-
- Read the Docs
- v: ${config.versions.current.slug}
-
-
-
- ${renderLanguages(config)}
- ${renderVersions(config)}
- ${renderDownloads(config)}
-
- On Read the Docs
-
- Project Home
-
-
- Builds
-
-
- Downloads
-
-
-
- Search
-
-
-
-
-
-
- Hosted by Read the Docs
-
-
-
- `;
-
- // Inject the generated flyout into the body HTML element.
- document.body.insertAdjacentHTML("beforeend", flyout);
-
- // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
- document
- .querySelector("#flyout-search-form")
- .addEventListener("focusin", () => {
- const event = new CustomEvent("readthedocs-search-show");
- document.dispatchEvent(event);
- });
- })
-}
-
-if (themeLanguageSelector || themeVersionSelector) {
- function onSelectorSwitch(event) {
- const option = event.target.selectedIndex;
- const item = event.target.options[option];
- window.location.href = item.dataset.url;
- }
-
- document.addEventListener("readthedocs-addons-data-ready", function (event) {
- const config = event.detail.data();
-
- const versionSwitch = document.querySelector(
- "div.switch-menus > div.version-switch",
- );
- if (themeVersionSelector) {
- let versions = config.versions.active;
- if (config.versions.current.hidden || config.versions.current.type === "external") {
- versions.unshift(config.versions.current);
- }
- const versionSelect = `
-
- ${versions
- .map(
- (version) => `
-
- ${version.slug}
- `,
- )
- .join("\n")}
-
- `;
-
- versionSwitch.innerHTML = versionSelect;
- versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
- }
-
- const languageSwitch = document.querySelector(
- "div.switch-menus > div.language-switch",
- );
-
- if (themeLanguageSelector) {
- if (config.projects.translations.length) {
- // Add the current language to the options on the selector
- let languages = config.projects.translations.concat(
- config.projects.current,
- );
- languages = languages.sort((a, b) =>
- a.language.name.localeCompare(b.language.name),
- );
-
- const languageSelect = `
-
- ${languages
- .map(
- (language) => `
-
- ${language.language.name}
- `,
- )
- .join("\n")}
-
- `;
-
- languageSwitch.innerHTML = languageSelect;
- languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
- }
- else {
- languageSwitch.remove();
- }
- }
- });
-}
-
-document.addEventListener("readthedocs-addons-data-ready", function (event) {
- // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
- document
- .querySelector("[role='search'] input")
- .addEventListener("focusin", () => {
- const event = new CustomEvent("readthedocs-search-show");
- document.dispatchEvent(event);
- });
-});
\ No newline at end of file
diff --git a/docs/_build/html/main/_static/language_data.js b/docs/_build/html/main/_static/language_data.js
deleted file mode 100644
index c7fe6c6..0000000
--- a/docs/_build/html/main/_static/language_data.js
+++ /dev/null
@@ -1,192 +0,0 @@
-/*
- * This script contains the language-specific data used by searchtools.js,
- * namely the list of stopwords, stemmer, scorer and splitter.
- */
-
-var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
-
-
-/* Non-minified version is copied as a separate JS file, if available */
-
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
- var step2list = {
- ational: 'ate',
- tional: 'tion',
- enci: 'ence',
- anci: 'ance',
- izer: 'ize',
- bli: 'ble',
- alli: 'al',
- entli: 'ent',
- eli: 'e',
- ousli: 'ous',
- ization: 'ize',
- ation: 'ate',
- ator: 'ate',
- alism: 'al',
- iveness: 'ive',
- fulness: 'ful',
- ousness: 'ous',
- aliti: 'al',
- iviti: 'ive',
- biliti: 'ble',
- logi: 'log'
- };
-
- var step3list = {
- icate: 'ic',
- ative: '',
- alize: 'al',
- iciti: 'ic',
- ical: 'ic',
- ful: '',
- ness: ''
- };
-
- var c = "[^aeiou]"; // consonant
- var v = "[aeiouy]"; // vowel
- var C = c + "[^aeiouy]*"; // consonant sequence
- var V = v + "[aeiou]*"; // vowel sequence
-
- var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
- var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
- var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
- var s_v = "^(" + C + ")?" + v; // vowel in stem
-
- this.stemWord = function (w) {
- var stem;
- var suffix;
- var firstch;
- var origword = w;
-
- if (w.length < 3)
- return w;
-
- var re;
- var re2;
- var re3;
- var re4;
-
- firstch = w.substr(0,1);
- if (firstch == "y")
- w = firstch.toUpperCase() + w.substr(1);
-
- // Step 1a
- re = /^(.+?)(ss|i)es$/;
- re2 = /^(.+?)([^s])s$/;
-
- if (re.test(w))
- w = w.replace(re,"$1$2");
- else if (re2.test(w))
- w = w.replace(re2,"$1$2");
-
- // Step 1b
- re = /^(.+?)eed$/;
- re2 = /^(.+?)(ed|ing)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- re = new RegExp(mgr0);
- if (re.test(fp[1])) {
- re = /.$/;
- w = w.replace(re,"");
- }
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1];
- re2 = new RegExp(s_v);
- if (re2.test(stem)) {
- w = stem;
- re2 = /(at|bl|iz)$/;
- re3 = new RegExp("([^aeiouylsz])\\1$");
- re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re2.test(w))
- w = w + "e";
- else if (re3.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
- else if (re4.test(w))
- w = w + "e";
- }
- }
-
- // Step 1c
- re = /^(.+?)y$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(s_v);
- if (re.test(stem))
- w = stem + "i";
- }
-
- // Step 2
- re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step2list[suffix];
- }
-
- // Step 3
- re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step3list[suffix];
- }
-
- // Step 4
- re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
- re2 = /^(.+?)(s|t)(ion)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- if (re.test(stem))
- w = stem;
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1] + fp[2];
- re2 = new RegExp(mgr1);
- if (re2.test(stem))
- w = stem;
- }
-
- // Step 5
- re = /^(.+?)e$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- re2 = new RegExp(meq1);
- re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
- w = stem;
- }
- re = /ll$/;
- re2 = new RegExp(mgr1);
- if (re.test(w) && re2.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
-
- // and turn initial Y back to y
- if (firstch == "y")
- w = firstch.toLowerCase() + w.substr(1);
- return w;
- }
-}
-
diff --git a/docs/_build/html/main/_static/minus.png b/docs/_build/html/main/_static/minus.png
deleted file mode 100644
index d96755f..0000000
Binary files a/docs/_build/html/main/_static/minus.png and /dev/null differ
diff --git a/docs/_build/html/main/_static/plus.png b/docs/_build/html/main/_static/plus.png
deleted file mode 100644
index 7107cec..0000000
Binary files a/docs/_build/html/main/_static/plus.png and /dev/null differ
diff --git a/docs/_build/html/main/_static/pygments.css b/docs/_build/html/main/_static/pygments.css
deleted file mode 100644
index 84ab303..0000000
--- a/docs/_build/html/main/_static/pygments.css
+++ /dev/null
@@ -1,75 +0,0 @@
-pre { line-height: 125%; }
-td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-.highlight .hll { background-color: #ffffcc }
-.highlight { background: #f8f8f8; }
-.highlight .c { color: #3D7B7B; font-style: italic } /* Comment */
-.highlight .err { border: 1px solid #FF0000 } /* Error */
-.highlight .k { color: #008000; font-weight: bold } /* Keyword */
-.highlight .o { color: #666666 } /* Operator */
-.highlight .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */
-.highlight .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: #9C6500 } /* Comment.Preproc */
-.highlight .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */
-.highlight .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */
-.highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
-.highlight .gd { color: #A00000 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
-.highlight .gr { color: #E40000 } /* Generic.Error */
-.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
-.highlight .gi { color: #008400 } /* Generic.Inserted */
-.highlight .go { color: #717171 } /* Generic.Output */
-.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
-.highlight .gt { color: #0044DD } /* Generic.Traceback */
-.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
-.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
-.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
-.highlight .kp { color: #008000 } /* Keyword.Pseudo */
-.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
-.highlight .kt { color: #B00040 } /* Keyword.Type */
-.highlight .m { color: #666666 } /* Literal.Number */
-.highlight .s { color: #BA2121 } /* Literal.String */
-.highlight .na { color: #687822 } /* Name.Attribute */
-.highlight .nb { color: #008000 } /* Name.Builtin */
-.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
-.highlight .no { color: #880000 } /* Name.Constant */
-.highlight .nd { color: #AA22FF } /* Name.Decorator */
-.highlight .ni { color: #717171; font-weight: bold } /* Name.Entity */
-.highlight .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */
-.highlight .nf { color: #0000FF } /* Name.Function */
-.highlight .nl { color: #767600 } /* Name.Label */
-.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
-.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
-.highlight .nv { color: #19177C } /* Name.Variable */
-.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
-.highlight .w { color: #bbbbbb } /* Text.Whitespace */
-.highlight .mb { color: #666666 } /* Literal.Number.Bin */
-.highlight .mf { color: #666666 } /* Literal.Number.Float */
-.highlight .mh { color: #666666 } /* Literal.Number.Hex */
-.highlight .mi { color: #666666 } /* Literal.Number.Integer */
-.highlight .mo { color: #666666 } /* Literal.Number.Oct */
-.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
-.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
-.highlight .sc { color: #BA2121 } /* Literal.String.Char */
-.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
-.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
-.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
-.highlight .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */
-.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
-.highlight .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */
-.highlight .sx { color: #008000 } /* Literal.String.Other */
-.highlight .sr { color: #A45A77 } /* Literal.String.Regex */
-.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
-.highlight .ss { color: #19177C } /* Literal.String.Symbol */
-.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #0000FF } /* Name.Function.Magic */
-.highlight .vc { color: #19177C } /* Name.Variable.Class */
-.highlight .vg { color: #19177C } /* Name.Variable.Global */
-.highlight .vi { color: #19177C } /* Name.Variable.Instance */
-.highlight .vm { color: #19177C } /* Name.Variable.Magic */
-.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/docs/_build/html/main/_static/searchtools.js b/docs/_build/html/main/_static/searchtools.js
deleted file mode 100644
index 2c774d1..0000000
--- a/docs/_build/html/main/_static/searchtools.js
+++ /dev/null
@@ -1,632 +0,0 @@
-/*
- * Sphinx JavaScript utilities for the full-text search.
- */
-"use strict";
-
-/**
- * Simple result scoring code.
- */
-if (typeof Scorer === "undefined") {
- var Scorer = {
- // Implement the following function to further tweak the score for each result
- // The function takes a result array [docname, title, anchor, descr, score, filename]
- // and returns the new score.
- /*
- score: result => {
- const [docname, title, anchor, descr, score, filename, kind] = result
- return score
- },
- */
-
- // query matches the full name of an object
- objNameMatch: 11,
- // or matches in the last dotted part of the object name
- objPartialMatch: 6,
- // Additive scores depending on the priority of the object
- objPrio: {
- 0: 15, // used to be importantResults
- 1: 5, // used to be objectResults
- 2: -5, // used to be unimportantResults
- },
- // Used when the priority is not in the mapping.
- objPrioDefault: 0,
-
- // query found in title
- title: 15,
- partialTitle: 7,
- // query found in terms
- term: 5,
- partialTerm: 2,
- };
-}
-
-// Global search result kind enum, used by themes to style search results.
-class SearchResultKind {
- static get index() { return "index"; }
- static get object() { return "object"; }
- static get text() { return "text"; }
- static get title() { return "title"; }
-}
-
-const _removeChildren = (element) => {
- while (element && element.lastChild) element.removeChild(element.lastChild);
-};
-
-/**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
- */
-const _escapeRegExp = (string) =>
- string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _displayItem = (item, searchTerms, highlightTerms) => {
- const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
- const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
- const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
- const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
- const contentRoot = document.documentElement.dataset.content_root;
-
- const [docName, title, anchor, descr, score, _filename, kind] = item;
-
- let listItem = document.createElement("li");
- // Add a class representing the item's type:
- // can be used by a theme's CSS selector for styling
- // See SearchResultKind for the class names.
- listItem.classList.add(`kind-${kind}`);
- let requestUrl;
- let linkUrl;
- if (docBuilder === "dirhtml") {
- // dirhtml builder
- let dirname = docName + "/";
- if (dirname.match(/\/index\/$/))
- dirname = dirname.substring(0, dirname.length - 6);
- else if (dirname === "index/") dirname = "";
- requestUrl = contentRoot + dirname;
- linkUrl = requestUrl;
- } else {
- // normal html builders
- requestUrl = contentRoot + docName + docFileSuffix;
- linkUrl = docName + docLinkSuffix;
- }
- let linkEl = listItem.appendChild(document.createElement("a"));
- linkEl.href = linkUrl + anchor;
- linkEl.dataset.score = score;
- linkEl.innerHTML = title;
- if (descr) {
- listItem.appendChild(document.createElement("span")).innerHTML =
- " (" + descr + ")";
- // highlight search terms in the description
- if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
- highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
- }
- else if (showSearchSummary)
- fetch(requestUrl)
- .then((responseData) => responseData.text())
- .then((data) => {
- if (data)
- listItem.appendChild(
- Search.makeSearchSummary(data, searchTerms, anchor)
- );
- // highlight search terms in the summary
- if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
- highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
- });
- Search.output.appendChild(listItem);
-};
-const _finishSearch = (resultCount) => {
- Search.stopPulse();
- Search.title.innerText = _("Search Results");
- if (!resultCount)
- Search.status.innerText = Documentation.gettext(
- "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
- );
- else
- Search.status.innerText = Documentation.ngettext(
- "Search finished, found one page matching the search query.",
- "Search finished, found ${resultCount} pages matching the search query.",
- resultCount,
- ).replace('${resultCount}', resultCount);
-};
-const _displayNextItem = (
- results,
- resultCount,
- searchTerms,
- highlightTerms,
-) => {
- // results left, load the summary and display it
- // this is intended to be dynamic (don't sub resultsCount)
- if (results.length) {
- _displayItem(results.pop(), searchTerms, highlightTerms);
- setTimeout(
- () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
- 5
- );
- }
- // search finished, update title and status message
- else _finishSearch(resultCount);
-};
-// Helper function used by query() to order search results.
-// Each input is an array of [docname, title, anchor, descr, score, filename, kind].
-// Order the results by score (in opposite order of appearance, since the
-// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
-const _orderResultsByScoreThenName = (a, b) => {
- const leftScore = a[4];
- const rightScore = b[4];
- if (leftScore === rightScore) {
- // same score: sort alphabetically
- const leftTitle = a[1].toLowerCase();
- const rightTitle = b[1].toLowerCase();
- if (leftTitle === rightTitle) return 0;
- return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
- }
- return leftScore > rightScore ? 1 : -1;
-};
-
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
- var splitQuery = (query) => query
- .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
- .filter(term => term) // remove remaining empty strings
-}
-
-/**
- * Search Module
- */
-const Search = {
- _index: null,
- _queued_query: null,
- _pulse_status: -1,
-
- htmlToText: (htmlString, anchor) => {
- const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
- for (const removalQuery of [".headerlink", "script", "style"]) {
- htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
- }
- if (anchor) {
- const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
- if (anchorContent) return anchorContent.textContent;
-
- console.warn(
- `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
- );
- }
-
- // if anchor not specified or not found, fall back to main content
- const docContent = htmlElement.querySelector('[role="main"]');
- if (docContent) return docContent.textContent;
-
- console.warn(
- "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
- );
- return "";
- },
-
- init: () => {
- const query = new URLSearchParams(window.location.search).get("q");
- document
- .querySelectorAll('input[name="q"]')
- .forEach((el) => (el.value = query));
- if (query) Search.performSearch(query);
- },
-
- loadIndex: (url) =>
- (document.body.appendChild(document.createElement("script")).src = url),
-
- setIndex: (index) => {
- Search._index = index;
- if (Search._queued_query !== null) {
- const query = Search._queued_query;
- Search._queued_query = null;
- Search.query(query);
- }
- },
-
- hasIndex: () => Search._index !== null,
-
- deferQuery: (query) => (Search._queued_query = query),
-
- stopPulse: () => (Search._pulse_status = -1),
-
- startPulse: () => {
- if (Search._pulse_status >= 0) return;
-
- const pulse = () => {
- Search._pulse_status = (Search._pulse_status + 1) % 4;
- Search.dots.innerText = ".".repeat(Search._pulse_status);
- if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
- };
- pulse();
- },
-
- /**
- * perform a search for something (or wait until index is loaded)
- */
- performSearch: (query) => {
- // create the required interface elements
- const searchText = document.createElement("h2");
- searchText.textContent = _("Searching");
- const searchSummary = document.createElement("p");
- searchSummary.classList.add("search-summary");
- searchSummary.innerText = "";
- const searchList = document.createElement("ul");
- searchList.setAttribute("role", "list");
- searchList.classList.add("search");
-
- const out = document.getElementById("search-results");
- Search.title = out.appendChild(searchText);
- Search.dots = Search.title.appendChild(document.createElement("span"));
- Search.status = out.appendChild(searchSummary);
- Search.output = out.appendChild(searchList);
-
- const searchProgress = document.getElementById("search-progress");
- // Some themes don't use the search progress node
- if (searchProgress) {
- searchProgress.innerText = _("Preparing search...");
- }
- Search.startPulse();
-
- // index already loaded, the browser was quick!
- if (Search.hasIndex()) Search.query(query);
- else Search.deferQuery(query);
- },
-
- _parseQuery: (query) => {
- // stem the search terms and add them to the correct list
- const stemmer = new Stemmer();
- const searchTerms = new Set();
- const excludedTerms = new Set();
- const highlightTerms = new Set();
- const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
- splitQuery(query.trim()).forEach((queryTerm) => {
- const queryTermLower = queryTerm.toLowerCase();
-
- // maybe skip this "word"
- // stopwords array is from language_data.js
- if (
- stopwords.indexOf(queryTermLower) !== -1 ||
- queryTerm.match(/^\d+$/)
- )
- return;
-
- // stem the word
- let word = stemmer.stemWord(queryTermLower);
- // select the correct list
- if (word[0] === "-") excludedTerms.add(word.substr(1));
- else {
- searchTerms.add(word);
- highlightTerms.add(queryTermLower);
- }
- });
-
- if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js
- localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
- }
-
- // console.debug("SEARCH: searching for:");
- // console.info("required: ", [...searchTerms]);
- // console.info("excluded: ", [...excludedTerms]);
-
- return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
- },
-
- /**
- * execute search (requires search index to be loaded)
- */
- _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const titles = Search._index.titles;
- const allTitles = Search._index.alltitles;
- const indexEntries = Search._index.indexentries;
-
- // Collect multiple result groups to be sorted separately and then ordered.
- // Each is an array of [docname, title, anchor, descr, score, filename, kind].
- const normalResults = [];
- const nonMainIndexResults = [];
-
- _removeChildren(document.getElementById("search-progress"));
-
- const queryLower = query.toLowerCase().trim();
- for (const [title, foundTitles] of Object.entries(allTitles)) {
- if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
- for (const [file, id] of foundTitles) {
- const score = Math.round(Scorer.title * queryLower.length / title.length);
- const boost = titles[file] === title ? 1 : 0; // add a boost for document titles
- normalResults.push([
- docNames[file],
- titles[file] !== title ? `${titles[file]} > ${title}` : title,
- id !== null ? "#" + id : "",
- null,
- score + boost,
- filenames[file],
- SearchResultKind.title,
- ]);
- }
- }
- }
-
- // search for explicit entries in index directives
- for (const [entry, foundEntries] of Object.entries(indexEntries)) {
- if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
- for (const [file, id, isMain] of foundEntries) {
- const score = Math.round(100 * queryLower.length / entry.length);
- const result = [
- docNames[file],
- titles[file],
- id ? "#" + id : "",
- null,
- score,
- filenames[file],
- SearchResultKind.index,
- ];
- if (isMain) {
- normalResults.push(result);
- } else {
- nonMainIndexResults.push(result);
- }
- }
- }
- }
-
- // lookup as object
- objectTerms.forEach((term) =>
- normalResults.push(...Search.performObjectSearch(term, objectTerms))
- );
-
- // lookup as search terms in fulltext
- normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
-
- // let the scorer override scores with a custom scoring function
- if (Scorer.score) {
- normalResults.forEach((item) => (item[4] = Scorer.score(item)));
- nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
- }
-
- // Sort each group of results by score and then alphabetically by name.
- normalResults.sort(_orderResultsByScoreThenName);
- nonMainIndexResults.sort(_orderResultsByScoreThenName);
-
- // Combine the result groups in (reverse) order.
- // Non-main index entries are typically arbitrary cross-references,
- // so display them after other results.
- let results = [...nonMainIndexResults, ...normalResults];
-
- // remove duplicate search results
- // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
- let seen = new Set();
- results = results.reverse().reduce((acc, result) => {
- let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
- if (!seen.has(resultStr)) {
- acc.push(result);
- seen.add(resultStr);
- }
- return acc;
- }, []);
-
- return results.reverse();
- },
-
- query: (query) => {
- const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
- const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
-
- // for debugging
- //Search.lastresults = results.slice(); // a copy
- // console.info("search results:", Search.lastresults);
-
- // print the results
- _displayNextItem(results, results.length, searchTerms, highlightTerms);
- },
-
- /**
- * search for object names
- */
- performObjectSearch: (object, objectTerms) => {
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const objects = Search._index.objects;
- const objNames = Search._index.objnames;
- const titles = Search._index.titles;
-
- const results = [];
-
- const objectSearchCallback = (prefix, match) => {
- const name = match[4]
- const fullname = (prefix ? prefix + "." : "") + name;
- const fullnameLower = fullname.toLowerCase();
- if (fullnameLower.indexOf(object) < 0) return;
-
- let score = 0;
- const parts = fullnameLower.split(".");
-
- // check for different match types: exact matches of full name or
- // "last name" (i.e. last dotted part)
- if (fullnameLower === object || parts.slice(-1)[0] === object)
- score += Scorer.objNameMatch;
- else if (parts.slice(-1)[0].indexOf(object) > -1)
- score += Scorer.objPartialMatch; // matches in last name
-
- const objName = objNames[match[1]][2];
- const title = titles[match[0]];
-
- // If more than one term searched for, we require other words to be
- // found in the name/title/description
- const otherTerms = new Set(objectTerms);
- otherTerms.delete(object);
- if (otherTerms.size > 0) {
- const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
- if (
- [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
- )
- return;
- }
-
- let anchor = match[3];
- if (anchor === "") anchor = fullname;
- else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
-
- const descr = objName + _(", in ") + title;
-
- // add custom score for some objects according to scorer
- if (Scorer.objPrio.hasOwnProperty(match[2]))
- score += Scorer.objPrio[match[2]];
- else score += Scorer.objPrioDefault;
-
- results.push([
- docNames[match[0]],
- fullname,
- "#" + anchor,
- descr,
- score,
- filenames[match[0]],
- SearchResultKind.object,
- ]);
- };
- Object.keys(objects).forEach((prefix) =>
- objects[prefix].forEach((array) =>
- objectSearchCallback(prefix, array)
- )
- );
- return results;
- },
-
- /**
- * search for full-text terms in the index
- */
- performTermsSearch: (searchTerms, excludedTerms) => {
- // prepare search
- const terms = Search._index.terms;
- const titleTerms = Search._index.titleterms;
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const titles = Search._index.titles;
-
- const scoreMap = new Map();
- const fileMap = new Map();
-
- // perform the search on the required terms
- searchTerms.forEach((word) => {
- const files = [];
- const arr = [
- { files: terms[word], score: Scorer.term },
- { files: titleTerms[word], score: Scorer.title },
- ];
- // add support for partial matches
- if (word.length > 2) {
- const escapedWord = _escapeRegExp(word);
- if (!terms.hasOwnProperty(word)) {
- Object.keys(terms).forEach((term) => {
- if (term.match(escapedWord))
- arr.push({ files: terms[term], score: Scorer.partialTerm });
- });
- }
- if (!titleTerms.hasOwnProperty(word)) {
- Object.keys(titleTerms).forEach((term) => {
- if (term.match(escapedWord))
- arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
- });
- }
- }
-
- // no match but word was a required one
- if (arr.every((record) => record.files === undefined)) return;
-
- // found search word in contents
- arr.forEach((record) => {
- if (record.files === undefined) return;
-
- let recordFiles = record.files;
- if (recordFiles.length === undefined) recordFiles = [recordFiles];
- files.push(...recordFiles);
-
- // set score for the word in each file
- recordFiles.forEach((file) => {
- if (!scoreMap.has(file)) scoreMap.set(file, {});
- scoreMap.get(file)[word] = record.score;
- });
- });
-
- // create the mapping
- files.forEach((file) => {
- if (!fileMap.has(file)) fileMap.set(file, [word]);
- else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
- });
- });
-
- // now check if the files don't contain excluded terms
- const results = [];
- for (const [file, wordList] of fileMap) {
- // check if all requirements are matched
-
- // as search terms with length < 3 are discarded
- const filteredTermCount = [...searchTerms].filter(
- (term) => term.length > 2
- ).length;
- if (
- wordList.length !== searchTerms.size &&
- wordList.length !== filteredTermCount
- )
- continue;
-
- // ensure that none of the excluded terms is in the search result
- if (
- [...excludedTerms].some(
- (term) =>
- terms[term] === file ||
- titleTerms[term] === file ||
- (terms[term] || []).includes(file) ||
- (titleTerms[term] || []).includes(file)
- )
- )
- break;
-
- // select one (max) score for the file.
- const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
- // add result to the result list
- results.push([
- docNames[file],
- titles[file],
- "",
- null,
- score,
- filenames[file],
- SearchResultKind.text,
- ]);
- }
- return results;
- },
-
- /**
- * helper function to return a node containing the
- * search summary for a given text. keywords is a list
- * of stemmed words.
- */
- makeSearchSummary: (htmlText, keywords, anchor) => {
- const text = Search.htmlToText(htmlText, anchor);
- if (text === "") return null;
-
- const textLower = text.toLowerCase();
- const actualStartPosition = [...keywords]
- .map((k) => textLower.indexOf(k.toLowerCase()))
- .filter((i) => i > -1)
- .slice(-1)[0];
- const startWithContext = Math.max(actualStartPosition - 120, 0);
-
- const top = startWithContext === 0 ? "" : "...";
- const tail = startWithContext + 240 < text.length ? "..." : "";
-
- let summary = document.createElement("p");
- summary.classList.add("context");
- summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
-
- return summary;
- },
-};
-
-_ready(Search.init);
diff --git a/docs/_build/html/main/_static/sphinx_highlight.js b/docs/_build/html/main/_static/sphinx_highlight.js
deleted file mode 100644
index 8a96c69..0000000
--- a/docs/_build/html/main/_static/sphinx_highlight.js
+++ /dev/null
@@ -1,154 +0,0 @@
-/* Highlighting utilities for Sphinx HTML documentation. */
-"use strict";
-
-const SPHINX_HIGHLIGHT_ENABLED = true
-
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
- if (node.nodeType === Node.TEXT_NODE) {
- const val = node.nodeValue;
- const parent = node.parentNode;
- const pos = val.toLowerCase().indexOf(text);
- if (
- pos >= 0 &&
- !parent.classList.contains(className) &&
- !parent.classList.contains("nohighlight")
- ) {
- let span;
-
- const closestNode = parent.closest("body, svg, foreignObject");
- const isInSVG = closestNode && closestNode.matches("svg");
- if (isInSVG) {
- span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
- } else {
- span = document.createElement("span");
- span.classList.add(className);
- }
-
- span.appendChild(document.createTextNode(val.substr(pos, text.length)));
- const rest = document.createTextNode(val.substr(pos + text.length));
- parent.insertBefore(
- span,
- parent.insertBefore(
- rest,
- node.nextSibling
- )
- );
- node.nodeValue = val.substr(0, pos);
- /* There may be more occurrences of search term in this node. So call this
- * function recursively on the remaining fragment.
- */
- _highlight(rest, addItems, text, className);
-
- if (isInSVG) {
- const rect = document.createElementNS(
- "http://www.w3.org/2000/svg",
- "rect"
- );
- const bbox = parent.getBBox();
- rect.x.baseVal.value = bbox.x;
- rect.y.baseVal.value = bbox.y;
- rect.width.baseVal.value = bbox.width;
- rect.height.baseVal.value = bbox.height;
- rect.setAttribute("class", className);
- addItems.push({ parent: parent, target: rect });
- }
- }
- } else if (node.matches && !node.matches("button, select, textarea")) {
- node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
- }
-};
-const _highlightText = (thisNode, text, className) => {
- let addItems = [];
- _highlight(thisNode, addItems, text, className);
- addItems.forEach((obj) =>
- obj.parent.insertAdjacentElement("beforebegin", obj.target)
- );
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const SphinxHighlight = {
-
- /**
- * highlight the search words provided in localstorage in the text
- */
- highlightSearchWords: () => {
- if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
-
- // get and clear terms from localstorage
- const url = new URL(window.location);
- const highlight =
- localStorage.getItem("sphinx_highlight_terms")
- || url.searchParams.get("highlight")
- || "";
- localStorage.removeItem("sphinx_highlight_terms")
- url.searchParams.delete("highlight");
- window.history.replaceState({}, "", url);
-
- // get individual terms from highlight string
- const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
- if (terms.length === 0) return; // nothing to do
-
- // There should never be more than one element matching "div.body"
- const divBody = document.querySelectorAll("div.body");
- const body = divBody.length ? divBody[0] : document.querySelector("body");
- window.setTimeout(() => {
- terms.forEach((term) => _highlightText(body, term, "highlighted"));
- }, 10);
-
- const searchBox = document.getElementById("searchbox");
- if (searchBox === null) return;
- searchBox.appendChild(
- document
- .createRange()
- .createContextualFragment(
- '
' +
- '' +
- _("Hide Search Matches") +
- "
"
- )
- );
- },
-
- /**
- * helper function to hide the search marks again
- */
- hideSearchWords: () => {
- document
- .querySelectorAll("#searchbox .highlight-link")
- .forEach((el) => el.remove());
- document
- .querySelectorAll("span.highlighted")
- .forEach((el) => el.classList.remove("highlighted"));
- localStorage.removeItem("sphinx_highlight_terms")
- },
-
- initEscapeListener: () => {
- // only install a listener if it is really needed
- if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
-
- document.addEventListener("keydown", (event) => {
- // bail for input elements
- if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
- // bail with special keys
- if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
- if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
- SphinxHighlight.hideSearchWords();
- event.preventDefault();
- }
- });
- },
-};
-
-_ready(() => {
- /* Do not call highlightSearchWords() when we are on the search page.
- * It will highlight words from the *previous* search query.
- */
- if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
- SphinxHighlight.initEscapeListener();
-});
diff --git a/docs/_build/html/main/api/core.html b/docs/_build/html/main/api/core.html
deleted file mode 100644
index 565207e..0000000
--- a/docs/_build/html/main/api/core.html
+++ /dev/null
@@ -1,886 +0,0 @@
-
-
-
-
-
-
-
-
-
ionique.core — ionique 0.1.0 documentation
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- ionique
-
-
-
-
-
-
-
-
-
-ionique.core
-
-
-This holds the core data types which may be abstracted in
-many different applications.
-This module defines a tree-based framework for representing, parsing, and annotating
-segments of ionic current data.
-
-
-class ionique.core. AbstractSegmentTree
-Bases: object
-Class for managing hierarchical segments.
-This class supports a tree structure where each segment can contain multiple
-child segments, enabling recursive parsing and analysis of nested data.
-It provides utilities to manage relationships between segments, apply parsers,
-and extract subsegments by rank.
-
-
-__init__ ( ) → None
-Initialize the segment
-
-
-
-
-add_child ( child : AnySegment ) → None
-Add a single child
-
-
-
-
-add_children ( children : list [ AnySegment ] ) → None
-Add multiple children if:
-1. Child position is the within parent’s segment.
-2. The length of the child’s segment is > 0
-3. There is no overlap between consecutive children
-Or add children with no check
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-clear_children ( )
-Clear the list of children
-
-
-
-
-climb_to_rank ( rank : str ) → AnySegment | None
-Go up and find the segment with the specified rank
-:return: segment
-
-
-
-
-end : int | None
-
-
-
-
-get_feature ( name : str )
-Gets the ‘name’ feature of the current segment or its parent
-
-
-
-
-get_top_parent ( ) → AnySegment
-Recursively go up and find top most parent
-:return: parent
-
-
-
-
-property n : int
-Get the length of the segment
-:return: len(segment)
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-parse ( parser newrank : str at_child_rank : str | None = None ** kwargs ) → bool
-Parses the data in the segment or at a particular rank of child segments
-into children segments with a new rank.
-
-Parameters:
-
-parser (Parser parse method and required input attributes.
newrank (str ) – Rank to assign to the newly created child segments.
at_child_rank (str or None ) – Determines whether to traverse the children tree down to a given rank.
kwargs – Additional arguments to pass to the parser.
-Returns:
-True if parsing was successful, otherwise raises an exception.
-Return type:
-bool
-
-
-
-
-
-rank : str
-
-
-
-
-property relative_end : int
-Get end position relative to parent segment
-:return: end position
-
-
-
-
-property relative_slice : ndarray
-Slice the array to get the data between end-start segment
-:return: sliced np array
-
-
-
-
-property relative_start : int
-Get the start position relative to parent segment
-:return: start position
-
-
-
-
-property slice : ndarray
-Slice the array to get the data between start-end segment
-:return: sliced np array
-
-
-
-
-start : int | None
-
-
-
-
-traverse_to_rank ( rank : str ) → list
-Traverse the tree rank and get the list of the segments of the rank
-:param: rank
-:return: list of segments
-
-
-
-
-
-
-class ionique.core. MetaSegment ( start : int end : int parent : AnySegment | None = None rank : str | None = None unique_features : dict | None = {} ** kwargs )
-Bases: AbstractSegmentTree
-The metadata on an abstract segment of ionic current. All information about a segment can be
-loaded, without the expectation of the array of floats.
-
-
-__init__ ( start : int end : int parent : AnySegment | None = None rank : str | None = None unique_features : dict | None = {} ** kwargs )
-
-Parameters:
-
-start (int ) – Start index of the segment.
end (int ) – End index of the segment.
parent (AbstractSegmentTree or None ) – Parent segment in the tree (optional).
rank (str or None ) – Rank identifier for this segment (optional).
unique_features (dict ) – Dictionary of metadata features.
-
-
-
-
-
-property current
-Get the current data of the segment if the segment correlates to the file
-:return: current
-
-
-
-
-delete ( )
-Delete itself. There are no arrays with which to delete references for.
-
-
-
-
-property duration : float
-Get the duration of the segment = start - end
-:return: duration
-
-
-
-
-classmethod from_json ( filename = None in_json = None )
-Read in a metasegment from a JSON and return a metasegment object.
-Either pass in a file which has a segment stored, or an actual JSON
-object.
-
-
-
-
-property max
-Calculate the maximum value of the current array.
-
-Returns:
-Maximum value of the current array.
-Return type:
-float
-
-
-
-
-
-property mean
-Calculate the mean of the current array.
-
-Returns:
-Mean value of the current array.
-Return type:
-float
-
-
-
-
-
-property min
-Calculate the minimum value of the current array.
-
-Returns:
-Minimum value of the current array.
-Return type:
-float
-
-
-
-
-
-property std
-Calculate the standard deviation of the current array.
-
-Returns:
-Standard deviation of the current array.
-Return type:
-float
-
-
-
-
-
-property time
-Get the time data of the segment if the corresponding rank is in file
-:return: time
-
-
-
-
-to_dict ( )
-Return a dict representation of the metadata, usually used prior to
-converting the dict to a JSON.
-
-
-
-
-to_json ( filename = None )
-Return a JSON representation of this, by reporting the important
-metadata.
-
-
-
-
-to_meta ( )
-Kept to allow for error handling, but since it’s already a metasegment
-it won’t actually do anything.
-
-
-
-
-unique_features
-
-
-
-
-
-
-class ionique.core. Segment ( current ** kwargs )
-Bases: AbstractSegmentTree
-A segment of ionic current, and methods relevant for collecting metadata. The ionic current is
-expected to be passed as a numpy array of floats. Metadata methods (mean, std..) are decorated
-as properties to reduce overall computational time, making them calculated on the fly rather
-than during analysis.
-
-
-__init__ ( current ** kwargs )
-The segment must have a list of ionic current, of which it stores some statistics about.
-It may also take in as many keyword arguments as needed, such as start time or duration
-if already known. Cannot override statistical measurements.
-:param current: Numpy array of current data points.
-:type current: np.ndarray
-:param kwargs: Additional attributes like ‘start’, ‘end’, ‘rank’, etc.
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-delete ( )
-Deleting this segment requires deleting its reference to the ionic
-current array, and then deleting itself.
-
-
-
-
-end : int | None
-
-
-
-
-classmethod from_json ( filename = None json = None )
-Read in a segment from a JSON and return a metasegment object. Either
-pass in a file which has a segment stored, or an actual JSON object.
-
-
-
-
-property max
-Calculate the maximum value of the current array.
-
-Returns:
-Maximum value of the current array.
-Return type:
-float
-
-
-
-
-
-property mean
-Calculate the mean of the current array.
-
-Returns:
-Mean value of the current array.
-Return type:
-float
-
-
-
-
-
-property min
-Calculate the minimum value of the current array.
-
-Returns:
-Minimum value of the current array.
-Return type:
-float
-
-
-
-
-
-property n
-Get the number of elements in the current array.
-
-Returns:
-Number of elements in the current array.
-Return type:
-int
-
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-rank : str
-
-
-
-
-scale ( sampling_freq )
-Rescale all of the values to go from samples to seconds.
-
-
-
-
-start : int | None
-
-
-
-
-property std
-Calculate the standard deviation of the current array.
-
-Returns:
-Standard deviation of the current array.
-Return type:
-float
-
-
-
-
-
-to_dict ( )
-Return a dict representation of the metadata, usually used prior to
-converting the dict to a JSON.
-
-
-
-
-to_json ( filename = None )
-Return a JSON representation of this, by reporting the important
-metadata.
-
-
-
-
-to_meta ( )
-Convert from a segment to a ‘metasegment’, which stores only metadata
-about the segment and not the full array of ionic current.
-
-
-
-
-
-
-ionique.core. ignored ( * exceptions )
-Replace the “try, except: pass” paradigm by replacing those three lines with a single line.
-Taken from the latest 3.4 python update push by Raymond Hettinger, see:
-http://hg.python.org/cpython/rev/406b47c64480
-
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\ No newline at end of file
diff --git a/docs/_build/html/main/api/cparsers.html b/docs/_build/html/main/api/cparsers.html
deleted file mode 100644
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+++ /dev/null
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ionique.cparsers — ionique 0.1.0 documentation
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-
-ionique.cparsers
-This contains cython implementations of ionic current parsers which are in
-parsers.py. Currently the only parser is StatSplit, which is implemented
-as FastStatSplit.
-
-
-class ionique.cparsers. FastStatSplit
-Bases: object
-A cython implementation of the segmenter written by Kevin Karplus. Sped up approximately 50-100x
-compared to the Python implementation depending on parameters.
-
-
-__init__ ( * args ** kwargs )
-
-
-
-
-best_single_split ( current )
-Wrapper for a single call to _best_single_split. It will find the
-single best split in a series of current, and return the index of
-that split. Returns a tuple of ( gain, index ) where gain is the
-gain in variance by splitting there, and index is the index at which
-the split should occur in the current array.
-
-
-
-
-min_gain
-
-
-
-
-parse ( current )
-Wrapper function for the segmentation, which is implemented in cython.
-
-
-
-
-parse_meta ( current )
-Wrapper function for the segmentation, which is implemented in cython. returns meta segments (no current array)
-
-
-
-
-score_samples ( current no_split = False )
-Return a series of lists scoring each sample. However, this isn’t just
-scoring each sample once. Every time a split is detected, it will return
-a new list with newly scored samples. In essence, it returns one list
-per scan of the current using the recursive method.
-
-
-
-
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-
-ionique.cparsers. pairwise ( iterable )
-
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\ No newline at end of file
diff --git a/docs/_build/html/main/api/datatypes.html b/docs/_build/html/main/api/datatypes.html
deleted file mode 100644
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+++ /dev/null
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ionique.datatypes — ionique 0.1.0 documentation
-
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-ionique.datatypes
-Data structure definitions and session file management for analysis.
-
-
-class ionique.datatypes. SessionFileManager ( * args ** kwargs )
-Bases: MetaSegment
-Singleton-based session manager for hierarchical data.
-This class inherits from MetaSegment and uses the Singleton metaclass
-to ensure only one instance exists during a given session. It serves as
-the root segment for a data analysis session and is responsible for managing
-metadata and registered “affectors” (i.e., objects that modify or
-interact with the session).
-
-
-__init__ ( ) → None
-Initialize the session manager.
-Sets the session start time and prepares the affector log structure.
-
-
-
-
-add_child ( child : AnySegment ) → None
-Add a single child segment to the session’s hierarchy.
-
-Parameters:
-child (AnySegment ) – The segment to be added as a child.
-
-
-
-
-
-add_children ( children : list [ AnySegment ] ) → None
-Add multiple child segments to the session.
-
-Parameters:
-children (list [ AnySegment ] ) – A list of segments to be added as children.
-
-
-
-
-
-rank : str = 'root'
-
-
-
-
-register_affector ( affector )
-Register an affector object that influences the session data.
-Records metadata including class name, string representation, and timestamp,
-and logs the event with a unique identifier.
-
-Parameters:
-affector (object ) – An object that modifies or interacts with the session.
-Returns:
-A UUID string uniquely identifying the registered affector.
-Return type:
-str
-
-
-
-
-
-unique_features
-
-
-
-
-
-
-class ionique.datatypes. TraceFile ( current : ndarray voltage = None rank = 'file' parent = None unique_features : dict = {} metadata : dict = {} )
-Bases: Segment
-Data structure for representing a single trace file with current and optional voltage information.
-This class inherits from Segment and encapsulates current and voltage data. It sets up segment metadata,
-defines parent-child hierarchy, and optionally initializes child segments based on voltage steps.
-Typical usage includes assigning metadata and organizing hierarchical
-data structures for downstream processing or visualization.
-
-
-__init__ ( current : ndarray voltage = None rank = 'file' parent = None unique_features : dict = {} metadata : dict = {} )
-If voltage steps are provided, corresponding child segments of rank “vstep” are created.
-
-Parameters:
-
-current (numpy.ndarray ) – current array.
voltage (list [ tuple [ tuple [ int , int ] , float ] ] or None ) – Optional list of tuples containing (start, end) index pairs and voltage values.
-Used to create child segments for each voltage step.
rank (str ) – Segment rank label, defaults to “file”.
parent (Segment or None ) – Optional parent segment to which this trace belongs.
unique_features (dict ) – Dictionary of metadata such as sampling frequency.
metadata (dict ) – Additional metadata.
-
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-delete ( )
-Delete the current object, removing it from its parent
-
-
-
-
-end : int | None
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-plot ( rank axes downsample_per_rank color_per_rank )
-Method for plotting
-
-
-
-
-rank : str
-
-
-
-
-start : int | None
-
-
-
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\ No newline at end of file
diff --git a/docs/_build/html/main/api/io.html b/docs/_build/html/main/api/io.html
deleted file mode 100644
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ionique.io — ionique 0.1.0 documentation
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-ionique.io
-Input/output utilities for data files.
-This module provides an interface for loading, parsing, and preprocessing
-signal data from various file formats (e.g., .edh , .opt , .abf , .dat , .xml ). It defines a base reader class
-and concrete implementations that handle format-specific logic, metadata extraction,
-data alignment, and optional preprocessing steps.
-This module is central to converting raw experimental data into analyzable form.
-
-
-class ionique.io. AbstractFileReader
-Bases: object
-An abstract class for reading various data files (.abf, .edh, .mat, .opt, .xml)
-This class defines the structure for file readers
-
-
-__init__ ( )
-
-
-
-
-accepted_keywords = []
-
-
-
-
-current_multiplier : float = 1.0
-
-
-
-
-ext = '___'
-
-
-
-
-read ( filename : str ** kwargs )
-Read a datafile or series of files. Files are identified according to their extension.
-Data formats that come with a header file must be referred to by the header file.
-If inheriting from the AbstractFileReader class, do not override this function;
-instead, create a custom _read method.
-
-Parameters:
-filename – file name or list of file names, typically
-
-given as a string or PathLike object.
-:type filename: str or os.PathLike or list[str] or list[os.PathLike]
-:param **
-
-
-
-
-voltage_multiplier : float = 1.0
-
-
-
-
-
-
-class ionique.io. EDHReader ( edh_filename voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Bases: AbstractFileReader
-Reader class for loading .edh data files and their associated current data.
-This class parses the .edh header file and automatically loads the corresponding signal data
-(either from .abf or .dat files in the same directory). It extracts metadata, converts raw
-current and voltage data to SI units, and supports optional preprocessing steps such as
-voltage step segmentation, downsampling, and signal filtering.
-
-
-__init__ ( edh_filename voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Initialize the EDHReader and load signal data from associated files.
-
-Parameters:
-
-edh_filename (str ) – Path to the .edh header file.
voltage_compress (bool ) – If True, splits signal into segments based on voltage steps.
n_remove (int ) – Number of samples to remove from the beginning of each voltage step.
downsample (int ) – Downsampling factor to reduce data size.
prefilter (callable or None ) – Callable to apply preprocessing to the current signal.
-
-
-
-
-
-accepted_keywords = ['voltage_compress', 'n_remove', 'downsample', 'prefilter']
-
-
-
-
-current_multiplier : float = 1e-09
-
-
-
-
-ext = '.edh'
-
-
-
-
-voltage_multiplier : float = 0.001
-
-
-
-
-
-
-class ionique.io. OPTReader ( opt_filename : str voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Bases: AbstractFileReader
-Reader for .opt data files with corresponding XML or _volt.opt metadata.
-This class reads current data from .opt files and attempts to extract or reconstruct
-corresponding voltage data using associated .xml or _volt.opt files found in the same
-directory. It handles metadata extraction, signal preprocessing, voltage alignment, and
-segment compression based on voltage steps.
-Supported XML structures include both standard HWtiming_cap_step formats and timestamp-based
-custom formats.
-
-
-__init__ ( opt_filename : str voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Initialize the OPTReader instance and load corresponding files.
-
-Parameters:
-
-opt_filename (str ) – Path to the .opt header file.
voltage_compress (bool ) – If True, splits signal into segments based on voltage steps.
n_remove (int ) – Number of samples to remove from the beginning of each voltage step.
downsample (int ) – Downsampling factor to reduce data size.
prefilter (callable or None ) – Callable to apply preprocessing to the current signal.
-
-
-
-
-
-accepted_keywords = ['voltage_compress', 'n_remove', 'downsample', 'prefilter']
-
-
-
-
-current_multiplier : float = 1000000000.0
-
-
-
-
-ext = '.opt'
-
-
-
-
-find_peaks_in_segment ( current_data start_index end_index )
-Apply “find_peaks” on a segment of the current data and return
-the indices of the found peak
-
-
-
-
-find_peaks_slide_window ( current start_index window_shift_duration = 0.002 sampling_frequency = 250000 sign = 'negative' )
-Find peaks in the current every time the voltage changes.
-The search for the peaks happens in the window of the timestamp 0.02 seconds.
-
-
-
-
-process_custom_xml ( )
-Processes the custom XML file to extract voltage data, align the voltage waveform
-with the current, and detect peaks in the current signal every time the voltage changes.
-
-
-
-
-
-
-ionique.io. load_files_GUI ( )
-
-Create a form with:
-‘Compress voltage (only uncheck if voltage is not stepwise, e.g., triangle wave)’ : checkbox
‘Apply Filter?’ : checkbox
If ‘Apply Filter?’ checked: dynamic form from the Filter dataclass (types, options, limits).
-Returns (frame, get_values) where get_values() -> dict with: {‘compress_voltage’: bool, ‘apply_filter’: bool, ‘filter’: Filter|None}
-
-
-
-
-
-ionique.io. select_files_GUI ( )
-Open a file dialog allowing multiple selection, but only for ONE file type at a time.
-Users can switch the dialog’s file-type dropdown, but the final selection must be
-homogeneous (all the same extension) and in the allowed list.
-
-Returns
-
-tuple[str, …] Absolute paths of the selected files (empty tuple if the user cancels).
-
-
-
-
-
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-
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\ No newline at end of file
diff --git a/docs/_build/html/main/api/parsers.html b/docs/_build/html/main/api/parsers.html
deleted file mode 100644
index 3d9d2f0..0000000
--- a/docs/_build/html/main/api/parsers.html
+++ /dev/null
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ionique.parsers — ionique 0.1.0 documentation
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-ionique.parsers
-Parser module for event and state segmentation in current recordings.
-This module provides a framework for parsing current trace data. It includes an abstract base Parser class
-and multiple concrete parser implementations for detecting spikes, noise, square pulses, and other event
-structures in ionic current signals.
-Some of the parsers and the parser base class were adapted from the
-PyPore Package by Jacob Schreiber and Kevin Karplus (https://github.com/jmschrei/PyPore )
-
-
-class ionique.parsers. AutoSquareParser ( threshold_baseline = 0.7 expected_conductance = 1.9 conductance_tolerance = 1.2 wrap_padding : int = 50 rules = [] )
-Bases: Parser
-Class for building a straightforward pipeline of data analysis
-to make it reusable with customized parameters
-
-
-__init__ ( threshold_baseline = 0.7 expected_conductance = 1.9 conductance_tolerance = 1.2 wrap_padding : int = 50 rules = [] )
-Initialize a Parser instance
-
-
-
-
-parse ( current eff_sampling_freq voltage )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current', 'eff_sampling_freq', 'voltage']
-
-
-
-
-
-
-class ionique.parsers. FilterDerivativeSegmenter ( low_threshold = 1 high_threshold = 2 cutoff_freq = 1000.0 sampling_freq = 100000.0 )
-Bases: Parser
-This parser will segment an event using a filter-derivative method. It will
-first apply a bessel filter at a certain cutoff to the current, then it will
-take the derivative of that, and segment when the derivative passes a
-threshold.
-
-
-__init__ ( low_threshold = 1 high_threshold = 2 cutoff_freq = 1000.0 sampling_freq = 100000.0 )
-Initialize a Parser instance
-
-
-
-
-parse ( current )
-Apply the filter-derivative method to filter the ionic current.
-
-
-
-
-set_params ( )
-
-Returns:
-
-
-
-
-
-
-
-
-class ionique.parsers. IVCurveAnalyzer ( current parent_segment sampling_frequency method = 'simple' )
-Bases: Parser
-THIS CLASS IS NOT IMPLEMENTED YET. DO NOT USE.
-Check for at Least Two Voltage Steps: confirm that the segment has at least two voltage steps.
-Divide into Voltage Segments: separate data into voltage segments.
-Call IVAnalyzer on Parent Segment: compute the mean and std of the current in each
-voltage segment.
-Return an Array of (voltage: (imean, istd)) pair:
-Also mean and std should be calulated in two ways
-
-
-__init__ ( current parent_segment sampling_frequency method = 'simple' )
-Initialize a Parser instance
-
-
-
-
-analyze ( )
-Analyze each voltage step in a parent segment
-
-
-
-
-
-
-class ionique.parsers. IVCurveParser ( voltage )
-Bases: object
-THIS CLASS IS NOT IMPLEMENTED YET! DO NOT USE.
-The class handles:
-
-
-Identifies a file where an IV curve is recorded based on the pattern.
-
-Here, the goal is to feed the voltage information to a module and have it search and do pattern matching to find one of
-these possibilities. .This can be a parser that accepts voltage array or compressed voltage as one of the inputs.
-
-
-__init__ ( voltage )
-
-
-
-
-parse ( )
-Searches for a pattern in the voltage data to identify IV.
-If none exists, the given datafile does not contain IV curves.
-If one or multiple IV curves are found, it returns start and
-end regions of each of them.
-Returns: list of indices: start and end for each deteced IV curve
-
-
-
-
-
-
-class ionique.parsers. MemoryParse ( starts ends )
-Bases: object
-A parser based on being fed previous split points, and splitting a raw file based
-those splits. Used predominately when loading previous split points from the
-database cache, to reconstruct a parsed file from “memory.
-
-
-__init__ ( starts ends )
-
-Parameters:
-
-
-
-
-
-
-
-parse ( current )
-
-
-
-
-
-
-class ionique.parsers. NoiseFilterParser ( noise_threshold = 60 detect_noise = True )
-Bases: Parser
-This unit is a parser subclass. when invoked on a segment (whole file or subsegment),
-it identifies the areas where bad things happened, and areas where the data is clean.
-Examples of bad things include high noise (60Hz noise from opening the cage), membrane
-rupture, and membrane formation attempts. it produces children from the good
-regions of the data and ignore the bad regions.
-
-
-__init__ ( noise_threshold = 60 detect_noise = True )
-Initialize a Parser instance
-
-
-
-
-parse ( data segment_start = 0 segment_end = None )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-
-
-class ionique.parsers. Parser
-Bases: ABC
-This class defines the interface and shared utility methods for parser implementations.
-Parsers are used to segment ionic current data
-into structured regions of interest (events).
-Subclasses should implement the parse method and define any additional logic
-required to detect features in the data.
-
-
-abstract __init__ ( )
-Initialize a Parser instance
-
-
-
-
-classmethod from_json ( _json )
-Critical: Not implemented yet, figure out how to make this work in pythion
-
-
-
-
-classmethod get_init_params ( )
-Get the initialization parameters of the parser class.
-
-Returns:
-A dictionary of initialization parameters, default None.
-Return type:
-dict or None
-
-
-
-
-
-classmethod get_name ( )
-Get the name of the parser class
-:return: The name of the class.
-:rtype: str
-
-
-
-
-classmethod get_process_inputs ( )
-
-
-
-
-classmethod get_process_outputs ( )
-Get the outputs generated by processing
-
-Returns:
-A list of outputs generated by parse method
-Return type:
-list or None
-
-
-
-
-
-get_required_parent_attributes ( )
-Get the list of required attributes from the parent segment
-
-Returns:
-A list of required parent attributes
-Return type:
-list[str]
-
-
-
-
-
-parse ( ** kwargs )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current']
-
-
-
-
-set_params ( param_dict )
-Updates each paramater presented in the GUI to the value input in the lineEdit
-corresponding to that value.
-:param param_dict: A dictionary
-:type param_dict: dict
-
-
-
-
-to_dict ( )
-Convert the parser’s attributes to a dictionary.
-
-Returns:
-A dictionary representation of the parser’s attributes.
-Return type:
-dict
-
-
-
-
-
-to_json ( filename = False )
-Convert the parser’s attributes to a JSON string.
-
-Parameters:
-filename (str , default bool ) – If filename, it writes the JSON string to the specified file.
-Returns:
-A dictionary representation of the parser’s attributes.
-Return type:
-str
-
-
-
-
-
-
-
-class ionique.parsers. SpeedyStatSplit ( sampling_freq min_width = 100 max_width = 1000000 window_width = 10000 min_gain_per_sample = None false_positive_rate = None prior_segments_per_second = None cutoff_freq = None )
-Bases: Parser
-A parser wrapper for the Cython-accelerated FastStatSplit algorithm.
-See cparsers.pyx FastStatSplit for full documentation. This is just a
-wrapper for the cyton implementation to add a GUI.
-
-
-__init__ ( sampling_freq min_width = 100 max_width = 1000000 window_width = 10000 min_gain_per_sample = None false_positive_rate = None prior_segments_per_second = None cutoff_freq = None )
-Initialize the SpeedyStatSplit instance
-
-Parameters:
-
-sampling_freq (int ) – The sampling frequency of the data
min_width (int , optional ) – Minimum width of a segment, default=100
max_width (int , optional ) – Maximum width of a segment, default=1000000.
window_width (int , optional ) – Width of the window, defaults to 10000.
min_gain_per_sample (float or None , optional ) – Minimum gain per sample, default=None
false_positive_rate (float or None , optional ) – Expected false positive rate , default=None
prior_segments_per_second (float or None , optional ) – Prior number of segments per second, default=None.
cutoff_freq (float or None , optional ) – Cutoff frequency for filtering, default=None
-
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-best_single_split ( current )
-Determine the best single split point in the current data
-
-Parameters:
-current (numpy.ndarray ) – The current data to be analyzed.
-Returns:
-The best split point
-Return type:
-int or None
-
-
-
-
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-parse ( current )
-Parse the current data to detect segments using the FastStatSplit
-
-Parameters:
-current (numpy.ndarray ) – The current data to be parsed
-Returns:
-A list of detected segments
-Return type:
-
-
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-parse_meta ( current )
-Parse the current data and extract meta information
-
-Parameters:
-current (numpy.ndarray ) – The current data to be parsed.
-
-
-
-
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-set_params ( )
-Set the parameters
-
-
-
-
-uiTemplate = [('sampling_freq', 'spin', {'bounds': [0, None], 'dec': True, 'siPrefix': True, 'step': 1, 'suffix': 'Hz', 'value': 200000}), ('min_width', 'spin', {'dec': True, 'int': True, 'min': 2, 'step': 1, 'value': 100}), ('max_width', 'spin', {'dec': True, 'int': True, 'step': 1, 'value': 1000000}), ('window_width', 'spin', {'dec': True, 'int': True, 'step': 1, 'value': 10000}), ('cutoff_freq', 'spin', {'hidden': True})]
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-class ionique.parsers. SpikeParser ( height = None threshold = None distance = None prominence = None prominence_snr = None width = None wlen = None rel_height : float = 0.5 plateu_size = None fractional : bool = True )
-Bases: Parser
-A parser for detecting spike-like events in ionic current data.
-This parser wraps scipy.signal.find_peaks , supporting thresholds
-for features like peak height, prominence, and width. It computes event characteristics such
-as dwell time, relative prominence, and peak positions.
-
-
-__init__ ( height = None threshold = None distance = None prominence = None prominence_snr = None width = None wlen = None rel_height : float = 0.5 plateu_size = None fractional : bool = True ) → None
-Initialize the SpikeParser.
-
-Parameters:
-
-height (float or tuple or None ) – Minimum spike height.
threshold (float or tuple or None ) – Minimum vertical threshold.
distance (float or tuple or None ) – Minimum horizontal distance between peaks.
prominence (float or tuple or None ) – Minimum peak prominence.
prominence_snr (tuple or None ) – Alternative to prominence, specified as a tuple of signal-to-noise (SNR) ratios.
width (float or tuple or None ) – Full width at half maximum of the peak.
wlen (float or None ) – Width of the window used to calculate prominence.
rel_height (float ) – Relative height used in width calculation (range 0 to 1).
plateu_size (float or None ) – Minimum length of a flat-topped peak.
fractional (bool ) – Whether to interpret height, prominence, etc., as a fraction of the signal mean.
-
-
-
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-parse ( current sampling_freq mean std start )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
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-required_parent_attributes : list [ str ] = ['current', 'sampling_freq', 'mean', 'start', 'std']
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-class ionique.parsers. lambda_event_parser ( threshold = 90 rules = None )
-Bases: Parser
-A simple rule-based parser which defines events as a sequential series
-of points which are below a certain threshold, then filtered based on other
-critereon such as total time or minimum current. Rules can be passed in at
-initiation, or set later, but must be a lambda function takes in a PreEvent
-object and performs some boolean operation.
-
-
-__init__ ( threshold = 90 rules = None )
-
-Parameters:
-
-threshold (float ) – The threshold for detecting events (e.g., current < threshold).
rules (list [ callable ] or None ) – A list of lambda functions that accept an event and return a bool.
-
-
-
-
-
-parse ( current )
-Perform a large capture of events by creating a boolean mask for when
-the current is below a threshold, then detecting the edges in those masks,
-and using the edges to partitition the sample. The events are
-then filtered before being returned.
-
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-set_params ( )
-Read in the data from the GUI and use it to customize
-the rules or threshold of the parser.
-
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-ionique.parsers. pairwise ( iterable )
-
-s -> (s0,s1), (s1,s2), (s2, s3), …
-
-
-Parameters:
-iterable
-Returns:
-
-
-
-
-
-
-class ionique.parsers. snakebase_parser ( threshold = 1.5 )
-Bases: Parser
-A simple parser based on dividing when the peak-to-peak
-amplitude of a wave exceeds a certain threshold.
-
-
-__init__ ( threshold = 1.5 )
-Initialize a Parser instance
-
-
-
-
-parse ( current )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
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-set_params ( )
-Updates each paramater presented in the GUI to the value input in the lineEdit
-corresponding to that value.
-:param param_dict: A dictionary
-:type param_dict: dict
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-ionique.utils
-Signal Processing and Data Extraction Utilities
-This module provides a set of utility functions and classes designed for use in signal
-processing workflows.
-These tools are intended for both internal processing and external use cases such as analysis
-pipelines and API integrations.
-
-
-class ionique.utils. Filter ( cutoff_frequency : float filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ] filter_method : Literal [ 'butter' , 'bessel' ] = 'butter' bidirectional : bool = True sampling_frequency : float = None )
-Bases: object
-This class allows a user to apply low-pass, high-pass,
-band-pass, or band-stop filters using standard filter types (Butterworth or Bessel).
-Filters are implemented using second-order sections (SOS) for numerical stability and
-can be applied in either forward-only or bidirectional mode.
-
-Parameters:
-
-cutoff_frequency (float or list [ float ] ) – The cutoff frequency or frequency band for the filter in Hz.
-For band filters, provide the band center or list of [low, high] values.
filter_type (Literal [ "lowpass" , "highpass" , "bandpass" , "bandstop" ] ) – The type of filter to apply. Options are “lowpass”, “highpass”, “bandpass”, or “bandstop”.
filter_method (Literal [ "butter" , "bessel" ] ) – The filter method. Supported options: “butter” (Butterworth), “bessel”. Defaults to “butter”.
order (int ) – The order of the filter. Must be >= 1. Defaults to 2.
bidirectional (bool ) – If True, applies filtering forward and backward using sosfiltfilt .
-If False, uses causal sosfilt . Defaults to True.
sampling_frequency (float , optional ) – Sampling frequency of the signal in Hz.
-
-
-
-__init__ ( cutoff_frequency : float filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ] filter_method : Literal [ 'butter' , 'bessel' ] = 'butter' bidirectional : bool = True sampling_frequency : float = None ) → None
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-bidirectional : bool = True
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-cutoff_frequency : float
-
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-filter_method : Literal [ 'butter' , 'bessel' ] = 'butter'
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-filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ]
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-sampling_frequency : float = None
-
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-
-
-class ionique.utils. Singleton ( * args ** kwargs )
-Bases: type
-Generic singleton metaclass.
-This metaclass ensures the same return every time the class is instanced.
-
-
-__init__ ( * args ** kwargs )
-Class initialization
-
-Parameters:
-
-
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-
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-class ionique.utils. Trimmer ( samples_to_remove : int rank : str = 'vstep' newrank : str = 'vstepgap' )
-Bases: object
-Segment trimming utility for hierarchical signal data.
-This class operates on a segment tree. It traverses segments of a given rank (e.g., “vstep”) and trims
-a fixed number of samples from the start of each segment. The resulting trimmed segments are added
-as new child segments with a specified new rank (e.g., “vstepgap”).
-This is useful when initial samples of each segment include artifacts that should be excluded from analysis.
-
-Parameters:
-
-samples_to_remove (int ) – Number of samples to trim from the beginning of each segment.
rank (str ) – The hierarchical rank of segments to target for trimming. Defaults to “vstep”.
newrank (str ) – The rank name to assign to newly created trimmed child segments. Defaults to “vstepgap”.
-
-
-
-__init__ ( samples_to_remove : int rank : str = 'vstep' newrank : str = 'vstepgap' ) → None
-
-
-
-
-newrank : str = 'vstepgap'
-
-
-
-
-rank : str = 'vstep'
-
-
-
-
-samples_to_remove : int
-
-
-
-
-
-
-Extract features from hierarchical segments into a DataFrame.
-Traverses a hierarchical segment structure down to bottom_rank , then collects feature values
-from each segment. Static features are retrieved via get_feature() , constants can be added via
-add_ons , and custom computed features can be provided through lambdas .
-This is useful for generating structured datasets from annotated traces for statistical analysis or
-machine learning.
-
-Parameters:
-
-seg (object ) – The root segment or trace object containing a hierarchical structure. It must implement traverse_to_rank() and support get_feature() .
bottom_rank (str ) – The rank name of the lowest-level segments from which to extract features.
extractions (list [ str ] ) – List of feature names to extract directly using get_feature() on each segment.
-Common examples include: ‘mean’, ‘frac’, ‘duration’, ‘baseline’, ‘current’, ‘wrap’, ‘start’.
add_ons (dict ) – A dictionary of fixed key-value pairs to include as constant columns in the resulting DataFrame.
lambdas (dict ) – A dictionary mapping column names to lambda functions that compute derived values from each segment.
-Returns:
-A pandas DataFrame where each row corresponds to a bottom-rank segment and columns represent extracted and computed features.
-Return type:
-pandas.DataFrame
-
-
-
-
-
-ionique.utils. si_eval ( value unit = None return_unit = False )
-Evaluate and convert a value with an SI unit prefix to its numeric base value.
-This function handles both string-based and numeric values with
-SI (International System of Units) prefixes such as “k” (kilo), “M” (mega), “μ” (micro), etc.
-It multiplies the input value by the appropriate factor based on the SI prefix.
-This utility is useful when parsing human-readable measurement strings or standardizing
-units across data pipelines that mix strings and numeric formats.
-
-Parameters:
-
-value (str or float ) – The value to be converted. Can be a string like “1.2 kHz” or a numeric value (int or float).
unit (str , optional ) – The unit string (e.g., “kHz”, “mV”). Required only if value is a numeric type.
return_unit (bool , optional ) – If True, returns a tuple containing the converted numeric value and the base unit (e.g., ‘Hz’).
-If False, only the numeric value is returned.
-Raises:
-
-
-Returns:
-The converted value, optionally paired with the base unit.
-Return type:
-float or tuple[float, str]
-
-
-
-
-
-ionique.utils. split_voltage_steps ( voltage : ndarray n_remove = 0 as_tuples = False )
-Split a voltage signal into segments based on step changes.
-This function detects changes in the voltage signal and splits it into individual
-segments (or steps) wherever a change in value occurs. It is useful in analyzing
-stepwise voltage protocols.
-Optionally, a number of initial samples can be removed from each segment using the
-n_remove parameter. The output can either be two separate arrays of start and end
-indices, or a list of tuples representing each segment.
-
-Parameters:
-
-voltage (numpy.ndarray ) – 1D voltage signal array to be segmented.
n_remove (int ) – Number of samples to remove from the start of each split. Defaults to 0.
as_tuples (bool ) – If True, returns a list of (start, end) index tuples. If False, returns two arrays: start_indices and end_indices. Defaults to False.
-Raises:
-ValueError – If n_remove is negative or larger than the start of the voltage changes.
-Returns:
-Either:
-- A tuple of (start_indices, end_indices), where each is a numpy array of indices.
-- A list of (start, end) tuples if as_tuples=True .
-Return type:
-tuple[numpy.ndarray, numpy.ndarray] or list[tuple[int, int]]
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API Reference — ionique 0.1.0 documentation
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Data Input — ionique 0.1.0 documentation
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<no title> — ionique 0.1.0 documentation
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Index — ionique 0.1.0 documentation
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- __init__() (ionique.core.AbstractSegmentTree method) , [1]
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- (ionique.core.MetaSegment method) , [1]
-(ionique.core.Segment method) , [1]
-(ionique.cparsers.FastStatSplit method) , [1]
-(ionique.datatypes.SessionFileManager method) , [1]
-(ionique.datatypes.TraceFile method) , [1]
-(ionique.io.AbstractFileReader method) , [1]
-(ionique.io.EDHReader method) , [1]
-(ionique.io.OPTReader method) , [1]
-(ionique.parsers.AutoSquareParser method) , [1]
-(ionique.parsers.FilterDerivativeSegmenter method) , [1]
-(ionique.parsers.IVCurveAnalyzer method) , [1]
-(ionique.parsers.IVCurveParser method) , [1]
-(ionique.parsers.lambda_event_parser method) , [1]
-(ionique.parsers.MemoryParse method) , [1]
-(ionique.parsers.NoiseFilterParser method) , [1]
-(ionique.parsers.Parser method) , [1]
-(ionique.parsers.snakebase_parser method) , [1]
-(ionique.parsers.SpeedyStatSplit method) , [1]
-(ionique.parsers.SpikeParser method) , [1]
-(ionique.utils.Filter method) , [1]
-(ionique.utils.Singleton method) , [1]
-(ionique.utils.Trimmer method) , [1]
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- (ionique.cparsers.FastStatSplit method) , [1]
-(ionique.parsers.AutoSquareParser method) , [1]
-(ionique.parsers.FilterDerivativeSegmenter method) , [1]
-(ionique.parsers.IVCurveParser method) , [1]
-(ionique.parsers.lambda_event_parser method) , [1]
-(ionique.parsers.MemoryParse method) , [1]
-(ionique.parsers.NoiseFilterParser method) , [1]
-(ionique.parsers.Parser method) , [1]
-(ionique.parsers.snakebase_parser method) , [1]
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Ionique: A Nanopore Signal Analysis Framework — ionique 0.1.0 documentation
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-Ionique: A Nanopore Signal Analysis Framework
-Authors:
-Ali Fallahi, Dinara Boyko
-Date:
-Feb 11, 2025
-Version:
-0.1.0 (Development)
-
-Overview
-Ionique is a Python module for processing and analyzing nanopore signal data. It provides tools for reading, filtering, segmenting, and analyzing nanopore experimental data. The framework supports structured workflows for signal processing and quality control, allowing researchers to extract relevant information from raw current and voltage traces.
-
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-Features
-
-File Handling
-- Supports .edh , .opt , and .xml formats.
-- Extracts raw current and voltage traces.
-- Parses experimental metadata for structured analysis.
Signal Processing
-- Segmentation and event detection.
Analysis Modules
-- IV curve computation and voltage pattern detection.
-- Step response and dwell-time analysis.
-- Customizable parsers and filters for flexible workflows.
Usability
-- Python library for scripting and automation.
-
-
-Installation (Development)
-To install the development version of Ionique, clone the repository and install it in editable mode:
- clone https://github.com/wanunulab/ionique.git
-cd ionique
-pip install ionique
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Requirements:
-- Python > 3.10
-- Dependencies listed in requirements.txt
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-Future Development
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Installation for beginners — ionique 0.1.0 documentation
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-Installation for beginners
-
-Welcome to Ionique!
-This tutorial is designed for non-coding users. If you have used Python and GitHub before, please proceed with the README.md
-Before diving into data analysis, let’s first install Python, GitHub, and Jupyter Notebook.
-
-
-Step 1: Install Python
-Download the package from Python Downloads .
-Choose the appropriate OS system for your machine. Then, scroll down to pick Python version 3.10 or later up to 3.12 .
-After Python has been successfully installed, we can go over some basic terminal commands.
-
-Linux Users:
-
-MacOS Users:
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-Windows Users:
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-To check the installation, type:
-
-Some versions of Python may require the following command:
-
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-Step 2: GitHub
-First, you need to create an account by following the instructions on GitHub .
-Now, open the repository link: Ionique Repository , click on the green “Code” button, and download the ZIP file. Unzip it.
-Move the folder to the desired location on your computer.
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-Step 3: Moving Between Folders in the Terminal
-You can find a short list of basic commands here: Linux vs Windows Commands .
-Check your current location using:
-
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-pwd (Unix, Linux, Mac)
cd (Windows)
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-To list items in the current directory:
-
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-ls (Unix, Linux, Mac)
chdir (Windows)
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-The most important command for this tutorial is cd , which stands for “change directory.” To move around folders in the terminal, type:
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-Find the ionique folder and move inside it.
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-Step 4: Virtual Environment
-A virtual environment is an encapsulated workspace on your computer that allows you to install software packages for a project. It helps to keep the versions of different packages within a project completely separate from other projects.
-To learn more about virtual environments, visit Python Virtual Environments .
-Let’s create your first virtual environment!
-Run the following command:
- -m venv ionique_env
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Then, activate it:
-source ionique_env/bin/activate # Linux/MacOS
-ionique_env\S cripts\a ctivate.bat # Windows
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Now, install the Ionique requirements:
-
-If your OS and Python version require the ``python3`` command, use ``pip3`` instead.
-Next, install JupyterLab:
-
-
-
-Step 5: Run the Notebooks
-Copy the notebook you want to run into the data folder. (You don’t need to use the terminal for this step.)
-Now, return to the terminal window and navigate to the folder where the copied notebook is stored.
-Then, run:
-
-You are now ready to work on data analysis!
-
-
-Step 6: Finish Workflow
-Once you are done with the analysis, go back to the terminal and press Ctrl + C .
-Then, type:
-
-to deactivate the virtual environment.
-Note: Starting from Step 5 , the procedure will be identical each time you want to run Ionique .
-Happy using Ionique!
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Install — ionique 0.1.0 documentation
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- ionique
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-Install
-
-Install ionique
-The most recent release of the ionique pip_
-
-If you are very new to coding, more detailed instructions, we provide guides on VSCode, Python3 and ionique installment.
-
-
-Installation from Source
-If you want to contribute to ionique you can build ionique from source using the
-instructions below.
-$ git clone https://github.com/wanunulab/ionique.git
-$ cd ionique
-$ pip install -e .
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ionique — ionique 0.1.0 documentation
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- ionique
-
-
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-
-
-
-ionique
-
-
-ionique.core
-
-
-This holds the core data types which may be abstracted in
-many different applications.
-This module defines a tree-based framework for representing, parsing, and annotating
-segments of ionic current data.
-
-
-class ionique.core. AbstractSegmentTree
-Bases: object
-Class for managing hierarchical segments.
-This class supports a tree structure where each segment can contain multiple
-child segments, enabling recursive parsing and analysis of nested data.
-It provides utilities to manage relationships between segments, apply parsers,
-and extract subsegments by rank.
-
-
-__init__ ( ) → None
-Initialize the segment
-
-
-
-
-add_child ( child : AnySegment ) → None
-Add a single child
-
-
-
-
-add_children ( children : list [ AnySegment ] ) → None
-Add multiple children if:
-1. Child position is the within parent’s segment.
-2. The length of the child’s segment is > 0
-3. There is no overlap between consecutive children
-Or add children with no check
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-clear_children ( )
-Clear the list of children
-
-
-
-
-climb_to_rank ( rank : str ) → AnySegment | None
-Go up and find the segment with the specified rank
-:return: segment
-
-
-
-
-end : int | None
-
-
-
-
-get_feature ( name : str )
-Gets the ‘name’ feature of the current segment or its parent
-
-
-
-
-get_top_parent ( ) → AnySegment
-Recursively go up and find top most parent
-:return: parent
-
-
-
-
-property n : int
-Get the length of the segment
-:return: len(segment)
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-parse ( parser newrank : str at_child_rank : str | None = None ** kwargs ) → bool
-Parses the data in the segment or at a particular rank of child segments
-into children segments with a new rank.
-
-Parameters:
-
-parser (Parser parse method and required input attributes.
newrank (str ) – Rank to assign to the newly created child segments.
at_child_rank (str or None ) – Determines whether to traverse the children tree down to a given rank.
kwargs – Additional arguments to pass to the parser.
-Returns:
-True if parsing was successful, otherwise raises an exception.
-Return type:
-bool
-
-
-
-
-
-rank : str
-
-
-
-
-property relative_end : int
-Get end position relative to parent segment
-:return: end position
-
-
-
-
-property relative_slice : ndarray
-Slice the array to get the data between end-start segment
-:return: sliced np array
-
-
-
-
-property relative_start : int
-Get the start position relative to parent segment
-:return: start position
-
-
-
-
-property slice : ndarray
-Slice the array to get the data between start-end segment
-:return: sliced np array
-
-
-
-
-start : int | None
-
-
-
-
-traverse_to_rank ( rank : str ) → list
-Traverse the tree rank and get the list of the segments of the rank
-:param: rank
-:return: list of segments
-
-
-
-
-
-
-class ionique.core. MetaSegment ( start : int end : int parent : AnySegment | None = None rank : str | None = None unique_features : dict | None = {} ** kwargs )
-Bases: AbstractSegmentTree
-The metadata on an abstract segment of ionic current. All information about a segment can be
-loaded, without the expectation of the array of floats.
-
-
-__init__ ( start : int end : int parent : AnySegment | None = None rank : str | None = None unique_features : dict | None = {} ** kwargs )
-
-Parameters:
-
-start (int ) – Start index of the segment.
end (int ) – End index of the segment.
parent (AbstractSegmentTree or None ) – Parent segment in the tree (optional).
rank (str or None ) – Rank identifier for this segment (optional).
unique_features (dict ) – Dictionary of metadata features.
-
-
-
-
-
-property current
-Get the current data of the segment if the segment correlates to the file
-:return: current
-
-
-
-
-delete ( )
-Delete itself. There are no arrays with which to delete references for.
-
-
-
-
-property duration : float
-Get the duration of the segment = start - end
-:return: duration
-
-
-
-
-classmethod from_json ( filename = None in_json = None )
-Read in a metasegment from a JSON and return a metasegment object.
-Either pass in a file which has a segment stored, or an actual JSON
-object.
-
-
-
-
-property max
-Calculate the maximum value of the current array.
-
-Returns:
-Maximum value of the current array.
-Return type:
-float
-
-
-
-
-
-property mean
-Calculate the mean of the current array.
-
-Returns:
-Mean value of the current array.
-Return type:
-float
-
-
-
-
-
-property min
-Calculate the minimum value of the current array.
-
-Returns:
-Minimum value of the current array.
-Return type:
-float
-
-
-
-
-
-property std
-Calculate the standard deviation of the current array.
-
-Returns:
-Standard deviation of the current array.
-Return type:
-float
-
-
-
-
-
-property time
-Get the time data of the segment if the corresponding rank is in file
-:return: time
-
-
-
-
-to_dict ( )
-Return a dict representation of the metadata, usually used prior to
-converting the dict to a JSON.
-
-
-
-
-to_json ( filename = None )
-Return a JSON representation of this, by reporting the important
-metadata.
-
-
-
-
-to_meta ( )
-Kept to allow for error handling, but since it’s already a metasegment
-it won’t actually do anything.
-
-
-
-
-unique_features
-
-
-
-
-
-
-class ionique.core. Segment ( current ** kwargs )
-Bases: AbstractSegmentTree
-A segment of ionic current, and methods relevant for collecting metadata. The ionic current is
-expected to be passed as a numpy array of floats. Metadata methods (mean, std..) are decorated
-as properties to reduce overall computational time, making them calculated on the fly rather
-than during analysis.
-
-
-__init__ ( current ** kwargs )
-The segment must have a list of ionic current, of which it stores some statistics about.
-It may also take in as many keyword arguments as needed, such as start time or duration
-if already known. Cannot override statistical measurements.
-:param current: Numpy array of current data points.
-:type current: np.ndarray
-:param kwargs: Additional attributes like ‘start’, ‘end’, ‘rank’, etc.
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-delete ( )
-Deleting this segment requires deleting its reference to the ionic
-current array, and then deleting itself.
-
-
-
-
-end : int | None
-
-
-
-
-classmethod from_json ( filename = None json = None )
-Read in a segment from a JSON and return a metasegment object. Either
-pass in a file which has a segment stored, or an actual JSON object.
-
-
-
-
-property max
-Calculate the maximum value of the current array.
-
-Returns:
-Maximum value of the current array.
-Return type:
-float
-
-
-
-
-
-property mean
-Calculate the mean of the current array.
-
-Returns:
-Mean value of the current array.
-Return type:
-float
-
-
-
-
-
-property min
-Calculate the minimum value of the current array.
-
-Returns:
-Minimum value of the current array.
-Return type:
-float
-
-
-
-
-
-property n
-Get the number of elements in the current array.
-
-Returns:
-Number of elements in the current array.
-Return type:
-int
-
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-rank : str
-
-
-
-
-scale ( sampling_freq )
-Rescale all of the values to go from samples to seconds.
-
-
-
-
-start : int | None
-
-
-
-
-property std
-Calculate the standard deviation of the current array.
-
-Returns:
-Standard deviation of the current array.
-Return type:
-float
-
-
-
-
-
-to_dict ( )
-Return a dict representation of the metadata, usually used prior to
-converting the dict to a JSON.
-
-
-
-
-to_json ( filename = None )
-Return a JSON representation of this, by reporting the important
-metadata.
-
-
-
-
-to_meta ( )
-Convert from a segment to a ‘metasegment’, which stores only metadata
-about the segment and not the full array of ionic current.
-
-
-
-
-
-
-ionique.core. ignored ( * exceptions )
-Replace the “try, except: pass” paradigm by replacing those three lines with a single line.
-Taken from the latest 3.4 python update push by Raymond Hettinger, see:
-http://hg.python.org/cpython/rev/406b47c64480
-
-
-
-
-ionique.cparsers
-This contains cython implementations of ionic current parsers which are in
-parsers.py. Currently the only parser is StatSplit, which is implemented
-as FastStatSplit.
-
-
-class ionique.cparsers. FastStatSplit
-Bases: object
-A cython implementation of the segmenter written by Kevin Karplus. Sped up approximately 50-100x
-compared to the Python implementation depending on parameters.
-
-
-__init__ ( * args ** kwargs )
-
-
-
-
-best_single_split ( current )
-Wrapper for a single call to _best_single_split. It will find the
-single best split in a series of current, and return the index of
-that split. Returns a tuple of ( gain, index ) where gain is the
-gain in variance by splitting there, and index is the index at which
-the split should occur in the current array.
-
-
-
-
-min_gain
-
-
-
-
-parse ( current )
-Wrapper function for the segmentation, which is implemented in cython.
-
-
-
-
-parse_meta ( current )
-Wrapper function for the segmentation, which is implemented in cython. returns meta segments (no current array)
-
-
-
-
-score_samples ( current no_split = False )
-Return a series of lists scoring each sample. However, this isn’t just
-scoring each sample once. Every time a split is detected, it will return
-a new list with newly scored samples. In essence, it returns one list
-per scan of the current using the recursive method.
-
-
-
-
-
-
-ionique.cparsers. pairwise ( iterable )
-
-
-
-
-ionique.datatypes
-Data structure definitions and session file management for analysis.
-
-
-class ionique.datatypes. SessionFileManager ( * args ** kwargs )
-Bases: MetaSegment
-Singleton-based session manager for hierarchical data.
-This class inherits from MetaSegment and uses the Singleton metaclass
-to ensure only one instance exists during a given session. It serves as
-the root segment for a data analysis session and is responsible for managing
-metadata and registered “affectors” (i.e., objects that modify or
-interact with the session).
-
-
-__init__ ( ) → None
-Initialize the session manager.
-Sets the session start time and prepares the affector log structure.
-
-
-
-
-add_child ( child : AnySegment ) → None
-Add a single child segment to the session’s hierarchy.
-
-Parameters:
-child (AnySegment ) – The segment to be added as a child.
-
-
-
-
-
-add_children ( children : list [ AnySegment ] ) → None
-Add multiple child segments to the session.
-
-Parameters:
-children (list [ AnySegment ] ) – A list of segments to be added as children.
-
-
-
-
-
-rank : str = 'root'
-
-
-
-
-register_affector ( affector )
-Register an affector object that influences the session data.
-Records metadata including class name, string representation, and timestamp,
-and logs the event with a unique identifier.
-
-Parameters:
-affector (object ) – An object that modifies or interacts with the session.
-Returns:
-A UUID string uniquely identifying the registered affector.
-Return type:
-str
-
-
-
-
-
-unique_features
-
-
-
-
-
-
-class ionique.datatypes. TraceFile ( current : ndarray voltage = None rank = 'file' parent = None unique_features : dict = {} metadata : dict = {} )
-Bases: Segment
-Data structure for representing a single trace file with current and optional voltage information.
-This class inherits from Segment and encapsulates current and voltage data. It sets up segment metadata,
-defines parent-child hierarchy, and optionally initializes child segments based on voltage steps.
-Typical usage includes assigning metadata and organizing hierarchical
-data structures for downstream processing or visualization.
-
-
-__init__ ( current : ndarray voltage = None rank = 'file' parent = None unique_features : dict = {} metadata : dict = {} )
-If voltage steps are provided, corresponding child segments of rank “vstep” are created.
-
-Parameters:
-
-current (numpy.ndarray ) – current array.
voltage (list [ tuple [ tuple [ int , int ] , float ] ] or None ) – Optional list of tuples containing (start, end) index pairs and voltage values.
-Used to create child segments for each voltage step.
rank (str ) – Segment rank label, defaults to “file”.
parent (Segment or None ) – Optional parent segment to which this trace belongs.
unique_features (dict ) – Dictionary of metadata such as sampling frequency.
metadata (dict ) – Additional metadata.
-
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-delete ( )
-Delete the current object, removing it from its parent
-
-
-
-
-end : int | None
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-plot ( rank axes downsample_per_rank color_per_rank )
-Method for plotting
-
-
-
-
-rank : str
-
-
-
-
-start : int | None
-
-
-
-
-
-
-ionique.io
-Input/output utilities for data files.
-This module provides an interface for loading, parsing, and preprocessing
-signal data from various file formats (e.g., .edh , .opt , .abf , .dat , .xml ). It defines a base reader class
-and concrete implementations that handle format-specific logic, metadata extraction,
-data alignment, and optional preprocessing steps.
-This module is central to converting raw experimental data into analyzable form.
-
-
-class ionique.io. AbstractFileReader
-Bases: object
-An abstract class for reading various data files (.abf, .edh, .mat, .opt, .xml)
-This class defines the structure for file readers
-
-
-__init__ ( )
-
-
-
-
-accepted_keywords = []
-
-
-
-
-current_multiplier : float = 1.0
-
-
-
-
-ext = '___'
-
-
-
-
-read ( filename : str ** kwargs )
-Read a datafile or series of files. Files are identified according to their extension.
-Data formats that come with a header file must be referred to by the header file.
-If inheriting from the AbstractFileReader class, do not override this function;
-instead, create a custom _read method.
-
-Parameters:
-filename – file name or list of file names, typically
-
-given as a string or PathLike object.
-:type filename: str or os.PathLike or list[str] or list[os.PathLike]
-:param **
-
-
-
-
-voltage_multiplier : float = 1.0
-
-
-
-
-
-
-class ionique.io. EDHReader ( edh_filename voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Bases: AbstractFileReader
-Reader class for loading .edh data files and their associated current data.
-This class parses the .edh header file and automatically loads the corresponding signal data
-(either from .abf or .dat files in the same directory). It extracts metadata, converts raw
-current and voltage data to SI units, and supports optional preprocessing steps such as
-voltage step segmentation, downsampling, and signal filtering.
-
-
-__init__ ( edh_filename voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Initialize the EDHReader and load signal data from associated files.
-
-Parameters:
-
-edh_filename (str ) – Path to the .edh header file.
voltage_compress (bool ) – If True, splits signal into segments based on voltage steps.
n_remove (int ) – Number of samples to remove from the beginning of each voltage step.
downsample (int ) – Downsampling factor to reduce data size.
prefilter (callable or None ) – Callable to apply preprocessing to the current signal.
-
-
-
-
-
-accepted_keywords = ['voltage_compress', 'n_remove', 'downsample', 'prefilter']
-
-
-
-
-current_multiplier : float = 1e-09
-
-
-
-
-ext = '.edh'
-
-
-
-
-voltage_multiplier : float = 0.001
-
-
-
-
-
-
-class ionique.io. OPTReader ( opt_filename : str voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Bases: AbstractFileReader
-Reader for .opt data files with corresponding XML or _volt.opt metadata.
-This class reads current data from .opt files and attempts to extract or reconstruct
-corresponding voltage data using associated .xml or _volt.opt files found in the same
-directory. It handles metadata extraction, signal preprocessing, voltage alignment, and
-segment compression based on voltage steps.
-Supported XML structures include both standard HWtiming_cap_step formats and timestamp-based
-custom formats.
-
-
-__init__ ( opt_filename : str voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Initialize the OPTReader instance and load corresponding files.
-
-Parameters:
-
-opt_filename (str ) – Path to the .opt header file.
voltage_compress (bool ) – If True, splits signal into segments based on voltage steps.
n_remove (int ) – Number of samples to remove from the beginning of each voltage step.
downsample (int ) – Downsampling factor to reduce data size.
prefilter (callable or None ) – Callable to apply preprocessing to the current signal.
-
-
-
-
-
-accepted_keywords = ['voltage_compress', 'n_remove', 'downsample', 'prefilter']
-
-
-
-
-current_multiplier : float = 1000000000.0
-
-
-
-
-ext = '.opt'
-
-
-
-
-find_peaks_in_segment ( current_data start_index end_index )
-Apply “find_peaks” on a segment of the current data and return
-the indices of the found peak
-
-
-
-
-find_peaks_slide_window ( current start_index window_shift_duration = 0.002 sampling_frequency = 250000 sign = 'negative' )
-Find peaks in the current every time the voltage changes.
-The search for the peaks happens in the window of the timestamp 0.02 seconds.
-
-
-
-
-process_custom_xml ( )
-Processes the custom XML file to extract voltage data, align the voltage waveform
-with the current, and detect peaks in the current signal every time the voltage changes.
-
-
-
-
-
-
-ionique.io. load_files_GUI ( )
-
-Create a form with:
-‘Compress voltage (only uncheck if voltage is not stepwise, e.g., triangle wave)’ : checkbox
‘Apply Filter?’ : checkbox
If ‘Apply Filter?’ checked: dynamic form from the Filter dataclass (types, options, limits).
-Returns (frame, get_values) where get_values() -> dict with: {‘compress_voltage’: bool, ‘apply_filter’: bool, ‘filter’: Filter|None}
-
-
-
-
-
-ionique.io. select_files_GUI ( )
-Open a file dialog allowing multiple selection, but only for ONE file type at a time.
-Users can switch the dialog’s file-type dropdown, but the final selection must be
-homogeneous (all the same extension) and in the allowed list.
-
-Returns
-
-tuple[str, …] Absolute paths of the selected files (empty tuple if the user cancels).
-
-
-
-
-
-
-ionique.parsers
-Parser module for event and state segmentation in current recordings.
-This module provides a framework for parsing current trace data. It includes an abstract base Parser class
-and multiple concrete parser implementations for detecting spikes, noise, square pulses, and other event
-structures in ionic current signals.
-Some of the parsers and the parser base class were adapted from the
-PyPore Package by Jacob Schreiber and Kevin Karplus (https://github.com/jmschrei/PyPore )
-
-
-class ionique.parsers. AutoSquareParser ( threshold_baseline = 0.7 expected_conductance = 1.9 conductance_tolerance = 1.2 wrap_padding : int = 50 rules = [] )
-Bases: Parser
-Class for building a straightforward pipeline of data analysis
-to make it reusable with customized parameters
-
-
-__init__ ( threshold_baseline = 0.7 expected_conductance = 1.9 conductance_tolerance = 1.2 wrap_padding : int = 50 rules = [] )
-Initialize a Parser instance
-
-
-
-
-parse ( current eff_sampling_freq voltage )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current', 'eff_sampling_freq', 'voltage']
-
-
-
-
-
-
-class ionique.parsers. FilterDerivativeSegmenter ( low_threshold = 1 high_threshold = 2 cutoff_freq = 1000.0 sampling_freq = 100000.0 )
-Bases: Parser
-This parser will segment an event using a filter-derivative method. It will
-first apply a bessel filter at a certain cutoff to the current, then it will
-take the derivative of that, and segment when the derivative passes a
-threshold.
-
-
-__init__ ( low_threshold = 1 high_threshold = 2 cutoff_freq = 1000.0 sampling_freq = 100000.0 )
-Initialize a Parser instance
-
-
-
-
-parse ( current )
-Apply the filter-derivative method to filter the ionic current.
-
-
-
-
-set_params ( )
-
-Returns:
-
-
-
-
-
-
-
-
-class ionique.parsers. IVCurveAnalyzer ( current parent_segment sampling_frequency method = 'simple' )
-Bases: Parser
-THIS CLASS IS NOT IMPLEMENTED YET. DO NOT USE.
-Check for at Least Two Voltage Steps: confirm that the segment has at least two voltage steps.
-Divide into Voltage Segments: separate data into voltage segments.
-Call IVAnalyzer on Parent Segment: compute the mean and std of the current in each
-voltage segment.
-Return an Array of (voltage: (imean, istd)) pair:
-Also mean and std should be calulated in two ways
-
-
-__init__ ( current parent_segment sampling_frequency method = 'simple' )
-Initialize a Parser instance
-
-
-
-
-analyze ( )
-Analyze each voltage step in a parent segment
-
-
-
-
-
-
-class ionique.parsers. IVCurveParser ( voltage )
-Bases: object
-THIS CLASS IS NOT IMPLEMENTED YET! DO NOT USE.
-The class handles:
-
-
-Identifies a file where an IV curve is recorded based on the pattern.
-
-Here, the goal is to feed the voltage information to a module and have it search and do pattern matching to find one of
-these possibilities. .This can be a parser that accepts voltage array or compressed voltage as one of the inputs.
-
-
-__init__ ( voltage )
-
-
-
-
-parse ( )
-Searches for a pattern in the voltage data to identify IV.
-If none exists, the given datafile does not contain IV curves.
-If one or multiple IV curves are found, it returns start and
-end regions of each of them.
-Returns: list of indices: start and end for each deteced IV curve
-
-
-
-
-
-
-class ionique.parsers. MemoryParse ( starts ends )
-Bases: object
-A parser based on being fed previous split points, and splitting a raw file based
-those splits. Used predominately when loading previous split points from the
-database cache, to reconstruct a parsed file from “memory.
-
-
-__init__ ( starts ends )
-
-Parameters:
-
-
-
-
-
-
-
-parse ( current )
-
-
-
-
-
-
-class ionique.parsers. NoiseFilterParser ( noise_threshold = 60 detect_noise = True )
-Bases: Parser
-This unit is a parser subclass. when invoked on a segment (whole file or subsegment),
-it identifies the areas where bad things happened, and areas where the data is clean.
-Examples of bad things include high noise (60Hz noise from opening the cage), membrane
-rupture, and membrane formation attempts. it produces children from the good
-regions of the data and ignore the bad regions.
-
-
-__init__ ( noise_threshold = 60 detect_noise = True )
-Initialize a Parser instance
-
-
-
-
-parse ( data segment_start = 0 segment_end = None )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-
-
-class ionique.parsers. Parser
-Bases: ABC
-This class defines the interface and shared utility methods for parser implementations.
-Parsers are used to segment ionic current data
-into structured regions of interest (events).
-Subclasses should implement the parse method and define any additional logic
-required to detect features in the data.
-
-
-abstract __init__ ( )
-Initialize a Parser instance
-
-
-
-
-classmethod from_json ( _json )
-Critical: Not implemented yet, figure out how to make this work in pythion
-
-
-
-
-classmethod get_init_params ( )
-Get the initialization parameters of the parser class.
-
-Returns:
-A dictionary of initialization parameters, default None.
-Return type:
-dict or None
-
-
-
-
-
-classmethod get_name ( )
-Get the name of the parser class
-:return: The name of the class.
-:rtype: str
-
-
-
-
-classmethod get_process_inputs ( )
-
-
-
-
-classmethod get_process_outputs ( )
-Get the outputs generated by processing
-
-Returns:
-A list of outputs generated by parse method
-Return type:
-list or None
-
-
-
-
-
-get_required_parent_attributes ( )
-Get the list of required attributes from the parent segment
-
-Returns:
-A list of required parent attributes
-Return type:
-list[str]
-
-
-
-
-
-parse ( ** kwargs )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current']
-
-
-
-
-set_params ( param_dict )
-Updates each paramater presented in the GUI to the value input in the lineEdit
-corresponding to that value.
-:param param_dict: A dictionary
-:type param_dict: dict
-
-
-
-
-to_dict ( )
-Convert the parser’s attributes to a dictionary.
-
-Returns:
-A dictionary representation of the parser’s attributes.
-Return type:
-dict
-
-
-
-
-
-to_json ( filename = False )
-Convert the parser’s attributes to a JSON string.
-
-Parameters:
-filename (str , default bool ) – If filename, it writes the JSON string to the specified file.
-Returns:
-A dictionary representation of the parser’s attributes.
-Return type:
-str
-
-
-
-
-
-
-
-class ionique.parsers. SpeedyStatSplit ( sampling_freq min_width = 100 max_width = 1000000 window_width = 10000 min_gain_per_sample = None false_positive_rate = None prior_segments_per_second = None cutoff_freq = None )
-Bases: Parser
-A parser wrapper for the Cython-accelerated FastStatSplit algorithm.
-See cparsers.pyx FastStatSplit for full documentation. This is just a
-wrapper for the cyton implementation to add a GUI.
-
-
-__init__ ( sampling_freq min_width = 100 max_width = 1000000 window_width = 10000 min_gain_per_sample = None false_positive_rate = None prior_segments_per_second = None cutoff_freq = None )
-Initialize the SpeedyStatSplit instance
-
-Parameters:
-
-sampling_freq (int ) – The sampling frequency of the data
min_width (int , optional ) – Minimum width of a segment, default=100
max_width (int , optional ) – Maximum width of a segment, default=1000000.
window_width (int , optional ) – Width of the window, defaults to 10000.
min_gain_per_sample (float or None , optional ) – Minimum gain per sample, default=None
false_positive_rate (float or None , optional ) – Expected false positive rate , default=None
prior_segments_per_second (float or None , optional ) – Prior number of segments per second, default=None.
cutoff_freq (float or None , optional ) – Cutoff frequency for filtering, default=None
-
-
-
-
-
-best_single_split ( current )
-Determine the best single split point in the current data
-
-Parameters:
-current (numpy.ndarray ) – The current data to be analyzed.
-Returns:
-The best split point
-Return type:
-int or None
-
-
-
-
-
-parse ( current )
-Parse the current data to detect segments using the FastStatSplit
-
-Parameters:
-current (numpy.ndarray ) – The current data to be parsed
-Returns:
-A list of detected segments
-Return type:
-
-
-
-
-
-
-parse_meta ( current )
-Parse the current data and extract meta information
-
-Parameters:
-current (numpy.ndarray ) – The current data to be parsed.
-
-
-
-
-
-set_params ( )
-Set the parameters
-
-
-
-
-uiTemplate = [('sampling_freq', 'spin', {'bounds': [0, None], 'dec': True, 'siPrefix': True, 'step': 1, 'suffix': 'Hz', 'value': 200000}), ('min_width', 'spin', {'dec': True, 'int': True, 'min': 2, 'step': 1, 'value': 100}), ('max_width', 'spin', {'dec': True, 'int': True, 'step': 1, 'value': 1000000}), ('window_width', 'spin', {'dec': True, 'int': True, 'step': 1, 'value': 10000}), ('cutoff_freq', 'spin', {'hidden': True})]
-
-
-
-
-
-
-class ionique.parsers. SpikeParser ( height = None threshold = None distance = None prominence = None prominence_snr = None width = None wlen = None rel_height : float = 0.5 plateu_size = None fractional : bool = True )
-Bases: Parser
-A parser for detecting spike-like events in ionic current data.
-This parser wraps scipy.signal.find_peaks , supporting thresholds
-for features like peak height, prominence, and width. It computes event characteristics such
-as dwell time, relative prominence, and peak positions.
-
-
-__init__ ( height = None threshold = None distance = None prominence = None prominence_snr = None width = None wlen = None rel_height : float = 0.5 plateu_size = None fractional : bool = True ) → None
-Initialize the SpikeParser.
-
-Parameters:
-
-height (float or tuple or None ) – Minimum spike height.
threshold (float or tuple or None ) – Minimum vertical threshold.
distance (float or tuple or None ) – Minimum horizontal distance between peaks.
prominence (float or tuple or None ) – Minimum peak prominence.
prominence_snr (tuple or None ) – Alternative to prominence, specified as a tuple of signal-to-noise (SNR) ratios.
width (float or tuple or None ) – Full width at half maximum of the peak.
wlen (float or None ) – Width of the window used to calculate prominence.
rel_height (float ) – Relative height used in width calculation (range 0 to 1).
plateu_size (float or None ) – Minimum length of a flat-topped peak.
fractional (bool ) – Whether to interpret height, prominence, etc., as a fraction of the signal mean.
-
-
-
-
-
-parse ( current sampling_freq mean std start )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current', 'sampling_freq', 'mean', 'start', 'std']
-
-
-
-
-
-
-class ionique.parsers. lambda_event_parser ( threshold = 90 rules = None )
-Bases: Parser
-A simple rule-based parser which defines events as a sequential series
-of points which are below a certain threshold, then filtered based on other
-critereon such as total time or minimum current. Rules can be passed in at
-initiation, or set later, but must be a lambda function takes in a PreEvent
-object and performs some boolean operation.
-
-
-__init__ ( threshold = 90 rules = None )
-
-Parameters:
-
-threshold (float ) – The threshold for detecting events (e.g., current < threshold).
rules (list [ callable ] or None ) – A list of lambda functions that accept an event and return a bool.
-
-
-
-
-
-parse ( current )
-Perform a large capture of events by creating a boolean mask for when
-the current is below a threshold, then detecting the edges in those masks,
-and using the edges to partitition the sample. The events are
-then filtered before being returned.
-
-
-
-
-set_params ( )
-Read in the data from the GUI and use it to customize
-the rules or threshold of the parser.
-
-
-
-
-
-
-ionique.parsers. pairwise ( iterable )
-
-s -> (s0,s1), (s1,s2), (s2, s3), …
-
-
-Parameters:
-iterable
-Returns:
-
-
-
-
-
-
-class ionique.parsers. snakebase_parser ( threshold = 1.5 )
-Bases: Parser
-A simple parser based on dividing when the peak-to-peak
-amplitude of a wave exceeds a certain threshold.
-
-
-__init__ ( threshold = 1.5 )
-Initialize a Parser instance
-
-
-
-
-parse ( current )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-set_params ( )
-Updates each paramater presented in the GUI to the value input in the lineEdit
-corresponding to that value.
-:param param_dict: A dictionary
-:type param_dict: dict
-
-
-
-
-
-
-ionique.utils
-Signal Processing and Data Extraction Utilities
-This module provides a set of utility functions and classes designed for use in signal
-processing workflows.
-These tools are intended for both internal processing and external use cases such as analysis
-pipelines and API integrations.
-
-
-class ionique.utils. Filter ( cutoff_frequency : float filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ] filter_method : Literal [ 'butter' , 'bessel' ] = 'butter' bidirectional : bool = True sampling_frequency : float = None )
-Bases: object
-This class allows a user to apply low-pass, high-pass,
-band-pass, or band-stop filters using standard filter types (Butterworth or Bessel).
-Filters are implemented using second-order sections (SOS) for numerical stability and
-can be applied in either forward-only or bidirectional mode.
-
-Parameters:
-
-cutoff_frequency (float or list [ float ] ) – The cutoff frequency or frequency band for the filter in Hz.
-For band filters, provide the band center or list of [low, high] values.
filter_type (Literal [ "lowpass" , "highpass" , "bandpass" , "bandstop" ] ) – The type of filter to apply. Options are “lowpass”, “highpass”, “bandpass”, or “bandstop”.
filter_method (Literal [ "butter" , "bessel" ] ) – The filter method. Supported options: “butter” (Butterworth), “bessel”. Defaults to “butter”.
order (int ) – The order of the filter. Must be >= 1. Defaults to 2.
bidirectional (bool ) – If True, applies filtering forward and backward using sosfiltfilt .
-If False, uses causal sosfilt . Defaults to True.
sampling_frequency (float , optional ) – Sampling frequency of the signal in Hz.
-
-
-
-__init__ ( cutoff_frequency : float filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ] filter_method : Literal [ 'butter' , 'bessel' ] = 'butter' bidirectional : bool = True sampling_frequency : float = None ) → None
-
-
-
-
-bidirectional : bool = True
-
-
-
-
-cutoff_frequency : float
-
-
-
-
-filter_method : Literal [ 'butter' , 'bessel' ] = 'butter'
-
-
-
-
-filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ]
-
-
-
-
-sampling_frequency : float = None
-
-
-
-
-
-
-class ionique.utils. Singleton ( * args ** kwargs )
-Bases: type
-Generic singleton metaclass.
-This metaclass ensures the same return every time the class is instanced.
-
-
-__init__ ( * args ** kwargs )
-Class initialization
-
-Parameters:
-
-
-
-
-
-
-
-
-
-class ionique.utils. Trimmer ( samples_to_remove : int rank : str = 'vstep' newrank : str = 'vstepgap' )
-Bases: object
-Segment trimming utility for hierarchical signal data.
-This class operates on a segment tree. It traverses segments of a given rank (e.g., “vstep”) and trims
-a fixed number of samples from the start of each segment. The resulting trimmed segments are added
-as new child segments with a specified new rank (e.g., “vstepgap”).
-This is useful when initial samples of each segment include artifacts that should be excluded from analysis.
-
-Parameters:
-
-samples_to_remove (int ) – Number of samples to trim from the beginning of each segment.
rank (str ) – The hierarchical rank of segments to target for trimming. Defaults to “vstep”.
newrank (str ) – The rank name to assign to newly created trimmed child segments. Defaults to “vstepgap”.
-
-
-
-__init__ ( samples_to_remove : int rank : str = 'vstep' newrank : str = 'vstepgap' ) → None
-
-
-
-
-newrank : str = 'vstepgap'
-
-
-
-
-rank : str = 'vstep'
-
-
-
-
-samples_to_remove : int
-
-
-
-
-
-
-Extract features from hierarchical segments into a DataFrame.
-Traverses a hierarchical segment structure down to bottom_rank , then collects feature values
-from each segment. Static features are retrieved via get_feature() , constants can be added via
-add_ons , and custom computed features can be provided through lambdas .
-This is useful for generating structured datasets from annotated traces for statistical analysis or
-machine learning.
-
-Parameters:
-
-seg (object ) – The root segment or trace object containing a hierarchical structure. It must implement traverse_to_rank() and support get_feature() .
bottom_rank (str ) – The rank name of the lowest-level segments from which to extract features.
extractions (list [ str ] ) – List of feature names to extract directly using get_feature() on each segment.
-Common examples include: ‘mean’, ‘frac’, ‘duration’, ‘baseline’, ‘current’, ‘wrap’, ‘start’.
add_ons (dict ) – A dictionary of fixed key-value pairs to include as constant columns in the resulting DataFrame.
lambdas (dict ) – A dictionary mapping column names to lambda functions that compute derived values from each segment.
-Returns:
-A pandas DataFrame where each row corresponds to a bottom-rank segment and columns represent extracted and computed features.
-Return type:
-pandas.DataFrame
-
-
-
-
-
-ionique.utils. si_eval ( value unit = None return_unit = False )
-Evaluate and convert a value with an SI unit prefix to its numeric base value.
-This function handles both string-based and numeric values with
-SI (International System of Units) prefixes such as “k” (kilo), “M” (mega), “μ” (micro), etc.
-It multiplies the input value by the appropriate factor based on the SI prefix.
-This utility is useful when parsing human-readable measurement strings or standardizing
-units across data pipelines that mix strings and numeric formats.
-
-Parameters:
-
-value (str or float ) – The value to be converted. Can be a string like “1.2 kHz” or a numeric value (int or float).
unit (str , optional ) – The unit string (e.g., “kHz”, “mV”). Required only if value is a numeric type.
return_unit (bool , optional ) – If True, returns a tuple containing the converted numeric value and the base unit (e.g., ‘Hz’).
-If False, only the numeric value is returned.
-Raises:
-
-
-Returns:
-The converted value, optionally paired with the base unit.
-Return type:
-float or tuple[float, str]
-
-
-
-
-
-ionique.utils. split_voltage_steps ( voltage : ndarray n_remove = 0 as_tuples = False )
-Split a voltage signal into segments based on step changes.
-This function detects changes in the voltage signal and splits it into individual
-segments (or steps) wherever a change in value occurs. It is useful in analyzing
-stepwise voltage protocols.
-Optionally, a number of initial samples can be removed from each segment using the
-n_remove parameter. The output can either be two separate arrays of start and end
-indices, or a list of tuples representing each segment.
-
-Parameters:
-
-voltage (numpy.ndarray ) – 1D voltage signal array to be segmented.
n_remove (int ) – Number of samples to remove from the start of each split. Defaults to 0.
as_tuples (bool ) – If True, returns a list of (start, end) index tuples. If False, returns two arrays: start_indices and end_indices. Defaults to False.
-Raises:
-ValueError – If n_remove is negative or larger than the start of the voltage changes.
-Returns:
-Either:
-- A tuple of (start_indices, end_indices), where each is a numpy array of indices.
-- A list of (start, end) tuples if as_tuples=True .
-Return type:
-tuple[numpy.ndarray, numpy.ndarray] or list[tuple[int, int]]
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Install Ionique — ionique 0.1.0 documentation
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-Install Ionique
-This guide walks you through the steps to download, set up, and install the Ionique package using a virtual environment.
-
-1. Download the Ionique Repository
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-2. Open the Ionique Folder in VS Code
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-Now your working directory in VS Code is set to the ionique
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-3. Set Up a Virtual Environment
-A virtual environment is an isolated workspace for installing project-specific dependencies.
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-Step 1: Open Terminal in VS Code
-
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-Step 2: Confirm You’re in the Correct Directory
-Use the following command:
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-You should see the contents of the ionique
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-Step 3: Create and Activate the Environment
-Run the command:
- -m venv ionique_env
-
-
-Note: On some systems (especially Linux/macOS), you may need to use python3 pip3 python pip
-
-Then activate it:
-
-Linux / macOS:
-source ionique_env/bin/activate
-
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Windows:
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-If activated successfully, you’ll see (ionique_env)
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-4. Install the Ionique Package
-Run the command:
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-Wait a few minutes while dependencies install.
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-If you see a similar message, congratulations — you’ve successfully installed the Ionique package!
-
-
-
-5. (Optional) Install JupyterLab
-To run data analysis in an interactive interface like Jupyter Notebook, install JupyterLab while the virtual environment is still active:
-
-
-
-6. Deactivate the Environment
-When you’re done, deactivate the virtual environment by typing:
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Nanopore Signal Analysis Framework — ionique 0.1.0 documentation
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Python Module Index — ionique 0.1.0 documentation
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Guide for VSCode and Python Installation — ionique 0.1.0 documentation
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-Guide for VSCode and Python Installation
-This guide is designed for users who do not have Python or Visual Studio Code installed on their machine.
-
-Note: The guide is divided into three parts: Windows , Linux and macOS users. Please follow the appropriate instructions for your operating system.
-
-First, you need a platform (Integrated Development Environment) to run Ionique . Visual Studio Code (VS Code) is a convenient IDE to start with.
-
-Installing VS Code
-
-Windows Users
-1. Install VS Code
-
-Go to the VS Code website , download.
Run the installer.
On the first screen, check the boxes Add Python to PATH and Register code … .
-
Finish installation.
-
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-2. Install Required Extensions
-A few package requirements include C++ extensions and compilers.
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-4. Install Python.
-
-Skip this step if you already have Python!
-Visit Python Downloads for Windows .
Choose a version between 3.10 and 3.12 and download.
Run the installer.
On the first screen, check the box for “Add Python to PATH” .
Click Install Now .
-
-
-
-Linux Users
-
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-1. Download and Install VS_Code
-
-
-
-2. Open a Terminal
-You can search for “shell” or use the shortcut Ctrl + Alt + T
-Check your current location:
-
-Navigate to the Downloads folder (or wherever you saved the .deb
-
-List files to confirm:
-
-Install VS Code:
- dpkg -i code_1.XXX.deb
-
-
-Replace code_1.XXX.deb
-
-
-
-3. Install Python
-
-Skip this step if you already have Python!
-Visit the Python Downloads page .
Download a version between 3.10 and 3.12 .
Use your package manager or install from source.
-For Ubuntu-based systems:
- apt update
-sudo apt install python3.XX
-
-
-Replace XX
-
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-macOS Users
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-1. Install VS Code
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-2. Install Python
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Search — ionique 0.1.0 documentation
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-Search.setIndex({"alltitles": {"1. Download and Install VS_Code": [[16, "download-and-install-vs-code"]], "1. Download the Ionique Repository": [[14, "download-the-ionique-repository"]], "1. Install VS Code": [[16, "install-vs-code"], [16, "id3"]], "2. Install Python": [[16, "id4"]], "2. Install Required Extensions": [[16, "install-required-extensions"]], "2. Open a Terminal": [[16, "open-a-terminal"]], "2. Open the Ionique Folder in VS Code": [[14, "open-the-ionique-folder-in-vs-code"]], "3. Install Compilers (Build Tools)": [[16, "install-compilers-build-tools"]], "3. Install Python": [[16, "id1"]], "3. Set Up a Virtual Environment": [[14, "set-up-a-virtual-environment"]], "4. Install Python.": [[16, "install-python"]], "4. Install the Ionique Package": [[14, "install-the-ionique-package"]], "5. (Optional) Install JupyterLab": [[14, "optional-install-jupyterlab"]], "6. Deactivate the Environment": [[14, "deactivate-the-environment"]], "API Reference": [[6, null]], "Contents:": [[10, null]], "Data Input": [[7, null]], "Features": [[10, "features"]], "Filtering with Filter class": [[19, "filtering-with-filter-class"]], "Future Development": [[10, "future-development"]], "Guide for VSCode and Python Installation": [[16, null]], "Guides": [[10, null]], "Indices and tables": [[10, "indices-and-tables"]], "Install": [[12, null]], "Install Ionique": [[14, null]], "Install ionique": [[12, "install-ionique"]], "Installation (Development)": [[10, "installation-development"]], "Installation for beginners": [[11, null]], "Installation from Source": [[12, "installation-from-source"]], "Installing VS Code": [[16, "installing-vs-code"]], "Ionique: A Nanopore Signal Analysis Framework": [[10, null]], "Module contents": [[13, "module-ionique"]], "Modules": [[13, "modules"]], "Nanopore Signal Analysis Framework": [[15, null], [15, null]], "Overview": [[10, "overview"]], "Returns": [[3, "returns"], [13, "returns"]], "Signal Analysis": [[18, null]], "Signal Preprocessing": [[19, null]], "Skip this step if you already have Python!": [[16, "skip-this-step-if-you-already-have-python"], [16, "id2"], [16, "id5"]], "Step 1: Install Python": [[11, "step-1-install-python"]], "Step 1: Open Terminal in VS Code": [[14, "step-1-open-terminal-in-vs-code"]], "Step 2: Confirm You\u2019re in the Correct Directory": [[14, "step-2-confirm-you-re-in-the-correct-directory"]], "Step 2: GitHub": [[11, "step-2-github"]], "Step 3: Create and Activate the Environment": [[14, "step-3-create-and-activate-the-environment"]], "Step 3: Moving Between Folders in the Terminal": [[11, "step-3-moving-between-folders-in-the-terminal"]], "Step 4: Virtual Environment": [[11, "step-4-virtual-environment"]], "Step 5: Run the Notebooks": [[11, "step-5-run-the-notebooks"]], "Step 6: Finish Workflow": [[11, "step-6-finish-workflow"]], "Summary": [[19, "summary"]], "Trimming with 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-To analyse the signal data, ionique invokes parsers module.
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-The statistical measures are computed for each segment
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-Signal preprocessing is an essential step before downstream analysis of nanopore signals.
-It typically involves filtering, trimming, downsampling, or compressing signal segments
-to reduce noise and improve performance.
-This page describes the following functionalities of Ionique:
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-Filtering with Filter class
-The Filter class allows users to apply different types of filters on current traces:
-Supported filter types:
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-lowpass
highpass
bandpass
bandstop
-Supported filter methods:
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-Filtering is applied using scipy.signal.sosfiltfilt (zero-phase, bidirectional) or
-scipy.signal.sosfilt depending on bidirectional=True/False .
-Usage Example:
-filt = Filter (
- cutoff_frequency = [ 0.1 , 10 ],
- filter_type = "bandpass" ,
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- order = 3 ,
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- sampling_frequency = 10000
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-filt ( current_trace )
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-Trimming with Trimmer class
-The Trimmer class removes a fixed number of samples from the start of each segment
-at a given hierarchical rank (e.g., “vstep”). This is useful for excluding artifacts
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-Usage Example:
-trimmer = Trimmer ( samples_to_remove = 200 )
-trimmer ( trace_file )
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-Summary
-Both Filter and Trimmer are designed to be composable tools for preprocessing current traces before
-event detection and feature extraction. You can chain them together in the analysis workflows.
-These operations are performed using tools in utils.py
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Guide for VSCode and Python Installation — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-Guide for VSCode and Python Installation
-This guide is designed for users who do not have Python or Visual Studio Code installed on their machine.
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-Note: The guide is divided into three parts: Windows , Linux and macOS users. Please follow the appropriate instructions for your operating system.
-
-First, you need a platform (Integrated Development Environment) to run Ionique . Visual Studio Code (VS Code) is a convenient IDE to start with.
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-Installing VS Code
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-Windows Users
-1. Install VS Code
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-Go to the VS Code website , download.
Run the installer.
On the first screen, check the boxes Add Python to PATH and Register code … .
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Finish installation.
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-2. Install Required Extensions
-A few package requirements include C++ extensions and compilers.
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-4. Install Python.
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-Skip this step if you already have Python!
-Visit Python Downloads for Windows .
Choose a version between 3.10 and 3.12 and download.
Run the installer.
On the first screen, check the box for “Add Python to PATH” .
Click Install Now .
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-Install VS Code:
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-3. Install Python
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-Skip this step if you already have Python!
-Visit the Python Downloads page .
Download a version between 3.10 and 3.12 .
Use your package manager or install from source.
-For Ubuntu-based systems:
- apt update
-sudo apt install python3.XX
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-Replace XX
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-macOS Users
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-Download VS Code for Mac from here .
Open your browser’s download list and find the installer.
If it’s a .zip
Drag Visual Studio Code.app Applications folder.
Open VS Code from the Applications folder or Launchpad.
-
-
-2. Install Python
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-Skip this step if you already have Python!
-Visit the Python Downloads page .
Choose a version between 3.10 and 3.12 .
Double-click the downloaded .pkg
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-To analyse the signal data, ionique invokes parsers module.
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Signal Preprocessing — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-Signal Preprocessing
-Signal preprocessing is an essential step before downstream analysis of nanopore signals.
-It typically involves filtering, trimming, downsampling, or compressing signal segments
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-This page describes the following functionalities of Ionique:
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-Filtering with Filter class
-The Filter class allows users to apply different types of filters on current traces:
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diff --git a/docs/_build/html/v0.3.0/_sources/api/core.rst.txt b/docs/_build/html/v0.3.0/_sources/api/core.rst.txt
deleted file mode 100644
index 868dd04..0000000
--- a/docs/_build/html/v0.3.0/_sources/api/core.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-ionique.core
-===========
-
-.. automodule:: ionique.core
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/docs/_build/html/v0.3.0/_sources/api/cparsers.rst.txt b/docs/_build/html/v0.3.0/_sources/api/cparsers.rst.txt
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--- a/docs/_build/html/v0.3.0/_sources/api/cparsers.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-ionique.cparsers
-===============
-
-.. automodule:: ionique.cparsers
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/docs/_build/html/v0.3.0/_sources/api/datatypes.rst.txt b/docs/_build/html/v0.3.0/_sources/api/datatypes.rst.txt
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--- a/docs/_build/html/v0.3.0/_sources/api/datatypes.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-ionique.datatypes
-================
-
-.. automodule:: ionique.datatypes
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/docs/_build/html/v0.3.0/_sources/api/io.rst.txt b/docs/_build/html/v0.3.0/_sources/api/io.rst.txt
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--- a/docs/_build/html/v0.3.0/_sources/api/io.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-ionique.io
-==========
-
-.. automodule:: ionique.io
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/docs/_build/html/v0.3.0/_sources/api/parsers.rst.txt b/docs/_build/html/v0.3.0/_sources/api/parsers.rst.txt
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--- a/docs/_build/html/v0.3.0/_sources/api/parsers.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-ionique.parsers
-==============
-
-.. automodule:: ionique.parsers
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/docs/_build/html/v0.3.0/_sources/api/utils.rst.txt b/docs/_build/html/v0.3.0/_sources/api/utils.rst.txt
deleted file mode 100644
index 7394ac5..0000000
--- a/docs/_build/html/v0.3.0/_sources/api/utils.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-ionique.utils
-============
-
-.. automodule:: ionique.utils
- :members:
- :undoc-members:
- :show-inheritance:
-
diff --git a/docs/_build/html/v0.3.0/_sources/api_reference.rst.txt b/docs/_build/html/v0.3.0/_sources/api_reference.rst.txt
deleted file mode 100644
index 3ee5155..0000000
--- a/docs/_build/html/v0.3.0/_sources/api_reference.rst.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-API Reference
-===============================
-
-.. toctree::
- :maxdepth: 2
-
- api/io
- api/core
- api/parsers
- api/utils
- api/datatypes
- api/cparsers
diff --git a/docs/_build/html/v0.3.0/_sources/data_input.rst.txt b/docs/_build/html/v0.3.0/_sources/data_input.rst.txt
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--- a/docs/_build/html/v0.3.0/_sources/data_input.rst.txt
+++ /dev/null
@@ -1,32 +0,0 @@
-Data Input
-^^^^^^^^^^^
-
-**File Readers**
-
-Ionique supports multiple file formats commonly utilized in nanopore experiments:
-
-- **.edh** – Element Data Header files containing metadata and pointers to raw data
-- **.opt** – Orbit Potential files with recorded current and voltage data
-- **.abf** – Axon Binary Format files used by pClamp software
-- **.xml** – XML configuration and metadata files associated with experiments
-
-The framework provides specialized reader classes (subclasses of ``AbstractFileReader``) to handle each format’s
-structure and extract both metadata and raw signal data.
-
-**File Reading Process**
-
-1. Identify file format based on extension
-2. Use the appropriate reader class to extract metadata
-3. Load raw current and voltage traces
-4. Align voltage to raw current
-5. Convert values to standardized SI units
-6. Apply optional pre-processing (e.g., downsampling, filtering, trimming)
-
-**Example**
-
-.. code-block:: python
-
- from ionique.io import EDHReader
-
- reader = EDHReader("path/to/file.edh", downsample=5)
- metadata, current, voltage = reader
\ No newline at end of file
diff --git a/docs/_build/html/v0.3.0/_sources/event_detect.rst.txt b/docs/_build/html/v0.3.0/_sources/event_detect.rst.txt
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diff --git a/docs/_build/html/v0.3.0/_sources/getting_started.rst.txt b/docs/_build/html/v0.3.0/_sources/getting_started.rst.txt
deleted file mode 100644
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diff --git a/docs/_build/html/v0.3.0/_sources/index.rst.txt b/docs/_build/html/v0.3.0/_sources/index.rst.txt
deleted file mode 100644
index 144f10d..0000000
--- a/docs/_build/html/v0.3.0/_sources/index.rst.txt
+++ /dev/null
@@ -1,88 +0,0 @@
-.. ionique documentation master file, created by
- sphinx-quickstart on Tue Feb 11 10:00:50 2025.
- You can adapt this file completely to your liking, but it should at least
- contain the root `toctree` directive.
-
-Ionique: A Nanopore Signal Analysis Framework
-==========================================
-
-**Authors:**
-Ali Fallahi, Dinara Boyko
-
-**Date:**
-Feb 11, 2025
-
-**Version:**
-0.1.0 (Development)
-
-Overview
---------
-
-Ionique is a Python module for processing and analyzing nanopore signal data. It provides tools for reading, filtering, segmenting, and analyzing nanopore experimental data. The framework supports structured workflows for signal processing and quality control, allowing researchers to extract relevant information from raw current and voltage traces.
-
-Features
---------
-
-- **File Handling**
- - Supports `.edh`, `.opt`, and `.xml` formats.
- - Extracts raw current and voltage traces.
- - Parses experimental metadata for structured analysis.
-
-- **Signal Processing**
- - Segmentation and event detection.
-
-
-- **Analysis Modules**
- - IV curve computation and voltage pattern detection.
- - Step response and dwell-time analysis.
- - Customizable parsers and filters for flexible workflows.
-
-- **Usability**
- - Python library for scripting and automation.
-
-
-Installation (Development)
---------------------------
-
-To install the development version of Ionique, clone the repository and install it in editable mode:
-
-.. code-block:: bash
-
- git clone https://github.com/wanunulab/ionique.git
- cd ionique
- pip install ionique
-
-**Requirements:**
-- Python > 3.10
-- Dependencies listed in `requirements.txt`
-
-Future Development
-------------------
-
-- **Additional analysis modules** for extended event detection and pattern recognition.
-- **Integration with Jupyter Notebooks** for interactive visualization.
-
-.. toctree::
- :maxdepth: 2
- :caption: Contents:
-
- modules
-
- signal_process
- api_reference
-
-.. toctree::
- :maxdepth: 2
- :caption: Guides
-
- install_beginner.rst
- ionique_starter.md
- python_vscode_start.md
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/docs/_build/html/v0.3.0/_sources/install_beginner.rst.txt b/docs/_build/html/v0.3.0/_sources/install_beginner.rst.txt
deleted file mode 100644
index 5a410aa..0000000
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@@ -1,140 +0,0 @@
-Installation for beginners
-===========================
-
-Welcome to Ionique!
-------------------
-
-This tutorial is designed for non-coding users. If you have used Python and GitHub before, please proceed with the ``README.md`` instructions.
-
-Before diving into data analysis, let's first install Python, GitHub, and Jupyter Notebook.
-
-Step 1: Install Python
------------------------
-
-Download the package from `Python Downloads
`_.
-
-Choose the appropriate OS system for your machine. Then, scroll down to pick Python version **3.10** or later up to **3.12**.
-
-After Python has been successfully installed, we can go over some basic terminal commands.
-
-**Linux Users:**
- - In the search window, type **"shell"** or use the hotkey **Ctrl-Alt-T**.
-
-**MacOS Users:**
- - In the search window, type **"Terminal"**.
-
-**Windows Users:**
- - In the search window, type **"Terminal"**.
-
-To check the installation, type:
-
-.. code-block:: sh
-
- python -V
-
-Some versions of Python may require the following command:
-
-.. code-block:: sh
-
- python3 -V
-
-
-Step 2: GitHub
---------------
-
-First, you need to create an account by following the instructions on `GitHub
`_.
-
-Now, open the repository link: `Ionique Repository `_, click on the green **"Code"** button, and download the ZIP file. Unzip it.
-
-Move the folder to the desired location on your computer.
-
-Step 3: Moving Between Folders in the Terminal
-----------------------------------------------
-
-You can find a short list of basic commands here: `Linux vs Windows Commands `_.
-
-Let's create your first virtual environment!
-
-Run the following command:
-
-.. code-block:: sh
-
- python -m venv ionique_env
-
-Then, activate it:
-
-.. code-block:: sh
-
- source ionique_env/bin/activate # Linux/MacOS
- ionique_env\Scripts\activate.bat # Windows
-
-Now, install the Ionique requirements:
-
-.. code-block:: sh
-
- pip install .
-
-*If your OS and Python version require the ``python3`` command, use ``pip3`` instead.*
-
-Next, install JupyterLab:
-
-.. code-block:: sh
-
- pip install jupyterlab
-
-Step 5: Run the Notebooks
--------------------------
-
-Copy the notebook you want to run into the **data** folder. (You don’t need to use the terminal for this step.)
-
-Now, return to the terminal window and navigate to the folder where the copied notebook is stored.
-
-Then, run:
-
-.. code-block:: sh
-
- jupyter lab
-
-You are now ready to work on data analysis!
-
-Step 6: Finish Workflow
------------------------
-
-Once you are done with the analysis, go back to the terminal and press **Ctrl + C**.
-
-Then, type:
-
-.. code-block:: sh
-
- deactivate
-
-to deactivate the virtual environment.
-
-**Note:** Starting from **Step 5**, the procedure will be identical each time you want to run **Ionique**.
-
-Happy using Ionique!
diff --git a/docs/_build/html/v0.3.0/_sources/installation.rst.txt b/docs/_build/html/v0.3.0/_sources/installation.rst.txt
deleted file mode 100644
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@@ -1,29 +0,0 @@
-=======
-Install
-=======
-
-Install ionique
-============
-
-The most recent release of the ``ionique`` package can be installed using pip_
-
-.. code-block:: console
-
- $ pip install ionique
-
-If you are very new to coding, more detailed instructions, we provide guides on VSCode, Python3 and ionique installment.
-
-
-Installation from Source
-========================
-
-If you want to contribute to ionique you can build ionique from source using the
-instructions below.
-
-.. code-block:: console
-
- $ git clone https://github.com/wanunulab/ionique.git
- $ cd ionique
- $ pip install -e .
-
-
diff --git a/docs/_build/html/v0.3.0/_sources/ioniq.rst.txt b/docs/_build/html/v0.3.0/_sources/ioniq.rst.txt
deleted file mode 100644
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+++ /dev/null
@@ -1,61 +0,0 @@
-ionique
-=============
-
-Modules
-----------
-
-ionique.core
------------------
-
-.. automodule:: ionique.core
- :members:
- :undoc-members:
- :show-inheritance:
-
-ionique.cparsers
----------------------
-
-.. automodule:: ionique.cparsers
- :members:
- :undoc-members:
- :show-inheritance:
-
-ionique.datatypes
-----------------------
-
-.. automodule:: ionique.datatypes
- :members:
- :undoc-members:
- :show-inheritance:
-
-ionique.io
----------------
-
-.. automodule:: ionique.io
- :members:
- :undoc-members:
- :show-inheritance:
-
-ionique.parsers
---------------------
-
-.. automodule:: ionique.parsers
- :members:
- :undoc-members:
- :show-inheritance:
-
-ionique.utils
-------------------
-
-.. automodule:: ionique.utils
- :members:
- :undoc-members:
- :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: ionique
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/docs/_build/html/v0.3.0/_sources/ionique_starter.md.txt b/docs/_build/html/v0.3.0/_sources/ionique_starter.md.txt
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index 6d4c279..0000000
--- a/docs/_build/html/v0.3.0/_sources/ionique_starter.md.txt
+++ /dev/null
@@ -1,115 +0,0 @@
-# Install Ionique
-
-This guide walks you through the steps to download, set up, and install the Ionique package using a virtual environment.
-
----
-
-## 1. Download the Ionique Repository
-
-- Open the WanunuLab GitHub repository:
- https://github.com/wanunulab/ionique
-
-- Click the green **Code** button and choose **Download ZIP**.
-
-
-
-- Unzip the folder and move it to your desired location (e.g., Desktop or Documents).
-
----
-
-## 2. Open the Ionique Folder in VS Code
-
-- Launch the **VS Code** application.
-- Click **File > Open...** and select the unzipped `ionique` folder.
-
-
-
-Now your working directory in VS Code is set to the `ionique` folder.
-
----
-
-## 3. Set Up a Virtual Environment
-
-A **virtual environment** is an isolated workspace for installing project-specific dependencies.
-
-> Learn more: [Python Virtual Environments](https://docs.python.org/3/tutorial/venv.html)
-
-### Step 1: Open Terminal in VS Code
-
-- Go to the **top menu** and click **Terminal > New Terminal**.
-
-
-
-### Step 2: Confirm You're in the Correct Directory
-
-Use the following command:
-
-```bash
-ls
-```
-
-You should see the contents of the `ionique` folder.
-
-
-### Step 3: Create and Activate the Environment
-
-Run the command:
-
-```bash
-python -m venv ionique_env
-```
-> **Note:** On some systems (especially Linux/macOS), you may need to use `python3` and `pip3` instead of `python` and `pip`.
-
-Then activate it:
-
-- **Linux / macOS:**
- ```bash
- source ionique_env/bin/activate
- ```
-
-- **Windows:**
- ```bat
- ionique_env\Scripts\activate
- ```
-
-If activated successfully, you’ll see `(ionique_env)` appear before your username in the terminal.
-
-
-
----
-
-## 4. Install the Ionique Package
-
-Run the command:
-
-```bash
-pip install .
-```
-
-Wait a few minutes while dependencies install.
-
-
-
-> If you see a similar message, congratulations — you've successfully installed the Ionique package!
-
----
-
-## 5. (Optional) Install JupyterLab
-
-To run data analysis in an interactive interface like Jupyter Notebook, install JupyterLab while the virtual environment is still active:
-
-```bash
-pip install jupyterlab
-```
-
----
-
-## 6. Deactivate the Environment
-
-When you're done, deactivate the virtual environment by typing:
-
-```bash
-deactivate
-```
-
-> It's good practice to **activate** your virtual environment when working on a project and **deactivate** it when you're finished.
diff --git a/docs/_build/html/v0.3.0/_sources/modules.rst.txt b/docs/_build/html/v0.3.0/_sources/modules.rst.txt
deleted file mode 100644
index 891d39b..0000000
--- a/docs/_build/html/v0.3.0/_sources/modules.rst.txt
+++ /dev/null
@@ -1,21 +0,0 @@
- Ionique
-=====
-
-`Click here for an introduction to nanopore sequencing and signal analysis `_
-
-`Quick start installation instructions can be found here
-Windows Users
-
----
-
-
-Linux Users
-```
-
-List files to confirm:
-
-```bash
-ls
-```
-
-Install VS Code:
-
-```bash
-sudo dpkg -i code_1.XXX.deb
-```
-
-> Replace `code_1.XXX.deb` with the actual filename.
-
-### 3. Install Python
-#### *Skip this step if you already have Python!*
-
-- Visit the [Python Downloads page](https://www.python.org/downloads/).
-- Download a version between **3.10 and 3.12**.
-- Use your package manager or install from source.
-
-For Ubuntu-based systems:
-
-```bash
-sudo apt update
-sudo apt install python3.XX
-```
-> Replace `XX` with a version here
-
-
-
----
-
-
-macOS Users
-
diff --git a/docs/_build/html/v0.3.0/_sources/setup_log.rst.txt b/docs/_build/html/v0.3.0/_sources/setup_log.rst.txt
deleted file mode 100644
index eaebb41..0000000
--- a/docs/_build/html/v0.3.0/_sources/setup_log.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-setup\_log module
-=================
-
-.. automodule:: setup_log
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/docs/_build/html/v0.3.0/_sources/signal_analysis.rst.txt b/docs/_build/html/v0.3.0/_sources/signal_analysis.rst.txt
deleted file mode 100644
index 8e3b316..0000000
--- a/docs/_build/html/v0.3.0/_sources/signal_analysis.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-Signal Analysis
-===========================
-
-To analyse the signal data, ionique invokes parsers module.
-
-In Parsers, the segnments of the signal are processed separately to get the most accurate data.
-
-The statistical measures are computed for each segment
diff --git a/docs/_build/html/v0.3.0/_sources/signal_preprocess.rst.txt b/docs/_build/html/v0.3.0/_sources/signal_preprocess.rst.txt
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index 7f3bba8..0000000
--- a/docs/_build/html/v0.3.0/_sources/signal_preprocess.rst.txt
+++ /dev/null
@@ -1,73 +0,0 @@
-Signal Preprocessing
-====================
-
-Signal preprocessing is an essential step before downstream analysis of nanopore signals.
-It typically involves filtering, trimming, downsampling, or compressing signal segments
-to reduce noise and improve performance.
-
-This page describes the following functionalities of Ionique:
-
-- Filtering: denoise signals.
-- Trimming: remove edge artifacts from signal segments.
-
-Filtering with `Filter` class
------------------------------
-
-The `Filter` class allows users to apply different types of filters on current traces:
-
-**Supported filter types:**
-
-- ``lowpass``: Passes low frequencies below a cutoff.
-- ``highpass``: Passes high frequencies above a cutoff.
-- ``bandpass``: Passes a frequency band between low and high cutoff.
-- ``bandstop``: Removes a frequency band between low and high cutoff.
-
-
-**Supported filter methods:**
-
-- ``butter``: Butterworth filter (default) – smooth and flat frequency response.
-- ``bessel``: Bessel filter – preserves waveform shape and group delay.
-
-Filtering is applied using `scipy.signal.sosfiltfilt` (zero-phase, bidirectional) or
-`scipy.signal.sosfilt` depending on `bidirectional=True/False`.
-
-
-**Usage Example:**
-
-.. code-block:: python
-
- filt = Filter(
- cutoff_frequency=[0.1, 10],
- filter_type="bandpass",
- filter_method="butter",
- order=3,
- bidirectional=True,
- sampling_frequency=10000
- )
- filt(current_trace)
-
-
-Trimming with `Trimmer` class
------------------------------
-
-The `Trimmer` class removes a fixed number of samples from the start of each segment
-at a given hierarchical rank (e.g., "vstep"). This is useful for excluding artifacts
- in step protocols.
-
-
-**Usage Example:**
-
-.. code-block:: python
-
- trimmer = Trimmer(samples_to_remove=200)
- trimmer(trace_file)
-
-
-Summary
--------
-
-Both `Filter` and `Trimmer` are designed to be composable tools for preprocessing current traces before
-event detection and feature extraction. You can chain them together in the analysis workflows.
-
-
-These operations are performed using tools in ``utils.py`` and parser modules
\ No newline at end of file
diff --git a/docs/_build/html/v0.3.0/_sources/tutorial.rst.txt b/docs/_build/html/v0.3.0/_sources/tutorial.rst.txt
deleted file mode 100644
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diff --git a/docs/_build/html/v0.3.0/_static/_sphinx_javascript_frameworks_compat.js b/docs/_build/html/v0.3.0/_static/_sphinx_javascript_frameworks_compat.js
deleted file mode 100644
index 8141580..0000000
--- a/docs/_build/html/v0.3.0/_static/_sphinx_javascript_frameworks_compat.js
+++ /dev/null
@@ -1,123 +0,0 @@
-/* Compatability shim for jQuery and underscores.js.
- *
- * Copyright Sphinx contributors
- * Released under the two clause BSD licence
- */
-
-/**
- * small helper function to urldecode strings
- *
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
- */
-jQuery.urldecode = function(x) {
- if (!x) {
- return x
- }
- return decodeURIComponent(x.replace(/\+/g, ' '));
-};
-
-/**
- * small helper function to urlencode strings
- */
-jQuery.urlencode = encodeURIComponent;
-
-/**
- * This function returns the parsed url parameters of the
- * current request. Multiple values per key are supported,
- * it will always return arrays of strings for the value parts.
- */
-jQuery.getQueryParameters = function(s) {
- if (typeof s === 'undefined')
- s = document.location.search;
- var parts = s.substr(s.indexOf('?') + 1).split('&');
- var result = {};
- for (var i = 0; i < parts.length; i++) {
- var tmp = parts[i].split('=', 2);
- var key = jQuery.urldecode(tmp[0]);
- var value = jQuery.urldecode(tmp[1]);
- if (key in result)
- result[key].push(value);
- else
- result[key] = [value];
- }
- return result;
-};
-
-/**
- * highlight a given string on a jquery object by wrapping it in
- * span elements with the given class name.
- */
-jQuery.fn.highlightText = function(text, className) {
- function highlight(node, addItems) {
- if (node.nodeType === 3) {
- var val = node.nodeValue;
- var pos = val.toLowerCase().indexOf(text);
- if (pos >= 0 &&
- !jQuery(node.parentNode).hasClass(className) &&
- !jQuery(node.parentNode).hasClass("nohighlight")) {
- var span;
- var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
- if (isInSVG) {
- span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
- } else {
- span = document.createElement("span");
- span.className = className;
- }
- span.appendChild(document.createTextNode(val.substr(pos, text.length)));
- node.parentNode.insertBefore(span, node.parentNode.insertBefore(
- document.createTextNode(val.substr(pos + text.length)),
- node.nextSibling));
- node.nodeValue = val.substr(0, pos);
- if (isInSVG) {
- var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
- var bbox = node.parentElement.getBBox();
- rect.x.baseVal.value = bbox.x;
- rect.y.baseVal.value = bbox.y;
- rect.width.baseVal.value = bbox.width;
- rect.height.baseVal.value = bbox.height;
- rect.setAttribute('class', className);
- addItems.push({
- "parent": node.parentNode,
- "target": rect});
- }
- }
- }
- else if (!jQuery(node).is("button, select, textarea")) {
- jQuery.each(node.childNodes, function() {
- highlight(this, addItems);
- });
- }
- }
- var addItems = [];
- var result = this.each(function() {
- highlight(this, addItems);
- });
- for (var i = 0; i < addItems.length; ++i) {
- jQuery(addItems[i].parent).before(addItems[i].target);
- }
- return result;
-};
-
-/*
- * backward compatibility for jQuery.browser
- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
- jQuery.uaMatch = function(ua) {
- ua = ua.toLowerCase();
-
- var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
- /(webkit)[ \/]([\w.]+)/.exec(ua) ||
- /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
- /(msie) ([\w.]+)/.exec(ua) ||
- ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
- [];
-
- return {
- browser: match[ 1 ] || "",
- version: match[ 2 ] || "0"
- };
- };
- jQuery.browser = {};
- jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
-}
diff --git a/docs/_build/html/v0.3.0/_static/basic.css b/docs/_build/html/v0.3.0/_static/basic.css
deleted file mode 100644
index 7ebbd6d..0000000
--- a/docs/_build/html/v0.3.0/_static/basic.css
+++ /dev/null
@@ -1,914 +0,0 @@
-/*
- * Sphinx stylesheet -- basic theme.
- */
-
-/* -- main layout ----------------------------------------------------------- */
-
-div.clearer {
- clear: both;
-}
-
-div.section::after {
- display: block;
- content: '';
- clear: left;
-}
-
-/* -- relbar ---------------------------------------------------------------- */
-
-div.related {
- width: 100%;
- font-size: 90%;
-}
-
-div.related h3 {
- display: none;
-}
-
-div.related ul {
- margin: 0;
- padding: 0 0 0 10px;
- list-style: none;
-}
-
-div.related li {
- display: inline;
-}
-
-div.related li.right {
- float: right;
- margin-right: 5px;
-}
-
-/* -- sidebar --------------------------------------------------------------- */
-
-div.sphinxsidebarwrapper {
- padding: 10px 5px 0 10px;
-}
-
-div.sphinxsidebar {
- float: left;
- width: 230px;
- margin-left: -100%;
- font-size: 90%;
- word-wrap: break-word;
- overflow-wrap : break-word;
-}
-
-div.sphinxsidebar ul {
- list-style: none;
-}
-
-div.sphinxsidebar ul ul,
-div.sphinxsidebar ul.want-points {
- margin-left: 20px;
- list-style: square;
-}
-
-div.sphinxsidebar ul ul {
- margin-top: 0;
- margin-bottom: 0;
-}
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-div.sphinxsidebar form {
- margin-top: 10px;
-}
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-div.sphinxsidebar input {
- border: 1px solid #98dbcc;
- font-family: sans-serif;
- font-size: 1em;
-}
-
-div.sphinxsidebar #searchbox form.search {
- overflow: hidden;
-}
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-div.sphinxsidebar #searchbox input[type="text"] {
- float: left;
- width: 80%;
- padding: 0.25em;
- box-sizing: border-box;
-}
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-div.sphinxsidebar #searchbox input[type="submit"] {
- float: left;
- width: 20%;
- border-left: none;
- padding: 0.25em;
- box-sizing: border-box;
-}
-
-
-img {
- border: 0;
- max-width: 100%;
-}
-
-/* -- search page ----------------------------------------------------------- */
-
-ul.search {
- margin-top: 10px;
-}
-
-ul.search li {
- padding: 5px 0;
-}
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-ul.search li a {
- font-weight: bold;
-}
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-ul.search li p.context {
- color: #888;
- margin: 2px 0 0 30px;
- text-align: left;
-}
-
-ul.keywordmatches li.goodmatch a {
- font-weight: bold;
-}
-
-/* -- index page ------------------------------------------------------------ */
-
-table.contentstable {
- width: 90%;
- margin-left: auto;
- margin-right: auto;
-}
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-table.contentstable p.biglink {
- line-height: 150%;
-}
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- font-size: 1.3em;
-}
-
-span.linkdescr {
- font-style: italic;
- padding-top: 5px;
- font-size: 90%;
-}
-
-/* -- general index --------------------------------------------------------- */
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- width: 100%;
-}
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-table.indextable td {
- text-align: left;
- vertical-align: top;
-}
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-table.indextable ul {
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- margin-bottom: 0;
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-}
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- padding-left: 0em;
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- height: 10px;
-}
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-table.indextable tr.cap {
- margin-top: 10px;
- background-color: #f2f2f2;
-}
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-img.toggler {
- margin-right: 3px;
- margin-top: 3px;
- cursor: pointer;
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- border-top: 1px solid #ddd;
- border-bottom: 1px solid #ddd;
- margin: 1em 0 1em 0;
- padding: 0.4em;
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- border-top: 1px solid #ddd;
- border-bottom: 1px solid #ddd;
- margin: 1em 0 1em 0;
- padding: 0.4em;
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-.first {
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- margin-right: 1em;
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- margin-left: 1em;
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-img.align-center, figure.align-center, .figure.align-center, object.align-center {
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- margin-right: auto;
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-aside.sidebar {
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- border: 1px solid #ddb;
- padding: 7px;
- background-color: #ffe;
- width: 40%;
- float: right;
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-}
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-p.sidebar-title {
- font-weight: bold;
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-/* -- topics ---------------------------------------------------------------- */
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-p.topic-title {
- font-size: 1.1em;
- font-weight: bold;
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- margin-top: 10px;
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-div.admonition dt {
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-p.admonition-title {
- margin: 0px 10px 5px 0px;
- font-weight: bold;
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-div.body p.centered {
- text-align: center;
- margin-top: 25px;
-}
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-/* -- content of sidebars/topics/admonitions -------------------------------- */
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-div.sidebar > :last-child,
-aside.sidebar > :last-child,
-nav.contents > :last-child,
-aside.topic > :last-child,
-div.topic > :last-child,
-div.admonition > :last-child {
- margin-bottom: 0;
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-div.sidebar::after,
-aside.sidebar::after,
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-aside.topic::after,
-div.topic::after,
-div.admonition::after,
-blockquote::after {
- display: block;
- content: '';
- clear: both;
-}
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-/* -- tables ---------------------------------------------------------------- */
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-table.docutils {
- margin-top: 10px;
- margin-bottom: 10px;
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- border-collapse: collapse;
-}
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-table.align-center {
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-table.align-default {
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-table caption span.caption-number {
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-table.docutils td, table.docutils th {
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-th {
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-table.citation {
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-div.figure p.caption, figcaption {
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-div.figure p.caption span.caption-number,
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- font-style: italic;
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-div.figure p.caption span.caption-text,
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-.sig-inline.c-texpr,
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deleted file mode 100644
index 0398ebb..0000000
--- a/docs/_build/html/v0.3.0/_static/doctools.js
+++ /dev/null
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-/*
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- */
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-
-const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
- "TEXTAREA",
- "INPUT",
- "SELECT",
- "BUTTON",
-]);
-
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- callback();
- } else {
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- },
-
- /**
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- */
- TRANSLATIONS: {},
- PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
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-
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-
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- },
-
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- "n",
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- );
- Documentation.LOCALE = catalog.locale;
- },
-
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-
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- break;
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-
- // some keyboard layouts may need Shift to get /
- switch (event.key) {
- case "/":
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-_ready(Documentation.init);
diff --git a/docs/_build/html/v0.3.0/_static/documentation_options.js b/docs/_build/html/v0.3.0/_static/documentation_options.js
deleted file mode 100644
index a49aa03..0000000
--- a/docs/_build/html/v0.3.0/_static/documentation_options.js
+++ /dev/null
@@ -1,13 +0,0 @@
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- LANGUAGE: 'en',
- COLLAPSE_INDEX: false,
- BUILDER: 'html',
- FILE_SUFFIX: '.html',
- LINK_SUFFIX: '.html',
- HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt',
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- });
-}
-
-document.addEventListener("readthedocs-addons-data-ready", function (event) {
- // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
- document
- .querySelector("[role='search'] input")
- .addEventListener("focusin", () => {
- const event = new CustomEvent("readthedocs-search-show");
- document.dispatchEvent(event);
- });
-});
\ No newline at end of file
diff --git a/docs/_build/html/v0.3.0/_static/language_data.js b/docs/_build/html/v0.3.0/_static/language_data.js
deleted file mode 100644
index c7fe6c6..0000000
--- a/docs/_build/html/v0.3.0/_static/language_data.js
+++ /dev/null
@@ -1,192 +0,0 @@
-/*
- * This script contains the language-specific data used by searchtools.js,
- * namely the list of stopwords, stemmer, scorer and splitter.
- */
-
-var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
-
-
-/* Non-minified version is copied as a separate JS file, if available */
-
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
- var step2list = {
- ational: 'ate',
- tional: 'tion',
- enci: 'ence',
- anci: 'ance',
- izer: 'ize',
- bli: 'ble',
- alli: 'al',
- entli: 'ent',
- eli: 'e',
- ousli: 'ous',
- ization: 'ize',
- ation: 'ate',
- ator: 'ate',
- alism: 'al',
- iveness: 'ive',
- fulness: 'ful',
- ousness: 'ous',
- aliti: 'al',
- iviti: 'ive',
- biliti: 'ble',
- logi: 'log'
- };
-
- var step3list = {
- icate: 'ic',
- ative: '',
- alize: 'al',
- iciti: 'ic',
- ical: 'ic',
- ful: '',
- ness: ''
- };
-
- var c = "[^aeiou]"; // consonant
- var v = "[aeiouy]"; // vowel
- var C = c + "[^aeiouy]*"; // consonant sequence
- var V = v + "[aeiou]*"; // vowel sequence
-
- var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
- var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
- var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
- var s_v = "^(" + C + ")?" + v; // vowel in stem
-
- this.stemWord = function (w) {
- var stem;
- var suffix;
- var firstch;
- var origword = w;
-
- if (w.length < 3)
- return w;
-
- var re;
- var re2;
- var re3;
- var re4;
-
- firstch = w.substr(0,1);
- if (firstch == "y")
- w = firstch.toUpperCase() + w.substr(1);
-
- // Step 1a
- re = /^(.+?)(ss|i)es$/;
- re2 = /^(.+?)([^s])s$/;
-
- if (re.test(w))
- w = w.replace(re,"$1$2");
- else if (re2.test(w))
- w = w.replace(re2,"$1$2");
-
- // Step 1b
- re = /^(.+?)eed$/;
- re2 = /^(.+?)(ed|ing)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- re = new RegExp(mgr0);
- if (re.test(fp[1])) {
- re = /.$/;
- w = w.replace(re,"");
- }
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1];
- re2 = new RegExp(s_v);
- if (re2.test(stem)) {
- w = stem;
- re2 = /(at|bl|iz)$/;
- re3 = new RegExp("([^aeiouylsz])\\1$");
- re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re2.test(w))
- w = w + "e";
- else if (re3.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
- else if (re4.test(w))
- w = w + "e";
- }
- }
-
- // Step 1c
- re = /^(.+?)y$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(s_v);
- if (re.test(stem))
- w = stem + "i";
- }
-
- // Step 2
- re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step2list[suffix];
- }
-
- // Step 3
- re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step3list[suffix];
- }
-
- // Step 4
- re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
- re2 = /^(.+?)(s|t)(ion)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- if (re.test(stem))
- w = stem;
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1] + fp[2];
- re2 = new RegExp(mgr1);
- if (re2.test(stem))
- w = stem;
- }
-
- // Step 5
- re = /^(.+?)e$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- re2 = new RegExp(meq1);
- re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
- w = stem;
- }
- re = /ll$/;
- re2 = new RegExp(mgr1);
- if (re.test(w) && re2.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
-
- // and turn initial Y back to y
- if (firstch == "y")
- w = firstch.toLowerCase() + w.substr(1);
- return w;
- }
-}
-
diff --git a/docs/_build/html/v0.3.0/_static/minus.png b/docs/_build/html/v0.3.0/_static/minus.png
deleted file mode 100644
index d96755f..0000000
Binary files a/docs/_build/html/v0.3.0/_static/minus.png and /dev/null differ
diff --git a/docs/_build/html/v0.3.0/_static/plus.png b/docs/_build/html/v0.3.0/_static/plus.png
deleted file mode 100644
index 7107cec..0000000
Binary files a/docs/_build/html/v0.3.0/_static/plus.png and /dev/null differ
diff --git a/docs/_build/html/v0.3.0/_static/pygments.css b/docs/_build/html/v0.3.0/_static/pygments.css
deleted file mode 100644
index 84ab303..0000000
--- a/docs/_build/html/v0.3.0/_static/pygments.css
+++ /dev/null
@@ -1,75 +0,0 @@
-pre { line-height: 125%; }
-td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-.highlight .hll { background-color: #ffffcc }
-.highlight { background: #f8f8f8; }
-.highlight .c { color: #3D7B7B; font-style: italic } /* Comment */
-.highlight .err { border: 1px solid #FF0000 } /* Error */
-.highlight .k { color: #008000; font-weight: bold } /* Keyword */
-.highlight .o { color: #666666 } /* Operator */
-.highlight .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */
-.highlight .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: #9C6500 } /* Comment.Preproc */
-.highlight .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */
-.highlight .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */
-.highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
-.highlight .gd { color: #A00000 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
-.highlight .gr { color: #E40000 } /* Generic.Error */
-.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
-.highlight .gi { color: #008400 } /* Generic.Inserted */
-.highlight .go { color: #717171 } /* Generic.Output */
-.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
-.highlight .gt { color: #0044DD } /* Generic.Traceback */
-.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
-.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
-.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
-.highlight .kp { color: #008000 } /* Keyword.Pseudo */
-.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
-.highlight .kt { color: #B00040 } /* Keyword.Type */
-.highlight .m { color: #666666 } /* Literal.Number */
-.highlight .s { color: #BA2121 } /* Literal.String */
-.highlight .na { color: #687822 } /* Name.Attribute */
-.highlight .nb { color: #008000 } /* Name.Builtin */
-.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
-.highlight .no { color: #880000 } /* Name.Constant */
-.highlight .nd { color: #AA22FF } /* Name.Decorator */
-.highlight .ni { color: #717171; font-weight: bold } /* Name.Entity */
-.highlight .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */
-.highlight .nf { color: #0000FF } /* Name.Function */
-.highlight .nl { color: #767600 } /* Name.Label */
-.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
-.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
-.highlight .nv { color: #19177C } /* Name.Variable */
-.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
-.highlight .w { color: #bbbbbb } /* Text.Whitespace */
-.highlight .mb { color: #666666 } /* Literal.Number.Bin */
-.highlight .mf { color: #666666 } /* Literal.Number.Float */
-.highlight .mh { color: #666666 } /* Literal.Number.Hex */
-.highlight .mi { color: #666666 } /* Literal.Number.Integer */
-.highlight .mo { color: #666666 } /* Literal.Number.Oct */
-.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
-.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
-.highlight .sc { color: #BA2121 } /* Literal.String.Char */
-.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
-.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
-.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
-.highlight .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */
-.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
-.highlight .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */
-.highlight .sx { color: #008000 } /* Literal.String.Other */
-.highlight .sr { color: #A45A77 } /* Literal.String.Regex */
-.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
-.highlight .ss { color: #19177C } /* Literal.String.Symbol */
-.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #0000FF } /* Name.Function.Magic */
-.highlight .vc { color: #19177C } /* Name.Variable.Class */
-.highlight .vg { color: #19177C } /* Name.Variable.Global */
-.highlight .vi { color: #19177C } /* Name.Variable.Instance */
-.highlight .vm { color: #19177C } /* Name.Variable.Magic */
-.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/docs/_build/html/v0.3.0/_static/searchtools.js b/docs/_build/html/v0.3.0/_static/searchtools.js
deleted file mode 100644
index 2c774d1..0000000
--- a/docs/_build/html/v0.3.0/_static/searchtools.js
+++ /dev/null
@@ -1,632 +0,0 @@
-/*
- * Sphinx JavaScript utilities for the full-text search.
- */
-"use strict";
-
-/**
- * Simple result scoring code.
- */
-if (typeof Scorer === "undefined") {
- var Scorer = {
- // Implement the following function to further tweak the score for each result
- // The function takes a result array [docname, title, anchor, descr, score, filename]
- // and returns the new score.
- /*
- score: result => {
- const [docname, title, anchor, descr, score, filename, kind] = result
- return score
- },
- */
-
- // query matches the full name of an object
- objNameMatch: 11,
- // or matches in the last dotted part of the object name
- objPartialMatch: 6,
- // Additive scores depending on the priority of the object
- objPrio: {
- 0: 15, // used to be importantResults
- 1: 5, // used to be objectResults
- 2: -5, // used to be unimportantResults
- },
- // Used when the priority is not in the mapping.
- objPrioDefault: 0,
-
- // query found in title
- title: 15,
- partialTitle: 7,
- // query found in terms
- term: 5,
- partialTerm: 2,
- };
-}
-
-// Global search result kind enum, used by themes to style search results.
-class SearchResultKind {
- static get index() { return "index"; }
- static get object() { return "object"; }
- static get text() { return "text"; }
- static get title() { return "title"; }
-}
-
-const _removeChildren = (element) => {
- while (element && element.lastChild) element.removeChild(element.lastChild);
-};
-
-/**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
- */
-const _escapeRegExp = (string) =>
- string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _displayItem = (item, searchTerms, highlightTerms) => {
- const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
- const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
- const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
- const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
- const contentRoot = document.documentElement.dataset.content_root;
-
- const [docName, title, anchor, descr, score, _filename, kind] = item;
-
- let listItem = document.createElement("li");
- // Add a class representing the item's type:
- // can be used by a theme's CSS selector for styling
- // See SearchResultKind for the class names.
- listItem.classList.add(`kind-${kind}`);
- let requestUrl;
- let linkUrl;
- if (docBuilder === "dirhtml") {
- // dirhtml builder
- let dirname = docName + "/";
- if (dirname.match(/\/index\/$/))
- dirname = dirname.substring(0, dirname.length - 6);
- else if (dirname === "index/") dirname = "";
- requestUrl = contentRoot + dirname;
- linkUrl = requestUrl;
- } else {
- // normal html builders
- requestUrl = contentRoot + docName + docFileSuffix;
- linkUrl = docName + docLinkSuffix;
- }
- let linkEl = listItem.appendChild(document.createElement("a"));
- linkEl.href = linkUrl + anchor;
- linkEl.dataset.score = score;
- linkEl.innerHTML = title;
- if (descr) {
- listItem.appendChild(document.createElement("span")).innerHTML =
- " (" + descr + ")";
- // highlight search terms in the description
- if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
- highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
- }
- else if (showSearchSummary)
- fetch(requestUrl)
- .then((responseData) => responseData.text())
- .then((data) => {
- if (data)
- listItem.appendChild(
- Search.makeSearchSummary(data, searchTerms, anchor)
- );
- // highlight search terms in the summary
- if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
- highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
- });
- Search.output.appendChild(listItem);
-};
-const _finishSearch = (resultCount) => {
- Search.stopPulse();
- Search.title.innerText = _("Search Results");
- if (!resultCount)
- Search.status.innerText = Documentation.gettext(
- "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
- );
- else
- Search.status.innerText = Documentation.ngettext(
- "Search finished, found one page matching the search query.",
- "Search finished, found ${resultCount} pages matching the search query.",
- resultCount,
- ).replace('${resultCount}', resultCount);
-};
-const _displayNextItem = (
- results,
- resultCount,
- searchTerms,
- highlightTerms,
-) => {
- // results left, load the summary and display it
- // this is intended to be dynamic (don't sub resultsCount)
- if (results.length) {
- _displayItem(results.pop(), searchTerms, highlightTerms);
- setTimeout(
- () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
- 5
- );
- }
- // search finished, update title and status message
- else _finishSearch(resultCount);
-};
-// Helper function used by query() to order search results.
-// Each input is an array of [docname, title, anchor, descr, score, filename, kind].
-// Order the results by score (in opposite order of appearance, since the
-// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
-const _orderResultsByScoreThenName = (a, b) => {
- const leftScore = a[4];
- const rightScore = b[4];
- if (leftScore === rightScore) {
- // same score: sort alphabetically
- const leftTitle = a[1].toLowerCase();
- const rightTitle = b[1].toLowerCase();
- if (leftTitle === rightTitle) return 0;
- return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
- }
- return leftScore > rightScore ? 1 : -1;
-};
-
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
- var splitQuery = (query) => query
- .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
- .filter(term => term) // remove remaining empty strings
-}
-
-/**
- * Search Module
- */
-const Search = {
- _index: null,
- _queued_query: null,
- _pulse_status: -1,
-
- htmlToText: (htmlString, anchor) => {
- const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
- for (const removalQuery of [".headerlink", "script", "style"]) {
- htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
- }
- if (anchor) {
- const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
- if (anchorContent) return anchorContent.textContent;
-
- console.warn(
- `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
- );
- }
-
- // if anchor not specified or not found, fall back to main content
- const docContent = htmlElement.querySelector('[role="main"]');
- if (docContent) return docContent.textContent;
-
- console.warn(
- "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
- );
- return "";
- },
-
- init: () => {
- const query = new URLSearchParams(window.location.search).get("q");
- document
- .querySelectorAll('input[name="q"]')
- .forEach((el) => (el.value = query));
- if (query) Search.performSearch(query);
- },
-
- loadIndex: (url) =>
- (document.body.appendChild(document.createElement("script")).src = url),
-
- setIndex: (index) => {
- Search._index = index;
- if (Search._queued_query !== null) {
- const query = Search._queued_query;
- Search._queued_query = null;
- Search.query(query);
- }
- },
-
- hasIndex: () => Search._index !== null,
-
- deferQuery: (query) => (Search._queued_query = query),
-
- stopPulse: () => (Search._pulse_status = -1),
-
- startPulse: () => {
- if (Search._pulse_status >= 0) return;
-
- const pulse = () => {
- Search._pulse_status = (Search._pulse_status + 1) % 4;
- Search.dots.innerText = ".".repeat(Search._pulse_status);
- if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
- };
- pulse();
- },
-
- /**
- * perform a search for something (or wait until index is loaded)
- */
- performSearch: (query) => {
- // create the required interface elements
- const searchText = document.createElement("h2");
- searchText.textContent = _("Searching");
- const searchSummary = document.createElement("p");
- searchSummary.classList.add("search-summary");
- searchSummary.innerText = "";
- const searchList = document.createElement("ul");
- searchList.setAttribute("role", "list");
- searchList.classList.add("search");
-
- const out = document.getElementById("search-results");
- Search.title = out.appendChild(searchText);
- Search.dots = Search.title.appendChild(document.createElement("span"));
- Search.status = out.appendChild(searchSummary);
- Search.output = out.appendChild(searchList);
-
- const searchProgress = document.getElementById("search-progress");
- // Some themes don't use the search progress node
- if (searchProgress) {
- searchProgress.innerText = _("Preparing search...");
- }
- Search.startPulse();
-
- // index already loaded, the browser was quick!
- if (Search.hasIndex()) Search.query(query);
- else Search.deferQuery(query);
- },
-
- _parseQuery: (query) => {
- // stem the search terms and add them to the correct list
- const stemmer = new Stemmer();
- const searchTerms = new Set();
- const excludedTerms = new Set();
- const highlightTerms = new Set();
- const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
- splitQuery(query.trim()).forEach((queryTerm) => {
- const queryTermLower = queryTerm.toLowerCase();
-
- // maybe skip this "word"
- // stopwords array is from language_data.js
- if (
- stopwords.indexOf(queryTermLower) !== -1 ||
- queryTerm.match(/^\d+$/)
- )
- return;
-
- // stem the word
- let word = stemmer.stemWord(queryTermLower);
- // select the correct list
- if (word[0] === "-") excludedTerms.add(word.substr(1));
- else {
- searchTerms.add(word);
- highlightTerms.add(queryTermLower);
- }
- });
-
- if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js
- localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
- }
-
- // console.debug("SEARCH: searching for:");
- // console.info("required: ", [...searchTerms]);
- // console.info("excluded: ", [...excludedTerms]);
-
- return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
- },
-
- /**
- * execute search (requires search index to be loaded)
- */
- _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const titles = Search._index.titles;
- const allTitles = Search._index.alltitles;
- const indexEntries = Search._index.indexentries;
-
- // Collect multiple result groups to be sorted separately and then ordered.
- // Each is an array of [docname, title, anchor, descr, score, filename, kind].
- const normalResults = [];
- const nonMainIndexResults = [];
-
- _removeChildren(document.getElementById("search-progress"));
-
- const queryLower = query.toLowerCase().trim();
- for (const [title, foundTitles] of Object.entries(allTitles)) {
- if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
- for (const [file, id] of foundTitles) {
- const score = Math.round(Scorer.title * queryLower.length / title.length);
- const boost = titles[file] === title ? 1 : 0; // add a boost for document titles
- normalResults.push([
- docNames[file],
- titles[file] !== title ? `${titles[file]} > ${title}` : title,
- id !== null ? "#" + id : "",
- null,
- score + boost,
- filenames[file],
- SearchResultKind.title,
- ]);
- }
- }
- }
-
- // search for explicit entries in index directives
- for (const [entry, foundEntries] of Object.entries(indexEntries)) {
- if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
- for (const [file, id, isMain] of foundEntries) {
- const score = Math.round(100 * queryLower.length / entry.length);
- const result = [
- docNames[file],
- titles[file],
- id ? "#" + id : "",
- null,
- score,
- filenames[file],
- SearchResultKind.index,
- ];
- if (isMain) {
- normalResults.push(result);
- } else {
- nonMainIndexResults.push(result);
- }
- }
- }
- }
-
- // lookup as object
- objectTerms.forEach((term) =>
- normalResults.push(...Search.performObjectSearch(term, objectTerms))
- );
-
- // lookup as search terms in fulltext
- normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
-
- // let the scorer override scores with a custom scoring function
- if (Scorer.score) {
- normalResults.forEach((item) => (item[4] = Scorer.score(item)));
- nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
- }
-
- // Sort each group of results by score and then alphabetically by name.
- normalResults.sort(_orderResultsByScoreThenName);
- nonMainIndexResults.sort(_orderResultsByScoreThenName);
-
- // Combine the result groups in (reverse) order.
- // Non-main index entries are typically arbitrary cross-references,
- // so display them after other results.
- let results = [...nonMainIndexResults, ...normalResults];
-
- // remove duplicate search results
- // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
- let seen = new Set();
- results = results.reverse().reduce((acc, result) => {
- let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
- if (!seen.has(resultStr)) {
- acc.push(result);
- seen.add(resultStr);
- }
- return acc;
- }, []);
-
- return results.reverse();
- },
-
- query: (query) => {
- const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
- const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
-
- // for debugging
- //Search.lastresults = results.slice(); // a copy
- // console.info("search results:", Search.lastresults);
-
- // print the results
- _displayNextItem(results, results.length, searchTerms, highlightTerms);
- },
-
- /**
- * search for object names
- */
- performObjectSearch: (object, objectTerms) => {
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const objects = Search._index.objects;
- const objNames = Search._index.objnames;
- const titles = Search._index.titles;
-
- const results = [];
-
- const objectSearchCallback = (prefix, match) => {
- const name = match[4]
- const fullname = (prefix ? prefix + "." : "") + name;
- const fullnameLower = fullname.toLowerCase();
- if (fullnameLower.indexOf(object) < 0) return;
-
- let score = 0;
- const parts = fullnameLower.split(".");
-
- // check for different match types: exact matches of full name or
- // "last name" (i.e. last dotted part)
- if (fullnameLower === object || parts.slice(-1)[0] === object)
- score += Scorer.objNameMatch;
- else if (parts.slice(-1)[0].indexOf(object) > -1)
- score += Scorer.objPartialMatch; // matches in last name
-
- const objName = objNames[match[1]][2];
- const title = titles[match[0]];
-
- // If more than one term searched for, we require other words to be
- // found in the name/title/description
- const otherTerms = new Set(objectTerms);
- otherTerms.delete(object);
- if (otherTerms.size > 0) {
- const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
- if (
- [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
- )
- return;
- }
-
- let anchor = match[3];
- if (anchor === "") anchor = fullname;
- else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
-
- const descr = objName + _(", in ") + title;
-
- // add custom score for some objects according to scorer
- if (Scorer.objPrio.hasOwnProperty(match[2]))
- score += Scorer.objPrio[match[2]];
- else score += Scorer.objPrioDefault;
-
- results.push([
- docNames[match[0]],
- fullname,
- "#" + anchor,
- descr,
- score,
- filenames[match[0]],
- SearchResultKind.object,
- ]);
- };
- Object.keys(objects).forEach((prefix) =>
- objects[prefix].forEach((array) =>
- objectSearchCallback(prefix, array)
- )
- );
- return results;
- },
-
- /**
- * search for full-text terms in the index
- */
- performTermsSearch: (searchTerms, excludedTerms) => {
- // prepare search
- const terms = Search._index.terms;
- const titleTerms = Search._index.titleterms;
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const titles = Search._index.titles;
-
- const scoreMap = new Map();
- const fileMap = new Map();
-
- // perform the search on the required terms
- searchTerms.forEach((word) => {
- const files = [];
- const arr = [
- { files: terms[word], score: Scorer.term },
- { files: titleTerms[word], score: Scorer.title },
- ];
- // add support for partial matches
- if (word.length > 2) {
- const escapedWord = _escapeRegExp(word);
- if (!terms.hasOwnProperty(word)) {
- Object.keys(terms).forEach((term) => {
- if (term.match(escapedWord))
- arr.push({ files: terms[term], score: Scorer.partialTerm });
- });
- }
- if (!titleTerms.hasOwnProperty(word)) {
- Object.keys(titleTerms).forEach((term) => {
- if (term.match(escapedWord))
- arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
- });
- }
- }
-
- // no match but word was a required one
- if (arr.every((record) => record.files === undefined)) return;
-
- // found search word in contents
- arr.forEach((record) => {
- if (record.files === undefined) return;
-
- let recordFiles = record.files;
- if (recordFiles.length === undefined) recordFiles = [recordFiles];
- files.push(...recordFiles);
-
- // set score for the word in each file
- recordFiles.forEach((file) => {
- if (!scoreMap.has(file)) scoreMap.set(file, {});
- scoreMap.get(file)[word] = record.score;
- });
- });
-
- // create the mapping
- files.forEach((file) => {
- if (!fileMap.has(file)) fileMap.set(file, [word]);
- else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
- });
- });
-
- // now check if the files don't contain excluded terms
- const results = [];
- for (const [file, wordList] of fileMap) {
- // check if all requirements are matched
-
- // as search terms with length < 3 are discarded
- const filteredTermCount = [...searchTerms].filter(
- (term) => term.length > 2
- ).length;
- if (
- wordList.length !== searchTerms.size &&
- wordList.length !== filteredTermCount
- )
- continue;
-
- // ensure that none of the excluded terms is in the search result
- if (
- [...excludedTerms].some(
- (term) =>
- terms[term] === file ||
- titleTerms[term] === file ||
- (terms[term] || []).includes(file) ||
- (titleTerms[term] || []).includes(file)
- )
- )
- break;
-
- // select one (max) score for the file.
- const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
- // add result to the result list
- results.push([
- docNames[file],
- titles[file],
- "",
- null,
- score,
- filenames[file],
- SearchResultKind.text,
- ]);
- }
- return results;
- },
-
- /**
- * helper function to return a node containing the
- * search summary for a given text. keywords is a list
- * of stemmed words.
- */
- makeSearchSummary: (htmlText, keywords, anchor) => {
- const text = Search.htmlToText(htmlText, anchor);
- if (text === "") return null;
-
- const textLower = text.toLowerCase();
- const actualStartPosition = [...keywords]
- .map((k) => textLower.indexOf(k.toLowerCase()))
- .filter((i) => i > -1)
- .slice(-1)[0];
- const startWithContext = Math.max(actualStartPosition - 120, 0);
-
- const top = startWithContext === 0 ? "" : "...";
- const tail = startWithContext + 240 < text.length ? "..." : "";
-
- let summary = document.createElement("p");
- summary.classList.add("context");
- summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
-
- return summary;
- },
-};
-
-_ready(Search.init);
diff --git a/docs/_build/html/v0.3.0/_static/sphinx_highlight.js b/docs/_build/html/v0.3.0/_static/sphinx_highlight.js
deleted file mode 100644
index 8a96c69..0000000
--- a/docs/_build/html/v0.3.0/_static/sphinx_highlight.js
+++ /dev/null
@@ -1,154 +0,0 @@
-/* Highlighting utilities for Sphinx HTML documentation. */
-"use strict";
-
-const SPHINX_HIGHLIGHT_ENABLED = true
-
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
- if (node.nodeType === Node.TEXT_NODE) {
- const val = node.nodeValue;
- const parent = node.parentNode;
- const pos = val.toLowerCase().indexOf(text);
- if (
- pos >= 0 &&
- !parent.classList.contains(className) &&
- !parent.classList.contains("nohighlight")
- ) {
- let span;
-
- const closestNode = parent.closest("body, svg, foreignObject");
- const isInSVG = closestNode && closestNode.matches("svg");
- if (isInSVG) {
- span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
- } else {
- span = document.createElement("span");
- span.classList.add(className);
- }
-
- span.appendChild(document.createTextNode(val.substr(pos, text.length)));
- const rest = document.createTextNode(val.substr(pos + text.length));
- parent.insertBefore(
- span,
- parent.insertBefore(
- rest,
- node.nextSibling
- )
- );
- node.nodeValue = val.substr(0, pos);
- /* There may be more occurrences of search term in this node. So call this
- * function recursively on the remaining fragment.
- */
- _highlight(rest, addItems, text, className);
-
- if (isInSVG) {
- const rect = document.createElementNS(
- "http://www.w3.org/2000/svg",
- "rect"
- );
- const bbox = parent.getBBox();
- rect.x.baseVal.value = bbox.x;
- rect.y.baseVal.value = bbox.y;
- rect.width.baseVal.value = bbox.width;
- rect.height.baseVal.value = bbox.height;
- rect.setAttribute("class", className);
- addItems.push({ parent: parent, target: rect });
- }
- }
- } else if (node.matches && !node.matches("button, select, textarea")) {
- node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
- }
-};
-const _highlightText = (thisNode, text, className) => {
- let addItems = [];
- _highlight(thisNode, addItems, text, className);
- addItems.forEach((obj) =>
- obj.parent.insertAdjacentElement("beforebegin", obj.target)
- );
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const SphinxHighlight = {
-
- /**
- * highlight the search words provided in localstorage in the text
- */
- highlightSearchWords: () => {
- if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
-
- // get and clear terms from localstorage
- const url = new URL(window.location);
- const highlight =
- localStorage.getItem("sphinx_highlight_terms")
- || url.searchParams.get("highlight")
- || "";
- localStorage.removeItem("sphinx_highlight_terms")
- url.searchParams.delete("highlight");
- window.history.replaceState({}, "", url);
-
- // get individual terms from highlight string
- const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
- if (terms.length === 0) return; // nothing to do
-
- // There should never be more than one element matching "div.body"
- const divBody = document.querySelectorAll("div.body");
- const body = divBody.length ? divBody[0] : document.querySelector("body");
- window.setTimeout(() => {
- terms.forEach((term) => _highlightText(body, term, "highlighted"));
- }, 10);
-
- const searchBox = document.getElementById("searchbox");
- if (searchBox === null) return;
- searchBox.appendChild(
- document
- .createRange()
- .createContextualFragment(
- '
' +
- '' +
- _("Hide Search Matches") +
- "
"
- )
- );
- },
-
- /**
- * helper function to hide the search marks again
- */
- hideSearchWords: () => {
- document
- .querySelectorAll("#searchbox .highlight-link")
- .forEach((el) => el.remove());
- document
- .querySelectorAll("span.highlighted")
- .forEach((el) => el.classList.remove("highlighted"));
- localStorage.removeItem("sphinx_highlight_terms")
- },
-
- initEscapeListener: () => {
- // only install a listener if it is really needed
- if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
-
- document.addEventListener("keydown", (event) => {
- // bail for input elements
- if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
- // bail with special keys
- if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
- if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
- SphinxHighlight.hideSearchWords();
- event.preventDefault();
- }
- });
- },
-};
-
-_ready(() => {
- /* Do not call highlightSearchWords() when we are on the search page.
- * It will highlight words from the *previous* search query.
- */
- if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
- SphinxHighlight.initEscapeListener();
-});
diff --git a/docs/_build/html/v0.3.0/api/core.html b/docs/_build/html/v0.3.0/api/core.html
deleted file mode 100644
index c22b784..0000000
--- a/docs/_build/html/v0.3.0/api/core.html
+++ /dev/null
@@ -1,886 +0,0 @@
-
-
-
-
-
-
-
-
-
ionique.core — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- ionique
-
-
-
-
-
-
-
-
-
-ionique.core
-
-
-This holds the core data types which may be abstracted in
-many different applications.
-This module defines a tree-based framework for representing, parsing, and annotating
-segments of ionic current data.
-
-
-class ionique.core. AbstractSegmentTree
-Bases: object
-Class for managing hierarchical segments.
-This class supports a tree structure where each segment can contain multiple
-child segments, enabling recursive parsing and analysis of nested data.
-It provides utilities to manage relationships between segments, apply parsers,
-and extract subsegments by rank.
-
-
-__init__ ( ) → None
-Initialize the segment
-
-
-
-
-add_child ( child : AnySegment ) → None
-Add a single child
-
-
-
-
-add_children ( children : list [ AnySegment ] ) → None
-Add multiple children if:
-1. Child position is the within parent’s segment.
-2. The length of the child’s segment is > 0
-3. There is no overlap between consecutive children
-Or add children with no check
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-clear_children ( )
-Clear the list of children
-
-
-
-
-climb_to_rank ( rank : str ) → AnySegment | None
-Go up and find the segment with the specified rank
-:return: segment
-
-
-
-
-end : int | None
-
-
-
-
-get_feature ( name : str )
-Gets the ‘name’ feature of the current segment or its parent
-
-
-
-
-get_top_parent ( ) → AnySegment
-Recursively go up and find top most parent
-:return: parent
-
-
-
-
-property n : int
-Get the length of the segment
-:return: len(segment)
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-parse ( parser newrank : str at_child_rank : str | None = None ** kwargs ) → bool
-Parses the data in the segment or at a particular rank of child segments
-into children segments with a new rank.
-
-Parameters:
-
-parser (Parser parse method and required input attributes.
newrank (str ) – Rank to assign to the newly created child segments.
at_child_rank (str or None ) – Determines whether to traverse the children tree down to a given rank.
kwargs – Additional arguments to pass to the parser.
-Returns:
-True if parsing was successful, otherwise raises an exception.
-Return type:
-bool
-
-
-
-
-
-rank : str
-
-
-
-
-property relative_end : int
-Get end position relative to parent segment
-:return: end position
-
-
-
-
-property relative_slice : ndarray
-Slice the array to get the data between end-start segment
-:return: sliced np array
-
-
-
-
-property relative_start : int
-Get the start position relative to parent segment
-:return: start position
-
-
-
-
-property slice : ndarray
-Slice the array to get the data between start-end segment
-:return: sliced np array
-
-
-
-
-start : int | None
-
-
-
-
-traverse_to_rank ( rank : str ) → list
-Traverse the tree rank and get the list of the segments of the rank
-:param: rank
-:return: list of segments
-
-
-
-
-
-
-class ionique.core. MetaSegment ( start : int end : int parent : AnySegment | None = None rank : str | None = None unique_features : dict | None = {} ** kwargs )
-Bases: AbstractSegmentTree
-The metadata on an abstract segment of ionic current. All information about a segment can be
-loaded, without the expectation of the array of floats.
-
-
-__init__ ( start : int end : int parent : AnySegment | None = None rank : str | None = None unique_features : dict | None = {} ** kwargs )
-
-Parameters:
-
-start (int ) – Start index of the segment.
end (int ) – End index of the segment.
parent (AbstractSegmentTree or None ) – Parent segment in the tree (optional).
rank (str or None ) – Rank identifier for this segment (optional).
unique_features (dict ) – Dictionary of metadata features.
-
-
-
-
-
-property current
-Get the current data of the segment if the segment correlates to the file
-:return: current
-
-
-
-
-delete ( )
-Delete itself. There are no arrays with which to delete references for.
-
-
-
-
-property duration : float
-Get the duration of the segment = start - end
-:return: duration
-
-
-
-
-classmethod from_json ( filename = None in_json = None )
-Read in a metasegment from a JSON and return a metasegment object.
-Either pass in a file which has a segment stored, or an actual JSON
-object.
-
-
-
-
-property max
-Calculate the maximum value of the current array.
-
-Returns:
-Maximum value of the current array.
-Return type:
-float
-
-
-
-
-
-property mean
-Calculate the mean of the current array.
-
-Returns:
-Mean value of the current array.
-Return type:
-float
-
-
-
-
-
-property min
-Calculate the minimum value of the current array.
-
-Returns:
-Minimum value of the current array.
-Return type:
-float
-
-
-
-
-
-property std
-Calculate the standard deviation of the current array.
-
-Returns:
-Standard deviation of the current array.
-Return type:
-float
-
-
-
-
-
-property time
-Get the time data of the segment if the corresponding rank is in file
-:return: time
-
-
-
-
-to_dict ( )
-Return a dict representation of the metadata, usually used prior to
-converting the dict to a JSON.
-
-
-
-
-to_json ( filename = None )
-Return a JSON representation of this, by reporting the important
-metadata.
-
-
-
-
-to_meta ( )
-Kept to allow for error handling, but since it’s already a metasegment
-it won’t actually do anything.
-
-
-
-
-unique_features
-
-
-
-
-
-
-class ionique.core. Segment ( current ** kwargs )
-Bases: AbstractSegmentTree
-A segment of ionic current, and methods relevant for collecting metadata. The ionic current is
-expected to be passed as a numpy array of floats. Metadata methods (mean, std..) are decorated
-as properties to reduce overall computational time, making them calculated on the fly rather
-than during analysis.
-
-
-__init__ ( current ** kwargs )
-The segment must have a list of ionic current, of which it stores some statistics about.
-It may also take in as many keyword arguments as needed, such as start time or duration
-if already known. Cannot override statistical measurements.
-:param current: Numpy array of current data points.
-:type current: np.ndarray
-:param kwargs: Additional attributes like ‘start’, ‘end’, ‘rank’, etc.
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-delete ( )
-Deleting this segment requires deleting its reference to the ionic
-current array, and then deleting itself.
-
-
-
-
-end : int | None
-
-
-
-
-classmethod from_json ( filename = None json = None )
-Read in a segment from a JSON and return a metasegment object. Either
-pass in a file which has a segment stored, or an actual JSON object.
-
-
-
-
-property max
-Calculate the maximum value of the current array.
-
-Returns:
-Maximum value of the current array.
-Return type:
-float
-
-
-
-
-
-property mean
-Calculate the mean of the current array.
-
-Returns:
-Mean value of the current array.
-Return type:
-float
-
-
-
-
-
-property min
-Calculate the minimum value of the current array.
-
-Returns:
-Minimum value of the current array.
-Return type:
-float
-
-
-
-
-
-property n
-Get the number of elements in the current array.
-
-Returns:
-Number of elements in the current array.
-Return type:
-int
-
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-rank : str
-
-
-
-
-scale ( sampling_freq )
-Rescale all of the values to go from samples to seconds.
-
-
-
-
-start : int | None
-
-
-
-
-property std
-Calculate the standard deviation of the current array.
-
-Returns:
-Standard deviation of the current array.
-Return type:
-float
-
-
-
-
-
-to_dict ( )
-Return a dict representation of the metadata, usually used prior to
-converting the dict to a JSON.
-
-
-
-
-to_json ( filename = None )
-Return a JSON representation of this, by reporting the important
-metadata.
-
-
-
-
-to_meta ( )
-Convert from a segment to a ‘metasegment’, which stores only metadata
-about the segment and not the full array of ionic current.
-
-
-
-
-
-
-ionique.core. ignored ( * exceptions )
-Replace the “try, except: pass” paradigm by replacing those three lines with a single line.
-Taken from the latest 3.4 python update push by Raymond Hettinger, see:
-http://hg.python.org/cpython/rev/406b47c64480
-
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diff --git a/docs/_build/html/v0.3.0/api/cparsers.html b/docs/_build/html/v0.3.0/api/cparsers.html
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ionique.cparsers — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-ionique.cparsers
-This contains cython implementations of ionic current parsers which are in
-parsers.py. Currently the only parser is StatSplit, which is implemented
-as FastStatSplit.
-
-
-class ionique.cparsers. FastStatSplit
-Bases: object
-A cython implementation of the segmenter written by Kevin Karplus. Sped up approximately 50-100x
-compared to the Python implementation depending on parameters.
-
-
-__init__ ( * args ** kwargs )
-
-
-
-
-best_single_split ( current )
-Wrapper for a single call to _best_single_split. It will find the
-single best split in a series of current, and return the index of
-that split. Returns a tuple of ( gain, index ) where gain is the
-gain in variance by splitting there, and index is the index at which
-the split should occur in the current array.
-
-
-
-
-min_gain
-
-
-
-
-parse ( current )
-Wrapper function for the segmentation, which is implemented in cython.
-
-
-
-
-parse_meta ( current )
-Wrapper function for the segmentation, which is implemented in cython. returns meta segments (no current array)
-
-
-
-
-score_samples ( current no_split = False )
-Return a series of lists scoring each sample. However, this isn’t just
-scoring each sample once. Every time a split is detected, it will return
-a new list with newly scored samples. In essence, it returns one list
-per scan of the current using the recursive method.
-
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-ionique.cparsers. pairwise ( iterable )
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\ No newline at end of file
diff --git a/docs/_build/html/v0.3.0/api/datatypes.html b/docs/_build/html/v0.3.0/api/datatypes.html
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ionique.datatypes — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-ionique.datatypes
-Data structure definitions and session file management for analysis.
-
-
-class ionique.datatypes. SessionFileManager ( * args ** kwargs )
-Bases: MetaSegment
-Singleton-based session manager for hierarchical data.
-This class inherits from MetaSegment and uses the Singleton metaclass
-to ensure only one instance exists during a given session. It serves as
-the root segment for a data analysis session and is responsible for managing
-metadata and registered “affectors” (i.e., objects that modify or
-interact with the session).
-
-
-__init__ ( ) → None
-Initialize the session manager.
-Sets the session start time and prepares the affector log structure.
-
-
-
-
-add_child ( child : AnySegment ) → None
-Add a single child segment to the session’s hierarchy.
-
-Parameters:
-child (AnySegment ) – The segment to be added as a child.
-
-
-
-
-
-add_children ( children : list [ AnySegment ] ) → None
-Add multiple child segments to the session.
-
-Parameters:
-children (list [ AnySegment ] ) – A list of segments to be added as children.
-
-
-
-
-
-rank : str = 'root'
-
-
-
-
-register_affector ( affector )
-Register an affector object that influences the session data.
-Records metadata including class name, string representation, and timestamp,
-and logs the event with a unique identifier.
-
-Parameters:
-affector (object ) – An object that modifies or interacts with the session.
-Returns:
-A UUID string uniquely identifying the registered affector.
-Return type:
-str
-
-
-
-
-
-unique_features
-
-
-
-
-
-
-class ionique.datatypes. TraceFile ( current : ndarray voltage = None rank = 'file' parent = None unique_features : dict = {} metadata : dict = {} )
-Bases: Segment
-Data structure for representing a single trace file with current and optional voltage information.
-This class inherits from Segment and encapsulates current and voltage data. It sets up segment metadata,
-defines parent-child hierarchy, and optionally initializes child segments based on voltage steps.
-Typical usage includes assigning metadata and organizing hierarchical
-data structures for downstream processing or visualization.
-
-
-__init__ ( current : ndarray voltage = None rank = 'file' parent = None unique_features : dict = {} metadata : dict = {} )
-If voltage steps are provided, corresponding child segments of rank “vstep” are created.
-
-Parameters:
-
-current (numpy.ndarray ) – current array.
voltage (list [ tuple [ tuple [ int , int ] , float ] ] or None ) – Optional list of tuples containing (start, end) index pairs and voltage values.
-Used to create child segments for each voltage step.
rank (str ) – Segment rank label, defaults to “file”.
parent (Segment or None ) – Optional parent segment to which this trace belongs.
unique_features (dict ) – Dictionary of metadata such as sampling frequency.
metadata (dict ) – Additional metadata.
-
-
-
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-children : list [ AnySegment ]
-
-
-
-
-delete ( )
-Delete the current object, removing it from its parent
-
-
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-end : int | None
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-plot ( rank axes downsample_per_rank color_per_rank )
-Method for plotting
-
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-rank : str
-
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-start : int | None
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\ No newline at end of file
diff --git a/docs/_build/html/v0.3.0/api/io.html b/docs/_build/html/v0.3.0/api/io.html
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ionique.io — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-ionique.io
-Input/output utilities for data files.
-This module provides an interface for loading, parsing, and preprocessing
-signal data from various file formats (e.g., .edh , .opt , .abf , .dat , .xml ). It defines a base reader class
-and concrete implementations that handle format-specific logic, metadata extraction,
-data alignment, and optional preprocessing steps.
-This module is central to converting raw experimental data into analyzable form.
-
-
-class ionique.io. AbstractFileReader
-Bases: object
-An abstract class for reading various data files (.abf, .edh, .mat, .opt, .xml)
-This class defines the structure for file readers
-
-
-__init__ ( )
-
-
-
-
-accepted_keywords = []
-
-
-
-
-current_multiplier : float = 1.0
-
-
-
-
-ext = '___'
-
-
-
-
-read ( filename : str ** kwargs )
-Read a datafile or series of files. Files are identified according to their extension.
-Data formats that come with a header file must be referred to by the header file.
-If inheriting from the AbstractFileReader class, do not override this function;
-instead, create a custom _read method.
-
-Parameters:
-filename – file name or list of file names, typically
-
-given as a string or PathLike object.
-:type filename: str or os.PathLike or list[str] or list[os.PathLike]
-:param **
-
-
-
-
-voltage_multiplier : float = 1.0
-
-
-
-
-
-
-class ionique.io. EDHReader ( edh_filename voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Bases: AbstractFileReader
-Reader class for loading .edh data files and their associated current data.
-This class parses the .edh header file and automatically loads the corresponding signal data
-(either from .abf or .dat files in the same directory). It extracts metadata, converts raw
-current and voltage data to SI units, and supports optional preprocessing steps such as
-voltage step segmentation, downsampling, and signal filtering.
-
-
-__init__ ( edh_filename voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Initialize the EDHReader and load signal data from associated files.
-
-Parameters:
-
-edh_filename (str ) – Path to the .edh header file.
voltage_compress (bool ) – If True, splits signal into segments based on voltage steps.
n_remove (int ) – Number of samples to remove from the beginning of each voltage step.
downsample (int ) – Downsampling factor to reduce data size.
prefilter (callable or None ) – Callable to apply preprocessing to the current signal.
-
-
-
-
-
-accepted_keywords = ['voltage_compress', 'n_remove', 'downsample', 'prefilter']
-
-
-
-
-current_multiplier : float = 1e-09
-
-
-
-
-ext = '.edh'
-
-
-
-
-voltage_multiplier : float = 0.001
-
-
-
-
-
-
-class ionique.io. OPTReader ( opt_filename : str voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Bases: AbstractFileReader
-Reader for .opt data files with corresponding XML or _volt.opt metadata.
-This class reads current data from .opt files and attempts to extract or reconstruct
-corresponding voltage data using associated .xml or _volt.opt files found in the same
-directory. It handles metadata extraction, signal preprocessing, voltage alignment, and
-segment compression based on voltage steps.
-Supported XML structures include both standard HWtiming_cap_step formats and timestamp-based
-custom formats.
-
-
-__init__ ( opt_filename : str voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Initialize the OPTReader instance and load corresponding files.
-
-Parameters:
-
-opt_filename (str ) – Path to the .opt header file.
voltage_compress (bool ) – If True, splits signal into segments based on voltage steps.
n_remove (int ) – Number of samples to remove from the beginning of each voltage step.
downsample (int ) – Downsampling factor to reduce data size.
prefilter (callable or None ) – Callable to apply preprocessing to the current signal.
-
-
-
-
-
-accepted_keywords = ['voltage_compress', 'n_remove', 'downsample', 'prefilter']
-
-
-
-
-current_multiplier : float = 1000000000.0
-
-
-
-
-ext = '.opt'
-
-
-
-
-find_peaks_in_segment ( current_data start_index end_index )
-Apply “find_peaks” on a segment of the current data and return
-the indices of the found peak
-
-
-
-
-find_peaks_slide_window ( current start_index window_shift_duration = 0.002 sampling_frequency = 250000 sign = 'negative' )
-Find peaks in the current every time the voltage changes.
-The search for the peaks happens in the window of the timestamp 0.02 seconds.
-
-
-
-
-process_custom_xml ( )
-Processes the custom XML file to extract voltage data, align the voltage waveform
-with the current, and detect peaks in the current signal every time the voltage changes.
-
-
-
-
-
-
-ionique.io. load_files_GUI ( )
-
-Create a form with:
-‘Compress voltage (only uncheck if voltage is not stepwise, e.g., triangle wave)’ : checkbox
‘Apply Filter?’ : checkbox
If ‘Apply Filter?’ checked: dynamic form from the Filter dataclass (types, options, limits).
-Returns (frame, get_values) where get_values() -> dict with: {‘compress_voltage’: bool, ‘apply_filter’: bool, ‘filter’: Filter|None}
-
-
-
-
-
-ionique.io. select_files_GUI ( )
-Open a file dialog allowing multiple selection, but only for ONE file type at a time.
-Users can switch the dialog’s file-type dropdown, but the final selection must be
-homogeneous (all the same extension) and in the allowed list.
-
-Returns
-
-tuple[str, …] Absolute paths of the selected files (empty tuple if the user cancels).
-
-
-
-
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-
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\ No newline at end of file
diff --git a/docs/_build/html/v0.3.0/api/parsers.html b/docs/_build/html/v0.3.0/api/parsers.html
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ionique.parsers — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-ionique.parsers
-Parser module for event and state segmentation in current recordings.
-This module provides a framework for parsing current trace data. It includes an abstract base Parser class
-and multiple concrete parser implementations for detecting spikes, noise, square pulses, and other event
-structures in ionic current signals.
-Some of the parsers and the parser base class were adapted from the
-PyPore Package by Jacob Schreiber and Kevin Karplus (https://github.com/jmschrei/PyPore )
-
-
-class ionique.parsers. AutoSquareParser ( threshold_baseline = 0.7 expected_conductance = 1.9 conductance_tolerance = 1.2 wrap_padding : int = 50 rules = [] )
-Bases: Parser
-Class for building a straightforward pipeline of data analysis
-to make it reusable with customized parameters
-
-
-__init__ ( threshold_baseline = 0.7 expected_conductance = 1.9 conductance_tolerance = 1.2 wrap_padding : int = 50 rules = [] )
-Initialize a Parser instance
-
-
-
-
-parse ( current eff_sampling_freq voltage )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current', 'eff_sampling_freq', 'voltage']
-
-
-
-
-
-
-class ionique.parsers. FilterDerivativeSegmenter ( low_threshold = 1 high_threshold = 2 cutoff_freq = 1000.0 sampling_freq = 100000.0 )
-Bases: Parser
-This parser will segment an event using a filter-derivative method. It will
-first apply a bessel filter at a certain cutoff to the current, then it will
-take the derivative of that, and segment when the derivative passes a
-threshold.
-
-
-__init__ ( low_threshold = 1 high_threshold = 2 cutoff_freq = 1000.0 sampling_freq = 100000.0 )
-Initialize a Parser instance
-
-
-
-
-parse ( current )
-Apply the filter-derivative method to filter the ionic current.
-
-
-
-
-set_params ( )
-
-Returns:
-
-
-
-
-
-
-
-
-class ionique.parsers. IVCurveAnalyzer ( current parent_segment sampling_frequency method = 'simple' )
-Bases: Parser
-THIS CLASS IS NOT IMPLEMENTED YET. DO NOT USE.
-Check for at Least Two Voltage Steps: confirm that the segment has at least two voltage steps.
-Divide into Voltage Segments: separate data into voltage segments.
-Call IVAnalyzer on Parent Segment: compute the mean and std of the current in each
-voltage segment.
-Return an Array of (voltage: (imean, istd)) pair:
-Also mean and std should be calulated in two ways
-
-
-__init__ ( current parent_segment sampling_frequency method = 'simple' )
-Initialize a Parser instance
-
-
-
-
-analyze ( )
-Analyze each voltage step in a parent segment
-
-
-
-
-
-
-class ionique.parsers. IVCurveParser ( voltage )
-Bases: object
-THIS CLASS IS NOT IMPLEMENTED YET! DO NOT USE.
-The class handles:
-
-
-Identifies a file where an IV curve is recorded based on the pattern.
-
-Here, the goal is to feed the voltage information to a module and have it search and do pattern matching to find one of
-these possibilities. .This can be a parser that accepts voltage array or compressed voltage as one of the inputs.
-
-
-__init__ ( voltage )
-
-
-
-
-parse ( )
-Searches for a pattern in the voltage data to identify IV.
-If none exists, the given datafile does not contain IV curves.
-If one or multiple IV curves are found, it returns start and
-end regions of each of them.
-Returns: list of indices: start and end for each deteced IV curve
-
-
-
-
-
-
-class ionique.parsers. MemoryParse ( starts ends )
-Bases: object
-A parser based on being fed previous split points, and splitting a raw file based
-those splits. Used predominately when loading previous split points from the
-database cache, to reconstruct a parsed file from “memory.
-
-
-__init__ ( starts ends )
-
-Parameters:
-
-
-
-
-
-
-
-parse ( current )
-
-
-
-
-
-
-class ionique.parsers. NoiseFilterParser ( noise_threshold = 60 detect_noise = True )
-Bases: Parser
-This unit is a parser subclass. when invoked on a segment (whole file or subsegment),
-it identifies the areas where bad things happened, and areas where the data is clean.
-Examples of bad things include high noise (60Hz noise from opening the cage), membrane
-rupture, and membrane formation attempts. it produces children from the good
-regions of the data and ignore the bad regions.
-
-
-__init__ ( noise_threshold = 60 detect_noise = True )
-Initialize a Parser instance
-
-
-
-
-parse ( data segment_start = 0 segment_end = None )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-
-
-class ionique.parsers. Parser
-Bases: ABC
-This class defines the interface and shared utility methods for parser implementations.
-Parsers are used to segment ionic current data
-into structured regions of interest (events).
-Subclasses should implement the parse method and define any additional logic
-required to detect features in the data.
-
-
-abstract __init__ ( )
-Initialize a Parser instance
-
-
-
-
-classmethod from_json ( _json )
-Critical: Not implemented yet, figure out how to make this work in pythion
-
-
-
-
-classmethod get_init_params ( )
-Get the initialization parameters of the parser class.
-
-Returns:
-A dictionary of initialization parameters, default None.
-Return type:
-dict or None
-
-
-
-
-
-classmethod get_name ( )
-Get the name of the parser class
-:return: The name of the class.
-:rtype: str
-
-
-
-
-classmethod get_process_inputs ( )
-
-
-
-
-classmethod get_process_outputs ( )
-Get the outputs generated by processing
-
-Returns:
-A list of outputs generated by parse method
-Return type:
-list or None
-
-
-
-
-
-get_required_parent_attributes ( )
-Get the list of required attributes from the parent segment
-
-Returns:
-A list of required parent attributes
-Return type:
-list[str]
-
-
-
-
-
-parse ( ** kwargs )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current']
-
-
-
-
-set_params ( param_dict )
-Updates each paramater presented in the GUI to the value input in the lineEdit
-corresponding to that value.
-:param param_dict: A dictionary
-:type param_dict: dict
-
-
-
-
-to_dict ( )
-Convert the parser’s attributes to a dictionary.
-
-Returns:
-A dictionary representation of the parser’s attributes.
-Return type:
-dict
-
-
-
-
-
-to_json ( filename = False )
-Convert the parser’s attributes to a JSON string.
-
-Parameters:
-filename (str , default bool ) – If filename, it writes the JSON string to the specified file.
-Returns:
-A dictionary representation of the parser’s attributes.
-Return type:
-str
-
-
-
-
-
-
-
-class ionique.parsers. SpeedyStatSplit ( sampling_freq min_width = 100 max_width = 1000000 window_width = 10000 min_gain_per_sample = None false_positive_rate = None prior_segments_per_second = None cutoff_freq = None )
-Bases: Parser
-A parser wrapper for the Cython-accelerated FastStatSplit algorithm.
-See cparsers.pyx FastStatSplit for full documentation. This is just a
-wrapper for the cyton implementation to add a GUI.
-
-
-__init__ ( sampling_freq min_width = 100 max_width = 1000000 window_width = 10000 min_gain_per_sample = None false_positive_rate = None prior_segments_per_second = None cutoff_freq = None )
-Initialize the SpeedyStatSplit instance
-
-Parameters:
-
-sampling_freq (int ) – The sampling frequency of the data
min_width (int , optional ) – Minimum width of a segment, default=100
max_width (int , optional ) – Maximum width of a segment, default=1000000.
window_width (int , optional ) – Width of the window, defaults to 10000.
min_gain_per_sample (float or None , optional ) – Minimum gain per sample, default=None
false_positive_rate (float or None , optional ) – Expected false positive rate , default=None
prior_segments_per_second (float or None , optional ) – Prior number of segments per second, default=None.
cutoff_freq (float or None , optional ) – Cutoff frequency for filtering, default=None
-
-
-
-
-
-best_single_split ( current )
-Determine the best single split point in the current data
-
-Parameters:
-current (numpy.ndarray ) – The current data to be analyzed.
-Returns:
-The best split point
-Return type:
-int or None
-
-
-
-
-
-parse ( current )
-Parse the current data to detect segments using the FastStatSplit
-
-Parameters:
-current (numpy.ndarray ) – The current data to be parsed
-Returns:
-A list of detected segments
-Return type:
-
-
-
-
-
-
-parse_meta ( current )
-Parse the current data and extract meta information
-
-Parameters:
-current (numpy.ndarray ) – The current data to be parsed.
-
-
-
-
-
-set_params ( )
-Set the parameters
-
-
-
-
-uiTemplate = [('sampling_freq', 'spin', {'bounds': [0, None], 'dec': True, 'siPrefix': True, 'step': 1, 'suffix': 'Hz', 'value': 200000}), ('min_width', 'spin', {'dec': True, 'int': True, 'min': 2, 'step': 1, 'value': 100}), ('max_width', 'spin', {'dec': True, 'int': True, 'step': 1, 'value': 1000000}), ('window_width', 'spin', {'dec': True, 'int': True, 'step': 1, 'value': 10000}), ('cutoff_freq', 'spin', {'hidden': True})]
-
-
-
-
-
-
-class ionique.parsers. SpikeParser ( height = None threshold = None distance = None prominence = None prominence_snr = None width = None wlen = None rel_height : float = 0.5 plateu_size = None fractional : bool = True )
-Bases: Parser
-A parser for detecting spike-like events in ionic current data.
-This parser wraps scipy.signal.find_peaks , supporting thresholds
-for features like peak height, prominence, and width. It computes event characteristics such
-as dwell time, relative prominence, and peak positions.
-
-
-__init__ ( height = None threshold = None distance = None prominence = None prominence_snr = None width = None wlen = None rel_height : float = 0.5 plateu_size = None fractional : bool = True ) → None
-Initialize the SpikeParser.
-
-Parameters:
-
-height (float or tuple or None ) – Minimum spike height.
threshold (float or tuple or None ) – Minimum vertical threshold.
distance (float or tuple or None ) – Minimum horizontal distance between peaks.
prominence (float or tuple or None ) – Minimum peak prominence.
prominence_snr (tuple or None ) – Alternative to prominence, specified as a tuple of signal-to-noise (SNR) ratios.
width (float or tuple or None ) – Full width at half maximum of the peak.
wlen (float or None ) – Width of the window used to calculate prominence.
rel_height (float ) – Relative height used in width calculation (range 0 to 1).
plateu_size (float or None ) – Minimum length of a flat-topped peak.
fractional (bool ) – Whether to interpret height, prominence, etc., as a fraction of the signal mean.
-
-
-
-
-
-parse ( current sampling_freq mean std start )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
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-required_parent_attributes : list [ str ] = ['current', 'sampling_freq', 'mean', 'start', 'std']
-
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-class ionique.parsers. lambda_event_parser ( threshold = 90 rules = None )
-Bases: Parser
-A simple rule-based parser which defines events as a sequential series
-of points which are below a certain threshold, then filtered based on other
-critereon such as total time or minimum current. Rules can be passed in at
-initiation, or set later, but must be a lambda function takes in a PreEvent
-object and performs some boolean operation.
-
-
-__init__ ( threshold = 90 rules = None )
-
-Parameters:
-
-threshold (float ) – The threshold for detecting events (e.g., current < threshold).
rules (list [ callable ] or None ) – A list of lambda functions that accept an event and return a bool.
-
-
-
-
-
-parse ( current )
-Perform a large capture of events by creating a boolean mask for when
-the current is below a threshold, then detecting the edges in those masks,
-and using the edges to partitition the sample. The events are
-then filtered before being returned.
-
-
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-set_params ( )
-Read in the data from the GUI and use it to customize
-the rules or threshold of the parser.
-
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-ionique.parsers. pairwise ( iterable )
-
-s -> (s0,s1), (s1,s2), (s2, s3), …
-
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-Parameters:
-iterable
-Returns:
-
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-class ionique.parsers. snakebase_parser ( threshold = 1.5 )
-Bases: Parser
-A simple parser based on dividing when the peak-to-peak
-amplitude of a wave exceeds a certain threshold.
-
-
-__init__ ( threshold = 1.5 )
-Initialize a Parser instance
-
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-
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-parse ( current )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
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-set_params ( )
-Updates each paramater presented in the GUI to the value input in the lineEdit
-corresponding to that value.
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-:type param_dict: dict
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-ionique.utils
-Signal Processing and Data Extraction Utilities
-This module provides a set of utility functions and classes designed for use in signal
-processing workflows.
-These tools are intended for both internal processing and external use cases such as analysis
-pipelines and API integrations.
-
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-class ionique.utils. Filter ( cutoff_frequency : float filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ] filter_method : Literal [ 'butter' , 'bessel' ] = 'butter' bidirectional : bool = True sampling_frequency : float = None )
-Bases: object
-This class allows a user to apply low-pass, high-pass,
-band-pass, or band-stop filters using standard filter types (Butterworth or Bessel).
-Filters are implemented using second-order sections (SOS) for numerical stability and
-can be applied in either forward-only or bidirectional mode.
-
-Parameters:
-
-cutoff_frequency (float or list [ float ] ) – The cutoff frequency or frequency band for the filter in Hz.
-For band filters, provide the band center or list of [low, high] values.
filter_type (Literal [ "lowpass" , "highpass" , "bandpass" , "bandstop" ] ) – The type of filter to apply. Options are “lowpass”, “highpass”, “bandpass”, or “bandstop”.
filter_method (Literal [ "butter" , "bessel" ] ) – The filter method. Supported options: “butter” (Butterworth), “bessel”. Defaults to “butter”.
order (int ) – The order of the filter. Must be >= 1. Defaults to 2.
bidirectional (bool ) – If True, applies filtering forward and backward using sosfiltfilt .
-If False, uses causal sosfilt . Defaults to True.
sampling_frequency (float , optional ) – Sampling frequency of the signal in Hz.
-
-
-
-__init__ ( cutoff_frequency : float filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ] filter_method : Literal [ 'butter' , 'bessel' ] = 'butter' bidirectional : bool = True sampling_frequency : float = None ) → None
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-bidirectional : bool = True
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-filter_method : Literal [ 'butter' , 'bessel' ] = 'butter'
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-filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ]
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-sampling_frequency : float = None
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-class ionique.utils. Singleton ( * args ** kwargs )
-Bases: type
-Generic singleton metaclass.
-This metaclass ensures the same return every time the class is instanced.
-
-
-__init__ ( * args ** kwargs )
-Class initialization
-
-Parameters:
-
-
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-
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-class ionique.utils. Trimmer ( samples_to_remove : int rank : str = 'vstep' newrank : str = 'vstepgap' )
-Bases: object
-Segment trimming utility for hierarchical signal data.
-This class operates on a segment tree. It traverses segments of a given rank (e.g., “vstep”) and trims
-a fixed number of samples from the start of each segment. The resulting trimmed segments are added
-as new child segments with a specified new rank (e.g., “vstepgap”).
-This is useful when initial samples of each segment include artifacts that should be excluded from analysis.
-
-Parameters:
-
-samples_to_remove (int ) – Number of samples to trim from the beginning of each segment.
rank (str ) – The hierarchical rank of segments to target for trimming. Defaults to “vstep”.
newrank (str ) – The rank name to assign to newly created trimmed child segments. Defaults to “vstepgap”.
-
-
-
-__init__ ( samples_to_remove : int rank : str = 'vstep' newrank : str = 'vstepgap' ) → None
-
-
-
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-newrank : str = 'vstepgap'
-
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-
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-rank : str = 'vstep'
-
-
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-samples_to_remove : int
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-
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-
-
-Extract features from hierarchical segments into a DataFrame.
-Traverses a hierarchical segment structure down to bottom_rank , then collects feature values
-from each segment. Static features are retrieved via get_feature() , constants can be added via
-add_ons , and custom computed features can be provided through lambdas .
-This is useful for generating structured datasets from annotated traces for statistical analysis or
-machine learning.
-
-Parameters:
-
-seg (object ) – The root segment or trace object containing a hierarchical structure. It must implement traverse_to_rank() and support get_feature() .
bottom_rank (str ) – The rank name of the lowest-level segments from which to extract features.
extractions (list [ str ] ) – List of feature names to extract directly using get_feature() on each segment.
-Common examples include: ‘mean’, ‘frac’, ‘duration’, ‘baseline’, ‘current’, ‘wrap’, ‘start’.
add_ons (dict ) – A dictionary of fixed key-value pairs to include as constant columns in the resulting DataFrame.
lambdas (dict ) – A dictionary mapping column names to lambda functions that compute derived values from each segment.
-Returns:
-A pandas DataFrame where each row corresponds to a bottom-rank segment and columns represent extracted and computed features.
-Return type:
-pandas.DataFrame
-
-
-
-
-
-ionique.utils. si_eval ( value unit = None return_unit = False )
-Evaluate and convert a value with an SI unit prefix to its numeric base value.
-This function handles both string-based and numeric values with
-SI (International System of Units) prefixes such as “k” (kilo), “M” (mega), “μ” (micro), etc.
-It multiplies the input value by the appropriate factor based on the SI prefix.
-This utility is useful when parsing human-readable measurement strings or standardizing
-units across data pipelines that mix strings and numeric formats.
-
-Parameters:
-
-value (str or float ) – The value to be converted. Can be a string like “1.2 kHz” or a numeric value (int or float).
unit (str , optional ) – The unit string (e.g., “kHz”, “mV”). Required only if value is a numeric type.
return_unit (bool , optional ) – If True, returns a tuple containing the converted numeric value and the base unit (e.g., ‘Hz’).
-If False, only the numeric value is returned.
-Raises:
-
-
-Returns:
-The converted value, optionally paired with the base unit.
-Return type:
-float or tuple[float, str]
-
-
-
-
-
-ionique.utils. split_voltage_steps ( voltage : ndarray n_remove = 0 as_tuples = False )
-Split a voltage signal into segments based on step changes.
-This function detects changes in the voltage signal and splits it into individual
-segments (or steps) wherever a change in value occurs. It is useful in analyzing
-stepwise voltage protocols.
-Optionally, a number of initial samples can be removed from each segment using the
-n_remove parameter. The output can either be two separate arrays of start and end
-indices, or a list of tuples representing each segment.
-
-Parameters:
-
-voltage (numpy.ndarray ) – 1D voltage signal array to be segmented.
n_remove (int ) – Number of samples to remove from the start of each split. Defaults to 0.
as_tuples (bool ) – If True, returns a list of (start, end) index tuples. If False, returns two arrays: start_indices and end_indices. Defaults to False.
-Raises:
-ValueError – If n_remove is negative or larger than the start of the voltage changes.
-Returns:
-Either:
-- A tuple of (start_indices, end_indices), where each is a numpy array of indices.
-- A list of (start, end) tuples if as_tuples=True .
-Return type:
-tuple[numpy.ndarray, numpy.ndarray] or list[tuple[int, int]]
-
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Data Input — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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<no title> — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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Index — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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- __init__() (ionique.core.AbstractSegmentTree method) , [1]
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- (ionique.core.MetaSegment method) , [1]
-(ionique.core.Segment method) , [1]
-(ionique.cparsers.FastStatSplit method) , [1]
-(ionique.datatypes.SessionFileManager method) , [1]
-(ionique.datatypes.TraceFile method) , [1]
-(ionique.io.AbstractFileReader method) , [1]
-(ionique.io.EDHReader method) , [1]
-(ionique.io.OPTReader method) , [1]
-(ionique.parsers.AutoSquareParser method) , [1]
-(ionique.parsers.FilterDerivativeSegmenter method) , [1]
-(ionique.parsers.IVCurveAnalyzer method) , [1]
-(ionique.parsers.IVCurveParser method) , [1]
-(ionique.parsers.lambda_event_parser method) , [1]
-(ionique.parsers.MemoryParse method) , [1]
-(ionique.parsers.NoiseFilterParser method) , [1]
-(ionique.parsers.Parser method) , [1]
-(ionique.parsers.snakebase_parser method) , [1]
-(ionique.parsers.SpeedyStatSplit method) , [1]
-(ionique.parsers.SpikeParser method) , [1]
-(ionique.utils.Filter method) , [1]
-(ionique.utils.Singleton method) , [1]
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- (ionique.cparsers.FastStatSplit method) , [1]
-(ionique.parsers.AutoSquareParser method) , [1]
-(ionique.parsers.FilterDerivativeSegmenter method) , [1]
-(ionique.parsers.IVCurveParser method) , [1]
-(ionique.parsers.lambda_event_parser method) , [1]
-(ionique.parsers.MemoryParse method) , [1]
-(ionique.parsers.NoiseFilterParser method) , [1]
-(ionique.parsers.Parser method) , [1]
-(ionique.parsers.snakebase_parser method) , [1]
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Ionique: A Nanopore Signal Analysis Framework — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-Ionique: A Nanopore Signal Analysis Framework
-Authors:
-Ali Fallahi, Dinara Boyko
-Date:
-Feb 11, 2025
-Version:
-0.1.0 (Development)
-
-Overview
-Ionique is a Python module for processing and analyzing nanopore signal data. It provides tools for reading, filtering, segmenting, and analyzing nanopore experimental data. The framework supports structured workflows for signal processing and quality control, allowing researchers to extract relevant information from raw current and voltage traces.
-
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-Features
-
-File Handling
-- Supports .edh , .opt , and .xml formats.
-- Extracts raw current and voltage traces.
-- Parses experimental metadata for structured analysis.
Signal Processing
-- Segmentation and event detection.
Analysis Modules
-- IV curve computation and voltage pattern detection.
-- Step response and dwell-time analysis.
-- Customizable parsers and filters for flexible workflows.
Usability
-- Python library for scripting and automation.
-
-
-Installation (Development)
-To install the development version of Ionique, clone the repository and install it in editable mode:
- clone https://github.com/wanunulab/ionique.git
-cd ionique
-pip install ionique
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Requirements:
-- Python > 3.10
-- Dependencies listed in requirements.txt
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-Future Development
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-Installation for beginners
-
-Welcome to Ionique!
-This tutorial is designed for non-coding users. If you have used Python and GitHub before, please proceed with the README.md
-Before diving into data analysis, let’s first install Python, GitHub, and Jupyter Notebook.
-
-
-Step 1: Install Python
-Download the package from Python Downloads .
-Choose the appropriate OS system for your machine. Then, scroll down to pick Python version 3.10 or later up to 3.12 .
-After Python has been successfully installed, we can go over some basic terminal commands.
-
-Linux Users:
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-MacOS Users:
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-Windows Users:
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-To check the installation, type:
-
-Some versions of Python may require the following command:
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-Step 2: GitHub
-First, you need to create an account by following the instructions on GitHub .
-Now, open the repository link: Ionique Repository , click on the green “Code” button, and download the ZIP file. Unzip it.
-Move the folder to the desired location on your computer.
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-Step 3: Moving Between Folders in the Terminal
-You can find a short list of basic commands here: Linux vs Windows Commands .
-Check your current location using:
-
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-pwd (Unix, Linux, Mac)
cd (Windows)
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-To list items in the current directory:
-
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-ls (Unix, Linux, Mac)
chdir (Windows)
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-The most important command for this tutorial is cd , which stands for “change directory.” To move around folders in the terminal, type:
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-Find the ionique folder and move inside it.
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-Step 4: Virtual Environment
-A virtual environment is an encapsulated workspace on your computer that allows you to install software packages for a project. It helps to keep the versions of different packages within a project completely separate from other projects.
-To learn more about virtual environments, visit Python Virtual Environments .
-Let’s create your first virtual environment!
-Run the following command:
- -m venv ionique_env
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Then, activate it:
-source ionique_env/bin/activate # Linux/MacOS
-ionique_env\S cripts\a ctivate.bat # Windows
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Now, install the Ionique requirements:
-
-If your OS and Python version require the ``python3`` command, use ``pip3`` instead.
-Next, install JupyterLab:
-
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-Step 5: Run the Notebooks
-Copy the notebook you want to run into the data folder. (You don’t need to use the terminal for this step.)
-Now, return to the terminal window and navigate to the folder where the copied notebook is stored.
-Then, run:
-
-You are now ready to work on data analysis!
-
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-Step 6: Finish Workflow
-Once you are done with the analysis, go back to the terminal and press Ctrl + C .
-Then, type:
-
-to deactivate the virtual environment.
-Note: Starting from Step 5 , the procedure will be identical each time you want to run Ionique .
-Happy using Ionique!
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- ionique
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-Install
-
-Install ionique
-The most recent release of the ionique pip_
-
-If you are very new to coding, more detailed instructions, we provide guides on VSCode, Python3 and ionique installment.
-
-
-Installation from Source
-If you want to contribute to ionique you can build ionique from source using the
-instructions below.
-$ git clone https://github.com/wanunulab/ionique.git
-$ cd ionique
-$ pip install -e .
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ionique — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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- ionique
-
-
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-
-
-
-
-ionique
-
-
-ionique.core
-
-
-This holds the core data types which may be abstracted in
-many different applications.
-This module defines a tree-based framework for representing, parsing, and annotating
-segments of ionic current data.
-
-
-class ionique.core. AbstractSegmentTree
-Bases: object
-Class for managing hierarchical segments.
-This class supports a tree structure where each segment can contain multiple
-child segments, enabling recursive parsing and analysis of nested data.
-It provides utilities to manage relationships between segments, apply parsers,
-and extract subsegments by rank.
-
-
-__init__ ( ) → None
-Initialize the segment
-
-
-
-
-add_child ( child : AnySegment ) → None
-Add a single child
-
-
-
-
-add_children ( children : list [ AnySegment ] ) → None
-Add multiple children if:
-1. Child position is the within parent’s segment.
-2. The length of the child’s segment is > 0
-3. There is no overlap between consecutive children
-Or add children with no check
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-clear_children ( )
-Clear the list of children
-
-
-
-
-climb_to_rank ( rank : str ) → AnySegment | None
-Go up and find the segment with the specified rank
-:return: segment
-
-
-
-
-end : int | None
-
-
-
-
-get_feature ( name : str )
-Gets the ‘name’ feature of the current segment or its parent
-
-
-
-
-get_top_parent ( ) → AnySegment
-Recursively go up and find top most parent
-:return: parent
-
-
-
-
-property n : int
-Get the length of the segment
-:return: len(segment)
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-parse ( parser newrank : str at_child_rank : str | None = None ** kwargs ) → bool
-Parses the data in the segment or at a particular rank of child segments
-into children segments with a new rank.
-
-Parameters:
-
-parser (Parser parse method and required input attributes.
newrank (str ) – Rank to assign to the newly created child segments.
at_child_rank (str or None ) – Determines whether to traverse the children tree down to a given rank.
kwargs – Additional arguments to pass to the parser.
-Returns:
-True if parsing was successful, otherwise raises an exception.
-Return type:
-bool
-
-
-
-
-
-rank : str
-
-
-
-
-property relative_end : int
-Get end position relative to parent segment
-:return: end position
-
-
-
-
-property relative_slice : ndarray
-Slice the array to get the data between end-start segment
-:return: sliced np array
-
-
-
-
-property relative_start : int
-Get the start position relative to parent segment
-:return: start position
-
-
-
-
-property slice : ndarray
-Slice the array to get the data between start-end segment
-:return: sliced np array
-
-
-
-
-start : int | None
-
-
-
-
-traverse_to_rank ( rank : str ) → list
-Traverse the tree rank and get the list of the segments of the rank
-:param: rank
-:return: list of segments
-
-
-
-
-
-
-class ionique.core. MetaSegment ( start : int end : int parent : AnySegment | None = None rank : str | None = None unique_features : dict | None = {} ** kwargs )
-Bases: AbstractSegmentTree
-The metadata on an abstract segment of ionic current. All information about a segment can be
-loaded, without the expectation of the array of floats.
-
-
-__init__ ( start : int end : int parent : AnySegment | None = None rank : str | None = None unique_features : dict | None = {} ** kwargs )
-
-Parameters:
-
-start (int ) – Start index of the segment.
end (int ) – End index of the segment.
parent (AbstractSegmentTree or None ) – Parent segment in the tree (optional).
rank (str or None ) – Rank identifier for this segment (optional).
unique_features (dict ) – Dictionary of metadata features.
-
-
-
-
-
-property current
-Get the current data of the segment if the segment correlates to the file
-:return: current
-
-
-
-
-delete ( )
-Delete itself. There are no arrays with which to delete references for.
-
-
-
-
-property duration : float
-Get the duration of the segment = start - end
-:return: duration
-
-
-
-
-classmethod from_json ( filename = None in_json = None )
-Read in a metasegment from a JSON and return a metasegment object.
-Either pass in a file which has a segment stored, or an actual JSON
-object.
-
-
-
-
-property max
-Calculate the maximum value of the current array.
-
-Returns:
-Maximum value of the current array.
-Return type:
-float
-
-
-
-
-
-property mean
-Calculate the mean of the current array.
-
-Returns:
-Mean value of the current array.
-Return type:
-float
-
-
-
-
-
-property min
-Calculate the minimum value of the current array.
-
-Returns:
-Minimum value of the current array.
-Return type:
-float
-
-
-
-
-
-property std
-Calculate the standard deviation of the current array.
-
-Returns:
-Standard deviation of the current array.
-Return type:
-float
-
-
-
-
-
-property time
-Get the time data of the segment if the corresponding rank is in file
-:return: time
-
-
-
-
-to_dict ( )
-Return a dict representation of the metadata, usually used prior to
-converting the dict to a JSON.
-
-
-
-
-to_json ( filename = None )
-Return a JSON representation of this, by reporting the important
-metadata.
-
-
-
-
-to_meta ( )
-Kept to allow for error handling, but since it’s already a metasegment
-it won’t actually do anything.
-
-
-
-
-unique_features
-
-
-
-
-
-
-class ionique.core. Segment ( current ** kwargs )
-Bases: AbstractSegmentTree
-A segment of ionic current, and methods relevant for collecting metadata. The ionic current is
-expected to be passed as a numpy array of floats. Metadata methods (mean, std..) are decorated
-as properties to reduce overall computational time, making them calculated on the fly rather
-than during analysis.
-
-
-__init__ ( current ** kwargs )
-The segment must have a list of ionic current, of which it stores some statistics about.
-It may also take in as many keyword arguments as needed, such as start time or duration
-if already known. Cannot override statistical measurements.
-:param current: Numpy array of current data points.
-:type current: np.ndarray
-:param kwargs: Additional attributes like ‘start’, ‘end’, ‘rank’, etc.
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-delete ( )
-Deleting this segment requires deleting its reference to the ionic
-current array, and then deleting itself.
-
-
-
-
-end : int | None
-
-
-
-
-classmethod from_json ( filename = None json = None )
-Read in a segment from a JSON and return a metasegment object. Either
-pass in a file which has a segment stored, or an actual JSON object.
-
-
-
-
-property max
-Calculate the maximum value of the current array.
-
-Returns:
-Maximum value of the current array.
-Return type:
-float
-
-
-
-
-
-property mean
-Calculate the mean of the current array.
-
-Returns:
-Mean value of the current array.
-Return type:
-float
-
-
-
-
-
-property min
-Calculate the minimum value of the current array.
-
-Returns:
-Minimum value of the current array.
-Return type:
-float
-
-
-
-
-
-property n
-Get the number of elements in the current array.
-
-Returns:
-Number of elements in the current array.
-Return type:
-int
-
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-rank : str
-
-
-
-
-scale ( sampling_freq )
-Rescale all of the values to go from samples to seconds.
-
-
-
-
-start : int | None
-
-
-
-
-property std
-Calculate the standard deviation of the current array.
-
-Returns:
-Standard deviation of the current array.
-Return type:
-float
-
-
-
-
-
-to_dict ( )
-Return a dict representation of the metadata, usually used prior to
-converting the dict to a JSON.
-
-
-
-
-to_json ( filename = None )
-Return a JSON representation of this, by reporting the important
-metadata.
-
-
-
-
-to_meta ( )
-Convert from a segment to a ‘metasegment’, which stores only metadata
-about the segment and not the full array of ionic current.
-
-
-
-
-
-
-ionique.core. ignored ( * exceptions )
-Replace the “try, except: pass” paradigm by replacing those three lines with a single line.
-Taken from the latest 3.4 python update push by Raymond Hettinger, see:
-http://hg.python.org/cpython/rev/406b47c64480
-
-
-
-
-ionique.cparsers
-This contains cython implementations of ionic current parsers which are in
-parsers.py. Currently the only parser is StatSplit, which is implemented
-as FastStatSplit.
-
-
-class ionique.cparsers. FastStatSplit
-Bases: object
-A cython implementation of the segmenter written by Kevin Karplus. Sped up approximately 50-100x
-compared to the Python implementation depending on parameters.
-
-
-__init__ ( * args ** kwargs )
-
-
-
-
-best_single_split ( current )
-Wrapper for a single call to _best_single_split. It will find the
-single best split in a series of current, and return the index of
-that split. Returns a tuple of ( gain, index ) where gain is the
-gain in variance by splitting there, and index is the index at which
-the split should occur in the current array.
-
-
-
-
-min_gain
-
-
-
-
-parse ( current )
-Wrapper function for the segmentation, which is implemented in cython.
-
-
-
-
-parse_meta ( current )
-Wrapper function for the segmentation, which is implemented in cython. returns meta segments (no current array)
-
-
-
-
-score_samples ( current no_split = False )
-Return a series of lists scoring each sample. However, this isn’t just
-scoring each sample once. Every time a split is detected, it will return
-a new list with newly scored samples. In essence, it returns one list
-per scan of the current using the recursive method.
-
-
-
-
-
-
-ionique.cparsers. pairwise ( iterable )
-
-
-
-
-ionique.datatypes
-Data structure definitions and session file management for analysis.
-
-
-class ionique.datatypes. SessionFileManager ( * args ** kwargs )
-Bases: MetaSegment
-Singleton-based session manager for hierarchical data.
-This class inherits from MetaSegment and uses the Singleton metaclass
-to ensure only one instance exists during a given session. It serves as
-the root segment for a data analysis session and is responsible for managing
-metadata and registered “affectors” (i.e., objects that modify or
-interact with the session).
-
-
-__init__ ( ) → None
-Initialize the session manager.
-Sets the session start time and prepares the affector log structure.
-
-
-
-
-add_child ( child : AnySegment ) → None
-Add a single child segment to the session’s hierarchy.
-
-Parameters:
-child (AnySegment ) – The segment to be added as a child.
-
-
-
-
-
-add_children ( children : list [ AnySegment ] ) → None
-Add multiple child segments to the session.
-
-Parameters:
-children (list [ AnySegment ] ) – A list of segments to be added as children.
-
-
-
-
-
-rank : str = 'root'
-
-
-
-
-register_affector ( affector )
-Register an affector object that influences the session data.
-Records metadata including class name, string representation, and timestamp,
-and logs the event with a unique identifier.
-
-Parameters:
-affector (object ) – An object that modifies or interacts with the session.
-Returns:
-A UUID string uniquely identifying the registered affector.
-Return type:
-str
-
-
-
-
-
-unique_features
-
-
-
-
-
-
-class ionique.datatypes. TraceFile ( current : ndarray voltage = None rank = 'file' parent = None unique_features : dict = {} metadata : dict = {} )
-Bases: Segment
-Data structure for representing a single trace file with current and optional voltage information.
-This class inherits from Segment and encapsulates current and voltage data. It sets up segment metadata,
-defines parent-child hierarchy, and optionally initializes child segments based on voltage steps.
-Typical usage includes assigning metadata and organizing hierarchical
-data structures for downstream processing or visualization.
-
-
-__init__ ( current : ndarray voltage = None rank = 'file' parent = None unique_features : dict = {} metadata : dict = {} )
-If voltage steps are provided, corresponding child segments of rank “vstep” are created.
-
-Parameters:
-
-current (numpy.ndarray ) – current array.
voltage (list [ tuple [ tuple [ int , int ] , float ] ] or None ) – Optional list of tuples containing (start, end) index pairs and voltage values.
-Used to create child segments for each voltage step.
rank (str ) – Segment rank label, defaults to “file”.
parent (Segment or None ) – Optional parent segment to which this trace belongs.
unique_features (dict ) – Dictionary of metadata such as sampling frequency.
metadata (dict ) – Additional metadata.
-
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-delete ( )
-Delete the current object, removing it from its parent
-
-
-
-
-end : int | None
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-plot ( rank axes downsample_per_rank color_per_rank )
-Method for plotting
-
-
-
-
-rank : str
-
-
-
-
-start : int | None
-
-
-
-
-
-
-ionique.io
-Input/output utilities for data files.
-This module provides an interface for loading, parsing, and preprocessing
-signal data from various file formats (e.g., .edh , .opt , .abf , .dat , .xml ). It defines a base reader class
-and concrete implementations that handle format-specific logic, metadata extraction,
-data alignment, and optional preprocessing steps.
-This module is central to converting raw experimental data into analyzable form.
-
-
-class ionique.io. AbstractFileReader
-Bases: object
-An abstract class for reading various data files (.abf, .edh, .mat, .opt, .xml)
-This class defines the structure for file readers
-
-
-__init__ ( )
-
-
-
-
-accepted_keywords = []
-
-
-
-
-current_multiplier : float = 1.0
-
-
-
-
-ext = '___'
-
-
-
-
-read ( filename : str ** kwargs )
-Read a datafile or series of files. Files are identified according to their extension.
-Data formats that come with a header file must be referred to by the header file.
-If inheriting from the AbstractFileReader class, do not override this function;
-instead, create a custom _read method.
-
-Parameters:
-filename – file name or list of file names, typically
-
-given as a string or PathLike object.
-:type filename: str or os.PathLike or list[str] or list[os.PathLike]
-:param **
-
-
-
-
-voltage_multiplier : float = 1.0
-
-
-
-
-
-
-class ionique.io. EDHReader ( edh_filename voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Bases: AbstractFileReader
-Reader class for loading .edh data files and their associated current data.
-This class parses the .edh header file and automatically loads the corresponding signal data
-(either from .abf or .dat files in the same directory). It extracts metadata, converts raw
-current and voltage data to SI units, and supports optional preprocessing steps such as
-voltage step segmentation, downsampling, and signal filtering.
-
-
-__init__ ( edh_filename voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Initialize the EDHReader and load signal data from associated files.
-
-Parameters:
-
-edh_filename (str ) – Path to the .edh header file.
voltage_compress (bool ) – If True, splits signal into segments based on voltage steps.
n_remove (int ) – Number of samples to remove from the beginning of each voltage step.
downsample (int ) – Downsampling factor to reduce data size.
prefilter (callable or None ) – Callable to apply preprocessing to the current signal.
-
-
-
-
-
-accepted_keywords = ['voltage_compress', 'n_remove', 'downsample', 'prefilter']
-
-
-
-
-current_multiplier : float = 1e-09
-
-
-
-
-ext = '.edh'
-
-
-
-
-voltage_multiplier : float = 0.001
-
-
-
-
-
-
-class ionique.io. OPTReader ( opt_filename : str voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Bases: AbstractFileReader
-Reader for .opt data files with corresponding XML or _volt.opt metadata.
-This class reads current data from .opt files and attempts to extract or reconstruct
-corresponding voltage data using associated .xml or _volt.opt files found in the same
-directory. It handles metadata extraction, signal preprocessing, voltage alignment, and
-segment compression based on voltage steps.
-Supported XML structures include both standard HWtiming_cap_step formats and timestamp-based
-custom formats.
-
-
-__init__ ( opt_filename : str voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Initialize the OPTReader instance and load corresponding files.
-
-Parameters:
-
-opt_filename (str ) – Path to the .opt header file.
voltage_compress (bool ) – If True, splits signal into segments based on voltage steps.
n_remove (int ) – Number of samples to remove from the beginning of each voltage step.
downsample (int ) – Downsampling factor to reduce data size.
prefilter (callable or None ) – Callable to apply preprocessing to the current signal.
-
-
-
-
-
-accepted_keywords = ['voltage_compress', 'n_remove', 'downsample', 'prefilter']
-
-
-
-
-current_multiplier : float = 1000000000.0
-
-
-
-
-ext = '.opt'
-
-
-
-
-find_peaks_in_segment ( current_data start_index end_index )
-Apply “find_peaks” on a segment of the current data and return
-the indices of the found peak
-
-
-
-
-find_peaks_slide_window ( current start_index window_shift_duration = 0.002 sampling_frequency = 250000 sign = 'negative' )
-Find peaks in the current every time the voltage changes.
-The search for the peaks happens in the window of the timestamp 0.02 seconds.
-
-
-
-
-process_custom_xml ( )
-Processes the custom XML file to extract voltage data, align the voltage waveform
-with the current, and detect peaks in the current signal every time the voltage changes.
-
-
-
-
-
-
-ionique.io. load_files_GUI ( )
-
-Create a form with:
-‘Compress voltage (only uncheck if voltage is not stepwise, e.g., triangle wave)’ : checkbox
‘Apply Filter?’ : checkbox
If ‘Apply Filter?’ checked: dynamic form from the Filter dataclass (types, options, limits).
-Returns (frame, get_values) where get_values() -> dict with: {‘compress_voltage’: bool, ‘apply_filter’: bool, ‘filter’: Filter|None}
-
-
-
-
-
-ionique.io. select_files_GUI ( )
-Open a file dialog allowing multiple selection, but only for ONE file type at a time.
-Users can switch the dialog’s file-type dropdown, but the final selection must be
-homogeneous (all the same extension) and in the allowed list.
-
-Returns
-
-tuple[str, …] Absolute paths of the selected files (empty tuple if the user cancels).
-
-
-
-
-
-
-ionique.parsers
-Parser module for event and state segmentation in current recordings.
-This module provides a framework for parsing current trace data. It includes an abstract base Parser class
-and multiple concrete parser implementations for detecting spikes, noise, square pulses, and other event
-structures in ionic current signals.
-Some of the parsers and the parser base class were adapted from the
-PyPore Package by Jacob Schreiber and Kevin Karplus (https://github.com/jmschrei/PyPore )
-
-
-class ionique.parsers. AutoSquareParser ( threshold_baseline = 0.7 expected_conductance = 1.9 conductance_tolerance = 1.2 wrap_padding : int = 50 rules = [] )
-Bases: Parser
-Class for building a straightforward pipeline of data analysis
-to make it reusable with customized parameters
-
-
-__init__ ( threshold_baseline = 0.7 expected_conductance = 1.9 conductance_tolerance = 1.2 wrap_padding : int = 50 rules = [] )
-Initialize a Parser instance
-
-
-
-
-parse ( current eff_sampling_freq voltage )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current', 'eff_sampling_freq', 'voltage']
-
-
-
-
-
-
-class ionique.parsers. FilterDerivativeSegmenter ( low_threshold = 1 high_threshold = 2 cutoff_freq = 1000.0 sampling_freq = 100000.0 )
-Bases: Parser
-This parser will segment an event using a filter-derivative method. It will
-first apply a bessel filter at a certain cutoff to the current, then it will
-take the derivative of that, and segment when the derivative passes a
-threshold.
-
-
-__init__ ( low_threshold = 1 high_threshold = 2 cutoff_freq = 1000.0 sampling_freq = 100000.0 )
-Initialize a Parser instance
-
-
-
-
-parse ( current )
-Apply the filter-derivative method to filter the ionic current.
-
-
-
-
-set_params ( )
-
-Returns:
-
-
-
-
-
-
-
-
-class ionique.parsers. IVCurveAnalyzer ( current parent_segment sampling_frequency method = 'simple' )
-Bases: Parser
-THIS CLASS IS NOT IMPLEMENTED YET. DO NOT USE.
-Check for at Least Two Voltage Steps: confirm that the segment has at least two voltage steps.
-Divide into Voltage Segments: separate data into voltage segments.
-Call IVAnalyzer on Parent Segment: compute the mean and std of the current in each
-voltage segment.
-Return an Array of (voltage: (imean, istd)) pair:
-Also mean and std should be calulated in two ways
-
-
-__init__ ( current parent_segment sampling_frequency method = 'simple' )
-Initialize a Parser instance
-
-
-
-
-analyze ( )
-Analyze each voltage step in a parent segment
-
-
-
-
-
-
-class ionique.parsers. IVCurveParser ( voltage )
-Bases: object
-THIS CLASS IS NOT IMPLEMENTED YET! DO NOT USE.
-The class handles:
-
-
-Identifies a file where an IV curve is recorded based on the pattern.
-
-Here, the goal is to feed the voltage information to a module and have it search and do pattern matching to find one of
-these possibilities. .This can be a parser that accepts voltage array or compressed voltage as one of the inputs.
-
-
-__init__ ( voltage )
-
-
-
-
-parse ( )
-Searches for a pattern in the voltage data to identify IV.
-If none exists, the given datafile does not contain IV curves.
-If one or multiple IV curves are found, it returns start and
-end regions of each of them.
-Returns: list of indices: start and end for each deteced IV curve
-
-
-
-
-
-
-class ionique.parsers. MemoryParse ( starts ends )
-Bases: object
-A parser based on being fed previous split points, and splitting a raw file based
-those splits. Used predominately when loading previous split points from the
-database cache, to reconstruct a parsed file from “memory.
-
-
-__init__ ( starts ends )
-
-Parameters:
-
-
-
-
-
-
-
-parse ( current )
-
-
-
-
-
-
-class ionique.parsers. NoiseFilterParser ( noise_threshold = 60 detect_noise = True )
-Bases: Parser
-This unit is a parser subclass. when invoked on a segment (whole file or subsegment),
-it identifies the areas where bad things happened, and areas where the data is clean.
-Examples of bad things include high noise (60Hz noise from opening the cage), membrane
-rupture, and membrane formation attempts. it produces children from the good
-regions of the data and ignore the bad regions.
-
-
-__init__ ( noise_threshold = 60 detect_noise = True )
-Initialize a Parser instance
-
-
-
-
-parse ( data segment_start = 0 segment_end = None )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-
-
-class ionique.parsers. Parser
-Bases: ABC
-This class defines the interface and shared utility methods for parser implementations.
-Parsers are used to segment ionic current data
-into structured regions of interest (events).
-Subclasses should implement the parse method and define any additional logic
-required to detect features in the data.
-
-
-abstract __init__ ( )
-Initialize a Parser instance
-
-
-
-
-classmethod from_json ( _json )
-Critical: Not implemented yet, figure out how to make this work in pythion
-
-
-
-
-classmethod get_init_params ( )
-Get the initialization parameters of the parser class.
-
-Returns:
-A dictionary of initialization parameters, default None.
-Return type:
-dict or None
-
-
-
-
-
-classmethod get_name ( )
-Get the name of the parser class
-:return: The name of the class.
-:rtype: str
-
-
-
-
-classmethod get_process_inputs ( )
-
-
-
-
-classmethod get_process_outputs ( )
-Get the outputs generated by processing
-
-Returns:
-A list of outputs generated by parse method
-Return type:
-list or None
-
-
-
-
-
-get_required_parent_attributes ( )
-Get the list of required attributes from the parent segment
-
-Returns:
-A list of required parent attributes
-Return type:
-list[str]
-
-
-
-
-
-parse ( ** kwargs )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current']
-
-
-
-
-set_params ( param_dict )
-Updates each paramater presented in the GUI to the value input in the lineEdit
-corresponding to that value.
-:param param_dict: A dictionary
-:type param_dict: dict
-
-
-
-
-to_dict ( )
-Convert the parser’s attributes to a dictionary.
-
-Returns:
-A dictionary representation of the parser’s attributes.
-Return type:
-dict
-
-
-
-
-
-to_json ( filename = False )
-Convert the parser’s attributes to a JSON string.
-
-Parameters:
-filename (str , default bool ) – If filename, it writes the JSON string to the specified file.
-Returns:
-A dictionary representation of the parser’s attributes.
-Return type:
-str
-
-
-
-
-
-
-
-class ionique.parsers. SpeedyStatSplit ( sampling_freq min_width = 100 max_width = 1000000 window_width = 10000 min_gain_per_sample = None false_positive_rate = None prior_segments_per_second = None cutoff_freq = None )
-Bases: Parser
-A parser wrapper for the Cython-accelerated FastStatSplit algorithm.
-See cparsers.pyx FastStatSplit for full documentation. This is just a
-wrapper for the cyton implementation to add a GUI.
-
-
-__init__ ( sampling_freq min_width = 100 max_width = 1000000 window_width = 10000 min_gain_per_sample = None false_positive_rate = None prior_segments_per_second = None cutoff_freq = None )
-Initialize the SpeedyStatSplit instance
-
-Parameters:
-
-sampling_freq (int ) – The sampling frequency of the data
min_width (int , optional ) – Minimum width of a segment, default=100
max_width (int , optional ) – Maximum width of a segment, default=1000000.
window_width (int , optional ) – Width of the window, defaults to 10000.
min_gain_per_sample (float or None , optional ) – Minimum gain per sample, default=None
false_positive_rate (float or None , optional ) – Expected false positive rate , default=None
prior_segments_per_second (float or None , optional ) – Prior number of segments per second, default=None.
cutoff_freq (float or None , optional ) – Cutoff frequency for filtering, default=None
-
-
-
-
-
-best_single_split ( current )
-Determine the best single split point in the current data
-
-Parameters:
-current (numpy.ndarray ) – The current data to be analyzed.
-Returns:
-The best split point
-Return type:
-int or None
-
-
-
-
-
-parse ( current )
-Parse the current data to detect segments using the FastStatSplit
-
-Parameters:
-current (numpy.ndarray ) – The current data to be parsed
-Returns:
-A list of detected segments
-Return type:
-
-
-
-
-
-
-parse_meta ( current )
-Parse the current data and extract meta information
-
-Parameters:
-current (numpy.ndarray ) – The current data to be parsed.
-
-
-
-
-
-set_params ( )
-Set the parameters
-
-
-
-
-uiTemplate = [('sampling_freq', 'spin', {'bounds': [0, None], 'dec': True, 'siPrefix': True, 'step': 1, 'suffix': 'Hz', 'value': 200000}), ('min_width', 'spin', {'dec': True, 'int': True, 'min': 2, 'step': 1, 'value': 100}), ('max_width', 'spin', {'dec': True, 'int': True, 'step': 1, 'value': 1000000}), ('window_width', 'spin', {'dec': True, 'int': True, 'step': 1, 'value': 10000}), ('cutoff_freq', 'spin', {'hidden': True})]
-
-
-
-
-
-
-class ionique.parsers. SpikeParser ( height = None threshold = None distance = None prominence = None prominence_snr = None width = None wlen = None rel_height : float = 0.5 plateu_size = None fractional : bool = True )
-Bases: Parser
-A parser for detecting spike-like events in ionic current data.
-This parser wraps scipy.signal.find_peaks , supporting thresholds
-for features like peak height, prominence, and width. It computes event characteristics such
-as dwell time, relative prominence, and peak positions.
-
-
-__init__ ( height = None threshold = None distance = None prominence = None prominence_snr = None width = None wlen = None rel_height : float = 0.5 plateu_size = None fractional : bool = True ) → None
-Initialize the SpikeParser.
-
-Parameters:
-
-height (float or tuple or None ) – Minimum spike height.
threshold (float or tuple or None ) – Minimum vertical threshold.
distance (float or tuple or None ) – Minimum horizontal distance between peaks.
prominence (float or tuple or None ) – Minimum peak prominence.
prominence_snr (tuple or None ) – Alternative to prominence, specified as a tuple of signal-to-noise (SNR) ratios.
width (float or tuple or None ) – Full width at half maximum of the peak.
wlen (float or None ) – Width of the window used to calculate prominence.
rel_height (float ) – Relative height used in width calculation (range 0 to 1).
plateu_size (float or None ) – Minimum length of a flat-topped peak.
fractional (bool ) – Whether to interpret height, prominence, etc., as a fraction of the signal mean.
-
-
-
-
-
-parse ( current sampling_freq mean std start )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current', 'sampling_freq', 'mean', 'start', 'std']
-
-
-
-
-
-
-class ionique.parsers. lambda_event_parser ( threshold = 90 rules = None )
-Bases: Parser
-A simple rule-based parser which defines events as a sequential series
-of points which are below a certain threshold, then filtered based on other
-critereon such as total time or minimum current. Rules can be passed in at
-initiation, or set later, but must be a lambda function takes in a PreEvent
-object and performs some boolean operation.
-
-
-__init__ ( threshold = 90 rules = None )
-
-Parameters:
-
-threshold (float ) – The threshold for detecting events (e.g., current < threshold).
rules (list [ callable ] or None ) – A list of lambda functions that accept an event and return a bool.
-
-
-
-
-
-parse ( current )
-Perform a large capture of events by creating a boolean mask for when
-the current is below a threshold, then detecting the edges in those masks,
-and using the edges to partitition the sample. The events are
-then filtered before being returned.
-
-
-
-
-set_params ( )
-Read in the data from the GUI and use it to customize
-the rules or threshold of the parser.
-
-
-
-
-
-
-ionique.parsers. pairwise ( iterable )
-
-s -> (s0,s1), (s1,s2), (s2, s3), …
-
-
-Parameters:
-iterable
-Returns:
-
-
-
-
-
-
-class ionique.parsers. snakebase_parser ( threshold = 1.5 )
-Bases: Parser
-A simple parser based on dividing when the peak-to-peak
-amplitude of a wave exceeds a certain threshold.
-
-
-__init__ ( threshold = 1.5 )
-Initialize a Parser instance
-
-
-
-
-parse ( current )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-set_params ( )
-Updates each paramater presented in the GUI to the value input in the lineEdit
-corresponding to that value.
-:param param_dict: A dictionary
-:type param_dict: dict
-
-
-
-
-
-
-ionique.utils
-Signal Processing and Data Extraction Utilities
-This module provides a set of utility functions and classes designed for use in signal
-processing workflows.
-These tools are intended for both internal processing and external use cases such as analysis
-pipelines and API integrations.
-
-
-class ionique.utils. Filter ( cutoff_frequency : float filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ] filter_method : Literal [ 'butter' , 'bessel' ] = 'butter' bidirectional : bool = True sampling_frequency : float = None )
-Bases: object
-This class allows a user to apply low-pass, high-pass,
-band-pass, or band-stop filters using standard filter types (Butterworth or Bessel).
-Filters are implemented using second-order sections (SOS) for numerical stability and
-can be applied in either forward-only or bidirectional mode.
-
-Parameters:
-
-cutoff_frequency (float or list [ float ] ) – The cutoff frequency or frequency band for the filter in Hz.
-For band filters, provide the band center or list of [low, high] values.
filter_type (Literal [ "lowpass" , "highpass" , "bandpass" , "bandstop" ] ) – The type of filter to apply. Options are “lowpass”, “highpass”, “bandpass”, or “bandstop”.
filter_method (Literal [ "butter" , "bessel" ] ) – The filter method. Supported options: “butter” (Butterworth), “bessel”. Defaults to “butter”.
order (int ) – The order of the filter. Must be >= 1. Defaults to 2.
bidirectional (bool ) – If True, applies filtering forward and backward using sosfiltfilt .
-If False, uses causal sosfilt . Defaults to True.
sampling_frequency (float , optional ) – Sampling frequency of the signal in Hz.
-
-
-
-__init__ ( cutoff_frequency : float filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ] filter_method : Literal [ 'butter' , 'bessel' ] = 'butter' bidirectional : bool = True sampling_frequency : float = None ) → None
-
-
-
-
-bidirectional : bool = True
-
-
-
-
-cutoff_frequency : float
-
-
-
-
-filter_method : Literal [ 'butter' , 'bessel' ] = 'butter'
-
-
-
-
-filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ]
-
-
-
-
-sampling_frequency : float = None
-
-
-
-
-
-
-class ionique.utils. Singleton ( * args ** kwargs )
-Bases: type
-Generic singleton metaclass.
-This metaclass ensures the same return every time the class is instanced.
-
-
-__init__ ( * args ** kwargs )
-Class initialization
-
-Parameters:
-
-
-
-
-
-
-
-
-
-class ionique.utils. Trimmer ( samples_to_remove : int rank : str = 'vstep' newrank : str = 'vstepgap' )
-Bases: object
-Segment trimming utility for hierarchical signal data.
-This class operates on a segment tree. It traverses segments of a given rank (e.g., “vstep”) and trims
-a fixed number of samples from the start of each segment. The resulting trimmed segments are added
-as new child segments with a specified new rank (e.g., “vstepgap”).
-This is useful when initial samples of each segment include artifacts that should be excluded from analysis.
-
-Parameters:
-
-samples_to_remove (int ) – Number of samples to trim from the beginning of each segment.
rank (str ) – The hierarchical rank of segments to target for trimming. Defaults to “vstep”.
newrank (str ) – The rank name to assign to newly created trimmed child segments. Defaults to “vstepgap”.
-
-
-
-__init__ ( samples_to_remove : int rank : str = 'vstep' newrank : str = 'vstepgap' ) → None
-
-
-
-
-newrank : str = 'vstepgap'
-
-
-
-
-rank : str = 'vstep'
-
-
-
-
-samples_to_remove : int
-
-
-
-
-
-
-Extract features from hierarchical segments into a DataFrame.
-Traverses a hierarchical segment structure down to bottom_rank , then collects feature values
-from each segment. Static features are retrieved via get_feature() , constants can be added via
-add_ons , and custom computed features can be provided through lambdas .
-This is useful for generating structured datasets from annotated traces for statistical analysis or
-machine learning.
-
-Parameters:
-
-seg (object ) – The root segment or trace object containing a hierarchical structure. It must implement traverse_to_rank() and support get_feature() .
bottom_rank (str ) – The rank name of the lowest-level segments from which to extract features.
extractions (list [ str ] ) – List of feature names to extract directly using get_feature() on each segment.
-Common examples include: ‘mean’, ‘frac’, ‘duration’, ‘baseline’, ‘current’, ‘wrap’, ‘start’.
add_ons (dict ) – A dictionary of fixed key-value pairs to include as constant columns in the resulting DataFrame.
lambdas (dict ) – A dictionary mapping column names to lambda functions that compute derived values from each segment.
-Returns:
-A pandas DataFrame where each row corresponds to a bottom-rank segment and columns represent extracted and computed features.
-Return type:
-pandas.DataFrame
-
-
-
-
-
-ionique.utils. si_eval ( value unit = None return_unit = False )
-Evaluate and convert a value with an SI unit prefix to its numeric base value.
-This function handles both string-based and numeric values with
-SI (International System of Units) prefixes such as “k” (kilo), “M” (mega), “μ” (micro), etc.
-It multiplies the input value by the appropriate factor based on the SI prefix.
-This utility is useful when parsing human-readable measurement strings or standardizing
-units across data pipelines that mix strings and numeric formats.
-
-Parameters:
-
-value (str or float ) – The value to be converted. Can be a string like “1.2 kHz” or a numeric value (int or float).
unit (str , optional ) – The unit string (e.g., “kHz”, “mV”). Required only if value is a numeric type.
return_unit (bool , optional ) – If True, returns a tuple containing the converted numeric value and the base unit (e.g., ‘Hz’).
-If False, only the numeric value is returned.
-Raises:
-
-
-Returns:
-The converted value, optionally paired with the base unit.
-Return type:
-float or tuple[float, str]
-
-
-
-
-
-ionique.utils. split_voltage_steps ( voltage : ndarray n_remove = 0 as_tuples = False )
-Split a voltage signal into segments based on step changes.
-This function detects changes in the voltage signal and splits it into individual
-segments (or steps) wherever a change in value occurs. It is useful in analyzing
-stepwise voltage protocols.
-Optionally, a number of initial samples can be removed from each segment using the
-n_remove parameter. The output can either be two separate arrays of start and end
-indices, or a list of tuples representing each segment.
-
-Parameters:
-
-voltage (numpy.ndarray ) – 1D voltage signal array to be segmented.
n_remove (int ) – Number of samples to remove from the start of each split. Defaults to 0.
as_tuples (bool ) – If True, returns a list of (start, end) index tuples. If False, returns two arrays: start_indices and end_indices. Defaults to False.
-Raises:
-ValueError – If n_remove is negative or larger than the start of the voltage changes.
-Returns:
-Either:
-- A tuple of (start_indices, end_indices), where each is a numpy array of indices.
-- A list of (start, end) tuples if as_tuples=True .
-Return type:
-tuple[numpy.ndarray, numpy.ndarray] or list[tuple[int, int]]
-
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Install Ionique — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-Install Ionique
-This guide walks you through the steps to download, set up, and install the Ionique package using a virtual environment.
-
-1. Download the Ionique Repository
-
-
-
-
-
-2. Open the Ionique Folder in VS Code
-
-
-Now your working directory in VS Code is set to the ionique
-
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-3. Set Up a Virtual Environment
-A virtual environment is an isolated workspace for installing project-specific dependencies.
-
-
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-Step 1: Open Terminal in VS Code
-
-
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-Step 2: Confirm You’re in the Correct Directory
-Use the following command:
-
-You should see the contents of the ionique
-
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-Step 3: Create and Activate the Environment
-Run the command:
- -m venv ionique_env
-
-
-Note: On some systems (especially Linux/macOS), you may need to use python3 pip3 python pip
-
-Then activate it:
-
-Linux / macOS:
-source ionique_env/bin/activate
-
-
Windows:
-
-
-If activated successfully, you’ll see (ionique_env)
-
-
-
-4. Install the Ionique Package
-Run the command:
-
-Wait a few minutes while dependencies install.
-
-
-If you see a similar message, congratulations — you’ve successfully installed the Ionique package!
-
-
-
-5. (Optional) Install JupyterLab
-To run data analysis in an interactive interface like Jupyter Notebook, install JupyterLab while the virtual environment is still active:
-
-
-
-6. Deactivate the Environment
-When you’re done, deactivate the virtual environment by typing:
-
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-It’s good practice to activate your virtual environment when working on a project and deactivate it when you’re finished.
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Nanopore Signal Analysis Framework — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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Python Module Index — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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Guide for VSCode and Python Installation — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-Guide for VSCode and Python Installation
-This guide is designed for users who do not have Python or Visual Studio Code installed on their machine.
-
-Note: The guide is divided into three parts: Windows , Linux and macOS users. Please follow the appropriate instructions for your operating system.
-
-First, you need a platform (Integrated Development Environment) to run Ionique . Visual Studio Code (VS Code) is a convenient IDE to start with.
-
-Installing VS Code
-
-Windows Users
-1. Install VS Code
-
-Go to the VS Code website , download.
Run the installer.
On the first screen, check the boxes Add Python to PATH and Register code … .
-
Finish installation.
-
-
-2. Install Required Extensions
-A few package requirements include C++ extensions and compilers.
-
-
-
-
-
-4. Install Python.
-
-Skip this step if you already have Python!
-Visit Python Downloads for Windows .
Choose a version between 3.10 and 3.12 and download.
Run the installer.
On the first screen, check the box for “Add Python to PATH” .
Click Install Now .
-
-
-
-Linux Users
-
-
-1. Download and Install VS_Code
-
-
-
-2. Open a Terminal
-You can search for “shell” or use the shortcut Ctrl + Alt + T
-Check your current location:
-
-Navigate to the Downloads folder (or wherever you saved the .deb
-
-List files to confirm:
-
-Install VS Code:
- dpkg -i code_1.XXX.deb
-
-
-Replace code_1.XXX.deb
-
-
-
-3. Install Python
-
-Skip this step if you already have Python!
-Visit the Python Downloads page .
Download a version between 3.10 and 3.12 .
Use your package manager or install from source.
-For Ubuntu-based systems:
- apt update
-sudo apt install python3.XX
-
-
-Replace XX
-
-
-
-macOS Users
-
-
-1. Install VS Code
-
-Download VS Code for Mac from here .
Open your browser’s download list and find the installer.
If it’s a .zip
Drag Visual Studio Code.app Applications folder.
Open VS Code from the Applications folder or Launchpad.
-
-
-2. Install Python
-
-Skip this step if you already have Python!
-Visit the Python Downloads page .
Choose a version between 3.10 and 3.12 .
Double-click the downloaded .pkg
Follow the on-screen steps. The default settings are usually sufficient.
You may be asked to enter your Mac password during installation.
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-Signal Analysis
-To analyse the signal data, ionique invokes parsers module.
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Signal Preprocessing — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-Signal Preprocessing
-Signal preprocessing is an essential step before downstream analysis of nanopore signals.
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-This page describes the following functionalities of Ionique:
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-Filtering with Filter class
-The Filter class allows users to apply different types of filters on current traces:
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diff --git a/docs/_build/html/v0.3.1/_sources/api/core.rst.txt b/docs/_build/html/v0.3.1/_sources/api/core.rst.txt
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--- a/docs/_build/html/v0.3.1/_sources/api/core.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-ionique.core
-===========
-
-.. automodule:: ionique.core
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/docs/_build/html/v0.3.1/_sources/api/cparsers.rst.txt b/docs/_build/html/v0.3.1/_sources/api/cparsers.rst.txt
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--- a/docs/_build/html/v0.3.1/_sources/api/cparsers.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-ionique.cparsers
-===============
-
-.. automodule:: ionique.cparsers
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/docs/_build/html/v0.3.1/_sources/api/datatypes.rst.txt b/docs/_build/html/v0.3.1/_sources/api/datatypes.rst.txt
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+++ /dev/null
@@ -1,7 +0,0 @@
-ionique.datatypes
-================
-
-.. automodule:: ionique.datatypes
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/docs/_build/html/v0.3.1/_sources/api/io.rst.txt b/docs/_build/html/v0.3.1/_sources/api/io.rst.txt
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--- a/docs/_build/html/v0.3.1/_sources/api/io.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-ionique.io
-==========
-
-.. automodule:: ionique.io
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/docs/_build/html/v0.3.1/_sources/api/parsers.rst.txt b/docs/_build/html/v0.3.1/_sources/api/parsers.rst.txt
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--- a/docs/_build/html/v0.3.1/_sources/api/parsers.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-ionique.parsers
-==============
-
-.. automodule:: ionique.parsers
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/docs/_build/html/v0.3.1/_sources/api/utils.rst.txt b/docs/_build/html/v0.3.1/_sources/api/utils.rst.txt
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--- a/docs/_build/html/v0.3.1/_sources/api/utils.rst.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-ionique.utils
-============
-
-.. automodule:: ionique.utils
- :members:
- :undoc-members:
- :show-inheritance:
-
diff --git a/docs/_build/html/v0.3.1/_sources/api_reference.rst.txt b/docs/_build/html/v0.3.1/_sources/api_reference.rst.txt
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--- a/docs/_build/html/v0.3.1/_sources/api_reference.rst.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-API Reference
-===============================
-
-.. toctree::
- :maxdepth: 2
-
- api/io
- api/core
- api/parsers
- api/utils
- api/datatypes
- api/cparsers
diff --git a/docs/_build/html/v0.3.1/_sources/data_input.rst.txt b/docs/_build/html/v0.3.1/_sources/data_input.rst.txt
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@@ -1,32 +0,0 @@
-Data Input
-^^^^^^^^^^^
-
-**File Readers**
-
-Ionique supports multiple file formats commonly utilized in nanopore experiments:
-
-- **.edh** – Element Data Header files containing metadata and pointers to raw data
-- **.opt** – Orbit Potential files with recorded current and voltage data
-- **.abf** – Axon Binary Format files used by pClamp software
-- **.xml** – XML configuration and metadata files associated with experiments
-
-The framework provides specialized reader classes (subclasses of ``AbstractFileReader``) to handle each format’s
-structure and extract both metadata and raw signal data.
-
-**File Reading Process**
-
-1. Identify file format based on extension
-2. Use the appropriate reader class to extract metadata
-3. Load raw current and voltage traces
-4. Align voltage to raw current
-5. Convert values to standardized SI units
-6. Apply optional pre-processing (e.g., downsampling, filtering, trimming)
-
-**Example**
-
-.. code-block:: python
-
- from ionique.io import EDHReader
-
- reader = EDHReader("path/to/file.edh", downsample=5)
- metadata, current, voltage = reader
\ No newline at end of file
diff --git a/docs/_build/html/v0.3.1/_sources/event_detect.rst.txt b/docs/_build/html/v0.3.1/_sources/event_detect.rst.txt
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diff --git a/docs/_build/html/v0.3.1/_sources/getting_started.rst.txt b/docs/_build/html/v0.3.1/_sources/getting_started.rst.txt
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diff --git a/docs/_build/html/v0.3.1/_sources/index.rst.txt b/docs/_build/html/v0.3.1/_sources/index.rst.txt
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--- a/docs/_build/html/v0.3.1/_sources/index.rst.txt
+++ /dev/null
@@ -1,88 +0,0 @@
-.. ionique documentation master file, created by
- sphinx-quickstart on Tue Feb 11 10:00:50 2025.
- You can adapt this file completely to your liking, but it should at least
- contain the root `toctree` directive.
-
-Ionique: A Nanopore Signal Analysis Framework
-==========================================
-
-**Authors:**
-Ali Fallahi, Dinara Boyko
-
-**Date:**
-Feb 11, 2025
-
-**Version:**
-0.1.0 (Development)
-
-Overview
---------
-
-Ionique is a Python module for processing and analyzing nanopore signal data. It provides tools for reading, filtering, segmenting, and analyzing nanopore experimental data. The framework supports structured workflows for signal processing and quality control, allowing researchers to extract relevant information from raw current and voltage traces.
-
-Features
---------
-
-- **File Handling**
- - Supports `.edh`, `.opt`, and `.xml` formats.
- - Extracts raw current and voltage traces.
- - Parses experimental metadata for structured analysis.
-
-- **Signal Processing**
- - Segmentation and event detection.
-
-
-- **Analysis Modules**
- - IV curve computation and voltage pattern detection.
- - Step response and dwell-time analysis.
- - Customizable parsers and filters for flexible workflows.
-
-- **Usability**
- - Python library for scripting and automation.
-
-
-Installation (Development)
---------------------------
-
-To install the development version of Ionique, clone the repository and install it in editable mode:
-
-.. code-block:: bash
-
- git clone https://github.com/wanunulab/ionique.git
- cd ionique
- pip install ionique
-
-**Requirements:**
-- Python > 3.10
-- Dependencies listed in `requirements.txt`
-
-Future Development
-------------------
-
-- **Additional analysis modules** for extended event detection and pattern recognition.
-- **Integration with Jupyter Notebooks** for interactive visualization.
-
-.. toctree::
- :maxdepth: 2
- :caption: Contents:
-
- modules
-
- signal_process
- api_reference
-
-.. toctree::
- :maxdepth: 2
- :caption: Guides
-
- install_beginner.rst
- ionique_starter.md
- python_vscode_start.md
-
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/docs/_build/html/v0.3.1/_sources/install_beginner.rst.txt b/docs/_build/html/v0.3.1/_sources/install_beginner.rst.txt
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@@ -1,140 +0,0 @@
-Installation for beginners
-===========================
-
-Welcome to Ionique!
-------------------
-
-This tutorial is designed for non-coding users. If you have used Python and GitHub before, please proceed with the ``README.md`` instructions.
-
-Before diving into data analysis, let's first install Python, GitHub, and Jupyter Notebook.
-
-Step 1: Install Python
------------------------
-
-Download the package from `Python Downloads
`_.
-
-Choose the appropriate OS system for your machine. Then, scroll down to pick Python version **3.10** or later up to **3.12**.
-
-After Python has been successfully installed, we can go over some basic terminal commands.
-
-**Linux Users:**
- - In the search window, type **"shell"** or use the hotkey **Ctrl-Alt-T**.
-
-**MacOS Users:**
- - In the search window, type **"Terminal"**.
-
-**Windows Users:**
- - In the search window, type **"Terminal"**.
-
-To check the installation, type:
-
-.. code-block:: sh
-
- python -V
-
-Some versions of Python may require the following command:
-
-.. code-block:: sh
-
- python3 -V
-
-
-Step 2: GitHub
---------------
-
-First, you need to create an account by following the instructions on `GitHub
`_.
-
-Now, open the repository link: `Ionique Repository `_, click on the green **"Code"** button, and download the ZIP file. Unzip it.
-
-Move the folder to the desired location on your computer.
-
-Step 3: Moving Between Folders in the Terminal
-----------------------------------------------
-
-You can find a short list of basic commands here: `Linux vs Windows Commands `_.
-
-Let's create your first virtual environment!
-
-Run the following command:
-
-.. code-block:: sh
-
- python -m venv ionique_env
-
-Then, activate it:
-
-.. code-block:: sh
-
- source ionique_env/bin/activate # Linux/MacOS
- ionique_env\Scripts\activate.bat # Windows
-
-Now, install the Ionique requirements:
-
-.. code-block:: sh
-
- pip install .
-
-*If your OS and Python version require the ``python3`` command, use ``pip3`` instead.*
-
-Next, install JupyterLab:
-
-.. code-block:: sh
-
- pip install jupyterlab
-
-Step 5: Run the Notebooks
--------------------------
-
-Copy the notebook you want to run into the **data** folder. (You don’t need to use the terminal for this step.)
-
-Now, return to the terminal window and navigate to the folder where the copied notebook is stored.
-
-Then, run:
-
-.. code-block:: sh
-
- jupyter lab
-
-You are now ready to work on data analysis!
-
-Step 6: Finish Workflow
------------------------
-
-Once you are done with the analysis, go back to the terminal and press **Ctrl + C**.
-
-Then, type:
-
-.. code-block:: sh
-
- deactivate
-
-to deactivate the virtual environment.
-
-**Note:** Starting from **Step 5**, the procedure will be identical each time you want to run **Ionique**.
-
-Happy using Ionique!
diff --git a/docs/_build/html/v0.3.1/_sources/installation.rst.txt b/docs/_build/html/v0.3.1/_sources/installation.rst.txt
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-=======
-Install
-=======
-
-Install ionique
-============
-
-The most recent release of the ``ionique`` package can be installed using pip_
-
-.. code-block:: console
-
- $ pip install ionique
-
-If you are very new to coding, more detailed instructions, we provide guides on VSCode, Python3 and ionique installment.
-
-
-Installation from Source
-========================
-
-If you want to contribute to ionique you can build ionique from source using the
-instructions below.
-
-.. code-block:: console
-
- $ git clone https://github.com/wanunulab/ionique.git
- $ cd ionique
- $ pip install -e .
-
-
diff --git a/docs/_build/html/v0.3.1/_sources/ioniq.rst.txt b/docs/_build/html/v0.3.1/_sources/ioniq.rst.txt
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@@ -1,61 +0,0 @@
-ionique
-=============
-
-Modules
-----------
-
-ionique.core
------------------
-
-.. automodule:: ionique.core
- :members:
- :undoc-members:
- :show-inheritance:
-
-ionique.cparsers
----------------------
-
-.. automodule:: ionique.cparsers
- :members:
- :undoc-members:
- :show-inheritance:
-
-ionique.datatypes
-----------------------
-
-.. automodule:: ionique.datatypes
- :members:
- :undoc-members:
- :show-inheritance:
-
-ionique.io
----------------
-
-.. automodule:: ionique.io
- :members:
- :undoc-members:
- :show-inheritance:
-
-ionique.parsers
---------------------
-
-.. automodule:: ionique.parsers
- :members:
- :undoc-members:
- :show-inheritance:
-
-ionique.utils
-------------------
-
-.. automodule:: ionique.utils
- :members:
- :undoc-members:
- :show-inheritance:
-
-Module contents
----------------
-
-.. automodule:: ionique
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/docs/_build/html/v0.3.1/_sources/ionique_starter.md.txt b/docs/_build/html/v0.3.1/_sources/ionique_starter.md.txt
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index 6d4c279..0000000
--- a/docs/_build/html/v0.3.1/_sources/ionique_starter.md.txt
+++ /dev/null
@@ -1,115 +0,0 @@
-# Install Ionique
-
-This guide walks you through the steps to download, set up, and install the Ionique package using a virtual environment.
-
----
-
-## 1. Download the Ionique Repository
-
-- Open the WanunuLab GitHub repository:
- https://github.com/wanunulab/ionique
-
-- Click the green **Code** button and choose **Download ZIP**.
-
-
-
-- Unzip the folder and move it to your desired location (e.g., Desktop or Documents).
-
----
-
-## 2. Open the Ionique Folder in VS Code
-
-- Launch the **VS Code** application.
-- Click **File > Open...** and select the unzipped `ionique` folder.
-
-
-
-Now your working directory in VS Code is set to the `ionique` folder.
-
----
-
-## 3. Set Up a Virtual Environment
-
-A **virtual environment** is an isolated workspace for installing project-specific dependencies.
-
-> Learn more: [Python Virtual Environments](https://docs.python.org/3/tutorial/venv.html)
-
-### Step 1: Open Terminal in VS Code
-
-- Go to the **top menu** and click **Terminal > New Terminal**.
-
-
-
-### Step 2: Confirm You're in the Correct Directory
-
-Use the following command:
-
-```bash
-ls
-```
-
-You should see the contents of the `ionique` folder.
-
-
-### Step 3: Create and Activate the Environment
-
-Run the command:
-
-```bash
-python -m venv ionique_env
-```
-> **Note:** On some systems (especially Linux/macOS), you may need to use `python3` and `pip3` instead of `python` and `pip`.
-
-Then activate it:
-
-- **Linux / macOS:**
- ```bash
- source ionique_env/bin/activate
- ```
-
-- **Windows:**
- ```bat
- ionique_env\Scripts\activate
- ```
-
-If activated successfully, you’ll see `(ionique_env)` appear before your username in the terminal.
-
-
-
----
-
-## 4. Install the Ionique Package
-
-Run the command:
-
-```bash
-pip install .
-```
-
-Wait a few minutes while dependencies install.
-
-
-
-> If you see a similar message, congratulations — you've successfully installed the Ionique package!
-
----
-
-## 5. (Optional) Install JupyterLab
-
-To run data analysis in an interactive interface like Jupyter Notebook, install JupyterLab while the virtual environment is still active:
-
-```bash
-pip install jupyterlab
-```
-
----
-
-## 6. Deactivate the Environment
-
-When you're done, deactivate the virtual environment by typing:
-
-```bash
-deactivate
-```
-
-> It's good practice to **activate** your virtual environment when working on a project and **deactivate** it when you're finished.
diff --git a/docs/_build/html/v0.3.1/_sources/modules.rst.txt b/docs/_build/html/v0.3.1/_sources/modules.rst.txt
deleted file mode 100644
index 891d39b..0000000
--- a/docs/_build/html/v0.3.1/_sources/modules.rst.txt
+++ /dev/null
@@ -1,21 +0,0 @@
- Ionique
-=====
-
-`Click here for an introduction to nanopore sequencing and signal analysis `_
-
-`Quick start installation instructions can be found here
-Windows Users
-
----
-
-
-Linux Users
-```
-
-List files to confirm:
-
-```bash
-ls
-```
-
-Install VS Code:
-
-```bash
-sudo dpkg -i code_1.XXX.deb
-```
-
-> Replace `code_1.XXX.deb` with the actual filename.
-
-### 3. Install Python
-#### *Skip this step if you already have Python!*
-
-- Visit the [Python Downloads page](https://www.python.org/downloads/).
-- Download a version between **3.10 and 3.12**.
-- Use your package manager or install from source.
-
-For Ubuntu-based systems:
-
-```bash
-sudo apt update
-sudo apt install python3.XX
-```
-> Replace `XX` with a version here
-
-
-
----
-
-
-macOS Users
-
diff --git a/docs/_build/html/v0.3.1/_sources/setup_log.rst.txt b/docs/_build/html/v0.3.1/_sources/setup_log.rst.txt
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index eaebb41..0000000
--- a/docs/_build/html/v0.3.1/_sources/setup_log.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-setup\_log module
-=================
-
-.. automodule:: setup_log
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/docs/_build/html/v0.3.1/_sources/signal_analysis.rst.txt b/docs/_build/html/v0.3.1/_sources/signal_analysis.rst.txt
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--- a/docs/_build/html/v0.3.1/_sources/signal_analysis.rst.txt
+++ /dev/null
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-Signal Analysis
-===========================
-
-To analyse the signal data, ionique invokes parsers module.
-
-In Parsers, the segnments of the signal are processed separately to get the most accurate data.
-
-The statistical measures are computed for each segment
diff --git a/docs/_build/html/v0.3.1/_sources/signal_preprocess.rst.txt b/docs/_build/html/v0.3.1/_sources/signal_preprocess.rst.txt
deleted file mode 100644
index 7f3bba8..0000000
--- a/docs/_build/html/v0.3.1/_sources/signal_preprocess.rst.txt
+++ /dev/null
@@ -1,73 +0,0 @@
-Signal Preprocessing
-====================
-
-Signal preprocessing is an essential step before downstream analysis of nanopore signals.
-It typically involves filtering, trimming, downsampling, or compressing signal segments
-to reduce noise and improve performance.
-
-This page describes the following functionalities of Ionique:
-
-- Filtering: denoise signals.
-- Trimming: remove edge artifacts from signal segments.
-
-Filtering with `Filter` class
------------------------------
-
-The `Filter` class allows users to apply different types of filters on current traces:
-
-**Supported filter types:**
-
-- ``lowpass``: Passes low frequencies below a cutoff.
-- ``highpass``: Passes high frequencies above a cutoff.
-- ``bandpass``: Passes a frequency band between low and high cutoff.
-- ``bandstop``: Removes a frequency band between low and high cutoff.
-
-
-**Supported filter methods:**
-
-- ``butter``: Butterworth filter (default) – smooth and flat frequency response.
-- ``bessel``: Bessel filter – preserves waveform shape and group delay.
-
-Filtering is applied using `scipy.signal.sosfiltfilt` (zero-phase, bidirectional) or
-`scipy.signal.sosfilt` depending on `bidirectional=True/False`.
-
-
-**Usage Example:**
-
-.. code-block:: python
-
- filt = Filter(
- cutoff_frequency=[0.1, 10],
- filter_type="bandpass",
- filter_method="butter",
- order=3,
- bidirectional=True,
- sampling_frequency=10000
- )
- filt(current_trace)
-
-
-Trimming with `Trimmer` class
------------------------------
-
-The `Trimmer` class removes a fixed number of samples from the start of each segment
-at a given hierarchical rank (e.g., "vstep"). This is useful for excluding artifacts
- in step protocols.
-
-
-**Usage Example:**
-
-.. code-block:: python
-
- trimmer = Trimmer(samples_to_remove=200)
- trimmer(trace_file)
-
-
-Summary
--------
-
-Both `Filter` and `Trimmer` are designed to be composable tools for preprocessing current traces before
-event detection and feature extraction. You can chain them together in the analysis workflows.
-
-
-These operations are performed using tools in ``utils.py`` and parser modules
\ No newline at end of file
diff --git a/docs/_build/html/v0.3.1/_sources/tutorial.rst.txt b/docs/_build/html/v0.3.1/_sources/tutorial.rst.txt
deleted file mode 100644
index e69de29..0000000
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deleted file mode 100644
index 8141580..0000000
--- a/docs/_build/html/v0.3.1/_static/_sphinx_javascript_frameworks_compat.js
+++ /dev/null
@@ -1,123 +0,0 @@
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deleted file mode 100644
index 0398ebb..0000000
--- a/docs/_build/html/v0.3.1/_static/doctools.js
+++ /dev/null
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-/*
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- */
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-
-const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
- "TEXTAREA",
- "INPUT",
- "SELECT",
- "BUTTON",
-]);
-
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- } else {
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- },
-
- /**
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- TRANSLATIONS: {},
- PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
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-
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- },
-
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- "n",
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- );
- Documentation.LOCALE = catalog.locale;
- },
-
- /**
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-
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- break;
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-
- // some keyboard layouts may need Shift to get /
- switch (event.key) {
- case "/":
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-_ready(Documentation.init);
diff --git a/docs/_build/html/v0.3.1/_static/documentation_options.js b/docs/_build/html/v0.3.1/_static/documentation_options.js
deleted file mode 100644
index a49aa03..0000000
--- a/docs/_build/html/v0.3.1/_static/documentation_options.js
+++ /dev/null
@@ -1,13 +0,0 @@
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- LANGUAGE: 'en',
- COLLAPSE_INDEX: false,
- BUILDER: 'html',
- FILE_SUFFIX: '.html',
- LINK_SUFFIX: '.html',
- HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt',
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- languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
- }
- else {
- languageSwitch.remove();
- }
- }
- });
-}
-
-document.addEventListener("readthedocs-addons-data-ready", function (event) {
- // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
- document
- .querySelector("[role='search'] input")
- .addEventListener("focusin", () => {
- const event = new CustomEvent("readthedocs-search-show");
- document.dispatchEvent(event);
- });
-});
\ No newline at end of file
diff --git a/docs/_build/html/v0.3.1/_static/language_data.js b/docs/_build/html/v0.3.1/_static/language_data.js
deleted file mode 100644
index c7fe6c6..0000000
--- a/docs/_build/html/v0.3.1/_static/language_data.js
+++ /dev/null
@@ -1,192 +0,0 @@
-/*
- * This script contains the language-specific data used by searchtools.js,
- * namely the list of stopwords, stemmer, scorer and splitter.
- */
-
-var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
-
-
-/* Non-minified version is copied as a separate JS file, if available */
-
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
- var step2list = {
- ational: 'ate',
- tional: 'tion',
- enci: 'ence',
- anci: 'ance',
- izer: 'ize',
- bli: 'ble',
- alli: 'al',
- entli: 'ent',
- eli: 'e',
- ousli: 'ous',
- ization: 'ize',
- ation: 'ate',
- ator: 'ate',
- alism: 'al',
- iveness: 'ive',
- fulness: 'ful',
- ousness: 'ous',
- aliti: 'al',
- iviti: 'ive',
- biliti: 'ble',
- logi: 'log'
- };
-
- var step3list = {
- icate: 'ic',
- ative: '',
- alize: 'al',
- iciti: 'ic',
- ical: 'ic',
- ful: '',
- ness: ''
- };
-
- var c = "[^aeiou]"; // consonant
- var v = "[aeiouy]"; // vowel
- var C = c + "[^aeiouy]*"; // consonant sequence
- var V = v + "[aeiou]*"; // vowel sequence
-
- var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
- var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
- var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
- var s_v = "^(" + C + ")?" + v; // vowel in stem
-
- this.stemWord = function (w) {
- var stem;
- var suffix;
- var firstch;
- var origword = w;
-
- if (w.length < 3)
- return w;
-
- var re;
- var re2;
- var re3;
- var re4;
-
- firstch = w.substr(0,1);
- if (firstch == "y")
- w = firstch.toUpperCase() + w.substr(1);
-
- // Step 1a
- re = /^(.+?)(ss|i)es$/;
- re2 = /^(.+?)([^s])s$/;
-
- if (re.test(w))
- w = w.replace(re,"$1$2");
- else if (re2.test(w))
- w = w.replace(re2,"$1$2");
-
- // Step 1b
- re = /^(.+?)eed$/;
- re2 = /^(.+?)(ed|ing)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- re = new RegExp(mgr0);
- if (re.test(fp[1])) {
- re = /.$/;
- w = w.replace(re,"");
- }
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1];
- re2 = new RegExp(s_v);
- if (re2.test(stem)) {
- w = stem;
- re2 = /(at|bl|iz)$/;
- re3 = new RegExp("([^aeiouylsz])\\1$");
- re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re2.test(w))
- w = w + "e";
- else if (re3.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
- else if (re4.test(w))
- w = w + "e";
- }
- }
-
- // Step 1c
- re = /^(.+?)y$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(s_v);
- if (re.test(stem))
- w = stem + "i";
- }
-
- // Step 2
- re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step2list[suffix];
- }
-
- // Step 3
- re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step3list[suffix];
- }
-
- // Step 4
- re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
- re2 = /^(.+?)(s|t)(ion)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- if (re.test(stem))
- w = stem;
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1] + fp[2];
- re2 = new RegExp(mgr1);
- if (re2.test(stem))
- w = stem;
- }
-
- // Step 5
- re = /^(.+?)e$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- re2 = new RegExp(meq1);
- re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
- w = stem;
- }
- re = /ll$/;
- re2 = new RegExp(mgr1);
- if (re.test(w) && re2.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
-
- // and turn initial Y back to y
- if (firstch == "y")
- w = firstch.toLowerCase() + w.substr(1);
- return w;
- }
-}
-
diff --git a/docs/_build/html/v0.3.1/_static/minus.png b/docs/_build/html/v0.3.1/_static/minus.png
deleted file mode 100644
index d96755f..0000000
Binary files a/docs/_build/html/v0.3.1/_static/minus.png and /dev/null differ
diff --git a/docs/_build/html/v0.3.1/_static/plus.png b/docs/_build/html/v0.3.1/_static/plus.png
deleted file mode 100644
index 7107cec..0000000
Binary files a/docs/_build/html/v0.3.1/_static/plus.png and /dev/null differ
diff --git a/docs/_build/html/v0.3.1/_static/pygments.css b/docs/_build/html/v0.3.1/_static/pygments.css
deleted file mode 100644
index 84ab303..0000000
--- a/docs/_build/html/v0.3.1/_static/pygments.css
+++ /dev/null
@@ -1,75 +0,0 @@
-pre { line-height: 125%; }
-td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
-td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
-.highlight .hll { background-color: #ffffcc }
-.highlight { background: #f8f8f8; }
-.highlight .c { color: #3D7B7B; font-style: italic } /* Comment */
-.highlight .err { border: 1px solid #FF0000 } /* Error */
-.highlight .k { color: #008000; font-weight: bold } /* Keyword */
-.highlight .o { color: #666666 } /* Operator */
-.highlight .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */
-.highlight .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: #9C6500 } /* Comment.Preproc */
-.highlight .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */
-.highlight .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */
-.highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
-.highlight .gd { color: #A00000 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
-.highlight .gr { color: #E40000 } /* Generic.Error */
-.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
-.highlight .gi { color: #008400 } /* Generic.Inserted */
-.highlight .go { color: #717171 } /* Generic.Output */
-.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
-.highlight .gt { color: #0044DD } /* Generic.Traceback */
-.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
-.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
-.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
-.highlight .kp { color: #008000 } /* Keyword.Pseudo */
-.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
-.highlight .kt { color: #B00040 } /* Keyword.Type */
-.highlight .m { color: #666666 } /* Literal.Number */
-.highlight .s { color: #BA2121 } /* Literal.String */
-.highlight .na { color: #687822 } /* Name.Attribute */
-.highlight .nb { color: #008000 } /* Name.Builtin */
-.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
-.highlight .no { color: #880000 } /* Name.Constant */
-.highlight .nd { color: #AA22FF } /* Name.Decorator */
-.highlight .ni { color: #717171; font-weight: bold } /* Name.Entity */
-.highlight .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */
-.highlight .nf { color: #0000FF } /* Name.Function */
-.highlight .nl { color: #767600 } /* Name.Label */
-.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
-.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
-.highlight .nv { color: #19177C } /* Name.Variable */
-.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
-.highlight .w { color: #bbbbbb } /* Text.Whitespace */
-.highlight .mb { color: #666666 } /* Literal.Number.Bin */
-.highlight .mf { color: #666666 } /* Literal.Number.Float */
-.highlight .mh { color: #666666 } /* Literal.Number.Hex */
-.highlight .mi { color: #666666 } /* Literal.Number.Integer */
-.highlight .mo { color: #666666 } /* Literal.Number.Oct */
-.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
-.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
-.highlight .sc { color: #BA2121 } /* Literal.String.Char */
-.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
-.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
-.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
-.highlight .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */
-.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
-.highlight .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */
-.highlight .sx { color: #008000 } /* Literal.String.Other */
-.highlight .sr { color: #A45A77 } /* Literal.String.Regex */
-.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
-.highlight .ss { color: #19177C } /* Literal.String.Symbol */
-.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #0000FF } /* Name.Function.Magic */
-.highlight .vc { color: #19177C } /* Name.Variable.Class */
-.highlight .vg { color: #19177C } /* Name.Variable.Global */
-.highlight .vi { color: #19177C } /* Name.Variable.Instance */
-.highlight .vm { color: #19177C } /* Name.Variable.Magic */
-.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/docs/_build/html/v0.3.1/_static/searchtools.js b/docs/_build/html/v0.3.1/_static/searchtools.js
deleted file mode 100644
index 2c774d1..0000000
--- a/docs/_build/html/v0.3.1/_static/searchtools.js
+++ /dev/null
@@ -1,632 +0,0 @@
-/*
- * Sphinx JavaScript utilities for the full-text search.
- */
-"use strict";
-
-/**
- * Simple result scoring code.
- */
-if (typeof Scorer === "undefined") {
- var Scorer = {
- // Implement the following function to further tweak the score for each result
- // The function takes a result array [docname, title, anchor, descr, score, filename]
- // and returns the new score.
- /*
- score: result => {
- const [docname, title, anchor, descr, score, filename, kind] = result
- return score
- },
- */
-
- // query matches the full name of an object
- objNameMatch: 11,
- // or matches in the last dotted part of the object name
- objPartialMatch: 6,
- // Additive scores depending on the priority of the object
- objPrio: {
- 0: 15, // used to be importantResults
- 1: 5, // used to be objectResults
- 2: -5, // used to be unimportantResults
- },
- // Used when the priority is not in the mapping.
- objPrioDefault: 0,
-
- // query found in title
- title: 15,
- partialTitle: 7,
- // query found in terms
- term: 5,
- partialTerm: 2,
- };
-}
-
-// Global search result kind enum, used by themes to style search results.
-class SearchResultKind {
- static get index() { return "index"; }
- static get object() { return "object"; }
- static get text() { return "text"; }
- static get title() { return "title"; }
-}
-
-const _removeChildren = (element) => {
- while (element && element.lastChild) element.removeChild(element.lastChild);
-};
-
-/**
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
- */
-const _escapeRegExp = (string) =>
- string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
-
-const _displayItem = (item, searchTerms, highlightTerms) => {
- const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
- const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
- const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
- const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
- const contentRoot = document.documentElement.dataset.content_root;
-
- const [docName, title, anchor, descr, score, _filename, kind] = item;
-
- let listItem = document.createElement("li");
- // Add a class representing the item's type:
- // can be used by a theme's CSS selector for styling
- // See SearchResultKind for the class names.
- listItem.classList.add(`kind-${kind}`);
- let requestUrl;
- let linkUrl;
- if (docBuilder === "dirhtml") {
- // dirhtml builder
- let dirname = docName + "/";
- if (dirname.match(/\/index\/$/))
- dirname = dirname.substring(0, dirname.length - 6);
- else if (dirname === "index/") dirname = "";
- requestUrl = contentRoot + dirname;
- linkUrl = requestUrl;
- } else {
- // normal html builders
- requestUrl = contentRoot + docName + docFileSuffix;
- linkUrl = docName + docLinkSuffix;
- }
- let linkEl = listItem.appendChild(document.createElement("a"));
- linkEl.href = linkUrl + anchor;
- linkEl.dataset.score = score;
- linkEl.innerHTML = title;
- if (descr) {
- listItem.appendChild(document.createElement("span")).innerHTML =
- " (" + descr + ")";
- // highlight search terms in the description
- if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
- highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
- }
- else if (showSearchSummary)
- fetch(requestUrl)
- .then((responseData) => responseData.text())
- .then((data) => {
- if (data)
- listItem.appendChild(
- Search.makeSearchSummary(data, searchTerms, anchor)
- );
- // highlight search terms in the summary
- if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
- highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
- });
- Search.output.appendChild(listItem);
-};
-const _finishSearch = (resultCount) => {
- Search.stopPulse();
- Search.title.innerText = _("Search Results");
- if (!resultCount)
- Search.status.innerText = Documentation.gettext(
- "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
- );
- else
- Search.status.innerText = Documentation.ngettext(
- "Search finished, found one page matching the search query.",
- "Search finished, found ${resultCount} pages matching the search query.",
- resultCount,
- ).replace('${resultCount}', resultCount);
-};
-const _displayNextItem = (
- results,
- resultCount,
- searchTerms,
- highlightTerms,
-) => {
- // results left, load the summary and display it
- // this is intended to be dynamic (don't sub resultsCount)
- if (results.length) {
- _displayItem(results.pop(), searchTerms, highlightTerms);
- setTimeout(
- () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
- 5
- );
- }
- // search finished, update title and status message
- else _finishSearch(resultCount);
-};
-// Helper function used by query() to order search results.
-// Each input is an array of [docname, title, anchor, descr, score, filename, kind].
-// Order the results by score (in opposite order of appearance, since the
-// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
-const _orderResultsByScoreThenName = (a, b) => {
- const leftScore = a[4];
- const rightScore = b[4];
- if (leftScore === rightScore) {
- // same score: sort alphabetically
- const leftTitle = a[1].toLowerCase();
- const rightTitle = b[1].toLowerCase();
- if (leftTitle === rightTitle) return 0;
- return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
- }
- return leftScore > rightScore ? 1 : -1;
-};
-
-/**
- * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
- * custom function per language.
- *
- * The regular expression works by splitting the string on consecutive characters
- * that are not Unicode letters, numbers, underscores, or emoji characters.
- * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
- */
-if (typeof splitQuery === "undefined") {
- var splitQuery = (query) => query
- .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
- .filter(term => term) // remove remaining empty strings
-}
-
-/**
- * Search Module
- */
-const Search = {
- _index: null,
- _queued_query: null,
- _pulse_status: -1,
-
- htmlToText: (htmlString, anchor) => {
- const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
- for (const removalQuery of [".headerlink", "script", "style"]) {
- htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
- }
- if (anchor) {
- const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
- if (anchorContent) return anchorContent.textContent;
-
- console.warn(
- `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
- );
- }
-
- // if anchor not specified or not found, fall back to main content
- const docContent = htmlElement.querySelector('[role="main"]');
- if (docContent) return docContent.textContent;
-
- console.warn(
- "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
- );
- return "";
- },
-
- init: () => {
- const query = new URLSearchParams(window.location.search).get("q");
- document
- .querySelectorAll('input[name="q"]')
- .forEach((el) => (el.value = query));
- if (query) Search.performSearch(query);
- },
-
- loadIndex: (url) =>
- (document.body.appendChild(document.createElement("script")).src = url),
-
- setIndex: (index) => {
- Search._index = index;
- if (Search._queued_query !== null) {
- const query = Search._queued_query;
- Search._queued_query = null;
- Search.query(query);
- }
- },
-
- hasIndex: () => Search._index !== null,
-
- deferQuery: (query) => (Search._queued_query = query),
-
- stopPulse: () => (Search._pulse_status = -1),
-
- startPulse: () => {
- if (Search._pulse_status >= 0) return;
-
- const pulse = () => {
- Search._pulse_status = (Search._pulse_status + 1) % 4;
- Search.dots.innerText = ".".repeat(Search._pulse_status);
- if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
- };
- pulse();
- },
-
- /**
- * perform a search for something (or wait until index is loaded)
- */
- performSearch: (query) => {
- // create the required interface elements
- const searchText = document.createElement("h2");
- searchText.textContent = _("Searching");
- const searchSummary = document.createElement("p");
- searchSummary.classList.add("search-summary");
- searchSummary.innerText = "";
- const searchList = document.createElement("ul");
- searchList.setAttribute("role", "list");
- searchList.classList.add("search");
-
- const out = document.getElementById("search-results");
- Search.title = out.appendChild(searchText);
- Search.dots = Search.title.appendChild(document.createElement("span"));
- Search.status = out.appendChild(searchSummary);
- Search.output = out.appendChild(searchList);
-
- const searchProgress = document.getElementById("search-progress");
- // Some themes don't use the search progress node
- if (searchProgress) {
- searchProgress.innerText = _("Preparing search...");
- }
- Search.startPulse();
-
- // index already loaded, the browser was quick!
- if (Search.hasIndex()) Search.query(query);
- else Search.deferQuery(query);
- },
-
- _parseQuery: (query) => {
- // stem the search terms and add them to the correct list
- const stemmer = new Stemmer();
- const searchTerms = new Set();
- const excludedTerms = new Set();
- const highlightTerms = new Set();
- const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
- splitQuery(query.trim()).forEach((queryTerm) => {
- const queryTermLower = queryTerm.toLowerCase();
-
- // maybe skip this "word"
- // stopwords array is from language_data.js
- if (
- stopwords.indexOf(queryTermLower) !== -1 ||
- queryTerm.match(/^\d+$/)
- )
- return;
-
- // stem the word
- let word = stemmer.stemWord(queryTermLower);
- // select the correct list
- if (word[0] === "-") excludedTerms.add(word.substr(1));
- else {
- searchTerms.add(word);
- highlightTerms.add(queryTermLower);
- }
- });
-
- if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js
- localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
- }
-
- // console.debug("SEARCH: searching for:");
- // console.info("required: ", [...searchTerms]);
- // console.info("excluded: ", [...excludedTerms]);
-
- return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
- },
-
- /**
- * execute search (requires search index to be loaded)
- */
- _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const titles = Search._index.titles;
- const allTitles = Search._index.alltitles;
- const indexEntries = Search._index.indexentries;
-
- // Collect multiple result groups to be sorted separately and then ordered.
- // Each is an array of [docname, title, anchor, descr, score, filename, kind].
- const normalResults = [];
- const nonMainIndexResults = [];
-
- _removeChildren(document.getElementById("search-progress"));
-
- const queryLower = query.toLowerCase().trim();
- for (const [title, foundTitles] of Object.entries(allTitles)) {
- if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
- for (const [file, id] of foundTitles) {
- const score = Math.round(Scorer.title * queryLower.length / title.length);
- const boost = titles[file] === title ? 1 : 0; // add a boost for document titles
- normalResults.push([
- docNames[file],
- titles[file] !== title ? `${titles[file]} > ${title}` : title,
- id !== null ? "#" + id : "",
- null,
- score + boost,
- filenames[file],
- SearchResultKind.title,
- ]);
- }
- }
- }
-
- // search for explicit entries in index directives
- for (const [entry, foundEntries] of Object.entries(indexEntries)) {
- if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
- for (const [file, id, isMain] of foundEntries) {
- const score = Math.round(100 * queryLower.length / entry.length);
- const result = [
- docNames[file],
- titles[file],
- id ? "#" + id : "",
- null,
- score,
- filenames[file],
- SearchResultKind.index,
- ];
- if (isMain) {
- normalResults.push(result);
- } else {
- nonMainIndexResults.push(result);
- }
- }
- }
- }
-
- // lookup as object
- objectTerms.forEach((term) =>
- normalResults.push(...Search.performObjectSearch(term, objectTerms))
- );
-
- // lookup as search terms in fulltext
- normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
-
- // let the scorer override scores with a custom scoring function
- if (Scorer.score) {
- normalResults.forEach((item) => (item[4] = Scorer.score(item)));
- nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
- }
-
- // Sort each group of results by score and then alphabetically by name.
- normalResults.sort(_orderResultsByScoreThenName);
- nonMainIndexResults.sort(_orderResultsByScoreThenName);
-
- // Combine the result groups in (reverse) order.
- // Non-main index entries are typically arbitrary cross-references,
- // so display them after other results.
- let results = [...nonMainIndexResults, ...normalResults];
-
- // remove duplicate search results
- // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
- let seen = new Set();
- results = results.reverse().reduce((acc, result) => {
- let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
- if (!seen.has(resultStr)) {
- acc.push(result);
- seen.add(resultStr);
- }
- return acc;
- }, []);
-
- return results.reverse();
- },
-
- query: (query) => {
- const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
- const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
-
- // for debugging
- //Search.lastresults = results.slice(); // a copy
- // console.info("search results:", Search.lastresults);
-
- // print the results
- _displayNextItem(results, results.length, searchTerms, highlightTerms);
- },
-
- /**
- * search for object names
- */
- performObjectSearch: (object, objectTerms) => {
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const objects = Search._index.objects;
- const objNames = Search._index.objnames;
- const titles = Search._index.titles;
-
- const results = [];
-
- const objectSearchCallback = (prefix, match) => {
- const name = match[4]
- const fullname = (prefix ? prefix + "." : "") + name;
- const fullnameLower = fullname.toLowerCase();
- if (fullnameLower.indexOf(object) < 0) return;
-
- let score = 0;
- const parts = fullnameLower.split(".");
-
- // check for different match types: exact matches of full name or
- // "last name" (i.e. last dotted part)
- if (fullnameLower === object || parts.slice(-1)[0] === object)
- score += Scorer.objNameMatch;
- else if (parts.slice(-1)[0].indexOf(object) > -1)
- score += Scorer.objPartialMatch; // matches in last name
-
- const objName = objNames[match[1]][2];
- const title = titles[match[0]];
-
- // If more than one term searched for, we require other words to be
- // found in the name/title/description
- const otherTerms = new Set(objectTerms);
- otherTerms.delete(object);
- if (otherTerms.size > 0) {
- const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
- if (
- [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
- )
- return;
- }
-
- let anchor = match[3];
- if (anchor === "") anchor = fullname;
- else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
-
- const descr = objName + _(", in ") + title;
-
- // add custom score for some objects according to scorer
- if (Scorer.objPrio.hasOwnProperty(match[2]))
- score += Scorer.objPrio[match[2]];
- else score += Scorer.objPrioDefault;
-
- results.push([
- docNames[match[0]],
- fullname,
- "#" + anchor,
- descr,
- score,
- filenames[match[0]],
- SearchResultKind.object,
- ]);
- };
- Object.keys(objects).forEach((prefix) =>
- objects[prefix].forEach((array) =>
- objectSearchCallback(prefix, array)
- )
- );
- return results;
- },
-
- /**
- * search for full-text terms in the index
- */
- performTermsSearch: (searchTerms, excludedTerms) => {
- // prepare search
- const terms = Search._index.terms;
- const titleTerms = Search._index.titleterms;
- const filenames = Search._index.filenames;
- const docNames = Search._index.docnames;
- const titles = Search._index.titles;
-
- const scoreMap = new Map();
- const fileMap = new Map();
-
- // perform the search on the required terms
- searchTerms.forEach((word) => {
- const files = [];
- const arr = [
- { files: terms[word], score: Scorer.term },
- { files: titleTerms[word], score: Scorer.title },
- ];
- // add support for partial matches
- if (word.length > 2) {
- const escapedWord = _escapeRegExp(word);
- if (!terms.hasOwnProperty(word)) {
- Object.keys(terms).forEach((term) => {
- if (term.match(escapedWord))
- arr.push({ files: terms[term], score: Scorer.partialTerm });
- });
- }
- if (!titleTerms.hasOwnProperty(word)) {
- Object.keys(titleTerms).forEach((term) => {
- if (term.match(escapedWord))
- arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
- });
- }
- }
-
- // no match but word was a required one
- if (arr.every((record) => record.files === undefined)) return;
-
- // found search word in contents
- arr.forEach((record) => {
- if (record.files === undefined) return;
-
- let recordFiles = record.files;
- if (recordFiles.length === undefined) recordFiles = [recordFiles];
- files.push(...recordFiles);
-
- // set score for the word in each file
- recordFiles.forEach((file) => {
- if (!scoreMap.has(file)) scoreMap.set(file, {});
- scoreMap.get(file)[word] = record.score;
- });
- });
-
- // create the mapping
- files.forEach((file) => {
- if (!fileMap.has(file)) fileMap.set(file, [word]);
- else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
- });
- });
-
- // now check if the files don't contain excluded terms
- const results = [];
- for (const [file, wordList] of fileMap) {
- // check if all requirements are matched
-
- // as search terms with length < 3 are discarded
- const filteredTermCount = [...searchTerms].filter(
- (term) => term.length > 2
- ).length;
- if (
- wordList.length !== searchTerms.size &&
- wordList.length !== filteredTermCount
- )
- continue;
-
- // ensure that none of the excluded terms is in the search result
- if (
- [...excludedTerms].some(
- (term) =>
- terms[term] === file ||
- titleTerms[term] === file ||
- (terms[term] || []).includes(file) ||
- (titleTerms[term] || []).includes(file)
- )
- )
- break;
-
- // select one (max) score for the file.
- const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
- // add result to the result list
- results.push([
- docNames[file],
- titles[file],
- "",
- null,
- score,
- filenames[file],
- SearchResultKind.text,
- ]);
- }
- return results;
- },
-
- /**
- * helper function to return a node containing the
- * search summary for a given text. keywords is a list
- * of stemmed words.
- */
- makeSearchSummary: (htmlText, keywords, anchor) => {
- const text = Search.htmlToText(htmlText, anchor);
- if (text === "") return null;
-
- const textLower = text.toLowerCase();
- const actualStartPosition = [...keywords]
- .map((k) => textLower.indexOf(k.toLowerCase()))
- .filter((i) => i > -1)
- .slice(-1)[0];
- const startWithContext = Math.max(actualStartPosition - 120, 0);
-
- const top = startWithContext === 0 ? "" : "...";
- const tail = startWithContext + 240 < text.length ? "..." : "";
-
- let summary = document.createElement("p");
- summary.classList.add("context");
- summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
-
- return summary;
- },
-};
-
-_ready(Search.init);
diff --git a/docs/_build/html/v0.3.1/_static/sphinx_highlight.js b/docs/_build/html/v0.3.1/_static/sphinx_highlight.js
deleted file mode 100644
index 8a96c69..0000000
--- a/docs/_build/html/v0.3.1/_static/sphinx_highlight.js
+++ /dev/null
@@ -1,154 +0,0 @@
-/* Highlighting utilities for Sphinx HTML documentation. */
-"use strict";
-
-const SPHINX_HIGHLIGHT_ENABLED = true
-
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
- if (node.nodeType === Node.TEXT_NODE) {
- const val = node.nodeValue;
- const parent = node.parentNode;
- const pos = val.toLowerCase().indexOf(text);
- if (
- pos >= 0 &&
- !parent.classList.contains(className) &&
- !parent.classList.contains("nohighlight")
- ) {
- let span;
-
- const closestNode = parent.closest("body, svg, foreignObject");
- const isInSVG = closestNode && closestNode.matches("svg");
- if (isInSVG) {
- span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
- } else {
- span = document.createElement("span");
- span.classList.add(className);
- }
-
- span.appendChild(document.createTextNode(val.substr(pos, text.length)));
- const rest = document.createTextNode(val.substr(pos + text.length));
- parent.insertBefore(
- span,
- parent.insertBefore(
- rest,
- node.nextSibling
- )
- );
- node.nodeValue = val.substr(0, pos);
- /* There may be more occurrences of search term in this node. So call this
- * function recursively on the remaining fragment.
- */
- _highlight(rest, addItems, text, className);
-
- if (isInSVG) {
- const rect = document.createElementNS(
- "http://www.w3.org/2000/svg",
- "rect"
- );
- const bbox = parent.getBBox();
- rect.x.baseVal.value = bbox.x;
- rect.y.baseVal.value = bbox.y;
- rect.width.baseVal.value = bbox.width;
- rect.height.baseVal.value = bbox.height;
- rect.setAttribute("class", className);
- addItems.push({ parent: parent, target: rect });
- }
- }
- } else if (node.matches && !node.matches("button, select, textarea")) {
- node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
- }
-};
-const _highlightText = (thisNode, text, className) => {
- let addItems = [];
- _highlight(thisNode, addItems, text, className);
- addItems.forEach((obj) =>
- obj.parent.insertAdjacentElement("beforebegin", obj.target)
- );
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const SphinxHighlight = {
-
- /**
- * highlight the search words provided in localstorage in the text
- */
- highlightSearchWords: () => {
- if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
-
- // get and clear terms from localstorage
- const url = new URL(window.location);
- const highlight =
- localStorage.getItem("sphinx_highlight_terms")
- || url.searchParams.get("highlight")
- || "";
- localStorage.removeItem("sphinx_highlight_terms")
- url.searchParams.delete("highlight");
- window.history.replaceState({}, "", url);
-
- // get individual terms from highlight string
- const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
- if (terms.length === 0) return; // nothing to do
-
- // There should never be more than one element matching "div.body"
- const divBody = document.querySelectorAll("div.body");
- const body = divBody.length ? divBody[0] : document.querySelector("body");
- window.setTimeout(() => {
- terms.forEach((term) => _highlightText(body, term, "highlighted"));
- }, 10);
-
- const searchBox = document.getElementById("searchbox");
- if (searchBox === null) return;
- searchBox.appendChild(
- document
- .createRange()
- .createContextualFragment(
- '
' +
- '' +
- _("Hide Search Matches") +
- "
"
- )
- );
- },
-
- /**
- * helper function to hide the search marks again
- */
- hideSearchWords: () => {
- document
- .querySelectorAll("#searchbox .highlight-link")
- .forEach((el) => el.remove());
- document
- .querySelectorAll("span.highlighted")
- .forEach((el) => el.classList.remove("highlighted"));
- localStorage.removeItem("sphinx_highlight_terms")
- },
-
- initEscapeListener: () => {
- // only install a listener if it is really needed
- if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
-
- document.addEventListener("keydown", (event) => {
- // bail for input elements
- if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
- // bail with special keys
- if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
- if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
- SphinxHighlight.hideSearchWords();
- event.preventDefault();
- }
- });
- },
-};
-
-_ready(() => {
- /* Do not call highlightSearchWords() when we are on the search page.
- * It will highlight words from the *previous* search query.
- */
- if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
- SphinxHighlight.initEscapeListener();
-});
diff --git a/docs/_build/html/v0.3.1/api/core.html b/docs/_build/html/v0.3.1/api/core.html
deleted file mode 100644
index 60c50dc..0000000
--- a/docs/_build/html/v0.3.1/api/core.html
+++ /dev/null
@@ -1,886 +0,0 @@
-
-
-
-
-
-
-
-
-
ionique.core — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
- ionique
-
-
-
-
-
-
-
-
-
-ionique.core
-
-
-This holds the core data types which may be abstracted in
-many different applications.
-This module defines a tree-based framework for representing, parsing, and annotating
-segments of ionic current data.
-
-
-class ionique.core. AbstractSegmentTree
-Bases: object
-Class for managing hierarchical segments.
-This class supports a tree structure where each segment can contain multiple
-child segments, enabling recursive parsing and analysis of nested data.
-It provides utilities to manage relationships between segments, apply parsers,
-and extract subsegments by rank.
-
-
-__init__ ( ) → None
-Initialize the segment
-
-
-
-
-add_child ( child : AnySegment ) → None
-Add a single child
-
-
-
-
-add_children ( children : list [ AnySegment ] ) → None
-Add multiple children if:
-1. Child position is the within parent’s segment.
-2. The length of the child’s segment is > 0
-3. There is no overlap between consecutive children
-Or add children with no check
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-clear_children ( )
-Clear the list of children
-
-
-
-
-climb_to_rank ( rank : str ) → AnySegment | None
-Go up and find the segment with the specified rank
-:return: segment
-
-
-
-
-end : int | None
-
-
-
-
-get_feature ( name : str )
-Gets the ‘name’ feature of the current segment or its parent
-
-
-
-
-get_top_parent ( ) → AnySegment
-Recursively go up and find top most parent
-:return: parent
-
-
-
-
-property n : int
-Get the length of the segment
-:return: len(segment)
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-parse ( parser newrank : str at_child_rank : str | None = None ** kwargs ) → bool
-Parses the data in the segment or at a particular rank of child segments
-into children segments with a new rank.
-
-Parameters:
-
-parser (Parser parse method and required input attributes.
newrank (str ) – Rank to assign to the newly created child segments.
at_child_rank (str or None ) – Determines whether to traverse the children tree down to a given rank.
kwargs – Additional arguments to pass to the parser.
-Returns:
-True if parsing was successful, otherwise raises an exception.
-Return type:
-bool
-
-
-
-
-
-rank : str
-
-
-
-
-property relative_end : int
-Get end position relative to parent segment
-:return: end position
-
-
-
-
-property relative_slice : ndarray
-Slice the array to get the data between end-start segment
-:return: sliced np array
-
-
-
-
-property relative_start : int
-Get the start position relative to parent segment
-:return: start position
-
-
-
-
-property slice : ndarray
-Slice the array to get the data between start-end segment
-:return: sliced np array
-
-
-
-
-start : int | None
-
-
-
-
-traverse_to_rank ( rank : str ) → list
-Traverse the tree rank and get the list of the segments of the rank
-:param: rank
-:return: list of segments
-
-
-
-
-
-
-class ionique.core. MetaSegment ( start : int end : int parent : AnySegment | None = None rank : str | None = None unique_features : dict | None = {} ** kwargs )
-Bases: AbstractSegmentTree
-The metadata on an abstract segment of ionic current. All information about a segment can be
-loaded, without the expectation of the array of floats.
-
-
-__init__ ( start : int end : int parent : AnySegment | None = None rank : str | None = None unique_features : dict | None = {} ** kwargs )
-
-Parameters:
-
-start (int ) – Start index of the segment.
end (int ) – End index of the segment.
parent (AbstractSegmentTree or None ) – Parent segment in the tree (optional).
rank (str or None ) – Rank identifier for this segment (optional).
unique_features (dict ) – Dictionary of metadata features.
-
-
-
-
-
-property current
-Get the current data of the segment if the segment correlates to the file
-:return: current
-
-
-
-
-delete ( )
-Delete itself. There are no arrays with which to delete references for.
-
-
-
-
-property duration : float
-Get the duration of the segment = start - end
-:return: duration
-
-
-
-
-classmethod from_json ( filename = None in_json = None )
-Read in a metasegment from a JSON and return a metasegment object.
-Either pass in a file which has a segment stored, or an actual JSON
-object.
-
-
-
-
-property max
-Calculate the maximum value of the current array.
-
-Returns:
-Maximum value of the current array.
-Return type:
-float
-
-
-
-
-
-property mean
-Calculate the mean of the current array.
-
-Returns:
-Mean value of the current array.
-Return type:
-float
-
-
-
-
-
-property min
-Calculate the minimum value of the current array.
-
-Returns:
-Minimum value of the current array.
-Return type:
-float
-
-
-
-
-
-property std
-Calculate the standard deviation of the current array.
-
-Returns:
-Standard deviation of the current array.
-Return type:
-float
-
-
-
-
-
-property time
-Get the time data of the segment if the corresponding rank is in file
-:return: time
-
-
-
-
-to_dict ( )
-Return a dict representation of the metadata, usually used prior to
-converting the dict to a JSON.
-
-
-
-
-to_json ( filename = None )
-Return a JSON representation of this, by reporting the important
-metadata.
-
-
-
-
-to_meta ( )
-Kept to allow for error handling, but since it’s already a metasegment
-it won’t actually do anything.
-
-
-
-
-unique_features
-
-
-
-
-
-
-class ionique.core. Segment ( current ** kwargs )
-Bases: AbstractSegmentTree
-A segment of ionic current, and methods relevant for collecting metadata. The ionic current is
-expected to be passed as a numpy array of floats. Metadata methods (mean, std..) are decorated
-as properties to reduce overall computational time, making them calculated on the fly rather
-than during analysis.
-
-
-__init__ ( current ** kwargs )
-The segment must have a list of ionic current, of which it stores some statistics about.
-It may also take in as many keyword arguments as needed, such as start time or duration
-if already known. Cannot override statistical measurements.
-:param current: Numpy array of current data points.
-:type current: np.ndarray
-:param kwargs: Additional attributes like ‘start’, ‘end’, ‘rank’, etc.
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-delete ( )
-Deleting this segment requires deleting its reference to the ionic
-current array, and then deleting itself.
-
-
-
-
-end : int | None
-
-
-
-
-classmethod from_json ( filename = None json = None )
-Read in a segment from a JSON and return a metasegment object. Either
-pass in a file which has a segment stored, or an actual JSON object.
-
-
-
-
-property max
-Calculate the maximum value of the current array.
-
-Returns:
-Maximum value of the current array.
-Return type:
-float
-
-
-
-
-
-property mean
-Calculate the mean of the current array.
-
-Returns:
-Mean value of the current array.
-Return type:
-float
-
-
-
-
-
-property min
-Calculate the minimum value of the current array.
-
-Returns:
-Minimum value of the current array.
-Return type:
-float
-
-
-
-
-
-property n
-Get the number of elements in the current array.
-
-Returns:
-Number of elements in the current array.
-Return type:
-int
-
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-rank : str
-
-
-
-
-scale ( sampling_freq )
-Rescale all of the values to go from samples to seconds.
-
-
-
-
-start : int | None
-
-
-
-
-property std
-Calculate the standard deviation of the current array.
-
-Returns:
-Standard deviation of the current array.
-Return type:
-float
-
-
-
-
-
-to_dict ( )
-Return a dict representation of the metadata, usually used prior to
-converting the dict to a JSON.
-
-
-
-
-to_json ( filename = None )
-Return a JSON representation of this, by reporting the important
-metadata.
-
-
-
-
-to_meta ( )
-Convert from a segment to a ‘metasegment’, which stores only metadata
-about the segment and not the full array of ionic current.
-
-
-
-
-
-
-ionique.core. ignored ( * exceptions )
-Replace the “try, except: pass” paradigm by replacing those three lines with a single line.
-Taken from the latest 3.4 python update push by Raymond Hettinger, see:
-http://hg.python.org/cpython/rev/406b47c64480
-
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\ No newline at end of file
diff --git a/docs/_build/html/v0.3.1/api/cparsers.html b/docs/_build/html/v0.3.1/api/cparsers.html
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ionique.cparsers — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-ionique.cparsers
-This contains cython implementations of ionic current parsers which are in
-parsers.py. Currently the only parser is StatSplit, which is implemented
-as FastStatSplit.
-
-
-class ionique.cparsers. FastStatSplit
-Bases: object
-A cython implementation of the segmenter written by Kevin Karplus. Sped up approximately 50-100x
-compared to the Python implementation depending on parameters.
-
-
-__init__ ( * args ** kwargs )
-
-
-
-
-best_single_split ( current )
-Wrapper for a single call to _best_single_split. It will find the
-single best split in a series of current, and return the index of
-that split. Returns a tuple of ( gain, index ) where gain is the
-gain in variance by splitting there, and index is the index at which
-the split should occur in the current array.
-
-
-
-
-min_gain
-
-
-
-
-parse ( current )
-Wrapper function for the segmentation, which is implemented in cython.
-
-
-
-
-parse_meta ( current )
-Wrapper function for the segmentation, which is implemented in cython. returns meta segments (no current array)
-
-
-
-
-score_samples ( current no_split = False )
-Return a series of lists scoring each sample. However, this isn’t just
-scoring each sample once. Every time a split is detected, it will return
-a new list with newly scored samples. In essence, it returns one list
-per scan of the current using the recursive method.
-
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-ionique.cparsers. pairwise ( iterable )
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\ No newline at end of file
diff --git a/docs/_build/html/v0.3.1/api/datatypes.html b/docs/_build/html/v0.3.1/api/datatypes.html
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ionique.datatypes — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-ionique.datatypes
-Data structure definitions and session file management for analysis.
-
-
-class ionique.datatypes. SessionFileManager ( * args ** kwargs )
-Bases: MetaSegment
-Singleton-based session manager for hierarchical data.
-This class inherits from MetaSegment and uses the Singleton metaclass
-to ensure only one instance exists during a given session. It serves as
-the root segment for a data analysis session and is responsible for managing
-metadata and registered “affectors” (i.e., objects that modify or
-interact with the session).
-
-
-__init__ ( ) → None
-Initialize the session manager.
-Sets the session start time and prepares the affector log structure.
-
-
-
-
-add_child ( child : AnySegment ) → None
-Add a single child segment to the session’s hierarchy.
-
-Parameters:
-child (AnySegment ) – The segment to be added as a child.
-
-
-
-
-
-add_children ( children : list [ AnySegment ] ) → None
-Add multiple child segments to the session.
-
-Parameters:
-children (list [ AnySegment ] ) – A list of segments to be added as children.
-
-
-
-
-
-rank : str = 'root'
-
-
-
-
-register_affector ( affector )
-Register an affector object that influences the session data.
-Records metadata including class name, string representation, and timestamp,
-and logs the event with a unique identifier.
-
-Parameters:
-affector (object ) – An object that modifies or interacts with the session.
-Returns:
-A UUID string uniquely identifying the registered affector.
-Return type:
-str
-
-
-
-
-
-unique_features
-
-
-
-
-
-
-class ionique.datatypes. TraceFile ( current : ndarray voltage = None rank = 'file' parent = None unique_features : dict = {} metadata : dict = {} )
-Bases: Segment
-Data structure for representing a single trace file with current and optional voltage information.
-This class inherits from Segment and encapsulates current and voltage data. It sets up segment metadata,
-defines parent-child hierarchy, and optionally initializes child segments based on voltage steps.
-Typical usage includes assigning metadata and organizing hierarchical
-data structures for downstream processing or visualization.
-
-
-__init__ ( current : ndarray voltage = None rank = 'file' parent = None unique_features : dict = {} metadata : dict = {} )
-If voltage steps are provided, corresponding child segments of rank “vstep” are created.
-
-Parameters:
-
-current (numpy.ndarray ) – current array.
voltage (list [ tuple [ tuple [ int , int ] , float ] ] or None ) – Optional list of tuples containing (start, end) index pairs and voltage values.
-Used to create child segments for each voltage step.
rank (str ) – Segment rank label, defaults to “file”.
parent (Segment or None ) – Optional parent segment to which this trace belongs.
unique_features (dict ) – Dictionary of metadata such as sampling frequency.
metadata (dict ) – Additional metadata.
-
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-delete ( )
-Delete the current object, removing it from its parent
-
-
-
-
-end : int | None
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-plot ( rank axes downsample_per_rank color_per_rank )
-Method for plotting
-
-
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-rank : str
-
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-start : int | None
-
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\ No newline at end of file
diff --git a/docs/_build/html/v0.3.1/api/io.html b/docs/_build/html/v0.3.1/api/io.html
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ionique.io — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-ionique.io
-Input/output utilities for data files.
-This module provides an interface for loading, parsing, and preprocessing
-signal data from various file formats (e.g., .edh , .opt , .abf , .dat , .xml ). It defines a base reader class
-and concrete implementations that handle format-specific logic, metadata extraction,
-data alignment, and optional preprocessing steps.
-This module is central to converting raw experimental data into analyzable form.
-
-
-class ionique.io. AbstractFileReader
-Bases: object
-An abstract class for reading various data files (.abf, .edh, .mat, .opt, .xml)
-This class defines the structure for file readers
-
-
-__init__ ( )
-
-
-
-
-accepted_keywords = []
-
-
-
-
-current_multiplier : float = 1.0
-
-
-
-
-ext = '___'
-
-
-
-
-read ( filename : str ** kwargs )
-Read a datafile or series of files. Files are identified according to their extension.
-Data formats that come with a header file must be referred to by the header file.
-If inheriting from the AbstractFileReader class, do not override this function;
-instead, create a custom _read method.
-
-Parameters:
-filename – file name or list of file names, typically
-
-given as a string or PathLike object.
-:type filename: str or os.PathLike or list[str] or list[os.PathLike]
-:param **
-
-
-
-
-voltage_multiplier : float = 1.0
-
-
-
-
-
-
-class ionique.io. EDHReader ( edh_filename voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Bases: AbstractFileReader
-Reader class for loading .edh data files and their associated current data.
-This class parses the .edh header file and automatically loads the corresponding signal data
-(either from .abf or .dat files in the same directory). It extracts metadata, converts raw
-current and voltage data to SI units, and supports optional preprocessing steps such as
-voltage step segmentation, downsampling, and signal filtering.
-
-
-__init__ ( edh_filename voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Initialize the EDHReader and load signal data from associated files.
-
-Parameters:
-
-edh_filename (str ) – Path to the .edh header file.
voltage_compress (bool ) – If True, splits signal into segments based on voltage steps.
n_remove (int ) – Number of samples to remove from the beginning of each voltage step.
downsample (int ) – Downsampling factor to reduce data size.
prefilter (callable or None ) – Callable to apply preprocessing to the current signal.
-
-
-
-
-
-accepted_keywords = ['voltage_compress', 'n_remove', 'downsample', 'prefilter']
-
-
-
-
-current_multiplier : float = 1e-09
-
-
-
-
-ext = '.edh'
-
-
-
-
-voltage_multiplier : float = 0.001
-
-
-
-
-
-
-class ionique.io. OPTReader ( opt_filename : str voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Bases: AbstractFileReader
-Reader for .opt data files with corresponding XML or _volt.opt metadata.
-This class reads current data from .opt files and attempts to extract or reconstruct
-corresponding voltage data using associated .xml or _volt.opt files found in the same
-directory. It handles metadata extraction, signal preprocessing, voltage alignment, and
-segment compression based on voltage steps.
-Supported XML structures include both standard HWtiming_cap_step formats and timestamp-based
-custom formats.
-
-
-__init__ ( opt_filename : str voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Initialize the OPTReader instance and load corresponding files.
-
-Parameters:
-
-opt_filename (str ) – Path to the .opt header file.
voltage_compress (bool ) – If True, splits signal into segments based on voltage steps.
n_remove (int ) – Number of samples to remove from the beginning of each voltage step.
downsample (int ) – Downsampling factor to reduce data size.
prefilter (callable or None ) – Callable to apply preprocessing to the current signal.
-
-
-
-
-
-accepted_keywords = ['voltage_compress', 'n_remove', 'downsample', 'prefilter']
-
-
-
-
-current_multiplier : float = 1000000000.0
-
-
-
-
-ext = '.opt'
-
-
-
-
-find_peaks_in_segment ( current_data start_index end_index )
-Apply “find_peaks” on a segment of the current data and return
-the indices of the found peak
-
-
-
-
-find_peaks_slide_window ( current start_index window_shift_duration = 0.002 sampling_frequency = 250000 sign = 'negative' )
-Find peaks in the current every time the voltage changes.
-The search for the peaks happens in the window of the timestamp 0.02 seconds.
-
-
-
-
-process_custom_xml ( )
-Processes the custom XML file to extract voltage data, align the voltage waveform
-with the current, and detect peaks in the current signal every time the voltage changes.
-
-
-
-
-
-
-ionique.io. load_files_GUI ( )
-
-Create a form with:
-‘Compress voltage (only uncheck if voltage is not stepwise, e.g., triangle wave)’ : checkbox
‘Apply Filter?’ : checkbox
If ‘Apply Filter?’ checked: dynamic form from the Filter dataclass (types, options, limits).
-Returns (frame, get_values) where get_values() -> dict with: {‘compress_voltage’: bool, ‘apply_filter’: bool, ‘filter’: Filter|None}
-
-
-
-
-
-ionique.io. select_files_GUI ( )
-Open a file dialog allowing multiple selection, but only for ONE file type at a time.
-Users can switch the dialog’s file-type dropdown, but the final selection must be
-homogeneous (all the same extension) and in the allowed list.
-
-Returns
-
-tuple[str, …] Absolute paths of the selected files (empty tuple if the user cancels).
-
-
-
-
-
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\ No newline at end of file
diff --git a/docs/_build/html/v0.3.1/api/parsers.html b/docs/_build/html/v0.3.1/api/parsers.html
deleted file mode 100644
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ionique.parsers — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-ionique.parsers
-Parser module for event and state segmentation in current recordings.
-This module provides a framework for parsing current trace data. It includes an abstract base Parser class
-and multiple concrete parser implementations for detecting spikes, noise, square pulses, and other event
-structures in ionic current signals.
-Some of the parsers and the parser base class were adapted from the
-PyPore Package by Jacob Schreiber and Kevin Karplus (https://github.com/jmschrei/PyPore )
-
-
-class ionique.parsers. AutoSquareParser ( threshold_baseline = 0.7 expected_conductance = 1.9 conductance_tolerance = 1.2 wrap_padding : int = 50 rules = [] )
-Bases: Parser
-Class for building a straightforward pipeline of data analysis
-to make it reusable with customized parameters
-
-
-__init__ ( threshold_baseline = 0.7 expected_conductance = 1.9 conductance_tolerance = 1.2 wrap_padding : int = 50 rules = [] )
-Initialize a Parser instance
-
-
-
-
-parse ( current eff_sampling_freq voltage )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current', 'eff_sampling_freq', 'voltage']
-
-
-
-
-
-
-class ionique.parsers. FilterDerivativeSegmenter ( low_threshold = 1 high_threshold = 2 cutoff_freq = 1000.0 sampling_freq = 100000.0 )
-Bases: Parser
-This parser will segment an event using a filter-derivative method. It will
-first apply a bessel filter at a certain cutoff to the current, then it will
-take the derivative of that, and segment when the derivative passes a
-threshold.
-
-
-__init__ ( low_threshold = 1 high_threshold = 2 cutoff_freq = 1000.0 sampling_freq = 100000.0 )
-Initialize a Parser instance
-
-
-
-
-parse ( current )
-Apply the filter-derivative method to filter the ionic current.
-
-
-
-
-set_params ( )
-
-Returns:
-
-
-
-
-
-
-
-
-class ionique.parsers. IVCurveAnalyzer ( current parent_segment sampling_frequency method = 'simple' )
-Bases: Parser
-THIS CLASS IS NOT IMPLEMENTED YET. DO NOT USE.
-Check for at Least Two Voltage Steps: confirm that the segment has at least two voltage steps.
-Divide into Voltage Segments: separate data into voltage segments.
-Call IVAnalyzer on Parent Segment: compute the mean and std of the current in each
-voltage segment.
-Return an Array of (voltage: (imean, istd)) pair:
-Also mean and std should be calulated in two ways
-
-
-__init__ ( current parent_segment sampling_frequency method = 'simple' )
-Initialize a Parser instance
-
-
-
-
-analyze ( )
-Analyze each voltage step in a parent segment
-
-
-
-
-
-
-class ionique.parsers. IVCurveParser ( voltage )
-Bases: object
-THIS CLASS IS NOT IMPLEMENTED YET! DO NOT USE.
-The class handles:
-
-
-Identifies a file where an IV curve is recorded based on the pattern.
-
-Here, the goal is to feed the voltage information to a module and have it search and do pattern matching to find one of
-these possibilities. .This can be a parser that accepts voltage array or compressed voltage as one of the inputs.
-
-
-__init__ ( voltage )
-
-
-
-
-parse ( )
-Searches for a pattern in the voltage data to identify IV.
-If none exists, the given datafile does not contain IV curves.
-If one or multiple IV curves are found, it returns start and
-end regions of each of them.
-Returns: list of indices: start and end for each deteced IV curve
-
-
-
-
-
-
-class ionique.parsers. MemoryParse ( starts ends )
-Bases: object
-A parser based on being fed previous split points, and splitting a raw file based
-those splits. Used predominately when loading previous split points from the
-database cache, to reconstruct a parsed file from “memory.
-
-
-__init__ ( starts ends )
-
-Parameters:
-
-
-
-
-
-
-
-parse ( current )
-
-
-
-
-
-
-class ionique.parsers. NoiseFilterParser ( noise_threshold = 60 detect_noise = True )
-Bases: Parser
-This unit is a parser subclass. when invoked on a segment (whole file or subsegment),
-it identifies the areas where bad things happened, and areas where the data is clean.
-Examples of bad things include high noise (60Hz noise from opening the cage), membrane
-rupture, and membrane formation attempts. it produces children from the good
-regions of the data and ignore the bad regions.
-
-
-__init__ ( noise_threshold = 60 detect_noise = True )
-Initialize a Parser instance
-
-
-
-
-parse ( data segment_start = 0 segment_end = None )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-
-
-class ionique.parsers. Parser
-Bases: ABC
-This class defines the interface and shared utility methods for parser implementations.
-Parsers are used to segment ionic current data
-into structured regions of interest (events).
-Subclasses should implement the parse method and define any additional logic
-required to detect features in the data.
-
-
-abstract __init__ ( )
-Initialize a Parser instance
-
-
-
-
-classmethod from_json ( _json )
-Critical: Not implemented yet, figure out how to make this work in pythion
-
-
-
-
-classmethod get_init_params ( )
-Get the initialization parameters of the parser class.
-
-Returns:
-A dictionary of initialization parameters, default None.
-Return type:
-dict or None
-
-
-
-
-
-classmethod get_name ( )
-Get the name of the parser class
-:return: The name of the class.
-:rtype: str
-
-
-
-
-classmethod get_process_inputs ( )
-
-
-
-
-classmethod get_process_outputs ( )
-Get the outputs generated by processing
-
-Returns:
-A list of outputs generated by parse method
-Return type:
-list or None
-
-
-
-
-
-get_required_parent_attributes ( )
-Get the list of required attributes from the parent segment
-
-Returns:
-A list of required parent attributes
-Return type:
-list[str]
-
-
-
-
-
-parse ( ** kwargs )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current']
-
-
-
-
-set_params ( param_dict )
-Updates each paramater presented in the GUI to the value input in the lineEdit
-corresponding to that value.
-:param param_dict: A dictionary
-:type param_dict: dict
-
-
-
-
-to_dict ( )
-Convert the parser’s attributes to a dictionary.
-
-Returns:
-A dictionary representation of the parser’s attributes.
-Return type:
-dict
-
-
-
-
-
-to_json ( filename = False )
-Convert the parser’s attributes to a JSON string.
-
-Parameters:
-filename (str , default bool ) – If filename, it writes the JSON string to the specified file.
-Returns:
-A dictionary representation of the parser’s attributes.
-Return type:
-str
-
-
-
-
-
-
-
-class ionique.parsers. SpeedyStatSplit ( sampling_freq min_width = 100 max_width = 1000000 window_width = 10000 min_gain_per_sample = None false_positive_rate = None prior_segments_per_second = None cutoff_freq = None )
-Bases: Parser
-A parser wrapper for the Cython-accelerated FastStatSplit algorithm.
-See cparsers.pyx FastStatSplit for full documentation. This is just a
-wrapper for the cyton implementation to add a GUI.
-
-
-__init__ ( sampling_freq min_width = 100 max_width = 1000000 window_width = 10000 min_gain_per_sample = None false_positive_rate = None prior_segments_per_second = None cutoff_freq = None )
-Initialize the SpeedyStatSplit instance
-
-Parameters:
-
-sampling_freq (int ) – The sampling frequency of the data
min_width (int , optional ) – Minimum width of a segment, default=100
max_width (int , optional ) – Maximum width of a segment, default=1000000.
window_width (int , optional ) – Width of the window, defaults to 10000.
min_gain_per_sample (float or None , optional ) – Minimum gain per sample, default=None
false_positive_rate (float or None , optional ) – Expected false positive rate , default=None
prior_segments_per_second (float or None , optional ) – Prior number of segments per second, default=None.
cutoff_freq (float or None , optional ) – Cutoff frequency for filtering, default=None
-
-
-
-
-
-best_single_split ( current )
-Determine the best single split point in the current data
-
-Parameters:
-current (numpy.ndarray ) – The current data to be analyzed.
-Returns:
-The best split point
-Return type:
-int or None
-
-
-
-
-
-parse ( current )
-Parse the current data to detect segments using the FastStatSplit
-
-Parameters:
-current (numpy.ndarray ) – The current data to be parsed
-Returns:
-A list of detected segments
-Return type:
-
-
-
-
-
-
-parse_meta ( current )
-Parse the current data and extract meta information
-
-Parameters:
-current (numpy.ndarray ) – The current data to be parsed.
-
-
-
-
-
-set_params ( )
-Set the parameters
-
-
-
-
-uiTemplate = [('sampling_freq', 'spin', {'bounds': [0, None], 'dec': True, 'siPrefix': True, 'step': 1, 'suffix': 'Hz', 'value': 200000}), ('min_width', 'spin', {'dec': True, 'int': True, 'min': 2, 'step': 1, 'value': 100}), ('max_width', 'spin', {'dec': True, 'int': True, 'step': 1, 'value': 1000000}), ('window_width', 'spin', {'dec': True, 'int': True, 'step': 1, 'value': 10000}), ('cutoff_freq', 'spin', {'hidden': True})]
-
-
-
-
-
-
-class ionique.parsers. SpikeParser ( height = None threshold = None distance = None prominence = None prominence_snr = None width = None wlen = None rel_height : float = 0.5 plateu_size = None fractional : bool = True )
-Bases: Parser
-A parser for detecting spike-like events in ionic current data.
-This parser wraps scipy.signal.find_peaks , supporting thresholds
-for features like peak height, prominence, and width. It computes event characteristics such
-as dwell time, relative prominence, and peak positions.
-
-
-__init__ ( height = None threshold = None distance = None prominence = None prominence_snr = None width = None wlen = None rel_height : float = 0.5 plateu_size = None fractional : bool = True ) → None
-Initialize the SpikeParser.
-
-Parameters:
-
-height (float or tuple or None ) – Minimum spike height.
threshold (float or tuple or None ) – Minimum vertical threshold.
distance (float or tuple or None ) – Minimum horizontal distance between peaks.
prominence (float or tuple or None ) – Minimum peak prominence.
prominence_snr (tuple or None ) – Alternative to prominence, specified as a tuple of signal-to-noise (SNR) ratios.
width (float or tuple or None ) – Full width at half maximum of the peak.
wlen (float or None ) – Width of the window used to calculate prominence.
rel_height (float ) – Relative height used in width calculation (range 0 to 1).
plateu_size (float or None ) – Minimum length of a flat-topped peak.
fractional (bool ) – Whether to interpret height, prominence, etc., as a fraction of the signal mean.
-
-
-
-
-
-parse ( current sampling_freq mean std start )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
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-required_parent_attributes : list [ str ] = ['current', 'sampling_freq', 'mean', 'start', 'std']
-
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-
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-class ionique.parsers. lambda_event_parser ( threshold = 90 rules = None )
-Bases: Parser
-A simple rule-based parser which defines events as a sequential series
-of points which are below a certain threshold, then filtered based on other
-critereon such as total time or minimum current. Rules can be passed in at
-initiation, or set later, but must be a lambda function takes in a PreEvent
-object and performs some boolean operation.
-
-
-__init__ ( threshold = 90 rules = None )
-
-Parameters:
-
-threshold (float ) – The threshold for detecting events (e.g., current < threshold).
rules (list [ callable ] or None ) – A list of lambda functions that accept an event and return a bool.
-
-
-
-
-
-parse ( current )
-Perform a large capture of events by creating a boolean mask for when
-the current is below a threshold, then detecting the edges in those masks,
-and using the edges to partitition the sample. The events are
-then filtered before being returned.
-
-
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-set_params ( )
-Read in the data from the GUI and use it to customize
-the rules or threshold of the parser.
-
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-ionique.parsers. pairwise ( iterable )
-
-s -> (s0,s1), (s1,s2), (s2, s3), …
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-Parameters:
-iterable
-Returns:
-
-
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-
-
-class ionique.parsers. snakebase_parser ( threshold = 1.5 )
-Bases: Parser
-A simple parser based on dividing when the peak-to-peak
-amplitude of a wave exceeds a certain threshold.
-
-
-__init__ ( threshold = 1.5 )
-Initialize a Parser instance
-
-
-
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-parse ( current )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
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-set_params ( )
-Updates each paramater presented in the GUI to the value input in the lineEdit
-corresponding to that value.
-:param param_dict: A dictionary
-:type param_dict: dict
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-ionique.utils
-Signal Processing and Data Extraction Utilities
-This module provides a set of utility functions and classes designed for use in signal
-processing workflows.
-These tools are intended for both internal processing and external use cases such as analysis
-pipelines and API integrations.
-
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-class ionique.utils. Filter ( cutoff_frequency : float filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ] filter_method : Literal [ 'butter' , 'bessel' ] = 'butter' bidirectional : bool = True sampling_frequency : float = None )
-Bases: object
-This class allows a user to apply low-pass, high-pass,
-band-pass, or band-stop filters using standard filter types (Butterworth or Bessel).
-Filters are implemented using second-order sections (SOS) for numerical stability and
-can be applied in either forward-only or bidirectional mode.
-
-Parameters:
-
-cutoff_frequency (float or list [ float ] ) – The cutoff frequency or frequency band for the filter in Hz.
-For band filters, provide the band center or list of [low, high] values.
filter_type (Literal [ "lowpass" , "highpass" , "bandpass" , "bandstop" ] ) – The type of filter to apply. Options are “lowpass”, “highpass”, “bandpass”, or “bandstop”.
filter_method (Literal [ "butter" , "bessel" ] ) – The filter method. Supported options: “butter” (Butterworth), “bessel”. Defaults to “butter”.
order (int ) – The order of the filter. Must be >= 1. Defaults to 2.
bidirectional (bool ) – If True, applies filtering forward and backward using sosfiltfilt .
-If False, uses causal sosfilt . Defaults to True.
sampling_frequency (float , optional ) – Sampling frequency of the signal in Hz.
-
-
-
-__init__ ( cutoff_frequency : float filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ] filter_method : Literal [ 'butter' , 'bessel' ] = 'butter' bidirectional : bool = True sampling_frequency : float = None ) → None
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-bidirectional : bool = True
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-cutoff_frequency : float
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-filter_method : Literal [ 'butter' , 'bessel' ] = 'butter'
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-filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ]
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-sampling_frequency : float = None
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-class ionique.utils. Singleton ( * args ** kwargs )
-Bases: type
-Generic singleton metaclass.
-This metaclass ensures the same return every time the class is instanced.
-
-
-__init__ ( * args ** kwargs )
-Class initialization
-
-Parameters:
-
-
-
-
-
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-
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-class ionique.utils. Trimmer ( samples_to_remove : int rank : str = 'vstep' newrank : str = 'vstepgap' )
-Bases: object
-Segment trimming utility for hierarchical signal data.
-This class operates on a segment tree. It traverses segments of a given rank (e.g., “vstep”) and trims
-a fixed number of samples from the start of each segment. The resulting trimmed segments are added
-as new child segments with a specified new rank (e.g., “vstepgap”).
-This is useful when initial samples of each segment include artifacts that should be excluded from analysis.
-
-Parameters:
-
-samples_to_remove (int ) – Number of samples to trim from the beginning of each segment.
rank (str ) – The hierarchical rank of segments to target for trimming. Defaults to “vstep”.
newrank (str ) – The rank name to assign to newly created trimmed child segments. Defaults to “vstepgap”.
-
-
-
-__init__ ( samples_to_remove : int rank : str = 'vstep' newrank : str = 'vstepgap' ) → None
-
-
-
-
-newrank : str = 'vstepgap'
-
-
-
-
-rank : str = 'vstep'
-
-
-
-
-samples_to_remove : int
-
-
-
-
-
-
-Extract features from hierarchical segments into a DataFrame.
-Traverses a hierarchical segment structure down to bottom_rank , then collects feature values
-from each segment. Static features are retrieved via get_feature() , constants can be added via
-add_ons , and custom computed features can be provided through lambdas .
-This is useful for generating structured datasets from annotated traces for statistical analysis or
-machine learning.
-
-Parameters:
-
-seg (object ) – The root segment or trace object containing a hierarchical structure. It must implement traverse_to_rank() and support get_feature() .
bottom_rank (str ) – The rank name of the lowest-level segments from which to extract features.
extractions (list [ str ] ) – List of feature names to extract directly using get_feature() on each segment.
-Common examples include: ‘mean’, ‘frac’, ‘duration’, ‘baseline’, ‘current’, ‘wrap’, ‘start’.
add_ons (dict ) – A dictionary of fixed key-value pairs to include as constant columns in the resulting DataFrame.
lambdas (dict ) – A dictionary mapping column names to lambda functions that compute derived values from each segment.
-Returns:
-A pandas DataFrame where each row corresponds to a bottom-rank segment and columns represent extracted and computed features.
-Return type:
-pandas.DataFrame
-
-
-
-
-
-ionique.utils. si_eval ( value unit = None return_unit = False )
-Evaluate and convert a value with an SI unit prefix to its numeric base value.
-This function handles both string-based and numeric values with
-SI (International System of Units) prefixes such as “k” (kilo), “M” (mega), “μ” (micro), etc.
-It multiplies the input value by the appropriate factor based on the SI prefix.
-This utility is useful when parsing human-readable measurement strings or standardizing
-units across data pipelines that mix strings and numeric formats.
-
-Parameters:
-
-value (str or float ) – The value to be converted. Can be a string like “1.2 kHz” or a numeric value (int or float).
unit (str , optional ) – The unit string (e.g., “kHz”, “mV”). Required only if value is a numeric type.
return_unit (bool , optional ) – If True, returns a tuple containing the converted numeric value and the base unit (e.g., ‘Hz’).
-If False, only the numeric value is returned.
-Raises:
-
-
-Returns:
-The converted value, optionally paired with the base unit.
-Return type:
-float or tuple[float, str]
-
-
-
-
-
-ionique.utils. split_voltage_steps ( voltage : ndarray n_remove = 0 as_tuples = False )
-Split a voltage signal into segments based on step changes.
-This function detects changes in the voltage signal and splits it into individual
-segments (or steps) wherever a change in value occurs. It is useful in analyzing
-stepwise voltage protocols.
-Optionally, a number of initial samples can be removed from each segment using the
-n_remove parameter. The output can either be two separate arrays of start and end
-indices, or a list of tuples representing each segment.
-
-Parameters:
-
-voltage (numpy.ndarray ) – 1D voltage signal array to be segmented.
n_remove (int ) – Number of samples to remove from the start of each split. Defaults to 0.
as_tuples (bool ) – If True, returns a list of (start, end) index tuples. If False, returns two arrays: start_indices and end_indices. Defaults to False.
-Raises:
-ValueError – If n_remove is negative or larger than the start of the voltage changes.
-Returns:
-Either:
-- A tuple of (start_indices, end_indices), where each is a numpy array of indices.
-- A list of (start, end) tuples if as_tuples=True .
-Return type:
-tuple[numpy.ndarray, numpy.ndarray] or list[tuple[int, int]]
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API Reference — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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Data Input — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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<no title> — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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Index — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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- __init__() (ionique.core.AbstractSegmentTree method) , [1]
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- (ionique.core.MetaSegment method) , [1]
-(ionique.core.Segment method) , [1]
-(ionique.cparsers.FastStatSplit method) , [1]
-(ionique.datatypes.SessionFileManager method) , [1]
-(ionique.datatypes.TraceFile method) , [1]
-(ionique.io.AbstractFileReader method) , [1]
-(ionique.io.EDHReader method) , [1]
-(ionique.io.OPTReader method) , [1]
-(ionique.parsers.AutoSquareParser method) , [1]
-(ionique.parsers.FilterDerivativeSegmenter method) , [1]
-(ionique.parsers.IVCurveAnalyzer method) , [1]
-(ionique.parsers.IVCurveParser method) , [1]
-(ionique.parsers.lambda_event_parser method) , [1]
-(ionique.parsers.MemoryParse method) , [1]
-(ionique.parsers.NoiseFilterParser method) , [1]
-(ionique.parsers.Parser method) , [1]
-(ionique.parsers.snakebase_parser method) , [1]
-(ionique.parsers.SpeedyStatSplit method) , [1]
-(ionique.parsers.SpikeParser method) , [1]
-(ionique.utils.Filter method) , [1]
-(ionique.utils.Singleton method) , [1]
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- parse() (ionique.core.AbstractSegmentTree method) , [1]
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- (ionique.cparsers.FastStatSplit method) , [1]
-(ionique.parsers.AutoSquareParser method) , [1]
-(ionique.parsers.FilterDerivativeSegmenter method) , [1]
-(ionique.parsers.IVCurveParser method) , [1]
-(ionique.parsers.lambda_event_parser method) , [1]
-(ionique.parsers.MemoryParse method) , [1]
-(ionique.parsers.NoiseFilterParser method) , [1]
-(ionique.parsers.Parser method) , [1]
-(ionique.parsers.snakebase_parser method) , [1]
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Ionique: A Nanopore Signal Analysis Framework — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-Ionique: A Nanopore Signal Analysis Framework
-Authors:
-Ali Fallahi, Dinara Boyko
-Date:
-Feb 11, 2025
-Version:
-0.1.0 (Development)
-
-Overview
-Ionique is a Python module for processing and analyzing nanopore signal data. It provides tools for reading, filtering, segmenting, and analyzing nanopore experimental data. The framework supports structured workflows for signal processing and quality control, allowing researchers to extract relevant information from raw current and voltage traces.
-
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-Features
-
-File Handling
-- Supports .edh , .opt , and .xml formats.
-- Extracts raw current and voltage traces.
-- Parses experimental metadata for structured analysis.
Signal Processing
-- Segmentation and event detection.
Analysis Modules
-- IV curve computation and voltage pattern detection.
-- Step response and dwell-time analysis.
-- Customizable parsers and filters for flexible workflows.
Usability
-- Python library for scripting and automation.
-
-
-Installation (Development)
-To install the development version of Ionique, clone the repository and install it in editable mode:
- clone https://github.com/wanunulab/ionique.git
-cd ionique
-pip install ionique
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Requirements:
-- Python > 3.10
-- Dependencies listed in requirements.txt
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-Future Development
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-Installation for beginners
-
-Welcome to Ionique!
-This tutorial is designed for non-coding users. If you have used Python and GitHub before, please proceed with the README.md
-Before diving into data analysis, let’s first install Python, GitHub, and Jupyter Notebook.
-
-
-Step 1: Install Python
-Download the package from Python Downloads .
-Choose the appropriate OS system for your machine. Then, scroll down to pick Python version 3.10 or later up to 3.12 .
-After Python has been successfully installed, we can go over some basic terminal commands.
-
-Linux Users:
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-MacOS Users:
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-Windows Users:
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-To check the installation, type:
-
-Some versions of Python may require the following command:
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-Step 2: GitHub
-First, you need to create an account by following the instructions on GitHub .
-Now, open the repository link: Ionique Repository , click on the green “Code” button, and download the ZIP file. Unzip it.
-Move the folder to the desired location on your computer.
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-Step 3: Moving Between Folders in the Terminal
-You can find a short list of basic commands here: Linux vs Windows Commands .
-Check your current location using:
-
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-pwd (Unix, Linux, Mac)
cd (Windows)
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-To list items in the current directory:
-
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-ls (Unix, Linux, Mac)
chdir (Windows)
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-The most important command for this tutorial is cd , which stands for “change directory.” To move around folders in the terminal, type:
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-Find the ionique folder and move inside it.
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-Step 4: Virtual Environment
-A virtual environment is an encapsulated workspace on your computer that allows you to install software packages for a project. It helps to keep the versions of different packages within a project completely separate from other projects.
-To learn more about virtual environments, visit Python Virtual Environments .
-Let’s create your first virtual environment!
-Run the following command:
- -m venv ionique_env
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Then, activate it:
-source ionique_env/bin/activate # Linux/MacOS
-ionique_env\S cripts\a ctivate.bat # Windows
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Now, install the Ionique requirements:
-
-If your OS and Python version require the ``python3`` command, use ``pip3`` instead.
-Next, install JupyterLab:
-
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-Step 5: Run the Notebooks
-Copy the notebook you want to run into the data folder. (You don’t need to use the terminal for this step.)
-Now, return to the terminal window and navigate to the folder where the copied notebook is stored.
-Then, run:
-
-You are now ready to work on data analysis!
-
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-Step 6: Finish Workflow
-Once you are done with the analysis, go back to the terminal and press Ctrl + C .
-Then, type:
-
-to deactivate the virtual environment.
-Note: Starting from Step 5 , the procedure will be identical each time you want to run Ionique .
-Happy using Ionique!
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Install — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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- ionique
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-Install
-
-Install ionique
-The most recent release of the ionique pip_
-
-If you are very new to coding, more detailed instructions, we provide guides on VSCode, Python3 and ionique installment.
-
-
-Installation from Source
-If you want to contribute to ionique you can build ionique from source using the
-instructions below.
-$ git clone https://github.com/wanunulab/ionique.git
-$ cd ionique
-$ pip install -e .
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ionique — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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- ionique
-
-
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-
-
-ionique
-
-
-ionique.core
-
-
-This holds the core data types which may be abstracted in
-many different applications.
-This module defines a tree-based framework for representing, parsing, and annotating
-segments of ionic current data.
-
-
-class ionique.core. AbstractSegmentTree
-Bases: object
-Class for managing hierarchical segments.
-This class supports a tree structure where each segment can contain multiple
-child segments, enabling recursive parsing and analysis of nested data.
-It provides utilities to manage relationships between segments, apply parsers,
-and extract subsegments by rank.
-
-
-__init__ ( ) → None
-Initialize the segment
-
-
-
-
-add_child ( child : AnySegment ) → None
-Add a single child
-
-
-
-
-add_children ( children : list [ AnySegment ] ) → None
-Add multiple children if:
-1. Child position is the within parent’s segment.
-2. The length of the child’s segment is > 0
-3. There is no overlap between consecutive children
-Or add children with no check
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-clear_children ( )
-Clear the list of children
-
-
-
-
-climb_to_rank ( rank : str ) → AnySegment | None
-Go up and find the segment with the specified rank
-:return: segment
-
-
-
-
-end : int | None
-
-
-
-
-get_feature ( name : str )
-Gets the ‘name’ feature of the current segment or its parent
-
-
-
-
-get_top_parent ( ) → AnySegment
-Recursively go up and find top most parent
-:return: parent
-
-
-
-
-property n : int
-Get the length of the segment
-:return: len(segment)
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-parse ( parser newrank : str at_child_rank : str | None = None ** kwargs ) → bool
-Parses the data in the segment or at a particular rank of child segments
-into children segments with a new rank.
-
-Parameters:
-
-parser (Parser parse method and required input attributes.
newrank (str ) – Rank to assign to the newly created child segments.
at_child_rank (str or None ) – Determines whether to traverse the children tree down to a given rank.
kwargs – Additional arguments to pass to the parser.
-Returns:
-True if parsing was successful, otherwise raises an exception.
-Return type:
-bool
-
-
-
-
-
-rank : str
-
-
-
-
-property relative_end : int
-Get end position relative to parent segment
-:return: end position
-
-
-
-
-property relative_slice : ndarray
-Slice the array to get the data between end-start segment
-:return: sliced np array
-
-
-
-
-property relative_start : int
-Get the start position relative to parent segment
-:return: start position
-
-
-
-
-property slice : ndarray
-Slice the array to get the data between start-end segment
-:return: sliced np array
-
-
-
-
-start : int | None
-
-
-
-
-traverse_to_rank ( rank : str ) → list
-Traverse the tree rank and get the list of the segments of the rank
-:param: rank
-:return: list of segments
-
-
-
-
-
-
-class ionique.core. MetaSegment ( start : int end : int parent : AnySegment | None = None rank : str | None = None unique_features : dict | None = {} ** kwargs )
-Bases: AbstractSegmentTree
-The metadata on an abstract segment of ionic current. All information about a segment can be
-loaded, without the expectation of the array of floats.
-
-
-__init__ ( start : int end : int parent : AnySegment | None = None rank : str | None = None unique_features : dict | None = {} ** kwargs )
-
-Parameters:
-
-start (int ) – Start index of the segment.
end (int ) – End index of the segment.
parent (AbstractSegmentTree or None ) – Parent segment in the tree (optional).
rank (str or None ) – Rank identifier for this segment (optional).
unique_features (dict ) – Dictionary of metadata features.
-
-
-
-
-
-property current
-Get the current data of the segment if the segment correlates to the file
-:return: current
-
-
-
-
-delete ( )
-Delete itself. There are no arrays with which to delete references for.
-
-
-
-
-property duration : float
-Get the duration of the segment = start - end
-:return: duration
-
-
-
-
-classmethod from_json ( filename = None in_json = None )
-Read in a metasegment from a JSON and return a metasegment object.
-Either pass in a file which has a segment stored, or an actual JSON
-object.
-
-
-
-
-property max
-Calculate the maximum value of the current array.
-
-Returns:
-Maximum value of the current array.
-Return type:
-float
-
-
-
-
-
-property mean
-Calculate the mean of the current array.
-
-Returns:
-Mean value of the current array.
-Return type:
-float
-
-
-
-
-
-property min
-Calculate the minimum value of the current array.
-
-Returns:
-Minimum value of the current array.
-Return type:
-float
-
-
-
-
-
-property std
-Calculate the standard deviation of the current array.
-
-Returns:
-Standard deviation of the current array.
-Return type:
-float
-
-
-
-
-
-property time
-Get the time data of the segment if the corresponding rank is in file
-:return: time
-
-
-
-
-to_dict ( )
-Return a dict representation of the metadata, usually used prior to
-converting the dict to a JSON.
-
-
-
-
-to_json ( filename = None )
-Return a JSON representation of this, by reporting the important
-metadata.
-
-
-
-
-to_meta ( )
-Kept to allow for error handling, but since it’s already a metasegment
-it won’t actually do anything.
-
-
-
-
-unique_features
-
-
-
-
-
-
-class ionique.core. Segment ( current ** kwargs )
-Bases: AbstractSegmentTree
-A segment of ionic current, and methods relevant for collecting metadata. The ionic current is
-expected to be passed as a numpy array of floats. Metadata methods (mean, std..) are decorated
-as properties to reduce overall computational time, making them calculated on the fly rather
-than during analysis.
-
-
-__init__ ( current ** kwargs )
-The segment must have a list of ionic current, of which it stores some statistics about.
-It may also take in as many keyword arguments as needed, such as start time or duration
-if already known. Cannot override statistical measurements.
-:param current: Numpy array of current data points.
-:type current: np.ndarray
-:param kwargs: Additional attributes like ‘start’, ‘end’, ‘rank’, etc.
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-delete ( )
-Deleting this segment requires deleting its reference to the ionic
-current array, and then deleting itself.
-
-
-
-
-end : int | None
-
-
-
-
-classmethod from_json ( filename = None json = None )
-Read in a segment from a JSON and return a metasegment object. Either
-pass in a file which has a segment stored, or an actual JSON object.
-
-
-
-
-property max
-Calculate the maximum value of the current array.
-
-Returns:
-Maximum value of the current array.
-Return type:
-float
-
-
-
-
-
-property mean
-Calculate the mean of the current array.
-
-Returns:
-Mean value of the current array.
-Return type:
-float
-
-
-
-
-
-property min
-Calculate the minimum value of the current array.
-
-Returns:
-Minimum value of the current array.
-Return type:
-float
-
-
-
-
-
-property n
-Get the number of elements in the current array.
-
-Returns:
-Number of elements in the current array.
-Return type:
-int
-
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-rank : str
-
-
-
-
-scale ( sampling_freq )
-Rescale all of the values to go from samples to seconds.
-
-
-
-
-start : int | None
-
-
-
-
-property std
-Calculate the standard deviation of the current array.
-
-Returns:
-Standard deviation of the current array.
-Return type:
-float
-
-
-
-
-
-to_dict ( )
-Return a dict representation of the metadata, usually used prior to
-converting the dict to a JSON.
-
-
-
-
-to_json ( filename = None )
-Return a JSON representation of this, by reporting the important
-metadata.
-
-
-
-
-to_meta ( )
-Convert from a segment to a ‘metasegment’, which stores only metadata
-about the segment and not the full array of ionic current.
-
-
-
-
-
-
-ionique.core. ignored ( * exceptions )
-Replace the “try, except: pass” paradigm by replacing those three lines with a single line.
-Taken from the latest 3.4 python update push by Raymond Hettinger, see:
-http://hg.python.org/cpython/rev/406b47c64480
-
-
-
-
-ionique.cparsers
-This contains cython implementations of ionic current parsers which are in
-parsers.py. Currently the only parser is StatSplit, which is implemented
-as FastStatSplit.
-
-
-class ionique.cparsers. FastStatSplit
-Bases: object
-A cython implementation of the segmenter written by Kevin Karplus. Sped up approximately 50-100x
-compared to the Python implementation depending on parameters.
-
-
-__init__ ( * args ** kwargs )
-
-
-
-
-best_single_split ( current )
-Wrapper for a single call to _best_single_split. It will find the
-single best split in a series of current, and return the index of
-that split. Returns a tuple of ( gain, index ) where gain is the
-gain in variance by splitting there, and index is the index at which
-the split should occur in the current array.
-
-
-
-
-min_gain
-
-
-
-
-parse ( current )
-Wrapper function for the segmentation, which is implemented in cython.
-
-
-
-
-parse_meta ( current )
-Wrapper function for the segmentation, which is implemented in cython. returns meta segments (no current array)
-
-
-
-
-score_samples ( current no_split = False )
-Return a series of lists scoring each sample. However, this isn’t just
-scoring each sample once. Every time a split is detected, it will return
-a new list with newly scored samples. In essence, it returns one list
-per scan of the current using the recursive method.
-
-
-
-
-
-
-ionique.cparsers. pairwise ( iterable )
-
-
-
-
-ionique.datatypes
-Data structure definitions and session file management for analysis.
-
-
-class ionique.datatypes. SessionFileManager ( * args ** kwargs )
-Bases: MetaSegment
-Singleton-based session manager for hierarchical data.
-This class inherits from MetaSegment and uses the Singleton metaclass
-to ensure only one instance exists during a given session. It serves as
-the root segment for a data analysis session and is responsible for managing
-metadata and registered “affectors” (i.e., objects that modify or
-interact with the session).
-
-
-__init__ ( ) → None
-Initialize the session manager.
-Sets the session start time and prepares the affector log structure.
-
-
-
-
-add_child ( child : AnySegment ) → None
-Add a single child segment to the session’s hierarchy.
-
-Parameters:
-child (AnySegment ) – The segment to be added as a child.
-
-
-
-
-
-add_children ( children : list [ AnySegment ] ) → None
-Add multiple child segments to the session.
-
-Parameters:
-children (list [ AnySegment ] ) – A list of segments to be added as children.
-
-
-
-
-
-rank : str = 'root'
-
-
-
-
-register_affector ( affector )
-Register an affector object that influences the session data.
-Records metadata including class name, string representation, and timestamp,
-and logs the event with a unique identifier.
-
-Parameters:
-affector (object ) – An object that modifies or interacts with the session.
-Returns:
-A UUID string uniquely identifying the registered affector.
-Return type:
-str
-
-
-
-
-
-unique_features
-
-
-
-
-
-
-class ionique.datatypes. TraceFile ( current : ndarray voltage = None rank = 'file' parent = None unique_features : dict = {} metadata : dict = {} )
-Bases: Segment
-Data structure for representing a single trace file with current and optional voltage information.
-This class inherits from Segment and encapsulates current and voltage data. It sets up segment metadata,
-defines parent-child hierarchy, and optionally initializes child segments based on voltage steps.
-Typical usage includes assigning metadata and organizing hierarchical
-data structures for downstream processing or visualization.
-
-
-__init__ ( current : ndarray voltage = None rank = 'file' parent = None unique_features : dict = {} metadata : dict = {} )
-If voltage steps are provided, corresponding child segments of rank “vstep” are created.
-
-Parameters:
-
-current (numpy.ndarray ) – current array.
voltage (list [ tuple [ tuple [ int , int ] , float ] ] or None ) – Optional list of tuples containing (start, end) index pairs and voltage values.
-Used to create child segments for each voltage step.
rank (str ) – Segment rank label, defaults to “file”.
parent (Segment or None ) – Optional parent segment to which this trace belongs.
unique_features (dict ) – Dictionary of metadata such as sampling frequency.
metadata (dict ) – Additional metadata.
-
-
-
-
-
-children : list [ AnySegment ]
-
-
-
-
-delete ( )
-Delete the current object, removing it from its parent
-
-
-
-
-end : int | None
-
-
-
-
-parent : AnySegment | None
-
-
-
-
-plot ( rank axes downsample_per_rank color_per_rank )
-Method for plotting
-
-
-
-
-rank : str
-
-
-
-
-start : int | None
-
-
-
-
-
-
-ionique.io
-Input/output utilities for data files.
-This module provides an interface for loading, parsing, and preprocessing
-signal data from various file formats (e.g., .edh , .opt , .abf , .dat , .xml ). It defines a base reader class
-and concrete implementations that handle format-specific logic, metadata extraction,
-data alignment, and optional preprocessing steps.
-This module is central to converting raw experimental data into analyzable form.
-
-
-class ionique.io. AbstractFileReader
-Bases: object
-An abstract class for reading various data files (.abf, .edh, .mat, .opt, .xml)
-This class defines the structure for file readers
-
-
-__init__ ( )
-
-
-
-
-accepted_keywords = []
-
-
-
-
-current_multiplier : float = 1.0
-
-
-
-
-ext = '___'
-
-
-
-
-read ( filename : str ** kwargs )
-Read a datafile or series of files. Files are identified according to their extension.
-Data formats that come with a header file must be referred to by the header file.
-If inheriting from the AbstractFileReader class, do not override this function;
-instead, create a custom _read method.
-
-Parameters:
-filename – file name or list of file names, typically
-
-given as a string or PathLike object.
-:type filename: str or os.PathLike or list[str] or list[os.PathLike]
-:param **
-
-
-
-
-voltage_multiplier : float = 1.0
-
-
-
-
-
-
-class ionique.io. EDHReader ( edh_filename voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Bases: AbstractFileReader
-Reader class for loading .edh data files and their associated current data.
-This class parses the .edh header file and automatically loads the corresponding signal data
-(either from .abf or .dat files in the same directory). It extracts metadata, converts raw
-current and voltage data to SI units, and supports optional preprocessing steps such as
-voltage step segmentation, downsampling, and signal filtering.
-
-
-__init__ ( edh_filename voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Initialize the EDHReader and load signal data from associated files.
-
-Parameters:
-
-edh_filename (str ) – Path to the .edh header file.
voltage_compress (bool ) – If True, splits signal into segments based on voltage steps.
n_remove (int ) – Number of samples to remove from the beginning of each voltage step.
downsample (int ) – Downsampling factor to reduce data size.
prefilter (callable or None ) – Callable to apply preprocessing to the current signal.
-
-
-
-
-
-accepted_keywords = ['voltage_compress', 'n_remove', 'downsample', 'prefilter']
-
-
-
-
-current_multiplier : float = 1e-09
-
-
-
-
-ext = '.edh'
-
-
-
-
-voltage_multiplier : float = 0.001
-
-
-
-
-
-
-class ionique.io. OPTReader ( opt_filename : str voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Bases: AbstractFileReader
-Reader for .opt data files with corresponding XML or _volt.opt metadata.
-This class reads current data from .opt files and attempts to extract or reconstruct
-corresponding voltage data using associated .xml or _volt.opt files found in the same
-directory. It handles metadata extraction, signal preprocessing, voltage alignment, and
-segment compression based on voltage steps.
-Supported XML structures include both standard HWtiming_cap_step formats and timestamp-based
-custom formats.
-
-
-__init__ ( opt_filename : str voltage_compress = False n_remove = 0 downsample = 1 prefilter = None )
-Initialize the OPTReader instance and load corresponding files.
-
-Parameters:
-
-opt_filename (str ) – Path to the .opt header file.
voltage_compress (bool ) – If True, splits signal into segments based on voltage steps.
n_remove (int ) – Number of samples to remove from the beginning of each voltage step.
downsample (int ) – Downsampling factor to reduce data size.
prefilter (callable or None ) – Callable to apply preprocessing to the current signal.
-
-
-
-
-
-accepted_keywords = ['voltage_compress', 'n_remove', 'downsample', 'prefilter']
-
-
-
-
-current_multiplier : float = 1000000000.0
-
-
-
-
-ext = '.opt'
-
-
-
-
-find_peaks_in_segment ( current_data start_index end_index )
-Apply “find_peaks” on a segment of the current data and return
-the indices of the found peak
-
-
-
-
-find_peaks_slide_window ( current start_index window_shift_duration = 0.002 sampling_frequency = 250000 sign = 'negative' )
-Find peaks in the current every time the voltage changes.
-The search for the peaks happens in the window of the timestamp 0.02 seconds.
-
-
-
-
-process_custom_xml ( )
-Processes the custom XML file to extract voltage data, align the voltage waveform
-with the current, and detect peaks in the current signal every time the voltage changes.
-
-
-
-
-
-
-ionique.io. load_files_GUI ( )
-
-Create a form with:
-‘Compress voltage (only uncheck if voltage is not stepwise, e.g., triangle wave)’ : checkbox
‘Apply Filter?’ : checkbox
If ‘Apply Filter?’ checked: dynamic form from the Filter dataclass (types, options, limits).
-Returns (frame, get_values) where get_values() -> dict with: {‘compress_voltage’: bool, ‘apply_filter’: bool, ‘filter’: Filter|None}
-
-
-
-
-
-ionique.io. select_files_GUI ( )
-Open a file dialog allowing multiple selection, but only for ONE file type at a time.
-Users can switch the dialog’s file-type dropdown, but the final selection must be
-homogeneous (all the same extension) and in the allowed list.
-
-Returns
-
-tuple[str, …] Absolute paths of the selected files (empty tuple if the user cancels).
-
-
-
-
-
-
-ionique.parsers
-Parser module for event and state segmentation in current recordings.
-This module provides a framework for parsing current trace data. It includes an abstract base Parser class
-and multiple concrete parser implementations for detecting spikes, noise, square pulses, and other event
-structures in ionic current signals.
-Some of the parsers and the parser base class were adapted from the
-PyPore Package by Jacob Schreiber and Kevin Karplus (https://github.com/jmschrei/PyPore )
-
-
-class ionique.parsers. AutoSquareParser ( threshold_baseline = 0.7 expected_conductance = 1.9 conductance_tolerance = 1.2 wrap_padding : int = 50 rules = [] )
-Bases: Parser
-Class for building a straightforward pipeline of data analysis
-to make it reusable with customized parameters
-
-
-__init__ ( threshold_baseline = 0.7 expected_conductance = 1.9 conductance_tolerance = 1.2 wrap_padding : int = 50 rules = [] )
-Initialize a Parser instance
-
-
-
-
-parse ( current eff_sampling_freq voltage )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current', 'eff_sampling_freq', 'voltage']
-
-
-
-
-
-
-class ionique.parsers. FilterDerivativeSegmenter ( low_threshold = 1 high_threshold = 2 cutoff_freq = 1000.0 sampling_freq = 100000.0 )
-Bases: Parser
-This parser will segment an event using a filter-derivative method. It will
-first apply a bessel filter at a certain cutoff to the current, then it will
-take the derivative of that, and segment when the derivative passes a
-threshold.
-
-
-__init__ ( low_threshold = 1 high_threshold = 2 cutoff_freq = 1000.0 sampling_freq = 100000.0 )
-Initialize a Parser instance
-
-
-
-
-parse ( current )
-Apply the filter-derivative method to filter the ionic current.
-
-
-
-
-set_params ( )
-
-Returns:
-
-
-
-
-
-
-
-
-class ionique.parsers. IVCurveAnalyzer ( current parent_segment sampling_frequency method = 'simple' )
-Bases: Parser
-THIS CLASS IS NOT IMPLEMENTED YET. DO NOT USE.
-Check for at Least Two Voltage Steps: confirm that the segment has at least two voltage steps.
-Divide into Voltage Segments: separate data into voltage segments.
-Call IVAnalyzer on Parent Segment: compute the mean and std of the current in each
-voltage segment.
-Return an Array of (voltage: (imean, istd)) pair:
-Also mean and std should be calulated in two ways
-
-
-__init__ ( current parent_segment sampling_frequency method = 'simple' )
-Initialize a Parser instance
-
-
-
-
-analyze ( )
-Analyze each voltage step in a parent segment
-
-
-
-
-
-
-class ionique.parsers. IVCurveParser ( voltage )
-Bases: object
-THIS CLASS IS NOT IMPLEMENTED YET! DO NOT USE.
-The class handles:
-
-
-Identifies a file where an IV curve is recorded based on the pattern.
-
-Here, the goal is to feed the voltage information to a module and have it search and do pattern matching to find one of
-these possibilities. .This can be a parser that accepts voltage array or compressed voltage as one of the inputs.
-
-
-__init__ ( voltage )
-
-
-
-
-parse ( )
-Searches for a pattern in the voltage data to identify IV.
-If none exists, the given datafile does not contain IV curves.
-If one or multiple IV curves are found, it returns start and
-end regions of each of them.
-Returns: list of indices: start and end for each deteced IV curve
-
-
-
-
-
-
-class ionique.parsers. MemoryParse ( starts ends )
-Bases: object
-A parser based on being fed previous split points, and splitting a raw file based
-those splits. Used predominately when loading previous split points from the
-database cache, to reconstruct a parsed file from “memory.
-
-
-__init__ ( starts ends )
-
-Parameters:
-
-
-
-
-
-
-
-parse ( current )
-
-
-
-
-
-
-class ionique.parsers. NoiseFilterParser ( noise_threshold = 60 detect_noise = True )
-Bases: Parser
-This unit is a parser subclass. when invoked on a segment (whole file or subsegment),
-it identifies the areas where bad things happened, and areas where the data is clean.
-Examples of bad things include high noise (60Hz noise from opening the cage), membrane
-rupture, and membrane formation attempts. it produces children from the good
-regions of the data and ignore the bad regions.
-
-
-__init__ ( noise_threshold = 60 detect_noise = True )
-Initialize a Parser instance
-
-
-
-
-parse ( data segment_start = 0 segment_end = None )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-
-
-class ionique.parsers. Parser
-Bases: ABC
-This class defines the interface and shared utility methods for parser implementations.
-Parsers are used to segment ionic current data
-into structured regions of interest (events).
-Subclasses should implement the parse method and define any additional logic
-required to detect features in the data.
-
-
-abstract __init__ ( )
-Initialize a Parser instance
-
-
-
-
-classmethod from_json ( _json )
-Critical: Not implemented yet, figure out how to make this work in pythion
-
-
-
-
-classmethod get_init_params ( )
-Get the initialization parameters of the parser class.
-
-Returns:
-A dictionary of initialization parameters, default None.
-Return type:
-dict or None
-
-
-
-
-
-classmethod get_name ( )
-Get the name of the parser class
-:return: The name of the class.
-:rtype: str
-
-
-
-
-classmethod get_process_inputs ( )
-
-
-
-
-classmethod get_process_outputs ( )
-Get the outputs generated by processing
-
-Returns:
-A list of outputs generated by parse method
-Return type:
-list or None
-
-
-
-
-
-get_required_parent_attributes ( )
-Get the list of required attributes from the parent segment
-
-Returns:
-A list of required parent attributes
-Return type:
-list[str]
-
-
-
-
-
-parse ( ** kwargs )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current']
-
-
-
-
-set_params ( param_dict )
-Updates each paramater presented in the GUI to the value input in the lineEdit
-corresponding to that value.
-:param param_dict: A dictionary
-:type param_dict: dict
-
-
-
-
-to_dict ( )
-Convert the parser’s attributes to a dictionary.
-
-Returns:
-A dictionary representation of the parser’s attributes.
-Return type:
-dict
-
-
-
-
-
-to_json ( filename = False )
-Convert the parser’s attributes to a JSON string.
-
-Parameters:
-filename (str , default bool ) – If filename, it writes the JSON string to the specified file.
-Returns:
-A dictionary representation of the parser’s attributes.
-Return type:
-str
-
-
-
-
-
-
-
-class ionique.parsers. SpeedyStatSplit ( sampling_freq min_width = 100 max_width = 1000000 window_width = 10000 min_gain_per_sample = None false_positive_rate = None prior_segments_per_second = None cutoff_freq = None )
-Bases: Parser
-A parser wrapper for the Cython-accelerated FastStatSplit algorithm.
-See cparsers.pyx FastStatSplit for full documentation. This is just a
-wrapper for the cyton implementation to add a GUI.
-
-
-__init__ ( sampling_freq min_width = 100 max_width = 1000000 window_width = 10000 min_gain_per_sample = None false_positive_rate = None prior_segments_per_second = None cutoff_freq = None )
-Initialize the SpeedyStatSplit instance
-
-Parameters:
-
-sampling_freq (int ) – The sampling frequency of the data
min_width (int , optional ) – Minimum width of a segment, default=100
max_width (int , optional ) – Maximum width of a segment, default=1000000.
window_width (int , optional ) – Width of the window, defaults to 10000.
min_gain_per_sample (float or None , optional ) – Minimum gain per sample, default=None
false_positive_rate (float or None , optional ) – Expected false positive rate , default=None
prior_segments_per_second (float or None , optional ) – Prior number of segments per second, default=None.
cutoff_freq (float or None , optional ) – Cutoff frequency for filtering, default=None
-
-
-
-
-
-best_single_split ( current )
-Determine the best single split point in the current data
-
-Parameters:
-current (numpy.ndarray ) – The current data to be analyzed.
-Returns:
-The best split point
-Return type:
-int or None
-
-
-
-
-
-parse ( current )
-Parse the current data to detect segments using the FastStatSplit
-
-Parameters:
-current (numpy.ndarray ) – The current data to be parsed
-Returns:
-A list of detected segments
-Return type:
-
-
-
-
-
-
-parse_meta ( current )
-Parse the current data and extract meta information
-
-Parameters:
-current (numpy.ndarray ) – The current data to be parsed.
-
-
-
-
-
-set_params ( )
-Set the parameters
-
-
-
-
-uiTemplate = [('sampling_freq', 'spin', {'bounds': [0, None], 'dec': True, 'siPrefix': True, 'step': 1, 'suffix': 'Hz', 'value': 200000}), ('min_width', 'spin', {'dec': True, 'int': True, 'min': 2, 'step': 1, 'value': 100}), ('max_width', 'spin', {'dec': True, 'int': True, 'step': 1, 'value': 1000000}), ('window_width', 'spin', {'dec': True, 'int': True, 'step': 1, 'value': 10000}), ('cutoff_freq', 'spin', {'hidden': True})]
-
-
-
-
-
-
-class ionique.parsers. SpikeParser ( height = None threshold = None distance = None prominence = None prominence_snr = None width = None wlen = None rel_height : float = 0.5 plateu_size = None fractional : bool = True )
-Bases: Parser
-A parser for detecting spike-like events in ionic current data.
-This parser wraps scipy.signal.find_peaks , supporting thresholds
-for features like peak height, prominence, and width. It computes event characteristics such
-as dwell time, relative prominence, and peak positions.
-
-
-__init__ ( height = None threshold = None distance = None prominence = None prominence_snr = None width = None wlen = None rel_height : float = 0.5 plateu_size = None fractional : bool = True ) → None
-Initialize the SpikeParser.
-
-Parameters:
-
-height (float or tuple or None ) – Minimum spike height.
threshold (float or tuple or None ) – Minimum vertical threshold.
distance (float or tuple or None ) – Minimum horizontal distance between peaks.
prominence (float or tuple or None ) – Minimum peak prominence.
prominence_snr (tuple or None ) – Alternative to prominence, specified as a tuple of signal-to-noise (SNR) ratios.
width (float or tuple or None ) – Full width at half maximum of the peak.
wlen (float or None ) – Width of the window used to calculate prominence.
rel_height (float ) – Relative height used in width calculation (range 0 to 1).
plateu_size (float or None ) – Minimum length of a flat-topped peak.
fractional (bool ) – Whether to interpret height, prominence, etc., as a fraction of the signal mean.
-
-
-
-
-
-parse ( current sampling_freq mean std start )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-required_parent_attributes : list [ str ] = ['current', 'sampling_freq', 'mean', 'start', 'std']
-
-
-
-
-
-
-class ionique.parsers. lambda_event_parser ( threshold = 90 rules = None )
-Bases: Parser
-A simple rule-based parser which defines events as a sequential series
-of points which are below a certain threshold, then filtered based on other
-critereon such as total time or minimum current. Rules can be passed in at
-initiation, or set later, but must be a lambda function takes in a PreEvent
-object and performs some boolean operation.
-
-
-__init__ ( threshold = 90 rules = None )
-
-Parameters:
-
-threshold (float ) – The threshold for detecting events (e.g., current < threshold).
rules (list [ callable ] or None ) – A list of lambda functions that accept an event and return a bool.
-
-
-
-
-
-parse ( current )
-Perform a large capture of events by creating a boolean mask for when
-the current is below a threshold, then detecting the edges in those masks,
-and using the edges to partitition the sample. The events are
-then filtered before being returned.
-
-
-
-
-set_params ( )
-Read in the data from the GUI and use it to customize
-the rules or threshold of the parser.
-
-
-
-
-
-
-ionique.parsers. pairwise ( iterable )
-
-s -> (s0,s1), (s1,s2), (s2, s3), …
-
-
-Parameters:
-iterable
-Returns:
-
-
-
-
-
-
-class ionique.parsers. snakebase_parser ( threshold = 1.5 )
-Bases: Parser
-A simple parser based on dividing when the peak-to-peak
-amplitude of a wave exceeds a certain threshold.
-
-
-__init__ ( threshold = 1.5 )
-Initialize a Parser instance
-
-
-
-
-parse ( current )
-Takes in a current segment, and returns a list of segment objects
-
-Parameters:
-kwargs (dict ) – Keyword arguments containing the data, such as current
-Returns:
-A list of tuples representing the start and end indices of the segment
-and an empty dictionary.
-Return type:
-list[tuple]
-
-
-
-
-
-set_params ( )
-Updates each paramater presented in the GUI to the value input in the lineEdit
-corresponding to that value.
-:param param_dict: A dictionary
-:type param_dict: dict
-
-
-
-
-
-
-ionique.utils
-Signal Processing and Data Extraction Utilities
-This module provides a set of utility functions and classes designed for use in signal
-processing workflows.
-These tools are intended for both internal processing and external use cases such as analysis
-pipelines and API integrations.
-
-
-class ionique.utils. Filter ( cutoff_frequency : float filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ] filter_method : Literal [ 'butter' , 'bessel' ] = 'butter' bidirectional : bool = True sampling_frequency : float = None )
-Bases: object
-This class allows a user to apply low-pass, high-pass,
-band-pass, or band-stop filters using standard filter types (Butterworth or Bessel).
-Filters are implemented using second-order sections (SOS) for numerical stability and
-can be applied in either forward-only or bidirectional mode.
-
-Parameters:
-
-cutoff_frequency (float or list [ float ] ) – The cutoff frequency or frequency band for the filter in Hz.
-For band filters, provide the band center or list of [low, high] values.
filter_type (Literal [ "lowpass" , "highpass" , "bandpass" , "bandstop" ] ) – The type of filter to apply. Options are “lowpass”, “highpass”, “bandpass”, or “bandstop”.
filter_method (Literal [ "butter" , "bessel" ] ) – The filter method. Supported options: “butter” (Butterworth), “bessel”. Defaults to “butter”.
order (int ) – The order of the filter. Must be >= 1. Defaults to 2.
bidirectional (bool ) – If True, applies filtering forward and backward using sosfiltfilt .
-If False, uses causal sosfilt . Defaults to True.
sampling_frequency (float , optional ) – Sampling frequency of the signal in Hz.
-
-
-
-__init__ ( cutoff_frequency : float filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ] filter_method : Literal [ 'butter' , 'bessel' ] = 'butter' bidirectional : bool = True sampling_frequency : float = None ) → None
-
-
-
-
-bidirectional : bool = True
-
-
-
-
-cutoff_frequency : float
-
-
-
-
-filter_method : Literal [ 'butter' , 'bessel' ] = 'butter'
-
-
-
-
-filter_type : Literal [ 'lowpass' , 'highpass' , 'bandpass' , 'bandstop' ]
-
-
-
-
-sampling_frequency : float = None
-
-
-
-
-
-
-class ionique.utils. Singleton ( * args ** kwargs )
-Bases: type
-Generic singleton metaclass.
-This metaclass ensures the same return every time the class is instanced.
-
-
-__init__ ( * args ** kwargs )
-Class initialization
-
-Parameters:
-
-
-
-
-
-
-
-
-
-class ionique.utils. Trimmer ( samples_to_remove : int rank : str = 'vstep' newrank : str = 'vstepgap' )
-Bases: object
-Segment trimming utility for hierarchical signal data.
-This class operates on a segment tree. It traverses segments of a given rank (e.g., “vstep”) and trims
-a fixed number of samples from the start of each segment. The resulting trimmed segments are added
-as new child segments with a specified new rank (e.g., “vstepgap”).
-This is useful when initial samples of each segment include artifacts that should be excluded from analysis.
-
-Parameters:
-
-samples_to_remove (int ) – Number of samples to trim from the beginning of each segment.
rank (str ) – The hierarchical rank of segments to target for trimming. Defaults to “vstep”.
newrank (str ) – The rank name to assign to newly created trimmed child segments. Defaults to “vstepgap”.
-
-
-
-__init__ ( samples_to_remove : int rank : str = 'vstep' newrank : str = 'vstepgap' ) → None
-
-
-
-
-newrank : str = 'vstepgap'
-
-
-
-
-rank : str = 'vstep'
-
-
-
-
-samples_to_remove : int
-
-
-
-
-
-
-Extract features from hierarchical segments into a DataFrame.
-Traverses a hierarchical segment structure down to bottom_rank , then collects feature values
-from each segment. Static features are retrieved via get_feature() , constants can be added via
-add_ons , and custom computed features can be provided through lambdas .
-This is useful for generating structured datasets from annotated traces for statistical analysis or
-machine learning.
-
-Parameters:
-
-seg (object ) – The root segment or trace object containing a hierarchical structure. It must implement traverse_to_rank() and support get_feature() .
bottom_rank (str ) – The rank name of the lowest-level segments from which to extract features.
extractions (list [ str ] ) – List of feature names to extract directly using get_feature() on each segment.
-Common examples include: ‘mean’, ‘frac’, ‘duration’, ‘baseline’, ‘current’, ‘wrap’, ‘start’.
add_ons (dict ) – A dictionary of fixed key-value pairs to include as constant columns in the resulting DataFrame.
lambdas (dict ) – A dictionary mapping column names to lambda functions that compute derived values from each segment.
-Returns:
-A pandas DataFrame where each row corresponds to a bottom-rank segment and columns represent extracted and computed features.
-Return type:
-pandas.DataFrame
-
-
-
-
-
-ionique.utils. si_eval ( value unit = None return_unit = False )
-Evaluate and convert a value with an SI unit prefix to its numeric base value.
-This function handles both string-based and numeric values with
-SI (International System of Units) prefixes such as “k” (kilo), “M” (mega), “μ” (micro), etc.
-It multiplies the input value by the appropriate factor based on the SI prefix.
-This utility is useful when parsing human-readable measurement strings or standardizing
-units across data pipelines that mix strings and numeric formats.
-
-Parameters:
-
-value (str or float ) – The value to be converted. Can be a string like “1.2 kHz” or a numeric value (int or float).
unit (str , optional ) – The unit string (e.g., “kHz”, “mV”). Required only if value is a numeric type.
return_unit (bool , optional ) – If True, returns a tuple containing the converted numeric value and the base unit (e.g., ‘Hz’).
-If False, only the numeric value is returned.
-Raises:
-
-
-Returns:
-The converted value, optionally paired with the base unit.
-Return type:
-float or tuple[float, str]
-
-
-
-
-
-ionique.utils. split_voltage_steps ( voltage : ndarray n_remove = 0 as_tuples = False )
-Split a voltage signal into segments based on step changes.
-This function detects changes in the voltage signal and splits it into individual
-segments (or steps) wherever a change in value occurs. It is useful in analyzing
-stepwise voltage protocols.
-Optionally, a number of initial samples can be removed from each segment using the
-n_remove parameter. The output can either be two separate arrays of start and end
-indices, or a list of tuples representing each segment.
-
-Parameters:
-
-voltage (numpy.ndarray ) – 1D voltage signal array to be segmented.
n_remove (int ) – Number of samples to remove from the start of each split. Defaults to 0.
as_tuples (bool ) – If True, returns a list of (start, end) index tuples. If False, returns two arrays: start_indices and end_indices. Defaults to False.
-Raises:
-ValueError – If n_remove is negative or larger than the start of the voltage changes.
-Returns:
-Either:
-- A tuple of (start_indices, end_indices), where each is a numpy array of indices.
-- A list of (start, end) tuples if as_tuples=True .
-Return type:
-tuple[numpy.ndarray, numpy.ndarray] or list[tuple[int, int]]
-
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Install Ionique — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-Install Ionique
-This guide walks you through the steps to download, set up, and install the Ionique package using a virtual environment.
-
-1. Download the Ionique Repository
-
-
-
-
-
-2. Open the Ionique Folder in VS Code
-
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-Now your working directory in VS Code is set to the ionique
-
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-3. Set Up a Virtual Environment
-A virtual environment is an isolated workspace for installing project-specific dependencies.
-
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-
-Step 1: Open Terminal in VS Code
-
-
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-Step 2: Confirm You’re in the Correct Directory
-Use the following command:
-
-You should see the contents of the ionique
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-Step 3: Create and Activate the Environment
-Run the command:
- -m venv ionique_env
-
-
-Note: On some systems (especially Linux/macOS), you may need to use python3 pip3 python pip
-
-Then activate it:
-
-Linux / macOS:
-source ionique_env/bin/activate
-
-
Windows:
-
-
-If activated successfully, you’ll see (ionique_env)
-
-
-
-4. Install the Ionique Package
-Run the command:
-
-Wait a few minutes while dependencies install.
-
-
-If you see a similar message, congratulations — you’ve successfully installed the Ionique package!
-
-
-
-5. (Optional) Install JupyterLab
-To run data analysis in an interactive interface like Jupyter Notebook, install JupyterLab while the virtual environment is still active:
-
-
-
-6. Deactivate the Environment
-When you’re done, deactivate the virtual environment by typing:
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Nanopore Signal Analysis Framework — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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Python Module Index — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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Guide for VSCode and Python Installation — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-Guide for VSCode and Python Installation
-This guide is designed for users who do not have Python or Visual Studio Code installed on their machine.
-
-Note: The guide is divided into three parts: Windows , Linux and macOS users. Please follow the appropriate instructions for your operating system.
-
-First, you need a platform (Integrated Development Environment) to run Ionique . Visual Studio Code (VS Code) is a convenient IDE to start with.
-
-Installing VS Code
-
-Windows Users
-1. Install VS Code
-
-Go to the VS Code website , download.
Run the installer.
On the first screen, check the boxes Add Python to PATH and Register code … .
-
Finish installation.
-
-
-2. Install Required Extensions
-A few package requirements include C++ extensions and compilers.
-
-
-
-
-
-4. Install Python.
-
-Skip this step if you already have Python!
-Visit Python Downloads for Windows .
Choose a version between 3.10 and 3.12 and download.
Run the installer.
On the first screen, check the box for “Add Python to PATH” .
Click Install Now .
-
-
-
-Linux Users
-
-
-1. Download and Install VS_Code
-
-
-
-2. Open a Terminal
-You can search for “shell” or use the shortcut Ctrl + Alt + T
-Check your current location:
-
-Navigate to the Downloads folder (or wherever you saved the .deb
-
-List files to confirm:
-
-Install VS Code:
- dpkg -i code_1.XXX.deb
-
-
-Replace code_1.XXX.deb
-
-
-
-3. Install Python
-
-Skip this step if you already have Python!
-Visit the Python Downloads page .
Download a version between 3.10 and 3.12 .
Use your package manager or install from source.
-For Ubuntu-based systems:
- apt update
-sudo apt install python3.XX
-
-
-Replace XX
-
-
-
-macOS Users
-
-
-1. Install VS Code
-
-Download VS Code for Mac from here .
Open your browser’s download list and find the installer.
If it’s a .zip
Drag Visual Studio Code.app Applications folder.
Open VS Code from the Applications folder or Launchpad.
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Search — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-Signal Analysis
-To analyse the signal data, ionique invokes parsers module.
-In Parsers, the segnments of the signal are processed separately to get the most accurate data.
-The statistical measures are computed for each segment
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Signal Preprocessing — ionique 0.3.1.dev0+g05af5db3a.d20251003 documentation
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-Signal Preprocessing
-Signal preprocessing is an essential step before downstream analysis of nanopore signals.
-It typically involves filtering, trimming, downsampling, or compressing signal segments
-to reduce noise and improve performance.
-This page describes the following functionalities of Ionique:
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-Filtering with Filter class
-The Filter class allows users to apply different types of filters on current traces:
-Supported filter types:
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-Supported filter methods:
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-Filtering is applied using scipy.signal.sosfiltfilt (zero-phase, bidirectional) or
-scipy.signal.sosfilt depending on bidirectional=True/False .
-Usage Example:
-filt = Filter (
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-filt ( current_trace )
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-Trimming with Trimmer class
-The Trimmer class removes a fixed number of samples from the start of each segment
-at a given hierarchical rank (e.g., “vstep”). This is useful for excluding artifacts
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-Usage Example:
-trimmer = Trimmer ( samples_to_remove = 200 )
-trimmer ( trace_file )
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-Summary
-Both Filter and Trimmer are designed to be composable tools for preprocessing current traces before
-event detection and feature extraction. You can chain them together in the analysis workflows.
-These operations are performed using tools in utils.py
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