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..bibliography:: directive output */ +table.docutils.citation, table.docutils.citation td, table.docutils.citation th { + border: none; + /* Below needed in some edge cases; if not applied, bottom shadows appear */ + -moz-box-shadow: none; + -webkit-box-shadow: none; + box-shadow: none; +} + + +/* relbar */ + +.related { + line-height: 30px; + width: 100%; + font-size: 0.9rem; +} + +.related.top { + border-bottom: 1px solid #EEE; + margin-bottom: 20px; +} + +.related.bottom { + border-top: 1px solid #EEE; +} + +.related ul { + padding: 0; + margin: 0; + list-style: none; +} + +.related li { + display: inline; +} + +nav#rellinks { + float: right; +} + +nav#rellinks li+li:before { + content: "|"; +} + +nav#breadcrumbs li+li:before { + content: "\00BB"; +} + +/* Hide certain items when printing */ +@media print { + div.related { + display: none; + } +} + +img.github { + position: absolute; + top: 0; + border: 0; + right: 0; +} \ No newline at end of file diff --git a/main/imspy/_static/basic.css b/main/imspy/_static/basic.css new file mode 100644 index 00000000..d9846dac --- /dev/null +++ b/main/imspy/_static/basic.css @@ -0,0 +1,914 @@ +/* + * 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; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +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; +} + +ul.search li a { + font-weight: bold; +} + +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; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: inherit; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +a:visited { + color: #551A8B; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, figure.align-right, .figure.align-right, object.align-right { + clear: right; + float: right; + margin-left: 1em; +} + +img.align-center, figure.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +img.align-default, figure.align-default, .figure.align-default { + display: block; + margin-left: auto; + margin-right: auto; +} + +.align-left { + text-align: left; +} + +.align-center { + text-align: center; +} + +.align-default { + text-align: center; +} + +.align-right { + text-align: right; +} + +/* -- sidebars -------------------------------------------------------------- */ + +div.sidebar, +aside.sidebar { + margin: 0 0 0.5em 1em; + border: 1px solid #ddb; + padding: 7px; + background-color: #ffe; + width: 40%; + float: right; + clear: right; + overflow-x: auto; +} + +p.sidebar-title { + font-weight: bold; +} + +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ + +nav.contents, +aside.topic, +div.topic { + border: 1px solid #ccc; + padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +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; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + +/* -- tables ---------------------------------------------------------------- */ + +table.docutils { + margin-top: 10px; + margin-bottom: 10px; + border: 0; + border-collapse: collapse; +} + +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + +table caption span.caption-number { + font-style: italic; +} + +table caption span.caption-text { +} + +table.docutils td, table.docutils th { + padding: 1px 8px 1px 5px; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + margin: 0.5em; + padding: 0.5em; +} + +div.figure p.caption, figcaption { + padding: 0.3em; +} + +div.figure p.caption span.caption-number, +figcaption span.caption-number { + font-style: italic; +} + +div.figure p.caption span.caption-text, +figcaption span.caption-text { +} + +/* -- field list styles ----------------------------------------------------- */ + +table.field-list td, table.field-list th { + border: 0 !important; +} + +.field-list ul { + margin: 0; + padding-left: 1em; +} + +.field-list p { + margin: 0; +} + +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist { + margin: 1em 0; +} + +table.hlist td { + vertical-align: top; +} + +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + + +/* -- other body styles ----------------------------------------------------- */ + +ol.arabic { + list-style: decimal; +} + +ol.loweralpha { + list-style: lower-alpha; +} + +ol.upperalpha { + list-style: upper-alpha; +} + +ol.lowerroman { + list-style: lower-roman; +} + +ol.upperroman { + list-style: upper-roman; +} + +:not(li) > ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { + margin-top: 0px; +} + +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { + margin-bottom: 0px; +} + +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} + +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dd { + padding-left: 0.5em; + margin-top: 0em; + margin-left: 0em; + margin-bottom: 0em; +} + +dl { + margin-bottom: 15px; +} + +dd > :first-child { + margin-top: 0px; +} + +dd ul, dd table { + margin-bottom: 10px; +} + +dd { + margin-top: 3px; + margin-bottom: 10px; + margin-left: 30px; +} + +.sig dd { + margin-top: 0px; + margin-bottom: 0px; +} + +.sig dl { + margin-top: 0px; + margin-bottom: 0px; +} + +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + +dt:target, span.highlighted { + background-color: #fbe54e; +} + +rect.highlighted { + fill: #fbe54e; +} + +dl.glossary dt { + font-weight: bold; + font-size: 1.1em; +} + +.versionmodified { + font-style: italic; +} + +.system-message { + background-color: #fda; + padding: 5px; + border: 3px solid red; +} + +.footnote:target { + background-color: #ffa; +} + +.line-block { + display: block; + margin-top: 1em; + margin-bottom: 1em; +} + +.line-block .line-block { + margin-top: 0; + margin-bottom: 0; + margin-left: 1.5em; +} + +.guilabel, .menuselection { + font-family: sans-serif; +} + +.accelerator { + text-decoration: underline; +} + +.classifier { + font-style: oblique; +} + +.classifier:before { + font-style: normal; + margin: 0 0.5em; + content: ":"; + display: inline-block; +} + +abbr, acronym { + border-bottom: dotted 1px; + cursor: help; +} + +.translated { + background-color: rgba(207, 255, 207, 0.2) +} + +.untranslated { + background-color: rgba(255, 207, 207, 0.2) +} + +/* -- code displays --------------------------------------------------------- */ + +pre { + overflow: auto; + overflow-y: hidden; /* fixes display issues on Chrome browsers */ +} + +pre, div[class*="highlight-"] { + clear: both; +} + +span.pre { + -moz-hyphens: none; + -ms-hyphens: none; + -webkit-hyphens: none; + hyphens: none; + white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; +} + +td.linenos pre { + border: 0; + background-color: transparent; + color: #aaa; +} + +table.highlighttable { + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; +} + +table.highlighttable td { + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; +} + +div.code-block-caption { + margin-top: 1em; + padding: 2px 5px; + font-size: small; +} + +div.code-block-caption code { + background-color: transparent; +} + +table.highlighttable td.linenos, +span.linenos, +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ +} + +div.code-block-caption span.caption-number { + padding: 0.1em 0.3em; + font-style: italic; +} + +div.code-block-caption span.caption-text { +} + +div.literal-block-wrapper { + margin: 1em 0; +} + +code.xref, a code { + background-color: transparent; + font-weight: bold; +} + +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + background-color: transparent; +} + +.viewcode-link { + float: right; +} + +.viewcode-back { + float: right; + font-family: sans-serif; +} + +div.viewcode-block:target { + margin: -1px -10px; + padding: 0 10px; +} + +/* -- math display ---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: absolute; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/main/imspy/_static/custom.css b/main/imspy/_static/custom.css new file mode 100644 index 00000000..2a924f1d --- /dev/null +++ b/main/imspy/_static/custom.css @@ -0,0 +1 @@ +/* This file intentionally left blank. */ diff --git a/main/imspy/_static/doctools.js b/main/imspy/_static/doctools.js new file mode 100644 index 00000000..0398ebb9 --- /dev/null +++ b/main/imspy/_static/doctools.js @@ -0,0 +1,149 @@ +/* + * Base JavaScript utilities for all Sphinx HTML documentation. + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !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.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/main/imspy/_static/documentation_options.js b/main/imspy/_static/documentation_options.js new file mode 100644 index 00000000..f2a4111a --- /dev/null +++ b/main/imspy/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '0.2.33', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/main/imspy/_static/file.png b/main/imspy/_static/file.png new file mode 100644 index 0000000000000000000000000000000000000000..a858a410e4faa62ce324d814e4b816fff83a6fb3 GIT binary patch literal 286 zcmV+(0pb3MP)s`hMrGg#P~ix$^RISR_I47Y|r1 z_CyJOe}D1){SET-^Amu_i71Lt6eYfZjRyw@I6OQAIXXHDfiX^GbOlHe=Ae4>0m)d(f|Me07*qoM6N<$f}vM^LjV8( literal 0 HcmV?d00001 diff --git a/main/imspy/_static/github-banner.svg b/main/imspy/_static/github-banner.svg new file mode 100644 index 00000000..c47d9dc0 --- /dev/null +++ b/main/imspy/_static/github-banner.svg @@ -0,0 +1,5 @@ + + + + + diff --git a/main/imspy/_static/language_data.js b/main/imspy/_static/language_data.js new file mode 100644 index 00000000..c7fe6c6f --- /dev/null +++ b/main/imspy/_static/language_data.js @@ -0,0 +1,192 @@ +/* + * 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 = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git 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the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +// Global search result kind enum, used by themes to style search results. +class SearchResultKind { + static get index() { return "index"; } + static get object() { return "object"; } + static get text() { return "text"; } + static get title() { return "title"; } +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/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/main/imspy/_static/sphinx_highlight.js b/main/imspy/_static/sphinx_highlight.js new file mode 100644 index 00000000..8a96c69a --- /dev/null +++ b/main/imspy/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* 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/main/imspy/genindex.html b/main/imspy/genindex.html new file mode 100644 index 00000000..0057c004 --- /dev/null +++ b/main/imspy/genindex.html @@ -0,0 +1,2092 @@ + + + + + + + Index — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ + +

Index

+ +
+ A + | B + | C + | D + | E + | F + | G + | I + | J + | K + | L + | M + | N + | O + | P + | R + | S + | T + | U + | V + | W + +
+

A

+ + + +
+ +

B

+ + + +
+ +

C

+ + + +
+ +

D

+ + + +
+ +

E

+ + + +
+ +

F

+ + + +
+ +

G

+ + + +
+ +

I

+ + + +
    +
  • im_lower (imspy.timstof.data.TimsDataset property) +
    • +
  • im_upper (imspy.timstof.data.TimsDataset property) +
    • +
  • + imspy + +
    • +
  • + imspy.algorithm + +
    • +
  • + imspy.algorithm.ccs + +
    • +
  • + imspy.algorithm.ccs.predictors + +
    • +
  • + imspy.algorithm.hashing + +
    • +
  • + imspy.algorithm.intensity + +
    • +
  • + imspy.algorithm.intensity.predictors + +
    • +
  • + imspy.algorithm.intensity.utility + +
    • +
  • + imspy.algorithm.ionization + +
    • +
  • + imspy.algorithm.ionization.predictors + +
    • +
  • + imspy.algorithm.pretrained + +
    • +
  • + imspy.algorithm.rt + +
    • +
  • + imspy.algorithm.rt.predictors + +
    • +
  • + imspy.algorithm.utility + +
    • +
  • + imspy.chemistry + +
    • +
  • + imspy.chemistry.amino_acids + +
    • +
  • + imspy.chemistry.constants + +
    • +
  • + imspy.chemistry.elements + +
    • +
  • + imspy.chemistry.mobility + +
    • +
  • + imspy.chemistry.sum_formula + +
    • +
  • + imspy.chemistry.unimod + +
    • +
  • + imspy.chemistry.utility + +
    • +
  • + imspy.data + +
    • +
  • + imspy.data.peptide + +
    • +
  • + imspy.data.spectrum + +
    • +
  • + imspy.simulation + +
    • +
  • + imspy.simulation.acquisition + +
    • +
  • + imspy.simulation.annotation + +
    • +
  • + imspy.simulation.experiment + +
    • +
  • + imspy.simulation.handle + +
    • +
  • + imspy.simulation.noise + +
    • +
  • + imspy.simulation.proteome + +
    • +
  • + imspy.simulation.tdf + +
    • +
  • + imspy.simulation.timsim + +
    • +
  • + imspy.simulation.timsim.jobs + +
    • +
  • + imspy.simulation.timsim.jobs.add_noise_from_real_data + +
    • +
  • + imspy.simulation.timsim.jobs.assemble_frames + +
    • +
  • + imspy.simulation.timsim.jobs.build_acquisition + +
    • +
  • + imspy.simulation.timsim.jobs.digest_fasta + +
    • +
  • + imspy.simulation.timsim.jobs.simulate_charge_states + +
    • +
+ +

J

+ + + +
+ +

K

+ + +
+ +

L

+ + + +
+ +

M

+ + + +
+ +

N

+ + + +
+ +

O

+ + + +
+ +

P

+ + + +
+ +

R

+ + + +
+ +

S

+ + + +
+ +

T

+ + + +
+ +

U

+ + +
+ +

V

+ + +
+ +

W

+ + + +
+ + + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.algorithm.ccs.html b/main/imspy/imspy.algorithm.ccs.html new file mode 100644 index 00000000..cd88029e --- /dev/null +++ b/main/imspy/imspy.algorithm.ccs.html @@ -0,0 +1,483 @@ + + + + + + + + imspy.algorithm.ccs package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.algorithm.ccs package¶

+
+

Submodules¶

+
+
+

imspy.algorithm.ccs.predictors module¶

+
+
+class imspy.algorithm.ccs.predictors.DeepPeptideIonMobilityApex(model, tokenizer, verbose=False, name='gru_predictor')¶
+

Bases: PeptideIonMobilityApex

+
+
+fine_tune_model(data, batch_size=64, re_compile=False, verbose=False, decoys_separate=True)¶
+
+ +
+
+simulate_ion_mobilities(sequences, charges, mz, batch_size=1024)¶
+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
+
+ +
+
+simulate_ion_mobilities_pandas(data, batch_size=1024, return_ccs=False, decoys_separate=True)¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+ +
+
+class imspy.algorithm.ccs.predictors.GRUCCSPredictor(*args, **kwargs)¶
+

Bases: Model

+
+
+build(input_shape)¶
+

Builds the model based on input shapes received.

+

This is to be used for subclassed models, which do not know at +instantiation time what their inputs look like.

+

This method only exists for users who want to call model.build() in a +standalone way (as a substitute for calling the model on real data to +build it). It will never be called by the framework (and thus it will +never throw unexpected errors in an unrelated workflow).

+
+
Parameters:
+

input_shape – Single tuple, TensorShape instance, or list/dict of +shapes, where shapes are tuples, integers, or TensorShape +instances.

+
+
Raises:
+
    +

  • ValueError –

      +

    1. In case of invalid user-provided data (not of type tuple, + list, TensorShape, or dict). + 2. If the model requires call arguments that are agnostic + to the input shapes (positional or keyword arg in call + signature). + 3. If not all layers were properly built. + 4. If float type inputs are not supported within the layers.

      2. +
    +

    • +

  • In each of these cases, the user should build their model by calling –

    • +

  • it on real tensor data. –

    • +
+
+
+
+ +
+
+call(inputs, training=False)¶
+

Calls the model on new inputs and returns the outputs as tensors.

+

In this case call() just reapplies +all ops in the graph to the new inputs +(e.g. build a new computational graph from the provided inputs).

+

Note: This method should not be called directly. It is only meant to be +overridden when subclassing tf.keras.Model. +To call a model on an input, always use the __call__() method, +i.e. model(inputs), which relies on the underlying call() method.

+
+
Parameters:
+
    +

  • inputs – Input tensor, or dict/list/tuple of input tensors.

    • +

  • training – Boolean or boolean scalar tensor, indicating whether to +run the Network in training mode or inference mode.

    • +

  • mask – A mask or list of masks. A mask can be either a boolean tensor +or None (no mask). For more details, check the guide +[here](https://www.tensorflow.org/guide/keras/masking_and_padding).

    • +
+
+
Returns:
+

A tensor if there is a single output, or +a list of tensors if there are more than one outputs.

+
+
+
+ +
+
+classmethod from_config(config)¶
+

Creates a layer from its config.

+

This method is the reverse of get_config, +capable of instantiating the same layer from the config +dictionary. It does not handle layer connectivity +(handled by Network), nor weights (handled by set_weights).

+
+
Parameters:
+

config – A Python dictionary, typically the +output of get_config.

+
+
Returns:
+

A layer instance.

+
+
+
+ +
+
+get_config()¶
+

Returns the config of the Model.

+

Config is a Python dictionary (serializable) containing the +configuration of an object, which in this case is a Model. This allows +the Model to be be reinstantiated later (without its trained weights) +from this configuration.

+

Note that get_config() does not guarantee to return a fresh copy of +dict every time it is called. The callers should make a copy of the +returned dict if they want to modify it.

+

Developers of subclassed Model are advised to override this method, +and continue to update the dict from super(MyModel, self).get_config() +to provide the proper configuration of this Model. The default config +will return config dict for init parameters if they are basic types. +Raises NotImplementedError when in cases where a custom +get_config() implementation is required for the subclassed model.

+
+
Returns:
+

Python dictionary containing the configuration of this Model.

+
+
+
+ +
+ +
+
+class imspy.algorithm.ccs.predictors.PeptideIonMobilityApex¶
+

Bases: ABC

+
+
+abstract simulate_ion_mobilities(sequences, charges)¶
+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
+
+ +
+
+abstract simulate_ion_mobilities_pandas(data)¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+ +
+
+class imspy.algorithm.ccs.predictors.SquareRootProjectionLayer(slopes, intercepts, trainable=True, **kwargs)¶
+

Bases: Layer

+
+
+build(input_shape)¶
+

Creates the variables of the layer (for subclass implementers).

+

This is a method that implementers of subclasses of Layer or Model +can override if they need a state-creation step in-between +layer instantiation and layer call. It is invoked automatically before +the first execution of call().

+

This is typically used to create the weights of Layer subclasses +(at the discretion of the subclass implementer).

+
+
Parameters:
+

input_shape – Instance of TensorShape, or list of instances of +TensorShape if the layer expects a list of inputs +(one instance per input).

+
+
+
+ +
+
+call(inputs)¶
+

This is where the layer’s logic lives.

+

The call() method may not create state (except in its first +invocation, wrapping the creation of variables or other resources in +tf.init_scope()). It is recommended to create state, including +tf.Variable instances and nested Layer instances,

+
+

in __init__(), or in the build() method that is

+
+

called automatically before call() executes for the first time.

+
+
Parameters:
+
    +

  • inputs –

    Input tensor, or dict/list/tuple of input tensors. +The first positional inputs argument is subject to special rules: +- inputs must be explicitly passed. A layer cannot have zero

    +
    +

    arguments, and inputs cannot be provided via the default value +of a keyword argument.

    +
    +
      +

    • NumPy array or Python scalar values in inputs get cast as +tensors.

      • +

    • Keras mask metadata is only collected from inputs.

      • +

    • Layers are built (build(input_shape) method) +using shape info from inputs only.

      • +

    • input_spec compatibility is only checked against inputs.

      • +

    • Mixed precision input casting is only applied to inputs. +If a layer has tensor arguments in *args or **kwargs, their +casting behavior in mixed precision should be handled manually.

      • +

    • The SavedModel input specification is generated using inputs +only.

      • +

    • Integration with various ecosystem packages like TFMOT, TFLite, +TF.js, etc is only supported for inputs and not for tensors in +positional and keyword arguments.

      • +
    +

    • +

  • *args – Additional positional arguments. May contain tensors, although +this is not recommended, for the reasons above.

    • +

  • **kwargs –

    Additional keyword arguments. May contain tensors, although +this is not recommended, for the reasons above. +The following optional keyword arguments are reserved: +- training: Boolean scalar tensor of Python boolean indicating

    +
    +

    whether the call is meant for training or inference.

    +
    +
      +

    • mask: Boolean input mask. If the layer’s call() method takes a +mask argument, its default value will be set to the mask +generated for inputs by the previous layer (if input did come +from a layer that generated a corresponding mask, i.e. if it came +from a Keras layer with masking support).

      • +
    +

    • +
+
+
Returns:
+

A tensor or list/tuple of tensors.

+
+
+
+ +
+
+classmethod from_config(config)¶
+

Creates a layer from its config.

+

This method is the reverse of get_config, +capable of instantiating the same layer from the config +dictionary. It does not handle layer connectivity +(handled by Network), nor weights (handled by set_weights).

+
+
Parameters:
+

config – A Python dictionary, typically the +output of get_config.

+
+
Returns:
+

A layer instance.

+
+
+
+ +
+
+get_config()¶
+

Returns the config of the layer.

+

A layer config is a Python dictionary (serializable) +containing the configuration of a layer. +The same layer can be reinstantiated later +(without its trained weights) from this configuration.

+

The config of a layer does not include connectivity +information, nor the layer class name. These are handled +by Network (one layer of abstraction above).

+

Note that get_config() does not guarantee to return a fresh copy of +dict every time it is called. The callers should make a copy of the +returned dict if they want to modify it.

+
+
Returns:
+

Python dictionary.

+
+
+
+ +
+ +
+
+imspy.algorithm.ccs.predictors.get_sqrt_slopes_and_intercepts(mz, charge, ccs, fit_charge_state_one=False)¶
+

Fit the square root model to the provided data. +:type mz: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]] +:param mz: The m/z values. +:type charge: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]] +:param charge: The charge states. +:type ccs: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]] +:param ccs: The collision cross sections. +:type fit_charge_state_one: bool +:param fit_charge_state_one: Whether to fit the charge state one.

+
+
Return type:
+

Tuple[ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]], ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]]

+
+
Returns:
+

The slopes and intercepts of the square root model fit.

+
+
+
+ +
+
+imspy.algorithm.ccs.predictors.load_deep_ccs_predictor()¶
+
+
Return type:
+

Model

+
+
+
+ +
+
+imspy.algorithm.ccs.predictors.predict_inverse_ion_mobility(psm_collection, refine_model=True, verbose=False)¶
+

Predicts the inverse ion mobility for a collection of peptide spectrum matches. +:type psm_collection: List[Psm] +:param psm_collection: A list of peptide spectrum matches. +:type refine_model: bool +:param refine_model: Whether to refine the model by fine-tuning it on the provided data. +:type verbose: bool +:param verbose: Whether to print additional information during the prediction.

+
+
Return type:
+

None

+
+
Returns:
+

None, the inverse ion mobility is set in the peptide spectrum matches in place.

+
+
+
+ +
+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.algorithm.html b/main/imspy/imspy.algorithm.html new file mode 100644 index 00000000..6f2c1a0e --- /dev/null +++ b/main/imspy/imspy.algorithm.html @@ -0,0 +1,400 @@ + + + + + + + + imspy.algorithm package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.algorithm package¶

+
+

Subpackages¶

+
+ +
+
+
+

Submodules¶

+
+
+

imspy.algorithm.hashing module¶

+
+
+class imspy.algorithm.hashing.CosimHasher(target_vector_length, trials=32, len_trial=20, seed=42)¶
+

Bases: object

+
+
+calculate_keys(W)¶
+
+
Return type:
+

Tensor

+
+
+
+ +
+ +
+
+class imspy.algorithm.hashing.TimsHasher(trials=32, len_trial=20, seed=5671, resolution=1, num_dalton=10)¶
+

Bases: CosimHasher

+

Class to create hash keys from a given set of weights.

+
+
Parameters:
+
    +

  • trials (int) – number of trials to use for random projection.

    • +

  • len_trial (int) – length of each trial.

    • +

  • seed (int) – seed for random projection.

    • +

  • resolution (int) – resolution of the random projection.

    • +

  • num_dalton (int) – number of dalton to use for random projection.

    • +
+
+
+
+ +
+
+

imspy.algorithm.mixture module¶

+
+
+

imspy.algorithm.utility module¶

+
+
+class imspy.algorithm.utility.InMemoryCheckpoint(validation_target='val_loss')¶
+

Bases: Callback

+
+
+on_epoch_end(epoch, logs=None)¶
+

Called at the end of an epoch.

+

Subclasses should override for any actions to run. This function should +only be called during TRAIN mode.

+
+
Parameters:
+
    +

  • epoch – Integer, index of epoch.

    • +

  • logs – Dict, metric results for this training epoch, and for the +validation epoch if validation is performed. Validation result +keys are prefixed with val_. For training epoch, the values of +the Model’s metrics are returned. Example: +{‘loss’: 0.2, ‘accuracy’: 0.7}.

    • +
+
+
+
+ +
+
+on_train_begin(logs=None)¶
+

Called at the beginning of training.

+

Subclasses should override for any actions to run.

+
+
Parameters:
+

logs – Dict. Currently no data is passed to this argument for this +method but that may change in the future.

+
+
+
+ +
+
+on_train_end(logs=None)¶
+

Called at the end of training.

+

Subclasses should override for any actions to run.

+
+
Parameters:
+

logs – Dict. Currently the output of the last call to +on_epoch_end() is passed to this argument for this method but +that may change in the future.

+
+
+
+ +
+ +
+
+imspy.algorithm.utility.get_model_path(model_name)¶
+

Get the path to a pretrained model

+
+
Parameters:
+

model_name (str) – The name of the model to load

+
+
Return type:
+

Traversable

+
+
Returns:
+

The path to the pretrained model

+
+
+
+ +
+
+imspy.algorithm.utility.load_tokenizer_from_resources(tokenizer_name)¶
+

Load a tokenizer from resources

+
+
Return type:
+

Tokenizer

+
+
Returns:
+

The pretrained tokenizer

+
+
+
+ +
+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.algorithm.intensity.html b/main/imspy/imspy.algorithm.intensity.html new file mode 100644 index 00000000..61cc5ea9 --- /dev/null +++ b/main/imspy/imspy.algorithm.intensity.html @@ -0,0 +1,392 @@ + + + + + + + + imspy.algorithm.intensity package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.algorithm.intensity package¶

+
+

Submodules¶

+
+
+

imspy.algorithm.intensity.predictors module¶

+
+
+class imspy.algorithm.intensity.predictors.IonIntensityPredictor¶
+

Bases: ABC

+

ABSTRACT INTERFACE for simulation of ion-mobility apex value

+
+
+abstract simulate_ion_intensities(sequences, charges, collision_energies)¶
+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
+
+ +
+
+abstract simulate_ion_intensities_pandas(data)¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+ +
+
+class imspy.algorithm.intensity.predictors.Prosit2023TimsTofWrapper(verbose=True, model_name='deep_ion_intensity_predictor')¶
+

Bases: IonIntensityPredictor

+

Wrapper for the Prosit 2023 TIMS-TOF predictor

+
+
+predict_intensities(sequences, charges, collision_energies, divide_collision_energy_by=100.0, batch_size=512, flatten=False)¶
+
+
Return type:
+

List[ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]]

+
+
+
+ +
+
+simulate_ion_intensities(sequences, charges, collision_energies, divide_collision_energy_by=100.0, batch_size=512)¶
+
+
Return type:
+

List[PeptideProductIonSeriesCollection]

+
+
+
+ +
+
+simulate_ion_intensities_pandas(data, batch_size=512, divide_collision_energy_by=100.0, verbose=False, flatten=False)¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+
+simulate_ion_intensities_pandas_batched(data, batch_size_tf_ds=1024, batch_size=400000, divide_collision_energy_by=100.0)¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+ +
+
+imspy.algorithm.intensity.predictors.get_collision_energy_calibration_factor(sample, model, lower=-30, upper=30, verbose=False)¶
+

Get the collision energy calibration factor for a given sample. +:type sample: List[Psm] +:param sample: a list of PeptideSpectrumMatch objects +:type model: Prosit2023TimsTofWrapper +:param model: a Prosit2023TimsTofWrapper object +:type lower: int +:param lower: lower bound for the search +:type upper: int +:param upper: upper bound for the search +:type verbose: bool +:param verbose: whether to print progress

+
+
Returns:
+

the collision energy calibration factor and the angle similarities

+
+
Return type:
+

Tuple[float, List[float]]

+
+
+
+ +
+
+imspy.algorithm.intensity.predictors.load_prosit_2023_timsTOF_predictor()¶
+

Get a pretrained deep predictor model +This model was downloaded from ZENODO: https://zenodo.org/records/8211811 +PAPER : https://doi.org/10.1101/2023.07.17.549401 +:returns: The pretrained deep predictor model

+
+ +
+
+imspy.algorithm.intensity.predictors.predict_intensities_prosit(psm_collection, calibrate_collision_energy=True, verbose=False, num_threads=-1)¶
+

Predict the fragment ion intensities using Prosit. +:type psm_collection: List[Psm] +:param psm_collection: a list of peptide-spectrum matches +:type calibrate_collision_energy: bool +:param calibrate_collision_energy: whether to calibrate the collision energy +:type verbose: bool +:param verbose: whether to print progress +:type num_threads: int +:param num_threads: number of threads to use

+
+
Return type:
+

None

+
+
Returns:
+

None, the fragment ion intensities are stored in the PeptideSpectrumMatch objects

+
+
+
+ +
+
+imspy.algorithm.intensity.predictors.remove_unimod_annotation(sequence)¶
+

Remove the unimod annotation from a peptide sequence. +:type sequence: str +:param sequence: a peptide sequence

+
+
Returns:
+

the peptide sequence without unimod annotation

+
+
Return type:
+

str

+
+
+
+ +
+
+

imspy.algorithm.intensity.utility module¶

+
+
+imspy.algorithm.intensity.utility.beta_score(fragments_observed, fragments_predicted)¶
+

The beta score is a variant of the OpenMS proposed score calculation, using predicted intensities instead of a constant value for the expected intensity. +:type fragments_observed: +:param fragments_observed: The Sage Fragment object containing the observed intensities +:type fragments_predicted: +:param fragments_predicted: The Sage Fragment object containing the predicted intensities, e.g. from Prosit

+
+
Returns:
+

The beta score, hyper score variant using predicted intensities instead of a constant value for the expected intensity

+
+
Return type:
+

float

+
+
+
+ +
+
+imspy.algorithm.intensity.utility.generate_prosit_intensity_prediction_dataset(sequences, charges, collision_energies=None)¶
+

Generate a dataset for predicting fragment intensities using Prosit. +:type sequences: List[str] +:param sequences: A list of peptide sequences. +:type charges: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]] +:param charges: A numpy array of precursor charges. +:type collision_energies: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]] | None +:param collision_energies: A numpy array of collision energies.

+
+
Returns:
+

A tf.data.Dataset object that yields batches of data in the format expected by the model.

+
+
+
+ +
+
+imspy.algorithm.intensity.utility.get_prosit_intensity_flat_labels()¶
+

Get the list of fragment ion labels for Prosit. +:rtype: List[str] +:returns: List of fragment ion labels, giving the returned order of fragment intensities.

+
+ +
+
+imspy.algorithm.intensity.utility.post_process_predicted_fragment_spectra(data_pred)¶
+

post process the predicted fragment intensities +:type data_pred: DataFrame +:param data_pred: dataframe containing the predicted fragment intensities

+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
Returns:
+

numpy array of fragment intensities

+
+
+
+ +
+
+imspy.algorithm.intensity.utility.seq_to_index(seq, max_length=30)¶
+

Convert a sequence to a list of indices into the alphabet.

+
+
Parameters:
+
    +

  • seq (str) – A string representing a sequence of amino acids.

    • +

  • max_length (int) – The maximum length of the sequence to allow.

    • +
+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
Returns:
+

A list of integers, each representing an index into the alphabet.

+
+
+
+ +
+
+imspy.algorithm.intensity.utility.to_prosit_tensor(sequences)¶
+

translate a list of fixed length numpy arrays into a tensorflow tensor +:type sequences: List +:param sequences: list of numpy arrays, representing peptide sequences

+
+
Return type:
+

Tensor

+
+
Returns:
+

tensorflow tensor

+
+
+
+ +
+
+imspy.algorithm.intensity.utility.unpack_dict(features)¶
+

Unpack the dictionary of features into the expected inputs for the model.

+
+
Parameters:
+

features – A dictionary of features, with keys ‘peptides_in’, ‘precursor_charge_in’, and ‘collision_energy_in’

+
+
Returns:
+

(peptides, precursor_charge, collision_energy)

+
+
Return type:
+

A tuple of the expected inputs for the model

+
+
+
+ +
+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.algorithm.ionization.html b/main/imspy/imspy.algorithm.ionization.html new file mode 100644 index 00000000..30cde8be --- /dev/null +++ b/main/imspy/imspy.algorithm.ionization.html @@ -0,0 +1,370 @@ + + + + + + + + imspy.algorithm.ionization package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.algorithm.ionization package¶

+
+

Submodules¶

+
+
+

imspy.algorithm.ionization.predictors module¶

+
+
+class imspy.algorithm.ionization.predictors.BinomialChargeStateDistributionModel(charged_probability=0.5, max_charge=4)¶
+

Bases: PeptideChargeStateDistribution, ABC

+
+
+simulate_charge_state_distribution_pandas(data, min_charge_contrib=0.005)¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+
+simulate_ionizations(sequences)¶
+
+
Return type:
+

array

+
+
+
+ +
+ +
+
+class imspy.algorithm.ionization.predictors.DeepChargeStateDistribution(model, tokenizer, allowed_charges=array([1, 2, 3, 4]), name='gru_predictor', verbose=True)¶
+

Bases: PeptideChargeStateDistribution, ABC

+
+
+simulate_charge_state_distribution_pandas(data, charge_state_one_probability=0.1, batch_size=1024, min_charge_contrib=0.005)¶
+

Simulate charge state distribution for a pandas DataFrame containing peptide sequences

+
+
Parameters:
+
    +

  • data (DataFrame) – pandas DataFrame containing peptide sequences

    • +

  • charge_state_one_probability (float) – probability of charge state 1

    • +

  • batch_size (int) – batch size for prediction

    • +

  • min_charge_contrib (float) – minimum relative abundance of a charge state to be included in the output

    • +
+
+
Return type:
+

DataFrame

+
+
Returns:
+

pandas DataFrame containing simulated charge state distributions

+
+
+
+ +
+
+simulate_ionizations(sequences, batch_size=1024)¶
+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
+
+ +
+ +
+
+class imspy.algorithm.ionization.predictors.GRUChargeStatePredictor(*args, **kwargs)¶
+

Bases: Model

+
+
+build(input_shape)¶
+

Builds the model based on input shapes received.

+

This is to be used for subclassed models, which do not know at +instantiation time what their inputs look like.

+

This method only exists for users who want to call model.build() in a +standalone way (as a substitute for calling the model on real data to +build it). It will never be called by the framework (and thus it will +never throw unexpected errors in an unrelated workflow).

+
+
Parameters:
+

input_shape – Single tuple, TensorShape instance, or list/dict of +shapes, where shapes are tuples, integers, or TensorShape +instances.

+
+
Raises:
+
    +

  • ValueError –

      +

    1. In case of invalid user-provided data (not of type tuple, + list, TensorShape, or dict). + 2. If the model requires call arguments that are agnostic + to the input shapes (positional or keyword arg in call + signature). + 3. If not all layers were properly built. + 4. If float type inputs are not supported within the layers.

      2. +
    +

    • +

  • In each of these cases, the user should build their model by calling –

    • +

  • it on real tensor data. –

    • +
+
+
+
+ +
+
+call(inputs, training=False)¶
+

Calls the model on new inputs and returns the outputs as tensors.

+

In this case call() just reapplies +all ops in the graph to the new inputs +(e.g. build a new computational graph from the provided inputs).

+

Note: This method should not be called directly. It is only meant to be +overridden when subclassing tf.keras.Model. +To call a model on an input, always use the __call__() method, +i.e. model(inputs), which relies on the underlying call() method.

+
+
Parameters:
+
    +

  • inputs – Input tensor, or dict/list/tuple of input tensors.

    • +

  • training – Boolean or boolean scalar tensor, indicating whether to +run the Network in training mode or inference mode.

    • +

  • mask – A mask or list of masks. A mask can be either a boolean tensor +or None (no mask). For more details, check the guide +[here](https://www.tensorflow.org/guide/keras/masking_and_padding).

    • +
+
+
Returns:
+

A tensor if there is a single output, or +a list of tensors if there are more than one outputs.

+
+
+
+ +
+
+classmethod from_config(config)¶
+

Creates a layer from its config.

+

This method is the reverse of get_config, +capable of instantiating the same layer from the config +dictionary. It does not handle layer connectivity +(handled by Network), nor weights (handled by set_weights).

+
+
Parameters:
+

config – A Python dictionary, typically the +output of get_config.

+
+
Returns:
+

A layer instance.

+
+
+
+ +
+
+get_config()¶
+

Returns the config of the Model.

+

Config is a Python dictionary (serializable) containing the +configuration of an object, which in this case is a Model. This allows +the Model to be be reinstantiated later (without its trained weights) +from this configuration.

+

Note that get_config() does not guarantee to return a fresh copy of +dict every time it is called. The callers should make a copy of the +returned dict if they want to modify it.

+

Developers of subclassed Model are advised to override this method, +and continue to update the dict from super(MyModel, self).get_config() +to provide the proper configuration of this Model. The default config +will return config dict for init parameters if they are basic types. +Raises NotImplementedError when in cases where a custom +get_config() implementation is required for the subclassed model.

+
+
Returns:
+

Python dictionary containing the configuration of this Model.

+
+
+
+ +
+ +
+
+class imspy.algorithm.ionization.predictors.PeptideChargeStateDistribution¶
+

Bases: ABC

+

ABSTRACT INTERFACE for ionization simulation of peptides

+
+
+abstract simulate_charge_state_distribution_pandas(data)¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+
+abstract simulate_ionizations(sequences)¶
+
+
Return type:
+

array

+
+
+
+ +
+ +
+
+imspy.algorithm.ionization.predictors.charge_state_distribution_from_sequence_rust(sequence, max_charge=None, charge_probability=None)¶
+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
+
+ +
+
+imspy.algorithm.ionization.predictors.charge_state_distributions_from_sequences_rust(sequences, n_threads=4, max_charge=None, charge_probability=None)¶
+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
+
+ +
+
+imspy.algorithm.ionization.predictors.load_deep_charge_state_predictor()¶
+

Get a pretrained deep predictor model

+
+
Return type:
+

Model

+
+
Returns:
+

The pretrained deep predictor model

+
+
+
+ +
+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.algorithm.pretrained.html b/main/imspy/imspy.algorithm.pretrained.html new file mode 100644 index 00000000..67368604 --- /dev/null +++ b/main/imspy/imspy.algorithm.pretrained.html @@ -0,0 +1,126 @@ + + + + + + + + imspy.algorithm.pretrained package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.algorithm.pretrained package¶

+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.algorithm.rt.html b/main/imspy/imspy.algorithm.rt.html new file mode 100644 index 00000000..0eed8404 --- /dev/null +++ b/main/imspy/imspy.algorithm.rt.html @@ -0,0 +1,373 @@ + + + + + + + + imspy.algorithm.rt package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.algorithm.rt package¶

+
+

Submodules¶

+
+
+

imspy.algorithm.rt.predictors module¶

+
+
+class imspy.algorithm.rt.predictors.DeepChromatographyApex(model, tokenizer, name='gru_predictor', verbose=False)¶
+

Bases: PeptideChromatographyApex

+
+
+fine_tune_model(data, batch_size=64, re_compile=False, verbose=False, decoys_separate=True)¶
+
+ +
+
+generate_tf_ds_inference(sequences)¶
+
+
Return type:
+

DatasetV2

+
+
+
+ +
+
+generate_tf_ds_train(sequences, rt_target)¶
+
+
Return type:
+

DatasetV2

+
+
+
+ +
+
+simulate_separation_times(sequences, batch_size=1024)¶
+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
+
+ +
+
+simulate_separation_times_pandas(data, batch_size=1024, gradient_length=None, decoys_separate=True)¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+ +
+
+class imspy.algorithm.rt.predictors.GRURetentionTimePredictor(*args, **kwargs)¶
+

Bases: Model

+
+
+build(input_shape)¶
+

Builds the model based on input shapes received.

+

This is to be used for subclassed models, which do not know at +instantiation time what their inputs look like.

+

This method only exists for users who want to call model.build() in a +standalone way (as a substitute for calling the model on real data to +build it). It will never be called by the framework (and thus it will +never throw unexpected errors in an unrelated workflow).

+
+
Parameters:
+

input_shape – Single tuple, TensorShape instance, or list/dict of +shapes, where shapes are tuples, integers, or TensorShape +instances.

+
+
Raises:
+
    +

  • ValueError –

      +

    1. In case of invalid user-provided data (not of type tuple, + list, TensorShape, or dict). + 2. If the model requires call arguments that are agnostic + to the input shapes (positional or keyword arg in call + signature). + 3. If not all layers were properly built. + 4. If float type inputs are not supported within the layers.

      2. +
    +

    • +

  • In each of these cases, the user should build their model by calling –

    • +

  • it on real tensor data. –

    • +
+
+
+
+ +
+
+call(inputs, training=False)¶
+

Calls the model on new inputs and returns the outputs as tensors.

+

In this case call() just reapplies +all ops in the graph to the new inputs +(e.g. build a new computational graph from the provided inputs).

+

Note: This method should not be called directly. It is only meant to be +overridden when subclassing tf.keras.Model. +To call a model on an input, always use the __call__() method, +i.e. model(inputs), which relies on the underlying call() method.

+
+
Parameters:
+
    +

  • inputs – Input tensor, or dict/list/tuple of input tensors.

    • +

  • training – Boolean or boolean scalar tensor, indicating whether to +run the Network in training mode or inference mode.

    • +

  • mask – A mask or list of masks. A mask can be either a boolean tensor +or None (no mask). For more details, check the guide +[here](https://www.tensorflow.org/guide/keras/masking_and_padding).

    • +
+
+
Returns:
+

A tensor if there is a single output, or +a list of tensors if there are more than one outputs.

+
+
+
+ +
+
+classmethod from_config(config)¶
+

Creates a layer from its config.

+

This method is the reverse of get_config, +capable of instantiating the same layer from the config +dictionary. It does not handle layer connectivity +(handled by Network), nor weights (handled by set_weights).

+
+
Parameters:
+

config – A Python dictionary, typically the +output of get_config.

+
+
Returns:
+

A layer instance.

+
+
+
+ +
+
+get_config()¶
+

Returns the config of the Model.

+

Config is a Python dictionary (serializable) containing the +configuration of an object, which in this case is a Model. This allows +the Model to be be reinstantiated later (without its trained weights) +from this configuration.

+

Note that get_config() does not guarantee to return a fresh copy of +dict every time it is called. The callers should make a copy of the +returned dict if they want to modify it.

+

Developers of subclassed Model are advised to override this method, +and continue to update the dict from super(MyModel, self).get_config() +to provide the proper configuration of this Model. The default config +will return config dict for init parameters if they are basic types. +Raises NotImplementedError when in cases where a custom +get_config() implementation is required for the subclassed model.

+
+
Returns:
+

Python dictionary containing the configuration of this Model.

+
+
+
+ +
+ +
+
+class imspy.algorithm.rt.predictors.PeptideChromatographyApex¶
+

Bases: ABC

+

ABSTRACT INTERFACE for a chromatographic separation for peptides

+
+
+abstract simulate_separation_times(sequences)¶
+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
+
+ +
+
+abstract simulate_separation_times_pandas(data)¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+ +
+
+imspy.algorithm.rt.predictors.get_rt_prediction_set(tokenizer, sequence)¶
+
+
Return type:
+

DatasetV2

+
+
+
+ +
+
+imspy.algorithm.rt.predictors.get_rt_train_set(tokenizer, sequence, rt)¶
+
+
Return type:
+

DatasetV2

+
+
+
+ +
+
+imspy.algorithm.rt.predictors.load_deep_retention_time_predictor()¶
+
+
Return type:
+

Model

+
+
+
+ +
+
+imspy.algorithm.rt.predictors.predict_retention_time(psm_collection, refine_model=True, verbose=False)¶
+

Predict the retention times for a collection of peptide-spectrum matches +:type psm_collection: List[Psm] +:param psm_collection: a list of peptide-spectrum matches +:type refine_model: bool +:param refine_model: whether to refine the model +:type verbose: bool +:param verbose: whether to print verbose output

+
+
Return type:
+

None

+
+
Returns:
+

None, retention times are set in the peptide-spectrum matches

+
+
+
+ +
+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.chemistry.html b/main/imspy/imspy.chemistry.html new file mode 100644 index 00000000..b05f670d --- /dev/null +++ b/main/imspy/imspy.chemistry.html @@ -0,0 +1,279 @@ + + + + + + + + imspy.chemistry package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.chemistry package¶

+
+

Submodules¶

+
+
+

imspy.chemistry.amino_acids module¶

+
+
+

imspy.chemistry.constants module¶

+
+
+

imspy.chemistry.elements module¶

+
+
+

imspy.chemistry.mobility module¶

+
+
+imspy.chemistry.mobility.ccs_to_one_over_k0(ccs, mz, charge, mass_gas=28.013, temp=31.85, t_diff=273.15)¶
+

Convert CCS to reduced ion mobility (1/k0).

+
+
Parameters:
+
    +

  • ccs – collision cross-section

    • +

  • mz – mass-over-charge of the ion

    • +

  • charge – charge state of the ion

    • +

  • mass_gas – mass of drift gas

    • +

  • temp – temperature of the drift gas in C°

    • +

  • t_diff – factor to translate from C° to K

    • +
+
+
Returns:
+

reduced ion mobility

+
+
Return type:
+

float

+
+
+
+ +
+
+imspy.chemistry.mobility.one_over_k0_to_ccs(one_over_k0, mz, charge, mass_gas=28.013, temp=31.85, t_diff=273.15)¶
+

Convert reduced ion mobility (1/k0) to CCS.

+
+
Parameters:
+
    +

  • one_over_k0 – reduced ion mobility

    • +

  • mz – mass-over-charge of the ion

    • +

  • charge – charge state of the ion

    • +

  • mass_gas – mass of drift gas

    • +

  • temp – temperature of the drift gas in C°

    • +

  • t_diff – factor to translate from C° to K

    • +
+
+
Returns:
+

collision cross-section

+
+
Return type:
+

float

+
+
+
+ +
+
+

imspy.chemistry.sum_formula module¶

+
+
+class imspy.chemistry.sum_formula.SumFormula(sum_formula)¶
+

Bases: RustWrapperObject

+
+
+property formula: str¶
+
+ +
+
+property formula_dict: dict¶
+
+ +
+
+classmethod from_py_ptr(py_ptr)¶
+
+ +
+
+generate_isotope_distribution(charge)¶
+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+
+get_py_ptr()¶
+
+ +
+
+property monoisotopic_mass: float¶
+
+ +
+ +
+
+

imspy.chemistry.unimod module¶

+
+
+

imspy.chemistry.utility module¶

+
+
+imspy.chemistry.utility.calculate_mz(mass, charge)¶
+

Calculate m/z value.

+
+
Parameters:
+
    +

  • mass (float) – Mass.

    • +

  • charge (int) – Charge.

    • +
+
+
Returns:
+

m/z value.

+
+
Return type:
+

float

+
+
+
+ +
+
+imspy.chemistry.utility.calculate_transmission_dependent_fragment_ion_isotope_distribution(target_spec, complement_spec, transmitted_isotopes, max_isotope)¶
+

Calculate transmission dependent fragment ion isotope distribution.

+
+
Parameters:
+
    +

  • target_spec (MzSpectrum) – Target spectrum.

    • +

  • complement_spec (MzSpectrum) – Complement spectrum.

    • +

  • transmitted_isotopes (MzSpectrum) – Transmitted isotopes.

    • +

  • max_isotope (int) – Maximum isotope.

    • +
+
+
Returns:
+

Transmission dependent fragment ion isotope distribution.

+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.data.html b/main/imspy/imspy.data.html new file mode 100644 index 00000000..02bf4498 --- /dev/null +++ b/main/imspy/imspy.data.html @@ -0,0 +1,1305 @@ + + + + + + + + imspy.data package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.data package¶

+
+

Submodules¶

+
+
+

imspy.data.peptide module¶

+
+
+class imspy.data.peptide.PeptideIon(sequence, charge, intensity, peptide_id=None)¶
+

Bases: RustWrapperObject

+
+
+property atomic_composition¶
+
+ +
+
+calculate_isotopic_spectrum(mass_tolerance=0.001, abundance_threshold=1e-08, max_result=200, intensity_min=0.0001)¶
+

Calculate the isotopic spectrum of the peptide ion.

+
+
Parameters:
+
    +

  • mass_tolerance (float) – The mass tolerance for the isotopic spectrum calculation.

    • +

  • abundance_threshold (float) – The abundance threshold for the isotopic spectrum calculation.

    • +

  • max_result (int) – The maximum number of results to return.

    • +

  • intensity_min (float) – The minimum intensity of the isotopic spectrum.

    • +
+
+
Return type:
+

MzSpectrum

+
+
Returns:
+

The isotopic spectrum of the peptide ion.

+
+
+
+ +
+
+calculate_isotopic_spectrum_annotated(mass_tolerance=0.001, abundance_threshold=1e-08, max_result=200, intensity_min=0.0001)¶
+

Calculate the isotopic spectrum of the peptide ion.

+
+
Parameters:
+
    +

  • mass_tolerance (float) – The mass tolerance for the isotopic spectrum calculation.

    • +

  • abundance_threshold (float) – The abundance threshold for the isotopic spectrum calculation.

    • +

  • max_result (int) – The maximum number of results to return.

    • +

  • intensity_min (float) – The minimum intensity of the isotopic spectrum.

    • +
+
+
Return type:
+

MzSpectrumAnnotated

+
+
Returns:
+

The isotopic spectrum of the peptide ion.

+
+
+
+ +
+
+property charge: int¶
+
+ +
+
+classmethod from_py_ptr(ion)¶
+
+ +
+
+get_py_ptr()¶
+
+ +
+
+property intensity: float¶
+
+ +
+
+property mz: float¶
+
+ +
+
+property peptide_id: None | int¶
+
+ +
+
+property sequence: PeptideSequence¶
+
+ +
+
+to_json()¶
+
+
Return type:
+

str

+
+
+
+ +
+ +
+
+class imspy.data.peptide.PeptideProductIon(kind, sequence, charge=1, intensity=1.0, peptide_id=None)¶
+

Bases: RustWrapperObject

+
+
+atomic_composition()¶
+
+ +
+
+property charge: int¶
+
+ +
+
+classmethod from_py_ptr(product_ion)¶
+
+ +
+
+get_py_ptr()¶
+
+ +
+
+property intensity: float¶
+
+ +
+
+isotope_distribution(mass_tolerance=0.001, abundance_threshold=1e-08, max_result=200, intensity_min=0.0001)¶
+

Calculate the isotope distribution of the product ion.

+
+
Parameters:
+
    +

  • mass_tolerance (float) – The mass tolerance for the isotope distribution calculation.

    • +

  • abundance_threshold (float) – The abundance threshold for the isotope distribution calculation.

    • +

  • max_result (int) – The maximum number of results to return.

    • +

  • intensity_min (float) – The minimum intensity of the isotope distribution.

    • +
+
+
Return type:
+

List[Tuple[float, float]]

+
+
Returns:
+

The isotope distribution of the product ion.

+
+
+
+ +
+
+property kind: str¶
+
+ +
+
+property mono_isotopic_mass: float¶
+
+ +
+
+property mz: float¶
+
+ +
+
+property sequence: str¶
+
+ +
+
+to_json()¶
+
+
Return type:
+

str

+
+
+
+ +
+ +
+
+class imspy.data.peptide.PeptideProductIonSeries(charge, n_ions, c_ions)¶
+

Bases: RustWrapperObject

+
+
+property c_ions: List[PeptideProductIon]¶
+
+ +
+
+property charge: int¶
+
+ +
+
+classmethod from_py_ptr(series)¶
+
+ +
+
+get_py_ptr()¶
+
+ +
+
+property n_ions: List[PeptideProductIon]¶
+
+ +
+
+to_json()¶
+
+
Return type:
+

str

+
+
+
+ +
+ +
+
+class imspy.data.peptide.PeptideProductIonSeriesCollection(series)¶
+

Bases: RustWrapperObject

+
+
+find_series(charge)¶
+

Find the product ion series with the given charge.

+
+
Parameters:
+

charge (int) – The charge of the product ion series.

+
+
Return type:
+

Optional[PeptideProductIonSeries]

+
+
Returns:
+

The product ion series with the given charge, or None if not found.

+
+
+
+ +
+
+classmethod from_py_ptr(collection)¶
+
+ +
+
+generate_isotopic_spectrum(mass_tolerance=0.001, abundance_threshold=1e-06, max_result=2000, intensity_min=1e-06)¶
+

Calculate the isotope distribution of the product ion series collection.

+
+
Parameters:
+
    +

  • mass_tolerance (float) – The mass tolerance for the isotope distribution calculation.

    • +

  • abundance_threshold (float) – The abundance threshold for the isotope distribution calculation.

    • +

  • max_result (int) – The maximum number of results to return.

    • +

  • intensity_min (float) – The minimum intensity of the isotope distribution.

    • +
+
+
Return type:
+

MzSpectrum

+
+
Returns:
+

The isotope distribution of the product ion series collection.

+
+
+
+ +
+
+generate_isotopic_spectrum_annotated(mass_tolerance=0.001, abundance_threshold=1e-06, max_result=2000, intensity_min=1e-06)¶
+

Calculate the isotope distribution of the product ion series collection.

+
+
Parameters:
+
    +

  • mass_tolerance (float) – The mass tolerance for the isotope distribution calculation.

    • +

  • abundance_threshold (float) – The abundance threshold for the isotope distribution calculation.

    • +

  • max_result (int) – The maximum number of results to return.

    • +

  • intensity_min (float) – The minimum intensity of the isotope distribution.

    • +
+
+
Return type:
+

MzSpectrumAnnotated

+
+
Returns:
+

The isotope distribution of the product ion series collection.

+
+
+
+ +
+
+get_py_ptr()¶
+
+ +
+
+property series: List[PeptideProductIonSeries]¶
+
+ +
+
+to_json()¶
+
+
Return type:
+

str

+
+
+
+ +
+ +
+
+class imspy.data.peptide.PeptideSequence(sequence, peptide_id=None)¶
+

Bases: RustWrapperObject

+
+
+property amino_acid_count: int¶
+
+ +
+
+associate_fragment_ion_series_with_prosit_intensities(flat_intensities, charge, fragment_type='b', normalize=True, half_charge_one=True)¶
+

Associate the peptide sequence with predicted intensities from Prosit intensity prediction.

+
+
Parameters:
+
    +

  • flat_intensities (List[float]) – The flat intensities.

    • +

  • TODO (#) – check how charge should be handled

    • +

  • charge (int) – The charge of the product ions.

    • +

  • fragment_type (str) – The type of the product ions, must be one of ‘a’, ‘b’, ‘c’, ‘x’, ‘y’, ‘z’.

    • +

  • normalize (bool) – Whether to normalize the intensities.

    • +

  • half_charge_one (bool) – Whether to use half charge one.

    • +
+
+
Return type:
+

PeptideProductIonSeriesCollection

+
+
Returns:
+

The b and y product ion series of the peptide sequence.

+
+
+
+ +
+
+property atomic_composition¶
+
+ +
+
+calculate_isotopic_product_ion_spectrum_annotated(charge=1, fragment_type='b', mass_tolerance=0.001, abundance_threshold=1e-08, max_result=200, intensity_min=0.0001)¶
+

Calculate the isotopic product ion spectrum of the peptide sequence.

+
+
Parameters:
+
    +

  • intensity_min (float)

    • +

  • max_result (int)

    • +

  • abundance_threshold (float)

    • +

  • mass_tolerance (float)

    • +

  • charge (int) – The charge of the product ions.

    • +

  • fragment_type (str) – The type of the product ions, must be one of ‘a’, ‘b’, ‘c’, ‘x’, ‘y’, ‘z’.

    • +
+
+
Return type:
+

MzSpectrumAnnotated

+
+
Returns:
+

The isotopic product ion spectrum of the peptide sequence.

+
+
+
+ +
+
+calculate_mono_isotopic_product_ion_spectrum(charge=1, fragment_type='b')¶
+

Calculate the mono-isotopic product ion spectrum of the peptide sequence.

+
+
Parameters:
+
    +

  • charge (int) – The charge of the product ions.

    • +

  • fragment_type (str) – The type of the product ions, must be one of ‘a’, ‘b’, ‘c’, ‘x’, ‘y’, ‘z’.

    • +
+
+
Return type:
+

MzSpectrum

+
+
Returns:
+

The mono-isotopic product ion spectrum of the peptide sequence.

+
+
+
+ +
+
+calculate_mono_isotopic_product_ion_spectrum_annotated(charge=1, fragment_type='b')¶
+

Calculate the mono-isotopic product ion spectrum of the peptide sequence.

+
+
Parameters:
+
    +

  • charge (int) – The charge of the product ions.

    • +

  • fragment_type (str) – The type of the product ions, must be one of ‘a’, ‘b’, ‘c’, ‘x’, ‘y’, ‘z’.

    • +
+
+
Return type:
+

MzSpectrumAnnotated

+
+
Returns:
+

The mono-isotopic product ion spectrum of the peptide sequence.

+
+
+
+ +
+
+calculate_product_ion_series(charge=1, fragment_type='b')¶
+

Calculate the b and y product ion series of the peptide sequence.

+
+
Parameters:
+
    +

  • charge (int) – The charge of the product ions.

    • +

  • fragment_type (str) – The type of the product ions, must be one of ‘a’, ‘b’, ‘c’, ‘x’, ‘y’, ‘z’.

    • +
+
+
Return type:
+

Tuple[List[PeptideProductIon], List[PeptideProductIon]]

+
+
Returns:
+

The b and y product ion series of the peptide sequence.

+
+
+
+ +
+
+classmethod from_py_ptr(obj)¶
+
+ +
+
+get_py_ptr()¶
+
+ +
+
+property mono_isotopic_mass: float¶
+
+ +
+
+property peptide_id: None | int¶
+
+ +
+
+property sequence: str¶
+
+ +
+
+to_json()¶
+
+
Return type:
+

str

+
+
+
+ +
+
+to_sage_representation()¶
+
+
Return type:
+

Tuple[str, List[float]]

+
+
+
+ +
+
+to_tokens(group_modifications=True)¶
+
+
Return type:
+

List[str]

+
+
+
+ +
+ +
+
+

imspy.data.spectrum module¶

+
+
+class imspy.data.spectrum.IndexedMzSpectrum(index, mz, intensity)¶
+

Bases: RustWrapperObject

+
+
+property df: DataFrame¶
+

Data.

+
+
Returns:
+

Data.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+filter(mz_min=0.0, mz_max=2000.0, intensity_min=0.0, intensity_max=1000000000.0)¶
+

Filter the spectrum for a given m/z range and intensity range.

+
+
Parameters:
+
    +

  • mz_min (float) – Minimum m/z value.

    • +

  • mz_max (float) – Maximum m/z value.

    • +

  • intensity_min (float, optional) – Minimum intensity value. Defaults to 0.0.

    • +

  • intensity_max (float, optional) – Maximum intensity value. Defaults to 1e9.

    • +
+
+
Returns:
+

Filtered spectrum.

+
+
Return type:
+

IndexedMzSpectrum

+
+
+
+ +
+
+classmethod from_py_ptr(spec)¶
+

Create a IndexedMzSpectrum from a PyIndexedMzSpectrum.

+
+
Parameters:
+

spec (pims.PyIndexedMzSpectrum) – PyIndexedMzSpectrum to create the IndexedMzSpectrum from.

+
+
Returns:
+

IndexedMzSpectrum created from the PyIndexedMzSpectrum.

+
+
Return type:
+

IndexedMzSpectrum

+
+
+
+ +
+
+get_py_ptr()¶
+

Get the spec_ptr.

+
+
Returns:
+

spec_ptr.

+
+
Return type:
+

pims.PyIndexedMzSpectrum

+
+
+
+ +
+
+property index: ndarray[Any, dtype[int32]]¶
+

Index.

+
+
Returns:
+

Index.

+
+
Return type:
+

NDArray[np.int32]

+
+
+
+ +
+
+property intensity: ndarray[Any, dtype[float64]]¶
+

Intensity.

+
+
Returns:
+

Intensity.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+
+property mz: ndarray[Any, dtype[float64]]¶
+

m/z.

+
+
Returns:
+

m/z.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+ +
+
+class imspy.data.spectrum.MzSpectrum(mz, intensity)¶
+

Bases: RustWrapperObject

+
+
+add_mz_noise_normal(noise_ppm)¶
+

Add normal noise to the intensity values of the spectrum.

+
+
Parameters:
+

noise_ppm (float) – Noise ppm, noise will be sampled from a normal distribution with mean 0 and std noise_ppm / 3.0.

+
+
Returns:
+

Spectrum with added noise.

+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+
+add_mz_noise_uniform(noise_ppm, right_drag=False)¶
+

Add uniform noise to the m/z values of the spectrum.

+
+
Parameters:
+
    +

  • noise_ppm (float) – Noise ppm, noise will be sampled from a uniform distribution with mean 0 and std noise_ppm.

    • +

  • right_drag (bool, optional) – If true, the noise will be shifted to the right. Defaults to False.

    • +
+
+
Returns:
+

Spectrum with added noise.

+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+
+property df: DataFrame¶
+

Data.

+
+
Returns:
+

Data.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+filter(mz_min=0.0, mz_max=2000.0, intensity_min=0.0, intensity_max=1000000000.0)¶
+

Filter the spectrum for a given m/z range and intensity range.

+
+
Parameters:
+
    +

  • mz_min (float) – Minimum m/z value.

    • +

  • mz_max (float) – Maximum m/z value.

    • +

  • intensity_min (float, optional) – Minimum intensity value. Defaults to 0.0.

    • +

  • intensity_max (float, optional) – Maximum intensity value. Defaults to 1e9.

    • +
+
+
Returns:
+

Filtered spectrum.

+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+
+classmethod from_jsons(jsons)¶
+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+
+classmethod from_mz_spectra_list(spectra_list, resolution)¶
+

Generates a convoluted mass spectrum by adding all spectra in the given list.

+
+
Parameters:
+
    +

  • spectra_list (List[MzSpectrum]) – List of mass spectra.

    • +

  • resolution (int) – Desired resolution of returned spectrum.

    • +
+
+
Returns:
+

Convoluted spectrum.

+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+
+classmethod from_py_ptr(spec)¶
+

Create a MzSpectrum from a PyMzSpectrum.

+
+
Parameters:
+

spec (pims.PyMzSpectrum) – PyMzSpectrum to create the MzSpectrum from.

+
+
Returns:
+

MzSpectrum created from the PyMzSpectrum.

+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+
+get_py_ptr()¶
+

Get the spec_ptr.

+
+
Returns:
+

spec_ptr.

+
+
Return type:
+

pims.PyMzSpectrum

+
+
+
+ +
+
+property intensity: ndarray[Any, dtype[float64]]¶
+

Intensity.

+
+
Returns:
+

Intensity.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+
+property mz: ndarray[Any, dtype[float64]]¶
+

m/z.

+
+
Returns:
+

m/z.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+
+to_centroided(baseline_noise_level=0.0, sigma=0.1, normalize=True)¶
+

Convert the spectrum to a centroided spectrum.

+
+
Returns:
+

Centroided spectrum.

+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+
+to_jsons()¶
+

generates json string representation of MzSpectrum

+
+
Return type:
+

str

+
+
+
+ +
+
+to_resolution(resolution)¶
+

Bins the spectrum’s m/z values to a +given resolution and sums the intensities.

+
+
Parameters:
+

resolution (int) – Negative decadic logarithm of bin size.

+
+
Returns:
+

A new MzSpectrum where m/z values are binned according to the given resolution.

+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+
+to_windows(window_length=10, overlapping=True, min_num_peaks=5, min_intensity=1)¶
+

Convert the spectrum to a list of windows.

+
+
Parameters:
+
    +

  • window_length (float, optional) – Window length. Defaults to 10.

    • +

  • overlapping (bool, optional) – Whether the windows should overlap. Defaults to True.

    • +

  • min_num_peaks (int, optional) – Minimum number of peaks in a window. Defaults to 5.

    • +

  • min_intensity (float, optional) – Minimum intensity of a peak in a window. Defaults to 1.

    • +
+
+
Returns:
+

List of windows.

+
+
Return type:
+

Tuple[NDArray, List[MzSpectrum]]

+
+
+
+ +
+
+vectorized(resolution=2)¶
+

Convert the spectrum to a vectorized spectrum.

+
+
Parameters:
+

resolution (int, optional) – Resolution. Defaults to 2.

+
+
Returns:
+

Vectorized spectrum.

+
+
Return type:
+

MzSpectrumVectorized

+
+
+
+ +
+ +
+
+class imspy.data.spectrum.MzSpectrumVectorized(indices, values, resolution)¶
+

Bases: RustWrapperObject

+
+
+classmethod from_py_ptr(spec)¶
+

Create a MzSpectrum from a PyMzSpectrum.

+
+
Parameters:
+

spec (pims.PyMzSpectrum) – PyMzSpectrum to create the MzSpectrum from.

+
+
Returns:
+

MzSpectrum created from the PyMzSpectrum.

+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+
+get_py_ptr()¶
+

Get the spec_ptr.

+
+
Returns:
+

spec_ptr.

+
+
Return type:
+

pims.PyMzSpectrumVectorized

+
+
+
+ +
+
+property indices: ndarray[Any, dtype[int32]]¶
+

m/z.

+
+
Returns:
+

m/z.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+
+property resolution: float¶
+

Resolution.

+
+
Returns:
+

Resolution.

+
+
Return type:
+

float

+
+
+
+ +
+
+to_centroided(integrate_method=<function get_peak_integral>)¶
+

Convert the spectrum to a centroided spectrum.

+
+
Returns:
+

Centroided spectrum.

+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+
+property values: ndarray[Any, dtype[float64]]¶
+

Intensity.

+
+
Returns:
+

Intensity.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+ +
+
+class imspy.data.spectrum.TimsSpectrum(frame_id, scan, retention_time, mobility, ms_type, index, mz, intensity)¶
+

Bases: RustWrapperObject

+
+
+property df: DataFrame¶
+

Data.

+
+
Returns:
+

Data.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+filter(mz_min=0.0, mz_max=2000.0, intensity_min=0.0, intensity_max=1000000000.0)¶
+

Filter the spectrum for a given m/z range and intensity range.

+
+
Parameters:
+
    +

  • mz_min (float) – Minimum m/z value.

    • +

  • mz_max (float) – Maximum m/z value.

    • +

  • intensity_min (float, optional) – Minimum intensity value. Defaults to 0.0.

    • +

  • intensity_max (float, optional) – Maximum intensity value. Defaults to 1e9.

    • +
+
+
Returns:
+

Filtered spectrum.

+
+
Return type:
+

TimsSpectrum

+
+
+
+ +
+
+property frame_id: int¶
+

Frame ID.

+
+
Returns:
+

Frame ID.

+
+
Return type:
+

int

+
+
+
+ +
+
+classmethod from_py_ptr(spec)¶
+

Create a TimsSpectrum from a PyTimsSpectrum.

+
+
Parameters:
+

spec (pims.PyTimsSpectrum) – PyTimsSpectrum to create the TimsSpectrum from.

+
+
Returns:
+

TimsSpectrum created from the PyTimsSpectrum.

+
+
Return type:
+

TimsSpectrum

+
+
+
+ +
+
+get_py_ptr()¶
+

Get the spec_ptr.

+
+
Returns:
+

spec_ptr.

+
+
Return type:
+

pims.PyTimsSpectrum

+
+
+
+ +
+
+property index: ndarray[Any, dtype[int32]]¶
+

Index.

+
+
Returns:
+

Index.

+
+
Return type:
+

NDArray[np.int32]

+
+
+
+ +
+
+property intensity: ndarray[Any, dtype[float64]]¶
+

Intensity.

+
+
Returns:
+

Intensity.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+
+property mobility: float¶
+

Inverse mobility.

+
+
Returns:
+

Inverse mobility.

+
+
Return type:
+

float

+
+
+
+ +
+
+property ms_type: str¶
+

MS type.

+
+
Returns:
+

MS type.

+
+
Return type:
+

str

+
+
+
+ +
+
+property mz: ndarray[Any, dtype[float64]]¶
+

m/z.

+
+
Returns:
+

m/z.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+
+property mz_spectrum: MzSpectrum¶
+

Get the MzSpectrum.

+
+
Returns:
+

Spectrum.

+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+
+property retention_time: float¶
+

Retention time.

+
+
Returns:
+

Retention time.

+
+
Return type:
+

float

+
+
+
+ +
+
+property scan: int¶
+

Scan.

+
+
Returns:
+

Scan.

+
+
Return type:
+

int

+
+
+
+ +
+ +
+
+imspy.data.spectrum.get_peak_integral(peaks, peak_info)¶
+

Calculates the integral of the peaks in a spectrum.

+
+
Parameters:
+
    +

  • peaks (NDArray[np.int32]) – Peak indices.

    • +

  • peak_info (dict) – Peak info.

    • +
+
+
Returns:
+

Peak integrals.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.html b/main/imspy/imspy.html new file mode 100644 index 00000000..66841afb --- /dev/null +++ b/main/imspy/imspy.html @@ -0,0 +1,125 @@ + + + + + + + + imspy Package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy Package¶

+

The imspy package provides tools for X, Y, and Z. This page introduces the main module and gives an overview of its submodules.

+
+

API Documentation¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.simulation.html b/main/imspy/imspy.simulation.html new file mode 100644 index 00000000..0493ff6f --- /dev/null +++ b/main/imspy/imspy.simulation.html @@ -0,0 +1,1578 @@ + + + + + + + + imspy.simulation package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.simulation package¶

+
+

Subpackages¶

+
+ +
+
+
+

Submodules¶

+
+
+

imspy.simulation.acquisition module¶

+
+
+class imspy.simulation.acquisition.TimsTofAcquisitionBuilder(path, reference_ds, gradient_length, rt_cycle_length, exp_name='RAW.d')¶
+

Bases: object

+
+
+abstract calculate_frame_types(*args)¶
+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
+
+ +
+
+classmethod from_existing(path, reference_ds)¶
+

Create an instance from existing data without calling __init__.

+
+
Return type:
+

TimsTofAcquisitionBuilder

+
+
+
+ +
+
+generate_frame_table(verbose=True)¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+
+generate_scan_table(verbose=True)¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+ +
+
+class imspy.simulation.acquisition.TimsTofAcquisitionBuilderDDA(path, reference_ds, verbose=True, precursor_every=7, gradient_length=7200, rt_cycle_length=0.109, exp_name='RAW.d')¶
+

Bases: TimsTofAcquisitionBuilder, ABC

+
+
+calculate_frame_types(table, precursor_every=7, verbose=True)¶
+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
+
+ +
+ +
+
+class imspy.simulation.acquisition.TimsTofAcquisitionBuilderDIA(path, reference_ds, window_group_file, acquisition_name='dia', exp_name='RAW', verbose=True, precursor_every=17, gradient_length=3000, rt_cycle_length=0.1054, use_reference_ds_layout=True, round_collision_energy=True, collision_energy_decimals=1)¶
+

Bases: TimsTofAcquisitionBuilder, ABC

+
+
+calculate_frame_types(verbose=True)¶
+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
+
+ +
+
+static from_config(path, reference_ds, exp_name, config, verbose=True, use_reference_layout=True, round_collision_energy=True, collision_energy_decimals=1)¶
+
+
Return type:
+

TimsTofAcquisitionBuilderDIA

+
+
+
+ +
+
+classmethod from_existing(path, reference_ds, use_reference_ds_layout=True, verbose=True)¶
+

Create an instance from existing data for DIA without calling __init__.

+
+
Return type:
+

TimsTofAcquisitionBuilderDIA

+
+
+
+ +
+
+generate_frame_to_window_group_table(verbose=True)¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+ +
+
+

imspy.simulation.annotation module¶

+
+
+class imspy.simulation.annotation.ContributionSource(intensity_contribution, source_type, signal_attributes=None)¶
+

Bases: RustWrapperObject

+
+
+classmethod from_py_ptr(contribution_source)¶
+
+
Return type:
+

ContributionSource

+
+
+
+ +
+
+get_py_ptr()¶
+
+
Return type:
+

PyContributionSource

+
+
+
+ +
+
+property intensity_contribution: float¶
+
+ +
+
+property signal_attributes: None | SignalAttributes¶
+
+ +
+
+property source_type: SourceType¶
+
+ +
+ +
+
+class imspy.simulation.annotation.MzSpectrumAnnotated(mz, intensity, annotations)¶
+

Bases: RustWrapperObject

+
+
+property annotations: list[PeakAnnotation]¶
+
+ +
+
+filter(mz_min=0.0, mz_max=1700.0, intensity_min=0.0, intensity_max=1000000000.0)¶
+
+
Return type:
+

MzSpectrumAnnotated

+
+
+
+ +
+
+classmethod from_py_ptr(mz_spectrum_annotated)¶
+
+
Return type:
+

MzSpectrumAnnotated

+
+
+
+ +
+
+get_py_ptr()¶
+
+
Return type:
+

PyMzSpectrumAnnotated

+
+
+
+ +
+
+property intensity: list[float]¶
+
+ +
+
+property mz: list[float]¶
+
+ +
+ +
+
+class imspy.simulation.annotation.PeakAnnotation(contributions)¶
+

Bases: RustWrapperObject

+
+
+property contributions: list[ContributionSource]¶
+
+ +
+
+classmethod from_py_ptr(peak_annotation)¶
+
+
Return type:
+

PeakAnnotation

+
+
+
+ +
+
+get_py_ptr()¶
+
+
Return type:
+

PyPeakAnnotation

+
+
+
+ +
+ +
+
+class imspy.simulation.annotation.RustWrapperObject¶
+

Bases: ABC

+
+
+abstract classmethod from_py_ptr(obj)¶
+
+ +
+
+abstract get_py_ptr()¶
+
+ +
+ +
+
+class imspy.simulation.annotation.SignalAttributes(charge_state, peptide_id, isotope_peak, description=None)¶
+

Bases: RustWrapperObject

+
+
+property charge_state¶
+
+ +
+
+property description: None | str¶
+
+ +
+
+classmethod from_py_ptr(signal_attributes)¶
+
+
Return type:
+

SignalAttributes

+
+
+
+ +
+
+get_py_ptr()¶
+
+
Return type:
+

PySignalAttributes

+
+
+
+ +
+
+property isotope_peak¶
+
+ +
+
+property peptide_id¶
+
+ +
+ +
+
+class imspy.simulation.annotation.SourceType(source_type)¶
+

Bases: RustWrapperObject

+
+
+classmethod from_py_ptr(source_type)¶
+
+
Return type:
+

SourceType

+
+
+
+ +
+
+get_py_ptr()¶
+
+
Return type:
+

PySourceType

+
+
+
+ +
+
+property source_type: str¶
+
+ +
+ +
+
+class imspy.simulation.annotation.TimsFrameAnnotated(frame_id, retention_time, ms_type, tof, mz, scan, inv_mobility, intensity, annotations)¶
+

Bases: RustWrapperObject

+
+
+property annotations: list[PeakAnnotation]¶
+
+ +
+
+property charge_states_first_only: ndarray[Any, dtype[int]]¶
+
+ +
+
+property df: DataFrame¶
+
+ +
+
+filter(mz_min=0.0, mz_max=1700.0, inv_mobility_min=0.0, inv_mobility_max=4.0, scan_min=0, scan_max=1000, intensity_min=0.0, intensity_max=1000000000.0)¶
+
+
Return type:
+

TimsFrameAnnotated

+
+
+
+ +
+
+property frame_id: int¶
+
+ +
+
+classmethod from_py_ptr(tims_frame_annotated)¶
+
+
Return type:
+

TimsFrameAnnotated

+
+
+
+ +
+
+get_py_ptr()¶
+
+
Return type:
+

PyTimsFrameAnnotated

+
+
+
+ +
+
+property intensity: list[float]¶
+
+ +
+
+property inv_mobility: list[float]¶
+
+ +
+
+property isotope_peaks_first_only: ndarray[Any, dtype[int]]¶
+
+ +
+
+property ms_type: int¶
+
+ +
+
+property ms_type_numeric: int¶
+
+ +
+
+property mz: list[float]¶
+
+ +
+
+property peptide_ids_first_only: ndarray[Any, dtype[int]]¶
+
+ +
+
+property retention_time: float¶
+
+ +
+
+property scan: list[int]¶
+
+ +
+
+property tof: list[int]¶
+
+ +
+ +
+
+

imspy.simulation.experiment module¶

+
+
+class imspy.simulation.experiment.SyntheticExperimentDataHandle(database_path, database_name='synthetic_data.db', verbose=True)¶
+

Bases: object

+
+
+append_table(table_name, table)¶
+
+ +
+
+close()¶
+
+ +
+
+create_table(table_name, table)¶
+
+ +
+
+create_table_sql(sql)¶
+
+ +
+
+get_table(table_name)¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+
+list_columns(table_name)¶
+
+ +
+
+list_tables()¶
+
+ +
+ +
+
+class imspy.simulation.experiment.SyntheticExperimentDataHandleDIA(database_path, database_name='synthetic_data.db', verbose=True)¶
+

Bases: SyntheticExperimentDataHandle, ABC

+
+
+get_frame_to_window_group()¶
+
+ +
+
+get_window_group_settings()¶
+
+ +
+ +
+
+class imspy.simulation.experiment.TimsTofSyntheticFrameBuilderDIA(db_path, with_annotations=False, num_threads=4)¶
+

Bases: RustWrapperObject

+
+
+build_frame(frame_id, fragment=True, mz_noise_precursor=False, mz_noise_uniform=False, precursor_noise_ppm=5.0, mz_noise_fragment=False, fragment_noise_ppm=5.0, right_drag=True)¶
+

Build a frame.

+
+
Parameters:
+
    +

  • frame_id (int) – Frame ID.

    • +

  • fragment (bool) – if true, frame will undergo synthetic fragmentation if it is a fragment frame,

    • +

  • fragmentation. (otherwise quadrupole isolation will still be applied but no)

    • +

  • mz_noise_precursor (bool) – if true, noise will be added to the precursor m/z values.

    • +

  • mz_noise_uniform (bool) – if true, noise will be added to the precursor m/z values uniformly.

    • +

  • precursor_noise_ppm (float) – PPM of the precursor noise.

    • +

  • mz_noise_fragment (bool) – if true, noise will be added to the fragment m/z values.

    • +

  • fragment_noise_ppm (float) – PPM of the fragment noise.

    • +

  • right_drag (bool) – if true, the noise will be shifted to the right.

    • +
+
+
Returns:
+

Frame.

+
+
Return type:
+

TimsFrame

+
+
+
+ +
+
+build_frame_annotated(frame_id, fragment=True, mz_noise_precursor=False, mz_noise_uniform=False, precursor_noise_ppm=5.0, mz_noise_fragment=False, fragment_noise_ppm=5.0, right_drag=True)¶
+

Build a frame. The frame will be annotated.

+
+
Parameters:
+
    +

  • frame_id (int) – Frame ID.

    • +

  • fragment (bool) – if true, frame will undergo synthetic fragmentation if it is a fragment frame,

    • +

  • fragmentation. (otherwise quadrupole isolation will still be applied but no)

    • +

  • mz_noise_precursor (bool) – if true, noise will be added to the precursor m/z values.

    • +

  • mz_noise_uniform (bool) – if true, noise will be added to the precursor m/z values uniformly.

    • +

  • precursor_noise_ppm (float) – PPM of the precursor noise.

    • +

  • mz_noise_fragment (bool) – if true, noise will be added to the fragment m/z values.

    • +

  • fragment_noise_ppm (float) – PPM of the fragment noise.

    • +

  • right_drag (bool) – if true, the noise will be shifted to the right.

    • +
+
+
Returns:
+

Frame.

+
+
Return type:
+

TimsFrameAnnotated

+
+
+
+ +
+
+build_frames(frame_ids, fragment=True, mz_noise_precursor=False, mz_noise_uniform=False, precursor_noise_ppm=5.0, mz_noise_fragment=False, fragment_noise_ppm=5.0, right_drag=True, num_threads=4)¶
+

Build frames.

+
+
Parameters:
+
    +

  • frame_ids (List[int]) – Frame IDs.

    • +

  • fragment (bool) – if true, frame will undergo synthetic fragmentation if it is a fragment frame,

    • +

  • fragmentation. (otherwise quadrupole isolation will still be applied but no)

    • +

  • mz_noise_precursor (bool) – if true, noise will be added to the precursor m/z values.

    • +

  • mz_noise_uniform (bool) – if true, noise will be added to the precursor m/z values uniformly.

    • +

  • precursor_noise_ppm (float) – PPM of the precursor noise.

    • +

  • mz_noise_fragment (bool) – if true, noise will be added to the fragment m/z values.

    • +

  • fragment_noise_ppm (float) – PPM of the fragment noise.

    • +

  • right_drag (bool) – if true, the noise will be shifted to the right.

    • +

  • num_threads (int) – Number of threads.

    • +
+
+
Returns:
+

Frames.

+
+
Return type:
+

List[TimsFrame]

+
+
+
+ +
+
+build_frames_annotated(frame_ids, fragment=True, mz_noise_precursor=False, mz_noise_uniform=False, precursor_noise_ppm=5.0, mz_noise_fragment=False, fragment_noise_ppm=5.0, right_drag=True, num_threads=4)¶
+

Build frames. The frames will be annotated.

+
+
Parameters:
+
    +

  • frame_ids (List[int]) – Frame IDs.

    • +

  • fragment (bool) – if true, frame will undergo synthetic fragmentation if it is a fragment frame,

    • +

  • fragmentation. (otherwise quadrupole isolation will still be applied but no)

    • +

  • mz_noise_precursor (bool) – if true, noise will be added to the precursor m/z values.

    • +

  • mz_noise_uniform (bool) – if true, noise will be added to the precursor m/z values uniformly.

    • +

  • precursor_noise_ppm (float) – PPM of the precursor noise.

    • +

  • mz_noise_fragment (bool) – if true, noise will be added to the fragment m/z values.

    • +

  • fragment_noise_ppm (float) – PPM of the fragment noise.

    • +

  • right_drag (bool) – if true, the noise will be shifted to the right.

    • +

  • num_threads (int) – Number of threads.

    • +
+
+
Returns:
+

Frames.

+
+
Return type:
+

List[TimsFrameAnnotated]

+
+
+
+ +
+
+count_number_transmissions(peptide_id, charge)¶
+
+
Return type:
+

(int, int)

+
+
+
+ +
+
+count_number_transmissions_parallel(peptide_ids, charges, num_threads=4)¶
+
+
Return type:
+

List[Tuple[int, int]]

+
+
+
+ +
+
+classmethod from_py_ptr(py_ptr)¶
+

Create a TimsTofSyntheticFrameBuilderDIA from a PyTimsTofSyntheticsFrameBuilderDIA.

+
+
Parameters:
+

py_ptr (ims.PyTimsTofSyntheticsFrameBuilderDIA) – PyTimsTofSyntheticsFrameBuilderDIA.

+
+
Returns:
+

TimsTofSyntheticFrameBuilderDIA.

+
+
Return type:
+

TimsTofSyntheticFrameBuilderDIA

+
+
+
+ +
+
+get_collision_energies(frame_ids, scan_ids)¶
+
+
Return type:
+

List[float]

+
+
+
+ +
+
+get_collision_energy(frame_id, scan_id)¶
+
+
Return type:
+

float

+
+
+
+ +
+
+get_ion_transmission_matrix(peptide_id, charge, include_precursor_frames=False)¶
+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
+
+ +
+
+get_py_ptr()¶
+
+
Return type:
+

PyTimsTofSyntheticsFrameBuilderDIA

+
+
+
+ +
+ +
+
+class imspy.simulation.experiment.TimsTofSyntheticPrecursorFrameBuilder(db_path)¶
+

Bases: RustWrapperObject

+
+
+build_precursor_frame(frame_id, mz_noise_precursor=False, mz_noise_uniform=False, precursor_noise_ppm=5.0, right_drag=True)¶
+
+
Return type:
+

TimsFrame

+
+
+
+ +
+
+build_precursor_frame_annotated(frame_id, mz_noise_precursor=False, mz_noise_uniform=False, precursor_noise_ppm=5.0, right_drag=True)¶
+
+
Return type:
+

TimsFrameAnnotated

+
+
+
+ +
+
+build_precursor_frames(frame_ids, mz_noise_precursor=False, mz_noise_uniform=False, precursor_noise_ppm=5.0, right_drag=True, num_threads=4)¶
+
+ +
+
+build_precursor_frames_annotated(frame_ids, mz_noise_precursor=False, mz_noise_uniform=False, precursor_noise_ppm=5.0, right_drag=True, num_threads=4)¶
+
+
Return type:
+

List[TimsFrameAnnotated]

+
+
+
+ +
+
+classmethod from_py_ptr(py_ptr)¶
+
+
Return type:
+

TimsTofSyntheticPrecursorFrameBuilder

+
+
+
+ +
+
+get_py_ptr()¶
+
+
Return type:
+

PyTimsTofSyntheticsPrecursorFrameBuilder

+
+
+
+ +
+ +
+
+

imspy.simulation.feature module¶

+
+
+

imspy.simulation.handle module¶

+
+
+class imspy.simulation.handle.TimsTofSyntheticsDataHandleRust(path)¶
+

Bases: object

+
+
+get_py_ptr()¶
+
+ +
+
+get_transmitted_ions(num_threads=4)¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+ +
+
+

imspy.simulation.isotopes module¶

+
+
+

imspy.simulation.noise module¶

+

This module contains several noise models, +such as detection noise, shot noise and baseline noise.

+
+
+imspy.simulation.noise.baseline_noise()¶
+
+ +
+
+imspy.simulation.noise.baseline_shot_noise(spectrum, window_size=50, expected_noise_peaks_per_Th=10, min_intensity=5, resolution=3)¶
+
+ +
+
+imspy.simulation.noise.baseline_shot_noise_window(window, window_theoretical_mz_min, window_theoretical_mz_max, expected_noise_peaks=5, expected_noise_intensity=10, expected_noise_sigma=0.001, resolution=3)¶
+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+
+imspy.simulation.noise.detection_noise(signal, method='poisson', custom_mu_function=None, custom_sigma_function=None)¶
+
+
Parameters:
+
    +

  • signal (Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]])

    • +

  • method (str)

    • +

  • custom_mu_function (Optional[Callable])

    • +

  • custom_sigma_function (Optional[Callable])

    • +
+
+
Return type:
+

Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]

+
+
+
+ +
+
+imspy.simulation.noise.generate_noise_peak(pos, sigma, intensity, min_intensity=0, resolution=3)¶
+
+ +
+
+imspy.simulation.noise.mu_function_normal_default(intensity)¶
+
+
Return type:
+

Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]

+
+
+
+ +
+
+imspy.simulation.noise.mu_function_poisson_default(intensity)¶
+
+
Return type:
+

Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]

+
+
+
+ +
+
+imspy.simulation.noise.sigma_function_normal_default(intensity)¶
+
+
Return type:
+

Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]

+
+
+
+ +
+
+

imspy.simulation.proteome module¶

+
+
+class imspy.simulation.proteome.PeptideDigest(fasta_path, missed_cleavages=2, min_len=7, max_len=50, cleave_at='KR', restrict='P', generate_decoys=False, c_terminal=True, verbose=True, variable_mods={'M': ['[UNIMOD:35]'], '[': ['[UNIMOD:1]']}, static_mods={'C': '[UNIMOD:4]'})¶
+

Bases: object

+
+ +
+
+

imspy.simulation.tdf module¶

+
+
+class imspy.simulation.tdf.TDFWriter(helper_handle, path='./', exp_name='RAW.d', offset_bytes=64)¶
+

Bases: object

+
+
+build_frame_meta_row(frame, scan_mode, frame_start_pos, only_frame_one=False)¶
+

Build a row for the frame meta data table from a TimsFrame object. +:type frame: TimsFrame +:param frame: TimsFrame object +:type scan_mode: int +:param scan_mode: int +:type frame_start_pos: int +:param frame_start_pos: int +:type only_frame_one: bool +:param only_frame_one: bool

+
+ +
+
+compress_frame(frame, only_frame_one=False)¶
+

Compress a single frame using zstd. +:type frame: TimsFrame +:param frame: TimsFrame object +:type only_frame_one: bool +:param only_frame_one: bool

+
+
Returns:
+

compressed data

+
+
Return type:
+

bytes

+
+
+
+ +
+
+get_frame_meta_data()¶
+
+
Return type:
+

DataFrame

+
+
+
+ +
+
+inv_mobility_to_scan(frame_id, inv_mobs)¶
+

Convert inverse mobility values to scan values for a given frame using the helper handle. +# CAUTION: This will use the calibration data from the reference handle.

+
+ +
+
+mz_to_tof(frame_id, mzs)¶
+

Convert m/z values to TOF values for a given frame using the helper handle. +# CAUTION: This will use the calibration data from the reference handle.

+
+ +
+
+scan_to_inv_mobility(frame_id, scans)¶
+

Convert scan values to inverse mobility values for a given frame using the helper handle. +# CAUTION: This will use the calibration data from the reference handle.

+
+ +
+
+tof_to_mz(frame_id, tofs)¶
+

Convert TOF values to m/z values for a given frame using the helper handle. +# CAUTION: This will use the calibration data from the reference handle.

+
+ +
+
+write_dia_ms_ms_info(dia_ms_ms_info)¶
+
+
Return type:
+

None

+
+
+
+ +
+
+write_dia_ms_ms_windows(dia_ms_ms_windows)¶
+
+
Return type:
+

None

+
+
+
+ +
+
+write_frame(frame, scan_mode, only_frame_one=False)¶
+

Write a single frame to the binary file. +:type frame: TimsFrame +:param frame: TimsFrame object +:type scan_mode: int +:param scan_mode: int +:type only_frame_one: bool +:param only_frame_one: bool

+
+
Return type:
+

None

+
+
+
+ +
+
+write_frame_meta_data()¶
+
+
Return type:
+

None

+
+
+
+ +
+ +
+
+

imspy.simulation.utility module¶

+
+
+imspy.simulation.utility.accumulated_intensity_cdf_numba(sample_start, sample_end, mean, std_dev)¶
+

Calculate the accumulated intensity between two points using the custom CDF.

+
+ +
+
+imspy.simulation.utility.add_uniform_noise(rt_abu, noise_level=1.0)¶
+
+ +
+
+imspy.simulation.utility.calculate_b_y_fragment_mz(sequence, modifications, is_y=False, charge=1)¶
+

Calculate the m/z value of a b or y fragment. +:type sequence: str +:param sequence: the peptide sequence +:type modifications: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]] +:param modifications: potential modifications +:type is_y: bool +:param is_y: is the fragment a y ion +:type charge: int +:param charge: the charge state of the peptide precursor

+
+
Return type:
+

float

+
+
Returns:
+

m/z value of the fragment

+
+
+
+ +
+
+imspy.simulation.utility.calculate_b_y_ion_series_ims(sequence, modifications, charge=1)¶
+

Calculate the b and y ion series for a given peptide sequence. +:type sequence: str +:param sequence: the peptide sequence +:type modifications: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]] +:param modifications: potential modifications +:type charge: int +:param charge: the charge state of the peptide precursor

+
+
Return type:
+

Tuple[List, List]

+
+
Returns:
+

b ion series, y ion series

+
+
+
+ +
+
+imspy.simulation.utility.calculate_bounds_numba(mean, std, z_score)¶
+

Calculate the bounds of a normal distribution for a given z-score.

+
+
Parameters:
+
    +

  • mean (float) – Mean of the normal distribution

    • +

  • std (float) – Standard deviation of the normal distribution

    • +

  • z_score (float) – Z-score of the normal distribution

    • +
+
+
Returns:
+

Lower bound of the normal distribution

+
+
Return type:
+

float

+
+
+
+ +
+
+imspy.simulation.utility.calculate_mobility_spacing(mobility_min, mobility_max, num_scans)¶
+

Calculate the amount of mobility that will be occupied by a single scan

+
+
Parameters:
+
    +

  • mobility_min (float) – Minimum mobility value

    • +

  • mobility_max (float) – Maximum mobility value

    • +

  • num_scans (int) – Number of scans that will be taken during the acquisition

    • +
+
+
Returns:
+

Mobility spacing

+
+
Return type:
+

float

+
+
+
+ +
+
+imspy.simulation.utility.calculate_number_frames(gradient_length, rt_cycle_length)¶
+

Calculate the number of frames that will be taken during the acquisition

+
+
Parameters:
+
    +

  • gradient_length (float) – Length of the gradient in seconds

    • +

  • rt_cycle_length (float) – Length of the RT cycle in seconds

    • +
+
+
Returns:
+

Number of frames that will be taken during the acquisition

+
+
Return type:
+

int

+
+
+
+ +
+
+imspy.simulation.utility.custom_cdf(x, mean, std_dev)¶
+

Custom implementation of the CDF for a normal distribution.

+
+ +
+
+imspy.simulation.utility.flat_intensity_to_sparse(intensity_flat, num_elements=174)¶
+
+ +
+
+imspy.simulation.utility.flatten_prosit_array(array)¶
+
+ +
+
+imspy.simulation.utility.generate_events(n, mean, min_val, max_val, mixture_contribution=1.0)¶
+
+ +
+
+imspy.simulation.utility.get_acquisition_builder_resource_path(acquisition_mode='dia')¶
+

Get the path to a pretrained model

+
+
Parameters:
+

acquisition_mode (str) – The name of the model to load

+
+
Return type:
+

Traversable

+
+
Returns:
+

The path to the pretrained model

+
+
+
+ +
+
+imspy.simulation.utility.get_compressible_data(tofs, scans, intensities, num_scans)¶
+
+ +
+
+imspy.simulation.utility.get_dilution_factors(path=None)¶
+
+
Return type:
+

Dict[str, float]

+
+
+
+ +
+
+imspy.simulation.utility.get_fasta_file_paths(fasta_path)¶
+

Check if the provided fasta path is a folder or file, if its a folder, check if it exists and return all fasta +:type fasta_path: +:param fasta_path: Path to the fasta file or folder containing fasta files

+
+
Return type:
+

Dict[str, str]

+
+
Returns:
+

List of fasta file paths

+
+
+
+ +
+
+imspy.simulation.utility.get_frames_numba(rt_value, times_array, std_rt, z_score)¶
+

Get the frames that will be acquired for a given retention time value. +:type rt_value: +:param rt_value: Retention time value +:type rt_value: float +:type times_array: +:param times_array: Array of retention times +:type times_array: NDArray +:type std_rt: +:param std_rt: Standard deviation of the retention time +:type std_rt: float +:type z_score: +:param z_score: Z-score of the normal distribution +:type z_score: float

+
+
Returns:
+

Array of frame indices

+
+
Return type:
+

NDArray

+
+
+
+ +
+
+imspy.simulation.utility.get_ms_ms_window_layout_resource_path(acquisition_mode)¶
+

Get the path to a pretrained model

+
+
Return type:
+

Traversable

+
+
Returns:
+

The path to the pretrained model

+
+
+
+ +
+
+imspy.simulation.utility.get_native_dataset_path(ds_name='NATIVE.d')¶
+

Get the path to a pretrained model

+
+
Parameters:
+

ds_name (str) – The name of the dataset to load

+
+
Return type:
+

str

+
+
Returns:
+

The path to the pretrained model

+
+
+
+ +
+
+imspy.simulation.utility.get_peak_cnts(total_scans, scans)¶
+
+ +
+
+imspy.simulation.utility.get_realdata(peak_cnts, interleaved)¶
+
+ +
+
+imspy.simulation.utility.get_realdata_loop(peak_cnts, interleaved, back_data, real_data)¶
+
+ +
+
+imspy.simulation.utility.get_scans_numba(im_value, ims_array, scans_array, std_im, z_score)¶
+

Get the scans that will be acquired for a given ion mobility value.

+
+ +
+
+imspy.simulation.utility.get_z_score_for_percentile(target_score=0.95)¶
+
+ +
+
+imspy.simulation.utility.irt_to_rts_numba(irt, new_min=0, new_max=120)¶
+

Scale an array of values from the original range (min_val, max_val) to a new range (new_min, new_max).

+
+
Parameters:
+
    +

  • irt (NDArray) – Array of values to be scaled.

    • +

  • new_min (float) – Minimum value of the new range.

    • +

  • new_max (float) – Maximum value of the new range.

    • +
+
+
Returns:
+

Array of scaled values.

+
+
Return type:
+

NDArray

+
+
+
+ +
+
+imspy.simulation.utility.json_string_to_python_list(json_string)¶
+
+ +
+
+imspy.simulation.utility.modify_tofs(tofs, scans)¶
+
+ +
+
+imspy.simulation.utility.np_zip(xx, yy)¶
+
+ +
+
+imspy.simulation.utility.python_list_to_json_string(lst, as_float=True, num_decimals=4)¶
+
+
Return type:
+

str

+
+
+
+ +
+
+imspy.simulation.utility.read_acquisition_config(acquisition_name='dia')¶
+
+
Return type:
+

Dict[str, Any]

+
+
+
+ +
+
+imspy.simulation.utility.sequence_to_all_ions(sequence, charge, intensities_flat, normalized=True, half_charge_one=True)¶
+

Simulate ion intensities for a peptide sequence, charge, and collision energy. +:type sequence: str +:param sequence: Peptide sequence +:type charge: int +:param charge: Peptide charge +:type intensities_flat: List[float] +:param intensities_flat: List of intensities +:type normalized: bool +:param normalized: Whether to normalize the intensities +:type half_charge_one: bool +:param half_charge_one: Whether to divide the intensity by 2 if the charge is 1

+
+
Returns:
+

Array of ion intensities

+
+
Return type:
+

NDArray

+
+
+
+ +
+
+imspy.simulation.utility.sequence_to_numpy(sequence, max_length=30)¶
+

translate a peptide sequence given as python string into a numpy array of characters with a fixed length +:type sequence: str +:param sequence: the peptide sequence +:type max_length: int +:param max_length: the maximum length a sequence can have

+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
Returns:
+

numpy array of characters

+
+
+
+ +
+
+imspy.simulation.utility.sequences_to_all_ions(sequences, charges, intensities_flat, normalized=True, half_charge_one=True, num_threads=4)¶
+

Simulate ion intensities for a list of peptide sequences, charges, and collision energies. +:type sequences: List[str] +:param sequences: List of peptide sequences +:type charges: List[int] +:param charges: List of peptide charges +:type intensities_flat: List[List[float]] +:param intensities_flat: List of intensities +:type normalized: bool +:param normalized: Whether to normalize the intensities +:type half_charge_one: bool +:param half_charge_one: Whether to divide the intensity by 2 if the charge is 1 +:type num_threads: int +:param num_threads: Number of threads to use for the calculation

+
+
Returns:
+

Array of ion intensities

+
+
Return type:
+

NDArray

+
+
+
+ +
+
+imspy.simulation.utility.set_percentage_to_zero(row, percentage)¶
+

Sets a given percentage of the non-zero elements of a numpy vector to zero, +where the probability of being set to 0 is inversely proportional to the element’s value +relative to other values in the vector.

+
+
Parameters:
+
    +

  • row (np.ndarray) – Input vector of arbitrary length

    • +

  • percentage (float) – Percentage of non-zero elements to set to zero (between 0 and 1)

    • +
+
+
Returns:
+

Modified vector with the specified percentage of non-zero elements set to zero

+
+
Return type:
+

np.ndarray

+
+
+
+ +
+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.simulation.timsim.html b/main/imspy/imspy.simulation.timsim.html new file mode 100644 index 00000000..c809754a --- /dev/null +++ b/main/imspy/imspy.simulation.timsim.html @@ -0,0 +1,198 @@ + + + + + + + + imspy.simulation.timsim package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.simulation.timsim package¶

+
+

Subpackages¶

+
+ +
+
+
+

Submodules¶

+
+
+

imspy.simulation.timsim.simulator module¶

+
+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.simulation.timsim.jobs.html b/main/imspy/imspy.simulation.timsim.jobs.html new file mode 100644 index 00000000..d5316d7f --- /dev/null +++ b/main/imspy/imspy.simulation.timsim.jobs.html @@ -0,0 +1,518 @@ + + + + + + + + imspy.simulation.timsim.jobs package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.simulation.timsim.jobs package¶

+
+

Submodules¶

+
+
+

imspy.simulation.timsim.jobs.add_noise_from_real_data module¶

+
+
+imspy.simulation.timsim.jobs.add_noise_from_real_data.add_real_data_noise_to_frames(acquisition_builder, frames, intensity_max_precursor=30, intensity_max_fragment=30, precursor_sample_fraction=0.2, fragment_sample_fraction=0.2, num_precursor_frames=5, num_fragment_frames=5)¶
+

Add noise to frame.

+
+
Parameters:
+
    +

  • acquisition_builder (TimsTofAcquisitionBuilderDIA) – Acquisition builder.

    • +

  • frames (List[TimsFrame]) – Frames.

    • +

  • intensity_max_precursor (float) – Maximum intensity for precursor.

    • +

  • intensity_max_fragment (float) – Maximum intensity for fragment.

    • +

  • precursor_sample_fraction (float) – Sample fraction for precursor.

    • +

  • fragment_sample_fraction (float) – Sample fraction for fragment.

    • +

  • num_precursor_frames (int) – Number of precursor frames.

    • +

  • num_fragment_frames (int) – Number of fragment frames.

    • +
+
+
Returns:
+

Frames.

+
+
Return type:
+

List[TimsFrame]

+
+
+
+ +
+
+

imspy.simulation.timsim.jobs.assemble_frames module¶

+
+
+imspy.simulation.timsim.jobs.assemble_frames.assemble_frames(acquisition_builder, frames, batch_size, verbose=False, mz_noise_precursor=False, mz_noise_uniform=False, precursor_noise_ppm=5.0, mz_noise_fragment=False, fragment_noise_ppm=5.0, num_threads=4, add_real_data_noise=False, intensity_max_precursor=150, intensity_max_fragment=75, precursor_sample_fraction=0.01, fragment_sample_fraction=0.05, num_precursor_frames=10, num_fragment_frames=10, fragment=True)¶
+

Assemble frames from frame ids and write them to the database.

+
+
Parameters:
+
    +

  • acquisition_builder (TimsTofAcquisitionBuilderDIA) – Acquisition builder object.

    • +

  • frames (DataFrame) – DataFrame containing frame ids.

    • +

  • batch_size (int) – Batch size for frame assembly, i.e. how many frames are assembled at once.

    • +

  • verbose (bool) – Verbosity.

    • +

  • mz_noise_precursor (bool) – Add noise to precursor m/z values.

    • +

  • mz_noise_uniform (bool) – Add uniform noise to m/z values.

    • +

  • precursor_noise_ppm (float) – PPM value for precursor noise.

    • +

  • mz_noise_fragment (bool) – Add noise to fragment m/z values.

    • +

  • fragment_noise_ppm (float) – PPM value for fragment noise.

    • +

  • num_threads (int) – Number of threads for frame assembly.

    • +

  • add_real_data_noise (bool) – Add real data noise to the frames.

    • +

  • intensity_max_precursor (float) – Maximum intensity for precursor noise.

    • +

  • intensity_max_fragment (float) – Maximum intensity for fragment noise.

    • +

  • precursor_sample_fraction (float) – Sample fraction for precursor noise.

    • +

  • fragment_sample_fraction (float) – Sample fraction for fragment noise.

    • +

  • num_precursor_frames (int) – Number of precursor frames.

    • +

  • num_fragment_frames (int) – Number of fragment frames.

    • +

  • fragment (bool) – if False, Quadrupole isolation will still be used, but no fragmentation will be performed.

    • +
+
+
Return type:
+

None

+
+
Returns:
+

None, writes frames to disk and metadata to database.

+
+
+
+ +
+
+

imspy.simulation.timsim.jobs.build_acquisition module¶

+
+
+imspy.simulation.timsim.jobs.build_acquisition.build_acquisition(path, reference_path, exp_name, acquisition_type='dia', verbose=False, gradient_length=None, use_reference_ds_layout=True, reference_in_memory=True, round_collision_energy=True, collision_energy_decimals=0, use_bruker_sdk=True)¶
+

Build acquisition object from reference path.

+
+
Parameters:
+
    +

  • path (str) – Path where the acquisition will be saved.

    • +

  • reference_path (str) – Path to the reference dataset.

    • +

  • exp_name (str) – Experiment name.

    • +

  • acquisition_type (str) – Acquisition type, must be ‘dia’, ‘midia’, ‘slice’ or ‘synchro’.

    • +

  • verbose (bool) – Verbosity.

    • +

  • gradient_length (float) – Gradient length.

    • +

  • use_reference_ds_layout (bool) – Use reference dataset layout for synthetic dataset.

    • +

  • reference_in_memory (bool) – Load reference dataset into memory.

    • +

  • round_collision_energy (bool) – Round collision energy.

    • +

  • collision_energy_decimals (int) – Number of decimals for collision energy (controls coarseness).

    • +

  • use_bruker_sdk (bool) – Use Bruker SDK for reading reference dataset.

    • +
+
+
Returns:
+

Acquisition object.

+
+
Return type:
+

TimsTofAcquisitionBuilderDIA

+
+
+
+ +
+
+

imspy.simulation.timsim.jobs.digest_fasta module¶

+
+
+imspy.simulation.timsim.jobs.digest_fasta.digest_fasta(fasta_file_path, missed_cleavages=2, min_len=6, max_len=30, cleave_at='KR', restrict=None, decoys=False, verbose=False, job_name='digest_fasta', static_mods={'C': '[UNIMOD:4]'}, variable_mods={'M': ['[UNIMOD:35]'], '[': ['[UNIMOD:1]']}, exclude_accumulated_gradient_start=True, min_rt_percent=2.0, gradient_length=3600)¶
+

Digest a fasta file.

+
+
Parameters:
+
    +

  • fasta_file_path (str) – Path to the fasta file.

    • +

  • missed_cleavages (int) – Number of missed cleavages.

    • +

  • min_len (int) – Minimum peptide length.

    • +

  • max_len (int) – Maximum peptide length.

    • +

  • cleave_at (str) – Cleavage sites.

    • +

  • restrict (str) – Restrict to specific proteins.

    • +

  • decoys (bool) – Generate decoys.

    • +

  • verbose (bool) – Verbosity.

    • +

  • job_name (str) – Job name.

    • +

  • static_mods (dict[str, str]) – Static modifications.

    • +

  • variable_mods (dict[str, list[str]]) – Variable modifications.

    • +

  • exclude_accumulated_gradient_start (bool) – Exclude low retention times.

    • +

  • min_rt_percent (float) – Minimum retention time in percent.

    • +

  • gradient_length (float) – Gradient length in seconds (in seconds).

    • +
+
+
Returns:
+

Peptide digest object.

+
+
Return type:
+

PeptideDigest

+
+
+
+ +
+
+

imspy.simulation.timsim.jobs.simulate_charge_states module¶

+
+
+imspy.simulation.timsim.jobs.simulate_charge_states.simulate_charge_states(peptides, mz_lower, mz_upper, p_charge=0.5, charge_state_one_probability=0.0, min_charge_contrib=0.15)¶
+

Simulate charge states for peptides.

+
+
Parameters:
+
    +

  • peptides (DataFrame) – Peptides DataFrame.

    • +

  • mz_lower (float) – Lower m/z value.

    • +

  • mz_upper (float) – Upper m/z value.

    • +

  • p_charge (float) – Probability of charge.

    • +

  • charge_state_one_probability (float) – Probability of charge state one.

    • +

  • min_charge_contrib (float) – Minimum charge contribution.

    • +
+
+
Returns:
+

Ions DataFrame.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+

imspy.simulation.timsim.jobs.simulate_fragment_intensities module¶

+
+
+imspy.simulation.timsim.jobs.simulate_fragment_intensities.simulate_fragment_intensities(path, name, acquisition_builder, batch_size, verbose, num_threads, down_sample_factor=0.5)¶
+

Simulate fragment ion intensity distributions.

+
+
Parameters:
+
    +

  • path (str) – Path to the synthetic data.

    • +

  • name (str) – Name of the synthetic data.

    • +

  • acquisition_builder (TimsTofAcquisitionBuilderDIA) – Acquisition builder object.

    • +

  • batch_size (int) – Batch size for frame assembly, i.e. how many frames are assembled at once.

    • +

  • verbose (bool) – Verbosity.

    • +

  • num_threads (int) – Number of threads for frame assembly.

    • +

  • down_sample_factor (int) – Down sample factor for fragment ion intensity distributions.

    • +
+
+
Return type:
+

None

+
+
Returns:
+

None, writes frames to disk and metadata to database.

+
+
+
+ +
+
+

imspy.simulation.timsim.jobs.simulate_frame_distributions module¶

+
+
+imspy.simulation.timsim.jobs.simulate_frame_distributions.simulate_frame_distributions(peptides, frames, z_score, std_rt, rt_cycle_length, verbose=False, add_noise=False, normalize=False)¶
+

Simulate frame distributions for peptides.

+
+
Parameters:
+
    +

  • peptides (DataFrame) – Peptide DataFrame.

    • +

  • frames (DataFrame) – Frame DataFrame.

    • +

  • z_score (float) – Z-score.

    • +

  • std_rt (float) – Standard deviation of retention time.

    • +

  • rt_cycle_length (float) – Retention time cycle length in seconds.

    • +

  • verbose (bool) – Verbosity.

    • +

  • add_noise (bool) – Add noise.

    • +

  • normalize (bool) – Normalize frame abundance.

    • +
+
+
Returns:
+

Peptide DataFrame with frame distributions.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+

imspy.simulation.timsim.jobs.simulate_frame_distributions_emg module¶

+
+
+imspy.simulation.timsim.jobs.simulate_frame_distributions_emg.sample_parameters_rejection(sigma_mean, sigma_variance, lambda_mean, lambda_variance, n)¶
+
+ +
+
+imspy.simulation.timsim.jobs.simulate_frame_distributions_emg.simulate_frame_distributions_emg(peptides, frames, mean_std_rt, variance_std_rt, mean_scewness, variance_scewness, target_p, step_size, rt_cycle_length, verbose=False, add_noise=False, normalize=False, n_steps=1000, num_threads=4, from_existing=False, sigmas=None, lambdas=None)¶
+

Simulate frame distributions for peptides.

+
+
Parameters:
+
    +

  • peptides (DataFrame) – Peptide DataFrame.

    • +

  • frames (DataFrame) – Frame DataFrame.

    • +

  • mean_std_rt (float) – mean retention time.

    • +

  • variance_std_rt (float) – variance retention time.

    • +

  • mean_scewness (float) – mean scewness.

    • +

  • variance_scewness (float) – variance scewness.

    • +

  • target_p (float) – target p.

    • +

  • step_size (float) – step size.

    • +

  • rt_cycle_length (float) – Retention time cycle length in seconds.

    • +

  • verbose (bool) – Verbosity.

    • +

  • add_noise (bool) – Add noise.

    • +

  • normalize (bool) – Normalize frame abundance.

    • +

  • n_steps (int) – number of steps.

    • +

  • num_threads (int) – number of threads.

    • +

  • from_existing (bool) – Use existing parameters.

    • +

  • sigmas (ndarray) – sigmas.

    • +

  • lambdas (ndarray) – lambdas.

    • +
+
+
Returns:
+

Peptide DataFrame with frame distributions.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+

imspy.simulation.timsim.jobs.simulate_ion_mobilities module¶

+
+
+imspy.simulation.timsim.jobs.simulate_ion_mobilities.simulate_ion_mobilities(ions, im_lower, im_upper, verbose=False)¶
+

Simulate ion mobilities.

+
+
Parameters:
+
    +

  • ions (DataFrame) – Ions DataFrame.

    • +

  • im_lower (float) – Lower ion mobility.

    • +

  • im_upper (float) – Upper ion mobility.

    • +

  • verbose (bool) – Verbosity.

    • +
+
+
Returns:
+

Ions DataFrame.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+

imspy.simulation.timsim.jobs.simulate_occurrences module¶

+
+
+imspy.simulation.timsim.jobs.simulate_occurrences.simulate_peptide_occurrences(peptides, intensity_mean, intensity_min, intensity_max, verbose=False, sample_occurrences=True, intensity_value=1000000.0, mixture_contribution=1.0)¶
+

Simulate peptide occurrences.

+
+
Parameters:
+
    +

  • peptides (DataFrame) – Peptides DataFrame.

    • +

  • intensity_mean (float) – Intensity mean.

    • +

  • intensity_min (float) – Intensity minimum.

    • +

  • intensity_max (float) – Intensity maximum.

    • +

  • verbose (bool) – Verbosity.

    • +

  • sample_occurrences (bool) – Sample occurrences.

    • +

  • intensity_value (float) – Intensity value.

    • +

  • mixture_contribution (float) – Mixture contribution.

    • +
+
+
Returns:
+

Peptides DataFrame.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+

imspy.simulation.timsim.jobs.simulate_precursor_spectra module¶

+
+
+

imspy.simulation.timsim.jobs.simulate_retention_time module¶

+
+
+imspy.simulation.timsim.jobs.simulate_retention_time.simulate_retention_times(peptides, verbose=False, gradient_length=3600)¶
+

Simulate retention times.

+
+
Parameters:
+
    +

  • peptides (DataFrame) – Peptides DataFrame.

    • +

  • verbose (bool) – Verbosity.

    • +

  • gradient_length (float) – Gradient length in seconds.

    • +
+
+
Returns:
+

Peptides DataFrame.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+

imspy.simulation.timsim.jobs.simulate_scan_distributions module¶

+
+
+imspy.simulation.timsim.jobs.simulate_scan_distributions.simulate_scan_distributions(ions, scans, z_score, mean_std_im=0.01, variance_std_im=0.0, verbose=False, add_noise=False, normalize=False, from_existing=False, std_means=None)¶
+

Simulate scan distributions for ions.

+
+
Parameters:
+
    +

  • ions (DataFrame) – Ions DataFrame.

    • +

  • scans (DataFrame) – Scan DataFrame.

    • +

  • z_score (float) – Z-score.

    • +

  • mean_std_im (float) – Standard deviation of ion mobility.

    • +

  • variance_std_im (float) – Variance of standard deviation of ion mobility.

    • +

  • verbose (bool) – Verbosity.

    • +

  • add_noise (bool) – Add noise.

    • +

  • normalize (bool) – Normalize scan abundance.

    • +

  • from_existing (bool) – Use existing parameters.

    • +

  • std_means (ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]) – Standard deviations.

    • +
+
+
Returns:
+

Ions DataFrame with scan distributions.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+

imspy.simulation.timsim.jobs.utility module¶

+
+
+imspy.simulation.timsim.jobs.utility.check_path(p)¶
+
+
Return type:
+

str

+
+
+
+ +
+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.timstof.dbsearch.html b/main/imspy/imspy.timstof.dbsearch.html new file mode 100644 index 00000000..35cfa180 --- /dev/null +++ b/main/imspy/imspy.timstof.dbsearch.html @@ -0,0 +1,494 @@ + + + + + + + + imspy.timstof.dbsearch package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.timstof.dbsearch package¶

+
+

Submodules¶

+
+
+

imspy.timstof.dbsearch.imspy_dda module¶

+
+
+imspy.timstof.dbsearch.imspy_dda.create_database(fasta, static, variab, enzyme_builder, generate_decoys, bucket_size, shuffle_decoys=True, keep_ends=True)¶
+
+ +
+
+imspy.timstof.dbsearch.imspy_dda.load_config(config_path)¶
+
+ +
+
+imspy.timstof.dbsearch.imspy_dda.main()¶
+
+ +
+
+

imspy.timstof.dbsearch.imspy_rescore_sage module¶

+
+
+imspy.timstof.dbsearch.imspy_rescore_sage.main()¶
+
+ +
+
+

imspy.timstof.dbsearch.sage_output_utility module¶

+
+
+class imspy.timstof.dbsearch.sage_output_utility.PatternReplacer(replacements, pattern='\\\\[.*?\\\\]')¶
+

Bases: object

+
+
+apply(string)¶
+
+
Return type:
+

str

+
+
+
+ +
+ +
+
+imspy.timstof.dbsearch.sage_output_utility.break_into_equal_size_sets(sequence_set, k=10)¶
+

Breaks a set of objects into k sets of equal size at random.

+
+
Parameters:
+
    +

  • sequence_set – Set of sequences to be divided

    • +

  • k (int) – Number of sets to divide the objects into

    • +
+
+
Returns:
+

A list containing k sets, each with equal number of randomly chosen sequences

+
+
+
+ +
+
+imspy.timstof.dbsearch.sage_output_utility.cosim_from_dict(observed, predicted)¶
+
+ +
+
+imspy.timstof.dbsearch.sage_output_utility.fragments_to_dict(fragments)¶
+
+ +
+
+imspy.timstof.dbsearch.sage_output_utility.generate_training_data(psms, method='psm', q_max=0.01, balance=True)¶
+

Generate training data. +:type psms: DataFrame +:param psms: List of PeptideSpectrumMatch objects +:type method: str +:param method: Method to use for training data generation +:type q_max: float +:param q_max: Maximum q-value allowed for positive examples +:type balance: bool +:param balance: Whether to balance the dataset

+
+
Returns:
+

X_train and Y_train

+
+
Return type:
+

Tuple[NDArray, NDArray]

+
+
+
+ +
+
+imspy.timstof.dbsearch.sage_output_utility.plot_summary(TARGET, DECOY, save_path, dpi=300, file_format='png')¶
+
+ +
+
+imspy.timstof.dbsearch.sage_output_utility.re_score_psms(psms, num_splits=10, verbose=True, balance=True, score='hyperscore', positive_example_q_max=0.01)¶
+

Re-score PSMs using LDA. +:type psms: DataFrame +:param psms: List of PeptideSpectrumMatch objects +:type num_splits: int +:param num_splits: Number of splits +:type verbose: bool +:param verbose: Whether to print progress +:type balance: bool +:param balance: Whether to balance the dataset +:type score: str +:param score: Score to use for re-scoring +:type positive_example_q_max: float +:param positive_example_q_max: Maximum q-value allowed for positive examples

+
+
Returns:
+

List of PeptideSpectrumMatch objects

+
+
Return type:
+

List[PeptideSpectrumMatch]

+
+
+
+ +
+
+imspy.timstof.dbsearch.sage_output_utility.remove_substrings(input_string)¶
+
+
Return type:
+

str

+
+
+
+ +
+
+imspy.timstof.dbsearch.sage_output_utility.row_to_fragment(r)¶
+
+ +
+
+imspy.timstof.dbsearch.sage_output_utility.split_dataframe_randomly(df, n)¶
+
+
Return type:
+

list

+
+
+
+ +
+
+

imspy.timstof.dbsearch.utility module¶

+
+
+imspy.timstof.dbsearch.utility.extract_timstof_dda_data(path, in_memory=False, use_bruker_sdk=False, isolation_window_lower=-3.0, isolation_window_upper=3.0, take_top_n=100, num_threads=16)¶
+

Extract TIMSTOF DDA data from bruker timsTOF TDF file. +:type path: str +:param path: Path to TIMSTOF DDA data +:type in_memory: bool +:param in_memory: Whether to load data in memory +:type use_bruker_sdk: bool +:param use_bruker_sdk: Whether to use bruker SDK for data extraction +:type isolation_window_lower: float +:param isolation_window_lower: Lower bound for isolation window (Da) +:type isolation_window_upper: float +:param isolation_window_upper: Upper bound for isolation window (Da) +:type take_top_n: int +:param take_top_n: Number of top peaks to take +:type num_threads: int +:param num_threads: Number of threads to use

+
+
Returns:
+

DataFrame containing timsTOF DDA data

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+imspy.timstof.dbsearch.utility.generate_balanced_im_dataset(psms)¶
+
+
Return type:
+

List[Psm]

+
+
+
+ +
+
+imspy.timstof.dbsearch.utility.generate_balanced_rt_dataset(psms)¶
+
+
Return type:
+

List[Psm]

+
+
+
+ +
+
+imspy.timstof.dbsearch.utility.generate_training_data(psms, method='psm', q_max=0.01, balance=True)¶
+

Generate training data. +:type psms: List[Psm] +:param psms: List of PeptideSpectrumMatch objects +:type method: str +:param method: Method to use for training data generation +:type q_max: float +:param q_max: Maximum q-value allowed for positive examples +:type balance: bool +:param balance: Whether to balance the dataset

+
+
Returns:
+

X_train and Y_train

+
+
Return type:
+

Tuple[NDArray, NDArray]

+
+
+
+ +
+
+imspy.timstof.dbsearch.utility.get_searchable_spec(precursor, raw_fragment_data, spec_processor, time, spec_id, file_id=0, ms_level=2)¶
+

Get SAGE searchable spectrum from raw data. +:type precursor: Precursor +:param precursor: Precursor object +:type raw_fragment_data: TimsFrame +:param raw_fragment_data: TimsFrame object +:type time: float +:param time: float +:type spec_processor: SpectrumProcessor +:param spec_processor: SpectrumProcessor object +:type spec_id: str +:param spec_id: str +:type file_id: int +:param file_id: int +:type ms_level: int +:param ms_level: int

+
+
Returns:
+

ProcessedSpectrum object

+
+
Return type:
+

ProcessedSpectrum

+
+
+
+ +
+
+imspy.timstof.dbsearch.utility.linear_map(value, old_min, old_max, new_min=0.0, new_max=60.0)¶
+
+ +
+
+imspy.timstof.dbsearch.utility.map_to_domain(data, gradient_length=120.0)¶
+

Maps the input data linearly into the domain [0, l].

+

Parameters: +- data: list or numpy array of numerical values +- l: float, the upper limit of the target domain [0, l]

+

Returns: +- mapped_data: list of values mapped into the domain [0, l]

+
+ +
+
+imspy.timstof.dbsearch.utility.merge_dicts_with_merge_dict(dicts)¶
+
+ +
+
+imspy.timstof.dbsearch.utility.sanitize_charge(charge)¶
+

Sanitize charge value. +:type charge: Optional[float] +:param charge: Charge value as float.

+
+
Returns:
+

Charge value as int.

+
+
Return type:
+

int

+
+
+
+ +
+
+imspy.timstof.dbsearch.utility.sanitize_mz(mz, mz_highest)¶
+

Sanitize mz value. +:type mz: Optional[float] +:param mz: Mz value as float. +:type mz_highest: float +:param mz_highest: Highest mz value.

+
+
Returns:
+

Mz value as float.

+
+
Return type:
+

float

+
+
+
+ +
+
+imspy.timstof.dbsearch.utility.split_fasta(fasta, num_splits=16, randomize=True)¶
+

Split a fasta file into multiple fasta files. +:type fasta: str +:param fasta: Fasta file as string. +:type num_splits: int +:param num_splits: Number of splits fasta file should be split into. +:type randomize: bool +:param randomize: Whether to randomize the order of sequences before splitting.

+
+
Return type:
+

List[str]

+
+
Returns:
+

List of fasta files as strings, will contain num_splits fasta files with equal number of sequences.

+
+
+
+ +
+
+imspy.timstof.dbsearch.utility.split_psms(psms, num_splits=10)¶
+

Split PSMs into multiple splits.

+
+
Parameters:
+
    +

  • psms (List[Psm]) – List of PeptideSpectrumMatch objects

    • +

  • num_splits (int) – Number of splits

    • +
+
+
Returns:
+

List of splits

+
+
Return type:
+

List[List[PeptideSpectrumMatch]]

+
+
+
+ +
+
+imspy.timstof.dbsearch.utility.transform_psm_to_pin(psm_df)¶
+
+ +
+
+imspy.timstof.dbsearch.utility.write_psms_binary(byte_array, folder_path, file_name, total=False)¶
+

Write PSMs to binary file. +:type byte_array: +:param byte_array: Byte array +:type folder_path: str +:param folder_path: Folder path +:type file_name: str +:param file_name: File name +:type total: bool +:param total: Whether to write to total folder

+
+ +
+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.timstof.html b/main/imspy/imspy.timstof.html new file mode 100644 index 00000000..e22f7879 --- /dev/null +++ b/main/imspy/imspy.timstof.html @@ -0,0 +1,1954 @@ + + + + + + + + imspy.timstof package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.timstof package¶

+
+

Subpackages¶

+
+ +
+
+
+

Submodules¶

+
+
+

imspy.timstof.collision module¶

+
+
+class imspy.timstof.collision.TimsTofCollisionEnergy¶
+

Bases: object

+
+
+abstract get_collision_energies()¶
+
+
Return type:
+

list[float]

+
+
+
+ +
+
+abstract get_collision_energy()¶
+
+
Return type:
+

float

+
+
+
+ +
+ +
+
+class imspy.timstof.collision.TimsTofCollisionEnergyDIA(frame, frame_window_group, window_group, scan_start, scan_end, collision_energy)¶
+

Bases: TimsTofCollisionEnergy

+
+
+get_collision_energies(frame_ids, scan_ids)¶
+
+
Return type:
+

list[float]

+
+
+
+ +
+
+get_collision_energy(frame_id, scan_id)¶
+
+
Return type:
+

float

+
+
+
+ +
+ +
+
+

imspy.timstof.data module¶

+
+
+class imspy.timstof.data.AcquisitionMode(mode)¶
+

Bases: RustWrapperObject

+
+
+classmethod from_py_ptr(ptr)¶
+

Get an AcquisitionMode from a pointer.

+
+
Parameters:
+

ptr (pims.AcquisitionMode) – Pointer to an acquisition mode.

+
+
Returns:
+

Acquisition mode.

+
+
Return type:
+

AcquisitionMode

+
+
+
+ +
+
+get_py_ptr()¶
+
+ +
+
+property mode: str¶
+

Get the acquisition mode.

+
+
Returns:
+

Acquisition mode.

+
+
Return type:
+

str

+
+
+
+ +
+ +
+
+class imspy.timstof.data.TimsDataset(data_path, in_memory=False, use_bruker_sdk=True)¶
+

Bases: ABC

+
+
+property acquisition_mode: str¶
+

Get the acquisition mode.

+
+
Returns:
+

Acquisition mode.

+
+
Return type:
+

str

+
+
+
+ +
+
+property acquisition_mode_numeric: int¶
+

Get the acquisition mode as a numerical value.

+
+
Returns:
+

Acquisition mode as a numerical value.

+
+
Return type:
+

int

+
+
+
+ +
+
+property average_cycle_length: float¶
+
+ +
+
+bytes_to_indexed_values(values)¶
+

Convert bytes to scan, tof, and intensity values.

+
+
Parameters:
+

values (NDArray[np.uint8]) – Bytes.

+
+
Returns:
+

Scan values. +NDArray[np.int32]: TOF values. +NDArray[np.float64]: Intensity values.

+
+
Return type:
+

NDArray[np.int32]

+
+
+
+ +
+
+compress_frames(frames, num_threads=4)¶
+

Compress a collection of frames.

+
+
Parameters:
+
    +

  • frames (List[TimsFrame]) – List of frames.

    • +

  • num_threads (int) – Number of threads to use.

    • +
+
+
Returns:
+

List of compressed bytes.

+
+
Return type:
+

List[NDArray[np.uint8]]

+
+
+
+ +
+
+compress_zstd(values)¶
+

Compress values using ZSTD.

+
+
Parameters:
+

values (NDArray[np.float64]) – Values to compress.

+
+
Returns:
+

Compressed values.

+
+
Return type:
+

NDArray[np.uint8]

+
+
+
+ +
+
+decompress_zstd(values, ignore_first_n=8)¶
+

Decompress values using ZSTD.

+
+
Parameters:
+
    +

  • values (NDArray[np.float64]) – Values to decompress.

    • +

  • ignore_first_n (int) – Number of bytes to ignore.

    • +
+
+
Returns:
+

Decompressed values.

+
+
Return type:
+

NDArray[np.uint8]

+
+
+
+ +
+
+property description: str¶
+
+ +
+
+property frame_count: int¶
+

Get the number of frames.

+
+
Returns:
+

Number of frames.

+
+
Return type:
+

int

+
+
+
+ +
+
+get_table(table_name)¶
+

Get a table.

+
+
Parameters:
+

table_name (str) – Table name.

+
+
Returns:
+

Table.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+get_tims_frame(frame_id)¶
+

Get a TimsFrame.

+
+
Parameters:
+

frame_id (int) – Frame ID.

+
+
Returns:
+

TimsFrame.

+
+
Return type:
+

TimsFrame

+
+
+
+ +
+
+get_tims_slice(frame_ids, num_threads=8)¶
+

Get a TimsFrame.

+
+
Parameters:
+
    +

  • frame_ids (int) – Frame ID.

    • +

  • num_threads (int) – Number of threads.

    • +
+
+
Returns:
+

TimsFrame.

+
+
Return type:
+

TimsFrame

+
+
+
+ +
+
+property im_lower¶
+
+ +
+
+property im_upper¶
+
+ +
+
+indexed_values_to_compressed_bytes(scan_values, tof_values, intensity_values, total_scans)¶
+

Convert scan and intensity values to bytes.

+
+
Parameters:
+
    +

  • scan_values (NDArray[np.int32]) – Scan values.

    • +

  • tof_values (NDArray[np.int32]) – TOF values.

    • +

  • intensity_values (NDArray[np.float64]) – Intensity values.

    • +

  • total_scans (int) – Total number of scans.

    • +
+
+
Returns:
+

Bytes.

+
+
Return type:
+

NDArray[np.uint8]

+
+
+
+ +
+
+inverse_mobility_to_scan(frame_id, im_values)¶
+

Convert inverse mobility values to scan values.

+
+
Parameters:
+
    +

  • frame_id (int) – Frame ID.

    • +

  • im_values (NDArray[np.float64]) – Inverse mobility values.

    • +
+
+
Returns:
+

Scan values.

+
+
Return type:
+

NDArray[np.int32]

+
+
+
+ +
+
+property mz_lower¶
+
+ +
+
+mz_to_tof(frame_id, mz_values)¶
+

Convert m/z values to TOF values.

+
+
Parameters:
+
    +

  • frame_id (int) – Frame ID.

    • +

  • mz_values (NDArray[np.float64]) – m/z values.

    • +
+
+
Returns:
+

TOF values.

+
+
Return type:
+

NDArray[np.int32]

+
+
+
+ +
+
+property mz_upper¶
+
+ +
+
+property num_scans: int¶
+

Get the number of scans.

+
+
Returns:
+

Number of scans.

+
+
Return type:
+

int

+
+
+
+ +
+
+scan_to_inverse_mobility(frame_id, scan_values)¶
+

Convert scan values to inverse mobility values.

+
+
Parameters:
+
    +

  • frame_id (int) – Frame ID.

    • +

  • scan_values (NDArray[np.int32]) – Scan values.

    • +
+
+
Returns:
+

Inverse mobility values.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+
+tof_to_mz(frame_id, tof_values)¶
+

Convert TOF values to m/z values.

+
+
Parameters:
+
    +

  • frame_id (int) – Frame ID.

    • +

  • tof_values (NDArray[np.int32]) – TOF values.

    • +
+
+
Returns:
+

m/z values.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+ +
+
+

imspy.timstof.dda module¶

+
+
+class imspy.timstof.dda.FragmentDDA(frame_id, precursor_id, collision_energy, selected_fragment)¶
+

Bases: RustWrapperObject

+
+
+property collision_energy: float¶
+
+ +
+
+property frame_id: int¶
+
+ +
+
+classmethod from_py_ptr(fragment)¶
+
+ +
+
+get_py_ptr()¶
+
+ +
+
+property precursor_id: int¶
+
+ +
+
+property selected_fragment: TimsFrame¶
+
+ +
+ +
+
+class imspy.timstof.dda.TimsDatasetDDA(data_path, in_memory=False, use_bruker_sdk=True)¶
+

Bases: TimsDataset, RustWrapperObject

+
+
+classmethod from_py_ptr(ptr)¶
+
+ +
+
+get_pasef_fragments(num_threads=1)¶
+

Get PASEF fragments.

+

Args: num_threads (int, optional): Number of threads. Defaults to 1. CAUTION: As long as connection to +datasets is established via bruker so / dll, using multiple threads is unstable.

+
+
Returns:
+

List of PASEF fragments.

+
+
Return type:
+

List[FragmentDDA]

+
+
+
+ +
+
+get_py_ptr()¶
+
+ +
+ +
+
+

imspy.timstof.dia module¶

+
+
+class imspy.timstof.dia.TimsDatasetDIA(data_path, in_memory=False, use_bruker_sdk=True)¶
+

Bases: TimsDataset, RustWrapperObject

+
+
+property dia_ms_ms_info¶
+

Get DIA MS/MS info.

+
+
Returns:
+

DIA MS/MS info.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+property dia_ms_ms_windows¶
+

Get PASEF meta data for DIA.

+
+
Returns:
+

PASEF meta data.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+classmethod from_py_ptr(obj)¶
+
+ +
+
+get_py_ptr()¶
+
+ +
+
+read_compressed_data_full()¶
+

Read compressed data.

+
+
Returns:
+

Compressed data.

+
+
Return type:
+

List[bytes]

+
+
+
+ +
+
+sample_fragment_signal(num_frames, window_group, max_intensity=25.0, take_probability=0.5)¶
+

Sample fragment signal.

+
+
Parameters:
+
    +

  • num_frames (int) – Number of frames.

    • +

  • window_group (int) – Window group to take frames from.

    • +

  • max_intensity (float) – Maximum intensity.

    • +

  • take_probability (float) – Probability to take signals from sampled frames.

    • +
+
+
Returns:
+

Frame.

+
+
Return type:
+

TimsFrame

+
+
+
+ +
+
+sample_precursor_signal(num_frames, max_intensity=25.0, take_probability=0.5)¶
+

Sample precursor signal.

+
+
Parameters:
+
    +

  • num_frames (int) – Number of frames.

    • +

  • max_intensity (float) – Maximum intensity.

    • +

  • take_probability (float) – Probability to take signals from sampled frames.

    • +
+
+
Returns:
+

Frame.

+
+
Return type:
+

TimsFrame

+
+
+
+ +
+ +
+
+

imspy.timstof.frame module¶

+
+
+class imspy.timstof.frame.TimsFrame(frame_id, ms_type, retention_time, scan, mobility, tof, mz, intensity)¶
+

Bases: RustWrapperObject

+
+
+property df: DataFrame¶
+

Data as a pandas DataFrame.

+
+
Returns:
+

Data.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+filter(mz_min=0.0, mz_max=2000.0, scan_min=0, scan_max=1000, mobility_min=0.0, mobility_max=2.0, intensity_min=0.0, intensity_max=1000000000.0)¶
+

Filter the frame for a given m/z range, scan range and intensity range.

+
+
Parameters:
+
    +

  • mz_min (float) – Minimum m/z value.

    • +

  • mz_max (float) – Maximum m/z value.

    • +

  • scan_min (int, optional) – Minimum scan value. Defaults to 0.

    • +

  • scan_max (int, optional) – Maximum scan value. Defaults to 1000.

    • +

  • mobility_min (float, optional) – Minimum inverse mobility value. Defaults to 0.0.

    • +

  • mobility_max (float, optional) – Maximum inverse mobility value. Defaults to 2.0.

    • +

  • intensity_min (float, optional) – Minimum intensity value. Defaults to 0.0.

    • +

  • intensity_max (float, optional) – Maximum intensity value. Defaults to 1e9.

    • +
+
+
Returns:
+

Filtered frame.

+
+
Return type:
+

TimsFrame

+
+
+
+ +
+
+property frame_id: int¶
+

Frame ID.

+
+
Returns:
+

Frame ID.

+
+
Return type:
+

int

+
+
+
+ +
+
+classmethod from_py_ptr(frame)¶
+

Create a TimsFrame from a PyTimsFrame.

+
+
Parameters:
+

frame (pims.PyTimsFrame) – PyTimsFrame to create the TimsFrame from.

+
+
Returns:
+

TimsFrame created from the PyTimsFrame.

+
+
Return type:
+

TimsFrame

+
+
+
+ +
+
+classmethod from_tims_spectra(spectra)¶
+

Create a TimsFrame from a list of TimsSpectrum.

+
+
Parameters:
+

spectra (List[TimsSpectrum]) – List of TimsSpectrum.

+
+
Returns:
+

TimsFrame created from the TimsSpectrum.

+
+
Return type:
+

TimsFrame

+
+
+
+ +
+
+classmethod from_windows(windows)¶
+

Create a TimsFrame from a list of windows.

+
+
Parameters:
+

windows (List[TimsSpectrum]) – List of windows.

+
+
Returns:
+

TimsFrame created from the windows.

+
+
Return type:
+

TimsFrame

+
+
+
+ +
+
+get_inverse_mobility_along_scan_marginal()¶
+

Get the inverse mobility along the scan marginal.

+
+
Returns:
+

Inverse mobility.

+
+
Return type:
+

float

+
+
+
+ +
+
+get_mobility_mean_and_variance()¶
+

Get the mean and variance of the inverse mobility.

+
+
Returns:
+

Mean and variance of the inverse mobility.

+
+
Return type:
+

Tuple[float, float]

+
+
+
+ +
+
+get_py_ptr()¶
+
+ +
+
+property intensity: ndarray[Any, dtype[float64]]¶
+

Intensity.

+
+
Returns:
+

Intensity.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+
+property mobility: ndarray[Any, dtype[float64]]¶
+

Inverse mobility.

+
+
Returns:
+

Inverse mobility.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+
+property ms_type: int¶
+

MS type.

+
+
Returns:
+

MS type.

+
+
Return type:
+

int

+
+
+
+ +
+
+property ms_type_as_string: str¶
+

MS type.

+
+
Returns:
+

MS type.

+
+
Return type:
+

int

+
+
+
+ +
+
+property mz: ndarray[Any, dtype[float64]]¶
+

m/z.

+
+
Returns:
+

m/z.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+
+random_subsample_frame(take_probability)¶
+

Randomly subsample the frame.

+

Args: +take_probability (float): Take probability.

+

Returns: +TimsFrame: Subsampled frame.

+
+
Return type:
+

TimsFrame

+
+
+
+ +
+
+property retention_time: float¶
+

Retention time.

+
+
Returns:
+

Retention time.

+
+
Return type:
+

float

+
+
+
+ +
+
+property scan: ndarray[Any, dtype[int32]]¶
+

Scan.

+
+
Returns:
+

Scan.

+
+
Return type:
+

NDArray[np.int32]

+
+
+
+ +
+
+to_dense_windows(window_length=10, resolution=1, overlapping=True, min_num_peaks=5, min_intensity=0.0)¶
+
+
Return type:
+

ndarray[Any, dtype[float64]]

+
+
+
+ +
+
+to_indexed_mz_spectrum()¶
+

Convert the frame to an IndexedMzSpectrum.

+
+
Returns:
+

IndexedMzSpectrum.

+
+
Return type:
+

IndexedMzSpectrum

+
+
+
+ +
+
+to_noise_annotated_tims_frame()¶
+

Convert the frame to a noise annotated frame.

+
+
Returns:
+

Noise annotated frame.

+
+
Return type:
+

TimsFrameAnnotated

+
+
+
+ +
+
+to_resolution(resolution)¶
+

Convert the frame to a given resolution.

+
+
Parameters:
+

resolution (int) – Resolution.

+
+
Returns:
+

Frame with the given resolution.

+
+
Return type:
+

TimsFrame

+
+
+
+ +
+
+to_tims_spectra()¶
+

Convert the frame to a list of TimsSpectrum.

+
+
Returns:
+

List of TimsSpectrum.

+
+
Return type:
+

List[TimsSpectrum]

+
+
+
+ +
+
+to_windows(window_length=10, overlapping=True, min_num_peaks=5, min_intensity=1)¶
+

Convert the frame to a list of windows.

+
+
Parameters:
+
    +

  • window_length (float, optional) – Window length. Defaults to 10.

    • +

  • overlapping (bool, optional) – Whether the windows should overlap. Defaults to True.

    • +

  • min_num_peaks (int, optional) – Minimum number of peaks in a window. Defaults to 5.

    • +

  • min_intensity (float, optional) – Minimum intensity of a peak in a window. Defaults to 1.

    • +
+
+
Returns:
+

List of windows.

+
+
Return type:
+

List[MzSpectrum]

+
+
+
+ +
+
+property tof: ndarray[Any, dtype[int32]]¶
+

Time of flight.

+
+
Returns:
+

Time of flight.

+
+
Return type:
+

NDArray[np.int32]

+
+
+
+ +
+
+vectorized(resolution=2)¶
+

Convert the frame to a vectorized frame.

+
+
Parameters:
+

resolution (int, optional) – Resolution. Defaults to 2.

+
+
Returns:
+

Vectorized frame.

+
+
Return type:
+

TimsFrameVectorized

+
+
+
+ +
+ +
+
+class imspy.timstof.frame.TimsFrameVectorized(frame_id, ms_type, retention_time, scan, mobility, tof, indices, intensity)¶
+

Bases: RustWrapperObject

+
+
+property df: DataFrame¶
+

Data as a pandas DataFrame.

+
+
Returns:
+

Data.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+filter(mz_min=0.0, mz_max=2000.0, scan_min=0, scan_max=1000, mobility_min=0.0, mobility_max=2.0, intensity_min=0.0, intensity_max=1000000000.0)¶
+

Filter the frame for a given m/z range, scan range and intensity range.

+
+
Parameters:
+
    +

  • mz_min (float) – Minimum m/z value.

    • +

  • mz_max (float) – Maximum m/z value.

    • +

  • scan_min (int, optional) – Minimum scan value. Defaults to 0.

    • +

  • scan_max (int, optional) – Maximum scan value. Defaults to 1000.

    • +

  • mobility_min (float, optional) – Minimum inverse mobility value. Defaults to 0.0.

    • +

  • mobility_max (float, optional) – Maximum inverse mobility value. Defaults to 2.0.

    • +

  • intensity_min (float, optional) – Minimum intensity value. Defaults to 0.0.

    • +

  • intensity_max (float, optional) – Maximum intensity value. Defaults to 1e9.

    • +
+
+
Returns:
+

Filtered frame.

+
+
Return type:
+

TimsFrameVectorized

+
+
+
+ +
+
+property frame_id: int¶
+

Frame ID.

+
+
Returns:
+

Frame ID.

+
+
Return type:
+

int

+
+
+
+ +
+
+classmethod from_py_ptr(frame)¶
+

Create a TimsFrameVectorized from a PyTimsFrameVectorized.

+
+
Parameters:
+

frame (pims.PyTimsFrameVectorized) – PyTimsFrameVectorized to create the TimsFrameVectorized from.

+
+
Returns:
+

TimsFrameVectorized created from the PyTimsFrameVectorized.

+
+
Return type:
+

TimsFrameVectorized

+
+
+
+ +
+
+get_arrays_at_index(index)¶
+

Get the arrays at a given index.

+
+
Parameters:
+

index (int) – Index.

+
+
Returns:
+

Arrays at the index.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+
+get_py_ptr()¶
+
+ +
+
+get_tensor_repr(dense=True, zero_indexed=True, re_index=True, scan_max=None, index_max=None)¶
+
+ +
+
+property indices: ndarray[Any, dtype[int32]]¶
+

Indices.

+
+
Returns:
+

Indices.

+
+
Return type:
+

NDArray[np.int32]

+
+
+
+ +
+
+property intensity: ndarray[Any, dtype[float64]]¶
+

Intensity.

+
+
Returns:
+

Intensity.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+
+property mobility: ndarray[Any, dtype[float64]]¶
+

Inverse mobility.

+
+
Returns:
+

Inverse mobility.

+
+
Return type:
+

NDArray[np.float64]

+
+
+
+ +
+
+property ms_type: str¶
+

MS type.

+
+
Returns:
+

MS type.

+
+
Return type:
+

int

+
+
+
+ +
+
+property retention_time: float¶
+

Retention time.

+
+
Returns:
+

Retention time.

+
+
Return type:
+

float

+
+
+
+ +
+
+property scan: ndarray[Any, dtype[int32]]¶
+

Scan.

+
+
Returns:
+

Scan.

+
+
Return type:
+

NDArray[np.int32]

+
+
+
+ +
+
+property tof: ndarray[Any, dtype[int32]]¶
+

Time of flight.

+
+
Returns:
+

Time of flight.

+
+
Return type:
+

NDArray[np.int32]

+
+
+
+ +
+ +
+
+

imspy.timstof.quadrupole module¶

+
+
+class imspy.timstof.quadrupole.TimsTofQuadrupoleDIA(frame, frame_window_group, window_group, scan_start, scan_end, isolation_mz, isolation_width, k=None)¶
+

Bases: object

+
+
+all_transmitted(frame_id, scan_id, mz, min_proba=None)¶
+
+
Return type:
+

bool

+
+
+
+ +
+
+any_transmitted(frame_id, scan_id, mz, min_proba=None)¶
+
+
Return type:
+

bool

+
+
+
+ +
+
+apply_transmission(frame_id, scan_id, mz)¶
+
+
Return type:
+

ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]

+
+
+
+ +
+
+frame_to_window_group(frame_id)¶
+
+
Return type:
+

int

+
+
+
+ +
+
+get_transmission_set(frame_id, scan_id, mz, min_proba=None)¶
+
+
Return type:
+

set[int]

+
+
+
+ +
+
+is_precursor(frame_id)¶
+
+
Return type:
+

bool

+
+
+
+ +
+
+is_transmitted(frame_id, scan_id, mz, min_proba=None)¶
+
+
Return type:
+

bool

+
+
+
+ +
+
+isotopes_transmitted(frame_id, scan_id, mz_mono, mz, min_proba=None)¶
+

Get the transmission probability for a list of isotopes +:type frame_id: int +:param frame_id: +:type scan_id: int +:param scan_id: +:type mz_mono: float +:param mz_mono: +:type mz: ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]] +:param mz: +:type min_proba: float | None +:param min_proba:

+

Returns:

+
+
Return type:
+

tuple[float, list[tuple[float, float]]]

+
+
+
+ +
+
+transmit_frame(frame, min_probability=None)¶
+
+
Return type:
+

TimsFrame

+
+
+
+ +
+
+transmit_ion(frame_ids, scan_ids, spectrum, min_probability)¶
+
+
Return type:
+

List[List[MzSpectrum]]

+
+
+
+ +
+
+transmit_spectrum(frame_id, scan_id, spectrum, min_probability=None)¶
+
+
Return type:
+

MzSpectrum

+
+
+
+ +
+ +
+
+

imspy.timstof.slice module¶

+
+
+class imspy.timstof.slice.TimsPlane¶
+

Bases: object

+
+
+property df¶
+
+ +
+
+property frame_ids¶
+
+ +
+
+classmethod from_py_tims_plane(plane)¶
+

Create a TimsPlane from a PyTimsPlane.

+
+
Parameters:
+

plane (pims.PyTimsPlane) – PyTimsPlane to create the TimsPlane from.

+
+
Returns:
+

TimsPlane created from the PyTimsPlane.

+
+
Return type:
+

TimsPlane

+
+
+
+ +
+
+property intensities¶
+
+ +
+
+property mobilities¶
+
+ +
+
+property mz_mean¶
+
+ +
+
+property mz_std¶
+
+ +
+
+property num_points¶
+
+ +
+
+property retention_times¶
+
+ +
+
+property scans¶
+
+ +
+
+property tof_mean¶
+
+ +
+
+property tof_std¶
+
+ +
+ +
+
+class imspy.timstof.slice.TimsSlice(frame_id, scan, tof, retention_time, mobility, mz, intensity)¶
+

Bases: object

+
+
+property df: DataFrame¶
+

Get the data as a pandas DataFrame.

+
+
Returns:
+

Data.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+filter(mz_min=0.0, mz_max=2000.0, scan_min=0, scan_max=1000, mobility_min=0.0, mobility_max=2.0, intensity_min=0.0, intensity_max=1000000000.0, num_threads=4)¶
+

Filter the slice by m/z, scan and intensity.

+
+
Parameters:
+
    +

  • mz_min (float) – Minimum m/z value.

    • +

  • mz_max (float) – Maximum m/z value.

    • +

  • scan_min (int, optional) – Minimum scan value. Defaults to 0.

    • +

  • scan_max (int, optional) – Maximum scan value. Defaults to 1000.

    • +

  • mobility_min (float, optional) – Minimum inverse mobility value. Defaults to 0.0.

    • +

  • mobility_max (float, optional) – Maximum inverse mobility value. Defaults to 2.0.

    • +

  • intensity_min (float, optional) – Minimum intensity value. Defaults to 0.0.

    • +

  • intensity_max (float, optional) – Maximum intensity value. Defaults to 1e9.

    • +

  • num_threads (int, optional) – Number of threads to use. Defaults to 4.

    • +
+
+
Returns:
+

Filtered slice.

+
+
Return type:
+

TimsSlice

+
+
+
+ +
+
+filter_by_type(mz_min_ms1=0, mz_max_ms1=2000, scan_min_ms1=0, scan_max_ms1=1000, inv_mob_min_ms1=0, inv_mob_max_ms1=2, intensity_min_ms1=0, intensity_max_ms1=1000000000.0, mz_min_ms2=0, mz_max_ms2=2000, scan_min_ms2=0, scan_max_ms2=1000, inv_mob_min_ms2=0, inv_mob_max_ms2=2, intensity_min_ms2=0, intensity_max_ms2=1000000000.0, num_threads=4)¶
+

Filter the slice by m/z, scan and intensity, for MS1 and MS2 with different ranges.

+
+
Parameters:
+
    +

  • mz_min_ms1 (float, optional) – Minimum m/z value for MS1. Defaults to 0.

    • +

  • mz_max_ms1 (float, optional) – Maximum m/z value for MS1. Defaults to 2000.

    • +

  • scan_min_ms1 (int, optional) – Minimum scan value for MS1. Defaults to 0.

    • +

  • scan_max_ms1 (int, optional) – Maximum scan value for MS1. Defaults to 1000.

    • +

  • inv_mob_min_ms1 (float, optional) – Minimum inverse mobility value for MS1. Defaults to 0.

    • +

  • inv_mob_max_ms1 (float, optional) – Maximum inverse mobility value for MS1. Defaults to 2.

    • +

  • intensity_min_ms1 (float, optional) – Minimum intensity value for MS1. Defaults to 0.

    • +

  • intensity_max_ms1 (float, optional) – Maximum intensity value for MS1. Defaults to 1e9.

    • +

  • mz_min_ms2 (float, optional) – Minimum m/z value for MS2. Defaults to 0.

    • +

  • mz_max_ms2 (float, optional) – Maximum m/z value for MS2. Defaults to 2000.

    • +

  • scan_min_ms2 (int, optional) – Minimum scan value for MS2. Defaults to 0.

    • +

  • scan_max_ms2 (int, optional) – Maximum scan value for MS2. Defaults to 1000.

    • +

  • inv_mob_min_ms2 (float, optional) – Minimum inverse mobility value for MS2. Defaults to 0.

    • +

  • inv_mob_max_ms2 (float, optional) – Maximum inverse mobility value for MS2. Defaults to 2.

    • +

  • intensity_min_ms2 (float, optional) – Minimum intensity value for MS2. Defaults to 0.

    • +

  • intensity_max_ms2 (float, optional) – Maximum intensity value for MS2. Defaults to 1e9.

    • +

  • num_threads (int, optional) – Number of threads to use. Defaults to 4.

    • +
+
+
Returns:
+

Filtered slice.

+
+
Return type:
+

TimsSlice

+
+
+
+ +
+
+property first_frame_id: int¶
+

First frame ID.

+
+
Returns:
+

First frame ID.

+
+
Return type:
+

int

+
+
+
+ +
+
+property fragments¶
+
+ +
+
+property frames: List[TimsFrame]¶
+

Get the frames.

+
+
Returns:
+

Frames.

+
+
Return type:
+

List[TimsFrame]

+
+
+
+ +
+
+classmethod from_frames(frames)¶
+

Create a TimsSlice from a list of TimsFrames.

+
+
Parameters:
+

frames (List[TimsFrame]) – List of TimsFrames.

+
+
Returns:
+

TimsSlice created from the list of TimsFrames.

+
+
Return type:
+

TimsSlice

+
+
+
+ +
+
+classmethod from_py_tims_slice(tims_slice)¶
+

Create a TimsSlice from a PyTimsSlice.

+
+
Parameters:
+

tims_slice (pims.PyTimsSlice) – PyTimsSlice to create the TimsSlice from.

+
+
Returns:
+

TimsSlice created from the PyTimsSlice.

+
+
Return type:
+

TimsSlice

+
+
+
+ +
+
+get_tims_planes(tof_max_value=400000, num_chunks=7, num_threads=4)¶
+
+
Return type:
+

List[TimsPlane]

+
+
+
+ +
+
+property last_frame_id: int¶
+

Last frame ID.

+
+
Returns:
+

Last frame ID.

+
+
Return type:
+

int

+
+
+
+ +
+
+property precursors¶
+
+ +
+
+to_dense_windows(window_length=10, resolution=1, overlapping=True, min_num_peaks=5, min_intensity=0.0, num_theads=4)¶
+
+
Return type:
+

tuple[list[ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]], list[ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]], list[ndarray[Any, dtype[TypeVar(_ScalarType_co, bound= generic, covariant=True)]]]]

+
+
+
+ +
+
+to_resolution(resolution, num_threads=4)¶
+

Convert the slice to a given resolution.

+
+
Parameters:
+
    +

  • resolution (int) – Resolution.

    • +

  • num_threads (int, optional) – Number of threads to use. Defaults to 4.

    • +
+
+
Returns:
+

Slice with given resolution.

+
+
Return type:
+

TimsSlice

+
+
+
+ +
+
+to_windows(window_length=10, overlapping=True, min_num_peaks=5, min_intensity=1, num_threads=4)¶
+

Convert the slice to a list of windows.

+
+
Parameters:
+
    +

  • window_length (float, optional) – Window length. Defaults to 10.

    • +

  • overlapping (bool, optional) – Whether the windows should overlap. Defaults to True.

    • +

  • min_num_peaks (int, optional) – Minimum number of peaks in a window. Defaults to 5.

    • +

  • min_intensity (float, optional) – Minimum intensity of a peak in a window. Defaults to 1.

    • +

  • num_threads (int, optional) – Number of threads to use. Defaults to 1.

    • +
+
+
Returns:
+

List of windows.

+
+
Return type:
+

List[MzSpectrum]

+
+
+
+ +
+
+vectorized(resolution=2, num_threads=4)¶
+

Get a vectorized version of the slice.

+
+
Parameters:
+
    +

  • resolution (int, optional) – Resolution. Defaults to 2.

    • +

  • num_threads (int, optional) – Number of threads to use. Defaults to 4.

    • +
+
+
Returns:
+

Vectorized version of the slice.

+
+
Return type:
+

TimsSliceVectorized

+
+
+
+ +
+ +
+
+class imspy.timstof.slice.TimsSliceVectorized¶
+

Bases: object

+
+
+property df: DataFrame¶
+

Get the data as a pandas DataFrame.

+
+
Returns:
+

Data.

+
+
Return type:
+

pd.DataFrame

+
+
+
+ +
+
+filter(mz_min=0.0, mz_max=2000.0, scan_min=0, scan_max=1000, mobility_min=0.0, mobility_max=2.0, intensity_min=0.0, intensity_max=1000000000.0, num_threads=4)¶
+

Filter the slice by m/z, scan and intensity.

+
+
Parameters:
+
    +

  • mz_min (float) – Minimum m/z value.

    • +

  • mz_max (float) – Maximum m/z value.

    • +

  • scan_min (int, optional) – Minimum scan value. Defaults to 0.

    • +

  • scan_max (int, optional) – Maximum scan value. Defaults to 1000.

    • +

  • mobility_min (float, optional) – Minimum inverse mobility value. Defaults to 0.0.

    • +

  • mobility_max (float, optional) – Maximum inverse mobility value. Defaults to 2.0.

    • +

  • intensity_min (float, optional) – Minimum intensity value. Defaults to 0.0.

    • +

  • intensity_max (float, optional) – Maximum intensity value. Defaults to 1e9.

    • +

  • num_threads (int, optional) – Number of threads to use. Defaults to 4.

    • +
+
+
Returns:
+

Filtered slice.

+
+
Return type:
+

TimsSlice

+
+
+
+ +
+
+property first_frame_id: int¶
+

First frame ID.

+
+
Returns:
+

First frame ID.

+
+
Return type:
+

int

+
+
+
+ +
+
+property fragments¶
+
+ +
+
+property frames: List[TimsFrameVectorized]¶
+

Get the frames.

+
+
Returns:
+

Frames.

+
+
Return type:
+

List[TimsFrame]

+
+
+
+ +
+
+classmethod from_vectorized_py_tims_slice(tims_slice)¶
+

Create a TimsSlice from a PyTimsSlice.

+
+
Parameters:
+

tims_slice (pims.PyTimsSlice) – PyTimsSlice to create the TimsSlice from.

+
+
Returns:
+

TimsSlice created from the PyTimsSlice.

+
+
Return type:
+

TimsSlice

+
+
+
+ +
+
+get_py_ptr()¶
+
+ +
+
+get_tensor_repr(dense=True, zero_index=True, re_index=True, frame_max=None, scan_max=None, index_max=None)¶
+
+ +
+
+property last_frame_id: int¶
+

Last frame ID.

+
+
Returns:
+

Last frame ID.

+
+
Return type:
+

int

+
+
+
+ +
+
+property precursors¶
+
+ +
+ +
+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.utility.html b/main/imspy/imspy.utility.html new file mode 100644 index 00000000..d51128eb --- /dev/null +++ b/main/imspy/imspy.utility.html @@ -0,0 +1,330 @@ + + + + + + + + imspy.utility package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.utility package¶

+
+

Submodules¶

+
+
+

imspy.utility.sequence module¶

+
+
+imspy.utility.sequence.tokenize_unimod_sequence(unimod_sequence)¶
+

Tokenizes a sequence of modified amino acids. +:type unimod_sequence: str +:param unimod_sequence: A string representing the sequence of amino acids with modifications.

+
+
Return type:
+

List[str]

+
+
Returns:
+

A list of tokenized amino acids.

+
+
+
+ +
+
+

imspy.utility.utilities module¶

+
+
+class imspy.utility.utilities.ExponentialGaussianDistribution(μ=-3, σ=1, λ=0.25)¶
+

Bases: object

+
+ +
+
+class imspy.utility.utilities.NormalDistribution(μ, σ)¶
+

Bases: object

+
+ +
+
+class imspy.utility.utilities.TokenSequence(sequence_tokenized=None, jsons=None)¶
+

Bases: object

+
+
+property jsons¶
+
+ +
+ +
+
+imspy.utility.utilities.exp_distribution(x, λ=1)¶
+

Exponential function +:type x: +:param x: +:type λ: float +:param λ: +:return:

+
+ +
+
+imspy.utility.utilities.exp_gaussian(x, μ=-3, σ=1, λ=0.25)¶
+

laplacian distribution with exponential decay +:type x: +:param x: +:type μ: float +:param μ: +:type σ: float +:param σ: +:type λ: float +:param λ: +:return:

+
+ +
+
+imspy.utility.utilities.gaussian(x, μ=0, σ=1)¶
+

Gaussian function +:type x: +:param x: +:type μ: float +:param μ: +:type σ: float +:param σ: +:return:

+
+ +
+
+imspy.utility.utilities.get_aa_num_proforma_sequence(sequence)¶
+

get number of amino acids in sequence

+
+
Parameters:
+

sequence (str) – proforma formatted aa sequence

+
+
Returns:
+

Number of amino acids

+
+
Return type:
+

int

+
+
+
+ +
+
+imspy.utility.utilities.is_unimod_end(char)¶
+

Tests if char is end of unimod +bracket

+
+
Parameters:
+

char (str) – Character of a proForma formatted aa sequence

+
+
Returns:
+

Whether char is end of unimod bracket

+
+
Return type:
+

bool

+
+
+
+ +
+
+imspy.utility.utilities.is_unimod_start(char)¶
+

Tests if char is start of unimod +bracket

+
+
Parameters:
+

char (str) – Character of a proForma formatted aa sequence

+
+
Returns:
+

Whether char is start of unimod bracket

+
+
Return type:
+

bool

+
+
+
+ +
+
+imspy.utility.utilities.normal_pdf(x, mass, s=0.001, inv_sqrt_2pi=0.3989422804014327, normalize=False)¶
+
+
Parameters:
+
    +

  • x (Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]])

    • +

  • mass (float)

    • +

  • s (float)

    • +

  • inv_sqrt_2pi (float)

    • +

  • normalize (bool)

    • +
+
+
+
+ +
+
+imspy.utility.utilities.re_index_indices(ids)¶
+

Re-index indices, i.e. replace gaps in indices with consecutive numbers. +Can be used, e.g., to re-index frame IDs from precursors for visualization. +:type ids: +:param ids: Indices.

+
+
Returns:
+

Indices.

+
+
+
+ +
+
+imspy.utility.utilities.tokenize_proforma_sequence(sequence)¶
+

Tokenize a ProForma formatted sequence string.

+
+
Parameters:
+

sequence (str) – Sequence string (ProForma formatted)

+
+
Returns:
+

List of tokens

+
+
Return type:
+

List

+
+
+
+ +
+
+imspy.utility.utilities.tokenizer_from_json(path)¶
+

load a pre-fit tokenizer from a json file +:type path: str +:param path: path to tokenizer as json file +:return: a keras tokenizer loaded from json

+
+ +
+
+imspy.utility.utilities.tokenizer_to_json(tokenizer, path)¶
+

save a fit keras tokenizer to json for later use +:type tokenizer: Tokenizer +:param tokenizer: fit keras tokenizer to save +:type path: str +:param path: path to save json to

+
+ +
+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/imspy.vis.html b/main/imspy/imspy.vis.html new file mode 100644 index 00000000..55bbe762 --- /dev/null +++ b/main/imspy/imspy.vis.html @@ -0,0 +1,128 @@ + + + + + + + + imspy.vis package — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy.vis package¶

+
+

Submodules¶

+
+
+

imspy.vis.pointcloud module¶

+
+
+

Module contents¶

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/index.html b/main/imspy/index.html new file mode 100644 index 00000000..67cdd433 --- /dev/null +++ b/main/imspy/index.html @@ -0,0 +1,133 @@ + + + + + + + + Welcome to imspy’s documentation! — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

Welcome to imspy’s documentation!¶

+

This is the main page of the imspy documentation.

+

Contents:

+
+

Main Modules

+ +
+
+
+

Indices and tables¶

+ +
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/modules.html b/main/imspy/modules.html new file mode 100644 index 00000000..d3ce1844 --- /dev/null +++ b/main/imspy/modules.html @@ -0,0 +1,198 @@ + + + + + + + + imspy Modules — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

imspy Modules¶

+

The imspy package contains the following submodules:

+
+ +
+
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/objects.inv b/main/imspy/objects.inv new file mode 100644 index 0000000000000000000000000000000000000000..4565449182e4f4403c101c2106e62d2234e72e7d GIT binary patch literal 5425 zcmV-170&7-AX9K?X>NERX>N99Zgg*Qc_4OWa&u{KZXhxWBOp+6Z)#;@bUGkuZF6vW z3L_v^WpZ8b#rNMXCQiPX<{x4c-qCC$&%c-5kT+r745LEQ!%!0zRBv5tq99*t7Xp-fg({= zm?jAd09&d$`8WJ~{t{#10`4S`sE(MBMlA9rmP8_v*k~@w>qB|{Q_j*VBM(14Q2pd^ zk$Lu8`|QWxJ^c8CeTuU)<5XVqi7(pU*1kO?iCFwz{l0~9C^<=KA{mFq9UciOpGYZb zNw+3)DNh1?KF^t`hQjOUkMkz zHgpXUbHU<5i*t}iR5K3mnQG#HRsUFSYint7Ia<{dYc*cTb=gR)a=)k_0+OdSrx0~{ z&lXnu)@reJZ*Fu2!_Wt1k3TlgH@Ol6d0~S3=D+!Ft=qXz61H?fz9~^=PCfd=Pk;EM zR=+Ro^b5yjNum_|oGizB#4YyO1l+aqccVGXU=!j)U2aB$Vtv#391UJjJ0w< z3MNhYLW-+o>jZ%I95wfFBoZT$Fis+*T4|zEKHUmxeQ4ikltF7_iX|3A#*r__qED(_ zq6{BNCSu4enJ_{VB3fsWr0B%)IVXkkR!ij0olNY2*4FJyydTZO{d;@A+#t6TlLK=|R$v|u^K+4~FTXY#CU40)tNa(b-v ztDl-c+^oMbNC2$luBS`PTS(~FRQ+}DZR&vUl*o<|D?K`%IFpt*wX`7c%`0b9+Tz!V zj`ek^*fuwj%S95G8wgvS zvc0gnBU4p~B|EDz{$+J4rxD@H-Zj@6EUX==$rDP28bX{1*NS>qsYX-cN^A|1ASJHD zA(G}Rv8s4tw^--x(pAs+nW#~b=6_N6O`Z+_euHlVA!XH@fe^CQe}Ry-(PP;zs_l;B zSgKik^mgST8MbNZs^Zd(Tdl{ecGHak1O<1V0B=;#l+I>fV7sU${@NE5fZr1PTgj_dnJ1z${!`%y3m{mR`G<0eemQ$a_x^#sZ;ygj5>9{)xBMj;u|A>}ZLyUrjOqL(A+5HwR>D|=N{_VUwYH4Vs%NQb4-^$JZNq^NRqVqEtZ zLAiA}hvzE)&>{N!w*=Q7QR^0rBUyBLpg#RH^L^ zs(~fR7#YY}!ED-Tf^w}Lo&|5G8H4n8zC+#3aF*`mH{bR+W~pAN5b9SmbDfe>^+$Tn ztereFv*|OMussEO#&Mz$^y8WQo1aSSgZh8zteqSii;l7S@j-dOxl*Tu<)xmEs|#SH zReO+K9kkkNXDX|FqCEhSn@RJ@g%G=Bm@rpxAuL<5J)}izvIo>#mll*c+fIWJ^&>>n zU{n(c4F)Nf;8!u(7i^_Gan)!O8PqB}#!TZ3P8)=dJ2V(ocYq)o@9+sr?1C?lAa}Z%`zVw(Ia9oA5K?^~%H0hHOcP1T~XZ09kFAkWIN}LO19d3*4-0 zY7wDO;kD>J%QCx(0ZyMfrk>zQk!bZ5JkgOL3DvsM8!Xy#Mc=5r3|E4NHM>qQ>{zVjP7Iq$ zTf;VEkcvMcwC{&ZNxrwdSUK&<3bn>9AIA2!I1u~O(tw@JmJ2jKYapF>s! z)gk>*rgs~FQU0OUYa=K?NUEUf?JOvxc0FS-GoT5|1cMUWs0rLT|wZ_3s|Y-(YndKwWpBU|R1mRaMYr@6dD^ z+1C{)DrC!Wu&+3)ytA)&1&8!??)U!PNRJZGk@Ptn=lebVIaXL>=)nRTL5~&Uf(<}n zy+{w{F4+lY2_&y20YTkhD7^?nC~mwOF_b70>}$7-Y7+cnVo+ypz17jWg(*(G%5{1t zYxm>KNY8lzCtoY-OiO~04)y5=@482S)+@rN4#F6U@Mk+Q9BYiUB2PIDfFg~hBHxv> z+nlQtO;j7~t}};^8M%{0t>SkO`1trPrhX{=8>qV?QIjhRRhR{0}B*fgdpu>Asv?~~%&0=U@jay=_cx3|kO+cNWMXZF+4{t!=peZ=Ss->+_HZh^{JiVg*`$77sr?n zS|ZQJl)vj)@~{FK5_=~FfT-U{LP|u!X$jWljRe3}=?EXSM%&e(dN>}ZcvdFEck@lJ z|E2Eq1GTYbL04i2hONGi3+?^rk--P;_X9dVVu4++vZt z&hQ?!u_5RWAZ2zU0Dvu7fN>??)kjpXfenl~b)duZwU8GgY4UUX>=8@+S6rl-uSc>K zf6$J8k%k50HVaEKChj1G6SLvQPT_sN=Gccf3duJS_MN0`Z1bXoq> z2saHLPX$}*DOAr%1QnjO+6?l7QtQL(AZ6YeCI+GCiy12A$d zIsJYo>Cvk8iIb$_f^kyv27BWh+=v=bT^@+qD&2k$YtSB}Ksx7J1mv4l0)Mh70VBs6a;fMvv^r5~ja?SdVf#%2yD1esT;`Mlmqgi>QisEVV^#?Fs3;3L;J7#6{YFj2x6&=86{^u9YT;?N2#45N`&0@rA$eM z&6e#cHt)Ug&e%xKv}D-KrKVp;CoH0=rz>J44>mWGH|oSv#A#s*X^529?#!IQ;W!bq zJSB5IMezzCAKch;hx`vkuBPXHxkn^Fzd;FTu?Kk!#&PfyqPc+6k7u{#HwQoWkeCUsb~C0pdFa|d&hp3HF?t2S^L#xi$?cj~KyT*^Cv&^1j~*yeCb%sIY>Q`-PJd2KF;2?a`b5Q&a< zcB{ROXOsQeCZ>sfQavDVLLTARlxFpHpt!vsGMs7#q3A?2TLMs2J3$K6dd;0oPP7kY z_{u67qwWgz=+)Mm(35>&U~_6l0;bA3q6Mc~4Gm?Cl`u%{o`5RAO(7X4`idG=m|UNf zL^A;UMfJ6H3~>pTT74oJ(tcTS*5vXkFX};azFZhvc`Wp#?e_^lk%|)CC_sHXIYjk| zY<2#_*-ABg(Ld(^hcIw^pR!6Z&N8&cgWg9~>Qr9gkODfTvS~;9AXvq|5zcmUwL{*_ zZuMR5`j;6$yZNOQ29{k)cee2JY_PG0j+gJ8Q5OjqXGr5>JoO!om+Og(w zMbgad)HXddb4||aET33Pc9-iEWM$2l4{>oBlMgVjGdtStvjwKB_0jrUeg);je0-qYQ9zO_UFwk>YG!&S3EIDs7)Z?ZL;WI|$OF%3X4QaR(^zCS(f0 zO9?!2`7naLJR6IjzdvFxZ)wAl7fbznb|r*2P-HZL0Mc9yRleC(P{Bo~V3j_I0|wS> zZ05e#S`*bn($}w`pE8k5QW97|dpqr!E7%kauz@GA(5j-rU8#FnSC?=a?3dOl2-i ztV3VX0bbND95ChPIuTaMvXW?i2F5q(6ZFHA4m|VXGbuBiOs$B8$iq;I-pZXVwd^2a zM_wPU%a9uN;|vLUBYIn@h*n9;_fq3~yK1hvS38cNW**A)ig87?(+83(1=oSN*8rjzSWB{&&_EMaRiA{{biKEI^G-? 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+
+
+ + +
+ + +

Python Module Index

+ +
+ i +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 
+ i
+ imspy +
    + imspy.algorithm +
    + imspy.algorithm.ccs +
    + imspy.algorithm.ccs.predictors +
    + imspy.algorithm.hashing +
    + imspy.algorithm.intensity +
    + imspy.algorithm.intensity.predictors +
    + imspy.algorithm.intensity.utility +
    + imspy.algorithm.ionization +
    + imspy.algorithm.ionization.predictors +
    + imspy.algorithm.pretrained +
    + imspy.algorithm.rt +
    + imspy.algorithm.rt.predictors +
    + imspy.algorithm.utility +
    + imspy.chemistry +
    + imspy.chemistry.amino_acids +
    + imspy.chemistry.constants +
    + imspy.chemistry.elements +
    + imspy.chemistry.mobility +
    + imspy.chemistry.sum_formula +
    + imspy.chemistry.unimod +
    + imspy.chemistry.utility +
    + imspy.data +
    + imspy.data.peptide +
    + imspy.data.spectrum +
    + imspy.simulation +
    + imspy.simulation.acquisition +
    + imspy.simulation.annotation +
    + imspy.simulation.experiment +
    + imspy.simulation.handle +
    + imspy.simulation.noise +
    + imspy.simulation.proteome +
    + imspy.simulation.tdf +
    + imspy.simulation.timsim +
    + imspy.simulation.timsim.jobs +
    + imspy.simulation.timsim.jobs.add_noise_from_real_data +
    + imspy.simulation.timsim.jobs.assemble_frames +
    + imspy.simulation.timsim.jobs.build_acquisition +
    + imspy.simulation.timsim.jobs.digest_fasta +
    + imspy.simulation.timsim.jobs.simulate_charge_states +
    + imspy.simulation.timsim.jobs.simulate_fragment_intensities +
    + imspy.simulation.timsim.jobs.simulate_frame_distributions +
    + imspy.simulation.timsim.jobs.simulate_frame_distributions_emg +
    + imspy.simulation.timsim.jobs.simulate_ion_mobilities +
    + imspy.simulation.timsim.jobs.simulate_occurrences +
    + imspy.simulation.timsim.jobs.simulate_retention_time +
    + imspy.simulation.timsim.jobs.simulate_scan_distributions +
    + imspy.simulation.timsim.jobs.utility +
    + imspy.simulation.utility +
    + imspy.timstof +
    + imspy.timstof.collision +
    + imspy.timstof.data +
    + imspy.timstof.dbsearch +
    + imspy.timstof.dbsearch.imspy_dda +
    + imspy.timstof.dbsearch.imspy_rescore_sage +
    + imspy.timstof.dbsearch.sage_output_utility +
    + imspy.timstof.dbsearch.utility +
    + imspy.timstof.dda +
    + imspy.timstof.dia +
    + imspy.timstof.frame +
    + imspy.timstof.quadrupole +
    + imspy.timstof.slice +
    + imspy.utility +
    + imspy.utility.sequence +
    + imspy.utility.utilities +
    + imspy.vis +
+ + +
+ +
+
+ +
+
+
+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + +
+ + + + + + \ No newline at end of file diff --git a/main/imspy/search.html b/main/imspy/search.html new file mode 100644 index 00000000..7dd50905 --- /dev/null +++ b/main/imspy/search.html @@ -0,0 +1,122 @@ + + + + + + + Search — imspy 0.2.33 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ ©2024, David Teschner. + + | + Powered by Sphinx 8.1.3 + & Alabaster 1.0.0 + +
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false]], "from_config() (imspy.algorithm.ccs.predictors.squarerootprojectionlayer class method)": [[2, "imspy.algorithm.ccs.predictors.SquareRootProjectionLayer.from_config", false]], "from_config() (imspy.algorithm.ionization.predictors.gruchargestatepredictor class method)": [[4, "imspy.algorithm.ionization.predictors.GRUChargeStatePredictor.from_config", false]], "from_config() (imspy.algorithm.rt.predictors.gruretentiontimepredictor class method)": [[6, "imspy.algorithm.rt.predictors.GRURetentionTimePredictor.from_config", false]], "from_config() (imspy.simulation.acquisition.timstofacquisitionbuilderdia static method)": [[9, "imspy.simulation.acquisition.TimsTofAcquisitionBuilderDIA.from_config", false]], "from_existing() (imspy.simulation.acquisition.timstofacquisitionbuilder class method)": [[9, "imspy.simulation.acquisition.TimsTofAcquisitionBuilder.from_existing", false]], "from_existing() (imspy.simulation.acquisition.timstofacquisitionbuilderdia class method)": [[9, 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false]], "from_py_ptr() (imspy.data.peptide.peptideproductionseriescollection class method)": [[8, "imspy.data.peptide.PeptideProductIonSeriesCollection.from_py_ptr", false]], "from_py_ptr() (imspy.data.peptide.peptidesequence class method)": [[8, "imspy.data.peptide.PeptideSequence.from_py_ptr", false]], "from_py_ptr() (imspy.data.spectrum.indexedmzspectrum class method)": [[8, "imspy.data.spectrum.IndexedMzSpectrum.from_py_ptr", false]], "from_py_ptr() (imspy.data.spectrum.mzspectrum class method)": [[8, "imspy.data.spectrum.MzSpectrum.from_py_ptr", false]], "from_py_ptr() (imspy.data.spectrum.mzspectrumvectorized class method)": [[8, "imspy.data.spectrum.MzSpectrumVectorized.from_py_ptr", false]], "from_py_ptr() (imspy.data.spectrum.timsspectrum class method)": [[8, "imspy.data.spectrum.TimsSpectrum.from_py_ptr", false]], "from_py_ptr() (imspy.simulation.annotation.contributionsource class method)": [[9, "imspy.simulation.annotation.ContributionSource.from_py_ptr", false]], 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false], [11, "module-imspy.simulation.timsim.jobs.simulate_occurrences", false], [11, "module-imspy.simulation.timsim.jobs.simulate_retention_time", false], [11, "module-imspy.simulation.timsim.jobs.simulate_scan_distributions", false], [11, "module-imspy.simulation.timsim.jobs.utility", false], [12, "module-imspy.timstof", false], [12, "module-imspy.timstof.collision", false], [12, "module-imspy.timstof.data", false], [12, "module-imspy.timstof.dda", false], [12, "module-imspy.timstof.dia", false], [12, "module-imspy.timstof.frame", false], [12, "module-imspy.timstof.quadrupole", false], [12, "module-imspy.timstof.slice", false], [13, "module-imspy.timstof.dbsearch", false], [13, "module-imspy.timstof.dbsearch.imspy_dda", false], [13, "module-imspy.timstof.dbsearch.imspy_rescore_sage", false], [13, "module-imspy.timstof.dbsearch.sage_output_utility", false], [13, "module-imspy.timstof.dbsearch.utility", false], [14, "module-imspy.utility", false], [14, "module-imspy.utility.sequence", false], [14, "module-imspy.utility.utilities", false], [15, "module-imspy.vis", false]], "mono_isotopic_mass (imspy.data.peptide.peptideproduction property)": [[8, "imspy.data.peptide.PeptideProductIon.mono_isotopic_mass", false]], "mono_isotopic_mass (imspy.data.peptide.peptidesequence property)": [[8, "imspy.data.peptide.PeptideSequence.mono_isotopic_mass", false]], "monoisotopic_mass (imspy.chemistry.sum_formula.sumformula property)": [[7, "imspy.chemistry.sum_formula.SumFormula.monoisotopic_mass", false]], "ms_type (imspy.data.spectrum.timsspectrum property)": [[8, "imspy.data.spectrum.TimsSpectrum.ms_type", false]], "ms_type (imspy.simulation.annotation.timsframeannotated property)": [[9, "imspy.simulation.annotation.TimsFrameAnnotated.ms_type", false]], "ms_type (imspy.timstof.frame.timsframe property)": [[12, "imspy.timstof.frame.TimsFrame.ms_type", false]], "ms_type (imspy.timstof.frame.timsframevectorized property)": [[12, "imspy.timstof.frame.TimsFrameVectorized.ms_type", false]], "ms_type_as_string (imspy.timstof.frame.timsframe property)": [[12, "imspy.timstof.frame.TimsFrame.ms_type_as_string", false]], "ms_type_numeric (imspy.simulation.annotation.timsframeannotated property)": [[9, "imspy.simulation.annotation.TimsFrameAnnotated.ms_type_numeric", false]], "mu_function_normal_default() (in module imspy.simulation.noise)": [[9, "imspy.simulation.noise.mu_function_normal_default", false]], "mu_function_poisson_default() (in module imspy.simulation.noise)": [[9, "imspy.simulation.noise.mu_function_poisson_default", false]], "mz (imspy.data.peptide.peptideion property)": [[8, "imspy.data.peptide.PeptideIon.mz", false]], "mz (imspy.data.peptide.peptideproduction property)": [[8, "imspy.data.peptide.PeptideProductIon.mz", false]], "mz (imspy.data.spectrum.indexedmzspectrum property)": [[8, "imspy.data.spectrum.IndexedMzSpectrum.mz", false]], "mz (imspy.data.spectrum.mzspectrum property)": [[8, "imspy.data.spectrum.MzSpectrum.mz", false]], "mz (imspy.data.spectrum.timsspectrum property)": [[8, "imspy.data.spectrum.TimsSpectrum.mz", false]], "mz (imspy.simulation.annotation.mzspectrumannotated property)": [[9, "imspy.simulation.annotation.MzSpectrumAnnotated.mz", false]], "mz (imspy.simulation.annotation.timsframeannotated property)": [[9, "imspy.simulation.annotation.TimsFrameAnnotated.mz", false]], "mz (imspy.timstof.frame.timsframe property)": [[12, "imspy.timstof.frame.TimsFrame.mz", false]], "mz_lower (imspy.timstof.data.timsdataset property)": [[12, "imspy.timstof.data.TimsDataset.mz_lower", false]], "mz_mean (imspy.timstof.slice.timsplane property)": [[12, "imspy.timstof.slice.TimsPlane.mz_mean", false]], "mz_spectrum (imspy.data.spectrum.timsspectrum property)": [[8, "imspy.data.spectrum.TimsSpectrum.mz_spectrum", false]], "mz_std (imspy.timstof.slice.timsplane property)": [[12, "imspy.timstof.slice.TimsPlane.mz_std", false]], "mz_to_tof() (imspy.simulation.tdf.tdfwriter method)": [[9, "imspy.simulation.tdf.TDFWriter.mz_to_tof", false]], "mz_to_tof() (imspy.timstof.data.timsdataset method)": [[12, "imspy.timstof.data.TimsDataset.mz_to_tof", false]], "mz_upper (imspy.timstof.data.timsdataset property)": [[12, "imspy.timstof.data.TimsDataset.mz_upper", false]], "mzspectrum (class in imspy.data.spectrum)": [[8, "imspy.data.spectrum.MzSpectrum", false]], "mzspectrumannotated (class in imspy.simulation.annotation)": [[9, "imspy.simulation.annotation.MzSpectrumAnnotated", false]], "mzspectrumvectorized (class in imspy.data.spectrum)": [[8, "imspy.data.spectrum.MzSpectrumVectorized", false]], "n_ions (imspy.data.peptide.peptideproductionseries property)": [[8, "imspy.data.peptide.PeptideProductIonSeries.n_ions", false]], "normal_pdf() (in module imspy.utility.utilities)": [[14, "imspy.utility.utilities.normal_pdf", false]], "normaldistribution (class in imspy.utility.utilities)": [[14, "imspy.utility.utilities.NormalDistribution", false]], "np_zip() (in module 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false]], "peakannotation (class in imspy.simulation.annotation)": [[9, "imspy.simulation.annotation.PeakAnnotation", false]], "peptide_id (imspy.data.peptide.peptideion property)": [[8, "imspy.data.peptide.PeptideIon.peptide_id", false]], "peptide_id (imspy.data.peptide.peptidesequence property)": [[8, "imspy.data.peptide.PeptideSequence.peptide_id", false]], "peptide_id (imspy.simulation.annotation.signalattributes property)": [[9, "imspy.simulation.annotation.SignalAttributes.peptide_id", false]], "peptide_ids_first_only (imspy.simulation.annotation.timsframeannotated property)": [[9, "imspy.simulation.annotation.TimsFrameAnnotated.peptide_ids_first_only", false]], "peptidechargestatedistribution (class in imspy.algorithm.ionization.predictors)": [[4, "imspy.algorithm.ionization.predictors.PeptideChargeStateDistribution", false]], "peptidechromatographyapex (class in imspy.algorithm.rt.predictors)": [[6, "imspy.algorithm.rt.predictors.PeptideChromatographyApex", false]], "peptidedigest (class in imspy.simulation.proteome)": [[9, "imspy.simulation.proteome.PeptideDigest", false]], "peptideion (class in imspy.data.peptide)": [[8, "imspy.data.peptide.PeptideIon", false]], "peptideionmobilityapex (class in imspy.algorithm.ccs.predictors)": [[2, "imspy.algorithm.ccs.predictors.PeptideIonMobilityApex", false]], "peptideproduction (class in imspy.data.peptide)": [[8, "imspy.data.peptide.PeptideProductIon", false]], "peptideproductionseries (class in imspy.data.peptide)": [[8, "imspy.data.peptide.PeptideProductIonSeries", false]], "peptideproductionseriescollection (class in imspy.data.peptide)": [[8, "imspy.data.peptide.PeptideProductIonSeriesCollection", false]], "peptidesequence (class in imspy.data.peptide)": [[8, "imspy.data.peptide.PeptideSequence", false]], "plot_summary() (in module imspy.timstof.dbsearch.sage_output_utility)": [[13, "imspy.timstof.dbsearch.sage_output_utility.plot_summary", false]], "post_process_predicted_fragment_spectra() (in module imspy.algorithm.intensity.utility)": [[3, "imspy.algorithm.intensity.utility.post_process_predicted_fragment_spectra", false]], "precursor_id (imspy.timstof.dda.fragmentdda property)": [[12, "imspy.timstof.dda.FragmentDDA.precursor_id", false]], "precursors (imspy.timstof.slice.timsslice property)": [[12, "imspy.timstof.slice.TimsSlice.precursors", false]], "precursors (imspy.timstof.slice.timsslicevectorized property)": [[12, "imspy.timstof.slice.TimsSliceVectorized.precursors", false]], "predict_intensities() (imspy.algorithm.intensity.predictors.prosit2023timstofwrapper method)": [[3, "imspy.algorithm.intensity.predictors.Prosit2023TimsTofWrapper.predict_intensities", false]], "predict_intensities_prosit() (in module imspy.algorithm.intensity.predictors)": [[3, "imspy.algorithm.intensity.predictors.predict_intensities_prosit", false]], "predict_inverse_ion_mobility() (in module imspy.algorithm.ccs.predictors)": [[2, "imspy.algorithm.ccs.predictors.predict_inverse_ion_mobility", false]], "predict_retention_time() (in module imspy.algorithm.rt.predictors)": [[6, "imspy.algorithm.rt.predictors.predict_retention_time", false]], "prosit2023timstofwrapper (class in imspy.algorithm.intensity.predictors)": [[3, "imspy.algorithm.intensity.predictors.Prosit2023TimsTofWrapper", false]], "python_list_to_json_string() (in module imspy.simulation.utility)": [[9, "imspy.simulation.utility.python_list_to_json_string", false]], "random_subsample_frame() (imspy.timstof.frame.timsframe method)": [[12, "imspy.timstof.frame.TimsFrame.random_subsample_frame", false]], "re_index_indices() (in module imspy.utility.utilities)": [[14, "imspy.utility.utilities.re_index_indices", false]], "re_score_psms() (in module imspy.timstof.dbsearch.sage_output_utility)": [[13, "imspy.timstof.dbsearch.sage_output_utility.re_score_psms", false]], "read_acquisition_config() (in module imspy.simulation.utility)": [[9, "imspy.simulation.utility.read_acquisition_config", false]], "read_compressed_data_full() (imspy.timstof.dia.timsdatasetdia method)": [[12, "imspy.timstof.dia.TimsDatasetDIA.read_compressed_data_full", false]], "remove_substrings() (in module imspy.timstof.dbsearch.sage_output_utility)": [[13, "imspy.timstof.dbsearch.sage_output_utility.remove_substrings", false]], "remove_unimod_annotation() (in module imspy.algorithm.intensity.predictors)": [[3, "imspy.algorithm.intensity.predictors.remove_unimod_annotation", false]], "resolution (imspy.data.spectrum.mzspectrumvectorized property)": [[8, "imspy.data.spectrum.MzSpectrumVectorized.resolution", false]], "retention_time (imspy.data.spectrum.timsspectrum property)": [[8, "imspy.data.spectrum.TimsSpectrum.retention_time", false]], "retention_time (imspy.simulation.annotation.timsframeannotated property)": [[9, "imspy.simulation.annotation.TimsFrameAnnotated.retention_time", false]], "retention_time (imspy.timstof.frame.timsframe property)": [[12, "imspy.timstof.frame.TimsFrame.retention_time", false]], "retention_time (imspy.timstof.frame.timsframevectorized property)": [[12, "imspy.timstof.frame.TimsFrameVectorized.retention_time", false]], "retention_times (imspy.timstof.slice.timsplane property)": [[12, "imspy.timstof.slice.TimsPlane.retention_times", false]], "row_to_fragment() (in module imspy.timstof.dbsearch.sage_output_utility)": [[13, "imspy.timstof.dbsearch.sage_output_utility.row_to_fragment", false]], "rustwrapperobject (class in imspy.simulation.annotation)": [[9, "imspy.simulation.annotation.RustWrapperObject", false]], "sample_fragment_signal() (imspy.timstof.dia.timsdatasetdia method)": [[12, "imspy.timstof.dia.TimsDatasetDIA.sample_fragment_signal", false]], "sample_parameters_rejection() (in module imspy.simulation.timsim.jobs.simulate_frame_distributions_emg)": [[11, "imspy.simulation.timsim.jobs.simulate_frame_distributions_emg.sample_parameters_rejection", false]], "sample_precursor_signal() (imspy.timstof.dia.timsdatasetdia method)": [[12, "imspy.timstof.dia.TimsDatasetDIA.sample_precursor_signal", false]], "sanitize_charge() (in module imspy.timstof.dbsearch.utility)": [[13, "imspy.timstof.dbsearch.utility.sanitize_charge", false]], "sanitize_mz() (in module imspy.timstof.dbsearch.utility)": [[13, "imspy.timstof.dbsearch.utility.sanitize_mz", false]], "scan (imspy.data.spectrum.timsspectrum property)": [[8, "imspy.data.spectrum.TimsSpectrum.scan", false]], "scan (imspy.simulation.annotation.timsframeannotated property)": [[9, "imspy.simulation.annotation.TimsFrameAnnotated.scan", false]], "scan (imspy.timstof.frame.timsframe property)": [[12, "imspy.timstof.frame.TimsFrame.scan", false]], "scan (imspy.timstof.frame.timsframevectorized property)": [[12, "imspy.timstof.frame.TimsFrameVectorized.scan", false]], "scan_to_inv_mobility() (imspy.simulation.tdf.tdfwriter method)": [[9, "imspy.simulation.tdf.TDFWriter.scan_to_inv_mobility", false]], 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"sequence_to_numpy() (in module imspy.simulation.utility)": [[9, "imspy.simulation.utility.sequence_to_numpy", false]], "sequences_to_all_ions() (in module imspy.simulation.utility)": [[9, "imspy.simulation.utility.sequences_to_all_ions", false]], "series (imspy.data.peptide.peptideproductionseriescollection property)": [[8, "imspy.data.peptide.PeptideProductIonSeriesCollection.series", false]], "set_percentage_to_zero() (in module imspy.simulation.utility)": [[9, "imspy.simulation.utility.set_percentage_to_zero", false]], "sigma_function_normal_default() (in module imspy.simulation.noise)": [[9, "imspy.simulation.noise.sigma_function_normal_default", false]], "signal_attributes (imspy.simulation.annotation.contributionsource property)": [[9, "imspy.simulation.annotation.ContributionSource.signal_attributes", false]], "signalattributes (class in imspy.simulation.annotation)": [[9, "imspy.simulation.annotation.SignalAttributes", false]], "simulate_charge_state_distribution_pandas() (imspy.algorithm.ionization.predictors.binomialchargestatedistributionmodel method)": [[4, "imspy.algorithm.ionization.predictors.BinomialChargeStateDistributionModel.simulate_charge_state_distribution_pandas", false]], "simulate_charge_state_distribution_pandas() (imspy.algorithm.ionization.predictors.deepchargestatedistribution method)": [[4, "imspy.algorithm.ionization.predictors.DeepChargeStateDistribution.simulate_charge_state_distribution_pandas", false]], "simulate_charge_state_distribution_pandas() (imspy.algorithm.ionization.predictors.peptidechargestatedistribution method)": [[4, "imspy.algorithm.ionization.predictors.PeptideChargeStateDistribution.simulate_charge_state_distribution_pandas", false]], "simulate_charge_states() (in module imspy.simulation.timsim.jobs.simulate_charge_states)": [[11, "imspy.simulation.timsim.jobs.simulate_charge_states.simulate_charge_states", false]], "simulate_fragment_intensities() (in module 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