str = 'AND' , level : str = 'patient' , subphenotype : bool = True , thresholds : Tuple [ int , int ] = ( 1 , 1 )) -> Phenotype If level='patient', keeps a specific patient if At least thresholds[0] visits are found in feature_1 AND/OR At least thresholds[1] visits are found in feature_2 If level='visit', keeps a specific visit if At least thresholds[0] events are found in feature_1 AND/OR At least thresholds[1] events are found in feature_2 PARAMETER DESCRIPTION input_feature_1 Name of the first input feature TYPE: str input_feature_2 Name of the second input feature TYPE: str output_feature Name of the input feature. If None, will be set to input_feature + \"_agg\" TYPE: str DEFAULT: None how Whether to perform a boolean \"AND\" or \"OR\" aggregation TYPE: str , optional DEFAULT: 'AND' level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True thresholds Repsective threshold for the first and second feature TYPE: Tuple [ int , int ], optional DEFAULT: (1, 1) RETURNS DESCRIPTION Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] Source code in eds_scikit/phenotype/base.py 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 def agg_two_features ( self , input_feature_1 : str , input_feature_2 : str , output_feature : str = None , how : str = \"AND\" , level : str = \"patient\" , subphenotype : bool = True , thresholds : Tuple [ int , int ] = ( 1 , 1 ), ) -> \"Phenotype\" : \"\"\" - If level='patient', keeps a specific patient if - At least `thresholds[0]` visits are found in feature_1 AND/OR - At least `thresholds[1]` visits are found in feature_2 - If level='visit', keeps a specific visit if - At least `thresholds[0]` events are found in feature_1 AND/OR - At least `thresholds[1]` events are found in feature_2 Parameters ---------- input_feature_1 : str Name of the first input feature input_feature_2 : str Name of the second input feature output_feature : str Name of the input feature. If None, will be set to input_feature + \"_agg\" how : str, optional Whether to perform a boolean \"AND\" or \"OR\" aggregation level : str On which level to do the aggregation, either \"patient\" or \"visit\" subphenotype : bool Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) thresholds : Tuple[int, int], optional Repsective threshold for the first and second feature Returns ------- Phenotype The current Phenotype object with an additional feature stored in self.features[output_feature] \"\"\" self . agg_single_feature ( input_feature = input_feature_1 , level = level , subphenotype = subphenotype , threshold = thresholds [ 0 ], ) self . agg_single_feature ( input_feature = input_feature_2 , level = level , subphenotype = subphenotype , threshold = thresholds [ 1 ], ) results_1 = self . features [ f \" { input_feature_1 } _agg\" ] results_2 = self . features [ f \" { input_feature_2 } _agg\" ] assert set ( results_1 . columns ) == set ( results_2 . columns ) if how == \"AND\" : result = results_1 . merge ( results_2 , on = list ( results_1 . columns ), how = \"inner\" ) elif how == \"OR\" : result = bd . concat ( [ results_1 , results_2 , ] ) . drop_duplicates () else : raise ValueError ( f \"'how' options are ('AND', 'OR'), got { how } .\" ) bd . cache ( result ) output_feature = output_feature or f \" { input_feature_1 } _ { how } _ { input_feature_2 } \" self . features [ output_feature ] = result self . logger . info ( f \"Aggregation from { input_feature_1 } { how } { input_feature_1 } stored in self.features[' { output_feature } '] \" f \"(N = { len ( result ) } )\" ) return self","title":"agg_two_features()"},{"location":"reference/phenotype/base/#eds_scikit.phenotype.base.Phenotype.compute","text":"compute ( ** kwargs ) Fetch all necessary features and perform aggregation Source code in eds_scikit/phenotype/base.py 325 326 327 328 329 def compute ( self , ** kwargs ): \"\"\" Fetch all necessary features and perform aggregation \"\"\" raise NotImplementedError ()","title":"compute()"},{"location":"reference/phenotype/base/#eds_scikit.phenotype.base.Phenotype.to_data","text":"to_data ( key : Optional [ str ] = None ) -> BaseData Appends the feature found in self.features[key] to the data object. If no key is provided, uses the last added feature PARAMETER DESCRIPTION key Key of the self.feature dictionary TYPE: Optional [ str ] DEFAULT: None RETURNS DESCRIPTION BaseData The data object with phenotype added to data.computed Source code in eds_scikit/phenotype/base.py 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 def to_data ( self , key : Optional [ str ] = None ) -> BaseData : \"\"\" Appends the feature found in self.features[key] to the data object. If no key is provided, uses the last added feature Parameters ---------- key : Optional[str] Key of the self.feature dictionary Returns ------- BaseData The data object with phenotype added to `data.computed` \"\"\" if not self . features : self . compute () if key is None : self . logger . info ( \"No key provided: Using last added feature.\" ) return self . _set ( self . features . last ()) else : assert ( key in self . features ), f \"Key { key } not found in features. Available { self . features } \" self . logger . info ( \"Using feature {key} \" ) return self . _set ( self . features [ key ])","title":"to_data()"},{"location":"reference/phenotype/base/#eds_scikit.phenotype.base.to_valid_variable_name","text":"to_valid_variable_name ( s : str ) Converts a string to a valid variable name Source code in eds_scikit/phenotype/base.py 415 416 417 418 419 420 421 422 423 424 425 426 def to_valid_variable_name ( s : str ): \"\"\" Converts a string to a valid variable name \"\"\" # Replace non-alphanumeric characters with underscores s = re . sub ( r \"\\W+\" , \"_\" , s ) # Remove leading underscores s = re . sub ( r \"^_+\" , \"\" , s ) # If the string is empty or starts with a number, prepend an underscore if not s or s [ 0 ] . isdigit (): s = \"_\" + s return s","title":"to_valid_variable_name()"},{"location":"reference/phenotype/cancer/","text":"eds_scikit.phenotype.cancer","title":"`eds_scikit.phenotype.cancer`"},{"location":"reference/phenotype/cancer/#eds_scikitphenotypecancer","text":"","title":"eds_scikit.phenotype.cancer"},{"location":"reference/phenotype/cancer/cancer/","text":"eds_scikit.phenotype.cancer.cancer CancerFromICD10 CancerFromICD10 ( data : BaseData , cancer_types : Optional [ List [ str ]] = None , level : str = 'patient' , subphenotype : bool = True , threshold : int = 1 ) Bases: Phenotype Phenotyping visits or patients using ICD10 cancer codes PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData cancer_types Optional list of cancer types to use for phenotyping TYPE: Optional[List[str]] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int DEFAULT: 1 Source code in eds_scikit/phenotype/cancer/cancer.py 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 def __init__ ( self , data : BaseData , cancer_types : Optional [ List [ str ]] = None , level : str = \"patient\" , subphenotype : bool = True , threshold : int = 1 , ): \"\"\" Parameters ---------- data : BaseData A BaseData object cancer_types : Optional[List[str]] Optional list of cancer types to use for phenotyping level : str On which level to do the aggregation, either \"patient\" or \"visit\" subphenotype : bool Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) threshold : int Minimal number of *events* (which definition depends on the `level` value) \"\"\" super () . __init__ ( data ) if cancer_types is None : cancer_types = self . ALL_CANCER_TYPES incorrect_cancer_types = set ( cancer_types ) - set ( self . ALL_CANCER_TYPES ) if incorrect_cancer_types : raise ValueError ( f \"Incorrect cancer types ( { incorrect_cancer_types } ). \" f \"Available cancer types are { self . ALL_CANCER_TYPES } \" ) self . icd10_codes = { k : v for k , v in self . ICD10_CODES . items () if k in cancer_types } self . level = level self . subphenotype = subphenotype self . threshold = threshold ICD10_CODES class-attribute ICD10_CODES = { cancer_type : { 'prefix' : df . code . to_list ()} for ( cancer_type , df ) in ICD10_CODES_DF . groupby ( 'Cancer type' )} For each cancer type, contains a set of corresponding ICD10 codes. ALL_CANCER_TYPES class-attribute ALL_CANCER_TYPES = list ( ICD10_CODES . keys ()) Available cancer types. compute compute () Fetch all necessary features and perform aggregation Source code in eds_scikit/phenotype/cancer/cancer.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 def compute ( self ): \"\"\" Fetch all necessary features and perform aggregation \"\"\" self . add_code_feature ( output_feature = \"icd10\" , source = \"icd10\" , codes = self . icd10_codes , additional_filtering = dict ( condition_status_source_value = { \"DP\" , \"DR\" }), ) self . agg_single_feature ( input_feature = \"icd10\" , level = self . level , subphenotype = self . subphenotype , threshold = self . threshold , )","title":"cancer"},{"location":"reference/phenotype/cancer/cancer/#eds_scikitphenotypecancercancer","text":"","title":"eds_scikit.phenotype.cancer.cancer"},{"location":"reference/phenotype/cancer/cancer/#eds_scikit.phenotype.cancer.cancer.CancerFromICD10","text":"CancerFromICD10 ( data : BaseData , cancer_types : Optional [ List [ str ]] = None , level : str = 'patient' , subphenotype : bool = True , threshold : int = 1 ) Bases: Phenotype Phenotyping visits or patients using ICD10 cancer codes PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData cancer_types Optional list of cancer types to use for phenotyping TYPE: Optional[List[str]] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int DEFAULT: 1 Source code in eds_scikit/phenotype/cancer/cancer.py 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 def __init__ ( self , data : BaseData , cancer_types : Optional [ List [ str ]] = None , level : str = \"patient\" , subphenotype : bool = True , threshold : int = 1 , ): \"\"\" Parameters ---------- data : BaseData A BaseData object cancer_types : Optional[List[str]] Optional list of cancer types to use for phenotyping level : str On which level to do the aggregation, either \"patient\" or \"visit\" subphenotype : bool Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) threshold : int Minimal number of *events* (which definition depends on the `level` value) \"\"\" super () . __init__ ( data ) if cancer_types is None : cancer_types = self . ALL_CANCER_TYPES incorrect_cancer_types = set ( cancer_types ) - set ( self . ALL_CANCER_TYPES ) if incorrect_cancer_types : raise ValueError ( f \"Incorrect cancer types ( { incorrect_cancer_types } ). \" f \"Available cancer types are { self . ALL_CANCER_TYPES } \" ) self . icd10_codes = { k : v for k , v in self . ICD10_CODES . items () if k in cancer_types } self . level = level self . subphenotype = subphenotype self . threshold = threshold","title":"CancerFromICD10"},{"location":"reference/phenotype/cancer/cancer/#eds_scikit.phenotype.cancer.cancer.CancerFromICD10.ICD10_CODES","text":"ICD10_CODES = { cancer_type : { 'prefix' : df . code . to_list ()} for ( cancer_type , df ) in ICD10_CODES_DF . groupby ( 'Cancer type' )} For each cancer type, contains a set of corresponding ICD10 codes.","title":"ICD10_CODES"},{"location":"reference/phenotype/cancer/cancer/#eds_scikit.phenotype.cancer.cancer.CancerFromICD10.ALL_CANCER_TYPES","text":"ALL_CANCER_TYPES = list ( ICD10_CODES . keys ()) Available cancer types.","title":"ALL_CANCER_TYPES"},{"location":"reference/phenotype/cancer/cancer/#eds_scikit.phenotype.cancer.cancer.CancerFromICD10.compute","text":"compute () Fetch all necessary features and perform aggregation Source code in eds_scikit/phenotype/cancer/cancer.py 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 def compute ( self ): \"\"\" Fetch all necessary features and perform aggregation \"\"\" self . add_code_feature ( output_feature = \"icd10\" , source = \"icd10\" , codes = self . icd10_codes , additional_filtering = dict ( condition_status_source_value = { \"DP\" , \"DR\" }), ) self . agg_single_feature ( input_feature = \"icd10\" , level = self . level , subphenotype = self . subphenotype , threshold = self . threshold , )","title":"compute()"},{"location":"reference/phenotype/diabetes/","text":"eds_scikit.phenotype.diabetes","title":"`eds_scikit.phenotype.diabetes`"},{"location":"reference/phenotype/diabetes/#eds_scikitphenotypediabetes","text":"","title":"eds_scikit.phenotype.diabetes"},{"location":"reference/phenotype/diabetes/diabetes/","text":"eds_scikit.phenotype.diabetes.diabetes DiabetesFromICD10 DiabetesFromICD10 ( data , diabetes_types : Optional [ List [ str ]] = None , level : str = 'visit' , subphenotype : bool = True , threshold : int = 1 ) Bases: Phenotype Phenotyping visits or patients using ICD10 diabetes codes PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData diabetes_types Optional list of diabetes types to use for phenotyping TYPE: Optional[List[str]] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'visit' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int DEFAULT: 1 Source code in eds_scikit/phenotype/diabetes/diabetes.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 def __init__ ( self , data , diabetes_types : Optional [ List [ str ]] = None , level : str = \"visit\" , subphenotype : bool = True , threshold : int = 1 , ): \"\"\" Parameters ---------- data : BaseData A BaseData object diabetes_types : Optional[List[str]] Optional list of diabetes types to use for phenotyping level : str On which level to do the aggregation, either \"patient\" or \"visit\" subphenotype : bool Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) threshold : int Minimal number of *events* (which definition depends on the `level` value) \"\"\" super () . __init__ ( data ) if diabetes_types is None : diabetes_types = self . ALL_DIABETES_TYPES incorrect_diabetes_types = set ( diabetes_types ) - set ( self . ALL_DIABETES_TYPES ) if incorrect_diabetes_types : raise ValueError ( f \"Incorrect diabetes types ( { incorrect_diabetes_types } ). \" f \"Available diabetes types are { self . ALL_DIABETES_TYPES } \" ) self . icd10_codes = { k : v for k , v in self . ICD10_CODES . items () if k in diabetes_types } self . level = level self . subphenotype = subphenotype self . threshold = threshold ALL_DIABETES_TYPES class-attribute ALL_DIABETES_TYPES = list ( ICD10_CODES . keys ()) Available diabetes types. compute compute () Fetch all necessary features and perform aggregation Source code in eds_scikit/phenotype/diabetes/diabetes.py 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 def compute ( self ): \"\"\" Fetch all necessary features and perform aggregation \"\"\" self . add_code_feature ( output_feature = \"icd10\" , source = \"icd10\" , codes = self . ICD10_CODES , additional_filtering = dict ( condition_status_source_value = { \"DP\" , \"DAS\" }), ) self . agg_single_feature ( input_feature = \"icd10\" , level = self . level , subphenotype = self . subphenotype , threshold = self . threshold , )","title":"diabetes"},{"location":"reference/phenotype/diabetes/diabetes/#eds_scikitphenotypediabetesdiabetes","text":"","title":"eds_scikit.phenotype.diabetes.diabetes"},{"location":"reference/phenotype/diabetes/diabetes/#eds_scikit.phenotype.diabetes.diabetes.DiabetesFromICD10","text":"DiabetesFromICD10 ( data , diabetes_types : Optional [ List [ str ]] = None , level : str = 'visit' , subphenotype : bool = True , threshold : int = 1 ) Bases: Phenotype Phenotyping visits or patients using ICD10 diabetes codes PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData diabetes_types Optional list of diabetes types to use for phenotyping TYPE: Optional[List[str]] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'visit' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int DEFAULT: 1 Source code in eds_scikit/phenotype/diabetes/diabetes.py 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 def __init__ ( self , data , diabetes_types : Optional [ List [ str ]] = None , level : str = \"visit\" , subphenotype : bool = True , threshold : int = 1 , ): \"\"\" Parameters ---------- data : BaseData A BaseData object diabetes_types : Optional[List[str]] Optional list of diabetes types to use for phenotyping level : str On which level to do the aggregation, either \"patient\" or \"visit\" subphenotype : bool Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) threshold : int Minimal number of *events* (which definition depends on the `level` value) \"\"\" super () . __init__ ( data ) if diabetes_types is None : diabetes_types = self . ALL_DIABETES_TYPES incorrect_diabetes_types = set ( diabetes_types ) - set ( self . ALL_DIABETES_TYPES ) if incorrect_diabetes_types : raise ValueError ( f \"Incorrect diabetes types ( { incorrect_diabetes_types } ). \" f \"Available diabetes types are { self . ALL_DIABETES_TYPES } \" ) self . icd10_codes = { k : v for k , v in self . ICD10_CODES . items () if k in diabetes_types } self . level = level self . subphenotype = subphenotype self . threshold = threshold","title":"DiabetesFromICD10"},{"location":"reference/phenotype/diabetes/diabetes/#eds_scikit.phenotype.diabetes.diabetes.DiabetesFromICD10.ALL_DIABETES_TYPES","text":"ALL_DIABETES_TYPES = list ( ICD10_CODES . keys ()) Available diabetes types.","title":"ALL_DIABETES_TYPES"},{"location":"reference/phenotype/diabetes/diabetes/#eds_scikit.phenotype.diabetes.diabetes.DiabetesFromICD10.compute","text":"compute () Fetch all necessary features and perform aggregation Source code in eds_scikit/phenotype/diabetes/diabetes.py 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 def compute ( self ): \"\"\" Fetch all necessary features and perform aggregation \"\"\" self . add_code_feature ( output_feature = \"icd10\" , source = \"icd10\" , codes = self . ICD10_CODES , additional_filtering = dict ( condition_status_source_value = { \"DP\" , \"DAS\" }), ) self . agg_single_feature ( input_feature = \"icd10\" , level = self . level , subphenotype = self . subphenotype , threshold = self . threshold , )","title":"compute()"},{"location":"reference/phenotype/psychiatric_disorder/","text":"eds_scikit.phenotype.psychiatric_disorder","title":"`eds_scikit.phenotype.psychiatric_disorder`"},{"location":"reference/phenotype/psychiatric_disorder/#eds_scikitphenotypepsychiatric_disorder","text":"","title":"eds_scikit.phenotype.psychiatric_disorder"},{"location":"reference/phenotype/psychiatric_disorder/psychiatric_disorder/","text":"eds_scikit.phenotype.psychiatric_disorder.psychiatric_disorder PsychiatricDisorderFromICD10 PsychiatricDisorderFromICD10 ( data , disorder_types : Optional [ List [ str ]] = None , level : str = 'patient' , subphenotype : bool = True , threshold : int = 1 ) Bases: Phenotype Phenotyping visits or patients with psychiatric disorders using ICD10 codes PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData disorder_types Optional list of disorder types to use for phenotyping TYPE: Optional[List[str]] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int DEFAULT: 1 Source code in eds_scikit/phenotype/psychiatric_disorder/psychiatric_disorder.py 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def __init__ ( self , data , disorder_types : Optional [ List [ str ]] = None , level : str = \"patient\" , subphenotype : bool = True , threshold : int = 1 , ): \"\"\" Parameters ---------- data : BaseData A BaseData object disorder_types : Optional[List[str]] Optional list of disorder types to use for phenotyping level : str On which level to do the aggregation, either \"patient\" or \"visit\" subphenotype : bool Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) threshold : int Minimal number of *events* (which definition depends on the `level` value) \"\"\" super () . __init__ ( data ) if disorder_types is None : disorder_types = self . ALL_DISORDER_TYPES incorrect_disorder_types = set ( disorder_types ) - set ( self . ALL_DISORDER_TYPES ) if incorrect_disorder_types : raise ValueError ( f \"Incorrect cancer types ( { incorrect_disorder_types } ). \" f \"Available cancer types are { self . ALL_DISORDER_TYPES } \" ) self . icd10_codes = { k : v for k , v in self . ICD10_CODES . items () if k in disorder_types } self . level = level self . subphenotype = subphenotype self . threshold = threshold ICD10_CODES class-attribute ICD10_CODES = { disorder_group : { 'exact' : df . ICD10_Code . to_list ()} for ( disorder_group , df ) in ICD10_CODES_DF . groupby ( 'disorder_group' )} ICD10 codes used for phenotyping ALL_DISORDER_TYPES class-attribute ALL_DISORDER_TYPES = list ( ICD10_CODES . keys ()) Available disorder types. compute compute () Fetch all necessary features and perform aggregation Source code in eds_scikit/phenotype/psychiatric_disorder/psychiatric_disorder.py 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 def compute ( self ): \"\"\" Fetch all necessary features and perform aggregation \"\"\" self . add_code_feature ( output_feature = \"icd10\" , source = \"icd10\" , codes = self . ICD10_CODES , ) self . agg_single_feature ( input_feature = \"icd10\" , level = self . level , subphenotype = self . subphenotype , threshold = self . threshold , )","title":"psychiatric_disorder"},{"location":"reference/phenotype/psychiatric_disorder/psychiatric_disorder/#eds_scikitphenotypepsychiatric_disorderpsychiatric_disorder","text":"","title":"eds_scikit.phenotype.psychiatric_disorder.psychiatric_disorder"},{"location":"reference/phenotype/psychiatric_disorder/psychiatric_disorder/#eds_scikit.phenotype.psychiatric_disorder.psychiatric_disorder.PsychiatricDisorderFromICD10","text":"PsychiatricDisorderFromICD10 ( data , disorder_types : Optional [ List [ str ]] = None , level : str = 'patient' , subphenotype : bool = True , threshold : int = 1 ) Bases: Phenotype Phenotyping visits or patients with psychiatric disorders using ICD10 codes PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData disorder_types Optional list of disorder types to use for phenotyping TYPE: Optional[List[str]] DEFAULT: None level On which level to do the aggregation, either \"patient\" or \"visit\" TYPE: str DEFAULT: 'patient' subphenotype Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) TYPE: bool DEFAULT: True threshold Minimal number of events (which definition depends on the level value) TYPE: int DEFAULT: 1 Source code in eds_scikit/phenotype/psychiatric_disorder/psychiatric_disorder.py 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 def __init__ ( self , data , disorder_types : Optional [ List [ str ]] = None , level : str = \"patient\" , subphenotype : bool = True , threshold : int = 1 , ): \"\"\" Parameters ---------- data : BaseData A BaseData object disorder_types : Optional[List[str]] Optional list of disorder types to use for phenotyping level : str On which level to do the aggregation, either \"patient\" or \"visit\" subphenotype : bool Whether the threshold should apply to the phenotype (\"phenotype\" column) of the subphenotype (\"subphenotype\" column) threshold : int Minimal number of *events* (which definition depends on the `level` value) \"\"\" super () . __init__ ( data ) if disorder_types is None : disorder_types = self . ALL_DISORDER_TYPES incorrect_disorder_types = set ( disorder_types ) - set ( self . ALL_DISORDER_TYPES ) if incorrect_disorder_types : raise ValueError ( f \"Incorrect cancer types ( { incorrect_disorder_types } ). \" f \"Available cancer types are { self . ALL_DISORDER_TYPES } \" ) self . icd10_codes = { k : v for k , v in self . ICD10_CODES . items () if k in disorder_types } self . level = level self . subphenotype = subphenotype self . threshold = threshold","title":"PsychiatricDisorderFromICD10"},{"location":"reference/phenotype/psychiatric_disorder/psychiatric_disorder/#eds_scikit.phenotype.psychiatric_disorder.psychiatric_disorder.PsychiatricDisorderFromICD10.ICD10_CODES","text":"ICD10_CODES = { disorder_group : { 'exact' : df . ICD10_Code . to_list ()} for ( disorder_group , df ) in ICD10_CODES_DF . groupby ( 'disorder_group' )} ICD10 codes used for phenotyping","title":"ICD10_CODES"},{"location":"reference/phenotype/psychiatric_disorder/psychiatric_disorder/#eds_scikit.phenotype.psychiatric_disorder.psychiatric_disorder.PsychiatricDisorderFromICD10.ALL_DISORDER_TYPES","text":"ALL_DISORDER_TYPES = list ( ICD10_CODES . keys ()) Available disorder types.","title":"ALL_DISORDER_TYPES"},{"location":"reference/phenotype/psychiatric_disorder/psychiatric_disorder/#eds_scikit.phenotype.psychiatric_disorder.psychiatric_disorder.PsychiatricDisorderFromICD10.compute","text":"compute () Fetch all necessary features and perform aggregation Source code in eds_scikit/phenotype/psychiatric_disorder/psychiatric_disorder.py 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 def compute ( self ): \"\"\" Fetch all necessary features and perform aggregation \"\"\" self . add_code_feature ( output_feature = \"icd10\" , source = \"icd10\" , codes = self . ICD10_CODES , ) self . agg_single_feature ( input_feature = \"icd10\" , level = self . level , subphenotype = self . subphenotype , threshold = self . threshold , )","title":"compute()"},{"location":"reference/phenotype/suicide_attempt/","text":"eds_scikit.phenotype.suicide_attempt","title":"`eds_scikit.phenotype.suicide_attempt`"},{"location":"reference/phenotype/suicide_attempt/#eds_scikitphenotypesuicide_attempt","text":"","title":"eds_scikit.phenotype.suicide_attempt"},{"location":"reference/phenotype/suicide_attempt/suicide_attempt/","text":"eds_scikit.phenotype.suicide_attempt.suicide_attempt SuicideAttemptFromICD10 SuicideAttemptFromICD10 ( data : BaseData , algo : str = 'Haguenoer2008' ) Bases: Phenotype Phenotyping visits related to a suicide attempt. Two algorithms are available: \"X60-X84\": The visit needs to have at least one ICD10 code in the range X60 to X84 \"Haguenoer2008\": The visit needs to have at least one ICD10 DAS code in the range X60 to X84, and a ICD10 DP code in the range S to T PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData algo The name of the algorithm. Should be either \"Haguenoer2008\" or \"X60-X84\" TYPE: str , optional DEFAULT: 'Haguenoer2008' Source code in eds_scikit/phenotype/suicide_attempt/suicide_attempt.py 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 def __init__ ( self , data : BaseData , algo : str = \"Haguenoer2008\" , ): \"\"\" Parameters ---------- data : BaseData A BaseData object algo : str, optional The name of the algorithm. Should be either \"Haguenoer2008\" or \"X60-X84\" \"\"\" super () . __init__ ( data , name = f \"SuicideAttemptFromICD10_ { algo } \" , ) self . algo = algo ICD10_CODES class-attribute ICD10_CODES = { 'X60-X84' : dict ( codes = { 'X60-X84' : dict ( regex = [ 'X[67]' , 'X8[0-4]' ])}), 'Haguenoer2008' : dict ( codes = { 'Haguenoer2008' : dict ( regex = [ 'S' , 'T[0-9]' ])}, additional_filtering = dict ( condition_status_source_value = 'DP' ))} ICD10 codes used by both algorithms compute compute () Fetch and aggregate features Source code in eds_scikit/phenotype/suicide_attempt/suicide_attempt.py 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def compute ( self ): \"\"\" Fetch and aggregate features \"\"\" if self . algo == \"X60-X84\" : self . add_code_feature ( output_feature = \"X60-X84\" , source = \"icd10\" , codes = self . ICD10_CODES [ \"X60-X84\" ][ \"codes\" ], ) self . agg_single_feature ( \"X60-X84\" , level = \"visit\" , subphenotype = False , threshold = 1 , ) elif self . algo == \"Haguenoer2008\" : self . add_code_feature ( output_feature = \"X60-X84\" , source = \"icd10\" , codes = self . ICD10_CODES [ \"X60-X84\" ][ \"codes\" ], additional_filtering = dict ( condition_status_source_value = \"DAS\" ), ) self . add_code_feature ( output_feature = \"DP\" , source = \"icd10\" , codes = self . ICD10_CODES [ \"Haguenoer2008\" ][ \"codes\" ], additional_filtering = self . ICD10_CODES [ \"Haguenoer2008\" ][ \"additional_filtering\" ], ) self . agg_two_features ( \"X60-X84\" , \"DP\" , output_feature = \"Haguenoer2008\" , how = \"AND\" , level = \"visit\" , subphenotype = False , thresholds = ( 1 , 1 ), )","title":"suicide_attempt"},{"location":"reference/phenotype/suicide_attempt/suicide_attempt/#eds_scikitphenotypesuicide_attemptsuicide_attempt","text":"","title":"eds_scikit.phenotype.suicide_attempt.suicide_attempt"},{"location":"reference/phenotype/suicide_attempt/suicide_attempt/#eds_scikit.phenotype.suicide_attempt.suicide_attempt.SuicideAttemptFromICD10","text":"SuicideAttemptFromICD10 ( data : BaseData , algo : str = 'Haguenoer2008' ) Bases: Phenotype Phenotyping visits related to a suicide attempt. Two algorithms are available: \"X60-X84\": The visit needs to have at least one ICD10 code in the range X60 to X84 \"Haguenoer2008\": The visit needs to have at least one ICD10 DAS code in the range X60 to X84, and a ICD10 DP code in the range S to T PARAMETER DESCRIPTION data A BaseData object TYPE: BaseData algo The name of the algorithm. Should be either \"Haguenoer2008\" or \"X60-X84\" TYPE: str , optional DEFAULT: 'Haguenoer2008' Source code in eds_scikit/phenotype/suicide_attempt/suicide_attempt.py 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 def __init__ ( self , data : BaseData , algo : str = \"Haguenoer2008\" , ): \"\"\" Parameters ---------- data : BaseData A BaseData object algo : str, optional The name of the algorithm. Should be either \"Haguenoer2008\" or \"X60-X84\" \"\"\" super () . __init__ ( data , name = f \"SuicideAttemptFromICD10_ { algo } \" , ) self . algo = algo","title":"SuicideAttemptFromICD10"},{"location":"reference/phenotype/suicide_attempt/suicide_attempt/#eds_scikit.phenotype.suicide_attempt.suicide_attempt.SuicideAttemptFromICD10.ICD10_CODES","text":"ICD10_CODES = { 'X60-X84' : dict ( codes = { 'X60-X84' : dict ( regex = [ 'X[67]' , 'X8[0-4]' ])}), 'Haguenoer2008' : dict ( codes = { 'Haguenoer2008' : dict ( regex = [ 'S' , 'T[0-9]' ])}, additional_filtering = dict ( condition_status_source_value = 'DP' ))} ICD10 codes used by both algorithms","title":"ICD10_CODES"},{"location":"reference/phenotype/suicide_attempt/suicide_attempt/#eds_scikit.phenotype.suicide_attempt.suicide_attempt.SuicideAttemptFromICD10.compute","text":"compute () Fetch and aggregate features Source code in eds_scikit/phenotype/suicide_attempt/suicide_attempt.py 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 def compute ( self ): \"\"\" Fetch and aggregate features \"\"\" if self . algo == \"X60-X84\" : self . add_code_feature ( output_feature = \"X60-X84\" , source = \"icd10\" , codes = self . ICD10_CODES [ \"X60-X84\" ][ \"codes\" ], ) self . agg_single_feature ( \"X60-X84\" , level = \"visit\" , subphenotype = False , threshold = 1 , ) elif self . algo == \"Haguenoer2008\" : self . add_code_feature ( output_feature = \"X60-X84\" , source = \"icd10\" , codes = self . ICD10_CODES [ \"X60-X84\" ][ \"codes\" ], additional_filtering = dict ( condition_status_source_value = \"DAS\" ), ) self . add_code_feature ( output_feature = \"DP\" , source = \"icd10\" , codes = self . ICD10_CODES [ \"Haguenoer2008\" ][ \"codes\" ], additional_filtering = self . ICD10_CODES [ \"Haguenoer2008\" ][ \"additional_filtering\" ], ) self . agg_two_features ( \"X60-X84\" , \"DP\" , output_feature = \"Haguenoer2008\" , how = \"AND\" , level = \"visit\" , subphenotype = False , thresholds = ( 1 , 1 ), )","title":"compute()"},{"location":"reference/plot/","text":"eds_scikit.plot","title":"`eds_scikit.plot`"},{"location":"reference/plot/#eds_scikitplot","text":"","title":"eds_scikit.plot"},{"location":"reference/plot/age_pyramid/","text":"eds_scikit.plot.age_pyramid plot_age_pyramid plot_age_pyramid ( person : DataFrame , datetime_ref : datetime = None , return_array : bool = False ) -> Tuple [ alt . ConcatChart , Series ] Plot an age pyramid from a 'person' pandas DataFrame. PARAMETER DESCRIPTION person The person table. Must have the following columns: - birth_datetime , dtype : datetime or str - person_id , dtype : any - gender_source_value , dtype : str, {'m', 'f'} TYPE: pd.DataFrame (ks.DataFrame not supported), datetime_ref : Union[datetime, str], default None The reference date to compute population age from. If a string, it searches for a column with the same name in the person table: each patient has his own datetime reference. If a datetime, the reference datetime is the same for all patients. If set to None, datetime.today() will be used instead. filename : str, default None The path to save figure at. savefig : bool, default False If set to True, filename must be set. The plot will be saved at the specified filename. return_array : bool, default False If set to True, return chart and its pd.Dataframe representation. RETURNS DESCRIPTION chart If savefig set to True, returns None. TYPE: alt . ConcatChart group_gender_age : Series, The total number of patients grouped by gender and binned age. Source code in eds_scikit/plot/age_pyramid.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 def plot_age_pyramid ( person : DataFrame , datetime_ref : datetime = None , return_array : bool = False , ) -> Tuple [ alt . ConcatChart , Series ]: \"\"\"Plot an age pyramid from a 'person' pandas DataFrame. Parameters ---------- person : pd.DataFrame (ks.DataFrame not supported), The person table. Must have the following columns: - `birth_datetime`, dtype : datetime or str - `person_id`, dtype : any - `gender_source_value`, dtype : str, {'m', 'f'} datetime_ref : Union[datetime, str], default None The reference date to compute population age from. If a string, it searches for a column with the same name in the person table: each patient has his own datetime reference. If a datetime, the reference datetime is the same for all patients. If set to None, datetime.today() will be used instead. filename : str, default None The path to save figure at. savefig : bool, default False If set to True, filename must be set. The plot will be saved at the specified filename. return_array : bool, default False If set to True, return chart and its pd.Dataframe representation. Returns ------- chart : alt.ConcatChart, If savefig set to True, returns None. group_gender_age : Series, The total number of patients grouped by gender and binned age. \"\"\" check_columns ( person , [ \"person_id\" , \"birth_datetime\" , \"gender_source_value\" ]) datetime_ref_raw = copy ( datetime_ref ) if datetime_ref is None : datetime_ref = datetime . today () elif isinstance ( datetime_ref , datetime ): datetime_ref = pd . to_datetime ( datetime_ref ) elif isinstance ( datetime_ref , str ): # A string type for datetime_ref could be either # a column name or a datetime in string format. if datetime_ref in person . columns : datetime_ref = person [ datetime_ref ] else : datetime_ref = pd . to_datetime ( datetime_ref , errors = \"coerce\" ) # In case of error, will return NaT if pd . isnull ( datetime_ref ): raise ValueError ( f \"`datetime_ref` must either be a column name or parseable date, \" f \"got string ' { datetime_ref_raw } '\" ) else : raise TypeError ( f \"`datetime_ref` must be either None, a parseable string date\" f \", a column name or a datetime. Got type: { type ( datetime_ref ) } , { datetime_ref } \" ) cols_to_keep = [ \"person_id\" , \"birth_datetime\" , \"gender_source_value\" ] person_ = bd . to_pandas ( person [ cols_to_keep ]) person_ [ \"age\" ] = ( datetime_ref - person_ [ \"birth_datetime\" ]) . dt . total_seconds () person_ [ \"age\" ] /= 365 * 24 * 3600 # Remove outliers mask_age_inliners = ( person_ [ \"age\" ] > 0 ) & ( person_ [ \"age\" ] < 125 ) n_outliers = ( ~ mask_age_inliners ) . sum () if n_outliers > 0 : perc_outliers = 100 * n_outliers / person_ . shape [ 0 ] logger . warning ( f \" { n_outliers } ( { perc_outliers : .4f } %) individuals' \" \"age is out of the (0, 125) interval, we skip them.\" ) person_ = person_ . loc [ mask_age_inliners ] # Aggregate rare age categories mask_rare_age_agg = person_ [ \"age\" ] > 90 person_ . loc [ mask_rare_age_agg , \"age\" ] = 99 bins = np . arange ( 0 , 100 , 10 ) labels = [ f \" { left } - { right } \" for left , right in zip ( bins [: - 1 ], bins [ 1 :])] person_ [ \"age_bins\" ] = pd . cut ( person_ [ \"age\" ], bins = bins , labels = labels ) person_ = person_ . loc [ person_ [ \"gender_source_value\" ] . isin ([ \"m\" , \"f\" ])] group_gender_age = person_ . groupby ([ \"gender_source_value\" , \"age_bins\" ])[ \"person_id\" ] . count () male = group_gender_age [ \"m\" ] . reset_index () female = group_gender_age [ \"f\" ] . reset_index () left = ( alt . Chart ( male ) . mark_bar () . encode ( y = alt . Y ( \"age_bins\" , axis = None , sort = alt . SortOrder ( \"descending\" )), x = alt . X ( \"person_id\" , sort = alt . SortOrder ( \"descending\" )), ) . properties ( title = \"Male\" ) ) right = ( alt . Chart ( female ) . mark_bar ( color = \"coral\" ) . encode ( y = alt . Y ( \"age_bins\" , axis = None , sort = alt . SortOrder ( \"descending\" )), x = alt . X ( \"person_id\" , title = \"N\" ), ) . properties ( title = \"Female\" ) ) middle = ( alt . Chart ( male ) . mark_text () . encode ( y = alt . Y ( \"age_bins\" , axis = None , sort = alt . SortOrder ( \"descending\" )), text = alt . Text ( \"age_bins\" ), ) ) chart = alt . concat ( left , middle , right , spacing = 5 ) if return_array : return group_gender_age return chart","title":"age_pyramid"},{"location":"reference/plot/age_pyramid/#eds_scikitplotage_pyramid","text":"","title":"eds_scikit.plot.age_pyramid"},{"location":"reference/plot/age_pyramid/#eds_scikit.plot.age_pyramid.plot_age_pyramid","text":"plot_age_pyramid ( person : DataFrame , datetime_ref : datetime = None , return_array : bool = False ) -> Tuple [ alt . ConcatChart , Series ] Plot an age pyramid from a 'person' pandas DataFrame. PARAMETER DESCRIPTION person The person table. Must have the following columns: - birth_datetime , dtype : datetime or str - person_id , dtype : any - gender_source_value , dtype : str, {'m', 'f'} TYPE: pd.DataFrame (ks.DataFrame not supported), datetime_ref : Union[datetime, str], default None The reference date to compute population age from. If a string, it searches for a column with the same name in the person table: each patient has his own datetime reference. If a datetime, the reference datetime is the same for all patients. If set to None, datetime.today() will be used instead. filename : str, default None The path to save figure at. savefig : bool, default False If set to True, filename must be set. The plot will be saved at the specified filename. return_array : bool, default False If set to True, return chart and its pd.Dataframe representation. RETURNS DESCRIPTION chart If savefig set to True, returns None. TYPE: alt . ConcatChart group_gender_age : Series, The total number of patients grouped by gender and binned age. Source code in eds_scikit/plot/age_pyramid.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 def plot_age_pyramid ( person : DataFrame , datetime_ref : datetime = None , return_array : bool = False , ) -> Tuple [ alt . ConcatChart , Series ]: \"\"\"Plot an age pyramid from a 'person' pandas DataFrame. Parameters ---------- person : pd.DataFrame (ks.DataFrame not supported), The person table. Must have the following columns: - `birth_datetime`, dtype : datetime or str - `person_id`, dtype : any - `gender_source_value`, dtype : str, {'m', 'f'} datetime_ref : Union[datetime, str], default None The reference date to compute population age from. If a string, it searches for a column with the same name in the person table: each patient has his own datetime reference. If a datetime, the reference datetime is the same for all patients. If set to None, datetime.today() will be used instead. filename : str, default None The path to save figure at. savefig : bool, default False If set to True, filename must be set. The plot will be saved at the specified filename. return_array : bool, default False If set to True, return chart and its pd.Dataframe representation. Returns ------- chart : alt.ConcatChart, If savefig set to True, returns None. group_gender_age : Series, The total number of patients grouped by gender and binned age. \"\"\" check_columns ( person , [ \"person_id\" , \"birth_datetime\" , \"gender_source_value\" ]) datetime_ref_raw = copy ( datetime_ref ) if datetime_ref is None : datetime_ref = datetime . today () elif isinstance ( datetime_ref , datetime ): datetime_ref = pd . to_datetime ( datetime_ref ) elif isinstance ( datetime_ref , str ): # A string type for datetime_ref could be either # a column name or a datetime in string format. if datetime_ref in person . columns : datetime_ref = person [ datetime_ref ] else : datetime_ref = pd . to_datetime ( datetime_ref , errors = \"coerce\" ) # In case of error, will return NaT if pd . isnull ( datetime_ref ): raise ValueError ( f \"`datetime_ref` must either be a column name or parseable date, \" f \"got string ' { datetime_ref_raw } '\" ) else : raise TypeError ( f \"`datetime_ref` must be either None, a parseable string date\" f \", a column name or a datetime. Got type: { type ( datetime_ref ) } , { datetime_ref } \" ) cols_to_keep = [ \"person_id\" , \"birth_datetime\" , \"gender_source_value\" ] person_ = bd . to_pandas ( person [ cols_to_keep ]) person_ [ \"age\" ] = ( datetime_ref - person_ [ \"birth_datetime\" ]) . dt . total_seconds () person_ [ \"age\" ] /= 365 * 24 * 3600 # Remove outliers mask_age_inliners = ( person_ [ \"age\" ] > 0 ) & ( person_ [ \"age\" ] < 125 ) n_outliers = ( ~ mask_age_inliners ) . sum () if n_outliers > 0 : perc_outliers = 100 * n_outliers / person_ . shape [ 0 ] logger . warning ( f \" { n_outliers } ( { perc_outliers : .4f } %) individuals' \" \"age is out of the (0, 125) interval, we skip them.\" ) person_ = person_ . loc [ mask_age_inliners ] # Aggregate rare age categories mask_rare_age_agg = person_ [ \"age\" ] > 90 person_ . loc [ mask_rare_age_agg , \"age\" ] = 99 bins = np . arange ( 0 , 100 , 10 ) labels = [ f \" { left } - { right } \" for left , right in zip ( bins [: - 1 ], bins [ 1 :])] person_ [ \"age_bins\" ] = pd . cut ( person_ [ \"age\" ], bins = bins , labels = labels ) person_ = person_ . loc [ person_ [ \"gender_source_value\" ] . isin ([ \"m\" , \"f\" ])] group_gender_age = person_ . groupby ([ \"gender_source_value\" , \"age_bins\" ])[ \"person_id\" ] . count () male = group_gender_age [ \"m\" ] . reset_index () female = group_gender_age [ \"f\" ] . reset_index () left = ( alt . Chart ( male ) . mark_bar () . encode ( y = alt . Y ( \"age_bins\" , axis = None , sort = alt . SortOrder ( \"descending\" )), x = alt . X ( \"person_id\" , sort = alt . SortOrder ( \"descending\" )), ) . properties ( title = \"Male\" ) ) right = ( alt . Chart ( female ) . mark_bar ( color = \"coral\" ) . encode ( y = alt . Y ( \"age_bins\" , axis = None , sort = alt . SortOrder ( \"descending\" )), x = alt . X ( \"person_id\" , title = \"N\" ), ) . properties ( title = \"Female\" ) ) middle = ( alt . Chart ( male ) . mark_text () . encode ( y = alt . Y ( \"age_bins\" , axis = None , sort = alt . SortOrder ( \"descending\" )), text = alt . Text ( \"age_bins\" ), ) ) chart = alt . concat ( left , middle , right , spacing = 5 ) if return_array : return group_gender_age return chart","title":"plot_age_pyramid()"},{"location":"reference/plot/altair_utils/","text":"eds_scikit.plot.altair_utils generate_cyclic_colors generate_cyclic_colors ( N : int ) -> List [ str ] Given an interger of N values, return a cyclic list of size N with repeated 20 altair standards colors. PARAMETER DESCRIPTION N TYPE: int RETURNS DESCRIPTION List [ str ] Source code in eds_scikit/plot/altair_utils.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 def generate_cyclic_colors ( N : int ) -> List [ str ]: \"\"\"Given an interger of N values, return a cyclic list of size N with repeated 20 altair standards colors. Parameters ---------- N : int Returns ------- List[str] \"\"\" category20_colors = [ \"#1f77b4\" , \"#ff7f0e\" , \"#2ca02c\" , \"#d62728\" , \"#9467bd\" , \"#8c564b\" , \"#e377c2\" , \"#7f7f7f\" , \"#bcbd22\" , \"#17becf\" , \"#aec7e8\" , \"#ffbb78\" , \"#98df8a\" , \"#ff9896\" , \"#c5b0d5\" , \"#c49c94\" , \"#f7b6d2\" , \"#c7c7c7\" , \"#dbdb8d\" , \"#9edae5\" , ] num_colors = len ( category20_colors ) return [ category20_colors [ i % num_colors ] for i in range ( N )] generate_color_map generate_color_map ( df : DataFrame , col : str ) -> alt . Scale Given a dataframe and a column name, generate an altair color scale for visualization purpose. PARAMETER DESCRIPTION df TYPE: DataFrame col TYPE: str RETURNS DESCRIPTION alt . Scale Source code in eds_scikit/plot/altair_utils.py 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 def generate_color_map ( df : DataFrame , col : str ) -> alt . Scale : \"\"\"Given a dataframe and a column name, generate an altair color scale for visualization purpose. Parameters ---------- df : DataFrame col : str Returns ------- alt.Scale \"\"\" check_columns ( df , required_columns = [ col ], ) category_values = df [ col ] . fillna ( \"NaN\" ) . unique () . tolist () if \"NaN\" in category_values : category_colors = generate_cyclic_colors ( len ( category_values ) - 1 ) category_values = [ category_value for category_value in category_values if category_value != \"NaN\" ] domain = [ * category_values , \"NaN\" ] range = [ * category_colors , \"black\" ] else : category_colors = generate_cyclic_colors ( len ( category_values )) domain = category_values range = category_colors color_scale = alt . Scale ( domain = domain , range = range ) return color_scale","title":"altair_utils"},{"location":"reference/plot/altair_utils/#eds_scikitplotaltair_utils","text":"","title":"eds_scikit.plot.altair_utils"},{"location":"reference/plot/altair_utils/#eds_scikit.plot.altair_utils.generate_cyclic_colors","text":"generate_cyclic_colors ( N : int ) -> List [ str ] Given an interger of N values, return a cyclic list of size N with repeated 20 altair standards colors. PARAMETER DESCRIPTION N TYPE: int RETURNS DESCRIPTION List [ str ] Source code in eds_scikit/plot/altair_utils.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 def generate_cyclic_colors ( N : int ) -> List [ str ]: \"\"\"Given an interger of N values, return a cyclic list of size N with repeated 20 altair standards colors. Parameters ---------- N : int Returns ------- List[str] \"\"\" category20_colors = [ \"#1f77b4\" , \"#ff7f0e\" , \"#2ca02c\" , \"#d62728\" , \"#9467bd\" , \"#8c564b\" , \"#e377c2\" , \"#7f7f7f\" , \"#bcbd22\" , \"#17becf\" , \"#aec7e8\" , \"#ffbb78\" , \"#98df8a\" , \"#ff9896\" , \"#c5b0d5\" , \"#c49c94\" , \"#f7b6d2\" , \"#c7c7c7\" , \"#dbdb8d\" , \"#9edae5\" , ] num_colors = len ( category20_colors ) return [ category20_colors [ i % num_colors ] for i in range ( N )]","title":"generate_cyclic_colors()"},{"location":"reference/plot/altair_utils/#eds_scikit.plot.altair_utils.generate_color_map","text":"generate_color_map ( df : DataFrame , col : str ) -> alt . Scale Given a dataframe and a column name, generate an altair color scale for visualization purpose. PARAMETER DESCRIPTION df TYPE: DataFrame col TYPE: str RETURNS DESCRIPTION alt . Scale Source code in eds_scikit/plot/altair_utils.py 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 def generate_color_map ( df : DataFrame , col : str ) -> alt . Scale : \"\"\"Given a dataframe and a column name, generate an altair color scale for visualization purpose. Parameters ---------- df : DataFrame col : str Returns ------- alt.Scale \"\"\" check_columns ( df , required_columns = [ col ], ) category_values = df [ col ] . fillna ( \"NaN\" ) . unique () . tolist () if \"NaN\" in category_values : category_colors = generate_cyclic_colors ( len ( category_values ) - 1 ) category_values = [ category_value for category_value in category_values if category_value != \"NaN\" ] domain = [ * category_values , \"NaN\" ] range = [ * category_colors , \"black\" ] else : category_colors = generate_cyclic_colors ( len ( category_values )) domain = category_values range = category_colors color_scale = alt . Scale ( domain = domain , range = range ) return color_scale","title":"generate_color_map()"},{"location":"reference/plot/event_sequences/","text":"eds_scikit.plot.event_sequences plot_event_sequences plot_event_sequences ( df_events : pd . DataFrame , event_col : Optional [ str ] = 'event' , event_start_datetime_col : Optional [ str ] = 'event_start_datetime' , event_end_datetime_col : Optional [ str ] = 'event_end_datetime' , dim_mapping : Optional [ Dict [ str , Dict [ str , Union [ Tuple [ int ], str ]]]] = None , index_date_col : Optional [ str ] = None , family_col : Optional [ str ] = None , family_to_index : Optional [ Dict [ str , int ]] = None , list_person_ids : Optional [ List [ str ]] = None , same_x_axis_scale : Optional [ bool ] = False , subplot_height : Optional [ int ] = 200 , subplot_width : Optional [ int ] = 500 , point_size : Optional [ int ] = 400 , bar_height : Optional [ int ] = 20 , title : Optional [ str ] = None , seed : Optional [ int ] = 0 ) -> alt . VConcatChart Plots individual sequences from an events DataFrame. Each event must be recorded with a start date, a name and a person_id . Events can be both one-time (only start date given) or longitudinal (both start and end dates). Events can also be aggregated in families using the family_col argument. Finally, events labelling and colors can be manually set by providing a dim_mapping dictionary. PARAMETER DESCRIPTION df_events DataFrame gathering the events information. Must contain at least person_id , event, t_start and t_end columns. TYPE: pd . DataFrame event_col Column name of the events. TYPE: Optional [ str ] DEFAULT: 'event' event_start_datetime_col Column name of the event start datetime. TYPE: Optional [ str ] DEFAULT: 'event_start_datetime' event_end_datetime_col Column name of the event end datetime. TYPE: Optional [ str ] DEFAULT: 'event_end_datetime' dim_mapping Mapping dictionary to provide plotting details on events. Must be of type : dim_labelling = { \"event_1\" : { \"color\" : ( 255 , 200 , 150 ), \"label\" : \"Event 1\" }, \"event_2\" : { \"color\" : ( 200 , 255 , 150 ), \"label\" : \"Event 2\" }, } TYPE: Optional [ Dict [ str , Dict [ str , Union [ Tuple [ int ], str ]]]] DEFAULT: None index_date_col Column name of the index date to compute relative datetimes for events. For example, it could be the date of inclusion for each patient. TYPE: Optional [ str ] DEFAULT: None family_col Column name of family events. Events of a given family will be plot on the same row. TYPE: Optional [ str ] DEFAULT: None family_to_index Dictionary mapping event family names to ordering indices. TYPE: Optional [ Dict [ str , int ]] DEFAULT: None list_person_ids List of person_ids to plot. If None given, only the first three individual sequences will be plot. TYPE: Optional [ List [ str ]] DEFAULT: None same_x_axis_scale Whether to use the same axis scale for all sequences. TYPE: Optional [ bool ] DEFAULT: False subplot_height Height of each plot. TYPE: Optional [ int ] DEFAULT: 200 subplot_width Width of each plot. TYPE: Optional [ int ] DEFAULT: 500 point_size Size of points for one-time events. TYPE: Optional [ int ] DEFAULT: 400 bar_height Height of bars for continuous events. TYPE: Optional [ int ] DEFAULT: 20 title Chart title. TYPE: Optional [ str ] DEFAULT: None seed Seed to randomly draw colors when not provided. TYPE: Optional [ int ] DEFAULT: 0 RETURNS DESCRIPTION chart Chart with the plotted individual event sequences. TYPE: alt . VConcatChart Source code in eds_scikit/plot/event_sequences.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 def plot_event_sequences ( df_events : pd . DataFrame , event_col : Optional [ str ] = \"event\" , event_start_datetime_col : Optional [ str ] = \"event_start_datetime\" , event_end_datetime_col : Optional [ str ] = \"event_end_datetime\" , dim_mapping : Optional [ Dict [ str , Dict [ str , Union [ Tuple [ int ], str ]]]] = None , index_date_col : Optional [ str ] = None , family_col : Optional [ str ] = None , family_to_index : Optional [ Dict [ str , int ]] = None , list_person_ids : Optional [ List [ str ]] = None , same_x_axis_scale : Optional [ bool ] = False , subplot_height : Optional [ int ] = 200 , subplot_width : Optional [ int ] = 500 , point_size : Optional [ int ] = 400 , bar_height : Optional [ int ] = 20 , title : Optional [ str ] = None , seed : Optional [ int ] = 0 , ) -> alt . VConcatChart : \"\"\" Plots individual sequences from an events DataFrame. Each event must be recorded with a start date, a name and a `person_id`. Events can be both one-time (only start date given) or longitudinal (both start and end dates). Events can also be aggregated in families using the `family_col` argument. Finally, events labelling and colors can be manually set by providing a `dim_mapping` dictionary. Parameters ---------- df_events: pd.DataFrame DataFrame gathering the events information. Must contain at least `person_id`, event, t_start and t_end columns. event_col: Optional[str] = \"event\" Column name of the events. event_start_datetime_col: Optional[str] = \"event_start_datetime\" Column name of the event start datetime. event_end_datetime_col: Optional[str] = \"event_end_datetime\" Column name of the event end datetime. dim_mapping: Optional[Dict[str,Dict[str,Union[tuple(int),str]]]] = None Mapping dictionary to provide plotting details on events. Must be of type : ```python dim_labelling = { \"event_1\": {\"color\": (255, 200, 150), \"label\": \"Event 1\"}, \"event_2\": {\"color\": (200, 255, 150), \"label\": \"Event 2\"}, } ``` index_date_col: Optional[str] = None Column name of the index date to compute relative datetimes for events. For example, it could be the date of inclusion for each patient. family_col: Optional[str] = None Column name of family events. Events of a given family will be plot on the same row. family_to_index: Optional[Dict[str,int]] = None Dictionary mapping event family names to ordering indices. list_person_ids: Optional[List[str]] = None List of person_ids to plot. If None given, only the first three individual sequences will be plot. same_x_axis_scale: Optional[bool] = False Whether to use the same axis scale for all sequences. subplot_height: Optional[int] = 200 Height of each plot. subplot_width: Optional[int] = 500 Width of each plot. point_size: Optional[int] = 400 Size of points for one-time events. bar_height: Optional[int] = 20 Height of bars for continuous events. title: Optional[str] = None Chart title. seed: int = 0 Seed to randomly draw colors when not provided. Returns ------- chart: alt.VConcatChart Chart with the plotted individual event sequences. \"\"\" rng = np . random . RandomState ( seed ) # Check required columns required_columns = [ \"person_id\" , event_col , event_start_datetime_col , event_end_datetime_col , ] if index_date_col is not None : required_columns . append ( index_date_col ) if family_col is not None : required_columns . append ( family_col ) check_columns ( df_events , required_columns = required_columns ) # Pre-selection of the sequences to plot if list_person_ids is None : list_person_ids = df_events . person_id . unique ()[: 3 ] df_events = df_events . query ( \"person_id in @list_person_ids\" ) # Ordering order = { val : idx for idx , val in enumerate ( list_person_ids )} df_events = df_events . sort_values ( by = \"person_id\" , key = lambda x : x . map ( order )) # Encoding events start and end dates if index_date_col is not None : df_events [ \"relative_event_start\" ] = ( df_events [ event_start_datetime_col ] - df_events [ index_date_col ] ) . dt . days . astype ( int ) df_events [ \"event_duration\" ] = ( ( df_events [ event_end_datetime_col ] - df_events [ event_start_datetime_col ]) . dt . days . fillna ( 1 ) . astype ( int ) ) df_events [ \"relative_event_end\" ] = ( df_events . relative_event_start + df_events . event_duration ) x_encoding = \"relative_event_start:Q\" x2_encoding = \"relative_event_end:Q\" else : x_encoding = f \" { event_start_datetime_col } :T\" x2_encoding = f \" { event_end_datetime_col } :T\" # Ordering events if family_col is not None : if family_to_index is None : family_to_index = { v : k for k , v in enumerate ( df_events [ family_col ] . unique ()) } df_events [ \"dim_id\" ] = df_events [ family_col ] . map ( family_to_index ) else : _ , classes = np . unique ( df_events [ event_col ], return_inverse = True ) df_events [ \"dim_id\" ] = classes # Mapping events towards colors and labels if dim_mapping is not None : df_events [ \"dim_label\" ] = df_events [ event_col ] . apply ( lambda x : dim_mapping [ x ][ \"label\" ] ) labels = [] colors = [] for event in dim_mapping . keys (): labels . append ( dim_mapping [ event ][ \"label\" ]) colors . append ( f \"rgb { dim_mapping [ event ][ 'color' ] } \" ) else : df_events [ \"dim_label\" ] = df_events [ event_col ] labels = list ( df_events [ \"dim_label\" ] . unique ()) colors = [ f \"rgb { tuple ( rng . randint ( 0 , 255 , size = 3 )) } \" for _ in labels ] # Base chart raw = alt . Chart ( df_events ) . encode ( x = alt . X ( x_encoding ), y = alt . Y ( \"dim_id:O\" , title = \"\" ), color = alt . Color ( \"dim_label:O\" , scale = alt . Scale ( domain = labels , range = colors ), title = \"Event type\" , ), ) # One-time events point_dim = ( raw . transform_filter ( { \"not\" : alt . FieldValidPredicate ( field = event_end_datetime_col , valid = True )} ) . mark_point ( filled = True , size = point_size , cursor = \"pointer\" ) . encode ( tooltip = [ f \" { event_col } \" , f \" { event_start_datetime_col } \" ], ) ) # Continuous events continuous_dim = ( raw . transform_filter ( alt . FieldValidPredicate ( event_end_datetime_col , valid = True ) ) . mark_bar ( filled = True , cursor = \"pointer\" , cornerRadius = bar_height / 2 , height = bar_height , ) . encode ( x2 = x2_encoding , tooltip = [ f \" { event_col } \" , f \" { event_start_datetime_col } \" , f \" { event_end_datetime_col } \" , ], ) ) # Aggregation base = ( point_dim + continuous_dim ) . properties ( width = subplot_width , height = subplot_height , ) # Vertical concatenation of all patients' sequences chart = ( alt . vconcat () . configure_legend ( labelFontSize = 13 , symbolSize = 150 , titleFontSize = 15 ) . configure_axisY ( disable = True ) ) for person_id in df_events . person_id . unique (): chart &= base . transform_filter ( alt . expr . datum . person_id == person_id ) . properties ( title = f \"Sequence of patient { person_id } \" ) if same_x_axis_scale : chart = chart . resolve_scale ( x = \"shared\" ) if title is not None : chart = chart . properties ( title = title ) return chart","title":"event_sequences"},{"location":"reference/plot/event_sequences/#eds_scikitplotevent_sequences","text":"","title":"eds_scikit.plot.event_sequences"},{"location":"reference/plot/event_sequences/#eds_scikit.plot.event_sequences.plot_event_sequences","text":"plot_event_sequences ( df_events : pd . DataFrame , event_col : Optional [ str ] = 'event' , event_start_datetime_col : Optional [ str ] = 'event_start_datetime' , event_end_datetime_col : Optional [ str ] = 'event_end_datetime' , dim_mapping : Optional [ Dict [ str , Dict [ str , Union [ Tuple [ int ], str ]]]] = None , index_date_col : Optional [ str ] = None , family_col : Optional [ str ] = None , family_to_index : Optional [ Dict [ str , int ]] = None , list_person_ids : Optional [ List [ str ]] = None , same_x_axis_scale : Optional [ bool ] = False , subplot_height : Optional [ int ] = 200 , subplot_width : Optional [ int ] = 500 , point_size : Optional [ int ] = 400 , bar_height : Optional [ int ] = 20 , title : Optional [ str ] = None , seed : Optional [ int ] = 0 ) -> alt . VConcatChart Plots individual sequences from an events DataFrame. Each event must be recorded with a start date, a name and a person_id . Events can be both one-time (only start date given) or longitudinal (both start and end dates). Events can also be aggregated in families using the family_col argument. Finally, events labelling and colors can be manually set by providing a dim_mapping dictionary. PARAMETER DESCRIPTION df_events DataFrame gathering the events information. Must contain at least person_id , event, t_start and t_end columns. TYPE: pd . DataFrame event_col Column name of the events. TYPE: Optional [ str ] DEFAULT: 'event' event_start_datetime_col Column name of the event start datetime. TYPE: Optional [ str ] DEFAULT: 'event_start_datetime' event_end_datetime_col Column name of the event end datetime. TYPE: Optional [ str ] DEFAULT: 'event_end_datetime' dim_mapping Mapping dictionary to provide plotting details on events. Must be of type : dim_labelling = { \"event_1\" : { \"color\" : ( 255 , 200 , 150 ), \"label\" : \"Event 1\" }, \"event_2\" : { \"color\" : ( 200 , 255 , 150 ), \"label\" : \"Event 2\" }, } TYPE: Optional [ Dict [ str , Dict [ str , Union [ Tuple [ int ], str ]]]] DEFAULT: None index_date_col Column name of the index date to compute relative datetimes for events. For example, it could be the date of inclusion for each patient. TYPE: Optional [ str ] DEFAULT: None family_col Column name of family events. Events of a given family will be plot on the same row. TYPE: Optional [ str ] DEFAULT: None family_to_index Dictionary mapping event family names to ordering indices. TYPE: Optional [ Dict [ str , int ]] DEFAULT: None list_person_ids List of person_ids to plot. If None given, only the first three individual sequences will be plot. TYPE: Optional [ List [ str ]] DEFAULT: None same_x_axis_scale Whether to use the same axis scale for all sequences. TYPE: Optional [ bool ] DEFAULT: False subplot_height Height of each plot. TYPE: Optional [ int ] DEFAULT: 200 subplot_width Width of each plot. TYPE: Optional [ int ] DEFAULT: 500 point_size Size of points for one-time events. TYPE: Optional [ int ] DEFAULT: 400 bar_height Height of bars for continuous events. TYPE: Optional [ int ] DEFAULT: 20 title Chart title. TYPE: Optional [ str ] DEFAULT: None seed Seed to randomly draw colors when not provided. TYPE: Optional [ int ] DEFAULT: 0 RETURNS DESCRIPTION chart Chart with the plotted individual event sequences. TYPE: alt . VConcatChart Source code in eds_scikit/plot/event_sequences.py 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 def plot_event_sequences ( df_events : pd . DataFrame , event_col : Optional [ str ] = \"event\" , event_start_datetime_col : Optional [ str ] = \"event_start_datetime\" , event_end_datetime_col : Optional [ str ] = \"event_end_datetime\" , dim_mapping : Optional [ Dict [ str , Dict [ str , Union [ Tuple [ int ], str ]]]] = None , index_date_col : Optional [ str ] = None , family_col : Optional [ str ] = None , family_to_index : Optional [ Dict [ str , int ]] = None , list_person_ids : Optional [ List [ str ]] = None , same_x_axis_scale : Optional [ bool ] = False , subplot_height : Optional [ int ] = 200 , subplot_width : Optional [ int ] = 500 , point_size : Optional [ int ] = 400 , bar_height : Optional [ int ] = 20 , title : Optional [ str ] = None , seed : Optional [ int ] = 0 , ) -> alt . VConcatChart : \"\"\" Plots individual sequences from an events DataFrame. Each event must be recorded with a start date, a name and a `person_id`. Events can be both one-time (only start date given) or longitudinal (both start and end dates). Events can also be aggregated in families using the `family_col` argument. Finally, events labelling and colors can be manually set by providing a `dim_mapping` dictionary. Parameters ---------- df_events: pd.DataFrame DataFrame gathering the events information. Must contain at least `person_id`, event, t_start and t_end columns. event_col: Optional[str] = \"event\" Column name of the events. event_start_datetime_col: Optional[str] = \"event_start_datetime\" Column name of the event start datetime. event_end_datetime_col: Optional[str] = \"event_end_datetime\" Column name of the event end datetime. dim_mapping: Optional[Dict[str,Dict[str,Union[tuple(int),str]]]] = None Mapping dictionary to provide plotting details on events. Must be of type : ```python dim_labelling = { \"event_1\": {\"color\": (255, 200, 150), \"label\": \"Event 1\"}, \"event_2\": {\"color\": (200, 255, 150), \"label\": \"Event 2\"}, } ``` index_date_col: Optional[str] = None Column name of the index date to compute relative datetimes for events. For example, it could be the date of inclusion for each patient. family_col: Optional[str] = None Column name of family events. Events of a given family will be plot on the same row. family_to_index: Optional[Dict[str,int]] = None Dictionary mapping event family names to ordering indices. list_person_ids: Optional[List[str]] = None List of person_ids to plot. If None given, only the first three individual sequences will be plot. same_x_axis_scale: Optional[bool] = False Whether to use the same axis scale for all sequences. subplot_height: Optional[int] = 200 Height of each plot. subplot_width: Optional[int] = 500 Width of each plot. point_size: Optional[int] = 400 Size of points for one-time events. bar_height: Optional[int] = 20 Height of bars for continuous events. title: Optional[str] = None Chart title. seed: int = 0 Seed to randomly draw colors when not provided. Returns ------- chart: alt.VConcatChart Chart with the plotted individual event sequences. \"\"\" rng = np . random . RandomState ( seed ) # Check required columns required_columns = [ \"person_id\" , event_col , event_start_datetime_col , event_end_datetime_col , ] if index_date_col is not None : required_columns . append ( index_date_col ) if family_col is not None : required_columns . append ( family_col ) check_columns ( df_events , required_columns = required_columns ) # Pre-selection of the sequences to plot if list_person_ids is None : list_person_ids = df_events . person_id . unique ()[: 3 ] df_events = df_events . query ( \"person_id in @list_person_ids\" ) # Ordering order = { val : idx for idx , val in enumerate ( list_person_ids )} df_events = df_events . sort_values ( by = \"person_id\" , key = lambda x : x . map ( order )) # Encoding events start and end dates if index_date_col is not None : df_events [ \"relative_event_start\" ] = ( df_events [ event_start_datetime_col ] - df_events [ index_date_col ] ) . dt . days . astype ( int ) df_events [ \"event_duration\" ] = ( ( df_events [ event_end_datetime_col ] - df_events [ event_start_datetime_col ]) . dt . days . fillna ( 1 ) . astype ( int ) ) df_events [ \"relative_event_end\" ] = ( df_events . relative_event_start + df_events . event_duration ) x_encoding = \"relative_event_start:Q\" x2_encoding = \"relative_event_end:Q\" else : x_encoding = f \" { event_start_datetime_col } :T\" x2_encoding = f \" { event_end_datetime_col } :T\" # Ordering events if family_col is not None : if family_to_index is None : family_to_index = { v : k for k , v in enumerate ( df_events [ family_col ] . unique ()) } df_events [ \"dim_id\" ] = df_events [ family_col ] . map ( family_to_index ) else : _ , classes = np . unique ( df_events [ event_col ], return_inverse = True ) df_events [ \"dim_id\" ] = classes # Mapping events towards colors and labels if dim_mapping is not None : df_events [ \"dim_label\" ] = df_events [ event_col ] . apply ( lambda x : dim_mapping [ x ][ \"label\" ] ) labels = [] colors = [] for event in dim_mapping . keys (): labels . append ( dim_mapping [ event ][ \"label\" ]) colors . append ( f \"rgb { dim_mapping [ event ][ 'color' ] } \" ) else : df_events [ \"dim_label\" ] = df_events [ event_col ] labels = list ( df_events [ \"dim_label\" ] . unique ()) colors = [ f \"rgb { tuple ( rng . randint ( 0 , 255 , size = 3 )) } \" for _ in labels ] # Base chart raw = alt . Chart ( df_events ) . encode ( x = alt . X ( x_encoding ), y = alt . Y ( \"dim_id:O\" , title = \"\" ), color = alt . Color ( \"dim_label:O\" , scale = alt . Scale ( domain = labels , range = colors ), title = \"Event type\" , ), ) # One-time events point_dim = ( raw . transform_filter ( { \"not\" : alt . FieldValidPredicate ( field = event_end_datetime_col , valid = True )} ) . mark_point ( filled = True , size = point_size , cursor = \"pointer\" ) . encode ( tooltip = [ f \" { event_col } \" , f \" { event_start_datetime_col } \" ], ) ) # Continuous events continuous_dim = ( raw . transform_filter ( alt . FieldValidPredicate ( event_end_datetime_col , valid = True ) ) . mark_bar ( filled = True , cursor = \"pointer\" , cornerRadius = bar_height / 2 , height = bar_height , ) . encode ( x2 = x2_encoding , tooltip = [ f \" { event_col } \" , f \" { event_start_datetime_col } \" , f \" { event_end_datetime_col } \" , ], ) ) # Aggregation base = ( point_dim + continuous_dim ) . properties ( width = subplot_width , height = subplot_height , ) # Vertical concatenation of all patients' sequences chart = ( alt . vconcat () . configure_legend ( labelFontSize = 13 , symbolSize = 150 , titleFontSize = 15 ) . configure_axisY ( disable = True ) ) for person_id in df_events . person_id . unique (): chart &= base . transform_filter ( alt . expr . datum . person_id == person_id ) . properties ( title = f \"Sequence of patient { person_id } \" ) if same_x_axis_scale : chart = chart . resolve_scale ( x = \"shared\" ) if title is not None : chart = chart . properties ( title = title ) return chart","title":"plot_event_sequences()"},{"location":"reference/plot/omop_teva/","text":"eds_scikit.plot.omop_teva generate_omop_teva generate_omop_teva ( data : HiveData , start_date : str , end_date : str , teva_config : dict = default_omop_teva_config , output_dir = 'omop_teva' ) Generate OMOP TEVA folder. PARAMETER DESCRIPTION data Must contain the visit_occurrence table. TYPE: HiveData start_date The start date for data extraction. TYPE: str end_date The end date for data extraction. TYPE: str teva_config OMOP TEVA configuration, by default default_omop_teva_config . Must start with visit_occurrence configuration. TYPE: dict , optional DEFAULT: default_omop_teva_config output_dir Output directory path, by default \"omop_teva\". TYPE: str , optional DEFAULT: 'omop_teva' Examples: Example configuration for teva_config : default_omop_teva_config = { \"visit_occurrence\": { \"category_columns\": [ \"visit_occurrence_id\", \"care_site_short_name\", \"stay_source_value\" ], \"date_column\": \"visit_start_datetime\", \"mapper\": { \"visit_occurrence_id\": {\"not NaN\": \". \"} } }, \"other_table\": { \"category_columns\": [ \"visit_occurrence_id\", \"column A\", \"column B\", \"column C\" ], \"date_column\": \"column_datetime\", \"mapper\": { \"column A\": {\"not NaN\": \". \"}, \"column B\": {\"X type\": \"X.*\", \"Y type\": \"Y\"} } } ... } Source code in eds_scikit/plot/omop_teva.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 def generate_omop_teva ( data : HiveData , start_date : str , end_date : str , teva_config : dict = default_omop_teva_config , output_dir = \"omop_teva\" , ): \"\"\" Generate OMOP TEVA folder. Parameters ---------- data : HiveData Must contain the visit_occurrence table. start_date : str The start date for data extraction. end_date : str The end date for data extraction. teva_config : dict, optional OMOP TEVA configuration, by default `default_omop_teva_config`. Must start with visit_occurrence configuration. output_dir : str, optional Output directory path, by default \"omop_teva\". Examples -------- Example configuration for `teva_config`: default_omop_teva_config = { \"visit_occurrence\": { \"category_columns\": [ \"visit_occurrence_id\", \"care_site_short_name\", \"stay_source_value\" ], \"date_column\": \"visit_start_datetime\", \"mapper\": { \"visit_occurrence_id\": {\"not NaN\": \".*\"} } }, \"other_table\": { \"category_columns\": [ \"visit_occurrence_id\", \"column A\", \"column B\", \"column C\" ], \"date_column\": \"column_datetime\", \"mapper\": { \"column A\": {\"not NaN\": \".*\"}, \"column B\": {\"X type\": \"X.*\", \"Y type\": \"Y\"} } } ... } \"\"\" if not os . path . exists ( f \" { output_dir } /\" ): os . makedirs ( f \" { output_dir } /\" ) # First, preprocess visit_occurrence which will be merged with remaining config tables try : visit_occurrence = data . visit_occurrence visit_occurrence = visit_occurrence . merge ( data . care_site [[ \"care_site_id\" , \"care_site_short_name\" ]], on = \"care_site_id\" ) teva_config [ \"visit_occurrence\" ] except AttributeError : raise Exception ( \"No visit_occurrence or care_site table in input data object. visit_occurrence and care_site table must be provided.\" ) # Iterate config tables for table_name , config in teva_config . items (): logger . info ( f \"Starting { table_name } processing.\" ) if table_name == \"visit_occurrence\" : visit_columns = [ * config [ \"category_columns\" ], config [ \"date_column\" ], \"visit_occurrence_id\" , ] visit_columns = list ( set ( visit_columns ) . intersection ( visit_occurrence . columns ) ) visit_occurrence = visit_occurrence [ visit_columns ] table = visit_occurrence . copy () else : try : table = data . _read_table ( table_name ) drop_columns = ( set ( visit_occurrence . columns ) . intersection ( table . columns ) ) . difference ([ \"visit_occurrence_id\" ]) if drop_columns : table = table . merge ( visit_occurrence . drop ( columns = drop_columns ), on = \"visit_occurrence_id\" , how = \"left\" , ) else : table = table . merge ( visit_occurrence , on = \"visit_occurrence_id\" , how = \"left\" ) except AttributeError : logger . warning ( f \"No { table_name } table in input data object. Skipping { table_name } .\" ) continue # Compute reduced table representation table [ \"visit_occurrence_id\" ] = table [ \"visit_occurrence_id\" ] . astype ( str ) table_count = reduce_table ( table , start_date = start_date , end_date = end_date , ** config ) table_count = table_count [ ~ ( table_count == 0 ) . any ( axis = 1 )] # Compute associated chart chart = visualize_table ( table_count , title = f \" { table_name } table dashboard\" ) # Save computations save_pickle ( f \" { output_dir } / { table_name } _count\" , table_count ) chart . save ( f \" { output_dir } / { table_name } _chart.html\" ) logger . info ( f \" { table_name } processing done.\" )","title":"omop_teva"},{"location":"reference/plot/omop_teva/#eds_scikitplotomop_teva","text":"","title":"eds_scikit.plot.omop_teva"},{"location":"reference/plot/omop_teva/#eds_scikit.plot.omop_teva.generate_omop_teva","text":"generate_omop_teva ( data : HiveData , start_date : str , end_date : str , teva_config : dict = default_omop_teva_config , output_dir = 'omop_teva' ) Generate OMOP TEVA folder. PARAMETER DESCRIPTION data Must contain the visit_occurrence table. TYPE: HiveData start_date The start date for data extraction. TYPE: str end_date The end date for data extraction. TYPE: str teva_config OMOP TEVA configuration, by default default_omop_teva_config . Must start with visit_occurrence configuration. TYPE: dict , optional DEFAULT: default_omop_teva_config output_dir Output directory path, by default \"omop_teva\". TYPE: str , optional DEFAULT: 'omop_teva' Examples: Example configuration for teva_config : default_omop_teva_config = { \"visit_occurrence\": { \"category_columns\": [ \"visit_occurrence_id\", \"care_site_short_name\", \"stay_source_value\" ], \"date_column\": \"visit_start_datetime\", \"mapper\": { \"visit_occurrence_id\": {\"not NaN\": \". \"} } }, \"other_table\": { \"category_columns\": [ \"visit_occurrence_id\", \"column A\", \"column B\", \"column C\" ], \"date_column\": \"column_datetime\", \"mapper\": { \"column A\": {\"not NaN\": \". \"}, \"column B\": {\"X type\": \"X.*\", \"Y type\": \"Y\"} } } ... } Source code in eds_scikit/plot/omop_teva.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 def generate_omop_teva ( data : HiveData , start_date : str , end_date : str , teva_config : dict = default_omop_teva_config , output_dir = \"omop_teva\" , ): \"\"\" Generate OMOP TEVA folder. Parameters ---------- data : HiveData Must contain the visit_occurrence table. start_date : str The start date for data extraction. end_date : str The end date for data extraction. teva_config : dict, optional OMOP TEVA configuration, by default `default_omop_teva_config`. Must start with visit_occurrence configuration. output_dir : str, optional Output directory path, by default \"omop_teva\". Examples -------- Example configuration for `teva_config`: default_omop_teva_config = { \"visit_occurrence\": { \"category_columns\": [ \"visit_occurrence_id\", \"care_site_short_name\", \"stay_source_value\" ], \"date_column\": \"visit_start_datetime\", \"mapper\": { \"visit_occurrence_id\": {\"not NaN\": \".*\"} } }, \"other_table\": { \"category_columns\": [ \"visit_occurrence_id\", \"column A\", \"column B\", \"column C\" ], \"date_column\": \"column_datetime\", \"mapper\": { \"column A\": {\"not NaN\": \".*\"}, \"column B\": {\"X type\": \"X.*\", \"Y type\": \"Y\"} } } ... } \"\"\" if not os . path . exists ( f \" { output_dir } /\" ): os . makedirs ( f \" { output_dir } /\" ) # First, preprocess visit_occurrence which will be merged with remaining config tables try : visit_occurrence = data . visit_occurrence visit_occurrence = visit_occurrence . merge ( data . care_site [[ \"care_site_id\" , \"care_site_short_name\" ]], on = \"care_site_id\" ) teva_config [ \"visit_occurrence\" ] except AttributeError : raise Exception ( \"No visit_occurrence or care_site table in input data object. visit_occurrence and care_site table must be provided.\" ) # Iterate config tables for table_name , config in teva_config . items (): logger . info ( f \"Starting { table_name } processing.\" ) if table_name == \"visit_occurrence\" : visit_columns = [ * config [ \"category_columns\" ], config [ \"date_column\" ], \"visit_occurrence_id\" , ] visit_columns = list ( set ( visit_columns ) . intersection ( visit_occurrence . columns ) ) visit_occurrence = visit_occurrence [ visit_columns ] table = visit_occurrence . copy () else : try : table = data . _read_table ( table_name ) drop_columns = ( set ( visit_occurrence . columns ) . intersection ( table . columns ) ) . difference ([ \"visit_occurrence_id\" ]) if drop_columns : table = table . merge ( visit_occurrence . drop ( columns = drop_columns ), on = \"visit_occurrence_id\" , how = \"left\" , ) else : table = table . merge ( visit_occurrence , on = \"visit_occurrence_id\" , how = \"left\" ) except AttributeError : logger . warning ( f \"No { table_name } table in input data object. Skipping { table_name } .\" ) continue # Compute reduced table representation table [ \"visit_occurrence_id\" ] = table [ \"visit_occurrence_id\" ] . astype ( str ) table_count = reduce_table ( table , start_date = start_date , end_date = end_date , ** config ) table_count = table_count [ ~ ( table_count == 0 ) . any ( axis = 1 )] # Compute associated chart chart = visualize_table ( table_count , title = f \" { table_name } table dashboard\" ) # Save computations save_pickle ( f \" { output_dir } / { table_name } _count\" , table_count ) chart . save ( f \" { output_dir } / { table_name } _chart.html\" ) logger . info ( f \" { table_name } processing done.\" )","title":"generate_omop_teva()"},{"location":"reference/plot/table_viz/","text":"eds_scikit.plot.table_viz map_column map_column ( table : DataFrame , mapping : dict , src_column : str , target_column : str , drop : bool = True ) -> DataFrame Map a dataframe column given a mapping dictionnary (of regex). If src_column == target_column , src_column will be renamed. Parameter\" table : DataFrame mapping : dict EXAMPLE : {\"column name\" : {\"CR\" : r\"^CR\", \"CRH\" : r\"^CRH\"}} src_column : str target_column : str RETURNS DESCRIPTION DataFrame Dataframe with mapped column Source code in eds_scikit/plot/table_viz.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 def map_column ( table : DataFrame , mapping : dict , src_column : str , target_column : str , drop : bool = True , ) -> DataFrame : \"\"\"Map a dataframe column given a mapping dictionnary (of regex). If ```src_column == target_column```, src_column will be renamed. Parameter\" ---------- table : DataFrame mapping : dict **EXAMPLE**: `{\"column name\" : {\"CR\" : r\"^CR\", \"CRH\" : r\"^CRH\"}}` src_column : str target_column : str Returns ------- DataFrame Dataframe with mapped column \"\"\" check_columns ( table , required_columns = [ src_column ], ) remove_columns = [] if src_column == target_column : table [ src_column + \"_src\" ] = table [ src_column ] src_column = src_column + \"_src\" remove_columns += [ src_column ] table [ target_column ] = \"Other\" table . loc [ table [ src_column ] . isna (), target_column ] = \"NaN\" for target , regex in mapping . items (): table . loc [ table [ src_column ] . str . contains ( regex , case = False , regex = True , na = False ), target_column , ] = target if drop : table = table [ set ( table . columns ) . difference ( remove_columns )] return table preprocess_table preprocess_table ( table : DataFrame , category_columns : List [ str ], date_column : str , start_date : str , end_date : str , mapper : dict = None ) -> DataFrame PARAMETER DESCRIPTION table Input dataframe to be reduced. TYPE: DataFrame category_columns Columns to perform reduction on. TYPE: List [ str ] date_column Date column. TYPE: str start_date start date TYPE: str end_date end date TYPE: str mapper EXAMPLE : {\"column 1\" : {\"CR\" : r\"^CR\", \"CRH\" : r\"^CRH\"}, \"column 2\" : {\"code a\" : r\"^A\", \"code b\" : r\"^B\"}} TYPE: dict DEFAULT: None RETURNS DESCRIPTION DataFrame Formated and preprocessed table RAISES DESCRIPTION ValueError Source code in eds_scikit/plot/table_viz.py 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 def preprocess_table ( table : DataFrame , category_columns : List [ str ], date_column : str , start_date : str , end_date : str , mapper : dict = None , ) -> DataFrame : \"\"\" Parameters ---------- table : DataFrame Input dataframe to be reduced. category_columns : List[str] Columns to perform reduction on. date_column : str Date column. start_date : str start date end_date : str end date mapper : dict **EXAMPLE**: `{\"column 1\" : {\"CR\" : r\"^CR\", \"CRH\" : r\"^CRH\"}, \"column 2\" : {\"code a\" : r\"^A\", \"code b\" : r\"^B\"}}` Returns ------- DataFrame Formated and preprocessed table Raises ------ ValueError \"\"\" # Check and format to string category columns remove_colums = [] for col in category_columns : if not ( col in table . columns ): logger . info ( f \"Column { col } not in table.\" ) remove_colums += [ col ] else : table [ col ] = table [ col ] . astype ( str ) for col in remove_colums : category_columns . remove ( col ) if category_columns == []: raise Exception ( \"No columns from category_columns in input table.\" ) category_columns = [ * category_columns , date_column ] table = table [ category_columns ] # Filter table on dates framework = get_framework ( table ) table = table [( table [ date_column ] >= start_date ) & ( table [ date_column ] <= end_date )] table [ \"datetime\" ] = framework . to_datetime ( table [ date_column ] . dt . strftime ( \"%Y-%m\" )) table = table . drop ( columns = [ date_column ]) # Map category columns if mapper : for col , mapping in mapper . items (): if col in category_columns : table = map_column ( table , mapping , col , col ) return table reduce_table reduce_table ( table : DataFrame , start_date : str , end_date : str , category_columns : List [ str ], date_column : str , mapper : dict = None ) -> DataFrame Reduce input table by counting each cartesian product values (col1, col2, ..., date_col) for each columns in category_columns and each date. Columns values must be under 50 . Use mapper to reduce this size. PARAMETER DESCRIPTION table Input dataframe to be reduced. TYPE: DataFrame start_date start date TYPE: str end_date end date TYPE: str category_columns Columns to perform reduction on. TYPE: List [ str ] date_column Date column. TYPE: str mapper EXAMPLE : {\"column 1\" : {\"CR\" : r\"^CR\", \"CRH\" : r\"^CRH\"}, \"column 2\" : {\"code a\" : r\"^A\", \"code b\" : r\"^B\"}} TYPE: dict DEFAULT: None RETURNS DESCRIPTION DataFrame Reducted DataFrame with columns category_columns, date_column and count. RAISES DESCRIPTION ValueError Source code in eds_scikit/plot/table_viz.py 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 def reduce_table ( table : DataFrame , start_date : str , end_date : str , category_columns : List [ str ], date_column : str , mapper : dict = None , ) -> DataFrame : \"\"\" Reduce input table by counting each cartesian product values (col1, col2, ..., date_col) for each columns in category_columns and each date. Columns values must be under 50 . Use mapper to reduce this size. Parameters ---------- table : DataFrame Input dataframe to be reduced. start_date : str start date end_date : str end date category_columns : List[str] Columns to perform reduction on. date_column : str Date column. mapper : dict **EXAMPLE**: `{\"column 1\" : {\"CR\" : r\"^CR\", \"CRH\" : r\"^CRH\"}, \"column 2\" : {\"code a\" : r\"^A\", \"code b\" : r\"^B\"}}` Returns ------- DataFrame Reducted DataFrame with columns category_columns, date_column and count. Raises ------ ValueError \"\"\" check_columns ( table , required_columns = [ date_column ], ) table = preprocess_table ( table , category_columns , date_column , start_date , end_date , mapper ) # to prevent computation issues shape = table . shape # noqa # raise error it too much categorical values nunique = table . nunique () oversized_columns = nunique [( nunique . index != \"datetime\" ) & ( nunique > 50 )] . index if len ( oversized_columns ) > 0 : raise ValueError ( f \"Input table columns can't have more then 50 values. Consider using eds_scikit.plot.map_column. Oversized columns: { oversized_columns } \" , ) # compute reducted table table_count = ( table . fillna ( \"NaN\" ) . groupby ([ \"datetime\" , * category_columns ]) . size () . reset_index ( name = \"count\" ) ) # to prevent computation issues shape = table_count . shape # noqa # final formatting table_count = to ( \"pandas\" , table_count ) table_count [ \"datetime\" ] = pd . to_datetime ( table_count [ \"datetime\" ]) date_dataframe = pd . DataFrame ( pd . date_range ( start = start_date , end = end_date , freq = \"MS\" ), columns = [ \"datetime\" ] ) table_count = table_count . merge ( date_dataframe , on = \"datetime\" , how = \"right\" ) table_count [ \"count\" ] = table_count [ \"count\" ] . fillna ( 0 ) table_count = table_count . fillna ( \"NaN\" ) return table_count visualize_table visualize_table ( table_count : DataFrame , title : str = 'table exploration dashboard' , description = True ) -> alt . Chart Generate reduced table dashboard. PARAMETER DESCRIPTION table_count Output from eds_scikit.plot.table_viz.reduce_table TYPE: DataFrame title Chart title TYPE: str DEFAULT: 'table exploration dashboard' RETURNS DESCRIPTION alt . Chart reduce_table dashboard RAISES DESCRIPTION ValueError description Source code in eds_scikit/plot/table_viz.py 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 def visualize_table ( table_count : DataFrame , title : str = \"table exploration dashboard\" , description = True , ) -> alt . Chart : \"\"\"Generate reduced table dashboard. Parameters ---------- table_count : DataFrame Output from eds_scikit.plot.table_viz.reduce_table title : str Chart title Returns ------- alt.Chart reduce_table dashboard Raises ------ ValueError _description_ \"\"\" check_columns = [ \"count\" , \"datetime\" ] for check_column in check_columns : if not ( check_column in table_count . columns ): raise ValueError ( f \"Input table must have a { check_column } column.\" ) selections = {} columns = [ col for col in table_count . columns if not ( col in [ \"datetime\" , \"count\" ])] for col in columns : selections [ col ] = alt . selection_point ( fields = [ col ], on = \"click\" , bind = \"legend\" , clear = \"dblclick\" ) charts = [] width , height = 300 , 50 # Two charts per column for i , col in enumerate ( columns ): color_scale = generate_color_map ( table_count , col ) selection_col = [ selections [ s ] for s in selections if ( s != col )] # Global volumetry chart chart = ( alt . Chart ( table_count ) . mark_bar () . encode ( x = col + \":N\" , y = \"sum(count):Q\" , color = alt . Color ( col + \":N\" , scale = color_scale ), opacity = alt . condition ( selections [ col ], alt . value ( 1 ), alt . value ( 0.3 )), tooltip = [ col ], ) . add_params ( selections [ col ]) ) if len ( selection_col ) > 0 : chart = chart . add_params ( selections [ col ]) . transform_filter ( reduce ( ( lambda x , y : x & y ), selection_col , ) ) # Temporal volumetry chart base_t = ( alt . Chart ( table_count ) . mark_line () . encode ( x = alt . X ( \"yearmonth(datetime):T\" ), y = alt . Y ( \"sum(count):Q\" , axis = alt . Axis ( format = \"s\" )), color = alt . Color ( col + \":N\" , scale = color_scale ), opacity = alt . condition ( selections [ col ], alt . value ( 1 ), alt . value ( 0.3 )), ) . add_params ( selections [ col ]) ) if len ( selection_col ) > 0 : base_t = base_t . transform_filter ( reduce ( ( lambda x , y : x & y ), selection_col , ) ) base_t = base_t . properties ( width = width , height = height ) chart = chart . properties ( width = width , height = height ) chart = ( chart | base_t ) . properties ( title = col ) charts . append ( chart ) charts = alt . vconcat ( * charts ) . resolve_scale ( color = \"independent\" ) if description : title = { \"text\" : [ title ], \"subtitle\" : [ \"ALT + SHIFT to select multiple categories\" , \"Double-click on legend to unselect\" , \"Reduce table column and values size for better interactivity\" , ], \"fontSize\" : 25 , \"subtitleFontSize\" : 15 , \"offset\" : 30 , \"subtitlePadding\" : 20 , } charts = charts . properties ( padding = { \"left\" : 50 , \"top\" : 50 , \"right\" : 50 , \"bottom\" : 50 }, title = title , ) . configure_legend ( columns = 4 , symbolLimit = 0 ) return charts","title":"table_viz"},{"location":"reference/plot/table_viz/#eds_scikitplottable_viz","text":"","title":"eds_scikit.plot.table_viz"},{"location":"reference/plot/table_viz/#eds_scikit.plot.table_viz.map_column","text":"map_column ( table : DataFrame , mapping : dict , src_column : str , target_column : str , drop : bool = True ) -> DataFrame Map a dataframe column given a mapping dictionnary (of regex). If src_column == target_column , src_column will be renamed.","title":"map_column()"},{"location":"reference/plot/table_viz/#eds_scikit.plot.table_viz.map_column--parameter","text":"table : DataFrame mapping : dict EXAMPLE : {\"column name\" : {\"CR\" : r\"^CR\", \"CRH\" : r\"^CRH\"}} src_column : str target_column : str RETURNS DESCRIPTION DataFrame Dataframe with mapped column Source code in eds_scikit/plot/table_viz.py 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 def map_column ( table : DataFrame , mapping : dict , src_column : str , target_column : str , drop : bool = True , ) -> DataFrame : \"\"\"Map a dataframe column given a mapping dictionnary (of regex). If ```src_column == target_column```, src_column will be renamed. Parameter\" ---------- table : DataFrame mapping : dict **EXAMPLE**: `{\"column name\" : {\"CR\" : r\"^CR\", \"CRH\" : r\"^CRH\"}}` src_column : str target_column : str Returns ------- DataFrame Dataframe with mapped column \"\"\" check_columns ( table , required_columns = [ src_column ], ) remove_columns = [] if src_column == target_column : table [ src_column + \"_src\" ] = table [ src_column ] src_column = src_column + \"_src\" remove_columns += [ src_column ] table [ target_column ] = \"Other\" table . loc [ table [ src_column ] . isna (), target_column ] = \"NaN\" for target , regex in mapping . items (): table . loc [ table [ src_column ] . str . contains ( regex , case = False , regex = True , na = False ), target_column , ] = target if drop : table = table [ set ( table . columns ) . difference ( remove_columns )] return table","title":"Parameter\""},{"location":"reference/plot/table_viz/#eds_scikit.plot.table_viz.preprocess_table","text":"preprocess_table ( table : DataFrame , category_columns : List [ str ], date_column : str , start_date : str , end_date : str , mapper : dict = None ) -> DataFrame PARAMETER DESCRIPTION table Input dataframe to be reduced. TYPE: DataFrame category_columns Columns to perform reduction on. TYPE: List [ str ] date_column Date column. TYPE: str start_date start date TYPE: str end_date end date TYPE: str mapper EXAMPLE : {\"column 1\" : {\"CR\" : r\"^CR\", \"CRH\" : r\"^CRH\"}, \"column 2\" : {\"code a\" : r\"^A\", \"code b\" : r\"^B\"}} TYPE: dict DEFAULT: None RETURNS DESCRIPTION DataFrame Formated and preprocessed table RAISES DESCRIPTION ValueError Source code in eds_scikit/plot/table_viz.py 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 def preprocess_table ( table : DataFrame , category_columns : List [ str ], date_column : str , start_date : str , end_date : str , mapper : dict = None , ) -> DataFrame : \"\"\" Parameters ---------- table : DataFrame Input dataframe to be reduced. category_columns : List[str] Columns to perform reduction on. date_column : str Date column. start_date : str start date end_date : str end date mapper : dict **EXAMPLE**: `{\"column 1\" : {\"CR\" : r\"^CR\", \"CRH\" : r\"^CRH\"}, \"column 2\" : {\"code a\" : r\"^A\", \"code b\" : r\"^B\"}}` Returns ------- DataFrame Formated and preprocessed table Raises ------ ValueError \"\"\" # Check and format to string category columns remove_colums = [] for col in category_columns : if not ( col in table . columns ): logger . info ( f \"Column { col } not in table.\" ) remove_colums += [ col ] else : table [ col ] = table [ col ] . astype ( str ) for col in remove_colums : category_columns . remove ( col ) if category_columns == []: raise Exception ( \"No columns from category_columns in input table.\" ) category_columns = [ * category_columns , date_column ] table = table [ category_columns ] # Filter table on dates framework = get_framework ( table ) table = table [( table [ date_column ] >= start_date ) & ( table [ date_column ] <= end_date )] table [ \"datetime\" ] = framework . to_datetime ( table [ date_column ] . dt . strftime ( \"%Y-%m\" )) table = table . drop ( columns = [ date_column ]) # Map category columns if mapper : for col , mapping in mapper . items (): if col in category_columns : table = map_column ( table , mapping , col , col ) return table","title":"preprocess_table()"},{"location":"reference/plot/table_viz/#eds_scikit.plot.table_viz.reduce_table","text":"reduce_table ( table : DataFrame , start_date : str , end_date : str , category_columns : List [ str ], date_column : str , mapper : dict = None ) -> DataFrame Reduce input table by counting each cartesian product values (col1, col2, ..., date_col) for each columns in category_columns and each date. Columns values must be under 50 . Use mapper to reduce this size. PARAMETER DESCRIPTION table Input dataframe to be reduced. TYPE: DataFrame start_date start date TYPE: str end_date end date TYPE: str category_columns Columns to perform reduction on. TYPE: List [ str ] date_column Date column. TYPE: str mapper EXAMPLE : {\"column 1\" : {\"CR\" : r\"^CR\", \"CRH\" : r\"^CRH\"}, \"column 2\" : {\"code a\" : r\"^A\", \"code b\" : r\"^B\"}} TYPE: dict DEFAULT: None RETURNS DESCRIPTION DataFrame Reducted DataFrame with columns category_columns, date_column and count. RAISES DESCRIPTION ValueError Source code in eds_scikit/plot/table_viz.py 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 def reduce_table ( table : DataFrame , start_date : str , end_date : str , category_columns : List [ str ], date_column : str , mapper : dict = None , ) -> DataFrame : \"\"\" Reduce input table by counting each cartesian product values (col1, col2, ..., date_col) for each columns in category_columns and each date. Columns values must be under 50 . Use mapper to reduce this size. Parameters ---------- table : DataFrame Input dataframe to be reduced. start_date : str start date end_date : str end date category_columns : List[str] Columns to perform reduction on. date_column : str Date column. mapper : dict **EXAMPLE**: `{\"column 1\" : {\"CR\" : r\"^CR\", \"CRH\" : r\"^CRH\"}, \"column 2\" : {\"code a\" : r\"^A\", \"code b\" : r\"^B\"}}` Returns ------- DataFrame Reducted DataFrame with columns category_columns, date_column and count. Raises ------ ValueError \"\"\" check_columns ( table , required_columns = [ date_column ], ) table = preprocess_table ( table , category_columns , date_column , start_date , end_date , mapper ) # to prevent computation issues shape = table . shape # noqa # raise error it too much categorical values nunique = table . nunique () oversized_columns = nunique [( nunique . index != \"datetime\" ) & ( nunique > 50 )] . index if len ( oversized_columns ) > 0 : raise ValueError ( f \"Input table columns can't have more then 50 values. Consider using eds_scikit.plot.map_column. Oversized columns: { oversized_columns } \" , ) # compute reducted table table_count = ( table . fillna ( \"NaN\" ) . groupby ([ \"datetime\" , * category_columns ]) . size () . reset_index ( name = \"count\" ) ) # to prevent computation issues shape = table_count . shape # noqa # final formatting table_count = to ( \"pandas\" , table_count ) table_count [ \"datetime\" ] = pd . to_datetime ( table_count [ \"datetime\" ]) date_dataframe = pd . DataFrame ( pd . date_range ( start = start_date , end = end_date , freq = \"MS\" ), columns = [ \"datetime\" ] ) table_count = table_count . merge ( date_dataframe , on = \"datetime\" , how = \"right\" ) table_count [ \"count\" ] = table_count [ \"count\" ] . fillna ( 0 ) table_count = table_count . fillna ( \"NaN\" ) return table_count","title":"reduce_table()"},{"location":"reference/plot/table_viz/#eds_scikit.plot.table_viz.visualize_table","text":"visualize_table ( table_count : DataFrame , title : str = 'table exploration dashboard' , description = True ) -> alt . Chart Generate reduced table dashboard. PARAMETER DESCRIPTION table_count Output from eds_scikit.plot.table_viz.reduce_table TYPE: DataFrame title Chart title TYPE: str DEFAULT: 'table exploration dashboard' RETURNS DESCRIPTION alt . Chart reduce_table dashboard RAISES DESCRIPTION ValueError description Source code in eds_scikit/plot/table_viz.py 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 def visualize_table ( table_count : DataFrame , title : str = \"table exploration dashboard\" , description = True , ) -> alt . Chart : \"\"\"Generate reduced table dashboard. Parameters ---------- table_count : DataFrame Output from eds_scikit.plot.table_viz.reduce_table title : str Chart title Returns ------- alt.Chart reduce_table dashboard Raises ------ ValueError _description_ \"\"\" check_columns = [ \"count\" , \"datetime\" ] for check_column in check_columns : if not ( check_column in table_count . columns ): raise ValueError ( f \"Input table must have a { check_column } column.\" ) selections = {} columns = [ col for col in table_count . columns if not ( col in [ \"datetime\" , \"count\" ])] for col in columns : selections [ col ] = alt . selection_point ( fields = [ col ], on = \"click\" , bind = \"legend\" , clear = \"dblclick\" ) charts = [] width , height = 300 , 50 # Two charts per column for i , col in enumerate ( columns ): color_scale = generate_color_map ( table_count , col ) selection_col = [ selections [ s ] for s in selections if ( s != col )] # Global volumetry chart chart = ( alt . Chart ( table_count ) . mark_bar () . encode ( x = col + \":N\" , y = \"sum(count):Q\" , color = alt . Color ( col + \":N\" , scale = color_scale ), opacity = alt . condition ( selections [ col ], alt . value ( 1 ), alt . value ( 0.3 )), tooltip = [ col ], ) . add_params ( selections [ col ]) ) if len ( selection_col ) > 0 : chart = chart . add_params ( selections [ col ]) . transform_filter ( reduce ( ( lambda x , y : x & y ), selection_col , ) ) # Temporal volumetry chart base_t = ( alt . Chart ( table_count ) . mark_line () . encode ( x = alt . X ( \"yearmonth(datetime):T\" ), y = alt . Y ( \"sum(count):Q\" , axis = alt . Axis ( format = \"s\" )), color = alt . Color ( col + \":N\" , scale = color_scale ), opacity = alt . condition ( selections [ col ], alt . value ( 1 ), alt . value ( 0.3 )), ) . add_params ( selections [ col ]) ) if len ( selection_col ) > 0 : base_t = base_t . transform_filter ( reduce ( ( lambda x , y : x & y ), selection_col , ) ) base_t = base_t . properties ( width = width , height = height ) chart = chart . properties ( width = width , height = height ) chart = ( chart | base_t ) . properties ( title = col ) charts . append ( chart ) charts = alt . vconcat ( * charts ) . resolve_scale ( color = \"independent\" ) if description : title = { \"text\" : [ title ], \"subtitle\" : [ \"ALT + SHIFT to select multiple categories\" , \"Double-click on legend to unselect\" , \"Reduce table column and values size for better interactivity\" , ], \"fontSize\" : 25 , \"subtitleFontSize\" : 15 , \"offset\" : 30 , \"subtitlePadding\" : 20 , } charts = charts . properties ( padding = { \"left\" : 50 , \"top\" : 50 , \"right\" : 50 , \"bottom\" : 50 }, title = title , ) . configure_legend ( columns = 4 , symbolLimit = 0 ) return charts","title":"visualize_table()"},{"location":"reference/resources/","text":"eds_scikit.resources","title":"`eds_scikit.resources`"},{"location":"reference/resources/#eds_scikitresources","text":"","title":"eds_scikit.resources"},{"location":"reference/resources/reg/","text":"eds_scikit.resources.reg Registry get get ( key : str , function_name : str ) Get a function from one of the registry PARAMETER DESCRIPTION key The registry's name. The function will be retrieved from self. TYPE: str function_name The function's name, The function will be retrieved via self. .get(function_name). Can be of the form \"function_name.version\" TYPE: str RETURNS DESCRIPTION Callable The registered function Source code in eds_scikit/resources/reg.py 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 def get ( self , key : str , function_name : str , ): \"\"\" Get a function from one of the registry Parameters ---------- key : str The registry's name. The function will be retrieved from self. function_name : str The function's name, The function will be retrieved via self..get(function_name). Can be of the form \"function_name.version\" Returns ------- Callable The registered function \"\"\" if not hasattr ( self , key ): raise ValueError ( f \"eds-scikit's registry has no { key } key !\" ) r = getattr ( self , key ) candidates = r . get_all () . keys () if function_name in candidates : # Exact match func = r . get ( function_name ) else : # Looking for a match excluding version string candidates = [ func for func in candidates if function_name == func . split ( \".\" )[ 0 ] ] if len ( candidates ) > 1 : # Multiple versions available, a specific one should be specified raise ValueError ( ( f \"Multiple functions are available under the name { function_name } : \\n \" f \" { candidates } \\n \" \"Please choose one of the implementation listed above.\" ) ) if not candidates : # No registered function raise ValueError ( ( f \"No function registered under the name { function_name } \" f \"was found in eds-scikit's { key } registry. \\n \" \"If you work in AP-HP's ecosystem, you should install \" 'extra resources via `pip install \"eds-scikit[aphp]\"' \"You can define your own and decorate it as follow: \\n \" \"from eds_scikit.resources import registry \\n \" f \"@registry. { key } (' { function_name } ')\" f \"def your_custom_func(args, **kwargs):\" , \" ...\" , ) ) func = r . get ( candidates [ 0 ]) return func","title":"reg"},{"location":"reference/resources/reg/#eds_scikitresourcesreg","text":"","title":"eds_scikit.resources.reg"},{"location":"reference/resources/reg/#eds_scikit.resources.reg.Registry","text":"","title":"Registry"},{"location":"reference/resources/reg/#eds_scikit.resources.reg.Registry.get","text":"get ( key : str , function_name : str ) Get a function from one of the registry PARAMETER DESCRIPTION key The registry's name. The function will be retrieved from self. TYPE: str function_name The function's name, The function will be retrieved via self. .get(function_name). Can be of the form \"function_name.version\" TYPE: str RETURNS DESCRIPTION Callable The registered function Source code in eds_scikit/resources/reg.py 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 def get ( self , key : str , function_name : str , ): \"\"\" Get a function from one of the registry Parameters ---------- key : str The registry's name. The function will be retrieved from self. function_name : str The function's name, The function will be retrieved via self..get(function_name). Can be of the form \"function_name.version\" Returns ------- Callable The registered function \"\"\" if not hasattr ( self , key ): raise ValueError ( f \"eds-scikit's registry has no { key } key !\" ) r = getattr ( self , key ) candidates = r . get_all () . keys () if function_name in candidates : # Exact match func = r . get ( function_name ) else : # Looking for a match excluding version string candidates = [ func for func in candidates if function_name == func . split ( \".\" )[ 0 ] ] if len ( candidates ) > 1 : # Multiple versions available, a specific one should be specified raise ValueError ( ( f \"Multiple functions are available under the name { function_name } : \\n \" f \" { candidates } \\n \" \"Please choose one of the implementation listed above.\" ) ) if not candidates : # No registered function raise ValueError ( ( f \"No function registered under the name { function_name } \" f \"was found in eds-scikit's { key } registry. \\n \" \"If you work in AP-HP's ecosystem, you should install \" 'extra resources via `pip install \"eds-scikit[aphp]\"' \"You can define your own and decorate it as follow: \\n \" \"from eds_scikit.resources import registry \\n \" f \"@registry. { key } (' { function_name } ')\" f \"def your_custom_func(args, **kwargs):\" , \" ...\" , ) ) func = r . get ( candidates [ 0 ]) return func","title":"get()"},{"location":"reference/resources/utils/","text":"eds_scikit.resources.utils versionize versionize ( algo : str ) -> Optional [ str ] Extract, if found, the version substring of an algorithm name. PARAMETER DESCRIPTION algo Of the form \" \" or \" . \" TYPE: str RETURNS DESCRIPTION Optional [ str ] The algo version suffix Source code in eds_scikit/resources/utils.py 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 def versionize ( algo : str ) -> Optional [ str ]: \"\"\" Extract, if found, the version substring of an algorithm name. Parameters ---------- algo : str Of the form \"\" or \".\" Returns ------- Optional[str] The algo version suffix \"\"\" splited = algo . split ( \".\" ) if len ( splited ) == 1 : return None return splited [ - 1 ]","title":"utils"},{"location":"reference/resources/utils/#eds_scikitresourcesutils","text":"","title":"eds_scikit.resources.utils"},{"location":"reference/resources/utils/#eds_scikit.resources.utils.versionize","text":"versionize ( algo : str ) -> Optional [ str ] Extract, if found, the version substring of an algorithm name. PARAMETER DESCRIPTION algo Of the form \" \" or \" . \" TYPE: str RETURNS DESCRIPTION Optional [ str ] The algo version suffix Source code in eds_scikit/resources/utils.py 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 def versionize ( algo : str ) -> Optional [ str ]: \"\"\" Extract, if found, the version substring of an algorithm name. Parameters ---------- algo : str Of the form \"\" or \".\" Returns ------- Optional[str] The algo version suffix \"\"\" splited = algo . split ( \".\" ) if len ( splited ) == 1 : return None return splited [ - 1 ]","title":"versionize()"},{"location":"reference/structures/","text":"eds_scikit.structures","title":"`eds_scikit.structures`"},{"location":"reference/structures/#eds_scikitstructures","text":"","title":"eds_scikit.structures"},{"location":"reference/structures/attributes/","text":"eds_scikit.structures.attributes ATTRIBUTE_REGEX_PATTERNS module-attribute ATTRIBUTE_REGEX_PATTERNS = [{ 'attribute' : 'IS_EMERGENCY' , 'pattern' : ' \\\\ bURG| \\\\ bSAU \\\\ b| \\\\ bUHCD \\\\ b| \\\\ bZHTCD \\\\ b' , 'true_examples' : [ 'URG' , 'URGENCES' , 'SAU' ], 'false_examples' : [ 'CHIRURGIE' ]}, { 'attribute' : 'IS_ICU' , 'pattern' : ' \\\\ bUSI| \\\\ bREA[N \\\\ s]| \\\\ bREA \\\\ b| \\\\ bUSC \\\\ b|SOINS.*INTENSIF|SURV.{0,15}CONT| \\\\ bSI \\\\ b| \\\\ bSC \\\\ b' , 'true_examples' : [ 'REA' , 'REA NEURO' , 'REANIMATION' ], 'false_examples' : [ 'CARREAU' ]}] Default argument of func: ~eds_scikit.structures.attributes.add_care_site_attributes . :meta private: Examples: :: ATTRIBUTE_REGEX_PATTERNS = [ { # required elements: name of attribute and pattern of regular expression \"attribute\": \"IS_EMERGENCY\", \"pattern\": r\"\bURG|\bSAU\b|\bUHCD\b|\bZHTCD\b\", # optional elements: list of test strings to validate the regular expression \"true_examples\": [\"URG\", \"URGENCES\", \"SAU\"], \"false_examples\": [\"CHIRURGIE\"], }, ... ] add_care_site_attributes add_care_site_attributes ( care_site : DataFrame , only_attributes : Optional [ List [ str ]] = None , attribute_regex_patterns : Optional [ List [ str ]] = None ) -> DataFrame Add boolean attributes as columns to care_site dataframe. This algo applies simple regular expressions to the care_site_name in order to compute boolean attributes of the care site. Implemented attributes are: IS_EMERGENCY IS_ICU In order to make the detection of attributes more robust, the column care_site_name is first transformed to a DESCRIPTION . This is done by func: ~eds_scikit.structures.description.add_care_site_description . PARAMETER DESCRIPTION care_site TYPE: DataFrame only_attributes if only a subset of all possible attributes should be computed TYPE: list of str DEFAULT: None attribute_regex_patterns If None , the default value is data: ~eds_scikit.structures.attributes.ATTRIBUTE_REGEX_PATTERNS TYPE: list (None) DEFAULT: None RETURNS DESCRIPTION care_site same as input with additional columns corresponding to boolean attributes. the column DESCRIPTION is also added : it contains of cleaner version of care_site_name . TYPE: DataFrame Examples: >>> care_site . head ( 2 ) care_site_id, care_site_name 21, HOSP ACCUEIL URG PED (UF) 22, HOSP CHIRURGIE DIGESTIVE 23, HOSP PEDIATRIE GEN ET SAU >>> care_site = add_care_site_attributes ( care_site , only_attributes = [ \"IS_EMERGENCY\" ]) >>> care_site . head ( 2 ) care_site_id, care_site_name, DESCRIPTION, IS_EMERGENCY 21, HOSP ACCUEIL URG PED (UF),ACCUEIL URG PED,True 22, HOSP CHIRURGIE DIGESTIVE,CHIRURGIE DIGESTIVE,False 23, HOSP PEDIATRIE GEN ET SAU,PEDIATRIE GEN ET SAU,True Specifying custom regular expressions. It is a good idea to provide true and false examples for each attribute. These examples will be tested against the provided regular expressions. >>> my_attributes = [ { \"attribute\": \"IS_EMERGENCY\", \"pattern\": r\"\bURG|\bSAU\b|\bUHCD\b|\bZHTCD\b\", \"true_examples\": [\"URG\", \"URGENCES\", \"SAU\"], \"false_examples\": [\"CHIRURGIE\"], }, { \"attribute\": \"IS_ICU\", \"pattern\": r\"\bREA\b|\bREANI\", \"true_examples\": [\"REA\", \"REA NEURO\", \"REANIMATION\"], \"false_examples\": [\"CARREAU\"], }, ] >>> care_site = add_care_site_attributes ( care_site , attribute_regex_patterns = my_attributes ) Source code in eds_scikit/structures/attributes.py 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 def add_care_site_attributes ( care_site : DataFrame , only_attributes : Optional [ List [ str ]] = None , attribute_regex_patterns : Optional [ List [ str ]] = None , ) -> DataFrame : \"\"\"Add boolean attributes as columns to care_site dataframe. This algo applies simple regular expressions to the ``care_site_name`` in order to compute boolean attributes of the care site. Implemented attributes are: - ``IS_EMERGENCY`` - ``IS_ICU`` In order to make the detection of attributes more robust, the column ``care_site_name`` is first transformed to a ``DESCRIPTION``. This is done by :py:func:`~eds_scikit.structures.description.add_care_site_description`. Parameters ---------- care_site : DataFrame only_attributes : list of str if only a subset of all possible attributes should be computed attribute_regex_patterns : list (None) If ``None``, the default value is :py:data:`~eds_scikit.structures.attributes.ATTRIBUTE_REGEX_PATTERNS` Returns ------- care_site: DataFrame same as input with additional columns corresponding to boolean attributes. the column ``DESCRIPTION`` is also added : it contains of cleaner version of ``care_site_name``. Examples -------- >>> care_site.head(2) care_site_id, care_site_name 21, HOSP ACCUEIL URG PED (UF) 22, HOSP CHIRURGIE DIGESTIVE 23, HOSP PEDIATRIE GEN ET SAU >>> care_site = add_care_site_attributes(care_site, only_attributes=[\"IS_EMERGENCY\"]) >>> care_site.head(2) care_site_id, care_site_name, DESCRIPTION, IS_EMERGENCY 21, HOSP ACCUEIL URG PED (UF),ACCUEIL URG PED,True 22, HOSP CHIRURGIE DIGESTIVE,CHIRURGIE DIGESTIVE,False 23, HOSP PEDIATRIE GEN ET SAU,PEDIATRIE GEN ET SAU,True Specifying custom regular expressions. It is a good idea to provide true and false examples for each attribute. These examples will be tested against the provided regular expressions. >>> my_attributes = [ { \"attribute\": \"IS_EMERGENCY\", \"pattern\": r\"\\bURG|\\bSAU\\b|\\bUHCD\\b|\\bZHTCD\\b\", \"true_examples\": [\"URG\", \"URGENCES\", \"SAU\"], \"false_examples\": [\"CHIRURGIE\"], }, { \"attribute\": \"IS_ICU\", \"pattern\": r\"\\bREA\\b|\\bREANI\", \"true_examples\": [\"REA\", \"REA NEURO\", \"REANIMATION\"], \"false_examples\": [\"CARREAU\"], }, ] >>> care_site = add_care_site_attributes(care_site, attribute_regex_patterns=my_attributes) \"\"\" # validate arguments if attribute_regex_patterns is None : attribute_regex_patterns = ATTRIBUTE_REGEX_PATTERNS if only_attributes : impossible = set ( only_attributes ) - set ( possible_concepts ) if impossible : raise ValueError ( f \"Unknown concepts: { impossible } \" ) attribute_regex_patterns = [ item for item in attribute_regex_patterns if item [ \"attribute\" ] in only_attributes ] validate_attribute_regex_patterns ( attribute_regex_patterns ) if \"DESCRIPTION\" not in care_site . columns : care_site = description . add_care_site_description ( care_site ) # apply algo for item in attribute_regex_patterns : new_column = { item [ \"attribute\" ]: care_site [ \"DESCRIPTION\" ] . str . contains ( item [ \"pattern\" ], regex = True ) } care_site = care_site . assign ( ** new_column ) if only_attributes : care_site = care_site . drop ([ \"DESCRIPTION\" ], axis = \"columns\" ) return care_site get_parent_attributes get_parent_attributes ( care_site : DataFrame , only_attributes : Optional [ List [ str ]] = None , version : Optional [ str ] = None , parent_type : str = 'Unit\u00e9 Fonctionnelle (UF)' ) -> DataFrame Get all known attributes from parent care sites and propagates them to each child care site PARAMETER DESCRIPTION care_site required columns: [\"care_site_id\", \"care_site_type_source_value\", \"care_site_name\"] TYPE: DataFrame only_attributes same as func: ~eds_scikit.structures.attributes.add_care_site_attributes TYPE: list of str DEFAULT: None version Optional version string for the care site hierarchy TYPE: Optional [ str ] DEFAULT: None parent_type Type of care site to consider as parent, by default \"Unit\u00e9 Fonctionnelle (UF)\". Corresponds to the \"care_site_type_source_value\" column TYPE: str DEFAULT: 'Unit\u00e9 Fonctionnelle (UF)' RETURNS DESCRIPTION care_site_attributes same index as input care_site. columns: care_site, is_emergency TYPE: DataFrame Warnings This algo requires that the care_site dataframe contains the parent care sites as well as the care sites that you want to tag. Examples: >>> attributes = get_parent_attributes ( care_site , only_attributes=[\"IS_EMERGENCY\"], parent_type=\"Unit\u00e9 Fonctionnelle (UF)\") >>> attributes . head () care_site_id, care_site_name, care_site_type_source_value, IS_EMERGENCY 92829 , ... , False 29820 , ... , True Source code in eds_scikit/structures/attributes.py 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 def get_parent_attributes ( care_site : DataFrame , only_attributes : Optional [ List [ str ]] = None , version : Optional [ str ] = None , parent_type : str = \"Unit\u00e9 Fonctionnelle (UF)\" , ) -> DataFrame : \"\"\"Get all known attributes from parent care sites and propagates them to each child care site Parameters ---------- care_site: DataFrame required columns: ``[\"care_site_id\", \"care_site_type_source_value\", \"care_site_name\"]`` only_attributes : list of str same as :py:func:`~eds_scikit.structures.attributes.add_care_site_attributes` version: Optional[str] Optional version string for the care site hierarchy parent_type: str Type of care site to consider as parent, by default \"Unit\u00e9 Fonctionnelle (UF)\". Corresponds to the `\"care_site_type_source_value\"` column Returns -------- care_site_attributes: DataFrame same index as input care_site. columns: care_site, is_emergency Warnings -------- This algo requires that the `care_site` dataframe contains the parent care sites as well as the care sites that you want to tag. Examples -------- >>> attributes = get_parent_attributes(care_site, only_attributes=[\"IS_EMERGENCY\"], parent_type=\"Unit\u00e9 Fonctionnelle (UF)\") >>> attributes.head() care_site_id, care_site_name, care_site_type_source_value, IS_EMERGENCY 92829 , ... , False 29820 , ... , True \"\"\" function_name = \"get_care_site_hierarchy\" if version is not None : function_name += f \". { version } \" hierarchy = registry . get ( \"data\" , function_name = function_name )() fw = framework . get_framework ( care_site ) hierarchy = framework . to ( fw , hierarchy ) # STEP 1: get attributes of parent parent_attributes = care_site . loc [ care_site [ \"care_site_type_source_value\" ] == parent_type , [ \"care_site_id\" , \"care_site_name\" ], ] parent_attributes = add_care_site_attributes ( parent_attributes , only_attributes = only_attributes ) boolean_columns = [ col for ( col , dtype ) in parent_attributes . dtypes . iteritems () if dtype == \"bool\" ] parent_attributes = parent_attributes . drop ( [ \"care_site_name\" ], axis = \"columns\" ) . rename ( columns = { \"care_site_id\" : \"parent_id\" }) # STEP 2: propagate attributes from parent to all children hierarchy = hierarchy . loc [:, [ \"care_site_id\" , parent_type ]] . rename ( columns = { parent_type : \"parent_id\" } ) children_attributes = hierarchy . merge ( parent_attributes , how = \"left\" , on = \"parent_id\" ) . drop ([ \"parent_id\" ], axis = \"columns\" ) # STEP 3 : merge to input dataframe old_columns = care_site . columns care_site = care_site . merge ( children_attributes , how = \"left\" , on = \"care_site_id\" ) for col in care_site . columns : if col in boolean_columns and col not in old_columns : care_site [ col ] = care_site [ col ] . fillna ( value = False ) return care_site # NOTE: this is how to return a single column that contains # EXACTLY the same index as the input dataframe. # For instance koalas requires the index name to be the same # for this operation to be valid: # >>> df[\"new_column\"] = compute_column(df) # attributes = ( # care_site.loc[:, [\"care_site_id\"]] # .reset_index() # .merge( # # drop_duplicates to ensure we keep same size as input # children_attributes.drop_duplicates(subset=[\"care_site_id\"]), # how=\"left\", # on=\"care_site_id\", # ) # .fillna(value=False) # # a merge \"forgets\" the index, we want to output the same as input # .set_index(\"index\") # ) # attributes.index.name = care_site.index.name","title":"attributes"},{"location":"reference/structures/attributes/#eds_scikitstructuresattributes","text":"","title":"eds_scikit.structures.attributes"},{"location":"reference/structures/attributes/#eds_scikit.structures.attributes.ATTRIBUTE_REGEX_PATTERNS","text":"ATTRIBUTE_REGEX_PATTERNS = [{ 'attribute' : 'IS_EMERGENCY' , 'pattern' : ' \\\\ bURG| \\\\ bSAU \\\\ b| \\\\ bUHCD \\\\ b| \\\\ bZHTCD \\\\ b' , 'true_examples' : [ 'URG' , 'URGENCES' , 'SAU' ], 'false_examples' : [ 'CHIRURGIE' ]}, { 'attribute' : 'IS_ICU' , 'pattern' : ' \\\\ bUSI| \\\\ bREA[N \\\\ s]| \\\\ bREA \\\\ b| \\\\ bUSC \\\\ b|SOINS.*INTENSIF|SURV.{0,15}CONT| \\\\ bSI \\\\ b| \\\\ bSC \\\\ b' , 'true_examples' : [ 'REA' , 'REA NEURO' , 'REANIMATION' ], 'false_examples' : [ 'CARREAU' ]}] Default argument of func: ~eds_scikit.structures.attributes.add_care_site_attributes . :meta private: Examples: :: ATTRIBUTE_REGEX_PATTERNS = [ { # required elements: name of attribute and pattern of regular expression \"attribute\": \"IS_EMERGENCY\", \"pattern\": r\"\bURG|\bSAU\b|\bUHCD\b|\bZHTCD\b\", # optional elements: list of test strings to validate the regular expression \"true_examples\": [\"URG\", \"URGENCES\", \"SAU\"], \"false_examples\": [\"CHIRURGIE\"], }, ... ]","title":"ATTRIBUTE_REGEX_PATTERNS"},{"location":"reference/structures/attributes/#eds_scikit.structures.attributes.add_care_site_attributes","text":"add_care_site_attributes ( care_site : DataFrame , only_attributes : Optional [ List [ str ]] = None , attribute_regex_patterns : Optional [ List [ str ]] = None ) -> DataFrame Add boolean attributes as columns to care_site dataframe. This algo applies simple regular expressions to the care_site_name in order to compute boolean attributes of the care site. Implemented attributes are: IS_EMERGENCY IS_ICU In order to make the detection of attributes more robust, the column care_site_name is first transformed to a DESCRIPTION . This is done by func: ~eds_scikit.structures.description.add_care_site_description . PARAMETER DESCRIPTION care_site TYPE: DataFrame only_attributes if only a subset of all possible attributes should be computed TYPE: list of str DEFAULT: None attribute_regex_patterns If None , the default value is data: ~eds_scikit.structures.attributes.ATTRIBUTE_REGEX_PATTERNS TYPE: list (None) DEFAULT: None RETURNS DESCRIPTION care_site same as input with additional columns corresponding to boolean attributes. the column DESCRIPTION is also added : it contains of cleaner version of care_site_name . TYPE: DataFrame Examples: >>> care_site . head ( 2 ) care_site_id, care_site_name 21, HOSP ACCUEIL URG PED (UF) 22, HOSP CHIRURGIE DIGESTIVE 23, HOSP PEDIATRIE GEN ET SAU >>> care_site = add_care_site_attributes ( care_site , only_attributes = [ \"IS_EMERGENCY\" ]) >>> care_site . head ( 2 ) care_site_id, care_site_name, DESCRIPTION, IS_EMERGENCY 21, HOSP ACCUEIL URG PED (UF),ACCUEIL URG PED,True 22, HOSP CHIRURGIE DIGESTIVE,CHIRURGIE DIGESTIVE,False 23, HOSP PEDIATRIE GEN ET SAU,PEDIATRIE GEN ET SAU,True Specifying custom regular expressions. It is a good idea to provide true and false examples for each attribute. These examples will be tested against the provided regular expressions. >>> my_attributes = [ { \"attribute\": \"IS_EMERGENCY\", \"pattern\": r\"\bURG|\bSAU\b|\bUHCD\b|\bZHTCD\b\", \"true_examples\": [\"URG\", \"URGENCES\", \"SAU\"], \"false_examples\": [\"CHIRURGIE\"], }, { \"attribute\": \"IS_ICU\", \"pattern\": r\"\bREA\b|\bREANI\", \"true_examples\": [\"REA\", \"REA NEURO\", \"REANIMATION\"], \"false_examples\": [\"CARREAU\"], }, ] >>> care_site = add_care_site_attributes ( care_site , attribute_regex_patterns = my_attributes ) Source code in eds_scikit/structures/attributes.py 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 def add_care_site_attributes ( care_site : DataFrame , only_attributes : Optional [ List [ str ]] = None , attribute_regex_patterns : Optional [ List [ str ]] = None , ) -> DataFrame : \"\"\"Add boolean attributes as columns to care_site dataframe. This algo applies simple regular expressions to the ``care_site_name`` in order to compute boolean attributes of the care site. Implemented attributes are: - ``IS_EMERGENCY`` - ``IS_ICU`` In order to make the detection of attributes more robust, the column ``care_site_name`` is first transformed to a ``DESCRIPTION``. This is done by :py:func:`~eds_scikit.structures.description.add_care_site_description`. Parameters ---------- care_site : DataFrame only_attributes : list of str if only a subset of all possible attributes should be computed attribute_regex_patterns : list (None) If ``None``, the default value is :py:data:`~eds_scikit.structures.attributes.ATTRIBUTE_REGEX_PATTERNS` Returns ------- care_site: DataFrame same as input with additional columns corresponding to boolean attributes. the column ``DESCRIPTION`` is also added : it contains of cleaner version of ``care_site_name``. Examples -------- >>> care_site.head(2) care_site_id, care_site_name 21, HOSP ACCUEIL URG PED (UF) 22, HOSP CHIRURGIE DIGESTIVE 23, HOSP PEDIATRIE GEN ET SAU >>> care_site = add_care_site_attributes(care_site, only_attributes=[\"IS_EMERGENCY\"]) >>> care_site.head(2) care_site_id, care_site_name, DESCRIPTION, IS_EMERGENCY 21, HOSP ACCUEIL URG PED (UF),ACCUEIL URG PED,True 22, HOSP CHIRURGIE DIGESTIVE,CHIRURGIE DIGESTIVE,False 23, HOSP PEDIATRIE GEN ET SAU,PEDIATRIE GEN ET SAU,True Specifying custom regular expressions. It is a good idea to provide true and false examples for each attribute. These examples will be tested against the provided regular expressions. >>> my_attributes = [ { \"attribute\": \"IS_EMERGENCY\", \"pattern\": r\"\\bURG|\\bSAU\\b|\\bUHCD\\b|\\bZHTCD\\b\", \"true_examples\": [\"URG\", \"URGENCES\", \"SAU\"], \"false_examples\": [\"CHIRURGIE\"], }, { \"attribute\": \"IS_ICU\", \"pattern\": r\"\\bREA\\b|\\bREANI\", \"true_examples\": [\"REA\", \"REA NEURO\", \"REANIMATION\"], \"false_examples\": [\"CARREAU\"], }, ] >>> care_site = add_care_site_attributes(care_site, attribute_regex_patterns=my_attributes) \"\"\" # validate arguments if attribute_regex_patterns is None : attribute_regex_patterns = ATTRIBUTE_REGEX_PATTERNS if only_attributes : impossible = set ( only_attributes ) - set ( possible_concepts ) if impossible : raise ValueError ( f \"Unknown concepts: { impossible } \" ) attribute_regex_patterns = [ item for item in attribute_regex_patterns if item [ \"attribute\" ] in only_attributes ] validate_attribute_regex_patterns ( attribute_regex_patterns ) if \"DESCRIPTION\" not in care_site . columns : care_site = description . add_care_site_description ( care_site ) # apply algo for item in attribute_regex_patterns : new_column = { item [ \"attribute\" ]: care_site [ \"DESCRIPTION\" ] . str . contains ( item [ \"pattern\" ], regex = True ) } care_site = care_site . assign ( ** new_column ) if only_attributes : care_site = care_site . drop ([ \"DESCRIPTION\" ], axis = \"columns\" ) return care_site","title":"add_care_site_attributes()"},{"location":"reference/structures/attributes/#eds_scikit.structures.attributes.get_parent_attributes","text":"get_parent_attributes ( care_site : DataFrame , only_attributes : Optional [ List [ str ]] = None , version : Optional [ str ] = None , parent_type : str = 'Unit\u00e9 Fonctionnelle (UF)' ) -> DataFrame Get all known attributes from parent care sites and propagates them to each child care site PARAMETER DESCRIPTION care_site required columns: [\"care_site_id\", \"care_site_type_source_value\", \"care_site_name\"] TYPE: DataFrame only_attributes same as func: ~eds_scikit.structures.attributes.add_care_site_attributes TYPE: list of str DEFAULT: None version Optional version string for the care site hierarchy TYPE: Optional [ str ] DEFAULT: None parent_type Type of care site to consider as parent, by default \"Unit\u00e9 Fonctionnelle (UF)\". Corresponds to the \"care_site_type_source_value\" column TYPE: str DEFAULT: 'Unit\u00e9 Fonctionnelle (UF)' RETURNS DESCRIPTION care_site_attributes same index as input care_site. columns: care_site, is_emergency TYPE: DataFrame","title":"get_parent_attributes()"},{"location":"reference/structures/attributes/#eds_scikit.structures.attributes.get_parent_attributes--warnings","text":"This algo requires that the care_site dataframe contains the parent care sites as well as the care sites that you want to tag. Examples: >>> attributes = get_parent_attributes ( care_site , only_attributes=[\"IS_EMERGENCY\"], parent_type=\"Unit\u00e9 Fonctionnelle (UF)\") >>> attributes . head () care_site_id, care_site_name, care_site_type_source_value, IS_EMERGENCY 92829 , ... , False 29820 , ... , True Source code in eds_scikit/structures/attributes.py 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 def get_parent_attributes ( care_site : DataFrame , only_attributes : Optional [ List [ str ]] = None , version : Optional [ str ] = None , parent_type : str = \"Unit\u00e9 Fonctionnelle (UF)\" , ) -> DataFrame : \"\"\"Get all known attributes from parent care sites and propagates them to each child care site Parameters ---------- care_site: DataFrame required columns: ``[\"care_site_id\", \"care_site_type_source_value\", \"care_site_name\"]`` only_attributes : list of str same as :py:func:`~eds_scikit.structures.attributes.add_care_site_attributes` version: Optional[str] Optional version string for the care site hierarchy parent_type: str Type of care site to consider as parent, by default \"Unit\u00e9 Fonctionnelle (UF)\". Corresponds to the `\"care_site_type_source_value\"` column Returns -------- care_site_attributes: DataFrame same index as input care_site. columns: care_site, is_emergency Warnings -------- This algo requires that the `care_site` dataframe contains the parent care sites as well as the care sites that you want to tag. Examples -------- >>> attributes = get_parent_attributes(care_site, only_attributes=[\"IS_EMERGENCY\"], parent_type=\"Unit\u00e9 Fonctionnelle (UF)\") >>> attributes.head() care_site_id, care_site_name, care_site_type_source_value, IS_EMERGENCY 92829 , ... , False 29820 , ... , True \"\"\" function_name = \"get_care_site_hierarchy\" if version is not None : function_name += f \". { version } \" hierarchy = registry . get ( \"data\" , function_name = function_name )() fw = framework . get_framework ( care_site ) hierarchy = framework . to ( fw , hierarchy ) # STEP 1: get attributes of parent parent_attributes = care_site . loc [ care_site [ \"care_site_type_source_value\" ] == parent_type , [ \"care_site_id\" , \"care_site_name\" ], ] parent_attributes = add_care_site_attributes ( parent_attributes , only_attributes = only_attributes ) boolean_columns = [ col for ( col , dtype ) in parent_attributes . dtypes . iteritems () if dtype == \"bool\" ] parent_attributes = parent_attributes . drop ( [ \"care_site_name\" ], axis = \"columns\" ) . rename ( columns = { \"care_site_id\" : \"parent_id\" }) # STEP 2: propagate attributes from parent to all children hierarchy = hierarchy . loc [:, [ \"care_site_id\" , parent_type ]] . rename ( columns = { parent_type : \"parent_id\" } ) children_attributes = hierarchy . merge ( parent_attributes , how = \"left\" , on = \"parent_id\" ) . drop ([ \"parent_id\" ], axis = \"columns\" ) # STEP 3 : merge to input dataframe old_columns = care_site . columns care_site = care_site . merge ( children_attributes , how = \"left\" , on = \"care_site_id\" ) for col in care_site . columns : if col in boolean_columns and col not in old_columns : care_site [ col ] = care_site [ col ] . fillna ( value = False ) return care_site # NOTE: this is how to return a single column that contains # EXACTLY the same index as the input dataframe. # For instance koalas requires the index name to be the same # for this operation to be valid: # >>> df[\"new_column\"] = compute_column(df) # attributes = ( # care_site.loc[:, [\"care_site_id\"]] # .reset_index() # .merge( # # drop_duplicates to ensure we keep same size as input # children_attributes.drop_duplicates(subset=[\"care_site_id\"]), # how=\"left\", # on=\"care_site_id\", # ) # .fillna(value=False) # # a merge \"forgets\" the index, we want to output the same as input # .set_index(\"index\") # ) # attributes.index.name = care_site.index.name","title":"Warnings"},{"location":"reference/structures/description/","text":"eds_scikit.structures.description add_care_site_description add_care_site_description ( care_site : DataFrame ) -> DataFrame Add column DESCRIPTION to care_site dataframe. This algo applies simple regular expression to simplify the care site name. This can be useful for post-processing the description, such as detecting the care_site characteristic from the description (is it an emergency care site ?) PARAMETER DESCRIPTION care_site with column care_site_name TYPE: DataFrame RETURNS DESCRIPTION care_site contains additional column DESCRIPTION TYPE: DataFrame Source code in eds_scikit/structures/description.py 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 @concept_checker ( concepts = [ \"DESCRIPTION\" ]) def add_care_site_description ( care_site : DataFrame ) -> DataFrame : \"\"\"Add column ``DESCRIPTION`` to care_site dataframe. This algo applies simple regular expression to simplify the care site name. This can be useful for post-processing the description, such as detecting the care_site characteristic from the description (is it an emergency care site ?) Parameters ---------- care_site : DataFrame with column ``care_site_name`` Returns ------- care_site : DataFrame contains additional column ``DESCRIPTION`` \"\"\" care_site = care_site . assign ( DESCRIPTION = description_from_care_site_name ( care_site [ \"care_site_name\" ]) ) return care_site","title":"description"},{"location":"reference/structures/description/#eds_scikitstructuresdescription","text":"","title":"eds_scikit.structures.description"},{"location":"reference/structures/description/#eds_scikit.structures.description.add_care_site_description","text":"add_care_site_description ( care_site : DataFrame ) -> DataFrame Add column DESCRIPTION to care_site dataframe. This algo applies simple regular expression to simplify the care site name. This can be useful for post-processing the description, such as detecting the care_site characteristic from the description (is it an emergency care site ?) PARAMETER DESCRIPTION care_site with column care_site_name TYPE: DataFrame RETURNS DESCRIPTION care_site contains additional column DESCRIPTION TYPE: DataFrame Source code in eds_scikit/structures/description.py 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 @concept_checker ( concepts = [ \"DESCRIPTION\" ]) def add_care_site_description ( care_site : DataFrame ) -> DataFrame : \"\"\"Add column ``DESCRIPTION`` to care_site dataframe. This algo applies simple regular expression to simplify the care site name. This can be useful for post-processing the description, such as detecting the care_site characteristic from the description (is it an emergency care site ?) Parameters ---------- care_site : DataFrame with column ``care_site_name`` Returns ------- care_site : DataFrame contains additional column ``DESCRIPTION`` \"\"\" care_site = care_site . assign ( DESCRIPTION = description_from_care_site_name ( care_site [ \"care_site_name\" ]) ) return care_site","title":"add_care_site_description()"},{"location":"reference/utils/","text":"eds_scikit.utils","title":"`eds_scikit.utils`"},{"location":"reference/utils/#eds_scikitutils","text":"","title":"eds_scikit.utils"},{"location":"reference/utils/bunch/","text":"eds_scikit.utils.bunch Vendored Bunch class from scikit-learn. Bunch Bunch ( ** kwargs ) Bases: dict Container object exposing keys as attributes. Bunch objects are sometimes used as an output for functions and methods. They extend dictionaries by enabling values to be accessed by key, bunch[\"value_key\"] , or by an attribute, bunch.value_key . Examples: >>> from sklearn.utils import Bunch >>> b = Bunch ( a = 1 , b = 2 ) >>> b [ 'b' ] 2 >>> b . b 2 >>> b . a = 3 >>> b [ 'a' ] 3 >>> b . c = 6 >>> b [ 'c' ] 6 Source code in eds_scikit/utils/bunch.py 29 30 def __init__ ( self , ** kwargs ): super () . __init__ ( kwargs )","title":"bunch"},{"location":"reference/utils/bunch/#eds_scikitutilsbunch","text":"Vendored Bunch class from scikit-learn.","title":"eds_scikit.utils.bunch"},{"location":"reference/utils/bunch/#eds_scikit.utils.bunch.Bunch","text":"Bunch ( ** kwargs ) Bases: dict Container object exposing keys as attributes. Bunch objects are sometimes used as an output for functions and methods. They extend dictionaries by enabling values to be accessed by key, bunch[\"value_key\"] , or by an attribute, bunch.value_key . Examples: >>> from sklearn.utils import Bunch >>> b = Bunch ( a = 1 , b = 2 ) >>> b [ 'b' ] 2 >>> b . b 2 >>> b . a = 3 >>> b [ 'a' ] 3 >>> b . c = 6 >>> b [ 'c' ] 6 Source code in eds_scikit/utils/bunch.py 29 30 def __init__ ( self , ** kwargs ): super () . __init__ ( kwargs )","title":"Bunch"},{"location":"reference/utils/checks/","text":"eds_scikit.utils.checks MissingConceptError MissingConceptError ( required_concepts : Union [ List [ str ], List [ Tuple [ str , str ]]], df_name : str = '' ) Bases: Exception Exception raised when a concept is missing Source code in eds_scikit/utils/checks.py 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 def __init__ ( self , required_concepts : Union [ List [ str ], List [ Tuple [ str , str ]]], df_name : str = \"\" , ): if all ( isinstance ( concept , tuple ) for concept in required_concepts ): to_display_per_concept = [ f \"- { concept } ( { msg } )\" for concept , msg in required_concepts ] else : to_display_per_concept = [ f \"- { concept } \" for concept in required_concepts ] str_to_display = \" \\n \" . join ( to_display_per_concept ) if df_name : df_name = f \" { df_name } \" message = ( f \"The { df_name } DataFrame is missing some columns, \" \"namely: \\n \" f \" { str_to_display } \" ) super () . __init__ ( message ) MissingTableError MissingTableError ( required_tables : Union [ List [ str ], List [ Tuple [ str , str ]]], data_name : str = '' ) Bases: Exception Exception raised when a table is missing in the Data Source code in eds_scikit/utils/checks.py 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 def __init__ ( self , required_tables : Union [ List [ str ], List [ Tuple [ str , str ]]], data_name : str = \"\" , ): if all ( isinstance ( table , tuple ) for table in required_tables ): to_display_per_table = [ f \"- { table } ( { msg } )\" for table , msg in required_tables ] else : to_display_per_table = [ f \"- { table } \" for table in required_tables ] str_to_display = \" \\n \" . join ( to_display_per_table ) if data_name : data_name = f \" { data_name } \" message = ( f \"The { data_name } Data is missing some tables, \" \"namely: \\n \" f \" { str_to_display } \" ) super () . __init__ ( message ) concept_checker concept_checker ( function : Callable , concepts : List [ str ] = None , only_adds_concepts : bool = True , * args , ** kwargs ) -> Any Decorator to use on functions that - Takes a DataFrame as first argument - Adds a concept to it The decorator checks: - If the first argument is a DataFrame - If the concepts to be added aren't already in the DataFrame - If the function correctly adds the concepts - If no additionnal columns are added (if only_adds_concepts is True) If one of this checks fails, raises an error Source code in eds_scikit/utils/checks.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 @decorator def concept_checker ( function : Callable , concepts : List [ str ] = None , only_adds_concepts : bool = True , * args , ** kwargs , ) -> Any : \"\"\" Decorator to use on functions that - Takes a DataFrame as first argument - Adds a concept to it The decorator checks: - If the first argument is a DataFrame - If the concepts to be added aren't already in the DataFrame - If the function correctly adds the concepts - If no additionnal columns are added (if only_adds_concepts is True) If one of this checks fails, raises an error \"\"\" # Is the first argument a DataFrame df = args [ 0 ] if ( type ( df ) != ks . DataFrame ) & ( type ( df ) != pd . DataFrame ): raise TypeError ( f \"The first argument of ' { function . __module__ } . { function . __name__ } ' \" \"should be a Pandas or Koalas DataFrame\" ) # Initial columns initial_cols = set ( df . columns ) # Is the concept already present if type ( concepts ) == str : concepts = [ concepts ] present_concepts = set ( concepts ) & set ( df . columns ) if present_concepts : raise ValueError ( f \"The concepts { present_concepts } are already present in the input dataframe \" f \"of ' { function . __module__ } . { function . __name__ } '. \\n \" \"You can either rename the column(s) or delete them before running \" \"the function again.\" ) result = function ( * args , ** kwargs ) # Was the concept correctly added missing_concepts = set ( concepts ) - set ( result . columns ) if len ( missing_concepts ) > 0 : raise ValueError ( f \"The concept(s) ' { missing_concepts } ' were not added to the DataFrame.\" ) # Check that no other columns were added if only_adds_concepts : result_cols = set ( result . columns ) additionnal_cols = result_cols - ( initial_cols | set ( concepts )) if additionnal_cols : logger . warning ( \"The columns\" + \"\" . join ([ f \" \\n - { s } \" for s in additionnal_cols ]) + f \" \\n were added/renamed by ' { function . __module__ } . { function . __name__ } ',\" + f \"although it should normally only add the columns { concepts } \" ) return result algo_checker algo_checker ( function : Callable , algos : Optional [ str ] = None , * args , ** kwargs ) -> Any Decorator to use on wrapper that calls specific functions based on the 'algo' argument The decorator checks if the provided algo is an implemented one. If this checks fails, raises an error Source code in eds_scikit/utils/checks.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 @decorator def algo_checker ( function : Callable , algos : Optional [ str ] = None , * args , ** kwargs , ) -> Any : \"\"\" Decorator to use on wrapper that calls specific functions based on the 'algo' argument The decorator checks if the provided algo is an implemented one. If this checks fails, raises an error \"\"\" algo = _get_arg_value ( function , \"algo\" , args , kwargs ) # Stripping eventual version suffix algo = algo . split ( \".\" )[ 0 ] if algo not in algos : raise ValueError ( f \"Method { algo } unknown for ' { function . __module__ } . { function . __name__ } '. \\n \" f \"Available algos are { algos } \" ) result = function ( * args , ** kwargs ) return result","title":"checks"},{"location":"reference/utils/checks/#eds_scikitutilschecks","text":"","title":"eds_scikit.utils.checks"},{"location":"reference/utils/checks/#eds_scikit.utils.checks.MissingConceptError","text":"MissingConceptError ( required_concepts : Union [ List [ str ], List [ Tuple [ str , str ]]], df_name : str = '' ) Bases: Exception Exception raised when a concept is missing Source code in eds_scikit/utils/checks.py 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 def __init__ ( self , required_concepts : Union [ List [ str ], List [ Tuple [ str , str ]]], df_name : str = \"\" , ): if all ( isinstance ( concept , tuple ) for concept in required_concepts ): to_display_per_concept = [ f \"- { concept } ( { msg } )\" for concept , msg in required_concepts ] else : to_display_per_concept = [ f \"- { concept } \" for concept in required_concepts ] str_to_display = \" \\n \" . join ( to_display_per_concept ) if df_name : df_name = f \" { df_name } \" message = ( f \"The { df_name } DataFrame is missing some columns, \" \"namely: \\n \" f \" { str_to_display } \" ) super () . __init__ ( message )","title":"MissingConceptError"},{"location":"reference/utils/checks/#eds_scikit.utils.checks.MissingTableError","text":"MissingTableError ( required_tables : Union [ List [ str ], List [ Tuple [ str , str ]]], data_name : str = '' ) Bases: Exception Exception raised when a table is missing in the Data Source code in eds_scikit/utils/checks.py 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 def __init__ ( self , required_tables : Union [ List [ str ], List [ Tuple [ str , str ]]], data_name : str = \"\" , ): if all ( isinstance ( table , tuple ) for table in required_tables ): to_display_per_table = [ f \"- { table } ( { msg } )\" for table , msg in required_tables ] else : to_display_per_table = [ f \"- { table } \" for table in required_tables ] str_to_display = \" \\n \" . join ( to_display_per_table ) if data_name : data_name = f \" { data_name } \" message = ( f \"The { data_name } Data is missing some tables, \" \"namely: \\n \" f \" { str_to_display } \" ) super () . __init__ ( message )","title":"MissingTableError"},{"location":"reference/utils/checks/#eds_scikit.utils.checks.concept_checker","text":"concept_checker ( function : Callable , concepts : List [ str ] = None , only_adds_concepts : bool = True , * args , ** kwargs ) -> Any Decorator to use on functions that - Takes a DataFrame as first argument - Adds a concept to it The decorator checks: - If the first argument is a DataFrame - If the concepts to be added aren't already in the DataFrame - If the function correctly adds the concepts - If no additionnal columns are added (if only_adds_concepts is True) If one of this checks fails, raises an error Source code in eds_scikit/utils/checks.py 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 @decorator def concept_checker ( function : Callable , concepts : List [ str ] = None , only_adds_concepts : bool = True , * args , ** kwargs , ) -> Any : \"\"\" Decorator to use on functions that - Takes a DataFrame as first argument - Adds a concept to it The decorator checks: - If the first argument is a DataFrame - If the concepts to be added aren't already in the DataFrame - If the function correctly adds the concepts - If no additionnal columns are added (if only_adds_concepts is True) If one of this checks fails, raises an error \"\"\" # Is the first argument a DataFrame df = args [ 0 ] if ( type ( df ) != ks . DataFrame ) & ( type ( df ) != pd . DataFrame ): raise TypeError ( f \"The first argument of ' { function . __module__ } . { function . __name__ } ' \" \"should be a Pandas or Koalas DataFrame\" ) # Initial columns initial_cols = set ( df . columns ) # Is the concept already present if type ( concepts ) == str : concepts = [ concepts ] present_concepts = set ( concepts ) & set ( df . columns ) if present_concepts : raise ValueError ( f \"The concepts { present_concepts } are already present in the input dataframe \" f \"of ' { function . __module__ } . { function . __name__ } '. \\n \" \"You can either rename the column(s) or delete them before running \" \"the function again.\" ) result = function ( * args , ** kwargs ) # Was the concept correctly added missing_concepts = set ( concepts ) - set ( result . columns ) if len ( missing_concepts ) > 0 : raise ValueError ( f \"The concept(s) ' { missing_concepts } ' were not added to the DataFrame.\" ) # Check that no other columns were added if only_adds_concepts : result_cols = set ( result . columns ) additionnal_cols = result_cols - ( initial_cols | set ( concepts )) if additionnal_cols : logger . warning ( \"The columns\" + \"\" . join ([ f \" \\n - { s } \" for s in additionnal_cols ]) + f \" \\n were added/renamed by ' { function . __module__ } . { function . __name__ } ',\" + f \"although it should normally only add the columns { concepts } \" ) return result","title":"concept_checker()"},{"location":"reference/utils/checks/#eds_scikit.utils.checks.algo_checker","text":"algo_checker ( function : Callable , algos : Optional [ str ] = None , * args , ** kwargs ) -> Any Decorator to use on wrapper that calls specific functions based on the 'algo' argument The decorator checks if the provided algo is an implemented one. If this checks fails, raises an error Source code in eds_scikit/utils/checks.py 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 @decorator def algo_checker ( function : Callable , algos : Optional [ str ] = None , * args , ** kwargs , ) -> Any : \"\"\" Decorator to use on wrapper that calls specific functions based on the 'algo' argument The decorator checks if the provided algo is an implemented one. If this checks fails, raises an error \"\"\" algo = _get_arg_value ( function , \"algo\" , args , kwargs ) # Stripping eventual version suffix algo = algo . split ( \".\" )[ 0 ] if algo not in algos : raise ValueError ( f \"Method { algo } unknown for ' { function . __module__ } . { function . __name__ } '. \\n \" f \"Available algos are { algos } \" ) result = function ( * args , ** kwargs ) return result","title":"algo_checker()"},{"location":"reference/utils/datetime_helpers/","text":"eds_scikit.utils.datetime_helpers add_timedelta add_timedelta ( series : Series , ** kwargs ) -> Series Adds a unique timedelta to a Pandas or Koalas Series Source code in eds_scikit/utils/datetime_helpers.py 9 10 11 12 13 def add_timedelta ( series : Series , ** kwargs ) -> Series : \"\"\" Adds a unique timedelta to a Pandas or Koalas Series \"\"\" return series . map ( lambda d : d + timedelta ( ** kwargs )) substract_datetime substract_datetime ( series_1 : Series , series_2 : Series , out : str = 'seconds' ) -> Series Substract 2 datetime series and return the number of seconds or hours between them. Source code in eds_scikit/utils/datetime_helpers.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 def substract_datetime ( series_1 : Series , series_2 : Series , out : str = \"seconds\" , ) -> Series : \"\"\" Substract 2 datetime series and return the number of seconds or hours between them. \"\"\" if out not in [ \"seconds\" , \"hours\" ]: raise ValueError ( \"the 'out' parameter should be in ['hours','seconds']\" ) if not ( np . issubdtype ( series_1 . dtype , np . datetime64 ) and np . issubdtype ( series_2 . dtype , np . datetime64 ) ): raise TypeError ( \"One of the provided Serie isn't a datetime Serie\" ) if is_pandas ( series_1 ) and is_pandas ( series_2 ): diff = ( series_1 - series_2 ) . dt . total_seconds () elif is_koalas ( series_1 ) and is_koalas ( series_2 ): diff = series_1 - series_2 else : raise TypeError ( \"Both series should either be a Koalas or Pandas Serie\" ) if out == \"hours\" : return diff / 3600 return diff","title":"datetime_helpers"},{"location":"reference/utils/datetime_helpers/#eds_scikitutilsdatetime_helpers","text":"","title":"eds_scikit.utils.datetime_helpers"},{"location":"reference/utils/datetime_helpers/#eds_scikit.utils.datetime_helpers.add_timedelta","text":"add_timedelta ( series : Series , ** kwargs ) -> Series Adds a unique timedelta to a Pandas or Koalas Series Source code in eds_scikit/utils/datetime_helpers.py 9 10 11 12 13 def add_timedelta ( series : Series , ** kwargs ) -> Series : \"\"\" Adds a unique timedelta to a Pandas or Koalas Series \"\"\" return series . map ( lambda d : d + timedelta ( ** kwargs ))","title":"add_timedelta()"},{"location":"reference/utils/datetime_helpers/#eds_scikit.utils.datetime_helpers.substract_datetime","text":"substract_datetime ( series_1 : Series , series_2 : Series , out : str = 'seconds' ) -> Series Substract 2 datetime series and return the number of seconds or hours between them. Source code in eds_scikit/utils/datetime_helpers.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 def substract_datetime ( series_1 : Series , series_2 : Series , out : str = \"seconds\" , ) -> Series : \"\"\" Substract 2 datetime series and return the number of seconds or hours between them. \"\"\" if out not in [ \"seconds\" , \"hours\" ]: raise ValueError ( \"the 'out' parameter should be in ['hours','seconds']\" ) if not ( np . issubdtype ( series_1 . dtype , np . datetime64 ) and np . issubdtype ( series_2 . dtype , np . datetime64 ) ): raise TypeError ( \"One of the provided Serie isn't a datetime Serie\" ) if is_pandas ( series_1 ) and is_pandas ( series_2 ): diff = ( series_1 - series_2 ) . dt . total_seconds () elif is_koalas ( series_1 ) and is_koalas ( series_2 ): diff = series_1 - series_2 else : raise TypeError ( \"Both series should either be a Koalas or Pandas Serie\" ) if out == \"hours\" : return diff / 3600 return diff","title":"substract_datetime()"},{"location":"reference/utils/framework/","text":"eds_scikit.utils.framework BackendDispatcher Dispatcher between pandas, koalas and custom methods. In addition to the methods below, use the BackendDispatcher class to access the custom functions defined in CustomImplem . Examples: Use a dispatcher function >>> from eds_scikit.utils.framework import bd >>> bd . is_pandas ( pd . DataFrame ()) True Use a custom implemented function >>> df = pd . DataFrame ({ \"categ\" : [ \"a\" , \"b\" , \"c\" ]}) >>> bd . add_unique_id ( df , col_name = \"id\" ) categ id 0 a 0 1 b 1 2 c 2 get_backend get_backend ( obj ) -> Optional [ ModuleType ] Return the backend of a given object. PARAMETER DESCRIPTION obj RETURNS DESCRIPTION backend TYPE: a backend among Examples: Get the backend from a DataFrame and create another DataFrame from it. This is especially useful at runtime, when you need to infer the backend of the input. >>> backend = bd . get_backend ( pd . DataFrame ()) >>> backend >>> df = backend . DataFrame () >>> bd . get_backend ( ks . DataFrame ()) For demo purposes, return the backend when provided directly >>> bd . get_backend ( ks ) >>> bd . get_backend ( spark ) None Source code in eds_scikit/utils/framework.py 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 def get_backend ( self , obj ) -> Optional [ ModuleType ]: \"\"\"Return the backend of a given object. Parameters ---------- obj: DataFrame or backend module among pandas or koalas. Returns ------- backend: a backend among {pd, ks} or None Examples -------- Get the backend from a DataFrame and create another DataFrame from it. This is especially useful at runtime, when you need to infer the backend of the input. >>> backend = bd.get_backend(pd.DataFrame()) >>> backend >>> df = backend.DataFrame() >>> bd.get_backend(ks.DataFrame()) For demo purposes, return the backend when provided directly >>> bd.get_backend(ks) >>> bd.get_backend(spark) None \"\"\" if isinstance ( obj , str ): return { \"pd\" : pd , \"pandas\" : pd , \"ks\" : ks , \"koalas\" : ks , } . get ( obj ) for backend in VALID_FRAMEWORKS : if ( obj . __class__ . __module__ . startswith ( backend . __name__ ) # DataFrame() or getattr ( obj , \"__name__\" , None ) == backend . __name__ # pd or ks ): return backend return None is_pandas is_pandas ( obj ) -> bool Return True when the obj is either a pd.DataFrame or the pandas module. Source code in eds_scikit/utils/framework.py 158 159 160 def is_pandas ( self , obj ) -> bool : \"\"\"Return True when the obj is either a pd.DataFrame or the pandas module.\"\"\" return self . get_backend ( obj ) is pd is_koalas is_koalas ( obj : Any ) -> bool Return True when the obj is either a ks.DataFrame or the koalas module. Source code in eds_scikit/utils/framework.py 162 163 164 def is_koalas ( self , obj : Any ) -> bool : \"\"\"Return True when the obj is either a ks.DataFrame or the koalas module.\"\"\" return self . get_backend ( obj ) is ks to to ( obj , backend ) Convert a dataframe to the provided backend. PARAMETER DESCRIPTION obj The object(s) to convert to the provided backend backend: str, DataFrame or pandas, koalas module The desired output backend. RETURNS DESCRIPTION out The converted object, in the same format as provided in input. TYPE: DataFrame or iterabel of DataFrame (list, tuple, dict) Examples: Convert a single DataFrame >>> df = pd . DataFrame ({ \"a\" : [ 1 , 2 ]}) >>> kdf = bd . to ( df , backend = \"koalas\" ) >>> type ( kdf ) databricks.koalas.frame.DataFrame Convert a list of DataFrame >>> extra_kdf = ks . DataFrame ({ \"b\" : [ 0 , 1 ]}) >>> another_kdf = ks . DataFrame ({ \"c\" : [ 2 , 3 ]}) >>> kdf_list = [ kdf , extra_kdf , another_kdf ] >>> df_list = bd . to ( kdf_list , backend = \"pandas\" ) >>> type ( df_list ) list >>> len ( df_list ) 3 >>> type ( df_list [ 0 ]) pandas.core.frame.DataFrame Convert a dictionnary of DataFrame >>> df_dict = { \"df_1\" : pd . DataFrame ({ \"a\" : [ 1 , 2 ]}), \"df_2\" : pd . DataFrame ({ \"a\" : [ 2 , 3 ]})} >>> kdf_dict = bd . to ( df_dict , backend = \"koalas\" ) >>> type ( kdf_dict ) dict >>> kdf_dict . keys () dict_keys([\"df_1\", \"df_2\"]) >>> type ( kdf_dict [ \"df_1\" ]) databricks.koalas.frame.DataFrame Source code in eds_scikit/utils/framework.py 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 def to ( self , obj , backend ): \"\"\"Convert a dataframe to the provided backend. Parameters ---------- obj: DataFrame or iterable of DataFrame (list, tuple, dict) The object(s) to convert to the provided backend backend: str, DataFrame or pandas, koalas module The desired output backend. Returns ------- out: DataFrame or iterabel of DataFrame (list, tuple, dict) The converted object, in the same format as provided in input. Examples -------- Convert a single DataFrame >>> df = pd.DataFrame({\"a\": [1, 2]}) >>> kdf = bd.to(df, backend=\"koalas\") >>> type(kdf) databricks.koalas.frame.DataFrame Convert a list of DataFrame >>> extra_kdf = ks.DataFrame({\"b\": [0, 1]}) >>> another_kdf = ks.DataFrame({\"c\": [2, 3]}) >>> kdf_list = [kdf, extra_kdf, another_kdf] >>> df_list = bd.to(kdf_list, backend=\"pandas\") >>> type(df_list) list >>> len(df_list) 3 >>> type(df_list[0]) pandas.core.frame.DataFrame Convert a dictionnary of DataFrame >>> df_dict = {\"df_1\": pd.DataFrame({\"a\": [1, 2]}), \"df_2\": pd.DataFrame({\"a\": [2, 3]})} >>> kdf_dict = bd.to(df_dict, backend=\"koalas\") >>> type(kdf_dict) dict >>> kdf_dict.keys() dict_keys([\"df_1\", \"df_2\"]) >>> type(kdf_dict[\"df_1\"]) databricks.koalas.frame.DataFrame \"\"\" if isinstance ( obj , ( list , tuple )): results = [] for _obj in obj : results . append ( self . to ( _obj , backend )) return results if isinstance ( obj , dict ): results = {} for k , _obj in obj . items (): results [ k ] = self . to ( _obj , backend ) return results backend = self . get_backend ( backend ) if self . is_pandas ( backend ): return self . to_pandas ( obj ) elif self . is_koalas ( backend ): return self . to_koalas ( obj ) else : raise ValueError ( \"Unknown backend\" )","title":"framework"},{"location":"reference/utils/framework/#eds_scikitutilsframework","text":"","title":"eds_scikit.utils.framework"},{"location":"reference/utils/framework/#eds_scikit.utils.framework.BackendDispatcher","text":"Dispatcher between pandas, koalas and custom methods. In addition to the methods below, use the BackendDispatcher class to access the custom functions defined in CustomImplem . Examples: Use a dispatcher function >>> from eds_scikit.utils.framework import bd >>> bd . is_pandas ( pd . DataFrame ()) True Use a custom implemented function >>> df = pd . DataFrame ({ \"categ\" : [ \"a\" , \"b\" , \"c\" ]}) >>> bd . add_unique_id ( df , col_name = \"id\" ) categ id 0 a 0 1 b 1 2 c 2","title":"BackendDispatcher"},{"location":"reference/utils/framework/#eds_scikit.utils.framework.BackendDispatcher.get_backend","text":"get_backend ( obj ) -> Optional [ ModuleType ] Return the backend of a given object. PARAMETER DESCRIPTION obj RETURNS DESCRIPTION backend TYPE: a backend among Examples: Get the backend from a DataFrame and create another DataFrame from it. This is especially useful at runtime, when you need to infer the backend of the input. >>> backend = bd . get_backend ( pd . DataFrame ()) >>> backend >>> df = backend . DataFrame () >>> bd . get_backend ( ks . DataFrame ()) For demo purposes, return the backend when provided directly >>> bd . get_backend ( ks ) >>> bd . get_backend ( spark ) None Source code in eds_scikit/utils/framework.py 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 def get_backend ( self , obj ) -> Optional [ ModuleType ]: \"\"\"Return the backend of a given object. Parameters ---------- obj: DataFrame or backend module among pandas or koalas. Returns ------- backend: a backend among {pd, ks} or None Examples -------- Get the backend from a DataFrame and create another DataFrame from it. This is especially useful at runtime, when you need to infer the backend of the input. >>> backend = bd.get_backend(pd.DataFrame()) >>> backend >>> df = backend.DataFrame() >>> bd.get_backend(ks.DataFrame()) For demo purposes, return the backend when provided directly >>> bd.get_backend(ks) >>> bd.get_backend(spark) None \"\"\" if isinstance ( obj , str ): return { \"pd\" : pd , \"pandas\" : pd , \"ks\" : ks , \"koalas\" : ks , } . get ( obj ) for backend in VALID_FRAMEWORKS : if ( obj . __class__ . __module__ . startswith ( backend . __name__ ) # DataFrame() or getattr ( obj , \"__name__\" , None ) == backend . __name__ # pd or ks ): return backend return None","title":"get_backend()"},{"location":"reference/utils/framework/#eds_scikit.utils.framework.BackendDispatcher.is_pandas","text":"is_pandas ( obj ) -> bool Return True when the obj is either a pd.DataFrame or the pandas module. Source code in eds_scikit/utils/framework.py 158 159 160 def is_pandas ( self , obj ) -> bool : \"\"\"Return True when the obj is either a pd.DataFrame or the pandas module.\"\"\" return self . get_backend ( obj ) is pd","title":"is_pandas()"},{"location":"reference/utils/framework/#eds_scikit.utils.framework.BackendDispatcher.is_koalas","text":"is_koalas ( obj : Any ) -> bool Return True when the obj is either a ks.DataFrame or the koalas module. Source code in eds_scikit/utils/framework.py 162 163 164 def is_koalas ( self , obj : Any ) -> bool : \"\"\"Return True when the obj is either a ks.DataFrame or the koalas module.\"\"\" return self . get_backend ( obj ) is ks","title":"is_koalas()"},{"location":"reference/utils/framework/#eds_scikit.utils.framework.BackendDispatcher.to","text":"to ( obj , backend ) Convert a dataframe to the provided backend. PARAMETER DESCRIPTION obj The object(s) to convert to the provided backend backend: str, DataFrame or pandas, koalas module The desired output backend. RETURNS DESCRIPTION out The converted object, in the same format as provided in input. TYPE: DataFrame or iterabel of DataFrame (list, tuple, dict) Examples: Convert a single DataFrame >>> df = pd . DataFrame ({ \"a\" : [ 1 , 2 ]}) >>> kdf = bd . to ( df , backend = \"koalas\" ) >>> type ( kdf ) databricks.koalas.frame.DataFrame Convert a list of DataFrame >>> extra_kdf = ks . DataFrame ({ \"b\" : [ 0 , 1 ]}) >>> another_kdf = ks . DataFrame ({ \"c\" : [ 2 , 3 ]}) >>> kdf_list = [ kdf , extra_kdf , another_kdf ] >>> df_list = bd . to ( kdf_list , backend = \"pandas\" ) >>> type ( df_list ) list >>> len ( df_list ) 3 >>> type ( df_list [ 0 ]) pandas.core.frame.DataFrame Convert a dictionnary of DataFrame >>> df_dict = { \"df_1\" : pd . DataFrame ({ \"a\" : [ 1 , 2 ]}), \"df_2\" : pd . DataFrame ({ \"a\" : [ 2 , 3 ]})} >>> kdf_dict = bd . to ( df_dict , backend = \"koalas\" ) >>> type ( kdf_dict ) dict >>> kdf_dict . keys () dict_keys([\"df_1\", \"df_2\"]) >>> type ( kdf_dict [ \"df_1\" ]) databricks.koalas.frame.DataFrame Source code in eds_scikit/utils/framework.py 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 def to ( self , obj , backend ): \"\"\"Convert a dataframe to the provided backend. Parameters ---------- obj: DataFrame or iterable of DataFrame (list, tuple, dict) The object(s) to convert to the provided backend backend: str, DataFrame or pandas, koalas module The desired output backend. Returns ------- out: DataFrame or iterabel of DataFrame (list, tuple, dict) The converted object, in the same format as provided in input. Examples -------- Convert a single DataFrame >>> df = pd.DataFrame({\"a\": [1, 2]}) >>> kdf = bd.to(df, backend=\"koalas\") >>> type(kdf) databricks.koalas.frame.DataFrame Convert a list of DataFrame >>> extra_kdf = ks.DataFrame({\"b\": [0, 1]}) >>> another_kdf = ks.DataFrame({\"c\": [2, 3]}) >>> kdf_list = [kdf, extra_kdf, another_kdf] >>> df_list = bd.to(kdf_list, backend=\"pandas\") >>> type(df_list) list >>> len(df_list) 3 >>> type(df_list[0]) pandas.core.frame.DataFrame Convert a dictionnary of DataFrame >>> df_dict = {\"df_1\": pd.DataFrame({\"a\": [1, 2]}), \"df_2\": pd.DataFrame({\"a\": [2, 3]})} >>> kdf_dict = bd.to(df_dict, backend=\"koalas\") >>> type(kdf_dict) dict >>> kdf_dict.keys() dict_keys([\"df_1\", \"df_2\"]) >>> type(kdf_dict[\"df_1\"]) databricks.koalas.frame.DataFrame \"\"\" if isinstance ( obj , ( list , tuple )): results = [] for _obj in obj : results . append ( self . to ( _obj , backend )) return results if isinstance ( obj , dict ): results = {} for k , _obj in obj . items (): results [ k ] = self . to ( _obj , backend ) return results backend = self . get_backend ( backend ) if self . is_pandas ( backend ): return self . to_pandas ( obj ) elif self . is_koalas ( backend ): return self . to_koalas ( obj ) else : raise ValueError ( \"Unknown backend\" )","title":"to()"},{"location":"reference/utils/hierarchy/","text":"eds_scikit.utils.hierarchy build_hierarchy build_hierarchy ( categories : pd . DataFrame , relationships : pd . DataFrame ) -> pd . DataFrame Build a dataframe with parent categories as columns Source code in eds_scikit/utils/hierarchy.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 def build_hierarchy ( categories : pd . DataFrame , relationships : pd . DataFrame , ) -> pd . DataFrame : \"\"\"Build a dataframe with parent categories as columns\"\"\" assert set ( categories . columns ) == { \"id\" , \"category\" } assert set ( relationships . columns ) == { \"child\" , \"parent\" } assert not categories [ \"id\" ] . duplicated () . any () assert not relationships . duplicated () . any () expanded_relationships = _follow_relationships ( relationships ) expanded_relationships = expanded_relationships . loc [ expanded_relationships [ \"child\" ] . isin ( categories [ \"id\" ]) ] relationships_with_category = _deduplicate_parent_category ( expanded_relationships , categories ) categories = _finalize_parent_categories ( categories , relationships_with_category ) return categories","title":"hierarchy"},{"location":"reference/utils/hierarchy/#eds_scikitutilshierarchy","text":"","title":"eds_scikit.utils.hierarchy"},{"location":"reference/utils/hierarchy/#eds_scikit.utils.hierarchy.build_hierarchy","text":"build_hierarchy ( categories : pd . DataFrame , relationships : pd . DataFrame ) -> pd . DataFrame Build a dataframe with parent categories as columns Source code in eds_scikit/utils/hierarchy.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 def build_hierarchy ( categories : pd . DataFrame , relationships : pd . DataFrame , ) -> pd . DataFrame : \"\"\"Build a dataframe with parent categories as columns\"\"\" assert set ( categories . columns ) == { \"id\" , \"category\" } assert set ( relationships . columns ) == { \"child\" , \"parent\" } assert not categories [ \"id\" ] . duplicated () . any () assert not relationships . duplicated () . any () expanded_relationships = _follow_relationships ( relationships ) expanded_relationships = expanded_relationships . loc [ expanded_relationships [ \"child\" ] . isin ( categories [ \"id\" ]) ] relationships_with_category = _deduplicate_parent_category ( expanded_relationships , categories ) categories = _finalize_parent_categories ( categories , relationships_with_category ) return categories","title":"build_hierarchy()"},{"location":"reference/utils/logging/","text":"eds_scikit.utils.logging formatter formatter ( record : dict ) Formats the logging message by: Adding color and bold Indenting the message Source code in eds_scikit/utils/logging.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def formatter ( record : dict ): \"\"\" Formats the logging message by: - Adding color and bold - Indenting the message \"\"\" base_format = ( \"\" # bold \"[eds-scikit]\" \"- \" \" {name} :\" # corresponds to __name__ \" {extra[classname]}{extra[sep]} \" # class name, if relevant \"\" \" {function} \" # function name ) colored_format = Colorizer . ansify ( base_format ) colored_message = Colorizer . ansify ( str ( record [ \"message\" ])) escaped_record = escape ( record ) base = colored_format . format ( ** escaped_record ) lines = colored_message . splitlines () new_message = \"\" . join ( \" \\n \" + line for line in lines ) + \" \\n \" return base + new_message escape escape ( record : dict ) Escape the \"<\" character before markup parsing Source code in eds_scikit/utils/logging.py 44 45 46 47 48 49 50 51 def escape ( record : dict ): \"\"\" Escape the \"<\" character before markup parsing \"\"\" return { k : v if not isinstance ( v , str ) else v . replace ( \"<\" , r \"\\<\" ) for k , v in record . items () }","title":"logging"},{"location":"reference/utils/logging/#eds_scikitutilslogging","text":"","title":"eds_scikit.utils.logging"},{"location":"reference/utils/logging/#eds_scikit.utils.logging.formatter","text":"formatter ( record : dict ) Formats the logging message by: Adding color and bold Indenting the message Source code in eds_scikit/utils/logging.py 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 def formatter ( record : dict ): \"\"\" Formats the logging message by: - Adding color and bold - Indenting the message \"\"\" base_format = ( \"\" # bold \"[eds-scikit]\" \"- \" \" {name} :\" # corresponds to __name__ \" {extra[classname]}{extra[sep]} \" # class name, if relevant \"\" \" {function} \" # function name ) colored_format = Colorizer . ansify ( base_format ) colored_message = Colorizer . ansify ( str ( record [ \"message\" ])) escaped_record = escape ( record ) base = colored_format . format ( ** escaped_record ) lines = colored_message . splitlines () new_message = \"\" . join ( \" \\n \" + line for line in lines ) + \" \\n \" return base + new_message","title":"formatter()"},{"location":"reference/utils/logging/#eds_scikit.utils.logging.escape","text":"escape ( record : dict ) Escape the \"<\" character before markup parsing Source code in eds_scikit/utils/logging.py 44 45 46 47 48 49 50 51 def escape ( record : dict ): \"\"\" Escape the \"<\" character before markup parsing \"\"\" return { k : v if not isinstance ( v , str ) else v . replace ( \"<\" , r \"\\<\" ) for k , v in record . items () }","title":"escape()"},{"location":"reference/utils/test_utils/","text":"eds_scikit.utils.test_utils","title":"test_utils"},{"location":"reference/utils/test_utils/#eds_scikitutilstest_utils","text":"","title":"eds_scikit.utils.test_utils"},{"location":"reference/utils/typing/","text":"eds_scikit.utils.typing","title":"typing"},{"location":"reference/utils/typing/#eds_scikitutilstyping","text":"","title":"eds_scikit.utils.typing"},{"location":"reference/utils/custom_implem/","text":"eds_scikit.utils.custom_implem","title":"`eds_scikit.utils.custom_implem`"},{"location":"reference/utils/custom_implem/#eds_scikitutilscustom_implem","text":"","title":"eds_scikit.utils.custom_implem"},{"location":"reference/utils/custom_implem/custom_implem/","text":"eds_scikit.utils.custom_implem.custom_implem CustomImplem A collection of custom pandas and koalas methods. All public facing methods must be stateless and defined as classmethods. add_unique_id classmethod add_unique_id ( obj : Any , col_name : str = 'id' , backend = None ) -> Any Add an ID column for koalas or pandas. Source code in eds_scikit/utils/custom_implem/custom_implem.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 @classmethod def add_unique_id ( cls , obj : Any , col_name : str = \"id\" , backend = None , ) -> Any : \"\"\"Add an ID column for koalas or pandas.\"\"\" if backend is pd : obj [ col_name ] = range ( obj . shape [ 0 ]) return obj elif backend is ks : return obj . koalas . attach_id_column ( id_type = \"distributed\" , column = col_name ) else : raise NotImplementedError ( f \"No method 'add_unique_id' is available for backend ' { backend } '.\" ) cut classmethod cut ( x , bins , right : bool = True , labels = None , retbins : bool = False , precision : int = 3 , include_lowest : bool = False , duplicates : str = 'raise' , ordered : bool = True , backend = None ) koalas version of pd.cut Notes Simplified vendoring from: https://github.com/pandas-dev/pandas/blob/v1.5.2/pandas/core/reshape/tile.py#L50-L305 Source code in eds_scikit/utils/custom_implem/custom_implem.py 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 @classmethod def cut ( cls , x , bins , right : bool = True , labels = None , retbins : bool = False , precision : int = 3 , include_lowest : bool = False , duplicates : str = \"raise\" , ordered : bool = True , backend = None , # unused because koalas only ): \"\"\"koalas version of pd.cut Notes ----- Simplified vendoring from: https://github.com/pandas-dev/pandas/blob/v1.5.2/pandas/core/reshape/tile.py#L50-L305 \"\"\" return cut ( x , bins , right , labels , retbins , precision , include_lowest , duplicates , ordered , )","title":"custom_implem"},{"location":"reference/utils/custom_implem/custom_implem/#eds_scikitutilscustom_implemcustom_implem","text":"","title":"eds_scikit.utils.custom_implem.custom_implem"},{"location":"reference/utils/custom_implem/custom_implem/#eds_scikit.utils.custom_implem.custom_implem.CustomImplem","text":"A collection of custom pandas and koalas methods. All public facing methods must be stateless and defined as classmethods.","title":"CustomImplem"},{"location":"reference/utils/custom_implem/custom_implem/#eds_scikit.utils.custom_implem.custom_implem.CustomImplem.add_unique_id","text":"add_unique_id ( obj : Any , col_name : str = 'id' , backend = None ) -> Any Add an ID column for koalas or pandas. Source code in eds_scikit/utils/custom_implem/custom_implem.py 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 @classmethod def add_unique_id ( cls , obj : Any , col_name : str = \"id\" , backend = None , ) -> Any : \"\"\"Add an ID column for koalas or pandas.\"\"\" if backend is pd : obj [ col_name ] = range ( obj . shape [ 0 ]) return obj elif backend is ks : return obj . koalas . attach_id_column ( id_type = \"distributed\" , column = col_name ) else : raise NotImplementedError ( f \"No method 'add_unique_id' is available for backend ' { backend } '.\" )","title":"add_unique_id()"},{"location":"reference/utils/custom_implem/custom_implem/#eds_scikit.utils.custom_implem.custom_implem.CustomImplem.cut","text":"cut ( x , bins , right : bool = True , labels = None , retbins : bool = False , precision : int = 3 , include_lowest : bool = False , duplicates : str = 'raise' , ordered : bool = True , backend = None ) koalas version of pd.cut","title":"cut()"},{"location":"reference/utils/custom_implem/custom_implem/#eds_scikit.utils.custom_implem.custom_implem.CustomImplem.cut--notes","text":"Simplified vendoring from: https://github.com/pandas-dev/pandas/blob/v1.5.2/pandas/core/reshape/tile.py#L50-L305 Source code in eds_scikit/utils/custom_implem/custom_implem.py 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 @classmethod def cut ( cls , x , bins , right : bool = True , labels = None , retbins : bool = False , precision : int = 3 , include_lowest : bool = False , duplicates : str = \"raise\" , ordered : bool = True , backend = None , # unused because koalas only ): \"\"\"koalas version of pd.cut Notes ----- Simplified vendoring from: https://github.com/pandas-dev/pandas/blob/v1.5.2/pandas/core/reshape/tile.py#L50-L305 \"\"\" return cut ( x , bins , right , labels , retbins , precision , include_lowest , duplicates , ordered , )","title":"Notes"},{"location":"reference/utils/custom_implem/cut/","text":"eds_scikit.utils.custom_implem.cut cut cut ( x , bins , right : bool = True , labels = None , retbins : bool = False , precision : int = 3 , include_lowest : bool = False , duplicates : str = 'raise' , ordered : bool = True ) Bin values into discrete intervals. Use cut when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a categorical variable. For example, cut could convert ages to groups of age ranges. Supports binning into an equal number of bins, or a pre-specified array of bins. See original function at: https://github.com/pandas-dev/pandas/blob/v1.5.2/pandas/core/reshape/tile.py#L50-L305 # noqa PARAMETER DESCRIPTION x The input array to be binned. Must be 1-dimensional. TYPE: koalas Series. bins The criteria to bin by. * int : Defines the number of equal-width bins in the range of x . The range of x is extended by .1% on each side to include the minimum and maximum values of x . * sequence of scalars : Defines the bin edges allowing for non-uniform width. No extension of the range of x is done. * IntervalIndex : Defines the exact bins to be used. Note that IntervalIndex for bins must be non-overlapping. TYPE: int, sequence of scalars, or IntervalIndex right Indicates whether bins includes the rightmost edge or not. If right == True (the default), then the bins [1, 2, 3, 4] indicate (1,2], (2,3], (3,4]. This argument is ignored when bins is an IntervalIndex. TYPE: bool, default True DEFAULT: True labels Specifies the labels for the returned bins. Must be the same length as the resulting bins. If False, returns only integer indicators of the bins. This affects the type of the output container (see below). This argument is ignored when bins is an IntervalIndex. If True, raises an error. When ordered=False , labels must be provided. TYPE: array or False, default None DEFAULT: None retbins Whether to return the bins or not. Useful when bins is provided as a scalar. TYPE: bool, default False DEFAULT: False precision The precision at which to store and display the bins labels. TYPE: int, default 3 DEFAULT: 3 include_lowest Whether the first interval should be left-inclusive or not. TYPE: bool, default False DEFAULT: False duplicates If bin edges are not unique, raise ValueError or drop non-uniques. TYPE: str DEFAULT: default 'raise' ordered Whether the labels are ordered or not. Applies to returned types Categorical and Series (with Categorical dtype). If True, the resulting categorical will be ordered. If False, the resulting categorical will be unordered (labels must be provided). .. versionadded:: 1.1.0 TYPE: bool, default True DEFAULT: True Returns out An array-like object representing the respective bin for each value of x . The type depends on the value of labels . * None (default) : returns a Series for Series x or a Categorical for all other inputs. The values stored within are Interval dtype. * sequence of scalars : returns a Series for Series x or a Categorical for all other inputs. The values stored within are whatever the type in the sequence is. * False : returns an ndarray of integers. TYPE: Categorical, Series, or ndarray bins The computed or specified bins. Only returned when retbins=True . For scalar or sequence bins , this is an ndarray with the computed bins. If set duplicates=drop , bins will drop non-unique bin. For an IntervalIndex bins , this is equal to bins . TYPE: numpy.ndarray or IntervalIndex. See Also qcut : Discretize variable into equal-sized buckets based on rank or based on sample quantiles. Categorical : Array type for storing data that come from a fixed set of values. Series : One-dimensional array with axis labels (including time series). IntervalIndex : Immutable Index implementing an ordered, sliceable set. Notes Any NA values will be NA in the result. Out of bounds values will be NA in the resulting Series or Categorical object. Reference :ref: the user guide for more examples. Examples: Discretize into three equal-sized bins. >>> from eds_scikit.utils.framework import bd >>> bd . cut ( ks . Series ( np . array ([ 1 , 7 , 5 , 4 , 6 , 3 ])), 3 ) ... [(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ... Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ... >>> bd . cut ( ks . Series ( np . array ([ 1 , 7 , 5 , 4 , 6 , 3 ])), 3 , retbins = True ) ... ([(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ... Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ... array([0.994, 3. , 5. , 7. ])) Discovers the same bins, but assign them specific labels. Notice that the returned Categorical's categories are labels and is ordered. >>> bd . cut ( ks . Series ( np . array ([ 1 , 7 , 5 , 4 , 6 , 3 ])), ... 3 , labels = [ \"bad\" , \"medium\" , \"good\" ]) ['bad', 'good', 'medium', 'medium', 'good', 'bad'] Categories (3, object): ['bad' < 'medium' < 'good'] ordered=False will result in unordered categories when labels are passed. This parameter can be used to allow non-unique labels: >>> bd . cut ( ks . Series ( np . array ([ 1 , 7 , 5 , 4 , 6 , 3 ])), 3 , ... labels = [ \"B\" , \"A\" , \"B\" ], ordered = False ) ['B', 'B', 'A', 'A', 'B', 'B'] Categories (2, object): ['A', 'B'] labels=False implies you just want the bins back. >>> bd . cut ( ks . Series ([ 0 , 1 , 1 , 2 ]), bins = 4 , labels = False ) array([0, 1, 1, 3]) Passing a Series as an input returns a Series with categorical dtype: >>> s = ks . Series ( np . array ([ 2 , 4 , 6 , 8 , 10 ]), ... index = [ 'a' , 'b' , 'c' , 'd' , 'e' ]) >>> bd . cut ( s , 3 ) ... a (1.992, 4.667] b (1.992, 4.667] c (4.667, 7.333] d (7.333, 10.0] e (7.333, 10.0] dtype: category Categories (3, interval[float64, right]): [(1.992, 4.667] < (4.667, ... Passing a Series as an input returns a Series with mapping value. It is used to map numerically to intervals based on bins. >>> s = ks . Series ( np . array ([ 2 , 4 , 6 , 8 , 10 ]), ... index = [ 'a' , 'b' , 'c' , 'd' , 'e' ]) >>> bd . cut ( s , [ 0 , 2 , 4 , 6 , 8 , 10 ], labels = False , retbins = True , right = False ) ... (a 1.0 b 2.0 c 3.0 d 4.0 e NaN dtype: float64, array([ 0, 2, 4, 6, 8, 10])) Use drop optional when bins is not unique >>> bd . cut ( s , [ 0 , 2 , 4 , 6 , 10 , 10 ], labels = False , retbins = True , ... right = False , duplicates = 'drop' ) ... (a 1.0 b 2.0 c 3.0 d 3.0 e NaN dtype: float64, array([ 0, 2, 4, 6, 10])) Passing an IntervalIndex for bins results in those categories exactly. Notice that values not covered by the IntervalIndex are set to NaN. 0 is to the left of the first bin (which is closed on the right), and 1.5 falls between two bins. >>> bins = pd . IntervalIndex . from_tuples ([( 0 , 1 ), ( 2 , 3 ), ( 4 , 5 )]) >>> bd . cut ( ks . Series ([ 0 , 0.5 , 1.5 , 2.5 , 4.5 ]), bins ) [NaN, (0.0, 1.0], NaN, (2.0, 3.0], (4.0, 5.0]] Categories (3, interval[int64, right]): [(0, 1] < (2, 3] < (4, 5]] Source code in eds_scikit/utils/custom_implem/cut.py 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 def cut ( x , bins , right : bool = True , labels = None , retbins : bool = False , precision : int = 3 , include_lowest : bool = False , duplicates : str = \"raise\" , ordered : bool = True , ): # pragma: no cover \"\"\" Bin values into discrete intervals. Use `cut` when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a categorical variable. For example, `cut` could convert ages to groups of age ranges. Supports binning into an equal number of bins, or a pre-specified array of bins. See original function at: https://github.com/pandas-dev/pandas/blob/v1.5.2/pandas/core/reshape/tile.py#L50-L305 # noqa Parameters ---------- x : koalas Series. The input array to be binned. Must be 1-dimensional. bins : int, sequence of scalars, or IntervalIndex The criteria to bin by. * int : Defines the number of equal-width bins in the range of `x`. The range of `x` is extended by .1% on each side to include the minimum and maximum values of `x`. * sequence of scalars : Defines the bin edges allowing for non-uniform width. No extension of the range of `x` is done. * IntervalIndex : Defines the exact bins to be used. Note that IntervalIndex for `bins` must be non-overlapping. right : bool, default True Indicates whether `bins` includes the rightmost edge or not. If ``right == True`` (the default), then the `bins` ``[1, 2, 3, 4]`` indicate (1,2], (2,3], (3,4]. This argument is ignored when `bins` is an IntervalIndex. labels : array or False, default None Specifies the labels for the returned bins. Must be the same length as the resulting bins. If False, returns only integer indicators of the bins. This affects the type of the output container (see below). This argument is ignored when `bins` is an IntervalIndex. If True, raises an error. When `ordered=False`, labels must be provided. retbins : bool, default False Whether to return the bins or not. Useful when bins is provided as a scalar. precision : int, default 3 The precision at which to store and display the bins labels. include_lowest : bool, default False Whether the first interval should be left-inclusive or not. duplicates : {default 'raise', 'drop'}, optional If bin edges are not unique, raise ValueError or drop non-uniques. ordered : bool, default True Whether the labels are ordered or not. Applies to returned types Categorical and Series (with Categorical dtype). If True, the resulting categorical will be ordered. If False, the resulting categorical will be unordered (labels must be provided). .. versionadded:: 1.1.0 Returns ------- out : Categorical, Series, or ndarray An array-like object representing the respective bin for each value of `x`. The type depends on the value of `labels`. * None (default) : returns a Series for Series `x` or a Categorical for all other inputs. The values stored within are Interval dtype. * sequence of scalars : returns a Series for Series `x` or a Categorical for all other inputs. The values stored within are whatever the type in the sequence is. * False : returns an ndarray of integers. bins : numpy.ndarray or IntervalIndex. The computed or specified bins. Only returned when `retbins=True`. For scalar or sequence `bins`, this is an ndarray with the computed bins. If set `duplicates=drop`, `bins` will drop non-unique bin. For an IntervalIndex `bins`, this is equal to `bins`. See Also -------- qcut : Discretize variable into equal-sized buckets based on rank or based on sample quantiles. Categorical : Array type for storing data that come from a fixed set of values. Series : One-dimensional array with axis labels (including time series). IntervalIndex : Immutable Index implementing an ordered, sliceable set. Notes ----- Any NA values will be NA in the result. Out of bounds values will be NA in the resulting Series or Categorical object. Reference :ref:`the user guide ` for more examples. Examples -------- Discretize into three equal-sized bins. >>> from eds_scikit.utils.framework import bd >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])), 3) ... # doctest: +ELLIPSIS [(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ... Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ... >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])), 3, retbins=True) ... # doctest: +ELLIPSIS ([(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ... Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ... array([0.994, 3. , 5. , 7. ])) Discovers the same bins, but assign them specific labels. Notice that the returned Categorical's categories are `labels` and is ordered. >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])), ... 3, labels=[\"bad\", \"medium\", \"good\"]) ['bad', 'good', 'medium', 'medium', 'good', 'bad'] Categories (3, object): ['bad' < 'medium' < 'good'] ``ordered=False`` will result in unordered categories when labels are passed. This parameter can be used to allow non-unique labels: >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])), 3, ... labels=[\"B\", \"A\", \"B\"], ordered=False) ['B', 'B', 'A', 'A', 'B', 'B'] Categories (2, object): ['A', 'B'] ``labels=False`` implies you just want the bins back. >>> bd.cut(ks.Series([0, 1, 1, 2]), bins=4, labels=False) array([0, 1, 1, 3]) Passing a Series as an input returns a Series with categorical dtype: >>> s = ks.Series(np.array([2, 4, 6, 8, 10]), ... index=['a', 'b', 'c', 'd', 'e']) >>> bd.cut(s, 3) ... # doctest: +ELLIPSIS a (1.992, 4.667] b (1.992, 4.667] c (4.667, 7.333] d (7.333, 10.0] e (7.333, 10.0] dtype: category Categories (3, interval[float64, right]): [(1.992, 4.667] < (4.667, ... Passing a Series as an input returns a Series with mapping value. It is used to map numerically to intervals based on bins. >>> s = ks.Series(np.array([2, 4, 6, 8, 10]), ... index=['a', 'b', 'c', 'd', 'e']) >>> bd.cut(s, [0, 2, 4, 6, 8, 10], labels=False, retbins=True, right=False) ... # doctest: +ELLIPSIS (a 1.0 b 2.0 c 3.0 d 4.0 e NaN dtype: float64, array([ 0, 2, 4, 6, 8, 10])) Use `drop` optional when bins is not unique >>> bd.cut(s, [0, 2, 4, 6, 10, 10], labels=False, retbins=True, ... right=False, duplicates='drop') ... # doctest: +ELLIPSIS (a 1.0 b 2.0 c 3.0 d 3.0 e NaN dtype: float64, array([ 0, 2, 4, 6, 10])) Passing an IntervalIndex for `bins` results in those categories exactly. Notice that values not covered by the IntervalIndex are set to NaN. 0 is to the left of the first bin (which is closed on the right), and 1.5 falls between two bins. >>> bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)]) >>> bd.cut(ks.Series([0, 0.5, 1.5, 2.5, 4.5]), bins) [NaN, (0.0, 1.0], NaN, (2.0, 3.0], (4.0, 5.0]] Categories (3, interval[int64, right]): [(0, 1] < (2, 3] < (4, 5]] \"\"\" if x . ndim != 1 : raise ValueError ( \"x must be 1D\" ) x , dtype = x . astype ( np . int64 ), x . dtype if not np . iterable ( bins ): if is_scalar ( bins ) and bins < 1 : raise ValueError ( \"`bins` should be a positive integer.\" ) try : # for array-like sz = x . size except AttributeError : x = np . asarray ( x ) sz = x . size if sz == 0 : raise ValueError ( \"Cannot cut empty array\" ) mn , mx = x . min (), x . max () if np . isinf ( mn ) or np . isinf ( mx ): raise ValueError ( \"cannot specify integer `bins` when input data contains infinity\" ) elif mn == mx : # adjust end points before binning mn -= 0.001 * abs ( mn ) if mn != 0 else 0.001 mx += 0.001 * abs ( mx ) if mx != 0 else 0.001 bins = np . linspace ( mn , mx , bins + 1 , endpoint = True ) else : # adjust end points after binning bins = np . linspace ( mn , mx , bins + 1 , endpoint = True ) adj = ( mx - mn ) * 0.001 # 0.1% of the range if right : bins [ 0 ] -= adj else : bins [ - 1 ] += adj elif isinstance ( bins , IntervalIndex ): if bins . is_overlapping : raise ValueError ( \"Overlapping IntervalIndex is not accepted.\" ) else : if is_datetime64tz_dtype ( bins ): bins = np . asarray ( bins , dtype = DT64NS_DTYPE ) else : bins = np . asarray ( bins ) bins = _convert_bin_to_numeric_type ( bins , dtype ) # GH 26045: cast to float64 to avoid an overflow if ( np . diff ( bins . astype ( \"float64\" )) < 0 ) . any (): raise ValueError ( \"bins must increase monotonically.\" ) fac , bins = _bins_to_cuts ( x , bins , right = right , labels = labels , precision = precision , include_lowest = include_lowest , dtype = dtype , duplicates = duplicates , ordered = ordered , ) if not retbins : return fac return fac , bins","title":"cut"},{"location":"reference/utils/custom_implem/cut/#eds_scikitutilscustom_implemcut","text":"","title":"eds_scikit.utils.custom_implem.cut"},{"location":"reference/utils/custom_implem/cut/#eds_scikit.utils.custom_implem.cut.cut","text":"cut ( x , bins , right : bool = True , labels = None , retbins : bool = False , precision : int = 3 , include_lowest : bool = False , duplicates : str = 'raise' , ordered : bool = True ) Bin values into discrete intervals. Use cut when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a categorical variable. For example, cut could convert ages to groups of age ranges. Supports binning into an equal number of bins, or a pre-specified array of bins. See original function at: https://github.com/pandas-dev/pandas/blob/v1.5.2/pandas/core/reshape/tile.py#L50-L305 # noqa PARAMETER DESCRIPTION x The input array to be binned. Must be 1-dimensional. TYPE: koalas Series. bins The criteria to bin by. * int : Defines the number of equal-width bins in the range of x . The range of x is extended by .1% on each side to include the minimum and maximum values of x . * sequence of scalars : Defines the bin edges allowing for non-uniform width. No extension of the range of x is done. * IntervalIndex : Defines the exact bins to be used. Note that IntervalIndex for bins must be non-overlapping. TYPE: int, sequence of scalars, or IntervalIndex right Indicates whether bins includes the rightmost edge or not. If right == True (the default), then the bins [1, 2, 3, 4] indicate (1,2], (2,3], (3,4]. This argument is ignored when bins is an IntervalIndex. TYPE: bool, default True DEFAULT: True labels Specifies the labels for the returned bins. Must be the same length as the resulting bins. If False, returns only integer indicators of the bins. This affects the type of the output container (see below). This argument is ignored when bins is an IntervalIndex. If True, raises an error. When ordered=False , labels must be provided. TYPE: array or False, default None DEFAULT: None retbins Whether to return the bins or not. Useful when bins is provided as a scalar. TYPE: bool, default False DEFAULT: False precision The precision at which to store and display the bins labels. TYPE: int, default 3 DEFAULT: 3 include_lowest Whether the first interval should be left-inclusive or not. TYPE: bool, default False DEFAULT: False duplicates If bin edges are not unique, raise ValueError or drop non-uniques. TYPE: str DEFAULT: default 'raise' ordered Whether the labels are ordered or not. Applies to returned types Categorical and Series (with Categorical dtype). If True, the resulting categorical will be ordered. If False, the resulting categorical will be unordered (labels must be provided). .. versionadded:: 1.1.0 TYPE: bool, default True DEFAULT: True Returns out An array-like object representing the respective bin for each value of x . The type depends on the value of labels . * None (default) : returns a Series for Series x or a Categorical for all other inputs. The values stored within are Interval dtype. * sequence of scalars : returns a Series for Series x or a Categorical for all other inputs. The values stored within are whatever the type in the sequence is. * False : returns an ndarray of integers. TYPE: Categorical, Series, or ndarray bins The computed or specified bins. Only returned when retbins=True . For scalar or sequence bins , this is an ndarray with the computed bins. If set duplicates=drop , bins will drop non-unique bin. For an IntervalIndex bins , this is equal to bins . TYPE: numpy.ndarray or IntervalIndex.","title":"cut()"},{"location":"reference/utils/custom_implem/cut/#eds_scikit.utils.custom_implem.cut.cut--see-also","text":"qcut : Discretize variable into equal-sized buckets based on rank or based on sample quantiles. Categorical : Array type for storing data that come from a fixed set of values. Series : One-dimensional array with axis labels (including time series). IntervalIndex : Immutable Index implementing an ordered, sliceable set.","title":"See Also"},{"location":"reference/utils/custom_implem/cut/#eds_scikit.utils.custom_implem.cut.cut--notes","text":"Any NA values will be NA in the result. Out of bounds values will be NA in the resulting Series or Categorical object. Reference :ref: the user guide for more examples. Examples: Discretize into three equal-sized bins. >>> from eds_scikit.utils.framework import bd >>> bd . cut ( ks . Series ( np . array ([ 1 , 7 , 5 , 4 , 6 , 3 ])), 3 ) ... [(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ... Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ... >>> bd . cut ( ks . Series ( np . array ([ 1 , 7 , 5 , 4 , 6 , 3 ])), 3 , retbins = True ) ... ([(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ... Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ... array([0.994, 3. , 5. , 7. ])) Discovers the same bins, but assign them specific labels. Notice that the returned Categorical's categories are labels and is ordered. >>> bd . cut ( ks . Series ( np . array ([ 1 , 7 , 5 , 4 , 6 , 3 ])), ... 3 , labels = [ \"bad\" , \"medium\" , \"good\" ]) ['bad', 'good', 'medium', 'medium', 'good', 'bad'] Categories (3, object): ['bad' < 'medium' < 'good'] ordered=False will result in unordered categories when labels are passed. This parameter can be used to allow non-unique labels: >>> bd . cut ( ks . Series ( np . array ([ 1 , 7 , 5 , 4 , 6 , 3 ])), 3 , ... labels = [ \"B\" , \"A\" , \"B\" ], ordered = False ) ['B', 'B', 'A', 'A', 'B', 'B'] Categories (2, object): ['A', 'B'] labels=False implies you just want the bins back. >>> bd . cut ( ks . Series ([ 0 , 1 , 1 , 2 ]), bins = 4 , labels = False ) array([0, 1, 1, 3]) Passing a Series as an input returns a Series with categorical dtype: >>> s = ks . Series ( np . array ([ 2 , 4 , 6 , 8 , 10 ]), ... index = [ 'a' , 'b' , 'c' , 'd' , 'e' ]) >>> bd . cut ( s , 3 ) ... a (1.992, 4.667] b (1.992, 4.667] c (4.667, 7.333] d (7.333, 10.0] e (7.333, 10.0] dtype: category Categories (3, interval[float64, right]): [(1.992, 4.667] < (4.667, ... Passing a Series as an input returns a Series with mapping value. It is used to map numerically to intervals based on bins. >>> s = ks . Series ( np . array ([ 2 , 4 , 6 , 8 , 10 ]), ... index = [ 'a' , 'b' , 'c' , 'd' , 'e' ]) >>> bd . cut ( s , [ 0 , 2 , 4 , 6 , 8 , 10 ], labels = False , retbins = True , right = False ) ... (a 1.0 b 2.0 c 3.0 d 4.0 e NaN dtype: float64, array([ 0, 2, 4, 6, 8, 10])) Use drop optional when bins is not unique >>> bd . cut ( s , [ 0 , 2 , 4 , 6 , 10 , 10 ], labels = False , retbins = True , ... right = False , duplicates = 'drop' ) ... (a 1.0 b 2.0 c 3.0 d 3.0 e NaN dtype: float64, array([ 0, 2, 4, 6, 10])) Passing an IntervalIndex for bins results in those categories exactly. Notice that values not covered by the IntervalIndex are set to NaN. 0 is to the left of the first bin (which is closed on the right), and 1.5 falls between two bins. >>> bins = pd . IntervalIndex . from_tuples ([( 0 , 1 ), ( 2 , 3 ), ( 4 , 5 )]) >>> bd . cut ( ks . Series ([ 0 , 0.5 , 1.5 , 2.5 , 4.5 ]), bins ) [NaN, (0.0, 1.0], NaN, (2.0, 3.0], (4.0, 5.0]] Categories (3, interval[int64, right]): [(0, 1] < (2, 3] < (4, 5]] Source code in eds_scikit/utils/custom_implem/cut.py 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 def cut ( x , bins , right : bool = True , labels = None , retbins : bool = False , precision : int = 3 , include_lowest : bool = False , duplicates : str = \"raise\" , ordered : bool = True , ): # pragma: no cover \"\"\" Bin values into discrete intervals. Use `cut` when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a categorical variable. For example, `cut` could convert ages to groups of age ranges. Supports binning into an equal number of bins, or a pre-specified array of bins. See original function at: https://github.com/pandas-dev/pandas/blob/v1.5.2/pandas/core/reshape/tile.py#L50-L305 # noqa Parameters ---------- x : koalas Series. The input array to be binned. Must be 1-dimensional. bins : int, sequence of scalars, or IntervalIndex The criteria to bin by. * int : Defines the number of equal-width bins in the range of `x`. The range of `x` is extended by .1% on each side to include the minimum and maximum values of `x`. * sequence of scalars : Defines the bin edges allowing for non-uniform width. No extension of the range of `x` is done. * IntervalIndex : Defines the exact bins to be used. Note that IntervalIndex for `bins` must be non-overlapping. right : bool, default True Indicates whether `bins` includes the rightmost edge or not. If ``right == True`` (the default), then the `bins` ``[1, 2, 3, 4]`` indicate (1,2], (2,3], (3,4]. This argument is ignored when `bins` is an IntervalIndex. labels : array or False, default None Specifies the labels for the returned bins. Must be the same length as the resulting bins. If False, returns only integer indicators of the bins. This affects the type of the output container (see below). This argument is ignored when `bins` is an IntervalIndex. If True, raises an error. When `ordered=False`, labels must be provided. retbins : bool, default False Whether to return the bins or not. Useful when bins is provided as a scalar. precision : int, default 3 The precision at which to store and display the bins labels. include_lowest : bool, default False Whether the first interval should be left-inclusive or not. duplicates : {default 'raise', 'drop'}, optional If bin edges are not unique, raise ValueError or drop non-uniques. ordered : bool, default True Whether the labels are ordered or not. Applies to returned types Categorical and Series (with Categorical dtype). If True, the resulting categorical will be ordered. If False, the resulting categorical will be unordered (labels must be provided). .. versionadded:: 1.1.0 Returns ------- out : Categorical, Series, or ndarray An array-like object representing the respective bin for each value of `x`. The type depends on the value of `labels`. * None (default) : returns a Series for Series `x` or a Categorical for all other inputs. The values stored within are Interval dtype. * sequence of scalars : returns a Series for Series `x` or a Categorical for all other inputs. The values stored within are whatever the type in the sequence is. * False : returns an ndarray of integers. bins : numpy.ndarray or IntervalIndex. The computed or specified bins. Only returned when `retbins=True`. For scalar or sequence `bins`, this is an ndarray with the computed bins. If set `duplicates=drop`, `bins` will drop non-unique bin. For an IntervalIndex `bins`, this is equal to `bins`. See Also -------- qcut : Discretize variable into equal-sized buckets based on rank or based on sample quantiles. Categorical : Array type for storing data that come from a fixed set of values. Series : One-dimensional array with axis labels (including time series). IntervalIndex : Immutable Index implementing an ordered, sliceable set. Notes ----- Any NA values will be NA in the result. Out of bounds values will be NA in the resulting Series or Categorical object. Reference :ref:`the user guide ` for more examples. Examples -------- Discretize into three equal-sized bins. >>> from eds_scikit.utils.framework import bd >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])), 3) ... # doctest: +ELLIPSIS [(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ... Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ... >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])), 3, retbins=True) ... # doctest: +ELLIPSIS ([(0.994, 3.0], (5.0, 7.0], (3.0, 5.0], (3.0, 5.0], (5.0, 7.0], ... Categories (3, interval[float64, right]): [(0.994, 3.0] < (3.0, 5.0] ... array([0.994, 3. , 5. , 7. ])) Discovers the same bins, but assign them specific labels. Notice that the returned Categorical's categories are `labels` and is ordered. >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])), ... 3, labels=[\"bad\", \"medium\", \"good\"]) ['bad', 'good', 'medium', 'medium', 'good', 'bad'] Categories (3, object): ['bad' < 'medium' < 'good'] ``ordered=False`` will result in unordered categories when labels are passed. This parameter can be used to allow non-unique labels: >>> bd.cut(ks.Series(np.array([1, 7, 5, 4, 6, 3])), 3, ... labels=[\"B\", \"A\", \"B\"], ordered=False) ['B', 'B', 'A', 'A', 'B', 'B'] Categories (2, object): ['A', 'B'] ``labels=False`` implies you just want the bins back. >>> bd.cut(ks.Series([0, 1, 1, 2]), bins=4, labels=False) array([0, 1, 1, 3]) Passing a Series as an input returns a Series with categorical dtype: >>> s = ks.Series(np.array([2, 4, 6, 8, 10]), ... index=['a', 'b', 'c', 'd', 'e']) >>> bd.cut(s, 3) ... # doctest: +ELLIPSIS a (1.992, 4.667] b (1.992, 4.667] c (4.667, 7.333] d (7.333, 10.0] e (7.333, 10.0] dtype: category Categories (3, interval[float64, right]): [(1.992, 4.667] < (4.667, ... Passing a Series as an input returns a Series with mapping value. It is used to map numerically to intervals based on bins. >>> s = ks.Series(np.array([2, 4, 6, 8, 10]), ... index=['a', 'b', 'c', 'd', 'e']) >>> bd.cut(s, [0, 2, 4, 6, 8, 10], labels=False, retbins=True, right=False) ... # doctest: +ELLIPSIS (a 1.0 b 2.0 c 3.0 d 4.0 e NaN dtype: float64, array([ 0, 2, 4, 6, 8, 10])) Use `drop` optional when bins is not unique >>> bd.cut(s, [0, 2, 4, 6, 10, 10], labels=False, retbins=True, ... right=False, duplicates='drop') ... # doctest: +ELLIPSIS (a 1.0 b 2.0 c 3.0 d 3.0 e NaN dtype: float64, array([ 0, 2, 4, 6, 10])) Passing an IntervalIndex for `bins` results in those categories exactly. Notice that values not covered by the IntervalIndex are set to NaN. 0 is to the left of the first bin (which is closed on the right), and 1.5 falls between two bins. >>> bins = pd.IntervalIndex.from_tuples([(0, 1), (2, 3), (4, 5)]) >>> bd.cut(ks.Series([0, 0.5, 1.5, 2.5, 4.5]), bins) [NaN, (0.0, 1.0], NaN, (2.0, 3.0], (4.0, 5.0]] Categories (3, interval[int64, right]): [(0, 1] < (2, 3] < (4, 5]] \"\"\" if x . ndim != 1 : raise ValueError ( \"x must be 1D\" ) x , dtype = x . astype ( np . int64 ), x . dtype if not np . iterable ( bins ): if is_scalar ( bins ) and bins < 1 : raise ValueError ( \"`bins` should be a positive integer.\" ) try : # for array-like sz = x . size except AttributeError : x = np . asarray ( x ) sz = x . size if sz == 0 : raise ValueError ( \"Cannot cut empty array\" ) mn , mx = x . min (), x . max () if np . isinf ( mn ) or np . isinf ( mx ): raise ValueError ( \"cannot specify integer `bins` when input data contains infinity\" ) elif mn == mx : # adjust end points before binning mn -= 0.001 * abs ( mn ) if mn != 0 else 0.001 mx += 0.001 * abs ( mx ) if mx != 0 else 0.001 bins = np . linspace ( mn , mx , bins + 1 , endpoint = True ) else : # adjust end points after binning bins = np . linspace ( mn , mx , bins + 1 , endpoint = True ) adj = ( mx - mn ) * 0.001 # 0.1% of the range if right : bins [ 0 ] -= adj else : bins [ - 1 ] += adj elif isinstance ( bins , IntervalIndex ): if bins . is_overlapping : raise ValueError ( \"Overlapping IntervalIndex is not accepted.\" ) else : if is_datetime64tz_dtype ( bins ): bins = np . asarray ( bins , dtype = DT64NS_DTYPE ) else : bins = np . asarray ( bins ) bins = _convert_bin_to_numeric_type ( bins , dtype ) # GH 26045: cast to float64 to avoid an overflow if ( np . diff ( bins . astype ( \"float64\" )) < 0 ) . any (): raise ValueError ( \"bins must increase monotonically.\" ) fac , bins = _bins_to_cuts ( x , bins , right = right , labels = labels , precision = precision , include_lowest = include_lowest , dtype = dtype , duplicates = duplicates , ordered = ordered , ) if not retbins : return fac return fac , bins","title":"Notes"},{"location":"reference/utils/flowchart/","text":"eds_scikit.utils.flowchart","title":"`eds_scikit.utils.flowchart`"},{"location":"reference/utils/flowchart/#eds_scikitutilsflowchart","text":"","title":"eds_scikit.utils.flowchart"},{"location":"reference/utils/flowchart/flowchart/","text":"eds_scikit.utils.flowchart.flowchart Flowchart Flowchart ( initial_description : str , data : Union [ DataFrame , Dict [ str , Iterable ]], concat_criterion_description : bool = True , to_count : str = 'person_id' ) Main class to define an flowchart (inclusion diagram) PARAMETER DESCRIPTION initial_description Description of the initial population TYPE: str data Either a Pandas/Koalas DataFrame, or a dictionary of iterables. If a dictionary, the initial cohort should be proivided under the initial key. TYPE: Union [ DataFrame , Dict [ str , Iterable ]] concat_criterion_description Whether to concatenate provided description together when adding multiple criteria TYPE: bool , optional DEFAULT: True to_count Only if data is a DataFrame: column of data from which the count is computed. Usually, this will be the column containing patient or stay IDs. TYPE: str , optional DEFAULT: 'person_id' Source code in eds_scikit/utils/flowchart/flowchart.py 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 def __init__ ( self , initial_description : str , data : Union [ DataFrame , Dict [ str , Iterable ]], concat_criterion_description : bool = True , to_count : str = \"person_id\" , ): \"\"\" Main class to define an flowchart (inclusion diagram) Parameters ---------- initial_description : str Description of the initial population data : Union[DataFrame, Dict[str, Iterable]] Either a Pandas/Koalas DataFrame, or a dictionary of iterables. If a dictionary, the initial cohort should be proivided under the **initial** key. concat_criterion_description : bool, optional Whether to concatenate provided description together when adding multiple criteria to_count : str, optional Only if `data` is a DataFrame: column of `data` from which the count is computed. Usually, this will be the column containing patient or stay IDs. \"\"\" self . initial_description = initial_description self . data = data self . to_count = to_count self . check_data () self . ids = self . get_unique () self . criteria = [] self . concat_criterion_description = concat_criterion_description self . final_split = None self . drawing = None add_criterion add_criterion ( description : str , criterion_name : str , excluded_description : str = '' ) Adds a criterion to the flowchart PARAMETER DESCRIPTION description Description of the cohort passing the criterion TYPE: str criterion_name If data is a DataFrame, criterion_name is a boolean column of data to split between passing cohort ( data[criterion_name] == True ) and excluded column ( data[criterion_name] == False ) If data is a dictionary, criterion_name is a key of data containing the passing cohort as an iterable of IDs (list, set , Series, array, etc.) TYPE: str excluded_description Description of the cohort excluded by the criterion TYPE: str DEFAULT: '' Source code in eds_scikit/utils/flowchart/flowchart.py 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 def add_criterion ( self , description : str , criterion_name : str , excluded_description : str = \"\" , ): \"\"\" Adds a criterion to the flowchart ![](../../../_static/flowchart/criterion.png) Parameters ---------- description : str Description of the cohort passing the criterion criterion_name : str - If `data` is a DataFrame, `criterion_name` is a boolean column of `data` to split between passing cohort (`data[criterion_name] == True`) and excluded column (`data[criterion_name] == False`) - If `data` is a dictionary, `criterion_name` is a key of `data` containing the passing cohort as an iterable of IDs (list, set , Series, array, etc.) excluded_description: str Description of the cohort excluded by the criterion \"\"\" input_data = ( Data ( self . ids , ) if not self . criteria else self . criteria [ - 1 ] . output_data ) passing_criterion_ids = self . get_unique ( criterion_name = criterion_name ) output_data = Data ( passing_criterion_ids & input_data . ids , ) excluded_data = Data ( input_data . ids - passing_criterion_ids , ) description = ( description if not self . concat_criterion_description else ( self . get_last_description () + description ) ) added_criterion = Criterion ( description = description , excluded_description = excluded_description , input_data = input_data , output_data = output_data , excluded_data = excluded_data , ) self . criteria . append ( added_criterion ) add_final_split add_final_split ( left_description : str , right_description : str , criterion_name : str , left_title : str = '' , right_title : str = '' ) Adds a final split in two distinct cohorts. Should be called after all other critera were added. PARAMETER DESCRIPTION left_description Description of the left cohort TYPE: str right_description Description of the right cohort TYPE: str criterion_name If data is a DataFrame, criterion_name is a boolean column of data to split between passing cohort ( data[criterion_name] == True ) and excluded column ( data[criterion_name] == False ) If data is a dictionary, criterion_name is a key of data containing the passing cohort as an iterable of IDs (list, set , Series, array, etc.) TYPE: str left_title Title of the left cohort TYPE: str , optional DEFAULT: '' right_title title of the right cohort TYPE: str , optional DEFAULT: '' Source code in eds_scikit/utils/flowchart/flowchart.py 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 def add_final_split ( self , left_description : str , right_description : str , criterion_name : str , left_title : str = \"\" , right_title : str = \"\" , ): \"\"\" Adds a final split in two distinct cohorts. Should be called after all other critera were added. ![](../../../_static/flowchart/split.png) Parameters ---------- left_description : str Description of the left cohort right_description : str Description of the right cohort criterion_name : str - If `data` is a DataFrame, `criterion_name` is a boolean column of `data` to split between passing cohort (`data[criterion_name] == True`) and excluded column (`data[criterion_name] == False`) - If `data` is a dictionary, `criterion_name` is a key of `data` containing the passing cohort as an iterable of IDs (list, set , Series, array, etc.) left_title : str, optional Title of the left cohort right_title : str, optional title of the right cohort \"\"\" input_data = ( Data ( self . ids , ) if not self . criteria else self . criteria [ - 1 ] . output_data ) left_criterion_ids = self . get_unique ( criterion_name = criterion_name ) left_data = Data ( left_criterion_ids & input_data . ids , ) right_data = Data ( input_data . ids - left_criterion_ids , ) left_description = ( left_description if not self . concat_criterion_description else ( self . get_last_description () + left_description ) ) right_description = ( right_description if not self . concat_criterion_description else ( self . get_last_description () + right_description ) ) added_criterion = Criterion ( description = left_description , excluded_description = right_description , input_data = input_data , output_data = left_data , excluded_data = right_data , ) added_criterion . left_title = left_title added_criterion . right_title = right_title self . final_split = added_criterion generate_flowchart generate_flowchart ( alternate : bool = False , fontsize : int = 10 ) Generate and display the flowchart PARAMETER DESCRIPTION alternate Wether to alternate the excluded box positions TYPE: bool , optional DEFAULT: False fontsize fontsize TYPE: int , optional DEFAULT: 10 Source code in eds_scikit/utils/flowchart/flowchart.py 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 def generate_flowchart ( self , alternate : bool = False , fontsize : int = 10 , ): \"\"\" Generate and display the flowchart Parameters ---------- alternate : bool, optional Wether to alternate the excluded box positions fontsize : int, optional fontsize \"\"\" max_criterion_width = max ( [ c . get_bbox ( fontsize = fontsize )[ \"w\" ] for c in self . criteria ] ) arrow_length = 1.2 * ( max_criterion_width / 2 ) directions = [ \"right\" , \"left\" ] if alternate else [ \"right\" , \"right\" ] d = Drawing () d . config ( font = \"dejavu sans\" , fontsize = fontsize , unit = 1 ) start_description = ( self . initial_description + \" \\n \" + f \"( { self . criteria [ 0 ] . input_data } )\" ) start_bbox = Criterion . get_bbox ( None , txt = start_description ) d += flow . Start ( ** start_bbox ) . label ( start_description ) for i , c in enumerate ( self . criteria ): d = c . draw ( d , arrow_length = arrow_length , direction = directions [ i % 2 ], fontsize = fontsize , ) if self . final_split is not None : d = self . final_split . draw ( d , final_split = True , fontsize = fontsize ) self . drawing = d return d save save ( filename : Union [ str , Path ], transparent : bool = False , dpi : int = 72 ) Save the generated flowchart PARAMETER DESCRIPTION filename path to the saved file (should end with svg or png) TYPE: Union [ str , Path ] transparent Wether to use a transparent background or not TYPE: bool , optional DEFAULT: False dpi Resolution (only when saving png) TYPE: int , optional DEFAULT: 72 Source code in eds_scikit/utils/flowchart/flowchart.py 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 def save ( self , filename : Union [ str , Path ], transparent : bool = False , dpi : int = 72 ): \"\"\" Save the generated flowchart Parameters ---------- filename : Union[str, Path] path to the saved file (should end with svg or png) transparent : bool, optional Wether to use a transparent background or not dpi : int, optional Resolution (only when saving png) \"\"\" self . drawing . save ( fname = filename , transparent = transparent , dpi = dpi )","title":"flowchart"},{"location":"reference/utils/flowchart/flowchart/#eds_scikitutilsflowchartflowchart","text":"","title":"eds_scikit.utils.flowchart.flowchart"},{"location":"reference/utils/flowchart/flowchart/#eds_scikit.utils.flowchart.flowchart.Flowchart","text":"Flowchart ( initial_description : str , data : Union [ DataFrame , Dict [ str , Iterable ]], concat_criterion_description : bool = True , to_count : str = 'person_id' ) Main class to define an flowchart (inclusion diagram) PARAMETER DESCRIPTION initial_description Description of the initial population TYPE: str data Either a Pandas/Koalas DataFrame, or a dictionary of iterables. If a dictionary, the initial cohort should be proivided under the initial key. TYPE: Union [ DataFrame , Dict [ str , Iterable ]] concat_criterion_description Whether to concatenate provided description together when adding multiple criteria TYPE: bool , optional DEFAULT: True to_count Only if data is a DataFrame: column of data from which the count is computed. Usually, this will be the column containing patient or stay IDs. TYPE: str , optional DEFAULT: 'person_id' Source code in eds_scikit/utils/flowchart/flowchart.py 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 def __init__ ( self , initial_description : str , data : Union [ DataFrame , Dict [ str , Iterable ]], concat_criterion_description : bool = True , to_count : str = \"person_id\" , ): \"\"\" Main class to define an flowchart (inclusion diagram) Parameters ---------- initial_description : str Description of the initial population data : Union[DataFrame, Dict[str, Iterable]] Either a Pandas/Koalas DataFrame, or a dictionary of iterables. If a dictionary, the initial cohort should be proivided under the **initial** key. concat_criterion_description : bool, optional Whether to concatenate provided description together when adding multiple criteria to_count : str, optional Only if `data` is a DataFrame: column of `data` from which the count is computed. Usually, this will be the column containing patient or stay IDs. \"\"\" self . initial_description = initial_description self . data = data self . to_count = to_count self . check_data () self . ids = self . get_unique () self . criteria = [] self . concat_criterion_description = concat_criterion_description self . final_split = None self . drawing = None","title":"Flowchart"},{"location":"reference/utils/flowchart/flowchart/#eds_scikit.utils.flowchart.flowchart.Flowchart.add_criterion","text":"add_criterion ( description : str , criterion_name : str , excluded_description : str = '' ) Adds a criterion to the flowchart PARAMETER DESCRIPTION description Description of the cohort passing the criterion TYPE: str criterion_name If data is a DataFrame, criterion_name is a boolean column of data to split between passing cohort ( data[criterion_name] == True ) and excluded column ( data[criterion_name] == False ) If data is a dictionary, criterion_name is a key of data containing the passing cohort as an iterable of IDs (list, set , Series, array, etc.) TYPE: str excluded_description Description of the cohort excluded by the criterion TYPE: str DEFAULT: '' Source code in eds_scikit/utils/flowchart/flowchart.py 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 def add_criterion ( self , description : str , criterion_name : str , excluded_description : str = \"\" , ): \"\"\" Adds a criterion to the flowchart ![](../../../_static/flowchart/criterion.png) Parameters ---------- description : str Description of the cohort passing the criterion criterion_name : str - If `data` is a DataFrame, `criterion_name` is a boolean column of `data` to split between passing cohort (`data[criterion_name] == True`) and excluded column (`data[criterion_name] == False`) - If `data` is a dictionary, `criterion_name` is a key of `data` containing the passing cohort as an iterable of IDs (list, set , Series, array, etc.) excluded_description: str Description of the cohort excluded by the criterion \"\"\" input_data = ( Data ( self . ids , ) if not self . criteria else self . criteria [ - 1 ] . output_data ) passing_criterion_ids = self . get_unique ( criterion_name = criterion_name ) output_data = Data ( passing_criterion_ids & input_data . ids , ) excluded_data = Data ( input_data . ids - passing_criterion_ids , ) description = ( description if not self . concat_criterion_description else ( self . get_last_description () + description ) ) added_criterion = Criterion ( description = description , excluded_description = excluded_description , input_data = input_data , output_data = output_data , excluded_data = excluded_data , ) self . criteria . append ( added_criterion )","title":"add_criterion()"},{"location":"reference/utils/flowchart/flowchart/#eds_scikit.utils.flowchart.flowchart.Flowchart.add_final_split","text":"add_final_split ( left_description : str , right_description : str , criterion_name : str , left_title : str = '' , right_title : str = '' ) Adds a final split in two distinct cohorts. Should be called after all other critera were added. PARAMETER DESCRIPTION left_description Description of the left cohort TYPE: str right_description Description of the right cohort TYPE: str criterion_name If data is a DataFrame, criterion_name is a boolean column of data to split between passing cohort ( data[criterion_name] == True ) and excluded column ( data[criterion_name] == False ) If data is a dictionary, criterion_name is a key of data containing the passing cohort as an iterable of IDs (list, set , Series, array, etc.) TYPE: str left_title Title of the left cohort TYPE: str , optional DEFAULT: '' right_title title of the right cohort TYPE: str , optional DEFAULT: '' Source code in eds_scikit/utils/flowchart/flowchart.py 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 def add_final_split ( self , left_description : str , right_description : str , criterion_name : str , left_title : str = \"\" , right_title : str = \"\" , ): \"\"\" Adds a final split in two distinct cohorts. Should be called after all other critera were added. ![](../../../_static/flowchart/split.png) Parameters ---------- left_description : str Description of the left cohort right_description : str Description of the right cohort criterion_name : str - If `data` is a DataFrame, `criterion_name` is a boolean column of `data` to split between passing cohort (`data[criterion_name] == True`) and excluded column (`data[criterion_name] == False`) - If `data` is a dictionary, `criterion_name` is a key of `data` containing the passing cohort as an iterable of IDs (list, set , Series, array, etc.) left_title : str, optional Title of the left cohort right_title : str, optional title of the right cohort \"\"\" input_data = ( Data ( self . ids , ) if not self . criteria else self . criteria [ - 1 ] . output_data ) left_criterion_ids = self . get_unique ( criterion_name = criterion_name ) left_data = Data ( left_criterion_ids & input_data . ids , ) right_data = Data ( input_data . ids - left_criterion_ids , ) left_description = ( left_description if not self . concat_criterion_description else ( self . get_last_description () + left_description ) ) right_description = ( right_description if not self . concat_criterion_description else ( self . get_last_description () + right_description ) ) added_criterion = Criterion ( description = left_description , excluded_description = right_description , input_data = input_data , output_data = left_data , excluded_data = right_data , ) added_criterion . left_title = left_title added_criterion . right_title = right_title self . final_split = added_criterion","title":"add_final_split()"},{"location":"reference/utils/flowchart/flowchart/#eds_scikit.utils.flowchart.flowchart.Flowchart.generate_flowchart","text":"generate_flowchart ( alternate : bool = False , fontsize : int = 10 ) Generate and display the flowchart PARAMETER DESCRIPTION alternate Wether to alternate the excluded box positions TYPE: bool , optional DEFAULT: False fontsize fontsize TYPE: int , optional DEFAULT: 10 Source code in eds_scikit/utils/flowchart/flowchart.py 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 def generate_flowchart ( self , alternate : bool = False , fontsize : int = 10 , ): \"\"\" Generate and display the flowchart Parameters ---------- alternate : bool, optional Wether to alternate the excluded box positions fontsize : int, optional fontsize \"\"\" max_criterion_width = max ( [ c . get_bbox ( fontsize = fontsize )[ \"w\" ] for c in self . criteria ] ) arrow_length = 1.2 * ( max_criterion_width / 2 ) directions = [ \"right\" , \"left\" ] if alternate else [ \"right\" , \"right\" ] d = Drawing () d . config ( font = \"dejavu sans\" , fontsize = fontsize , unit = 1 ) start_description = ( self . initial_description + \" \\n \" + f \"( { self . criteria [ 0 ] . input_data } )\" ) start_bbox = Criterion . get_bbox ( None , txt = start_description ) d += flow . Start ( ** start_bbox ) . label ( start_description ) for i , c in enumerate ( self . criteria ): d = c . draw ( d , arrow_length = arrow_length , direction = directions [ i % 2 ], fontsize = fontsize , ) if self . final_split is not None : d = self . final_split . draw ( d , final_split = True , fontsize = fontsize ) self . drawing = d return d","title":"generate_flowchart()"},{"location":"reference/utils/flowchart/flowchart/#eds_scikit.utils.flowchart.flowchart.Flowchart.save","text":"save ( filename : Union [ str , Path ], transparent : bool = False , dpi : int = 72 ) Save the generated flowchart PARAMETER DESCRIPTION filename path to the saved file (should end with svg or png) TYPE: Union [ str , Path ] transparent Wether to use a transparent background or not TYPE: bool , optional DEFAULT: False dpi Resolution (only when saving png) TYPE: int , optional DEFAULT: 72 Source code in eds_scikit/utils/flowchart/flowchart.py 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 def save ( self , filename : Union [ str , Path ], transparent : bool = False , dpi : int = 72 ): \"\"\" Save the generated flowchart Parameters ---------- filename : Union[str, Path] path to the saved file (should end with svg or png) transparent : bool, optional Wether to use a transparent background or not dpi : int, optional Resolution (only when saving png) \"\"\" self . drawing . save ( fname = filename , transparent = transparent , dpi = dpi )","title":"save()"}]} \ No newline at end of file diff --git a/main/sitemap.xml b/main/sitemap.xml new file mode 100644 index 00000000..0f8724ef --- /dev/null +++ b/main/sitemap.xml @@ -0,0 +1,3 @@ + + + \ No newline at end of file diff --git a/main/sitemap.xml.gz b/main/sitemap.xml.gz new file mode 100644 index 00000000..5e0a4449 Binary files /dev/null and b/main/sitemap.xml.gz differ diff --git a/versions.json b/versions.json index 66593bf9..1f3dc0c2 100644 --- a/versions.json +++ b/versions.json @@ -1 +1 @@ -[{"version": "v0.1.8", "title": "v0.1.8", "aliases": ["latest"]}, {"version": "v0.1.7", "title": "v0.1.7", "aliases": []}, {"version": "v0.1.6", "title": "v0.1.6", "aliases": []}, {"version": "v0.1.5", "title": "v0.1.5", "aliases": []}, {"version": "v0.1.5dev1", "title": "v0.1.5dev1", "aliases": []}, {"version": "v0.1.4", "title": "v0.1.4", "aliases": []}, {"version": "v0.1.3", "title": "v0.1.3", "aliases": []}, {"version": "v0.1.2", "title": "v0.1.2", "aliases": []}, {"version": "trigger_doc_PR", "title": "trigger_doc_PR", "aliases": []}, {"version": "table-viz", "title": "table-viz", "aliases": []}, {"version": "pyarrow-upgrade", "title": "pyarrow-upgrade", "aliases": []}, {"version": "edit-bioclean", "title": "edit-bioclean", "aliases": []}, {"version": "add_doc_warning_message", "title": "add_doc_warning_message", "aliases": []}] \ No newline at end of file +[{"version": "v0.1.8", "title": "v0.1.8", "aliases": ["latest"]}, {"version": "v0.1.7", "title": "v0.1.7", "aliases": []}, {"version": "v0.1.6", "title": "v0.1.6", "aliases": []}, {"version": "v0.1.5", "title": "v0.1.5", "aliases": []}, {"version": "v0.1.5dev1", "title": "v0.1.5dev1", "aliases": []}, {"version": "v0.1.4", "title": "v0.1.4", "aliases": []}, {"version": "v0.1.3", "title": "v0.1.3", "aliases": []}, {"version": "v0.1.2", "title": "v0.1.2", "aliases": []}, {"version": "trigger_doc_PR", "title": "trigger_doc_PR", "aliases": []}, {"version": "table-viz", "title": "table-viz", "aliases": []}, {"version": "pyarrow-upgrade", "title": "pyarrow-upgrade", "aliases": []}, {"version": "main", "title": "main", "aliases": []}, {"version": "edit-bioclean", "title": "edit-bioclean", "aliases": []}, {"version": "add_doc_warning_message", "title": "add_doc_warning_message", "aliases": []}] \ No newline at end of file