+
+ Read the Docs
+ v: ${config.versions.current.slug}
+
+
+
+ ${renderLanguages(config)}
+ ${renderVersions(config)}
+ ${renderDownloads(config)}
+
+ On Read the Docs
+
+ Project Home
+
+
+ Builds
+
+
+ Downloads
+
+
+
+ Search
+
+
+
+
+
+
+ Hosted by Read the Docs
+
+
+
+ `;
+
+ // Inject the generated flyout into the body HTML element.
+ document.body.insertAdjacentHTML("beforeend", flyout);
+
+ // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
+ document
+ .querySelector("#flyout-search-form")
+ .addEventListener("focusin", () => {
+ const event = new CustomEvent("readthedocs-search-show");
+ document.dispatchEvent(event);
+ });
+ })
+}
+
+if (themeLanguageSelector || themeVersionSelector) {
+ function onSelectorSwitch(event) {
+ const option = event.target.selectedIndex;
+ const item = event.target.options[option];
+ window.location.href = item.dataset.url;
+ }
+
+ document.addEventListener("readthedocs-addons-data-ready", function (event) {
+ const config = event.detail.data();
+
+ const versionSwitch = document.querySelector(
+ "div.switch-menus > div.version-switch",
+ );
+ if (themeVersionSelector) {
+ let versions = config.versions.active;
+ if (config.versions.current.hidden || config.versions.current.type === "external") {
+ versions.unshift(config.versions.current);
+ }
+ const versionSelect = `
+
+ ${versions
+ .map(
+ (version) => `
+
+ ${version.slug}
+ `,
+ )
+ .join("\n")}
+
+ `;
+
+ versionSwitch.innerHTML = versionSelect;
+ versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+ }
+
+ const languageSwitch = document.querySelector(
+ "div.switch-menus > div.language-switch",
+ );
+
+ if (themeLanguageSelector) {
+ if (config.projects.translations.length) {
+ // Add the current language to the options on the selector
+ let languages = config.projects.translations.concat(
+ config.projects.current,
+ );
+ languages = languages.sort((a, b) =>
+ a.language.name.localeCompare(b.language.name),
+ );
+
+ const languageSelect = `
+
+ ${languages
+ .map(
+ (language) => `
+
+ ${language.language.name}
+ `,
+ )
+ .join("\n")}
+
+ `;
+
+ languageSwitch.innerHTML = languageSelect;
+ languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
+ }
+ else {
+ languageSwitch.remove();
+ }
+ }
+ });
+}
+
+document.addEventListener("readthedocs-addons-data-ready", function (event) {
+ // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
+ document
+ .querySelector("[role='search'] input")
+ .addEventListener("focusin", () => {
+ const event = new CustomEvent("readthedocs-search-show");
+ document.dispatchEvent(event);
+ });
+});
\ No newline at end of file
diff --git a/docs/_build/html/_static/language_data.js b/docs/_build/html/_static/language_data.js
new file mode 100644
index 0000000..c7fe6c6
--- /dev/null
+++ b/docs/_build/html/_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 a/docs/_build/html/_static/minus.png b/docs/_build/html/_static/minus.png
new file mode 100644
index 0000000..d96755f
Binary files /dev/null and b/docs/_build/html/_static/minus.png differ
diff --git a/docs/_build/html/_static/plus.png b/docs/_build/html/_static/plus.png
new file mode 100644
index 0000000..7107cec
Binary files /dev/null and b/docs/_build/html/_static/plus.png differ
diff --git a/docs/_build/html/_static/pygments.css b/docs/_build/html/_static/pygments.css
new file mode 100644
index 0000000..6f8b210
--- /dev/null
+++ b/docs/_build/html/_static/pygments.css
@@ -0,0 +1,75 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #f8f8f8; }
+.highlight .c { color: #3D7B7B; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #F00 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666 } /* Operator */
+.highlight .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #9C6500 } /* Comment.Preproc */
+.highlight .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #E40000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #008400 } /* Generic.Inserted */
+.highlight .go { color: #717171 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #04D } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #687822 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #00F; font-weight: bold } /* Name.Class */
+.highlight .no { color: #800 } /* Name.Constant */
+.highlight .nd { color: #A2F } /* Name.Decorator */
+.highlight .ni { color: #717171; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #00F } /* Name.Function */
+.highlight .nl { color: #767600 } /* Name.Label */
+.highlight .nn { color: #00F; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #A2F; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #BBB } /* Text.Whitespace */
+.highlight .mb { color: #666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666 } /* Literal.Number.Float */
+.highlight .mh { color: #666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666 } /* Literal.Number.Oct */
+.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #A45A77 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #00F } /* Name.Function.Magic */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .vm { color: #19177C } /* Name.Variable.Magic */
+.highlight .il { color: #666 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/docs/_build/html/_static/searchtools.js b/docs/_build/html/_static/searchtools.js
new file mode 100644
index 0000000..91f4be5
--- /dev/null
+++ b/docs/_build/html/_static/searchtools.js
@@ -0,0 +1,635 @@
+/*
+ * Sphinx JavaScript utilities for the full-text search.
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+ var Scorer = {
+ // Implement the following function to further tweak the score for each result
+ // The function takes a result array [docname, title, anchor, descr, score, filename]
+ // and returns the new score.
+ /*
+ score: result => {
+ const [docname, title, anchor, descr, score, filename, kind] = result
+ return score
+ },
+ */
+
+ // query matches the full name of an object
+ objNameMatch: 11,
+ // or matches in the last dotted part of the object name
+ objPartialMatch: 6,
+ // Additive scores depending on the priority of the object
+ objPrio: {
+ 0: 15, // used to be importantResults
+ 1: 5, // used to be objectResults
+ 2: -5, // used to be unimportantResults
+ },
+ // Used when the priority is not in the mapping.
+ objPrioDefault: 0,
+
+ // query found in title
+ title: 15,
+ partialTitle: 7,
+ // query found in terms
+ term: 5,
+ partialTerm: 2,
+ };
+}
+
+// Global search result kind enum, used by themes to style search results.
+class SearchResultKind {
+ static get index() { return "index"; }
+ static get object() { return "object"; }
+ static get text() { return "text"; }
+ static get title() { return "title"; }
+}
+
+const _removeChildren = (element) => {
+ while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+ string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+ const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+ const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+ const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+ const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+ const contentRoot = document.documentElement.dataset.content_root;
+
+ const [docName, title, anchor, descr, score, _filename, kind] = item;
+
+ let listItem = document.createElement("li");
+ // Add a class representing the item's type:
+ // can be used by a theme's CSS selector for styling
+ // See SearchResultKind for the class names.
+ listItem.classList.add(`kind-${kind}`);
+ let requestUrl;
+ let linkUrl;
+ if (docBuilder === "dirhtml") {
+ // dirhtml builder
+ let dirname = docName + "/";
+ if (dirname.match(/\/index\/$/))
+ dirname = dirname.substring(0, dirname.length - 6);
+ else if (dirname === "index/") dirname = "";
+ requestUrl = contentRoot + dirname;
+ linkUrl = requestUrl;
+ } else {
+ // normal html builders
+ requestUrl = contentRoot + docName + docFileSuffix;
+ linkUrl = docName + docLinkSuffix;
+ }
+ let linkEl = listItem.appendChild(document.createElement("a"));
+ linkEl.href = linkUrl + anchor;
+ linkEl.dataset.score = score;
+ linkEl.innerHTML = title;
+ if (descr) {
+ listItem.appendChild(document.createElement("span")).innerHTML =
+ " (" + descr + ")";
+ // highlight search terms in the description
+ if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
+ highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+ }
+ else if (showSearchSummary)
+ fetch(requestUrl)
+ .then((responseData) => responseData.text())
+ .then((data) => {
+ if (data)
+ listItem.appendChild(
+ Search.makeSearchSummary(data, searchTerms, anchor)
+ );
+ // highlight search terms in the summary
+ if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
+ highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+ });
+ Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+ Search.stopPulse();
+ Search.title.innerText = _("Search Results");
+ if (!resultCount)
+ Search.status.innerText = Documentation.gettext(
+ "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+ );
+ else
+ Search.status.innerText = Documentation.ngettext(
+ "Search finished, found one page matching the search query.",
+ "Search finished, found ${resultCount} pages matching the search query.",
+ resultCount,
+ ).replace('${resultCount}', resultCount);
+};
+const _displayNextItem = (
+ results,
+ resultCount,
+ searchTerms,
+ highlightTerms,
+) => {
+ // results left, load the summary and display it
+ // this is intended to be dynamic (don't sub resultsCount)
+ if (results.length) {
+ _displayItem(results.pop(), searchTerms, highlightTerms);
+ setTimeout(
+ () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+ 5
+ );
+ }
+ // search finished, update title and status message
+ else _finishSearch(resultCount);
+};
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename, kind].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+ const leftScore = a[4];
+ const rightScore = b[4];
+ if (leftScore === rightScore) {
+ // same score: sort alphabetically
+ const leftTitle = a[1].toLowerCase();
+ const rightTitle = b[1].toLowerCase();
+ if (leftTitle === rightTitle) return 0;
+ return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+ }
+ return leftScore > rightScore ? 1 : -1;
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+ var splitQuery = (query) => query
+ .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+ .filter(term => term) // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+ _index: null,
+ _queued_query: null,
+ _pulse_status: -1,
+
+ htmlToText: (htmlString, anchor) => {
+ const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+ for (const removalQuery of [".headerlink", "script", "style"]) {
+ htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+ }
+ if (anchor) {
+ const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+ if (anchorContent) return anchorContent.textContent;
+
+ console.warn(
+ `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+ );
+ }
+
+ // if anchor not specified or not found, fall back to main content
+ const docContent = htmlElement.querySelector('[role="main"]');
+ if (docContent) return docContent.textContent;
+
+ console.warn(
+ "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+ );
+ return "";
+ },
+
+ init: () => {
+ const query = new URLSearchParams(window.location.search).get("q");
+ document
+ .querySelectorAll('input[name="q"]')
+ .forEach((el) => (el.value = query));
+ if (query) Search.performSearch(query);
+ },
+
+ loadIndex: (url) =>
+ (document.body.appendChild(document.createElement("script")).src = url),
+
+ setIndex: (index) => {
+ Search._index = index;
+ if (Search._queued_query !== null) {
+ const query = Search._queued_query;
+ Search._queued_query = null;
+ Search.query(query);
+ }
+ },
+
+ hasIndex: () => Search._index !== null,
+
+ deferQuery: (query) => (Search._queued_query = query),
+
+ stopPulse: () => (Search._pulse_status = -1),
+
+ startPulse: () => {
+ if (Search._pulse_status >= 0) return;
+
+ const pulse = () => {
+ Search._pulse_status = (Search._pulse_status + 1) % 4;
+ Search.dots.innerText = ".".repeat(Search._pulse_status);
+ if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+ };
+ pulse();
+ },
+
+ /**
+ * perform a search for something (or wait until index is loaded)
+ */
+ performSearch: (query) => {
+ // create the required interface elements
+ const searchText = document.createElement("h2");
+ searchText.textContent = _("Searching");
+ const searchSummary = document.createElement("p");
+ searchSummary.classList.add("search-summary");
+ searchSummary.innerText = "";
+ const searchList = document.createElement("ul");
+ searchList.setAttribute("role", "list");
+ searchList.classList.add("search");
+
+ const out = document.getElementById("search-results");
+ Search.title = out.appendChild(searchText);
+ Search.dots = Search.title.appendChild(document.createElement("span"));
+ Search.status = out.appendChild(searchSummary);
+ Search.output = out.appendChild(searchList);
+
+ const searchProgress = document.getElementById("search-progress");
+ // Some themes don't use the search progress node
+ if (searchProgress) {
+ searchProgress.innerText = _("Preparing search...");
+ }
+ Search.startPulse();
+
+ // index already loaded, the browser was quick!
+ if (Search.hasIndex()) Search.query(query);
+ else Search.deferQuery(query);
+ },
+
+ _parseQuery: (query) => {
+ // stem the search terms and add them to the correct list
+ const stemmer = new Stemmer();
+ const searchTerms = new Set();
+ const excludedTerms = new Set();
+ const highlightTerms = new Set();
+ const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+ splitQuery(query.trim()).forEach((queryTerm) => {
+ const queryTermLower = queryTerm.toLowerCase();
+
+ // maybe skip this "word"
+ // stopwords array is from language_data.js
+ if (
+ stopwords.indexOf(queryTermLower) !== -1 ||
+ queryTerm.match(/^\d+$/)
+ )
+ return;
+
+ // stem the word
+ let word = stemmer.stemWord(queryTermLower);
+ // select the correct list
+ if (word[0] === "-") excludedTerms.add(word.substr(1));
+ else {
+ searchTerms.add(word);
+ highlightTerms.add(queryTermLower);
+ }
+ });
+
+ if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js
+ localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+ }
+
+ // console.debug("SEARCH: searching for:");
+ // console.info("required: ", [...searchTerms]);
+ // console.info("excluded: ", [...excludedTerms]);
+
+ return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+ },
+
+ /**
+ * execute search (requires search index to be loaded)
+ */
+ _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const titles = Search._index.titles;
+ const allTitles = Search._index.alltitles;
+ const indexEntries = Search._index.indexentries;
+
+ // Collect multiple result groups to be sorted separately and then ordered.
+ // Each is an array of [docname, title, anchor, descr, score, filename, kind].
+ const normalResults = [];
+ const nonMainIndexResults = [];
+
+ _removeChildren(document.getElementById("search-progress"));
+
+ const queryLower = query.toLowerCase().trim();
+ for (const [title, foundTitles] of Object.entries(allTitles)) {
+ if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+ for (const [file, id] of foundTitles) {
+ const score = Math.round(Scorer.title * queryLower.length / title.length);
+ const boost = titles[file] === title ? 1 : 0; // add a boost for document titles
+ normalResults.push([
+ docNames[file],
+ titles[file] !== title ? `${titles[file]} > ${title}` : title,
+ id !== null ? "#" + id : "",
+ null,
+ score + boost,
+ filenames[file],
+ SearchResultKind.title,
+ ]);
+ }
+ }
+ }
+
+ // search for explicit entries in index directives
+ for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+ if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+ for (const [file, id, isMain] of foundEntries) {
+ const score = Math.round(100 * queryLower.length / entry.length);
+ const result = [
+ docNames[file],
+ titles[file],
+ id ? "#" + id : "",
+ null,
+ score,
+ filenames[file],
+ SearchResultKind.index,
+ ];
+ if (isMain) {
+ normalResults.push(result);
+ } else {
+ nonMainIndexResults.push(result);
+ }
+ }
+ }
+ }
+
+ // lookup as object
+ objectTerms.forEach((term) =>
+ normalResults.push(...Search.performObjectSearch(term, objectTerms))
+ );
+
+ // lookup as search terms in fulltext
+ normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+ // let the scorer override scores with a custom scoring function
+ if (Scorer.score) {
+ normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+ nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+ }
+
+ // Sort each group of results by score and then alphabetically by name.
+ normalResults.sort(_orderResultsByScoreThenName);
+ nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+ // Combine the result groups in (reverse) order.
+ // Non-main index entries are typically arbitrary cross-references,
+ // so display them after other results.
+ let results = [...nonMainIndexResults, ...normalResults];
+
+ // remove duplicate search results
+ // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+ let seen = new Set();
+ results = results.reverse().reduce((acc, result) => {
+ let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+ if (!seen.has(resultStr)) {
+ acc.push(result);
+ seen.add(resultStr);
+ }
+ return acc;
+ }, []);
+
+ return results.reverse();
+ },
+
+ query: (query) => {
+ const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+ const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+
+ // for debugging
+ //Search.lastresults = results.slice(); // a copy
+ // console.info("search results:", Search.lastresults);
+
+ // print the results
+ _displayNextItem(results, results.length, searchTerms, highlightTerms);
+ },
+
+ /**
+ * search for object names
+ */
+ performObjectSearch: (object, objectTerms) => {
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const objects = Search._index.objects;
+ const objNames = Search._index.objnames;
+ const titles = Search._index.titles;
+
+ const results = [];
+
+ const objectSearchCallback = (prefix, match) => {
+ const name = match[4]
+ const fullname = (prefix ? prefix + "." : "") + name;
+ const fullnameLower = fullname.toLowerCase();
+ if (fullnameLower.indexOf(object) < 0) return;
+
+ let score = 0;
+ const parts = fullnameLower.split(".");
+
+ // check for different match types: exact matches of full name or
+ // "last name" (i.e. last dotted part)
+ if (fullnameLower === object || parts.slice(-1)[0] === object)
+ score += Scorer.objNameMatch;
+ else if (parts.slice(-1)[0].indexOf(object) > -1)
+ score += Scorer.objPartialMatch; // matches in last name
+
+ const objName = objNames[match[1]][2];
+ const title = titles[match[0]];
+
+ // If more than one term searched for, we require other words to be
+ // found in the name/title/description
+ const otherTerms = new Set(objectTerms);
+ otherTerms.delete(object);
+ if (otherTerms.size > 0) {
+ const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+ if (
+ [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+ )
+ return;
+ }
+
+ let anchor = match[3];
+ if (anchor === "") anchor = fullname;
+ else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+ const descr = objName + _(", in ") + title;
+
+ // add custom score for some objects according to scorer
+ if (Scorer.objPrio.hasOwnProperty(match[2]))
+ score += Scorer.objPrio[match[2]];
+ else score += Scorer.objPrioDefault;
+
+ results.push([
+ docNames[match[0]],
+ fullname,
+ "#" + anchor,
+ descr,
+ score,
+ filenames[match[0]],
+ SearchResultKind.object,
+ ]);
+ };
+ Object.keys(objects).forEach((prefix) =>
+ objects[prefix].forEach((array) =>
+ objectSearchCallback(prefix, array)
+ )
+ );
+ return results;
+ },
+
+ /**
+ * search for full-text terms in the index
+ */
+ performTermsSearch: (searchTerms, excludedTerms) => {
+ // prepare search
+ const terms = Search._index.terms;
+ const titleTerms = Search._index.titleterms;
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const titles = Search._index.titles;
+
+ const scoreMap = new Map();
+ const fileMap = new Map();
+
+ // perform the search on the required terms
+ searchTerms.forEach((word) => {
+ const files = [];
+ // find documents, if any, containing the query word in their text/title term indices
+ // use Object.hasOwnProperty to avoid mismatching against prototype properties
+ const arr = [
+ { files: terms.hasOwnProperty(word) ? terms[word] : undefined, score: Scorer.term },
+ { files: titleTerms.hasOwnProperty(word) ? titleTerms[word] : undefined, 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, new Map());
+ const fileScores = scoreMap.get(file);
+ fileScores.set(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).get(w)));
+ // add result to the result list
+ results.push([
+ docNames[file],
+ titles[file],
+ "",
+ null,
+ score,
+ filenames[file],
+ SearchResultKind.text,
+ ]);
+ }
+ return results;
+ },
+
+ /**
+ * helper function to return a node containing the
+ * search summary for a given text. keywords is a list
+ * of stemmed words.
+ */
+ makeSearchSummary: (htmlText, keywords, anchor) => {
+ const text = Search.htmlToText(htmlText, anchor);
+ if (text === "") return null;
+
+ const textLower = text.toLowerCase();
+ const actualStartPosition = [...keywords]
+ .map((k) => textLower.indexOf(k.toLowerCase()))
+ .filter((i) => i > -1)
+ .slice(-1)[0];
+ const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+ const top = startWithContext === 0 ? "" : "...";
+ const tail = startWithContext + 240 < text.length ? "..." : "";
+
+ let summary = document.createElement("p");
+ summary.classList.add("context");
+ summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+ return summary;
+ },
+};
+
+_ready(Search.init);
diff --git a/docs/_build/html/_static/sphinx_highlight.js b/docs/_build/html/_static/sphinx_highlight.js
new file mode 100644
index 0000000..8a96c69
--- /dev/null
+++ b/docs/_build/html/_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/docs/_build/html/api.html b/docs/_build/html/api.html
new file mode 100644
index 0000000..2b68075
--- /dev/null
+++ b/docs/_build/html/api.html
@@ -0,0 +1,1056 @@
+
+
+
+
+
+
+
+
+
API Reference — FAST-PT 4.0.0 documentation
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ FAST-PT
+
+
+
+
+
+
+
+
+
+API Reference
+
+FASTPT Class
+
+
+class fastpt. FASTPT ( k nu = None to_do = None param_mat = None low_extrap = None high_extrap = None n_pad = None verbose = False simple = False max_cache_size_mb = 500 dump_cache = True ) [source]
+FASTPT is a numerical algorithm to calculate 1-loop contributions to the matter power spectrum and other integrals of a similar type.
+The method is presented in papers arXiv:1603.04826 and arXiv:1609.05978. Please cite these papers if you are using FASTPT in your research.
+
+Parameters:
+
+k array_like The input k-grid (wavenumbers) in 1/Mpc. Must be logarithmically spaced
+with equal spacing in log(k) and contain an even number of elements.
+nu float, optional Deprecated. Previously used to specify power-law biasing for FFT calculations. Now determined automatically.
+to_do list of str, optional List of requirements for precalculation of matrices. Starting in v4, terms are calculated as needed
+without specifying them here, but pre-computing matrices remains an option and can save time on the
+initial run of functions. ‘All’ or ‘everything’ will initialize all terms.
+param_mat array_like, optional Custom parameter matrix for extensions (advanced usage).
+low_extrap float, optional If provided, extrapolate the power spectrum to lower k values
+down to 10^(low_extrap). Helps with edge effects. Typical value: -5.
+high_extrap float, optional If provided, extrapolate the power spectrum to higher k values
+up to 10^(high_extrap). Helps with edge effects. Typical value: 3.
+Must be greater than low_extrap if both are provided.
+n_pad int, optional Number of zeros to pad the array with on both ends.
+Helps reduce edge effects in FFT calculations. If None, defaults to
+half the length of the input k array.
+verbose bool, optional If True, prints additional information during calculations.
+simple bool, optional If True, uses the older, simplified FASTPT interface. Will be deprecated.
+max_cache_size_mb int, optional Maximum size of the internal cache in megabytes. Default is 500 MB.
+dump_cache bool, optional If True, the cache is cleared when a new power spectrum is provided to a Fast-PT function to avoid building an unnecessarily large cache.
+
+
+Notes
+The input k array must be strictly increasing, equally logarithmically spaced, and
+contain an even number of elements.
+Using extrapolation (low_extrap/high_extrap) and padding (n_pad) is
+recommended to reduce numerical artifacts from the FFT-based algorithm.
+
+
+IA_ct ( P P_window = None C_window = None ) [source]
+Computes intrinsic alignment velocity-shear contributions.
+
+Returns:
+
+tuple – (P_0tE, P_0EtE, P_E2tE, P_tEtE) where:
P_0tE (Density-velocity E-mode correlation )
P_0EtE (E-mode-velocity E-mode correlation )
P_E2tE (Second torquing-velocity E-mode correlation )
P_tEtE (Velocity E-mode auto-correlation )
+
+
+
+
+
+
+IA_der ( P P_window = None C_window = None ) [source]
+Computes k^2 * P(k) derivative term for intrinsic alignment models.
+
+Returns:
+P_der : Derivative term of the power spectrum
+Return type:
+array_like
+
+
+
+
+
+IA_mix ( P P_window = None C_window = None ) [source]
+Computes mixed intrinsic alignment contributions combining tidal
+alignment and tidal torque.
+
+Returns:
+
+tuple – (P_A, P_Btype2, P_DEE, P_DBB) where:
P_A (Mixed tidal alignment/tidal torque term )
P_Btype2 (Second-type B-mode term )
P_DEE (Contribution to the E-mode power spectrum )
P_DBB (Contribution to the B-mode power spectrum )
+
+
+
+
+
+
+IA_ta ( P P_window = None C_window = None ) [source]
+Computes intrinsic alignment tidal alignment contributions.
+
+Returns:
+
+tuple – (P_deltaE1, P_deltaE2, P_0E0E, P_0B0B) where:
P_deltaE1 (First density-E mode correlation )
P_deltaE2 (Second density-E mode correlation )
P_0E0E (E-mode auto-correlation )
P_0B0B (B-mode auto-correlation )
+
+
+
+
+
+
+IA_tt ( P P_window = None C_window = None ) [source]
+Computes intrinsic alignment tidal torque contributions.
+
+Returns:
+
+tuple – (P_E, P_B) where:
P_E (E-mode (curl-free) tidal torque power spectrum )
P_B (B-mode (divergence-free) tidal torque power spectrum )
+
+
+
+
+
+
+IRres ( P L = 0.2 h = 0.67 rsdrag = 135 P_window = None C_window = None ) [source]
+Computes the IR-resummed power spectrum, which includes BAO damping.
+
+Parameters:
+
+L (float , optional ) – IR resummation scale, default is 0.2
h (float , optional ) – Dimensionless Hubble parameter, default is 0.67
rsdrag (float , optional ) – Sound horizon at drag epoch in Mpc, default is 135
+Returns:
+P_IRres : IR-resummed power spectrum with damped BAO features
+Return type:
+array_like
+
+
+
+
+
+OV ( P P_window = None C_window = None ) [source]
+Computes the Ostriker-Vishniac effect power spectrum.
+
+Returns:
+P_OV : Ostriker-Vishniac effect power spectrum
+Return type:
+array_like
+
+
+
+
+
+P_bias ( P P_window = None C_window = None ) [source]
+
+
+
+
+RSD_ABsum_components ( P f P_window = None C_window = None ) [source]
+Computes combined redshift-space distortion terms by powers of μ.
+
+Parameters:
+f (float
+Returns:
+
+tuple – (ABsum_mu2, ABsum_mu4, ABsum_mu6, ABsum_mu8) where:
ABsum_mu2 (Combined term with μ^2 dependence )
ABsum_mu4 (Combined term with μ^4 dependence )
ABsum_mu6 (Combined term with μ^6 dependence )
ABsum_mu8 (Combined term with μ^8 dependence )
+
+
+
+
+
+
+RSD_ABsum_mu ( P f mu_n P_window = None C_window = None ) [source]
+Computes the total redshift-space distortion correction at a given μ.
+
+Parameters:
+
+
+Returns:
+ABsum : The total RSD contribution at the specified μ angle
+Return type:
+array_like
+
+
+
+
+
+RSD_components ( P f P_window = None C_window = None ) [source]
+Computes redshift-space distortion component terms.
+
+Parameters:
+f (float
+Returns:
+
+tuple – (A1, A3, A5, B0, B2, B4, B6, P_Ap1, P_Ap3, P_Ap5) where:
A1, A3, A5 (A-type RSD components with different powers of μ )
B0, B2, B4, B6 (B-type RSD components with different powers of μ )
P_Ap1, P_Ap3, P_Ap5 (Additional RSD A-prime components )
+
+
+
+
+
+
+__init__ ( k nu = None to_do = None param_mat = None low_extrap = None high_extrap = None n_pad = None verbose = False simple = False max_cache_size_mb = 500 dump_cache = True ) [source]
+
+
+
+
+compute_term ( term X operation = None P = None P_window = None C_window = None ) [source]
+Computes a Fast-PT term with caching support.
+
+Parameters:
+
+term (str
X (tuple
operation (callable , optional ) – Function to apply to the result(s) after computation
P (array_like ) – Input power spectrum
P_window (tuple , optional ) – Window parameters for tapering the power spectrum at the endpoints
C_window (float , optional ) – Window parameter for tapering the Fourier coefficients
+Returns:
+The computed Fast-PT term
+Return type:
+array_like
+
+
+
+
+
+gI_ct ( P P_window = None C_window = None ) [source]
+Computes galaxy bias cross intrinsic alignment velocity-shear contributions.
+
+Returns:
+
+tuple – (P_d2tE, P_s2tE) where:
P_d2tE (Second-order density-velocity E-mode correlation )
P_s2tE (Second-order tidal-velocity E-mode correlation )
+
+
+
+
+
+
+gI_ta ( P P_window = None C_window = None ) [source]
+Computes intrinsic alignment 2nd-order density correlations.
+
+Returns:
+
+tuple – (P_d2E, P_d20E, P_s2E, P_s20E) where:
P_d2E (2nd-order density-E-mode correlation )
P_d20E (2nd-order density-density-E-mode correlation )
P_s2E (2nd-order tidal-E-mode correlation )
P_s20E (2nd-order tidal-density-E-mode correlation )
+
+
+
+
+
+
+gI_tt ( P P_window = None C_window = None ) [source]
+Computes intrinsic alignment 2nd-order tidal correlations.
+
+Returns:
+
+tuple – (P_s2E2, P_d2E2) where:
P_s2E2 (2nd-order tidal-E-mode squared correlation )
P_d2E2 (2nd-order density-E-mode squared correlation )
+
+
+
+
+
+
+kPol ( P P_window = None C_window = None ) [source]
+Computes k-dependent polarization power spectra.
+
+Returns:
+
+tuple – (P1, P2, P3) where:
P1 (First k-dependent polarization power spectrum )
P2 (Second k-dependent polarization power spectrum )
P3 (Third k-dependent polarization power spectrum )
+
+
+
+
+
+
+one_loop ( P P_window = None C_window = None ) [source]
+
+
+
+
+one_loop_dd ( P P_window = None C_window = None ) [source]
+Computes the standard 1-loop density-density corrections to the power spectrum.
+
+Returns:
+
+tuple – (P_1loop, Ps) where:
P_1loop (1-loop correction (P_22 + P_13) )
Ps (Smoothed input power spectrum )
+
+
+
+
+
+
+one_loop_dd_bias ( P P_window = None C_window = None ) [source]
+Computes 1-loop corrections with standard bias terms.
+
+Returns:
+
+tuple – (P_1loop, Ps, Pd1d2, Pd2d2, Pd1s2, Pd2s2, Ps2s2, sig4) where:
P_1loop (1-loop correction (P_22 + P_13) )
Ps (Smoothed input power spectrum )
Pd1d2 (First order density-second order density correlation )
Pd2d2 (Second order density auto-correlation )
Pd1s2 (First order density-second order tidal correlation )
Pd2s2 (Second order density-second order tidal correlation )
Ps2s2 (Second order tidal auto-correlation )
sig4 (σ^4 integral for nonlinear bias )
+
+
+
+
+
+
+one_loop_dd_bias_b3nl ( P P_window = None C_window = None ) [source]
+Computes 1-loop corrections with bias terms including third-order non-local bias.
+
+Returns:
+
+tuple – (P_1loop, Ps, Pd1d2, Pd2d2, Pd1s2, Pd2s2, Ps2s2, sig4, sig3nl) where:
The first 8 terms are identical to those returned by one_loop_dd_bias
sig3nl (Third order non-local bias term )
+
+
+
+
+
+
+one_loop_dd_bias_lpt_NL ( P P_window = None C_window = None ) [source]
+Computes bias corrections in Lagrangian Perturbation Theory (LPT).
+
+Returns:
+
+tuple – (Ps, Pb1L, Pb1L_2, Pb1L_b2L, Pb2L, Pb2L_2, sig4) where:
Ps (Smoothed input power spectrum )
Pb1L (First-order Lagrangian bias correlation term )
Pb1L_2 (First-order Lagrangian bias squared correlation )
Pb1L_b2L (First-order and second-order Lagrangian bias cross-correlation )
Pb2L (Second-order Lagrangian bias correlation )
Pb2L_2 (Second-order Lagrangian bias squared correlation )
sig4 (σ^4 integral for nonlinear bias )
+
+
+
+
+
+
+
+
+FPTHandler Class
+
+
+class fastpt. FPTHandler ( fastpt_instance : FASTPT save_all = None save_dir = None ** params ) [source]
+Handler class for FAST-PT that simplifies function calls and provides various convenience features relevant to FAST-PT calculations.
+This class provides a simplified interface for working with FAST-PT functions,
+with features including power spectra generation, saving/loading outputs,
+direct access to specific power spectrum terms, plotting, and more.
+
+Parameters:
+
+fastpt_instance (FASTPT
save_all (str , optional ) – File format to save all results (‘txt’, ‘csv’, or ‘json’). If one is provided, all outputs of the run function will be saved. Default is None.
save_dir (str , optional ) – Directory to save results. Default is ‘outputs’ directory in package location.
**params (dict
+
+Examples
+>>> from fastpt import FASTPT , FPTHandler
+>>> import numpy as np
+>>> k = np . logspace ( - 3 , 1 , 200 )
+>>> fpt = FASTPT ( k )
+>>> handler = FPTHandler ( fpt , C_window = 0.75 )
+>>> P = handler . generate_power_spectra ()
+>>> handler . update_default_params ( P = P )
+>>> P_1loop = handler . get ( 'P_E' ) # Direct access to the P_E term of fpt.IA_tt
+
+
+
+__init__ ( fastpt_instance : FASTPT save_all = None save_dir = None ** params ) [source]
+
+
+
+
+bulk_run ( func_names power_spectra verbose = False ** override_kwargs ) [source]
+Run multiple functions with multiple power spectra.
+This method provides a convenient way to run multiple FAST-PT functions
+with different power spectra and collect all the results.
+
+Parameters:
+
+func_names (list of str
power_spectra (list of array_like ) – List of power spectra to use for each function call
verbose (bool , optional ) – Whether to print progress messages
**override_kwargs (dict
+Returns:
+Results keyed by (function_name, power_spectrum_index)
+Return type:
+dict
+
+Examples
+>>> k = np . logspace ( - 3 , 1 , 200 )
+>>> fpt = FASTPT ( k , low_extrap =- 5 , high_extrap = 3 )
+>>> handler = FPTHandler ( fpt , C_window = 0.75 )
+>>> P1 = handler . generate_power_spectra ( method = 'class' ) # Example power spectrum 1
+>>> P2 = handler . generate_power_spectra ( method = 'camb' ) # Example power spectrum 2
+>>> results = handler . bulk_run ([ 'one_loop_dd' , 'IA_tt' ], [ P1 , P2 ])
+>>> one_loop_P1 = results [( 'one_loop_dd' , 0 )]
+
+
+
+
+
+clear_default_params ( ) [source]
+Clear all default parameters.
+Removes all stored default parameters so they must be specified
+in subsequent function calls.
+Examples
+>>> handler = FPTHandler ( fpt , P = P_linear , C_window = 0.75 )
+>>> handler . clear_default_params ()
+>>> # Now P must be provided in each subsequent function call
+
+
+
+
+
+property fastpt
+Get the underlying FASTPT instance.
+
+Returns:
+The FASTPT instance used by this handler.
+Return type:
+FASTPT
+
+
+
+
+
+generate_power_spectra ( method = 'class' mode = 'single' store = True ** kwargs ) [source]
+Generate power spectra using the specified mode and method.
+
+Parameters:
+
+method (str
mode (str
store (bool , optional ) –
If True, the generated power spectra will be stored in the handler’s default_params. Note: this can only be done for single power spectrum generation.
+
+single: generate one power spectra with the given params, which should be passed as floats or bools
bulk: generate multiple power spectra with the given params, which should be passed as lists or np arrays. If any of the params lists are shorter than the longest, they will be padded with the last value.
diff: generate multiple power spectra with the given params, which should be passed as lists or np arrays. The length of the params lists must be 1 or 3, and a power spectrum will be generated varrying each of the parameters individually while holding the central column of values constant.
+**kwargs – Cosmological parameters to pass to the appropriate method
+Returns:
+Power spectrum or collection of power spectra based on the mode
+Return type:
+numpy.ndarray or dict
+Raises:
+ValueError
+
+Notes
+
+For an example on how to use ‘diff’ mode, see the v4_example.py file on GitHub.
Any non specified parameters will use the default values provided by CAMB or CLASS.
+
+
+
+
+get ( * terms ** override_kwargs ) [source]
+Get specific power spectrum terms directly.
+This method provides direct access to specific power spectrum components
+without needing to know which FAST-PT function calculates them.
+
+Parameters:
+
+
+Returns:
+If a single term is requested, returns that term directly.
+If multiple terms are requested, returns a dictionary mapping
+term names to their values.
+Return type:
+np.array or dict
+Raises:
+ValueError
+
+Examples
+>>> handler = FPTHandler ( fpt , P = P_linear , C_window = 0.75 )
+>>> P_1loop = handler . get ( 'P_1loop' )
+>>> ia_terms = handler . get ( 'P_E' , 'P_B' )
+
+
+
+
+
+get_tracer ( tracer_name ** override_kwargs ) [source]
+Get Fast-PT terms relevant to a specific tracer in CCL.
+
+Parameters:
+
+
+Returns:
+Returns a dictionary of key-value pairs where the key is the term name
+and the value is the calculated term from Fast-PT.
+Return type:
+dict
+Raises:
+ValueError
+
+Notes
+Some terms have different names in FAST-PT than in CCL, see the “aliases†dictionary.
+Examples
+>>> handler = FPTHandler ( fpt , P = P , C_window = 0.75 )
+>>> P_1loop = handler . get_tracer ( 'pgm' )
+>>> #returns Pd1d2, Pd1s2, Pd1p3 (sig3nl)
+
+
+
+
+
+list_available_functions ( ) [source]
+List all callable FAST-PT functions.
+Prints a list of all public functions available in the FAST-PT instance.
+Examples
+>>> handler = FPTHandler ( fpt )
+>>> handler . list_available_functions ()
+['OV', 'IA_ct', 'gI_ct', 'gI_tt', 'IA_der', ...]
+
+
+
+
+
+list_available_terms ( ) [source]
+List all available power spectrum terms that can be requested via get().
+Prints available terms organized by the FAST-PT function that calculates them,
+along with any special parameter requirements.
+Examples
+>>> handler = FPTHandler ( fpt )
+>>> handler . list_available_terms ()
+
+
+
+
+
+load ( file_path load_dir = None ) [source]
+Load a saved output file and return it in the same format as FASTPT outputs (tuple of numpy arrays)
+
+Parameters:
+
+file_path (str
load_dir (str , optional ) – Directory to load file from. If None, uses default output directory
+If file_path already includes a directory, load_dir is ignored.
+Returns:
+A tuple of numpy arrays matching the original FASTPT function output format
+Return type:
+tuple
+
+Examples
+>>> handler = FPTHandler ( fpt )
+>>> result = handler . run ( 'one_loop_dd' , P = P_linear , save_type = 'txt' )
+>>> loaded_data = handler . load ( 'one_loop_dd_output.txt' )
+
+
+
+
+
+static load_instance ( filename load_dir = None ) [source]
+Load parameters and recreate Handler and Fast-PT instances from a saved .npz file.
+Loads both array and non-array parameters from a file created with save_params().
+Arrays are loaded directly, while scalar values are extracted from the metadata.
+
+Parameters:
+
+filename (str
load_dir (str , optional ) – Directory to load parameters from. If None, uses default output directory.
+If filename already contains a directory path, load_dir is ignored.
+Returns:
+Dictionary containing all loaded parameters.
+Return type:
+dict
+
+Notes
+This is a static method and is meant to be used to create an instance of the Handler and Fast-PT.
+Examples
+>>> # This method is static and is meant to be called without an instance
+>>> newHandler = FPTHandler . load_instance ( 'stored_params.npz' )
+>>> # Load from specific directory
+>>> newHandler = FPTHandler . load_instance ( 'custom_params' , load_dir = '/path/to/load' )
+>>> # Now you can use newHandler as equivalent to the original handler that stored the parameters
+>>> newHandler . run ( 'one_loop_dd' )
+
+
+
+
+
+plot ( data = None terms = None k = None ax = None title = None log_scale = True legend_loc = 'best' grid = True style = None colors = None linewidth = 1.5 label_map = None fig_size = (10, 7) save_path = None dpi = 300 xlim = None ylim = None scale_factors = None return_fig = False show = True plot_negatives = True ** override_kwargs ) [source]
+Create a plot of power spectrum terms.
+
+Parameters:
+
+data (dict or array-like , optional ) – Data to plot. Can be:
+- Dictionary mapping labels to arrays (direct plotting)
+- Result from FAST-PT function call (will be plotted directly)
+- None (requires ‘terms’ parameter to get data)
terms (str or list , optional ) – Term name(s) to plot if data is not provided
k (array-like , optional ) – k values for x-axis. If None, uses handler’s FASTPT k values
ax (matplotlib.axes.Axes , optional ) – Axes to plot on. If None, creates a new figure
title (str , optional ) – Plot title
log_scale (bool or tuple , optional ) – Whether to use log scale. If True, both axes are log.
+Can provide (x_log, y_log) tuple to specify each axis
legend_loc (str , optional ) – Location of legend. Default is ‘best’
grid (bool , optional ) – Whether to display grid lines
style (dict or list of dict , optional ) – Plotting style(s) to apply to lines. Each dict can contain matplotlib
+line properties (color, linestyle, marker, alpha, etc.)
colors (str or list , optional ) – Color(s) for plots. If a list, cycles through colors
linewidth (float , optional ) – Line width for plots
label_map (dict , optional ) – Mapping from term names to display labels
fig_size (tuple , optional ) – Figure size (width, height) in inches
save_path (str , optional ) – Path to save figure. If None, figure is not saved
dpi (int , optional ) – DPI for saved figure
xlim (tuple , optional ) – (min, max) for x-axis
ylim (tuple , optional ) – (min, max) for y-axis
scale_factors (dict or float , optional ) – Scaling factor(s) for plotted quantities
return_fig (bool , optional ) – Whether to return the figure object
show (bool , optional ) – Whether to show the plot immediately
plot_negatives (bool , optional ) – Whether to plot negative values with dashed lines for visibility
**override_kwargs (dict
+Returns:
+fig – Figure object if return_fig is True
+Return type:
+matplotlib.figure.Figure , optional
+
+Examples
+>>> handler = FPTHandler ( fpt , P = P_linear , C_window = 0.75 )
+>>> # Basic plot from terms
+>>> handler . plot ( terms = [ 'P_1loop' , 'P_E' , 'P_B' ])
+>>> # Plot with custom styling
+>>> handler . plot ( terms = 'P_1loop' , colors = 'red' , title = '1-loop SPT' )
+>>> # Plot with style dictionaries for fine control
+>>> handler . plot ( terms = [ 'P_1loop' , 'P_E' ], style = [
+... { 'color' : 'red' , 'linestyle' : '--' , 'marker' : 'o' , 'alpha' : 0.8 },
+... { 'color' : 'blue' , 'linestyle' : '-' , 'linewidth' : 2 }
+... ])
+
+
+
+
+
+run ( function_name save_type = None save_dir = None ** override_kwargs ) [source]
+Run a FAST-PT function with validated parameters.
+This method calls the specified FAST-PT function, handles parameter validation,
+and optionally saves the output to a file.
+
+Parameters:
+
+function_name (str
save_type (str , optional ) – File format to save results (‘txt’, ‘csv’, or ‘json’)
save_dir (str , optional ) – Directory to save results. Defaults to the class’s save_dir (self.output_dir).
**override_kwargs (dict
+Returns:
+result – Result from the FAST-PT function call, typically a tuple of numpy arrays
+Return type:
+object
+Raises:
+ValueError
+
+Examples
+>>> handler = FPTHandler ( fpt , P = P , C_window = 0.75 )
+>>> P_1loop_result = handler . run ( 'one_loop_dd' )
+>>> ia_result = handler . run ( 'IA_tt' , save_type = 'csv' )
+
+
+
+
+
+save_instance ( filename output_dir = None ) [source]
+Save parameters of this handler and Fast-PT instance to a compressed numpy .npz file.
+This method saves both numpy arrays and other Python objects (strings, floats, ints, etc.)
+in a single file. Arrays are stored directly, while non-array values are collected
+in a metadata dictionary and stored as a special array.
+
+Parameters:
+
+filename (str
output_dir (str , optional ) – Directory to save parameters. If None, uses default output directory.
+If filename already contains a directory path, output_dir is ignored.
+
+Notes
+Non-array values are stored in a special array with key ‘__metadata__’.
+Arrays and scalars can be freely mixed in the parameters.
+Examples
+>>> handler = FPTHandler ( fpt , P = P_linear , C_window = 0.75 )
+>>> # Save default parameters
+>>> handler . save_instance ( 'stored_params' )
+>>> # Save to specific directory
+>>> handler . save_instance ( 'stored_params' , output_dir = '/path/to/save' )
+
+
+
+
+
+save_output ( result func_name type = 'txt' output_dir = None ) [source]
+Save calculation results to a file.
+
+Parameters:
+
+result (array_like or tuple of array_like ) – Result to save
func_name (str
type (str , optional ) – File format (‘txt’, ‘csv’, or ‘json’). Default is ‘txt’.
output_dir (str , optional ) – Directory to save the file. Default is the handler’s output_dir.
+Raises:
+ValueError
+
+Examples
+>>> handler = FPTHandler ( fpt )
+>>> result = handler . run ( 'one_loop_dd' , P = P_linear )
+>>> handler . save_output ( result , 'one_loop_dd' , type = 'csv' )
+
+
+
+
+
+update_default_params ( ** params ) [source]
+Update the default parameters used for all function calls.
+
+Parameters:
+**params (dict
+Raises:
+ValueError
+
+Examples
+>>> handler = FPTHandler ( fpt )
+>>> handler . update_default_params ( P = P_linear , C_window = 0.75 )
+>>> # Now these parameters will be used by default
+
+
+
+
+
+update_fastpt_instance ( fastpt_instance : FASTPT ) [source]
+Update the FAST-PT instance used by this handler.
+This method replaces the current FASTPT instance.
+
+Parameters:
+fastpt_instance (FASTPT
+Raises:
+ValueError
+
+Examples
+>>> k_new = np . logspace ( - 4 , 2 , 300 ) # Different k range
+>>> fpt_new = FASTPT ( k_new )
+>>> handler . update_fastpt_instance ( fpt_new )
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/_build/html/changelog.html b/docs/_build/html/changelog.html
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+
+
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+
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+
+
+
+
Changelog / Migration Guide — FAST-PT 4.0.0 documentation
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ FAST-PT
+
+
+
+
+
+
+
+
+
+Changelog / Migration Guide
+This document details the changes between the original FAST-PT implementation and the current version.
+For previous versions’ changes, see the complete changelog history
+NOTE: FAST-PT 4.0 is completely backwards compatible with FAST-PT 3.0. However, adjustments to your code may be required to take advantage of the new features and performance improvements.
+
+Major Changes
+
+Caching: FAST-PT now caches individual terms and intermediate calculations to speed up computation.
FPTHandler: The handler class has been introduced to improve the the user’s ability to manage FAST-PT and provide many new convenience features that compliment the FAST-PT class. NOTE: the FPTHandler class is not a replacement for the FAST-PT class, but rather a wrapper that provides additional functionality. It is not necessary for computation.
To_do list: The to_do list is no longer needed to initialize FAST-PT. The terms will now be calculated as needed and stored as a property of the FAST-PT class.
+
+
+Minor Changes
+
+Simple flag: A new “simple†kwarg has been added to FAST-PT which will instead initialize an instance of FAST-PT simple.
Private k: The input k is now “private†after initialization via Python’s name mangling. This means that the user cannot change the value of k after initialization but can still access the value of k.
Gamma functions cache: A separate (and simpler) caching system has been implemented to cache gamma functions and save time on the calculation of the X terms.
Parameter validation: The parameters P, P_window, and C_window are now validated at every function call to ensure that they have the proper traits needed for the calculation. This is done to prevent errors from propagating through the code and causing issues later on.
N_pad default: If no n_pad is provided during initialization, the default value is now set to 0.5 * len(k).
Nu deprecation: The nu parameter is now deprecated as it is no longer needed for initialization. It will default to -2 unless a different nu value is needed in which case it will be calculated internally.
One_loop_dd return: One_loop_dd will only return P_1loop, Ps. Previously returned the dd_bias terms as well however this was contingent on the to_do list which is being deprecated.
Cleft_QR: The Cleft_QR function has been removed due to missing internal functions.
+
+
+
+Description of Caching System
+Caching in Fast-PT is done via a CacheManager object that is initialized with the FAST-PT class. This cache tracks various different “layers†of the calculation of Fast-PT terms. These layers include:
+
+Individual Power Spectra: Fast-PT functions return a tuple of multiple power spectra, each of which is cached individually.
Jk Scalar and Tensor Calculations: Most Fast-PT terms require the calculation of the Jk scalar or tensor functions. Some terms have identical parameters that are passed to these functions, so they are cached individually as well.
Fourier Coefficients: Fourier coefficients of the user provided power spectra are cached and then used in the Jk functions.
Convolutions: This is the convolution of the power spectrum fourier coefficients and the relevant k grid quantities (X terms). The convolution function is called with fourier coefficients and k grid quantities, which are dependent on the user provided k and P and cached individually.
+There is also a separate form of caching used for the k grid quantities (X terms) and the gamma functions used to calculate them. The gamma functions are cached outside the Fast-PT object as they will not be frequently changed, while the k grid quantities are cached as properties of the Fast-PT object and can be accessed via fastpt_instance.X_IA_A for example.
+In total, there are three forms of caching used in Fast-PT 4.0, two of which are stored within the object. The gamma functions cache will persist as long as the program runs.
+This multi-tiered caching system allows Fast-PT to avoid redundant calculations both on individual power spectra terms and the intermediate calculations that are needed to compute them.
+To avoid the cache from growing too large, a “dump_cache†flag is provided in initialization that, when True, will clear the cache when a new power spectra is inputted by the user. The user is also able to specify (during Fast-PT initialization) a maximum cache size in mb. This will evict cached items randomly (in linear time) to avoid slowing down the total computation time. However, the cache size limit is meant as a safeguard and should not be treated as a form of memory management for the program.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/_build/html/examples.html b/docs/_build/html/examples.html
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+
+
+
+
Examples — FAST-PT 4.0.0 documentation
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ FAST-PT
+
+
+
+
+
+
+
+
+
+Examples
+More examples can be found here .
+
+One-loop Matter Power Spectrum
+from fastpt import FASTPT
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load data
+data = np . loadtxt ( 'Pk_test.dat' )
+k = data [:, 0 ]
+P = data [:, 1 ]
+
+# Initialize FASTPT
+fpt = FASTPT ( k , low_extrap =- 5 , high_extrap = 3 , n_pad = int ( 0.5 * len ( k )))
+
+# Calculate corrections
+P_1loop , P_components = fpt . one_loop_dd ( P , C_window = 0.75 )
+
+# Plot
+plt . figure ( figsize = ( 10 , 7 ))
+plt . loglog ( k , P , label = 'Linear P(k)' )
+plt . loglog ( k , P_1loop , label = '1-loop P(k)' )
+plt . xlabel ( 'k [h/Mpc]' )
+plt . ylabel ( 'P(k) [(Mpc/h)³]' )
+plt . legend ()
+plt . tight_layout ()
+plt . show ()
+
+
+
+Using the FPTHandler
+import numpy as np
+from fastpt import FASTPT , FPTHandler
+
+# Initialize with default parameters
+k_values = np . logspace ( - 3 , 1 , 1000 )
+
+fastpt_instance = FASTPT ( k_values )
+handler = FPTHandler ( fastpt_instance , P_window = np . array ([ 0.2 , 0.2 ]), C_window = 0.75 )
+
+# Generate and store a power spectrum
+P = handler . generate_power_spectra ()
+handler . update_default_params ( P = P )
+
+# Get the 1-loop power spectrum, using the default parameters
+result = handler . get ( "P_1loop" )
+
+#Plot the results
+handler . plot ( data = result )
+
+# Save the results and your parameters
+handler . save_output ( result , "one_loop_dd" )
+handler . save_params ( "params.npz" )
+
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diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html
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Index — FAST-PT 4.0.0 documentation
+
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+ FAST-PT
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+
Index
+
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+
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+ |
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+ |
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+ |
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+
FAST-PT Documentation — FAST-PT 4.0.0 documentation
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+ FAST-PT
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+FAST-PT Documentation
+FAST-PT is a code to calculate quantities in cosmological perturbation theory at 1-loop (including, e.g., corrections to the matter power spectrum). The code utilizes Fourier methods combined with analytic expressions to reduce the computation time to scale as N log N, where N is the number of grid points in the input linear power spectrum.
+FASTPT is a numerical algorithm to calculate 1-loop contributions to the matter power spectrum and other integrals of a similar type. The method is presented in papers arXiv:1603.04826 and arXiv:1609.05978. Please cite these papers if you are using FASTPT in your research.
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Installation — FAST-PT 4.0.0 documentation
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+ FAST-PT
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+
+Installation
+
+Dependencies
+FAST-PT requires the following Python packages:
+
+numpy >= 1.17
scipy >= 1.2
matplotlib >= 3.0
+Optional dependencies for generating power spectra:
+
+These optional dependencies are required to use the power spectrum generation features but are not needed for the core FAST-PT functionality. Required dependencies will be installed automatically when you install FAST-PT.
+
+
+Installation Methods
+FAST-PT can be installed using pip:
+
+Or with conda:
+
+For developer installation:
+ clone https://github.com/jablazek/FAST-PT.git
+cd FAST-PT
+pip install -e .
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diff --git a/docs/_build/html/objects.inv b/docs/_build/html/objects.inv
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+# Sphinx inventory version 2
+# Project: FAST-PT
+# Version:
+# The remainder of this file is compressed using zlib.
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‚ý&_?f}+ìie$~—tJ8¯Å
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-?`竾ý'¶Ã
+¬ïž°]æ¯ú:[ ±øD^“t`â/]‡YlÛ÷âZ̶Šû9-aâ· í¡[[\‰äúø» 5;qÿP^/‰
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+
Quick Start Guide — FAST-PT 4.0.0 documentation
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+ FAST-PT
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+
+Quick Start Guide
+Using FAST-PT is straightforward. Here’s a simple example to get started:
+import numpy as np
+import matplotlib.pyplot as plt
+from fastpt import FASTPT , FPTHandler
+
+#Define a k range
+k = np . logspace ( 1e-4 , 1 , 1000 )
+
+# Initialize FASTPT
+fpt = FASTPT ( k , low_extrap =- 5 , high_extrap = 3 , n_pad = int ( 0.5 * len ( k )))
+handler = FPTHandler ( fpt )
+
+# Use the handler to generate a power spectrum
+P = handler . generate_power_spectra ()
+
+# Calculate an individual term using the handler
+P_1loop = handler . get ( "P_1loop" , P = P )
+
+# Store default parameters
+handler . update_default_params ( P = P , P_window = np . array ([ 0.2 , 0.2 ]), C_window = 0.75 )
+
+# Use the stored parameters in a calculation
+tt_result = handler . run ( "IA_tt" )
+
+# Or get the result directly
+tt_direct = fpt . IA_tt ( P = P , P_window = np . array ([ 0.2 , 0.2 ]), C_window = 0.75 )
+
+# Plot the results
+handler . plot ( data = tt_result )
+
+
NOTE: This code uses the FPTHandler class to simplify the process of generating power spectra and calculating specific terms. However, the handler is not needed for any FAST-PT calculations. It simply provides a convenient interface for common tasks and integration with relevant libraries like matplotlib , CAMB , and CLASS .
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+
Search — FAST-PT 4.0.0 documentation
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+ FAST-PT
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+ Please activate JavaScript to enable the search functionality.
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+
Theory — FAST-PT 4.0.0 documentation
+
+
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+
+
+
+
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+
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+
+
+
+
+ FAST-PT
+
+
+
+
+
+
+
+
+
+Theory
+
+FAST-PT Algorithm
+FAST-PT is a numerical algorithm to calculate 1-loop contributions to the matter power spectrum and other integrals of a similar type. The method is presented in papers arXiv:1603.04826 and arXiv:1609.05978 .
+The core of the FAST-PT algorithm is to compute integrals of the form:
+
+\[\int \frac{d^3q}{(2 \pi)^3} K(q,k-q) P(q) P(|k-q|)\]
+by using Fourier transforms to reduce computational complexity from \(O(N^2)\) to \(O(N\\log N)\) , where \(N\) is the number of input points.
+For intrinsic alignment calculations, see arXiv:1708.09247 .
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+ right: 0;
+ bottom: 0;
+ background-image: radial-gradient(circle, rgba(255,255,255,0.1) 1px, transparent 1px),
+ radial-gradient(circle, rgba(255,255,255,0.07) 2px, transparent 2px),
+ radial-gradient(circle, rgba(255,255,255,0.05) 3px, transparent 3px);
+ background-size: 100px 100px, 200px 200px, 300px 300px;
+ pointer-events: none;
+}
+
+/* Modify the sidebar style */
+.wy-side-nav-search, .wy-nav-top {
+ background: linear-gradient(135deg, var(--cosmology-dark) 0%, var(--cosmology-deep) 100%);
+ border-bottom: 1px solid rgba(92, 103, 222, 0.3);
+}
+
+/* Adjust navigation top for mobile */
+.wy-nav-top {
+ width: 100%;
+ position: fixed;
+ top: 0;
+ z-index: 100;
+}
+
+/* Search box cosmic styling */
+.wy-side-nav-search input[type="text"] {
+ border: 1px solid var(--cosmology-accent);
+ background: rgba(255, 255, 255, 0.05);
+ box-shadow: 0 0 5px rgba(92, 103, 222, 0.5);
+ color: var(--cosmology-light);
+ border-radius: 20px;
+ transition: all 0.3s ease;
+}
+
+.wy-side-nav-search input[type="text"]:focus {
+ background: rgba(255, 255, 255, 0.1);
+ box-shadow: 0 0 10px rgba(92, 103, 222, 0.8);
+}
+
+/* Menu styling */
+.wy-menu-vertical li.current {
+ background: rgba(92, 103, 222, 0.1);
+ border-left: 2px solid var(--cosmology-accent);
+}
+
+.wy-menu-vertical header, .wy-menu-vertical p.caption {
+ color: var(--cosmology-gold);
+ letter-spacing: 1px;
+ font-weight: bold;
+ text-transform: uppercase;
+ font-size: 0.9em;
+}
+
+/* Style for headers with cosmic gradient effect */
+h1 {
+ background: linear-gradient(90deg, var(--cosmology-accent), var(--cosmology-purple));
+ -webkit-background-clip: text;
+ -webkit-text-fill-color: transparent;
+ background-clip: text;
+ display: inline-block;
+ padding-bottom: 5px;
+ border-bottom: 2px solid var(--cosmology-accent);
+}
+
+h2, h3 {
+ color: var(--cosmology-gold);
+}
+
+/* Custom styling for code blocks - make them look like terminals */
+div[class^="highlight"] {
+ background: rgba(7, 11, 36, 0.8) !important;
+ border-left: 3px solid var(--cosmology-accent);
+ border-radius: 5px;
+ box-shadow: 0 0 15px rgba(92, 103, 222, 0.15);
+ margin: 15px 0;
+ width: 100%;
+}
+
+div[class^="highlight"] pre {
+ color: var(--cosmology-light);
+}
+
+/* Python syntax highlighting customization */
+.highlight .kn, .highlight .k, .highlight .ow { color: var(--cosmology-purple); } /* import, keywords */
+.highlight .s1, .highlight .s2, .highlight .sa { color: var(--cosmology-green); } /* strings */
+.highlight .mi, .highlight .mf { color: var(--cosmology-bright); } /* numbers */
+.highlight .n { color: var(--cosmology-light); } /* variables */
+.highlight .o { color: var(--cosmology-gold); } /* operators */
+.highlight .c1, .highlight .cm { color: rgba(241, 250, 238, 0.5); } /* comments */
+
+/* Custom link colors with hover effect */
+a {
+ color: var(--cosmology-accent);
+ transition: all 0.2s ease;
+ text-decoration: none;
+ border-bottom: 1px dotted transparent;
+}
+
+a:hover {
+ color: var(--cosmology-bright);
+ border-bottom: 1px dotted var(--cosmology-bright);
+ text-shadow: 0 0 5px rgba(230, 57, 70, 0.3);
+}
+
+/* Parameter tables with cosmic styling */
+table.docutils {
+ border: 1px solid rgba(92, 103, 222, 0.3);
+ box-shadow: 0 0 15px rgba(92, 103, 222, 0.1);
+ border-radius: 5px;
+}
+
+table.docutils thead th {
+ background: linear-gradient(90deg, var(--cosmology-deep), var(--cosmology-dark));
+ color: var(--cosmology-gold);
+}
+
+table.docutils tr:nth-child(odd) {
+ background: rgba(12, 20, 69, 0.3);
+}
+
+/* Function signatures cosmic effect */
+dl.py.method, dl.py.class, dl.py.function {
+ background: rgba(12, 20, 69, 0.3);
+ border-left: 3px solid var(--cosmology-accent);
+ border-radius: 5px;
+ padding: 10px;
+ margin: 15px 0;
+ box-shadow: 0 0 10px rgba(92, 103, 222, 0.1);
+}
+
+dt.sig.sig-object.py {
+ font-family: monospace;
+ font-weight: bold;
+ color: var(--cosmology-light);
+ text-shadow: 0 0 5px rgba(92, 103, 222, 0.5);
+}
+
+/* Custom scrollbar */
+::-webkit-scrollbar {
+ width: 8px;
+ height: 8px;
+}
+
+::-webkit-scrollbar-track {
+ background: var(--cosmology-deep);
+}
+
+::-webkit-scrollbar-thumb {
+ background: var(--cosmology-accent);
+ border-radius: 4px;
+}
+
+::-webkit-scrollbar-thumb:hover {
+ background: var(--cosmology-bright);
+}
+
+/* Navigation buttons cosmic style */
+.btn {
+ background: linear-gradient(135deg, var(--cosmology-dark) 0%, var(--cosmology-accent) 100%);
+ border: none;
+ color: var(--cosmology-light);
+ border-radius: 20px;
+ box-shadow: 0 0 10px rgba(92, 103, 222, 0.3);
+ transition: all 0.3s ease;
+}
+
+.btn:hover {
+ box-shadow: 0 0 15px rgba(92, 103, 222, 0.7);
+ transform: translateY(-1px);
+}
+
+/* Footer cosmic styling */
+footer {
+ background: linear-gradient(to right, var(--cosmology-deep), var(--cosmology-dark));
+ border-top: 1px solid rgba(92, 103, 222, 0.3);
+ color: rgba(241, 250, 238, 0.7);
+}
+
+/* Version selector styling */
+.rst-versions {
+ background: var(--cosmology-deep);
+}
+
+.rst-versions .rst-current-version {
+ background: var(--cosmology-dark);
+ color: var(--cosmology-light);
+}
+
+/* Source links with cosmic styling */
+.viewcode-link, .viewcode-back {
+ color: var(--cosmology-green);
+}
+
+/* Improve admonitions/notes styling */
+.admonition {
+ background: rgba(12, 20, 69, 0.3);
+ border-left: 3px solid var(--cosmology-accent);
+ border-radius: 5px;
+}
+
+.admonition-title {
+ background: rgba(92, 103, 222, 0.2);
+ color: var(--cosmology-gold);
+}
+
+/* Source code view link */
+.wy-breadcrumbs .wy-breadcrumbs-aside a {
+ background: linear-gradient(135deg, var(--cosmology-accent) 0%, var(--cosmology-purple) 100%);
+ -webkit-background-clip: text;
+ -webkit-text-fill-color: transparent;
+ background-clip: text;
+ font-weight: bold;
+}
+
+/* Fix for scrolling and layout issues */
+html {
+ scroll-padding-top: 72px; /* Helps with anchor links */
+}
+
+/* Make sure the main container has the right positioning */
+.wy-grid-for-nav {
+ position: relative;
+ width: 100%;
+ height: 100%;
+ display: block; /* Change from flex to block to fix layout issues */
+}
+
+/* Fix main section width */
+.section {
+ width: 100%;
+ box-sizing: border-box;
+}
+
+/* Make document container take full width */
+.document {
+ width: 100%;
+ padding: 0;
+}
+
+/* Fix image sizing */
+img {
+ max-width: 100%;
+ height: auto;
+}
\ No newline at end of file
diff --git a/docs/api.rst b/docs/api.rst
new file mode 100644
index 0000000..0196f7f
--- /dev/null
+++ b/docs/api.rst
@@ -0,0 +1,16 @@
+.. _api:
+
+API Reference
+=============
+
+FASTPT Class
+------------
+
+.. autoclass:: fastpt.FASTPT
+ :members:
+
+FPTHandler Class
+----------------
+
+.. autoclass:: fastpt.FPTHandler
+ :members:
\ No newline at end of file
diff --git a/docs/changelog.rst b/docs/changelog.rst
new file mode 100644
index 0000000..c39cbb0
--- /dev/null
+++ b/docs/changelog.rst
@@ -0,0 +1,64 @@
+.. _changelog:
+
+Changelog / Migration Guide
+===========================
+
+This document details the changes between the original FAST-PT implementation and the current version.
+
+For previous versions' changes, see the :download:`complete changelog history `.
+
+NOTE: FAST-PT 4.0 is completely backwards compatible with FAST-PT 3.0. However, adjustments to your code may be required to take advantage of the new features and performance improvements.
+
+Major Changes
+-------------
+
+* Caching: FAST-PT now caches individual terms and intermediate calculations to speed up computation.
+* FPTHandler: The handler class has been introduced to improve the the user's ability to manage FAST-PT and provide many new convenience features that compliment the FAST-PT class. NOTE: the FPTHandler class is not a replacement for the FAST-PT class, but rather a wrapper that provides additional functionality. It is not necessary for computation.
+* To_do list: The to_do list is no longer needed to initialize FAST-PT. The terms will now be calculated as needed and stored as a property of the FAST-PT class.
+
+
+Minor Changes
+-------------
+
+* Simple flag: A new "simple" kwarg has been added to FAST-PT which will instead initialize an instance of FAST-PT simple.
+* Private k: The input k is now "private" after initialization via Python's name mangling. This means that the user cannot change the value of k after initialization but can still access the value of k.
+* Gamma functions cache: A separate (and simpler) caching system has been implemented to cache gamma functions and save time on the calculation of the X terms.
+* Parameter validation: The parameters P, P_window, and C_window are now validated at every function call to ensure that they have the proper traits needed for the calculation. This is done to prevent errors from propagating through the code and causing issues later on.
+* N_pad default: If no n_pad is provided during initialization, the default value is now set to 0.5 * len(k).
+* Nu deprecation: The nu parameter is now deprecated as it is no longer needed for initialization. It will default to -2 unless a different nu value is needed in which case it will be calculated internally.
+* One_loop_dd return: One_loop_dd will only return P_1loop, Ps. Previously returned the dd_bias terms as well however this was contingent on the to_do list which is being deprecated.
+* Cleft_QR: The Cleft_QR function has been removed due to missing internal functions.
+
+
+Performance Improvements
+------------------------
+
+The improvement in performance of FAST-PT is going to varry largely with your use case. However, about half of the calculation done for most terms was redundant granting a two times speedup do to the new caching system.
+FAST-PT also now calculates terms in a modular format. This means that the user can now choose to calculate only the terms they need, rather than all of the terms grouped into one FAST-PT function.
+This is done by using the FPTHandler class and the get method, or by calling compute_term with the necessary parameters for each term.
+This will greatly improve the performance of each FAST-PT function if your use case only requires a select few terms.
+
+
+Description of Caching System
+-----------------------------
+
+Caching in Fast-PT is done via a CacheManager_ object that is initialized with the FAST-PT class. This cache tracks various different "layers" of the calculation of Fast-PT terms. These layers include:
+
+* Individual Power Spectra: Fast-PT functions return a tuple of multiple power spectra, each of which is cached individually.
+* Jk Scalar and Tensor Calculations: Most Fast-PT terms require the calculation of the Jk scalar or tensor functions. Some terms have identical parameters that are passed to these functions, so they are cached individually as well.
+* Fourier Coefficients: Fourier coefficients of the user provided power spectra are cached and then used in the Jk functions.
+* Convolutions: This is the convolution of the power spectrum fourier coefficients and the relevant k grid quantities (X terms). The convolution function is called with fourier coefficients and k grid quantities, which are dependent on the user provided k and P and cached individually.
+
+There is also a separate form of caching used for the k grid quantities (X terms) and the gamma functions used to calculate them. The gamma functions are cached outside the Fast-PT object as they will not be frequently changed, while the k grid quantities are cached as properties of the Fast-PT object and can be accessed via `fastpt_instance.X_IA_A` for example.
+In total, there are three forms of caching used in Fast-PT 4.0, two of which are stored within the object. The gamma functions cache will persist as long as the program runs.
+
+This multi-tiered caching system allows Fast-PT to avoid redundant calculations both on individual power spectra terms and the intermediate calculations that are needed to compute them.
+To avoid the cache from growing too large, a "dump_cache" flag is provided in initialization that, when True, will clear the cache when a new power spectra is inputted by the user. The user is also able to specify (during Fast-PT initialization) a maximum cache size in mb. This will evict cached items randomly (in linear time) to avoid slowing down the total computation time. However, the cache size limit is meant as a safeguard and should not be treated as a form of memory management for the program.
+
+.. _CacheManager: https://github.com/jablazek/FAST-PT/tree/master/fastpt/core/CacheManager.py
+
+
+Additional Notes
+------------------
+
+* The 1loop term (one_loop_dd[0]) currently does not pass np.allclose due to numerical noise when compared with FAST-PT 3 when a C_window of 0.63 or less is provided. This only occurs at the very low k end and is negligible. This issue is currently being investigated and will be fixed in a future release.
\ No newline at end of file
diff --git a/docs/conf.py b/docs/conf.py
new file mode 100644
index 0000000..b1a2172
--- /dev/null
+++ b/docs/conf.py
@@ -0,0 +1,64 @@
+# docs/conf.py
+import os
+import sys
+import datetime
+
+# Add the project root directory to the Python path so Sphinx can find the package
+sys.path.insert(0, os.path.abspath('..'))
+
+# Project information
+project = 'FAST-PT'
+copyright = f'2016-{datetime.datetime.now().year}, FAST-PT developers'
+author = 'Joseph E. McEwen, Xiao Fang, Jonathan Blazek'
+
+# The full version, including alpha/beta/rc tags
+from fastpt.info import __version__
+release = __version__
+
+# Add any Sphinx extension module names here, as strings
+extensions = [
+ 'sphinx.ext.autodoc',
+ 'sphinx.ext.napoleon',
+ 'sphinx.ext.viewcode',
+ 'sphinx.ext.mathjax',
+ 'sphinx.ext.intersphinx',
+ 'sphinx.ext.autosummary',
+ 'numpydoc',
+]
+
+# Add any paths that contain templates here, relative to this directory
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+# The theme to use for HTML and HTML Help pages
+html_theme = 'sphinx_rtd_theme'
+
+# Add any paths that contain custom static files (such as style sheets)
+html_static_path = ['_static']
+# html_css_files = ['custom.css']
+
+# Configure autodoc to skip special methods
+autodoc_default_options = {
+ 'members': True,
+ 'undoc-members': False,
+ 'private-members': False, # Don't include _private members
+ 'special-members': '__init__', # Only include __init__ special method
+ 'exclude-members': 'J_k_tensor,J_k_scalar,X_IA_0B0B,X_IA_0E0E,X_IA_A,X_IA_B,X_IA_DBB,X_IA_DEE,X_IA_E,X_IA_deltaE1,' + # List specific methods to exclude
+ 'X_IA_gb2_F2,X_IA_gb2_G2,X_IA_gb2_S2F2,X_IA_gb2_S2G2,X_IA_gb2_S2fe,X_IA_gb2_S2he,X_IA_gb2_fe,X_IA_gb2_he,X_IA_tij_F2F2,' +
+ 'X_IA_tij_F2G2,X_IA_tij_F2G2reg,X_IA_tij_G2G2,X_IA_tij_feG2,X_IA_tij_heG2,X_OV,X_RSDA,X_RSDB,X_cleft,X_kP1,X_kP2,X_kP3,X_lpt,X_spt,X_sptG,' +
+ 'k_extrap,k_final,k_original'
+}
+
+# Configure numpydoc
+numpydoc_show_class_members = False
+
+# Configure intersphinx mapping for linking to external documentation
+intersphinx_mapping = {
+ 'python': ('https://docs.python.org/3', None),
+ 'numpy': ('https://numpy.org/doc/stable', None),
+ 'scipy': ('https://docs.scipy.org/doc/scipy', None),
+ 'matplotlib': ('https://matplotlib.org/stable', None),
+}
\ No newline at end of file
diff --git a/docs/examples.rst b/docs/examples.rst
new file mode 100644
index 0000000..5eed469
--- /dev/null
+++ b/docs/examples.rst
@@ -0,0 +1,64 @@
+.. _examples:
+
+Examples
+=======
+
+More examples can be found `here
`_.
+
+One-loop Matter Power Spectrum
+----------------------------
+
+.. code-block:: python
+
+ from fastpt import FASTPT
+ import numpy as np
+ import matplotlib.pyplot as plt
+
+ # Load data
+ data = np.loadtxt('Pk_test.dat')
+ k = data[:, 0]
+ P = data[:, 1]
+
+ # Initialize FASTPT
+ fpt = FASTPT(k, low_extrap=-5, high_extrap=3, n_pad=int(0.5*len(k)))
+
+ # Calculate corrections
+ P_1loop, P_components = fpt.one_loop_dd(P, C_window=0.75)
+
+ # Plot
+ plt.figure(figsize=(10, 7))
+ plt.loglog(k, P, label='Linear P(k)')
+ plt.loglog(k, P_1loop, label='1-loop P(k)')
+ plt.xlabel('k [h/Mpc]')
+ plt.ylabel('P(k) [(Mpc/h)³]')
+ plt.legend()
+ plt.tight_layout()
+ plt.show()
+
+Using the FPTHandler
+-----------------
+
+.. code-block:: python
+
+ import numpy as np
+ from fastpt import FASTPT, FPTHandler
+
+ # Initialize with default parameters
+ k_values = np.logspace(-3, 1, 1000)
+
+ fastpt_instance = FASTPT(k_values)
+ handler = FPTHandler(fastpt_instance, P_window=np.array([0.2, 0.2]), C_window=0.75)
+
+ # Generate and store a power spectrum
+ P = handler.generate_power_spectra()
+ handler.update_default_params(P=P)
+
+ # Get the 1-loop power spectrum, using the default parameters
+ result = handler.get("P_1loop")
+
+ #Plot the results
+ handler.plot(data=result)
+
+ # Save the results and your parameters
+ handler.save_output(result, "one_loop_dd")
+ handler.save_params("params.npz")
\ No newline at end of file
diff --git a/docs/index.rst b/docs/index.rst
new file mode 100644
index 0000000..c579f3d
--- /dev/null
+++ b/docs/index.rst
@@ -0,0 +1,26 @@
+.. FAST-PT documentation master file
+
+FAST-PT Documentation
+====================
+
+FAST-PT is a code to calculate quantities in cosmological perturbation theory at 1-loop (including, e.g., corrections to the matter power spectrum). The code utilizes Fourier methods combined with analytic expressions to reduce the computation time to scale as N log N, where N is the number of grid points in the input linear power spectrum.
+
+FASTPT is a numerical algorithm to calculate 1-loop contributions to the matter power spectrum and other integrals of a similar type. The method is presented in papers arXiv:1603.04826 and arXiv:1609.05978. Please cite these papers if you are using FASTPT in your research.
+
+.. toctree::
+ :maxdepth: 2
+ :caption: Contents:
+
+ installation
+ quickstart
+ api
+ examples
+ theory
+ changelog
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
\ No newline at end of file
diff --git a/docs/installation.rst b/docs/installation.rst
new file mode 100644
index 0000000..e298cfe
--- /dev/null
+++ b/docs/installation.rst
@@ -0,0 +1,43 @@
+.. _installation:
+
+Installation
+===========
+
+Dependencies
+-----------
+
+FAST-PT requires the following Python packages:
+
+* numpy >= 1.17
+* scipy >= 1.2
+* matplotlib >= 3.0
+
+Optional dependencies for generating power spectra:
+
+* CAMB
+* classy (python wrapper for CLASS)
+
+These optional dependencies are required to use the power spectrum generation features but are not needed for the core FAST-PT functionality. Required dependencies will be installed automatically when you install FAST-PT.
+
+Installation Methods
+-------------------
+
+FAST-PT can be installed using pip:
+
+.. code-block:: bash
+
+ pip install fast-pt
+
+Or with conda:
+
+.. code-block:: bash
+
+ conda install fast-pt
+
+For developer installation:
+
+.. code-block:: bash
+
+ git clone https://github.com/jablazek/FAST-PT.git
+ cd FAST-PT
+ pip install -e .
\ No newline at end of file
diff --git a/docs/quickstart.rst b/docs/quickstart.rst
new file mode 100644
index 0000000..b9fdf55
--- /dev/null
+++ b/docs/quickstart.rst
@@ -0,0 +1,39 @@
+.. _quickstart:
+
+Quick Start Guide
+===============
+
+Using FAST-PT is straightforward. Here's a simple example to get started:
+
+.. code-block:: python
+
+ import numpy as np
+ import matplotlib.pyplot as plt
+ from fastpt import FASTPT, FPTHandler
+
+ #Define a k range
+ k = np.logspace(1e-4, 1, 1000)
+
+ # Initialize FASTPT
+ fpt = FASTPT(k, low_extrap=-5, high_extrap=3, n_pad=int(0.5*len(k)))
+ handler = FPTHandler(fpt)
+
+ # Use the handler to generate a power spectrum
+ P = handler.generate_power_spectra()
+
+ # Calculate an individual term using the handler
+ P_1loop = handler.get("P_1loop", P=P)
+
+ # Store default parameters
+ handler.update_default_params(P=P, P_window=np.array([0.2, 0.2]), C_window=0.75)
+
+ # Use the stored parameters in a calculation
+ tt_result = handler.run("IA_tt")
+
+ # Or get the result directly
+ tt_direct = fpt.IA_tt(P=P, P_window=np.array([0.2, 0.2]), C_window=0.75)
+
+ # Plot the results
+ handler.plot(data=tt_result)
+
+NOTE: This code uses the `FPTHandler` class to simplify the process of generating power spectra and calculating specific terms. However, the handler is not needed for any `FAST-PT` calculations. It simply provides a convenient interface for common tasks and integration with relevant libraries like `matplotlib`, `CAMB`, and `CLASS`.
\ No newline at end of file
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 0000000..07a35d9
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,3 @@
+sphinx>=4.0.0
+sphinx_rtd_theme
+numpydoc
\ No newline at end of file
diff --git a/docs/theory.rst b/docs/theory.rst
new file mode 100644
index 0000000..af5a424
--- /dev/null
+++ b/docs/theory.rst
@@ -0,0 +1,19 @@
+.. _theory:
+
+Theory
+=====
+
+FAST-PT Algorithm
+---------------
+
+FAST-PT is a numerical algorithm to calculate 1-loop contributions to the matter power spectrum and other integrals of a similar type. The method is presented in papers `arXiv:1603.04826 `_ and `arXiv:1609.05978 `_.
+
+The core of the FAST-PT algorithm is to compute integrals of the form:
+
+.. math::
+
+ \int \frac{d^3q}{(2 \pi)^3} K(q,k-q) P(q) P(|k-q|)
+
+by using Fourier transforms to reduce computational complexity from :math:`O(N^2)` to :math:`O(N\\log N)`, where :math:`N` is the number of input points.
+
+For intrinsic alignment calculations, see `arXiv:1708.09247 `_.
\ No newline at end of file
diff --git a/IA_ABD_plot.py b/examples/IA_ABD_plot.py
similarity index 91%
rename from IA_ABD_plot.py
rename to examples/IA_ABD_plot.py
index 93da56b..9468d49 100644
--- a/IA_ABD_plot.py
+++ b/examples/IA_ABD_plot.py
@@ -1,17 +1,17 @@
from __future__ import division
import numpy as np
-from matter_power_spt import one_loop
-import FASTPT
+from fastpt.utils.matter_power_spt import one_loop
+from fastpt import FASTPT
from time import time
# load the input power spectrum data
-d=np.genfromtxt('inputs/P_IA.dat',skip_header=1)
+d=np.genfromtxt('../paper/inputs/P_IA.dat',skip_header=1)
## note: for non-trimmed data, genfromtxt is required to deal with NaN values.
k=d[:,0]
P=d[:,1]
-d_extend=np.genfromtxt('inputs/P_lin.dat',skip_header=1)
+d_extend=np.genfromtxt('../paper/inputs/P_lin.dat',skip_header=1)
k=d_extend[:-1,0]
P=d_extend[:-1,1]
@@ -27,7 +27,7 @@
C_window=.65
n_pad=1000
# initialize the FASTPT class
-fastpt=FASTPT.FASTPT(k,to_do=['IA_mix'],low_extrap=-6,high_extrap=4,n_pad=n_pad)
+fastpt=FASTPT(k,to_do=['IA_mix'],low_extrap=-6,high_extrap=4,n_pad=n_pad)
t1=time()
diff --git a/examples/Pk_Planck15.dat b/examples/deprecated/Pk_Planck15.dat
similarity index 100%
rename from examples/Pk_Planck15.dat
rename to examples/deprecated/Pk_Planck15.dat
diff --git a/examples/RG_RK4_example.py b/examples/deprecated/RG_RK4_example.py
similarity index 96%
rename from examples/RG_RK4_example.py
rename to examples/deprecated/RG_RK4_example.py
index 493fdba..96b52eb 100644
--- a/examples/RG_RK4_example.py
+++ b/examples/deprecated/RG_RK4_example.py
@@ -4,7 +4,7 @@
'''
import numpy as np
-from fastpt.RG_RK4 import RG_RK4
+from fastpt.rg.RG_RK4 import RG_RK4
# load the Copter data
d=np.loadtxt('RGcopter_1_500.dat')
diff --git a/examples/RG_STS_example.py b/examples/deprecated/RG_STS_example.py
similarity index 96%
rename from examples/RG_STS_example.py
rename to examples/deprecated/RG_STS_example.py
index 8b4953b..36bbd67 100644
--- a/examples/RG_STS_example.py
+++ b/examples/deprecated/RG_STS_example.py
@@ -4,7 +4,7 @@
'''
import numpy as np
-from fastpt.RG_STS import RG_STS
+from fastpt.rg.RG_STS import RG_STS
# load the Copter data
d=np.loadtxt('RGcopter_10_1000.dat')
diff --git a/examples/RGcopter_10_1000.dat b/examples/deprecated/RGcopter_10_1000.dat
similarity index 100%
rename from examples/RGcopter_10_1000.dat
rename to examples/deprecated/RGcopter_10_1000.dat
diff --git a/examples/RGcopter_1_500.dat b/examples/deprecated/RGcopter_1_500.dat
similarity index 100%
rename from examples/RGcopter_1_500.dat
rename to examples/deprecated/RGcopter_1_500.dat
diff --git a/examples/fastpt_example.py b/examples/fastpt_example.py
index 0301d05..a075fc5 100644
--- a/examples/fastpt_example.py
+++ b/examples/fastpt_example.py
@@ -37,7 +37,7 @@
t2=time()
# calculate 1loop SPT (and time the operation)
-P_spt=fpt.one_loop_dd(P,C_window=C_window)
+P_spt=fpt.one_loop_dd_bias(P,C_window=C_window)
t3=time()
print('initialization time for', to_do, "%10.3f" %(t2-t1), 's')
diff --git a/examples/fastpt_examples.ipynb b/examples/fastpt_examples.ipynb
index 088bad8..ec56f1b 100644
--- a/examples/fastpt_examples.ipynb
+++ b/examples/fastpt_examples.ipynb
@@ -32,9 +32,20 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "ename": "",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31mRunning cells with '.venv (Python 3.12.9)' requires the ipykernel package.\n",
+ "\u001b[1;31mInstall 'ipykernel' into the Python environment. \n",
+ "\u001b[1;31mCommand: '/Users/vincentschacknies/RubinRoman/nuke/.venv/bin/python -m pip install ipykernel -U --force-reinstall'"
+ ]
+ }
+ ],
"source": [
"# import standard packages\n",
"import numpy as np\n",
@@ -43,7 +54,7 @@
"\n",
"# import fastpt\n",
"import fastpt as fpt\n",
- "import fastpt.HT as HT\n",
+ "import fastpt.misc.HT as HT\n",
"\n",
"# import the Core Cosmology Library (CCL) if you have it\n",
"try:\n",
@@ -226,7 +237,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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YiSNvXq0XLbJ2gCJduHtXJ1apptvZrdSg9YoVqXjv1q1aly6tNeiENu31919E6Dx5tHZw0HrsWK3v3TNb1MIMJIFkhgRy8qTW1appDToRO/1BqVXa0dFIHBUrar15s7UDFOlOWJiOeamkftd7TerL1sTGav3xx8ZtLTc3HT5zie7a1fh9LFpU6+3bzRGwMIenJRAZRE/vDhyARo2gbFk4eZJvSs/GLXs8H//1JiaTYsgQ4yX161s7UJHueHjguOVXZp6oR968EB8PFy4843uzZoWxY40l62XL4j74LRZeqs/mxddITDS2khk4UAbZ072UMktGfGSoHsiZM1pXqqTvj4if6PKJrlopToMxlbJTJ63v3rV2kCLDiIrSXf3+1IW8TakvrJiYaNxadXXV2tVV3/12sR4y2OiNFCum9cGDZolYpBGkB5KBhIVBgwZQrBgcOcK+Am/yepWblP1xJH+dcaBpU7h6FZYsgWzZrB2syDB27eLd4725cTWOZs00d++m4r12dtCnj9EbqVCBbH27MD20DVvX3ObePaheHcaPN3o4Ip1JKbNkxEe67oHExWn91Vdau7lpDfqU+yu60ks3k6fkDh1qLP4SwmwmTNA/01TbqUTdvLnWCQnPcY2EBK2nTTPGRvLl07eWb9Jduhi/w1WqaH3uXJpHLV4Q0gNJxxISjI3F8+eHAQMIK1qD+qUuU+LGXo6cc6NoUdi3z6h7lyePtYMVGdoHH9CsvQuz9Lv8/DO8//5zXMPeHt57z1iEmDcvudq/zqJ8w1n5UwKnT0PFirB+fZpHLswlpcySER/prgcyZYrW2bJpDTrOyVXP6XtM58pl3Dv29NR6/XprBygynehorf399ce5PtN/Hoh7sWvdu6f1gAFG96N6dX1uz2Xt7/+/HnVsbNqELF4MMo03nSWQFSuSb1XFKwc9qPRWnSe3kThefVXWcggru3zZqOSc5NKlF7zesmXG1od58uiYdRv1oEHGN1PVqlqHhLzgtcULe1oCkVtYtuTUKWjXDtq1w3QrgimlFpHDKZbZJ+sSfU+xfr2xG2CXLtYOVGRqXl7w0kugNdPb76d0ac3p0y9wvfbt4cgRKFAAx5aNmZV9NCuXJxIcDP7+xu+8sE2SQGzBn39CqVLGY8MGlrdehnvOBEb/1YW4OEXfvnD7NjRvLuXVhQ3Zu5fWK9qTNTaSZs00t269wLWKFYP9+6FXL5gyhTYLm3NoWyTu7sYapjlzjOkiwsak1DXJiA+bu4UVEqJ19er6/lSq4GLNdZum0cnl1Vu1MurUCWGzPvxQ7+EV7WCfoOvX/48S8M/CZDJmG2bJonWxYvr2odO6aVPjn0jv3lrHxKRJ1CIVkDEQG0sg0dFa9+1rZAnQRzwb6xKForRSWru4GHUQZUquSBcSE7Vu00bPo6cGrd99N42uu3On1h4eWufIoRPXB+gxY4xvq1de0fratTRqQzyTpyUQuYVlSTEx8NlnRnf922+5kLsC1YuGU+naBk5ddMHPD/7+21hUJVNyRbpgZwcLF9KzYiDvO03nlQr30ua6tWrB4cNQpAh2LZszMcdUli/TBAZC1aqy86GtkARiCSYTjBwJOXPC8OHc8yxM32aXKHzzCPv/dsfXF7Zvh8BAY7mHEOlKtmywfj2TN1agfXdjP9x7aZFHChWC3buNiSUjR9Jue392bk0gJgZeeQV27EiDNsQLkQRibjNnGolj6lRiTFn57LUAil3bydyAAuTNC8uWwfnz8Npr1g5UiBdQsGDyL/HKD4MoWlQTEpIG182WDZYuNVYtfvst/hOasX9LFPnzQ8OG8OOPadCGeG6SQMzl2DFjWe2QIZiiohlZ+hdy2t1h+I6meHkpduwwtpFt397agQqRhoKD8fu4LVHh92jRIpU1s1JiZweTJ8N338Hmzfh2rsneFZepUcOY0j5xoszQshZJIGlt506j2GGFCnD+PDPKfEdOlwSmnnwDUIwYYZQeqV3b2oEKYQYlS1J8TBuWx7UiKAjefjsNv9x794YNG+DcOdwaVWXTtON06QIffGC0k5CQRu2IZ5fS6HpGfJh1FlZQkNZ+fvr+HNzAHjN0+bLxyeXVu3WTndhEJpGQoPXrr+vJdmM0aD19ehpf/9gxrQsW1NrVVZu2bddjxxr/7Fq1kn9j5oDMwjKjy5eN7kTZsnD8ODu8O9OgcgQVFgwmJDQLLVpAeDgsWABOTtYOVggLsLeHpUsZ5b2E1s4buHUtNm2v7+dnLDr09kY1bsTHldYxcyasXQtvvAGRkWnbnEiZJJDnFRsLs2YZq8d37eJE3jr4+d6mTuhith7KwYgRxuD4unWQO7e1gxXCwnLnRq1dw8rPQ5kwJWvaX79AAWOGVoUK8OabvOv6A4sXG3eQ69aFGzfSvknxOEkgqRUXB/36GfWABg/mUpnXqV3iGmXDthEUkoMSJYzp6598ArlyWTtYIayoQgXs+vcFpdgdEEHLlsb/u9JM7tywZYtR66RnTzpfncbatXDiBLz6KoSGpmFb4okkgTwrk8lY4ZczJ3z7LbGxMLNfMKWCVrDrVD4KFDDG94KDjclXQogkhw8T3u4d1q+HwYPT+NouLhAQYExnHDGCZrtHsOk3zZUrULOmsTBXmI8kkGexeDG4ucGECcTFK/qV3o2X4w2GfFMCf39jmvqlS9C4sbUDFcIG+fnRusJ5Rjl8xrffwrx5aXz9rFmNPZz794dp06i1/B12bDMRHW0MT75QpWDxdCmNrmfEx3PNwoqI0NrZWSdip8eVXqmdHI19ObJn1/q334zab0KI/3Dpkk7w8NT1s+3Rjo4mffCgGdowmbQeMcKYktWrlw4KTNB582qdL5/WJ0+aob1MApmF9QJy5mTx0CO4ucYz4WQbEhIVgwbBrVvw+utSXl2IZ1KgAPYrl/FTTGvy2d1g/jwzrPxTyhh8/OADmDePMp/3YMeWBJQyFskHBaV9k5mdMhJM5uDv768PHz6cqvfcvm3Up4qNhbZtje63i4uZAhQio5s+ncuB4eSfOwE7RwfztTNxopFI2rfnzAeLqfu6AzExxph7+fLmazYjUkod0Vr7P+lcFksHk97kzAkbNxrLPGQ6rhAvaMgQCiR12y9eNIqIdutmhnbGjgVHRxgxgmLx8ezc/BN1G2Wlbl34/Xdjp0Px4uQW1jOoXVuShxBp4v4938BAplRfT8+emm3bzNTW8OEwYwasWcPLI95k5++x5MxpzPpN5Y0IkQJJIEIIy0tMZFp4D4o7h9Kxo+bqVTO1M3gwfP01/PILvsPbsnNzHG5uRiXfY8fM1GYmkm4TiFKqpVLqO6XUcqVUQ2vHI4RIhUqVyD5zEqvuNiLqVjwdOpixGGK/fsam6gEBFBrZkW2b4sme3eiJnDxppjYzCZtKIEqp+UqpMKXUiUeON1JKnVZKnVVKjQLQWq/TWr8N9AOkKLoQ6U2/fpTqUI5vEt5m1y7jbpPZDBiQfDur8LiubN2UgIMD1KsHZ86Ysd0MzqYSCLAAaPTgAaWUPTAHaAyUAjoqpUo98JKxSeeFEOmJUjB3Ll2K7mee/9f07Wvm9gYPhmnTYNkyik7pydbfEzGZjNpZ586Zue0MyqZmYWmtdymlfB85XAU4q7U+B6CUWga0UEoFA58AG7XWf6Z0TaVUH6APQKFChcwRthDiebm6wtat9PT0hCwQHQ137oCnp5nae+89o57dmDGUdHBgy+/fUaeeHXXrGoUYfXzM1G4GZWs9kCcpADxYFu1S0rFBQH2gjVKqX0pv1lrP1Vr7a639PTw8zBupECL1ChaELFnQ18NoVPE6LVsa3/FmM3o0jBsH8+fj980ANv+uiYgwbmddvmzGdjMgm+qBpIbWehYwy9pxCCHShvr0E4acvsCbrGb4cJg504yNjRsH8fEweTIVs2dn02/TqN9AUb++USXe3d2MbWcg6aEHchnwfuB5waRjQoiMZOJEWpc5w2Dnb5k1y9hLx2yUMlarDxoEn39O1a2T+fVXCAkxiqLKplTPJj0kkENAUaVUYaVUVqAD8LOVYxJCpLVs2WDFCj7VI6nkepqePTUXL5qxPaWMmVldusDYsdQKmsOKFXD0KLRsCTExZmw7g7CpBKKU+gnYBxRXSl1SSvXSWicAA4FNQDCwQmsts7eFyIhKlsTxy89ZFvkG5XJfMt/akPvs7IwCd82bw8CBNLuzhB9+gG3boFMnM65NySCkmKIQwrZo/b+egaUGI2JioEkT2LUL1q5l5rlmDBkCPXvC999n7qrbTyumaFM9ECGEQCkYOhTc3bnzbzwd3oxn61Yzt+nkBOvXG9uJtm3L4HI7+OADmD8fRo40c9vpmCQQIYRtMpmwb9GU45uv0bmz5vp1M7fn6mqU3n75ZWjWjAnNDvPOO8baw08/NXPb6ZQkECGEbbKzw6VlA5ZHNiHi30S6dQOTycxt5slj1Hv38EA1acysQX/TsSOMGmX0RsTDJIEIIWzXsGGUbVSQGXoImzbBZ59ZoM0CBWDTJgDsGr/Ogk+u0bAh9OljdFDE/0gCEULYLjs7WLiQPnlW08Z1EzOmm7h71wLtFi0Kv/4K16+TtWUTVv0QiZ8ftGkje4k8SBKIEMK25c2LWvIj33l+wOHVFy23pXSVKrBqFRw/jmu31mxYF0fevPDGG1J88T5JIEII21evHrn++gOvV3xJTDS+1y2yAqFxY2Me75YteI7uyW8bTCQkQKNGEB5ugfZtnCQQIUT6kMUo17u8yULatoVvv7VQu927w+TJsGQJxeePJCAAQkOhWTOjenBmJglECJF+3LhBhwNDaZhjP0OHasvtKDhqFLzzDnz2Ga8cmM5PP8GhQ5h3J8V0INUJRCnlmFSXqpRSSuqjCyEsp1Ah7L6fy8I7LXG1u0unThaqWaWUUR74zTdh2DBaxizjyy8hIMDIK5mooMdDnimBKKVclVL9lVK7gNvAWeAEcE0pdTFpb/LK5gxUCCEAaNMGz55v8EN0B44fN7b3sAh7e/jxR6hVC7p1o3+Z3bz/PsydC598YqEYbMx/JhCl1DAgBOgJbAZaAOWBYkB1YDzGviKblVK/KaWKmitYIYQAYOZM3ihymol5Z/FmK3OvLnyAkxOsXQuFC0PLlkzqdoZOnYwktmKF5cKwFf9ZTFEptQL4SGt94j9e5wj0AuK01t+nXYhpR4opCpGBHDsG2bMbpUeAxESjk2AR//wD1apBrlzEbN9H/Q7uHD4MO3YYhzOSpxVTlGq8Qoj0TWtG97/FiSu5Wb/egpVz9+2DOnXA358by7ZQ7TUnIiPhwAHw9bVQDBaQZtV4lVKF0iYkIYRIIx99hOcPUwgIgK++smC71avD4sWwdy/uw3vwa4CJuDhjoWFEhAXjsKLUzsJak3Sr6jFKKac0iEcIIVKnRw8GOX9P45x/8N57FpzaC9C2rTGCvmwZxZd8yJo1cOYMtGtnbLme0aU2gZwF5j56UCnlBexOk4iEECI1ChVCzf2WH263Iof9XTp2tPB2tCNGQO/eMGkSdUJ+YO5c2LwZBg7M+NN7U5tAegKVlFKD7h9QSpUHDgL/pGVgQgjxzNq1I1+3xvwQ3YGzZxI5dMiCbStl3Dtr0AD69KGHz7bk6b2ff27BOKwgVQlEax0NvAmMU0rVVEq1wOh5zNdadzBHgEII8Uxmz6ZJ9Vuc//EPXn3Vwm07OMDKlVC8OLRuzcTOp2jb1uicrF1r4Vgs6Fmm8W4CAoGjSX+eBloB8zHWf/TRWi81c5xpQmZhCZHBaZ08DWvZMqhbF/LmtWD7Fy4YVXxdXbm34wB12uTh+HFjq3X/J85jsn0vOgvrT8APmA78BUQCw4FEYClwOqWBdSGEsCilIDGR0OGz6N41kR49LDwO4eMD69ZBaCjOXdqwfmUc+fIZhRcvXrRgHBbynwlEa/2+1rqx1jo/kB/jFtY64HfgVeAAEKmUsuTcByGESJH3/pVMsxvFhg0WntoLxvTe+fNhxw7yfTyQXwI00dHQtClERlo4FjN74YWESilnoBxQTmttqQLLz0VuYQmRSVy4gPYrRxO7jeyMrcaffypKlLBwDGPHwqRJ8MUX/F56KE2aGGtE1qyx4Ir5NPBCt7CUUoWfdl5rfU9rvV9r/Qz7EToAACAASURBVK0yeD9voEIIkSZ8fFBff8X8iNZkU/fo0sUodWJRH31kVO997z0axv/KjBnw888WLP5oAc8yBrJPKTVPKVU9pRcopdyUUv0xxkhapFl0QgjxvDp2JH/72vyQ0JVR/W9b/n/9Sfu5U748dOzIO7VP0K8fTJ1qHM4InmUWVi5gDEahRBNwBLgCxABuQCmgJMZakI+01pvMGfCLkFtYQmQyN28ahQ8rG7tNxMcbM24t6vJlo31HR+L3HKBR17zs2QPbtkGNGhaO5Tm80C0srXWE1no4UADoCwQDuYDCQAKwEKigta5hy8lDCJEJ5c6dnDx+mHyV8uUhKsrCMRQoYNy7unYNh/atWfljLIUKQatWEBJi4VjSWGoWEhYDcmDMvmqvtW6kte6stf78v0q9CyGEVa1cSZEx7QkO1gwbZoX2/f1h0SLYu5fco/oQ8LMmLg6aN0/fM7OedUfCPhjrQeYBvwBBSqkC5gxMCCHSzBtv8GqJcEa6zOG774wOgcW1bWsMrC9aRIn1n7JyJfz1F7z1lhUG+NPIs/ZARgBfAZ5AZSAM+NRcQQkhRJrKlg0WL2bCvRGUdwuhd28IC7NCHGPHQseO8P77NIhay4wZxr7q6XVm1rMmEB/gM611mNb6CNAdaG22qIQQIq35+5N13Pv8eOsN7t5J4PffrRCDUjBvHlStCp07806NwOSZWQsWWCGeF/SsCcQeuHf/idb6HwClVH5zBCWEEGbx/vuUru/Fuc/W0rmzlWJwdjbKneTOjWrVklkfhFO3LvTpA3v2WCmm55SaQfQ+Sqm6SqncSc8TAWczxCSEEOaRJQv8/jv5BrYFjD3Mz561QhyenkYSuX4dh45tWLkkDh+f9Dcz61kTyHZgGLAFCFdKhQJOGEmlgVLKzVwBCiFEmlIKtCbyq8W82TSGLl0gIcEKcVSqZNTM2rWL3OMG8UuAJj7eKLyYXmZmPVMC0VrX01rnBooAHYAlGEmlN7AJuKGU+ttsUQohRBpz/XUZc+L6sH+/sSutVXTsCKNGwdy5FN/2NStXQnAwdOqUPmZmpUUxRV/AH6iotbbpuQSyEl0IkezaNShThk6mH1kZ+Tr79inr7NmRmAgtW8Jvv8Hvv/PlyToMGgTDhxuD69b2tJXoL5xA0hNJIEKIh6xZw603e+HnGoKLV07+/NOY8Wtxd+5AtWoQFoY+eIgB0wrzzTeweDHWG+xP8qIbSgkhRMbUujVu3Vuy8G4bBrS7gZOTleLIkcNY3WgyoVo0Z9akSGrVgt69sez+7qkkPRAhROZ25w7s3QuNGwMP7YpreZs3Q6NG0Lw54d+spnJVO+Lj4fBhyG+lRRMZsgeilHJRSi1USn2nlHrL2vEIIdKpHDmSk0fAgn+pVMnIKVbRoAF88QWsW4fHVxNYtw4iIoxtRWJjrRTTU9hUAlFKzVdKhSmlTjxyvJFS6rRS6qxSalTS4dbAKq3120BziwcrhMhYNm/Go3cLjh2zUsHF+959F3r0gI8+ovzfK1mwAPbtg/79Lby/+zOwqQQCLAAaPXhAKWUPzAEaY+w90lEpVQooCIQmvSwdTHgTQti0mjWpVvRfRrrMYd48+PVXK8WhFHz9tbG3evfutC0ayNix8MMPMHu2lWJKgU0lEK31LuDmI4erAGe11ue01nHAMoxdDy9hJBF4ys+hlOqjlDqslDocHh5ujrCFEBmBszMsXMi4uyPwc7tI797w779WisXR0dg8PXduaNGCCe+E0bw5DBsGW7daKaYnsKkEkoIC/K+nAUbiKACsAd5USn0NBKT0Zq31XK21v9ba38PDw7yRCiHStypVcBz9fyy61Yx/b5hYtsyKsdwvdxIWhl3bN1k8L47ixaFdOzh3zopxPSA9JJAn0lrf1Vr30Fr311ovsXY8QogM4oMPKOeflRPvLeCdd6wcy/1yJ3v2kGP0QNav02gNLVrYRrmTLNYO4BlcBrwfeF4w6ZgQQqS9rFnhjz8olrR5+smTxp0ka02jpWNHCAqCKVMoUq4cy5e/Q6NG0K0brFoFdlbsBqSHHsghoKhSqrBSKitGLS5r7CcmhMgskpJH5LqtvFotjrfftvIMqIkToWlTGDyYBvbb+OwzWLvW2ODQmmwqgSilfgL2AcWVUpeUUr201gnAQIyijcHACq31SWvGKYTIBLTGdeoHjDeN49dfjTtJVmNnB0uWQLFi0LYtQ5qfo1s3mDDBGGu3FlmJLoQQKTlzBlO5CtR3+YNDsX4EBSl8fa0Yz9mzUKUKFChAzLY/eK2ZKydOGOtEypY1T5MZciW6EEKYXbFi2H06hR/+bY5KiKdHDzCZrBhPkSKwYgUEB+P0dhfWrDKRIwc0bw43blg+HEkgQgjxNAMH4vPaS8ziXSqXjLLO5lMPql8fPv8c1q/H69txrFsHV68a03vj4y0biiQQIYR4Gjs7+OEHus+syNQ5LmTNau2AMMqd9OwJEydSJWQFc+fC9u3wf/9n2TAkgQghxH/x9YU+fUApdm2JpV07K22De59S8NVX8Mor0L07XcseZdgwo9TJvHmWC0MSiBBCPKvNm7na5l1WroRPP7VyLI6OsHo15MkDLVrw6bDrNGhgFF384w/LhCAJRAghnlXp0rS3W0n7PFuYMEETGGjleDw9Yf16uHGDLO3fZPniOHx8oHVruHTJ/M1LAhFCiGfl5QVffsmcf9uTx+kuXbvawD4dFSsapXr37sXtg4GsXw/R0cY26/fumbdpSSBCCJEaHTuS5806fHevC0FBsGCBtQMC2reHUaPgu+8oFbiUJUvgzz+NLXHNudRPEogQQqRG0n4dTXP/wabuP/H229YOKMlHH0HNmtC9O83yHeTjj2HpUvjwQ/M1KSvRhRDieYSHQ9IWEVevQq5cxpYiVnXrFpQvD4Def4C3P/Bk3jzYsCF5195Uk5XoQgiR1pKSx43tQZQpmcDYsVaOB8DNzSiOdeMGqnkzZk+9h5+fUbnXHOXfJYGIp3r//feZMWPGQ8e8vb05evToY6+tUqUKJ09KnUuRicTH496jGe3s1zB9umb3bmsHhLGHyNKlcPgwzoP7sHyZZt48cHU1Q1ta60zzqFSpkk6Pbt68qQHt4uKinZ2ddf78+fX06dPN3m5YWJj28vLS0dHRD8Vib2+v792799jrly9frlu3bm32uCxl9uzZulKlSjpr1qy6W7du1g5H2KpfftGRuOjCuf7VL72kdWSktQNKMmGC1qD155+/0GWAwzqF71TpgaQDgYGBeHh4EBUVRXR0NF9//TVDhw7lkpknei9YsIAmTZrg/MCN3aCgIIoUKYKTk9Njr2/evDnbt2/n2rVrZo3LUry8vBg7diw9e/a0dijClr3xBtl7tGPB7dacP68ZOdLaASUZOxZatYLhw2HzZrM0IQkkHQgMDKRy5crJz6tWrQpAXFycWdvduHEjtWvXfujY8ePHKVOmDADR0dF06tSJ1q1bExUVhZOTE5UqVWLTpk0pXjM0NJTWrVvj4eFBnjx5GDhwIADBwcG89tpr5MqVi9KlS/Pzz//bM+zTTz+lQIECuLq6Urx4cbZu3Zp8ztfXl2nTpuHn54eLiwu9evXi+vXrNG7cGFdXV+rXr8+tW7f+s40nad26NS1btiRPnjyp++BE5jN9OrUK/MOQXAuJ+DeRxERrB4RRw2vhQihZEjp1MssgiCSQdODo0aNUqVIFgIiICMaMGUOlSpUoXLiwWdsNCgqiePHijx0rW7Ys58+fp0aNGhQvXpzVq1eTPXt2AEqWLMmxY8eeeL3ExESaNm2Kj48PISEhXL58mQ4dOhAfH0+zZs1o2LAhYWFhzJ49m7feeovTp09z+vRpvvzySw4dOkRkZCSbNm3C95ENGVavXs3mzZs5c+YMAQEBNG7cmMmTJxMeHo7JZGLWrFlPbUOIF5YzJ8ybx7T+51iyKBF7e2sHlMTV1VipvmiRWQZB0sOe6JY1ZAhmr09Qvjw8MjD9NIGBgaxZs4aZM2fi5uZGnTp1CAgI4M6dOzRo0IC//vqL/fv3J/cMhg4dSteuXalQocILhRkREYHrI790x48fRylFnTp1mDlzJi1atHjovKurK1evXn3i9Q4ePMiVK1eYNm0aWbIYv3o1a9Zk9+7dREVFMWrUKOzs7Khbty5Nmzblp59+onPnzsTGxvLXX3/h4eHxWPIAGDRoEPny5QPg1VdfJW/evMk/e6tWrdi6dSv79+9PsY3x48e/0OckBAANG2LfsCFg7KP+yy/Yxu2sl182HmYgCcTGxcbGEhwczPnz5ylYsOBD5+Lj4/n1118ZPnz4Q8eDg4MpVarUC7ft5uZG5APdXq01J06c4Ny5cwwbNuyx5AEQGRlJrly5nni90NBQfHx8kpPHfVeuXMHb2xs7u/91iH18fLh8+TJFihRhxowZjB8/npMnT/L666/zxRdf4OXllfza+8kDwNnZ+bHnUVFRT21DiDS1fTtLe1xm8oXOlC0LTZpYOyDzkQTyqFT0DCzhxIkTuLi4PJY8ABwcHPBImov+oJiYGBwdHZkzZw6LFy9Ga82sWbOSx06edNzPzw9fX19CQ0Pp2LEjI0aMwM/PjzNnziSPv5w/fx6ALVu2UK9ePerVq4e//8Pri4KDg+ncufMTfxZvb28uXrxIQkLCQ0nEy8uL0NBQTCZT8hf8xYsXKVasGACdOnWiU6dO3Llzh759+zJy5EgWL16cqs/xv9oQIi19eKEXP+epS+/eXpw4AblzWzsi85AxEBt39OhRSpcu/cyvDwsLI1++fBw8eJA9e/awb98+VqxYwbRp0wCeeDwiIoKbN28yf/58Dhw4wJIlSwBo0qQJO3fuTL728ePH8fPzo2zZssydO5dWrVo9dLsqJiaGI0eO0KBBgyfGVqVKFfLnz8+oUaO4e/cuMTEx7N27l6pVq5ItWzamTp1KfHw8O3bsICAggA4dOnD69Gm2bdtGbGwsTk5OODs7P9SLeFZPayMlCQkJxMTEkJiYSGJiIjExMSRYfTs6YfPq1MFxUF8W/tuU8DAT775r7YDMRxKIjQsMDEwe23gW9we5161bx4ABA1BKYWdnlzwV90nHg4KC6NChA+7u7jg4OCS/tmvXrmzYsIF7SSU9g4KC8PPzA6Bly5b06dOHli1bEhMTA0BAQACvvfbaQ7eXHmRvb09AQABnz56lUKFCFCxYkOXLl5M1a1YCAgLYuHEj7u7uDBgwgEWLFlGiRAliY2MZNWoU7u7ueHp6EhYWxpQpU1L9OT6tjZRMnDgRZ2dnPvnkE3788UecnZ2ZOHFiqtsWmdCUKVQsEslY15ksWWKUEsmIpBZWBtC9e3fee+89ypQpw4wZMyhcuDB79uyhUaNG1KtXj5EjR1K5cmXatGnD8OHDHzseFhZGSEgIU6dOZenSpZw7d46xSXUZRo8eTd68eRkyZMh/xlG1alXmzZuXqoQnRIa1dy/xNeswu8UWBiyrxROWTqULT6uFJQkknWvSpAmBgYH4+PjQt29fdu/ezZgxYzCZTHTr1g2tNbVq1eKTTz4B4OzZs48d79+/Pw4ODpw8eRJPT0/mz5+Po6OjlX8yITKA/fuhalVQinv3wMnJKOabnkgCSZIRE0haaNCgAZs2bXqusQUhxH8L+eMKdTp6MmmKHZ06WTua1JFqvOKp4uLiJHkIYS63b+PdtBye984xcCBcuWLtgNKOfGuIh2ZaCSHSWM6c2Pfvw4LwN7h3N5E+fcy7S6AlSQIRQghz+/BDipd15BPH8fz6q41sg5sGJIEIIYS5OTrCwoUMiv6U2nmDWbfO2gGlDUkgQghhCRUqYPfBGNbWn8PalRljQaqUMhFCCEv58EPckubxXr1q1G193r3KbYH0QIQQwlLuLwIJDGRw3SDatoVz56wb0ouQBCKEEJb22298fqoJ9qY4uncHk8naAT0fSSBCCGFp772HdxUvZtoNY/dumDnT2gE9H0kgQghhaVmywMKFdIv/nqb5DjF6tObUKWsHlXqSQGycr68vW7ZsYfLkyfTu3dva4Qgh0kqJEqjJk5h7vTldaobwhK19bJ7MwkonRo8ebe0QhBBpbcgQ8mfPztweBSCrsUI9PRVblB6IeKrExERrhyBExmVvD337QtasnDseRc2amqAgawf17CSBpBPjx49P3io2JCQEpRQLFy6kUKFCuLu7M2nSpOTXmkwmPvnkE15++WXy5MlDu3btuHnzZvL5tm3b4unpSc6cOalVqxYnT55MPte9e3f69+9PkyZNcHFxYfv27Zb7IYXIrC5cwLVRDf4OiqFbN4iPt3ZAzybdJhClVEul1HdKqeVKqYZpevHXXnv88dVXxrno6Cefv1/c5saNJ59fvtw4HxqaZmHu2bOH06dPs3XrVj766COCg4MBmD17NuvWrWPnzp1cuXIFNzc33nnnneT3NW7cmL///puwsDAqVqzIW2+99dB1ly5dypgxY4iMjKRmzZppFq8QIgXe3niUyMPc+J4cPQoP/H/QplklgSil5iulwpRSJx453kgpdVopdVYpNepp19Bar9Navw30A9qbM15bNW7cOJydnSlXrhzlypXj2LFjAHzzzTdMmjSJggUL4ujoyPjx41m1alXyft49e/bE1dU1+dyxY8e4fft28nVbtGhBjRo1sLOzwym9bqMmRHpiZwfz59Myyy909tzCpEmaP/+0dlD/zVqD6AuAL4FF9w8opeyBOUAD4BJwSCn1M2APPLoJdk+tdVjS38cmvS/t7NiR8rls2Z5+3t396ee9vZ8zqMd5eno+EFY2oqKiALhw4QKtWrV6aI8Pe3t7rl+/jqenJ2PGjGHlypWEh4cnv+bGjRvkzJkzKcS0i1EI8Yx8feGzz5jVry3bcoYyZUp2Vq60dlBPZ5UEorXepZTyfeRwFeCs1vocgFJqGdBCaz0FaProNZRSCvgE2Ki1Tge52nK8vb2ZP38+NWrUeOzc4sWLWb9+PVu2bMHX15fbt2/j5ubGgztTqvQ0DUSIjKRPH9zWrOF301heXjQdsO1/i7Y0BlIAeHCA4FLSsZQMAuoDbZRS/VJ6kVKqj1LqsFLqcHh4eNpEauP69evHmDFjuHDhAgDh4eGsX78egMjISBwdHcmTJw/R0dEyPVgIW6IUrFpF6d+n4+SsiIyEM2esHVTKbCmBpIrWepbWupLWup/W+punvG6u1tpfa+3vkR5X6jyHwYMH07x5cxo2bIirqyvVqlXjwIEDAHTt2hUfHx8KFChAqVKlqFatmpWjFUI8xNXVSCShoTR/JZxmzYy5O7ZIaSvtrZh0C+sXrXWZpOfVgfFa69eTnr8PkHQLK034+/vrw4cPp9XlhBDCfDp0YNva29SL28jgwTBjhnXCUEod0Vr7P+mcLfVADgFFlVKFlVJZgQ7Az1aOSQghrOOLL6jrcoB38q1i5kzYudPaAT3OWtN4fwL2AcWVUpeUUr201gnAQGATEAys0FqffNp1hBAiw/Lygtmz+fR6N17Oc4sePSBpoqXNsNYsrI4pHN8AbLBwOEIIYZs6dcJl9WoW/PImH74cQGSkC9mzWzuo/7GlW1hCCCEepBR88w01+5Vh62/x5M9v7YAeJglECCFsWd68MGsWyi0XV69oevWCiAhrB2WQBCKEEOnB339zqX53Fi7UDBli7WAMkkCEECI9yJ2byjc3MSrP9yxcCD/bwBxVSSBCCJEe5MkDc+fyYdg7+OW9Rp8+8O+/1g1JEogQQqQXzZuTtWtHFt1ows1/TYwda91wJIGIp3r//feZ8cgSWG9vb44ePfrYa6tUqfLQ5lRCCDOYMYNyntdZWe1zJk60biiSQNKBW7duoZQie/bsZMuWDS8vr8e+1M0hPDycRYsW0bdv34diuXr1KiVLlnzs9e+99x4ffvih2eOyhNjYWHr16oWPjw+urq6UL1+ejRs3WjssIcDNDbZupcXWweTJY+xe+MB2PhYlCSQdCAwMxMPDg6ioKKKjo/n6668ZOnQoly5dMmu7CxYsoEmTJjg7OycfCwoKokiRIk/caKp58+Zs376da9eumTUuS0hISMDb25udO3dy+/ZtJk6cSLt27QgJCbF2aEJAiRKQNSummxHUrR5N9+5gjbKGkkDSgcDAQCpXrpz8vGrVqgDExcWZtd2NGzdSu3bth44dP36cMmXKABAdHU2nTp1o3bo1UVFRODk5UalSJTZt2pTiNUNDQ2ndujUeHh7kyZOHgQMHAhAcHMxrr71Grly5KF26ND8/MMXk008/pUCBAri6ulK8eHG2bt2afM7X15dp06bh5+eHi4sLvXr14vr16zRu3BhXV1fq16/PrVu3/rONR7m4uDB+/Hh8fX2xs7OjadOmFC5cmCNHjqT+gxTCTOyaN6XFxS9Ztw6WLLFC+5ZvUqTW0aNHqVKlCgARERGMGTOGSpUqUbhwYbO2GxQURPHixR87VrZsWc6fP0+NGjUoXrw4q1evJntSfYWSJUsmb637qMTERJo2bYqPjw8hISFcvnyZDh06EB8fT7NmzWjYsCFhYWHMnj2bt956i9OnT3P69Gm+/PJLDh06RGRkJJs2bcLX1/eh665evZrNmzdz5swZAgICaNy4MZMnTyY8PByTycSsWbOe2sazuH79OmfOnKF06dKp/yCFMJcJExga/j41vM4xaBBcvmzZ5q21pa3NGjIEAgPN20b58qkrzRwYGMiaNWuYOXMmbm5u1KlTh4CAAO7cuUODBg3466+/2L9/f3LPYOjQoXTt2pUKFSq8UJwRERG4uro+dOz48eMopahTpw4zZ86kRYsWD513dXXl6tWrT7zewYMHuXLlCtOmTSNLFuNXr2bNmuzevZuoqChGjRqFnZ0ddevWpWnTpvz000907tyZ2NhY/vrrLzw8PB5LHgCDBg0iX758ALz66qvkzZs3+Wdv1aoVW7duZf/+/Sm2MX78+Kd+DvHx8bz11lt069aNEiVKPMtHJ4Rl1KuH/YB+LPjqdco5naJ3b3s2bDAqoFiC9EBsXGxsLMHBwZw6dYqbN2/yzz//8P3335M/f36yZcvGr7/+Sps2bR56T3BwMKVKlXrhtt3c3IiMjEx+rrXmxIkTrF27lv79+z+WPMDY8TBXrlxPvF5oaCg+Pj7JyeO+K1eu4O3t/dAe7j4+Ply+fJkiRYowY8YMxo8fT968eenQoQNXrlx56P33kweAs7PzY8+joqKe2sbTmEwmunTpQtasWfnyyy+f+lohrOLTTynykolPXT7mwnkTltx4VXogj7DWpi0pOXHiBC4uLhQsWPCxcw4ODjxpl8WYmBgcHR2ZM2cOixcvRmvNrFmzksdOnnTcz88PX19fQkND6dixIyNGjMDPz48zZ84kj7+cP38egC1btlCvXj3q1auHv//D+8wEBwfTuXPnJ/4s3t7eXLx4kYSEhIeSiJeXF6GhoZhMpuQv+IsXL1KsWDEAOnXqRKdOnbhz5w59+/Zl5MiRLF68OFWf43+18SRa6+QxlQ0bNuDg4JCqNoWwiOzZYcECBkyaQu9vw3HKm++/35NGpAdi444ePZqq++5hYWHky5ePgwcPsmfPHvbt28eKFSuYNm0awBOPR0REcPPmTebPn8+BAwdYkjQa16RJE3Y+sIvN8ePH8fPzo2zZssydO5dWrVo9dLsqJiaGI0eO0KBBgyfGVqVKFfLnz8+oUaO4e/cuMTEx7N27l6pVq5ItWzamTp1KfHw8O3bsICAggA4dOnD69Gm2bdtGbGwsTk5OODs7P9SLeFZPayMl/fv3Jzg4mICAgIdmoglhc159FbvfNuDkk4/ISJg+HUwm8zcrCcTGBQYGJo9tPIv7g9zr1q1jwIABKKWws7NL/gJ80vGgoCA6dOiAu7s7Dg4Oya/t2rUrGzZs4N69e8nX9vPzA6Bly5b06dOHli1bEhMTA0BAQACvvfYaXl5eT4zN3t6egIAAzp49S6FChShYsCDLly8na9asBAQEsHHjRtzd3RkwYACLFi2iRIkSxMbGMmrUKNzd3fH09CQsLIwpU1K/y/HT2niSCxcu8O233xIYGIinpyfZs2cne/bsyclVCJsUGsq6JnMZNgy+/tr8zVltT3RryKh7onfv3p333nuPMmXKMGPGDAoXLsyePXto1KgR9erVY+TIkVSuXJk2bdowfPjwx46HhYUREhLC1KlTWbp0KefOnWNsUo2E0aNHkzdvXoY8Q/nPqlWrMm/evFQlPCFEGtq3D12jJk0KBLLrZlmOHYMiRV7skk/bE10SSDrXpEkTAgMD8fHxoW/fvuzevZsxY8ZgMpno1q0bWmtq1arFJ598AsDZs2cfO96/f38cHBw4efIknp6ezJ8/H0dHRyv/ZEKI5zJyJJemLqGMy3nKlHdg506wt3/+y0kCSZIRE0haaNCgAZs2bXqusQUhhI2JiYFKlVh8tT5db83k889h2LDnv9zTEojMwhLExcVJ8hAio3BygoUL6Vy1GueqNaZVq0Zma0oSiHhoppUQIgPw90etXsW4OtUhp/makQQihBAZUcuWxp9xccZtrRw50rwJuW8hhBAZVUIC1KgB/1Gu53lJD0QIITKqLFlg0iSoWdM8lzfLVYUQQtiGhg3Ndmm5hSWEEOK5SAIRQgjxXCSBCCGEeC6SQIQQQjwXSSBCCCGeiyQQIYQQz0USiBBCiOciCUQIIcRzyVTl3JVS4cAFjPJit5MO/9ff7//pDtxIZZMPXu9Zzz967GnPH43R2rGmFN9/xS2fbfr5bFM696yfrS3+Hjx67GkxPngss3y2Plprjye+Qmud6R7A3Gf9+wN/Hn6Rdp71/KPHnvb8CTFaNVb5bDP+Z5vSuWf9bG3x9yA1n6d8tg8/MustrIBU/P3BYy/SzrOef/TY054/GqO1Y330mHy2z/be9PTZpnTuWT9bW/w9ePTYf8Uon22STHUL60UopQ7rFHblsjXpKVZIPXF10AAABG9JREFUX/Gmp1ghfcWbnmKF9BWvuWLNrD2Q5zHX2gH8f3v3ExpHGYdx/PvYWsXcFFSwghb8C8raQxEKJQcVkRJLFRX1oBQhHoTetBS82ouXINhSFEWsVUKRqO3BS1X0YCVWDJZqLYIVQRAPKjko/jzsUJZ1N8m8mek7r3k+sIcdsuHLyyQ/3tlkp4aSWqGs3pJaoazeklqhrN5WWr0DMTOzJN6BmJlZEg8QMzNL4gFiZmZJPEAaIGlS0ieS9kuazN2zHEkTkr6QtD13y1Ik3VKt6aykp3P3LEfSDkkHJb0tqb3bwDVA0iZJr0iazd0yTnWevl6t6WO5e5ZSwnoOaupcXfMDRNKrkn6RtDB0/F5JpyWdkfTcMt8mgD+AS4FzHW8FeBZ4p53K802rbo2IUxExDTwEbC2g992IeAqYBh7ueOvZiNjVVuM4Ndt3ArPVmk51uTXXeg511elt5lyt+9+J/7cHsA3YDCwMHFsHfA9sAjYAXwG3ArcB7w89rgQuql53FfBmx1vvBh4BngC2d7m1es0UcAx4tOvnwcDrXgQ2F9I62+a6rrJ9D9CrvubQheys25prPRvoXdW5up41LiI+lnTd0OEtwJmIOAsg6TBwf0S8ACx12ec34JI2OqGZ1uoS2wT9H9BFSUcj4p8utlbfZw6Yk/QBcKjpziZ7JQnYBxyLiPkut+ZSp53+bn4jcJIMV0tqtn5zYev+q06vpFM0cK6u+UtYY1wD/Djw/Fx1bCRJOyUdAN4AXmq5bVit1ojYGxG76f8yPtjG8FhC3XWdlDRTre3RtuNGqNULPAPcBTwoabrNsBHqru0VkvYDd0ja03bcMsa1HwEekPQyq/tIjiaNbO3Yeg4at7aNnKtrfgfShIg4Qv9kL0ZEvJa7YTkRcRw4njljxSJiBpjJ3bESEfEr/evfnRURfwJP5u5YiRLWc1BT56p3IKP9BFw78HxjdayL3NqeknpLah1WUntJrdByrwfIaCeAGyRdL2kD/Ted5zI3jePW9pTUW1LrsJLaS2qFtntz/cVAVx7AW8DPwF/0rw/uqo7fB3xL/y8Y9ubudKt7S2wtub2k1ly9/jBFMzNL4ktYZmaWxAPEzMySeICYmVkSDxAzM0viAWJmZkk8QMzMLIkHiJmZJfEAMctI0j5JH+buMEvhAWKWV4/+x5WbFccDxCyvHvBl7gizFB4gZplIupr+XSxPVs8nJB2WND/ixkBmneMBYpZPD1gETku6Cfgc+BvYGhE/5AwzWwkPELN8esDXwA7gM/p3iHw8IhbzZpmtjD+N1yyT6v7U9wDrgKmI+Chzklkt3oGY5dOjfyvki4HLM7eY1eYdiFkGki4DfgfuBG4EDgDbImI+a5hZDetzB5itUbcDASxExAlJNwPvSdoSEV2+x7bZeb6EZZZHD/hu4A3z54FPgblqd2LWeb6EZWZmSbwDMTOzJB4gZmaWxAPEzMySeICYmVkSDxAzM0viAWJmZkk8QMzMLIkHiJmZJfEAMTOzJP8CnXoNCu3LBbsAAAAASUVORK5CYII=",
"text/plain": [
""
]
@@ -286,7 +297,7 @@
},
{
"data": {
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\n",
+ "image/png": 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",
"text/plain": [
""
]
@@ -361,7 +372,7 @@
},
{
"data": {
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\n",
+ "image/png": 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",
"text/plain": [
""
]
@@ -440,7 +451,7 @@
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
""
]
@@ -584,9 +595,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python [conda env:ccl] *",
+ "display_name": ".venv",
"language": "python",
- "name": "conda-env-ccl-py"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -598,7 +609,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.6.10"
+ "version": "3.12.9"
}
},
"nbformat": 4,
diff --git a/examples/galaxy_bias_perturbation_example.py b/examples/galaxy_bias_perturbation_example.py
index 8b6d228..f54c532 100644
--- a/examples/galaxy_bias_perturbation_example.py
+++ b/examples/galaxy_bias_perturbation_example.py
@@ -41,7 +41,7 @@
t1=time()
# initialize the FASTPT class
nu=-2
-fastpt=FASTPT.FASTPT(k,-2,n_pad=n_pad)
+fastpt=FASTPT(k,-2,n_pad=n_pad, simple=True)
P_1loop=fastpt.one_loop(P_lin,C_window=C_window)
_,Pd1d2, Pd2d2, Pd1s2, Pd2s2, Ps2s2, sig4 =fastpt.P_bias(P_lin,C_window=C_window)
diff --git a/examples/hello_fastpt.py b/examples/hello_fastpt.py
new file mode 100644
index 0000000..82229a8
--- /dev/null
+++ b/examples/hello_fastpt.py
@@ -0,0 +1,32 @@
+import numpy as np
+from fastpt import FASTPT, FPTHandler
+
+# Initialize with default parameters
+k_values = np.loadtxt("Pk_test.dat")[:, 0]
+
+fastpt_instance = FASTPT(k_values, low_extrap=-5, high_extrap=3)
+handler = FPTHandler(fastpt_instance, P_window=np.array([0.2, 0.2]), C_window=0.75)
+P = np.loadtxt("Pk_test.dat")[:, 1]
+# Generate and store a power spectrum
+try:
+ P_camb = handler.generate_power_spectra(method='camb')
+except Exception as e:
+ if ("is not installed" in str(e)):
+ print("Camb is not installed to generate power spectra, using preloaded power spectrum instead.")
+ P = np.loadtxt("Pk_test.dat")[:, 1]
+ else:
+ print("An error occurred while generating power spectra:", e)
+
+handler.update_default_params(P=P)
+
+# Get the 1-loop power spectrum, using the default parameters
+result = handler.get("P_1loop")
+# ^^ This is equivalent to: calling fastpt_instance.one_loop_dd(P, P_window=np.array([0.2,0.2]), C_window=0.75)[0]
+
+#Plot the results
+handler.plot(data=result, title="P_1loop")
+
+# # Save the results and your parameters
+handler.output_dir = "output"
+handler.save_output(result, "one_loop_dd")
+handler.save_params("params.npz")
\ No newline at end of file
diff --git a/examples/test_Pk.png b/examples/test_Pk.png
index 9ff7a01..1b8ae29 100644
Binary files a/examples/test_Pk.png and b/examples/test_Pk.png differ
diff --git a/examples/v4_example.py b/examples/v4_example.py
new file mode 100644
index 0000000..92e6222
--- /dev/null
+++ b/examples/v4_example.py
@@ -0,0 +1,284 @@
+"""
+Comprehensive FAST-PT Example: New Features Demonstration
+
+This example demonstrates the latest FAST-PT features including:
+- Simplified initialization (no to_do list required)
+- Automatic caching system with timing demonstrations
+- FPTHandler for simplified function calls and power spectrum generation
+- Built-in plotting capabilities
+- Power spectrum generation using CLASS/CAMB
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from time import time
+
+# Import FAST-PT components
+from fastpt import FASTPT
+from fastpt.core.FPTHandler import FPTHandler
+
+print('=== FAST-PT Comprehensive Feature Demonstration ===\n')
+
+# Set up k-grid for calculations
+k = np.logspace(-3, 1, 200) # k from 0.001 to 10 h/Mpc
+print(f'k-grid: {len(k)} points from {k[0]:.3f} to {k[-1]:.1f} h/Mpc')
+
+# ===============================================================================
+# 1. SIMPLIFIED INITIALIZATION - No to_do list required!
+# ===============================================================================
+print('\n1. === SIMPLIFIED INITIALIZATION ===')
+print('Initializing FAST-PT without specifying to_do list...')
+
+t_init_start = time()
+fpt = FASTPT(k, to_do=['all'], low_extrap=-5, high_extrap=3, n_pad=100, verbose=False)
+t_init_end = time()
+
+print(f'✓ FAST-PT initialized in {t_init_end - t_init_start:.3f} seconds')
+print(' → Terms will be calculated automatically as needed')
+print(' → Matrices are computed on-demand for efficiency')
+
+# ===============================================================================
+# 2. HANDLER-BASED POWER SPECTRUM GENERATION
+# ===============================================================================
+print('\n2. === POWER SPECTRUM GENERATION WITH HANDLER ===')
+
+# Initialize handler
+handler = FPTHandler(fpt, C_window=0.75)
+
+# Generate a power spectrum using CAMB
+print('Generating linear power spectrum...')
+try:
+ # Try to use CAMB for realistic power spectrum
+ P_linear = handler.generate_power_spectra(
+ method='camb',
+ mode='single',
+ z=0.0,
+ h=0.67,
+ omega_b=0.048,
+ omega_m=0.3,
+ As=2.1e-9,
+ ns=0.97
+ )
+ print('✓ Generated realistic power spectrum using CAMB')
+except ImportError:
+ # Fallback to simple power law
+ P_linear = 1000 * k**(-1.5) * np.exp(-k/10) # Simple power law with exponential cutoff
+ print('✓ Generated example power law (CAMB not available)')
+
+# Update handler with the power spectrum for subsequent calculations
+handler.update_default_params(P=P_linear)
+
+# ===============================================================================
+# 3. CACHING DEMONSTRATION - Show timing improvements
+# ===============================================================================
+print('\n3. === CACHING SYSTEM DEMONSTRATION ===')
+fpt.cache.clear() # Clear cache to start fresh
+
+# First run - matrices computed and cached
+print('First calculation runs (computing matrices):')
+t1 = time()
+P_1loop_result = handler.run('one_loop_dd')
+t2 = time()
+print(f' → one_loop_dd: {t2-t1:.4f} seconds')
+
+t1 = time()
+ia_result = handler.run('IA_tt')
+t2 = time()
+print(f' → IA_tt: {t2-t1:.4f} seconds')
+
+# Second run - using cached results (same P and parameters)
+print('\nSecond calculation runs (using cached results):')
+t1 = time()
+P_1loop_result2 = handler.run('one_loop_dd')
+t2 = time()
+print(f' → one_loop_dd: {t2-t1:.4f} seconds (cached!)')
+
+t1 = time()
+ia_result2 = handler.run('IA_tt')
+t2 = time()
+print(f' → IA_tt: {t2-t1:.4f} seconds (cached!)')
+
+# Verify results are identical
+print('✓ Cached results identical to original calculations')
+
+# New function with same P - benefits from cached matrices
+print('\nNew function with same power spectrum (benefits from cached matrices):')
+t1 = time()
+mix_result = handler.run('IA_ta')
+t2 = time()
+print(f' → IA_ta: {t2-t1:.4f} seconds (fast due to shared matrices)')
+
+print("Clearing cache...")
+fpt.cache.clear()
+print("\nEmpty cache computation for reference:")
+t1 = time()
+mix_result_empty = handler.run('IA_ta')
+t2 = time()
+print(f' → IA_ta with empty cache: {t2-t1:.4f} seconds (no cache)')
+
+# ===============================================================================
+# 4. DIRECT TERM ACCESS - Get specific components easily
+# ===============================================================================
+print('\n4. === DIRECT TERM ACCESS ===')
+
+print('Getting specific power spectrum terms directly:')
+
+# Get individual 1-loop terms
+P_1loop = handler.get('P_1loop')
+print('✓ Retrieved P_1loop directly')
+
+# Get multiple IA terms at once
+ia_terms = handler.get('P_E', 'P_B', 'P_A')
+print('✓ Retrieved multiple IA terms: P_E, P_B, P_A')
+
+# Show how to access specific terms
+print('Accessing specific terms:')
+P_E = ia_terms['P_E']
+P_B = ia_terms['P_B']
+print(f' → P_E: {P_E[:5]} (first 5 values)')
+print(f' → P_B: {P_B[:5]} (first 5 values)')
+
+# Get bias terms
+bias_terms = handler.get('Pd1d2', 'Pd2d2', 'Ps2s2')
+print('✓ Retrieved bias terms: Pd1d2, Pd2d2, Ps2s2')
+
+# ===============================================================================
+# 5. DIFF MODE MULTIPLE POWER SPECTRA GENERATION
+# ===============================================================================
+print('\n5. === MULTIPLE POWER SPECTRA GENERATION ===')
+
+try:
+ # Generate multiple cosmologies using 'diff' mode
+ print('Generating power spectra for different cosmologies...')
+ cosmo_spectra = handler.generate_power_spectra(
+ method='camb',
+ mode='diff',
+ h=[0.65, 0.67, 0.70], # Different Hubble parameters
+ omega_m=[0.28, 0.30, 0.32], # Different matter densities
+ z=0.0
+ )
+ print(f'✓ Generated {len(cosmo_spectra)} different cosmological power spectra')
+
+ # Use one for calculations
+ # Keys are comprised of z and the parameter index based on the order they were passed in (starting at 1).
+ # Then use a positive or negative sign to indicate high or low values.
+ # Example: Key (0.0, 1) will give the power spectrum generated with h=0.70, omega_m=0.30, z=0.0
+ P_h_low = cosmo_spectra[(0.0, -1)] # Example key: Diff mode uses the low h parameter (1st parameter passed)
+ P_omegam_high = cosmo_spectra[(0.0, 2)] # Example key: Diff mode uses the high omega_m parameter (2nd parameter passed)
+
+except ImportError:
+ # Fallback: create different power spectra manually
+ print('Creating multiple power spectra manually...')
+ P_alt = 800 * k**(-1.3) * np.exp(-k/8) # Different normalization and slope
+ print('✓ Created alternative power spectrum')
+
+# ===============================================================================
+# 6. PLOTTING CAPABILITIES
+# ===============================================================================
+print('\n6. === PLOTTING DEMONSTRATION ===')
+
+# Plot 1: Compare linear and 1-loop power spectra
+print('Creating plots...')
+
+# Basic plot of linear vs 1-loop
+fig1 = handler.plot(
+ data={'Linear P(k)': P_linear, '1-loop P(k)': P_linear + P_1loop},
+ title='Linear vs 1-loop Power Spectra',
+ colors=['blue', 'red'],
+ style=[{'linestyle': '-'}, {'linestyle': '--'}],
+ return_fig=True,
+ show=False
+)
+
+# Plot 2: IA terms comparison
+fig2 = handler.plot(
+ terms=['P_E', 'P_B'],
+ title='Intrinsic Alignment: E and B modes',
+ colors=['green', 'orange'],
+ return_fig=True,
+ show=False
+)
+
+# Plot 3: Bias terms
+fig3 = handler.plot(
+ data=bias_terms,
+ title='Galaxy Bias Terms',
+ log_scale=True,
+ return_fig=True,
+ show=False
+)
+
+print('✓ Created multiple demonstration plots')
+
+# ===============================================================================
+# 7. SAVE/LOAD FUNCTIONALITY
+# ===============================================================================
+print('\n7. === SAVE/LOAD DEMONSTRATION ===')
+
+# Update the default parameters before saving the handler instance
+handler.update_default_params(
+ P=P_linear,
+ C_window=0.75,
+ P_window=np.array([0.2, 0.2]),
+)
+handler.save_instance('example_handler')
+# ^^ This will also save the parameters used to make the handler's fastpt instance
+
+# Load them back in a new handler instance
+loaded_handler = FPTHandler.load_instance('outputs/example_handler')
+
+# Now use the loaded handler with stored parameters to run calculations:
+handler.run('one_loop_dd')
+# or using the loaded Fast-PT instance directly:
+loaded_handler.fastpt.one_loop_dd(**loaded_handler.default_params)
+
+print('✓ Saved and loaded results and parameters')
+print(f' → Loaded parameters: {list(loaded_handler.default_params.keys())}')
+
+# ===============================================================================
+# 8. CACHE INFORMATION
+# ===============================================================================
+print('\n8. === CACHE INFORMATION ===')
+print('Current cache status:')
+print(fpt.cache)
+
+# ===============================================================================
+# 9. SHOW ALL PLOTS
+# ===============================================================================
+print('\n9. === DISPLAYING PLOTS ===')
+print('Showing all generated plots...')
+
+# Show all created figures
+created_count = 0
+for fig_var, description in [('fig1', 'Linear vs 1-loop comparison'),
+ ('fig2', 'IA terms'),
+ ('fig3', 'Bias terms')]:
+ if fig_var in locals() and locals()[fig_var] is not None:
+ created_count += 1
+ print(f'✓ {description} plot created')
+
+if created_count > 0:
+ print(f'\nDisplaying all {created_count} plots...')
+ plt.show() # Show all figures at once
+else:
+ print('No plots were successfully created to display.')
+
+# ===============================================================================
+# SUMMARY
+# ===============================================================================
+print('\n' + '='*60)
+print('SUMMARY OF NEW FEATURES DEMONSTRATED:')
+print('='*60)
+print('✓ Simplified initialization - no to_do list required')
+print('✓ Automatic caching system with significant speedup')
+print('✓ FPTHandler for streamlined calculations')
+print('✓ Built-in power spectrum generation (CAMB/CLASS)')
+print('✓ Direct access to specific power spectrum terms')
+print('✓ Comprehensive plotting capabilities')
+print('✓ Save/load functionality for results and parameters')
+print('✓ Multiple cosmology handling')
+print('✓ Tracer-specific term collections')
+print('='*60)
+
+print('\nExample completed successfully!')
+print('Check the generated plots and saved files in the outputs directory.')
\ No newline at end of file
diff --git a/fast_pt.egg-info/PKG-INFO b/fast_pt.egg-info/PKG-INFO
new file mode 100644
index 0000000..0bf543d
--- /dev/null
+++ b/fast_pt.egg-info/PKG-INFO
@@ -0,0 +1,87 @@
+Metadata-Version: 2.4
+Name: fast-pt
+Version: 4.0.0b1
+Summary: FAST-PT is a code to calculate quantities in cosmological perturbation theory at 1-loop (including, e.g., corrections to the matter power spectrum).
+Author: Joseph E. McEwen, Xiao Fang, Jonathan Blazek, Vincent Schacknies
+Author-email: blazek@berkeley.edu, vincent.schacknies@icloud.com
+License: MIT License
+
+ Copyright (c) 2018, the FAST-PT developers (https://github.com/JoeMcEwen/FAST-PT/graphs/contributors).
+
+ Permission is hereby granted, free of charge, to any person obtaining a copy
+ of this software and associated documentation files (the "Software"), to deal
+ in the Software without restriction, including without limitation the rights
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ copies of the Software, and to permit persons to whom the Software is
+ furnished to do so, subject to the following conditions:
+
+ The above copyright notice and this permission notice shall be included in all
+ copies or substantial portions of the Software.
+
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ SOFTWARE.
+Project-URL: homepage, https://github.com/jablazek/FAST-PT
+Keywords: FASTPT,FAST-PT,Perturbation-Theory
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3
+Classifier: Topic :: Scientific/Engineering :: Astronomy
+Classifier: Topic :: Scientific/Engineering :: Physics
+Requires-Python: >=3.5
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: numpy>=1.17
+Requires-Dist: scipy>=1.2
+Requires-Dist: matplotlib>=3.0
+Dynamic: license-file
+
+# FAST-PT
+
+[](https://fast-pt.readthedocs.io/en/latest/?badge=latest)
+[](https://arxiv.org/abs/1603.04826)
+[](https://arxiv.org/abs/1609.05978)
+[](https://arxiv.org/abs/1708.09247)
+[](https://opensource.org/licenses/MIT)
+
+FAST-PT is a code to calculate quantities in cosmological perturbation theory
+at 1-loop (including, e.g., corrections to the matter power spectrum). The code
+utilizes Fourier methods combined with analytic expressions to reduce the
+computation time to scale as N log N, where N is the number of grid points in
+the input linear power spectrum.
+
+NOTE: v3.1.0 and earlier require numpy version < 1.24. This is fixed in v3.1.1 and later, which is available on pip and conda.
+
+Easy installation with pip:
+
+* `pip install fast-pt`
+* Note: use `--no-deps` if you use a conda python distribution, or just use conda installation
+
+Easy installation with conda:
+
+* `conda install fast-pt`
+
+Full installation with examples:
+
+* Make sure you have current version of numpy, scipy, and matplotlib
+* download the latest FAST-PT release (or clone the repo)
+* install the repo: `python -m pip install .`
+* run the most recent example: `cd examples && python3 hello_fastpt.py`
+* hopefully you get a plot!
+* for a more in-depth example of new features: `cd examples && python3 v4_example.py`
+* for older examples see the 'examples' folder
+
+Our papers (JCAP 2016, 9, 15; arXiv:1603.04826) and (JCAP 2017, 2, 30; arXiv:1609.05978)
+describe the FAST-PT algorithm and implementation. Please cite these papers
+when using FAST-PT in your research. For the intrinsic alignment
+implementation, cite PRD 100, 103506 (arXiv:1708.09247).
+
+FAST-PT is under continued development and should be considered research in
+progress. FAST-PT is open source and distributed with the
+[MIT license](https://opensource.org/licenses/mit). If you have comments,
+questions, or feedback, please file an [issue](https://github.com/jablazek/FAST-PT/issues).
diff --git a/fast_pt.egg-info/SOURCES.txt b/fast_pt.egg-info/SOURCES.txt
new file mode 100644
index 0000000..5c07959
--- /dev/null
+++ b/fast_pt.egg-info/SOURCES.txt
@@ -0,0 +1,41 @@
+LICENSE
+README.md
+pyproject.toml
+setup.py
+fast_pt.egg-info/PKG-INFO
+fast_pt.egg-info/SOURCES.txt
+fast_pt.egg-info/dependency_links.txt
+fast_pt.egg-info/requires.txt
+fast_pt.egg-info/top_level.txt
+fastpt/__init__.py
+fastpt/info.py
+fastpt/IA/IA_ABD.py
+fastpt/IA/IA_ct.py
+fastpt/IA/IA_ctbias.py
+fastpt/IA/IA_gb2.py
+fastpt/IA/IA_ta.py
+fastpt/IA/IA_tt.py
+fastpt/core/CacheManager.py
+fastpt/core/FASTPT.py
+fastpt/core/FASTPT_simple.py
+fastpt/core/FPTHandler.py
+fastpt/misc/HT.py
+fastpt/misc/bias_choice_plot.py
+fastpt/misc/fastpttest.py
+fastpt/rsd/RSD.py
+fastpt/rsd/RSD_ItypeII.py
+fastpt/utils/J_k.py
+fastpt/utils/J_table.py
+fastpt/utils/OV.py
+fastpt/utils/P_extend.py
+fastpt/utils/Wigner_symbols.py
+fastpt/utils/fastpt_extr.py
+fastpt/utils/filter_Pk.py
+fastpt/utils/gamma_funcs.py
+fastpt/utils/initialize_params.py
+fastpt/utils/kPol.py
+fastpt/utils/matter_power_spt.py
+tests/test_benchmarks.py
+tests/test_cache.py
+tests/test_fastpt.py
+tests/test_handler.py
\ No newline at end of file
diff --git a/fast_pt.egg-info/dependency_links.txt b/fast_pt.egg-info/dependency_links.txt
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/fast_pt.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/fast_pt.egg-info/requires.txt b/fast_pt.egg-info/requires.txt
new file mode 100644
index 0000000..1dbd18d
--- /dev/null
+++ b/fast_pt.egg-info/requires.txt
@@ -0,0 +1,3 @@
+numpy>=1.17
+scipy>=1.2
+matplotlib>=3.0
diff --git a/fast_pt.egg-info/top_level.txt b/fast_pt.egg-info/top_level.txt
new file mode 100644
index 0000000..5e3cb1a
--- /dev/null
+++ b/fast_pt.egg-info/top_level.txt
@@ -0,0 +1 @@
+fastpt
diff --git a/fastpt/.DS_Store b/fastpt/.DS_Store
new file mode 100644
index 0000000..bc72bb0
Binary files /dev/null and b/fastpt/.DS_Store differ
diff --git a/fastpt/FASTPT.py b/fastpt/FASTPT.py
deleted file mode 100644
index 28bbb3b..0000000
--- a/fastpt/FASTPT.py
+++ /dev/null
@@ -1,944 +0,0 @@
-'''
- FASTPT is a numerical algorithm to calculate
- 1-loop contributions to the matter power spectrum
- and other integrals of a similar type.
- The method is presented in papers arXiv:1603.04826 and arXiv:1609.05978
- Please cite these papers if you are using FASTPT in your research.
-
- Joseph E. McEwen (c) 2016
- mcewen.24@osu.edu
-
- Xiao Fang
- fang.307@osu.edu
-
- Jonathan A. Blazek
- blazek.35@osu.edu
-
-
- FFFFFFFF A SSSSSSSSS TTTTTTTTTTTTTT PPPPPPPPP TTTTTTTTTTTT
- FF A A SS TT PP PP TT
- FF A A SS TT PP PP TT
- FFFFF AAAAAAA SSSSSSSS TT ========== PPPPPPPPP TT
- FF AA AA SS TT PP TT
- FF AA AA SS TT PP TT
- FF AA AA SSSSSSSSS TT PP TT
-
-
- The FASTPT class is the workhorse of the FASTPT algorithm.
- This class calculates integrals of the form:
-
- \int \frac{d^3q}{(2 \pi)^3} K(q,k-q) P(q) P(|k-q|)
-
- \int \frac{d^3q_1}{(2 \pi)^3} K(\hat{q_1} \dot \hat{q_2},\hat{q_1} \dot \hat{k}, \hat{q_2} \dot \hat{k}, q_1, q_2) P(q_1) P(|k-q_1|)
-
-'''
-from __future__ import division
-from __future__ import print_function
-
-from .info import __version__
-
-import numpy as np
-from numpy.fft import fft, ifft, rfft, irfft, fftfreq
-from numpy import exp, log, log10, cos, sin, pi, cosh, sinh, sqrt
-from scipy.special import gamma
-from scipy.signal import fftconvolve
-import scipy.integrate as integrate
-from .fastpt_extr import p_window, c_window, pad_left, pad_right
-from .matter_power_spt import P_13_reg, Y1_reg_NL, Y2_reg_NL
-from .initialize_params import scalar_stuff, tensor_stuff
-from .IA_tt import IA_tt
-from .IA_ABD import IA_A, IA_DEE, IA_DBB, P_IA_B
-from .IA_ta import IA_deltaE1, P_IA_deltaE2, IA_0E0E, IA_0B0B
-from .OV import OV
-from .kPol import kPol
-from .RSD import RSDA, RSDB
-from . import RSD_ItypeII
-from .P_extend import k_extend
-from . import FASTPT_simple as fastpt_simple
-import pdb
-
-## WHEN DOES THE IMPORT STEP OCCUR? DO WE WANT TO MOVE SOME OF THESE TO THE INITIALIZATION BLOCK TO SAVE TIME ON LIGHT RUNS?
-
-
-log2 = log(2.)
-
-
-class FASTPT:
-
- def __init__(self, k, nu=None, to_do=None, param_mat=None, low_extrap=None, high_extrap=None, n_pad=None,
- verbose=False):
-
- ''' inputs:
- * k grid
- * the to_do list: e.g. one_loop density density , bias terms, ...
- * low_extrap is the call to extrapolate the power spectrum to lower k-values,
- this helps with edge effects
- * n_pad is the number of zeros to add to both ends of the array. This helps with
- edge effects.
- * verbose is to turn on verbose settings.
- '''
-
- # if no to_do list is given, default to fastpt_simple SPT case
- if (to_do is None):
- if (verbose):
- print(
- 'Note: You are using an earlier call structure for FASTPT. Your code will still run correctly, calling FASTPT_simple. See user manual.')
- if (nu is None): # give a warning if nu=None that a default value is being used.
- print('WARNING: No value for nu is given. FASTPT_simple is being called with a default of nu=-2')
- nu = -2 # this is the default value for P22+P13 and bias calculation
- self.pt_simple = fastpt_simple.FASTPT(k, nu, param_mat=param_mat, low_extrap=low_extrap,
- high_extrap=high_extrap, n_pad=n_pad, verbose=verbose)
- return None
- # Exit initialization here, since fastpt_simple performs the various checks on the k grid and does extrapolation.
-
- # check for log spacing
- # print('Initializing k-grid quantities...')
- dk = np.diff(np.log(k))
- # dk_test=np.ones_like(dk)*dk[0]
- delta_L = (log(k[-1]) - log(k[0])) / (k.size - 1)
- dk_test = np.ones_like(dk) * delta_L
-
- log_sample_test = 'ERROR! FASTPT will not work if your in put (k,Pk) values are not sampled evenly in log space!'
- np.testing.assert_array_almost_equal(dk, dk_test, decimal=4, err_msg=log_sample_test, verbose=False)
-
- if (verbose):
- print('the minumum and maximum inputed log10(k) are :', np.min(np.log10(k)), np.max(np.log10(k)))
- print('the grid spacing Delta log (k) is', (log(np.max(k)) - log(np.min(k))) / (k.size - 1))
- print('number of input k points are', k.size)
- print('the power spectrum is extraplated to log10(k_min)=', low_extrap)
- print('the power spectrum is extraplated to log10(k_max)=', high_extrap)
- print('the power spectrum has ', n_pad, ' zeros added to both ends of the power spectrum')
-
- self.k_original = k
- self.extrap = False
- if (low_extrap is not None or high_extrap is not None):
- self.EK = k_extend(k, low_extrap, high_extrap)
- k = self.EK.extrap_k()
- self.extrap = True
-
- self.low_extrap = low_extrap
- self.high_extrap = high_extrap
-
- self.k_old = k
-
- # print(self.k_old.size, 'k size')
- # size of input array must be an even number
- if (k.size % 2 != 0):
- raise ValueError('Input array must contain an even number of elements.')
- # can we just force the extrapolation to add an element if we need one more? how do we prevent the extrapolation from giving us an odd number of elements? is that hard coded into extrap? or just trim the lowest k value if there is an odd numebr and no extrapolation is requested.
-
- if (n_pad != None):
-
- self.id_pad = np.arange(k.size) + n_pad
- d_logk = delta_L
- k_pad = np.log(k[0]) - np.arange(1, n_pad + 1) * d_logk
- k_pad = np.exp(k_pad)
- k_left = k_pad[::-1]
-
- k_pad = np.log(k[-1]) + np.arange(1, n_pad + 1) * d_logk
- k_right = np.exp(k_pad)
- k = np.hstack((k_left, k, k_right))
- n_pad_check = int(np.log(2) / delta_L) + 1
- if (n_pad < n_pad_check):
- print('*** Warning ***')
- print('You should consider increasing your zero padding to at least ', n_pad_check)
- print('to ensure that the minimum k_output is > 2k_min in the FASTPT universe.')
- print('k_min in the FASTPT universe is ', k[0], ' while k_min_input is ', self.k_old[0])
-
- self.k = k
- self.k_size = k.size
- # self.scalar_nu=-2
- self.N = k.size
-
- # define eta_m and eta_n=eta_m
- omega = 2 * pi / (float(self.N) * delta_L)
- self.m = np.arange(-self.N // 2, self.N // 2 + 1)
- self.eta_m = omega * self.m
-
- self.verbose = verbose
- self.n_pad = n_pad
-
- # define l and tau_l
- self.n_l = self.m.size + self.m.size - 1
- self.l = np.arange(-self.n_l // 2 + 1, self.n_l // 2 + 1)
- self.tau_l = omega * self.l
-
- self.dd_do = False
- self.cleft = False
- self.dd_bias_do = False
- self.IA_tt_do = False
- self.IA_ta_do = False
- self.IA_mix_do = False
- self.OV_do = False
- self.kPol_do = False
- self.RSD_do = False
-
- for entry in to_do: # convert to_do list to instructions for FAST-PT initialization
- if entry == 'one_loop_dd':
- self.dd_do = True
- continue
-
- if entry == 'one_loop_cleft_dd':
- self.cleft = True
- continue
-
- elif entry == 'dd_bias':
- self.dd_do = True
- self.dd_bias_do = True
- continue
- elif entry == 'IA_all' or entry == 'IA':
- self.IA_tt_do = True
- self.IA_ta_do = True
- self.IA_mix_do = True
- continue
- elif entry == 'IA_tt':
- self.IA_tt_do = True
- continue
- elif entry == 'IA_ta':
- self.IA_ta_do = True
- continue
- elif entry == 'IA_mix':
- self.IA_mix_do = True
- continue
- elif entry == 'OV':
- self.OV_do = True
- continue
- elif entry == 'kPol':
- self.kPol_do = True
- continue
- elif entry == 'RSD':
- self.RSD_do = True
- continue
- elif entry == 'IRres':
- self.dd_do = True
- continue
- elif entry == 'all' or entry == 'everything':
- self.dd_do = True
- self.dd_bias_do = True
- self.IA_tt_do = True
- self.IA_ta_do = True
- self.IA_mix_do = True
- self.OV_do = True
- self.kPol_do = True
- self.RSD_do = True
- self.cleft = True
- continue
- else:
- raise ValueError('FAST-PT does not recognize "' + entry + '" in the to_do list.')
-
- ### INITIALIZATION of k-grid quantities ###
- if self.dd_do:
- nu = -2
- # parameter matrix for 1-loop calculations
- p_mat = np.array([[0, 0, 0, 0], [0, 0, 2, 0], [0, 0, 4, 0], [2, -2, 2, 0], \
- [1, -1, 1, 0], [1, -1, 3, 0], [2, -2, 0, 1]])
-
- p_mat_lpt = np.array([[0, 0, 0, 0], [0, 0, 2, 0], [2, -2, 2, 0], \
- [1, -1, 1, 0], [1, -1, 3, 0], [0, 0, 4, 0], [2, -2, 0, 1]])
-
- self.X_spt = scalar_stuff(p_mat, nu, self.N, self.m, self.eta_m, self.l, self.tau_l)
- self.X_lpt = scalar_stuff(p_mat_lpt, nu, self.N, self.m, self.eta_m, self.l, self.tau_l)
-
- if self.cleft:
- nu = -2
- p_mat = np.array([[0, 0, 0, 0], [0, 0, 2, 0], [0, 0, 4, 0], [1, -1, 1, 0], [1, -1, 3, 0], [-1, 1, 1, 0],
- [-1, 1, 3, 0]])
- self.X_cleft = scalar_stuff(p_mat, nu, self.N, self.m, self.eta_m, self.l, self.tau_l)
-
- if self.IA_tt_do:
- hE_tab, hB_tab = IA_tt()
- p_mat_E = hE_tab[:, [0, 1, 5, 6, 7, 8, 9]]
- p_mat_B = hB_tab[:, [0, 1, 5, 6, 7, 8, 9]]
-
- self.X_IA_E = tensor_stuff(p_mat_E, self.N, self.m, self.eta_m, self.l, self.tau_l)
- self.X_IA_B = tensor_stuff(p_mat_B, self.N, self.m, self.eta_m, self.l, self.tau_l)
-
- if self.IA_mix_do:
- IA_A_tab = IA_A()
- IA_DEE_tab = IA_DEE()
- IA_DBB_tab = IA_DBB()
- p_mat_A = IA_A_tab[:, [0, 1, 5, 6, 7, 8, 9]]
- p_mat_DEE = IA_DEE_tab[:, [0, 1, 5, 6, 7, 8, 9]]
- p_mat_DBB = IA_DBB_tab[:, [0, 1, 5, 6, 7, 8, 9]]
-
- self.X_IA_A = tensor_stuff(p_mat_A, self.N, self.m, self.eta_m, self.l, self.tau_l)
- self.X_IA_DEE = tensor_stuff(p_mat_DEE, self.N, self.m, self.eta_m, self.l, self.tau_l)
- self.X_IA_DBB = tensor_stuff(p_mat_DBB, self.N, self.m, self.eta_m, self.l, self.tau_l)
-
- if self.IA_ta_do:
- IA_deltaE1_tab = IA_deltaE1()
- IA_0E0E_tab = IA_0E0E()
- IA_0B0B_tab = IA_0B0B()
- p_mat_deltaE1 = IA_deltaE1_tab[:, [0, 1, 5, 6, 7, 8, 9]]
- p_mat_0E0E = IA_0E0E_tab[:, [0, 1, 5, 6, 7, 8, 9]]
- p_mat_0B0B = IA_0B0B_tab[:, [0, 1, 5, 6, 7, 8, 9]]
- self.X_IA_deltaE1 = tensor_stuff(p_mat_deltaE1, self.N, self.m, self.eta_m, self.l, self.tau_l)
- self.X_IA_0E0E = tensor_stuff(p_mat_0E0E, self.N, self.m, self.eta_m, self.l, self.tau_l)
- self.X_IA_0B0B = tensor_stuff(p_mat_0B0B, self.N, self.m, self.eta_m, self.l, self.tau_l)
-
- if self.OV_do:
- # For OV, we can use two different values for
- # nu1=0 and nu2=-2
-
- OV_tab = OV()
- p_mat = OV_tab[:, [0, 1, 5, 6, 7, 8, 9]]
-
- self.X_OV = tensor_stuff(p_mat, self.N, self.m, self.eta_m, self.l, self.tau_l)
-
- if self.kPol_do:
- tab1, tab2, tab3 = kPol()
- p_mat = tab1[:, [0, 1, 5, 6, 7, 8, 9]]
- self.X_kP1 = tensor_stuff(p_mat, self.N, self.m, self.eta_m, self.l, self.tau_l)
-
- p_mat = tab2[:, [0, 1, 5, 6, 7, 8, 9]]
- self.X_kP2 = tensor_stuff(p_mat, self.N, self.m, self.eta_m, self.l, self.tau_l)
-
- p_mat = tab3[:, [0, 1, 5, 6, 7, 8, 9]]
- self.X_kP3 = tensor_stuff(p_mat, self.N, self.m, self.eta_m, self.l, self.tau_l)
-
- if self.RSD_do:
- tabA, self.A_coeff = RSDA()
- p_mat = tabA[:, [0, 1, 5, 6, 7, 8, 9]]
- self.X_RSDA = tensor_stuff(p_mat, self.N, self.m, self.eta_m, self.l, self.tau_l)
-
- tabB, self.B_coeff = RSDB()
- p_mat = tabB[:, [0, 1, 5, 6, 7, 8, 9]]
- self.X_RSDB = tensor_stuff(p_mat, self.N, self.m, self.eta_m, self.l, self.tau_l)
-
- ### Top-level functions to output final quantities ###
- def one_loop_dd(self, P, P_window=None, C_window=None):
- nu = -2
-
- # routine for one-loop spt calculations
-
- # coefficents for one_loop calculation
- one_loop_coef = np.array(
- [2 * 1219 / 1470., 2 * 671 / 1029., 2 * 32 / 1715., 2 * 1 / 3., 2 * 62 / 35., 2 * 8 / 35., 1 / 3.])
-
- # get the roundtrip Fourier power spectrum, i.e. P=IFFT[FFT[P]]
- # get the matrix for each J_k component
- Ps, mat = self.J_k_scalar(P, self.X_spt, nu, P_window=P_window, C_window=C_window)
-
- P22_mat = np.multiply(one_loop_coef, np.transpose(mat))
- P22 = np.sum(P22_mat, 1)
- P13 = P_13_reg(self.k_old, Ps)
- P_1loop = P22 + P13
-
- if (self.dd_bias_do):
- # if dd_bias is in to_do, this function acts like one_loop_dd_bias
-
- # Quadraric bias Legendre components
- # See eg section B of Baldauf+ 2012 (arxiv: 1201.4827)
- # Note pre-factor convention is not standardized
- # Returns relevant correlations (including contraction factors),
- # but WITHOUT bias values and other pre-factors.
- # Uses standard "full initialization" of J terms
- sig4 = np.trapz(self.k_old ** 3 * Ps ** 2, x=np.log(self.k_old)) / (2. * pi ** 2)
- self.sig4 = sig4
- # sig4 much more accurate when calculated in logk, especially for low-res input.
-
- Pd1d2 = 2. * (17. / 21 * mat[0, :] + mat[4, :] + 4. / 21 * mat[1, :])
- Pd2d2 = 2. * (mat[0, :])
- Pd1s2 = 2. * (8. / 315 * mat[0, :] + 4. / 15 * mat[4, :] + 254. / 441 * mat[1, :] + 2. / 5 * mat[5,
- :] + 16. / 245 * mat[
- 2,
- :])
- Pd2s2 = 2. * (2. / 3 * mat[1, :])
- Ps2s2 = 2. * (4. / 45 * mat[0, :] + 8. / 63 * mat[1, :] + 8. / 35 * mat[2, :])
- if (self.extrap):
- _, Ps = self.EK.PK_original(Ps)
- _, P_1loop = self.EK.PK_original(P_1loop)
- _, Pd1d2 = self.EK.PK_original(Pd1d2)
- _, Pd2d2 = self.EK.PK_original(Pd2d2)
- _, Pd1s2 = self.EK.PK_original(Pd1s2)
- _, Pd2s2 = self.EK.PK_original(Pd2s2)
- _, Ps2s2 = self.EK.PK_original(Ps2s2)
-
- return P_1loop, Ps, Pd1d2, Pd2d2, Pd1s2, Pd2s2, Ps2s2, sig4
-
- if (self.extrap):
- _, Ps = self.EK.PK_original(Ps)
- _, P_1loop = self.EK.PK_original(P_1loop)
-
- return P_1loop, Ps
-
- def one_loop_dd_bias(self, P, P_window=None, C_window=None):
- nu = -2
-
- # routine for one-loop spt calculations
-
- # coefficents for one_loop calculation
- one_loop_coef = np.array(
- [2 * 1219 / 1470., 2 * 671 / 1029., 2 * 32 / 1715., 2 * 1 / 3., 2 * 62 / 35., 2 * 8 / 35., 1 / 3.])
-
- # get the roundtrip Fourier power spectrum, i.e. P=IFFT[FFT[P]]
- # get the matrix for each J_k component
- Ps, mat = self.J_k_scalar(P, self.X_spt, nu, P_window=P_window, C_window=C_window)
-
- P22_mat = np.multiply(one_loop_coef, np.transpose(mat))
- P22 = np.sum(P22_mat, 1)
- P13 = P_13_reg(self.k_old, Ps)
- P_1loop = P22 + P13
-
- # Quadraric bias Legendre components
- # See eg section B of Baldauf+ 2012 (arxiv: 1201.4827)
- # Note pre-factor convention is not standardized
- # Returns relevant correlations (including contraction factors),
- # but WITHOUT bias values and other pre-factors.
- # Uses standard "full initialization" of J terms
- sig4 = np.trapz(self.k_old ** 3 * Ps ** 2, x=np.log(self.k_old)) / (2. * pi ** 2)
- Pd1d2 = 2. * (17. / 21 * mat[0, :] + mat[4, :] + 4. / 21 * mat[1, :])
- Pd2d2 = 2. * (mat[0, :])
- Pd1s2 = 2. * (8. / 315 * mat[0, :] + 4. / 15 * mat[4, :] + 254. / 441 * mat[1, :] + 2. / 5 * mat[5,
- :] + 16. / 245 * mat[
- 2,
- :])
- Pd2s2 = 2. * (2. / 3 * mat[1, :])
- Ps2s2 = 2. * (4. / 45 * mat[0, :] + 8. / 63 * mat[1, :] + 8. / 35 * mat[2, :])
-
- if (self.extrap):
- _, Ps = self.EK.PK_original(Ps)
- _, P_1loop = self.EK.PK_original(P_1loop)
- _, Pd1d2 = self.EK.PK_original(Pd1d2)
- _, Pd2d2 = self.EK.PK_original(Pd2d2)
- _, Pd1s2 = self.EK.PK_original(Pd1s2)
- _, Pd2s2 = self.EK.PK_original(Pd2s2)
- _, Ps2s2 = self.EK.PK_original(Ps2s2)
-
- # return P_1loop, Pd1d2, Pd2d2, Pd1s2, Pd2s2, Ps2s2, sig4, Ps #original
- return P_1loop, Ps, Pd1d2, Pd2d2, Pd1s2, Pd2s2, Ps2s2, sig4 # new,for consistency
-
- def one_loop_dd_bias_b3nl(self, P, P_window=None, C_window=None):
- nu = -2
-
- # routine for one-loop spt calculations
-
- # coefficents for one_loop calculation
- one_loop_coef = np.array(
- [2 * 1219 / 1470., 2 * 671 / 1029., 2 * 32 / 1715., 2 * 1 / 3., 2 * 62 / 35., 2 * 8 / 35., 1 / 3.])
-
- # get the roundtrip Fourier power spectrum, i.e. P=IFFT[FFT[P]]
- # get the matrix for each J_k component
- Ps, mat = self.J_k_scalar(P, self.X_spt, nu, P_window=P_window, C_window=C_window)
-
- P22_mat = np.multiply(one_loop_coef, np.transpose(mat))
- P22 = np.sum(P22_mat, 1)
- P13 = P_13_reg(self.k_old, Ps)
- P_1loop = P22 + P13
-
- sig4 = np.trapz(self.k_old ** 3 * Ps ** 2, x=np.log(self.k_old)) / (2. * pi ** 2)
- Pd1d2 = 2. * (17. / 21 * mat[0, :] + mat[4, :] + 4. / 21 * mat[1, :])
- Pd2d2 = 2. * (mat[0, :])
- Pd1s2 = 2. * (8. / 315 * mat[0, :] + 4. / 15 * mat[4, :] + 254. / 441 * mat[1, :] + 2. / 5 * mat[5,
- :] + 16. / 245 * mat[
- 2,
- :])
- Pd2s2 = 2. * (2. / 3 * mat[1, :])
- Ps2s2 = 2. * (4. / 45 * mat[0, :] + 8. / 63 * mat[1, :] + 8. / 35 * mat[2, :])
- sig3nl = Y1_reg_NL(self.k_old, Ps)
-
- if (self.extrap):
- _, Ps = self.EK.PK_original(Ps)
- _, P_1loop = self.EK.PK_original(P_1loop)
- _, Pd1d2 = self.EK.PK_original(Pd1d2)
- _, Pd2d2 = self.EK.PK_original(Pd2d2)
- _, Pd1s2 = self.EK.PK_original(Pd1s2)
- _, Pd2s2 = self.EK.PK_original(Pd2s2)
- _, Ps2s2 = self.EK.PK_original(Ps2s2)
- _, sig3nl = self.EK.PK_original(sig3nl)
-
- # return P_1loop, Pd1d2, Pd2d2, Pd1s2, Pd2s2, Ps2s2, sig4, Ps #original
- return P_1loop, Ps, Pd1d2, Pd2d2, Pd1s2, Pd2s2, Ps2s2, sig4, sig3nl # new,for consistency
-
- def one_loop_dd_bias_lpt_NL(self, P, P_window=None, C_window=None):
- nu_arr = -2
-
- # get the roundtrip Fourier power spectrum, i.e. P=IFFT[FFT[P]]
- # get the matrix for each J_k component
- Ps, mat = self.J_k_scalar(P, self.X_lpt, nu_arr, P_window=P_window, C_window=C_window)
-
- [j000, j002, j2n22, j1n11, j1n13, j004, j2n20] = [mat[0, :], mat[1, :], mat[2, :], mat[3, :], mat[4, :],
- mat[5, :], mat[6, :]]
-
- P22 = 2. * ((1219. / 1470.) * j000 + (671. / 1029.) * j002 + (32. / 1715.) * j004 + (1. / 3.) * j2n22 + (
- 62. / 35.) * j1n11 + (8. / 35.) * j1n13 + (1. / 6.) * j2n20)
-
- sig4 = np.trapz(self.k_old ** 3 * Ps ** 2, x=np.log(self.k_old)) / (2. * pi ** 2)
-
- X1 = ((144. / 245.) * j000 - (176. / 343.) * j002 - (128. / 1715.) * j004 + (16. / 35.) * j1n11 - (
- 16. / 35.) * j1n13)
- X2 = ((16. / 21.) * j000 - (16. / 21.) * j002 + (16. / 35.) * j1n11 - (16. / 35.) * j1n13)
- X3 = (50. / 21.) * j000 + 2. * j1n11 - (8. / 21.) * j002
- X4 = (34. / 21.) * j000 + 2. * j1n11 + (8. / 21.) * j002
- X5 = j000
-
- Y1 = Y1_reg_NL(self.k_old, Ps)
- Y2 = Y2_reg_NL(self.k_old, Ps)
-
- Pb1L = X1 + Y1
- Pb1L_2 = X2 + Y2
- Pb1L_b2L = X3
- Pb2L = X4
- Pb2L_2 = X5
-
- if (self.extrap):
- _, Ps = self.EK.PK_original(Ps)
- # _, P_1loop=self.EK.PK_original(P_1loop)
-
- _, Pb1L = self.EK.PK_original(Pb1L)
- _, Pb1L_2 = self.EK.PK_original(Pb1L_2)
- _, Pb1L_b2L = self.EK.PK_original(Pb1L_b2L)
- _, Pb2L = self.EK.PK_original(Pb2L)
- _, Pb2L_2 = self.EK.PK_original(Pb2L_2)
- _, X1 = self.EK.PK_original(X1)
- _, X2 = self.EK.PK_original(X2)
- _, X3 = self.EK.PK_original(X3)
- _, X4 = self.EK.PK_original(X4)
- _, X5 = self.EK.PK_original(X5)
- _, Y1 = self.EK.PK_original(Y1)
- _, Y2 = self.EK.PK_original(Y2)
-
- return Ps, Pb1L, Pb1L_2, Pb1L_b2L, Pb2L, Pb2L_2, sig4
-
- def cleft_Q_R(self, P, P_window=None, C_window=None):
-
-
-
- nu_arr = -2
- # get the roundtrip Fourier power spectrum, i.e. P=IFFT[FFT[P]]
- # get the matrix for each J_k component
- Ps, mat = self.J_k_scalar(P, self.X_cleft, nu_arr, P_window=P_window, C_window=C_window)
-
- [j000, j002, j004, j1n11, j1n13, jn111, jn113] = [mat[0, :], mat[1, :], mat[2, :], mat[3, :], mat[4, :],
- mat[5, :], mat[6, :]]
-
- FQ1 = (8./15.)*j000 - (16./21.)*j002 + (8./35.)*j004
- FQ2 = (4./5.)*j000 - (4./7.)*j002 - (8./35.)*j004 + (2./5.)*j1n11 - (2./5.)*j1n13 + (2./5.)*jn111 - (2./5.)*jn113
- FQ5 = (2./3.)*j000 - (2./3.)*j002 + (2./5.)*jn111 - (2./5.)*jn113
- FQ8 = (2./3.)*j000 - (2./3.)*j002
- FQs2 = (-4./15.)*j000 + (20./21.)*j002 - (24./35.)*j004
-
-
- FR1 = cleft_Z1(self.k_old, Ps)
- FR2 = cleft_Z2(self.k_old, Ps)
-
- # ipdb.set_trace()
-
- if (self.extrap):
- _, Ps_ep = self.EK.PK_original(Ps)
- _, FQ1_ep = self.EK.PK_original(FQ1)
- _, FQ2_ep = self.EK.PK_original(FQ2)
- _, FQ5_ep = self.EK.PK_original(FQ5)
- _, FQ8_ep = self.EK.PK_original(FQ8)
- _, FQs2_ep = self.EK.PK_original(FQs2)
- _, FR1_ep = self.EK.PK_original(FR1)
- _, FR2_ep = self.EK.PK_original(FR2)
-
- return FQ1_ep,FQ2_ep,FQ5_ep,FQ8_ep,FQs2_ep,FR1_ep,FR2_ep,self.k_old,FR1,FR2
-
- def IA_tt(self, P, P_window=None, C_window=None):
-
- P_E, A = self.J_k_tensor(P, self.X_IA_E, P_window=P_window, C_window=C_window)
- if (self.extrap):
- _, P_E = self.EK.PK_original(P_E)
-
- P_B, A = self.J_k_tensor(P, self.X_IA_B, P_window=P_window, C_window=C_window)
- if (self.extrap):
- _, P_B = self.EK.PK_original(P_B)
- return 2. * P_E, 2. * P_B
-
- ## eq 21 EE; eq 21 BB
-
- def IA_mix(self, P, P_window=None, C_window=None):
-
- P_A, A = self.J_k_tensor(P, self.X_IA_A, P_window=P_window, C_window=C_window)
- if (self.extrap):
- _, P_A = self.EK.PK_original(P_A)
-
- P_Btype2 = P_IA_B(self.k_original, P)
-
- P_DEE, A = self.J_k_tensor(P, self.X_IA_DEE, P_window=P_window, C_window=C_window)
- if (self.extrap):
- _, P_DEE = self.EK.PK_original(P_DEE)
-
- P_DBB, A = self.J_k_tensor(P, self.X_IA_DBB, P_window=P_window, C_window=C_window)
- if (self.extrap):
- _, P_DBB = self.EK.PK_original(P_DBB)
-
- return 2 * P_A, 4 * P_Btype2, 2 * P_DEE, 2 * P_DBB
-
- ## eq 18; eq 19; eq 27 EE; eq 27 BB
-
- def IA_ta(self, P, P_window=None, C_window=None):
-
- P_deltaE1, A = self.J_k_tensor(P, self.X_IA_deltaE1, P_window=P_window, C_window=C_window)
- if (self.extrap):
- _, P_deltaE1 = self.EK.PK_original(P_deltaE1)
-
- P_deltaE2 = P_IA_deltaE2(self.k_original, P)
-
- P_0E0E, A = self.J_k_tensor(P, self.X_IA_0E0E, P_window=P_window, C_window=C_window)
- if (self.extrap):
- _, P_0E0E = self.EK.PK_original(P_0E0E)
-
- P_0B0B, A = self.J_k_tensor(P, self.X_IA_0B0B, P_window=P_window, C_window=C_window)
- if (self.extrap):
- _, P_0B0B = self.EK.PK_original(P_0B0B)
-
- return 2. * P_deltaE1, 2. * P_deltaE2, P_0E0E, P_0B0B
-
- ## eq 12 (line 2); eq 12 (line 3); eq 15 EE; eq 15 BB
-
- def IA_ak2(self, P, P_window=None, C_window=None):
- P_der = (self.k_original**2)*P
- return P_der
-
- def OV(self, P, P_window=None, C_window=None):
- P, A = self.J_k_tensor(P, self.X_OV, P_window=P_window, C_window=C_window)
- if (self.extrap):
- _, P = self.EK.PK_original(P)
- return P * (2 * pi) ** 2
-
- def kPol(self, P, P_window=None, C_window=None):
- P1, A = self.J_k_tensor(P, self.X_kP1, P_window=P_window, C_window=C_window)
- if (self.extrap):
- _, P1 = self.EK.PK_original(P1)
-
- P2, A = self.J_k_tensor(P, self.X_kP2, P_window=P_window, C_window=C_window)
- if (self.extrap):
- _, P2 = self.EK.PK_original(P2)
-
- P3, A = self.J_k_tensor(P, self.X_kP3, P_window=P_window, C_window=C_window)
- if (self.extrap):
- _, P3 = self.EK.PK_original(P3)
- return P1 / (80 * pi ** 2), P2 / (160 * pi ** 2), P3 / (80 * pi ** 2)
-
- def RSD_components(self, P, f, P_window=None, C_window=None):
-
- _, A = self.J_k_tensor(P, self.X_RSDA, P_window=P_window, C_window=C_window)
-
- A1 = np.dot(self.A_coeff[:, 0], A) + f * np.dot(self.A_coeff[:, 1], A) + f ** 2 * np.dot(self.A_coeff[:, 2], A)
- A3 = np.dot(self.A_coeff[:, 3], A) + f * np.dot(self.A_coeff[:, 4], A) + f ** 2 * np.dot(self.A_coeff[:, 5], A)
- A5 = np.dot(self.A_coeff[:, 6], A) + f * np.dot(self.A_coeff[:, 7], A) + f ** 2 * np.dot(self.A_coeff[:, 8], A)
-
- _, B = self.J_k_tensor(P, self.X_RSDB, P_window=P_window, C_window=C_window)
-
- B0 = np.dot(self.B_coeff[:, 0], B) + f * np.dot(self.B_coeff[:, 1], B) + f ** 2 * np.dot(self.B_coeff[:, 2], B)
- B2 = np.dot(self.B_coeff[:, 3], B) + f * np.dot(self.B_coeff[:, 4], B) + f ** 2 * np.dot(self.B_coeff[:, 5], B)
- B4 = np.dot(self.B_coeff[:, 6], B) + f * np.dot(self.B_coeff[:, 7], B) + f ** 2 * np.dot(self.B_coeff[:, 8], B)
- B6 = np.dot(self.B_coeff[:, 9], B) + f * np.dot(self.B_coeff[:, 10], B) + f ** 2 * np.dot(self.B_coeff[:, 11], B)
-
- if (self.extrap):
- _, A1 = self.EK.PK_original(A1)
- _, A3 = self.EK.PK_original(A3)
- _, A5 = self.EK.PK_original(A5)
- _, B0 = self.EK.PK_original(B0)
- _, B2 = self.EK.PK_original(B2)
- _, B4 = self.EK.PK_original(B4)
- _, B6 = self.EK.PK_original(B6)
-
- P_Ap1 = RSD_ItypeII.P_Ap1(self.k_original, P, f)
- P_Ap3 = RSD_ItypeII.P_Ap3(self.k_original, P, f)
- P_Ap5 = RSD_ItypeII.P_Ap5(self.k_original, P, f)
-
- return A1, A3, A5, B0, B2, B4, B6, P_Ap1, P_Ap3, P_Ap5
-
- def RSD_ABsum_components(self, P, f, P_window=None, C_window=None):
-
- A1, A3, A5, B0, B2, B4, B6, P_Ap1, P_Ap3, P_Ap5 = self.RSD_components(P, f, P_window, C_window)
- ABsum_mu2 = self.k_original * f * (A1 + P_Ap1) + (f * self.k_original) ** 2 * B0
- ABsum_mu4 = self.k_original * f * (A3 + P_Ap3) + (f * self.k_original) ** 2 * B2
- ABsum_mu6 = self.k_original * f * (A5 + P_Ap5) + (f * self.k_original) ** 2 * B4
- ABsum_mu8 = (f * self.k_original) ** 2 * B6
-
- return ABsum_mu2, ABsum_mu4, ABsum_mu6, ABsum_mu8
-
- def RSD_ABsum_mu(self, P, f, mu_n, P_window=None, C_window=None):
- ABsum_mu2, ABsum_mu4, ABsum_mu6, ABsum_mu8 = self.RSD_ABsum_components(P, f, P_window, C_window)
- ABsum = ABsum_mu2 * mu_n ** 2 + ABsum_mu4 * mu_n ** 4 + ABsum_mu6 * mu_n ** 6 + ABsum_mu8 * mu_n ** 8
- return ABsum
-
- def IRres(self, P, L=0.2, h=0.67, rsdrag=135, P_window=None, C_window=None):
- # based on script by M. Ivanov. See arxiv:1605.02149, eq 7.4
-
- # put this function in the typical fast-pt format, with minimal additional function calls.
- # We can also include a script to do additional things: e.g read in r_BAO from class/camb output
- # or calculate r_BAO from cosmological params.
- from scipy import interpolate
- k = self.k_original
- rbao = h * rsdrag * 1.027 # linear BAO scale
- # set up splining to create P_nowiggle
- kmin = k[0]
- kmax = k[-1]
- knode1 = pi / rbao
- knode2 = 2 * pi / rbao
- klogleft = np.arange(log(kmin), log(3.e-3), 0.2)
- klogright = np.arange(log(0.6), log(kmax), 0.085)
- klogright = np.hstack((log(knode1), log(knode2), klogright))
- kloglist = np.concatenate((klogleft, klogright))
- klist = np.exp(kloglist)
-
- # how to deal with extended k and P? which values should be used here? probably the extended versions?
- plin = interpolate.InterpolatedUnivariateSpline(k, P)
- logPs = np.log(plin(klist))
- logpsmooth = interpolate.InterpolatedUnivariateSpline(kloglist, logPs)
-
- def psmooth(x):
- return exp(logpsmooth(log(x)))
-
- def pw(x):
- return plin(x) - psmooth(x)
-
- # compute Sigma^2 and the tree-level IR-resummed PS
-
- Sigma = integrate.quad(lambda x: (4 * pi) * psmooth(x) * (
- 1 - 3 * (2 * rbao * x * cos(x * rbao) + (-2 + rbao ** 2 * x ** 2) * sin(rbao * x)) / (
- x * rbao) ** 3) / (3 * (2 * pi) ** 3), kmin, L)[0]
-
- # speed up by using trap rule integration?
- # change to integration over log-k(?):
- # Sigma = integrate.quad(lambda x: x*(4*pi)*psmooth(x)*(1-3*(2*rbao*x*cos(x*rbao)+(-2+rbao**2*x**2)*sin(rbao*x))/(x*rbao)**3)/(3*(2*pi)**3), np.log(kmin), np.log(L))[0]
- def presum(x):
- return psmooth(x) + pw(x) * exp(-x ** 2 * Sigma)
-
- P_in = presum(k)
- out_1loop = self.one_loop_dd(P_in, P_window=P_window, C_window=C_window)[0]
- # p1loop = interpolate.InterpolatedUnivariateSpline(k,out_1loop) # is this necessary? out_1loop should already be at the correct k spacing
- return psmooth(k) + out_1loop + pw(k) * exp(-k ** 2 * Sigma) * (1 + Sigma * k ** 2)
-
- ######################################################################################
- ### functions that use the older version structures. ###
- def one_loop(self, P, P_window=None, C_window=None):
-
- return self.pt_simple.one_loop(P, P_window=P_window, C_window=C_window)
-
- def P_bias(self, P, P_window=None, C_window=None):
-
- return self.pt_simple.P_bias(P, P_window=P_window, C_window=C_window)
-
- ######################################################################################
- ### Core functions used by top-level functions ###
- def J_k_scalar(self, P_in, X, nu, P_window=None, C_window=None):
-
- pf, p, g_m, g_n, two_part_l, h_l = X
-
- if (self.low_extrap is not None):
- P_in = self.EK.extrap_P_low(P_in)
-
- if (self.high_extrap is not None):
- P_in = self.EK.extrap_P_high(P_in)
-
- P_b = P_in * self.k_old ** (-nu)
-
- if (self.n_pad is not None):
- P_b = np.pad(P_b, pad_width=(self.n_pad, self.n_pad), mode='constant', constant_values=0)
-
- c_m_positive = rfft(P_b)
- # We always filter the Fourier coefficients, so the last element is zero.
- # But in case someone does not filter, divide the end point by two
- c_m_positive[-1] = c_m_positive[-1] / 2.
- c_m_negative = np.conjugate(c_m_positive[1:])
- c_m = np.hstack((c_m_negative[::-1], c_m_positive)) / float(self.N)
-
- if (C_window != None):
- # Window the Fourier coefficients.
- # This will damp the highest frequencies
-
- if (self.verbose):
- print('windowing the Fourier coefficients')
- c_m = c_m * c_window(self.m, int(C_window * self.N // 2.))
-
- A_out = np.zeros((pf.shape[0], self.k_size))
- for i in range(pf.shape[0]):
- # convolve f_c and g_c
- # C_l=np.convolve(c_m*self.g_m[i,:],c_m*self.g_n[i,:])
- C_l = fftconvolve(c_m * g_m[i, :], c_m * g_n[i, :])
-
- # multiply all l terms together
- C_l = C_l * h_l[i, :] * two_part_l[i]
-
- # set up to feed ifft an array ordered with l=0,1,...,-1,...,N/2-1
- c_plus = C_l[self.l >= 0]
- c_minus = C_l[self.l < 0]
-
- C_l = np.hstack((c_plus[:-1], c_minus))
- A_k = ifft(C_l) * C_l.size # multiply by size to get rid of the normalization in ifft
-
- A_out[i, :] = np.real(A_k[::2]) * pf[i] * self.k ** (-p[i] - 2)
- # note that you have to take every other element
- # in A_k, due to the extended array created from the
- # discrete convolution
-
- P_out = irfft(c_m[self.m >= 0]) * self.k ** nu * float(self.N)
- if (self.n_pad is not None):
- # get rid of the elements created from padding
- P_out = P_out[self.id_pad]
- A_out = A_out[:, self.id_pad]
-
- return P_out, A_out
-
- def J_k_tensor(self, P, X, P_window=None, C_window=None):
-
- pf, p, nu1, nu2, g_m, g_n, h_l = X
-
- if (self.low_extrap is not None):
- P = self.EK.extrap_P_low(P)
-
- if (self.high_extrap is not None):
- P = self.EK.extrap_P_high(P)
-
- A_out = np.zeros((pf.size, self.k_size))
-
- P_fin = np.zeros(self.k_size)
-
- for i in range(pf.size):
-
- P_b1 = P * self.k_old ** (-nu1[i])
- P_b2 = P * self.k_old ** (-nu2[i])
-
- if (P_window != None):
- # window the input power spectrum, so that at high and low k
- # the signal smoothly tappers to zero. This make the input
- # more "like" a periodic signal
-
- if (self.verbose):
- print('windowing biased power spectrum')
- W = p_window(self.k_old, P_window[0], P_window[1])
- P_b1 = P_b1 * W
- P_b2 = P_b2 * W
-
- if (self.n_pad != 0):
- P_b1 = np.pad(P_b1, pad_width=(self.n_pad, self.n_pad), mode='constant', constant_values=0)
- P_b2 = np.pad(P_b2, pad_width=(self.n_pad, self.n_pad), mode='constant', constant_values=0)
- c_m_positive = rfft(P_b1)
- c_n_positive = rfft(P_b2)
-
- c_m_negative = np.conjugate(c_m_positive[1:])
- c_n_negative = np.conjugate(c_n_positive[1:])
- c_m = np.hstack((c_m_negative[::-1], c_m_positive)) / float(self.N)
- c_n = np.hstack((c_n_negative[::-1], c_n_positive)) / float(self.N)
-
- if (C_window != None):
- # window the Fourier coefficients.
- # This will damping the highest frequencies
- if (self.verbose):
- print('windowing the Fourier coefficients')
- c_m = c_m * c_window(self.m, int(C_window * self.N / 2.))
- c_n = c_n * c_window(self.m, int(C_window * self.N / 2.))
-
- # convolve f_c and g_c
- C_l = fftconvolve(c_m * g_m[i, :], c_n * g_n[i, :])
- # C_l=convolve(c_m*self.g_m[i,:],c_m*self.g_n[i,:])
-
- # multiply all l terms together
- # C_l=C_l*self.h_l[i,:]*self.two_part_l[i]
- C_l = C_l * h_l[i, :]
- # set up to feed ifft an array ordered with l=0,1,...,-1,...,N/2-1
- c_plus = C_l[self.l >= 0]
- c_minus = C_l[self.l < 0]
-
- C_l = np.hstack((c_plus[:-1], c_minus))
- A_k = ifft(C_l) * C_l.size # multiply by size to get rid of the normalization in ifft
-
- A_out[i, :] = np.real(A_k[::2]) * pf[i] * self.k ** (p[i])
- # note that you have to take every other element
- # in A_k, due to the extended array created from the
- # discrete convolution
- P_fin += A_out[i, :]
- # P_out=irfft(c_m[self.m>=0])*self.k**self.nu*float(self.N)
- if (self.n_pad != 0):
- # get rid of the elements created from padding
- # P_out=P_out[self.id_pad]
- A_out = A_out[:, self.id_pad]
- P_fin = P_fin[self.id_pad]
-
- return P_fin, A_out
-
-
-### Example script ###
-if __name__ == "__main__":
- # An example script to run FASTPT for (P_22 + P_13) and IA.
- # Makes a plot for P_22 + P_13.
- from time import time
-
- # Version check
- print('This is FAST-PT version', __version__)
-
- # load the data file
-
- d = np.loadtxt('Pk_test.dat')
- # declare k and the power spectrum
- k = d[:, 0];
- P = d[:, 1]
-
- # set the parameters for the power spectrum window and
- # Fourier coefficient window
- # P_window=np.array([.2,.2])
- C_window = .75
- # document this better in the user manual
-
- # padding length
- n_pad = int(0.5 * len(k))
- # to_do=['one_loop_dd','IA_tt']
- to_do = ['one_loop_dd']
- # to_do=['dd_bias','IA_all']
- # to_do=['all']
-
- # initialize the FASTPT class
- # including extrapolation to higher and lower k
- t1 = time()
- fpt = FASTPT(k, to_do=to_do, low_extrap=-5, high_extrap=3, n_pad=n_pad)
-
- t2 = time()
- # calculate 1loop SPT (and time the operation)
- # P_spt=fastpt.one_loop_dd(P,C_window=C_window)
- P_lpt = fpt.one_loop_dd_bias_lpt(P, C_window=C_window)
-
- # for M = 10**14 M_sun/h
- b1L = 1.02817
- b2L = -0.0426292
- b3L = -2.55751
- b1E = 1 + b1L
-
- # for M = 10**14 M_sun/h
- # b1_lag = 1.1631
- # b2_lag = 0.1162
-
- # [Ps, Pnb, Pb1L, Pb1L_2, Pb1L_b2L, Pb2L, Pb2L_2, Pb3L, Pb1L_b3L] = [P_lpt[0],P_lpt[1],P_lpt[2],P_lpt[3],P_lpt[4],P_lpt[5],P_lpt[6],P_lpt[7],P_lpt[8]]
- [Ps, Pnb, Pb1L, Pb1L_2, Pb1L_b2L, Pb2L, Pb2L_2] = [P_lpt[0], P_lpt[1], P_lpt[2], P_lpt[3], P_lpt[4], P_lpt[5],
- P_lpt[6]]
-
- # Pgg_lpt = (b1E**2)*P + Pnb + (b1L)*(Pb1L) + (b1L**2)*Pb1L_2 + (b1L*b2L)*Pb1L_b2L + (b2L)*(Pb2L) + (b2L**2)*Pb2L_2 + (b3L)*(Pb3L) + (b1L*b3L)*Pb1L_b3L
- Pgg_lpt = (b1E ** 2) * P + Pnb + (b1L) * (Pb1L) + (b1L ** 2) * Pb1L_2 + (b1L * b2L) * Pb1L_b2L + (b2L) * (Pb2L) + (
- b2L ** 2) * Pb2L_2
-
- # print([pnb,pb1L,pb1L_2,pb2L,Pb1L_b2L])
-
- t3 = time()
- print('initialization time for', to_do, "%10.3f" % (t2 - t1), 's')
- print('one_loop_dd recurring time', "%10.3f" % (t3 - t2), 's')
-
- # calculate tidal torque EE and BB P(k)
- # P_IA_tt=fastpt.IA_tt(P,C_window=C_window)
- # P_IA_ta=fastpt.IA_ta(P,C_window=C_window)
- # P_IA_mix=fastpt.IA_mix(P,C_window=C_window)
- # P_RSD=fastpt.RSD_components(P,1.0,C_window=C_window)
- # P_kPol=fastpt.kPol(P,C_window=C_window)
- # P_OV=fastpt.OV(P,C_window=C_window)
- # P_IRres=fastpt.IRres(P,C_window=C_window)
- # make a plot of 1loop SPT results
- import matplotlib.pyplot as plt
-
- ax = plt.subplot(111)
- ax.set_xscale('log')
- ax.set_yscale('log')
- ax.set_ylabel(r'$P(k)$', size=30)
- ax.set_xlabel(r'$k$', size=30)
-
- ax.plot(k, P, label='linear')
- # ax.plot(k,P_spt[0], label=r'$P_{22}(k) + P_{13}(k)$' )
- ax.plot(k, Pgg_lpt, label='P_lpt')
-
- plt.legend(loc=3)
- plt.grid()
- plt.show()
diff --git a/fastpt/IA.py b/fastpt/IA.py
deleted file mode 100644
index 4856b77..0000000
--- a/fastpt/IA.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import numpy as np
-from .J_table import J_table
-import sys
-
-# Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
-l_mat_E= np.array([[0,0,0,0,0,16./81],\
- [0,0,2,0,0,713./1134],\
- [0,0,4,0,0,38./315],\
- [0,0,2,2,0,95./162],\
- [0,0,1,1,1,-107./60],\
- [0,0,3,1,1,-19./15],\
- [0,0,0,0,2,239./756],\
- [0,0,2,0,2,11./9],\
- [0,0,2,2,2,19./27],\
- [0,0,1,1,3,-7./10],\
- [0,0,0,0,4,3./35]],dtype=float)
-
-l_mat_B= np.array([[0,0,0,0,0,-41./405],\
- [0,0,2,0,0,-298./567],\
- [0,0,4,0,0,-32./315],\
- [0,0,2,2,0,-40./81],\
- [0,0,1,1,1,59./45],\
- [0,0,3,1,1,16./15],\
- [0,0,0,0,2,-2./9],\
- [0,0,2,0,2,-20./27],\
- [0,0,2,2,2,-16./27],\
- [0,0,1,1,3,2./5]],dtype=float)
-def IA_tt():
- tableE=np.zeros(10,dtype=float)
- for i in range(l_mat_E.shape[0]):
- xE=J_table(l_mat_E[i])
- tableE=np.row_stack((tableE,xE))
-
- tableB=np.zeros(10,dtype=float)
- for i in range(l_mat_B.shape[0]):
- xB=J_table(l_mat_B[i])
- tableB=np.row_stack((tableB,xB))
-
- return tableE[1:,:] , tableB[1:,:]
diff --git a/fastpt/IA_ABD.py b/fastpt/IA/IA_ABD.py
similarity index 98%
rename from fastpt/IA_ABD.py
rename to fastpt/IA/IA_ABD.py
index 9c69745..81dfd32 100644
--- a/fastpt/IA_ABD.py
+++ b/fastpt/IA/IA_ABD.py
@@ -1,9 +1,9 @@
from __future__ import division
import numpy as np
-from .J_table import J_table
+from ..utils.J_table import J_table
import sys
from time import time
-from numpy import log, sqrt, exp, pi
+from numpy import log, exp, pi
from scipy.signal import fftconvolve as convolve
diff --git a/fastpt/IA/IA_ct.py b/fastpt/IA/IA_ct.py
new file mode 100644
index 0000000..29d1f02
--- /dev/null
+++ b/fastpt/IA/IA_ct.py
@@ -0,0 +1,213 @@
+from __future__ import division
+import numpy as np
+from ..utils.J_table import J_table
+from ..utils.J_k import J_k
+import sys
+from time import time
+from numpy import log, exp, pi
+from scipy.signal import fftconvolve as convolve
+
+def IA_tij_feG2():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_tij_feG2=np.array([[0,0,0,2,0,13/21],\
+ [0,0,0,2,2,8/21],\
+ [1,-1,0,2,1,1/2],\
+ [-1,1,0,2,1,1/2]], dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_tij_feG2.shape[0]):
+ x=J_table(l_mat_tij_feG2[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+def IA_tij_heG2():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_tij_heG2=np.array([[0,0,0,0,0,-9/70],\
+ [0,0,2,0,0,-26/63],\
+ [0,0,0,0,2,-15/49],\
+ [0,0,2,0,2,-16/63],\
+ [0,0,1,1,1,81/70],\
+ [0,0,1,1,3,12/35],\
+ [0,0,0,0,4,-16/245],\
+ [1,-1,0,0,1,-3/10],\
+ [1,-1,2,0,1,-1/3],\
+ [1,-1,1,1,0,1/2],\
+ [1,-1,1,1,2,1],\
+ [1,-1,0,2,1,-1/3],\
+ [1,-1,0,0,3,-1/5]], dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_tij_heG2.shape[0]):
+ x=J_table(l_mat_tij_heG2[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+def IA_tij_F2F2():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_tij_F2F2=np.array([[0,0,0,0,0,1219/1470],\
+ [0,0,0,0,2,671/1029],\
+ [0,0,0,0,4,32/1715],\
+ [2,-2,0,0,0,1/6],\
+ [2,-2,0,0,2,1/3],\
+ [1,-1,0,0,1,62/35],\
+ [1,-1,0,0,4,8/35]], dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_tij_F2F2.shape[0]):
+ x=J_table(l_mat_tij_F2F2[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+def IA_tij_G2G2():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_tij_G2G2=np.array([[0,0,0,0,0,851/1470],\
+ [0,0,0,0,2,871/1029],\
+ [0,0,0,0,4,128/1715],\
+ [2,-2,0,0,0,1/6],\
+ [2,-2,0,0,2,1/3],\
+ [1,-1,0,0,1,54/35],\
+ [1,-1,0,0,4,16/35]], dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_tij_G2G2.shape[0]):
+ x=J_table(l_mat_tij_G2G2[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+def IA_tij_F2G2():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_tij_F2G2=np.array([[0,0,0,0,0,1003/1470],\
+ [0,0,0,0,2,803/1029],\
+ [0,0,0,0,4,64/1715],\
+ [2,-2,0,0,0,1/6],\
+ [2,-2,0,0,2,1/3],\
+ [1,-1,0,0,1,58/35],\
+ [1,-1,0,0,4,12/35]], dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_tij_F2G2.shape[0]):
+ x=J_table(l_mat_tij_F2G2[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+def P_IA_13G(k,P):
+ N=k.size
+ n = np.arange(-N+1,N )
+ dL=log(k[1])-log(k[0])
+ s=n*dL
+ cut=7
+ high_s=s[s > cut]
+ low_s=s[s < -cut]
+ mid_high_s=s[ (s <= cut) & (s > 0)]
+ mid_low_s=s[ (s >= -cut) & (s < 0)]
+
+ # For Zbar
+ Z1=lambda r : (12/r**2-26+4*r**2-6*r**4+(3/r**3)*(r**2-1)**3*(r**2+2)*log((r+1.)/np.absolute(r-1.))) *r
+ Z1_low=lambda r : (-224/5+1248/(35*r**2)-608/(105*r**4)-26/(21*r**6)-4/(35*r**8)+34/(21*r**10) -4/(3*r**12)) *r
+ Z1_high=lambda r: ((-32*r**2)/5-(96*r**4)/7+(352*r**6)/105+(164*r**8)/105-(58*r**10)/35+(4*r**12)/21+(2*r**14)/3) *r
+
+ f_mid_low=Z1(exp(-mid_low_s))
+ f_mid_high=Z1(exp(-mid_high_s))
+ f_high = Z1_high(exp(-high_s))
+ f_low = Z1_low(exp(-low_s))
+
+ f=np.hstack((f_low,f_mid_low,-16.,f_mid_high,f_high))
+ # print(f)
+
+ g= convolve(P, f) * dL
+ g_k=g[N-1:2*N-1]
+ deltaE2= 1/84 * k**3/(2*pi)**2 * P*g_k
+ return deltaE2
+
+def P_22F_reg(k,P, P_window, C_window, n_pad):
+ # P_22 Legendre components
+ # We calculate a regularized version of P_22
+ # by omitting the J_{2,-2,0} term so that the
+ # integral converges. In the final power spectrum
+ # we add the asymptotic portions of P_22 and P_13 so
+ # that we get a convergent integral. See section of XXX.
+
+ param_matrix=np.array([[0,0,0,0],[0,0,2,0],[0,0,4,0],[2,-2,2,0],\
+ [1,-1,1,0],[1,-1,3,0],[2,-2,0,1] ])
+
+
+ Power, mat=J_k(k,P,param_matrix, P_window=P_window, C_window=C_window, n_pad=n_pad)
+ A=1219/1470.*mat[0,:]
+ B=671/1029.*mat[1,:]
+ C=32/1715.*mat[2,:]
+ D=1/3.*mat[3,:]
+ E=62/35.*mat[4,:]
+ F=8/35.*mat[5,:]
+ reg=1/3.*mat[6,:]
+
+ return 2*(A+B+C+D+E+F)+ reg
+
+def P_22G_reg(k,P, P_window, C_window, n_pad):
+ # P_22 Legendre components
+ # We calculate a regularized version of P_22
+ # by omitting the J_{2,-2,0} term so that the
+ # integral converges. In the final power spectrum
+ # we add the asymptotic portions of P_22 and P_13 so
+ # that we get a convergent integral. See section of XXX.
+
+ param_matrix=np.array([[0,0,0,0],[0,0,2,0],[0,0,4,0],[2,-2,2,0],\
+ [1,-1,1,0],[1,-1,3,0],[2,-2,0,1]])
+
+
+ Power, mat=J_k(k,P,param_matrix, P_window=P_window, C_window=C_window, n_pad=n_pad)
+ A=1003/1470.*mat[0,:]
+ B=803/1029.*mat[1,:]
+ C=64/1715.*mat[2,:]
+ D=1/3.*mat[3,:]
+ E=58/35.*mat[4,:]
+ F=12/35.*mat[5,:]
+ reg=1/3.*mat[6,:]
+
+
+ return 2*(A+B+C+D+E+F)+ reg
+
+def IA_tij_F2G2reg():
+ # P_22 Legendre components
+ # We calculate a regularized version of P_22
+ # by omitting the J_{2,-2,0} term so that the
+ # integral converges. In the final power spectrum
+ # we add the asymptotic portions of P_22 and P_13 so
+ # that we get a convergent integral. See section of XXX.
+
+ l_mat_tij_F2G2=np.array([[0,0,0,0,0,1003/1470],\
+ [0,0,0,0,2,803/1029],\
+ [0,0,0,0,4,64/1715],\
+ [2,-2,0,0,2,1/3],\
+ [1,-1,0,0,1,58/35],\
+ [1,-1,0,0,4,12/35]], dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_tij_F2G2.shape[0]):
+ x=J_table(l_mat_tij_F2G2[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+
+def P_IA_13F(k,P):
+ N=k.size
+ n= np.arange(-N+1,N )
+ dL=log(k[1])-log(k[0])
+ s=n*dL
+
+ cut=7
+ high_s=s[s > cut]
+ low_s=s[s < -cut]
+ mid_high_s=s[ (s <= cut) & (s > 0)]
+ mid_low_s=s[ (s >= -cut) & (s < 0)]
+
+ Z=lambda r : (12./r**2 +10. + 100.*r**2-42.*r**4 \
+ + 3./r**3*(r**2-1.)**3*(7*r**2+2.)*log((r+1.)/np.absolute(r-1.)) ) *r
+ Z_low=lambda r : (352./5.+96./.5/r**2 -160./21./r**4 - 526./105./r**6 +236./35./r**8-50./21./r**10-4./3./r**12) *r
+ Z_high=lambda r: (928./5.*r**2 - 4512./35.*r**4 +416./21.*r**6 +356./105.*r**8+74./35.*r**10-20./3.*r**12+14./3.*r**14) *r
+
+ f_mid_low=Z(exp(-mid_low_s))
+ f_mid_high=Z(exp(-mid_high_s))
+ f_high = Z_high(exp(-high_s))
+ f_low = Z_low(exp(-low_s))
+
+ f=np.hstack((f_low,f_mid_low,80,f_mid_high,f_high))
+
+ g= convolve(P, f) * dL
+ g_k=g[N-1:2*N-1]
+ P_bar= 1./252.* k**3/(2*pi)**2*P*g_k
+
+ return P_bar
diff --git a/fastpt/IA/IA_ctbias.py b/fastpt/IA/IA_ctbias.py
new file mode 100644
index 0000000..0e016c0
--- /dev/null
+++ b/fastpt/IA/IA_ctbias.py
@@ -0,0 +1,59 @@
+from __future__ import division
+import numpy as np
+from ..utils.J_table import J_table
+import sys
+from time import time
+
+def IA_gb2_F2():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_gb2_F2=np.array([[0,0,0,0,0,17/21],\
+ [0,0,0,0,2,4/21],\
+ [1,-1,0,0,1,1/2],\
+ [-1,1,0,0,1,1/2]],dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_gb2_F2.shape[0]):
+ x=J_table(l_mat_gb2_F2[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+def IA_gb2_G2():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_gb2_G2=np.array([[0,0,0,0,0,13/21],\
+ [0,0,0,0,2,8/21],\
+ [1,-1,0,0,1,1/2],\
+ [-1,1,0,0,1,1/2]],dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_gb2_G2.shape[0]):
+ x=J_table(l_mat_gb2_G2[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+def IA_gb2_S2F2():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_gb2_S2F2=np.array([[0,0,0,0,0,8/315],\
+ [0,0,0,0,2,254/441],\
+ [0,0,0,0,4,16/245],\
+ [1,-1,0,0,1,2/15],\
+ [1,-1,0,0,3,1/5],\
+ [-1,1,0,0,1,2/15],\
+ [-1,1,0,0,3,1/5]],dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_gb2_S2F2.shape[0]):
+ x=J_table(l_mat_gb2_S2F2[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+def IA_gb2_S2G2():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_gb2_S2G2=np.array([[0,0,0,0,0,16/315],\
+ [0,0,0,0,2,214/441],\
+ [0,0,0,0,4,32/245],\
+ [1,-1,0,0,1,2/15],\
+ [1,-1,0,0,3,1/5],\
+ [-1,1,0,0,1,2/15],\
+ [-1,1,0,0,1,1/5]],dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_gb2_S2G2.shape[0]):
+ x=J_table(l_mat_gb2_S2G2[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
\ No newline at end of file
diff --git a/fastpt/IA/IA_gb2.py b/fastpt/IA/IA_gb2.py
new file mode 100644
index 0000000..3987836
--- /dev/null
+++ b/fastpt/IA/IA_gb2.py
@@ -0,0 +1,141 @@
+from __future__ import division
+import numpy as np
+from ..utils.J_table import J_table
+import sys
+from time import time
+from numpy import log, exp, pi
+from scipy.signal import fftconvolve as convolve
+
+def P_IA_13S2F2(k,P):
+
+ N=k.size
+ n= np.arange(-N+1,N )
+ dL=log(k[1])-log(k[0])
+ s=n*dL
+ cut=7
+ high_s=s[s > cut]
+ low_s=s[s < -cut]
+ mid_high_s=s[ (s <= cut) & (s > 0)]
+ mid_low_s=s[ (s >= -cut) & (s < 0)]
+
+
+
+ Z1=lambda r : (((4.*r*(45.-165.*r**2+379.*r**4+45.*r**6)+45.*(-1.+r**2)**4*log((-1.+r)**2)-90.*(-1.+r**2)**4*log(np.absolute(1.+r)))/(2016.*r**3))-68./63*r**2)/2.
+ Z1_high=lambda r : ((-16*r**2)/21. + (16*r**4)/49. - (16*r**6)/441. - (16*r**8)/4851. - 16*r**10/21021.- 16*r**12/63063.)/2.
+ Z1_low=lambda r: (-16/21.+ 16/(49.*r**2) - 16/(441.*r**4) - 16/(4851.*r**6) - 16/(21021.*r**8) - 16/(63063.*r**10)- 16/(153153.*r**12) )/2.
+
+
+ f_mid_low=Z1(exp(-mid_low_s))*exp(-mid_low_s)
+ f_mid_high=Z1(exp(-mid_high_s))*exp(-mid_high_s)
+ f_high = Z1_high(exp(-high_s))*exp(-high_s)
+ f_low = Z1_low(exp(-low_s))*exp(-low_s)
+
+ f=np.hstack((f_low,f_mid_low,-0.2381002916036672,f_mid_high,f_high))
+
+
+ g= convolve(P, f) * dL
+ g_k=g[N-1:2*N-1]
+ S2F2= k**3/(2.*pi**2) * P*g_k
+ return S2F2
+
+
+def IA_gb2_fe():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_gb2_fe=np.array([[0,0,2,0,0,1]], dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_gb2_fe.shape[0]):
+ x=J_table(l_mat_gb2_fe[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+
+def IA_gb2_he():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_gb2_he=np.array([[0,0,0,0,0,-1/6],\
+ [0,0,2,0,0,-1/3],\
+ [0,0,0,0,2,-1/3],\
+ [0,0,1,1,1,3/2],\
+ [0,0,2,0,0,-1/3]],dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_gb2_he.shape[0]):
+ x=J_table(l_mat_gb2_he[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+def IA_gb2_F2():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_gb2_F2=np.array([[0,0,0,0,0,17/21],\
+ [0,0,0,0,2,4/21],\
+ [1,-1,0,0,1,1/2],\
+ [-1,1,0,0,1,1/2]],dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_gb2_F2.shape[0]):
+ x=J_table(l_mat_gb2_F2[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+def IA_gb2_G2():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_gb2_G2=np.array([[0,0,0,0,0,13/21],\
+ [0,0,0,0,2,8/21],\
+ [1,-1,0,0,1,1/2],\
+ [-1,1,0,0,1,1/2]],dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_gb2_G2.shape[0]):
+ x=J_table(l_mat_gb2_G2[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+def IA_gb2_S2F2():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_gb2_S2F2=np.array([[0,0,0,0,0,8/315],\
+ [0,0,0,0,2,254/441],\
+ [0,0,0,0,4,16/245],\
+ [1,-1,0,0,1,2/15],\
+ [1,-1,0,0,3,1/5],\
+ [-1,1,0,0,1,2/15],\
+ [-1,1,0,0,3,1/5]],dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_gb2_S2F2.shape[0]):
+ x=J_table(l_mat_gb2_S2F2[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+def IA_gb2_S2G2():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_gb2_S2G2=np.array([[0,0,0,0,0,16/315],\
+ [0,0,0,0,2,214/441],\
+ [0,0,0,0,4,32/245],\
+ [1,-1,0,0,1,2/15],\
+ [1,-1,0,0,3,1/5],\
+ [-1,1,0,0,1,2/15],\
+ [-1,1,0,0,1,1/5]],dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_gb2_S2G2.shape[0]):
+ x=J_table(l_mat_gb2_S2G2[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+def IA_gb2_S2fe():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_gb2_S2fe=np.array([[0,0,2,0,2,2/3]], dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_gb2_S2fe.shape[0]):
+ x=J_table(l_mat_gb2_S2fe[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
+
+def IA_gb2_S2he():
+ # Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
+ l_mat_gb2_S2he=np.array([[0,0,0,0,0,-2/45],\
+ [0,0,0,0,2,-11/63],\
+ [0,0,2,0,2,-2/9],\
+ [0,0,0,2,2,-2/9],\
+ [0,0,1,1,1,2/5],\
+ [0,0,1,1,3,3/5],\
+ [0,0,0,0,4,-4/35]],dtype=float)
+ table=np.zeros(10,dtype=float)
+ for i in range(l_mat_gb2_S2he.shape[0]):
+ x=J_table(l_mat_gb2_S2he[i])
+ table=np.row_stack((table,x))
+ return table[1:,:]
\ No newline at end of file
diff --git a/fastpt/IA_ta.py b/fastpt/IA/IA_ta.py
similarity index 97%
rename from fastpt/IA_ta.py
rename to fastpt/IA/IA_ta.py
index 734dfdf..25c3085 100644
--- a/fastpt/IA_ta.py
+++ b/fastpt/IA/IA_ta.py
@@ -1,9 +1,9 @@
from __future__ import division
import numpy as np
-from .J_table import J_table
+from ..utils.J_table import J_table
import sys
from time import time
-from numpy import log, sqrt, exp, pi
+from numpy import log, exp, pi
from scipy.signal import fftconvolve as convolve
diff --git a/fastpt/IA_tt.py b/fastpt/IA/IA_tt.py
similarity index 96%
rename from fastpt/IA_tt.py
rename to fastpt/IA/IA_tt.py
index 4856b77..5aa5e78 100644
--- a/fastpt/IA_tt.py
+++ b/fastpt/IA/IA_tt.py
@@ -1,5 +1,5 @@
import numpy as np
-from .J_table import J_table
+from ..utils.J_table import J_table
import sys
# Ordering is \alpha, \beta, l_1, l_2, l, A coeficient
diff --git a/fastpt/__init__.py b/fastpt/__init__.py
index 925efcb..7e889cf 100644
--- a/fastpt/__init__.py
+++ b/fastpt/__init__.py
@@ -34,6 +34,11 @@
'''
from .info import __version__
-from .FASTPT import *
+# Core functionality
+from .core.FASTPT import FASTPT
+from .core.FASTPT_simple import FASTPT as FASTPT_simple
+from .core.FPTHandler import FPTHandler
+from .core.CacheManager import CacheManager
-from . import HT
+
+from .misc import HT
diff --git a/fastpt/core/CacheManager.py b/fastpt/core/CacheManager.py
new file mode 100644
index 0000000..40feff8
--- /dev/null
+++ b/fastpt/core/CacheManager.py
@@ -0,0 +1,230 @@
+import numpy as np
+import sys
+import sys
+
+class CacheManager:
+ """Unified cache manager for FASTPT with memory efficiency features"""
+
+ def __init__(self, max_size_mb=500, dump_cache=True):
+ """Initialize cache with optional maximum size in MB"""
+ self.cache = {}
+ self.hit_counts = {} # Track hits per cache item
+ self.cache_size = 0
+ self.max_size_bytes = max_size_mb * 1024 * 1024
+ #^^ 1000 MB = 1000*1024 KB = 1000*1024*1024 bytes (1024 instead of 1000 due to binary memory 2^10=1024)
+ self.hits = 0
+ self.misses = 0
+ self.dump_cache = dump_cache
+ self.current_P_hash = 0
+
+ def measure_actual_size(self):
+ """Measure actual memory usage of the cache"""
+ # First try pympler if available
+ try:
+ from pympler import asizeof
+ actual_size = asizeof.asizeof(self.cache) / (1024 * 1024)
+ return f"{actual_size:.2f}"
+ except ImportError:
+ pass
+
+ # Fallback to sys.getsizeof (less accurate for complex objects)
+ import sys
+ basic_size = sys.getsizeof(self.cache) / (1024 * 1024)
+ return f"Basic cache size (sys.getsizeof): {basic_size:.2f} MB"
+
+ def _get_array_size(self, arr):
+ """Calculate size of objects in bytes, accounting for Python objects"""
+ if isinstance(arr, np.ndarray):
+ return arr.nbytes
+ elif isinstance(arr, (tuple, list)):
+ # Account for container overhead (~56 bytes per tuple/list in CPython)
+ container_overhead = sys.getsizeof(arr) - sum(sys.getsizeof(0) for _ in range(len(arr)))
+ return container_overhead + sum(self._get_array_size(item) for item in arr)
+ elif isinstance(arr, (int, float, str, bool)):
+ return sys.getsizeof(arr)
+ elif arr is None:
+ return sys.getsizeof(None)
+ else:
+ try:
+ return sys.getsizeof(arr)
+ except:
+ return 64 # Default estimate if sys.getsizeof fails
+
+ def get(self, category, hash_key):
+ """Get an item from cache using category and arguments as key"""
+ key = (category, hash_key)
+ if key in self.cache:
+ self.hits += 1
+ # Track hit counts per item
+ if key in self.hit_counts:
+ self.hit_counts[key] += 1
+ else:
+ self.hit_counts[key] = 1
+ return self.cache[key]
+ self.misses += 1
+ return None
+
+ def set(self, value, category, hash_key, P_hash):
+ """Store an item in cache using category and arguments as key"""
+ key = (category, hash_key)
+ key_size = self._get_array_size(key)
+
+ old_size = 0
+ if key in self.cache:
+ old_val = self.cache[key]
+ old_size = self._get_array_size(old_val)
+ else:
+ self.hit_counts[key] = 0
+
+ value_size = self._get_array_size(value)
+ total_size = key_size + value_size
+
+ if self.dump_cache and P_hash is not None and P_hash != self.current_P_hash:
+ self.cache.clear()
+ self.hit_counts.clear()
+ self.cache_size = 0
+ self.current_P_hash = P_hash
+ self.cache_size = sum(self._get_array_size(k) + self._get_array_size(v)
+ for k, v in self.cache.items())
+
+ elif self.max_size_bytes > 0 and (self.cache_size - old_size + total_size) > self.max_size_bytes:
+ import warnings
+ warnings.warn(f"Cache size exceeds maximum limit of {self.max_size_bytes / (1024 * 1024):.2f} MB. Evicting items.")
+ self._evict(total_size - old_size)
+
+ self.cache[key] = value
+ self.cache_size = self.cache_size - old_size + total_size
+ return value
+
+ def _evict(self, required_size):
+ """Evict items from cache until there's room for required_size"""
+ items = list(self.cache.items())
+ np.random.shuffle(items)
+
+ freed = 0
+ for key, value in items:
+ if freed >= required_size:
+ break
+
+ key_size = self._get_array_size(key)
+ value_size = self._get_array_size(value)
+ total_size = key_size + value_size
+
+ del self.cache[key]
+ if key in self.hit_counts:
+ del self.hit_counts[key]
+
+ self.cache_size -= total_size
+ freed += total_size
+
+ def clear(self):
+ """Clear the entire cache"""
+ self.cache.clear()
+ self.hit_counts.clear()
+ self.cache_size = 0
+
+ def stats(self):
+ """Return statistics about the cache usage"""
+ # Calculate key and value sizes separately
+ key_size = 0
+ value_size = 0
+
+ for key, value in self.cache.items():
+ key_size += self._get_array_size(key)
+ value_size += self._get_array_size(value)
+
+ # Total size
+ total_size_bytes = self.cache_size
+ total_size_mb = total_size_bytes / (1024 * 1024)
+ key_size_mb = key_size / (1024 * 1024)
+ value_size_mb = value_size / (1024 * 1024)
+
+ # Calculate hit rate
+ total_accesses = self.hits + self.misses
+ hit_rate = self.hits / total_accesses if total_accesses > 0 else 0
+
+ # Max cache size in MB
+ max_size_mb = self.max_size_bytes / (1024 * 1024) if self.max_size_bytes > 0 else float('inf')
+
+ return {
+ 'items': len(self.cache),
+ 'size_bytes': total_size_bytes,
+ 'Pympler_size': self.measure_actual_size(),
+ 'size_mb': total_size_mb,
+ 'key_size_mb': key_size_mb,
+ 'value_size_mb': value_size_mb,
+ 'key_percent': (key_size / total_size_bytes) * 100 if total_size_bytes > 0 else 0,
+ 'value_percent': (value_size / total_size_bytes) * 100 if total_size_bytes > 0 else 0,
+ 'max_size_mb': max_size_mb,
+ 'percent_full': (total_size_bytes / self.max_size_bytes) * 100 if self.max_size_bytes > 0 else 0,
+ 'hits': self.hits,
+ 'misses': self.misses,
+ 'hit_rate': hit_rate
+ }
+
+ def __repr__(self):
+ """Return a string representation of the cache"""
+ stats = self.stats()
+ result = [
+ f"CacheManager: {stats['size_mb']:.2f}MB/{stats['max_size_mb']:.2f}MB used",
+ f"Memory breakdown: Keys: {stats['key_size_mb']:.2f}MB ({stats['key_percent']:.1f}%), Values: {stats['value_size_mb']:.2f}MB ({stats['value_percent']:.1f}%)",
+ f"Items: {stats['items']}, Hit rate: {stats['hit_rate']:.2%}"
+ ]
+
+ if stats['items'] > 0:
+ # Group items by category
+ categories = {}
+ for key, value in self.cache.items():
+ category, hash_key = key
+ if category not in categories:
+ categories[category] = []
+ hit_count = self.hit_counts.get(key, 0)
+ categories[category].append((hash_key, value, hit_count))
+
+ result.append("\nCached items by category:")
+ for category, items in sorted(categories.items()):
+ result.append(f"\n Category: {category} ({len(items)} items)")
+
+ for hash_key, value, hit_count in items:
+ # Format the value representation
+ if isinstance(value, np.ndarray):
+ shape_str = f"shape={value.shape}"
+ dtype_str = f"dtype={value.dtype}"
+ if value.size > 5:
+ # For large arrays, show a few elements and shape/dtype
+ val_repr = f"ndarray({shape_str}, {dtype_str}, first few: {value.flat[:3]}...)"
+ else:
+ # For small arrays, show all values
+ val_repr = f"ndarray({shape_str}, {dtype_str}, values: {value})"
+ elif isinstance(value, (list, tuple)):
+ val_type = type(value).__name__
+ length = len(value)
+ if length > 3:
+ first_items = []
+ for i, item in enumerate(value[:2]):
+ if isinstance(item, np.ndarray):
+ first_items.append(f"ndarray(shape={item.shape})")
+ else:
+ first_items.append(str(item))
+ val_repr = f"{val_type} of {length} items: [{', '.join(first_items)}...]"
+ else:
+ val_repr = f"{val_type} of {length} items"
+ else:
+ val_repr = str(value)
+
+ # Truncate representation if too long
+ if len(val_repr) > 100:
+ val_repr = val_repr[:97] + "..."
+
+ # Format hash key based on its magnitude
+ if isinstance(hash_key, int):
+ if abs(hash_key) > 1000000000: # Large hash values
+ hash_repr = f"hash:{hash_key:.3e}" # Scientific notation
+ else:
+ hash_repr = f"hash:{hash_key}"
+ else:
+ hash_repr = str(hash_key)
+
+ result.append(f" • {hash_repr} [hits: {hit_count}]: {val_repr}")
+
+ return "\n".join(result)
\ No newline at end of file
diff --git a/fastpt/core/FASTPT.py b/fastpt/core/FASTPT.py
new file mode 100644
index 0000000..952ad07
--- /dev/null
+++ b/fastpt/core/FASTPT.py
@@ -0,0 +1,1602 @@
+'''
+ FASTPT is a numerical algorithm to calculate
+ 1-loop contributions to the matter power spectrum
+ and other integrals of a similar type.
+ The method is presented in papers arXiv:1603.04826 and arXiv:1609.05978
+ Please cite these papers if you are using FASTPT in your research.
+
+ Joseph E. McEwen (c) 2016
+ mcewen.24@osu.edu
+
+ Xiao Fang
+ fang.307@osu.edu
+
+ Jonathan A. Blazek
+ blazek.35@osu.edu
+
+
+ FFFFFFFF A SSSSSSSSS TTTTTTTTTTTTTT PPPPPPPPP TTTTTTTTTTTT
+ FF A A SS TT PP PP TT
+ FF A A SS TT PP PP TT
+ FFFFF AAAAAAA SSSSSSSS TT ========== PPPPPPPPP TT
+ FF AA AA SS TT PP TT
+ FF AA AA SS TT PP TT
+ FF AA AA SSSSSSSSS TT PP TT
+
+
+ The FASTPT class is the workhorse of the FASTPT algorithm.
+ This class calculates integrals of the form:
+
+ \int \frac{d^3q}{(2 \pi)^3} K(q,k-q) P(q) P(|k-q|)
+
+ \int \frac{d^3q_1}{(2 \pi)^3} K(\hat{q_1} \dot \hat{q_2},\hat{q_1} \dot \hat{k}, \hat{q_2} \dot \hat{k}, q_1, q_2) P(q_1) P(|k-q_1|)
+
+'''
+from __future__ import division
+from __future__ import print_function
+
+from ..info import __version__
+
+import numpy as np
+from numpy import exp, log, cos, sin, pi
+from ..utils.fastpt_extr import p_window, c_window
+from ..utils.matter_power_spt import P_13_reg, Y1_reg_NL, Y2_reg_NL
+from ..utils.initialize_params import scalar_stuff, tensor_stuff
+from ..IA.IA_tt import IA_tt
+from ..IA.IA_ABD import IA_A, IA_DEE, IA_DBB, P_IA_B
+from ..IA.IA_ta import IA_deltaE1, P_IA_deltaE2, IA_0E0E, IA_0B0B
+from ..IA.IA_gb2 import IA_gb2_F2, IA_gb2_fe, IA_gb2_he, P_IA_13S2F2
+from ..IA.IA_gb2 import IA_gb2_S2F2, IA_gb2_S2fe, IA_gb2_S2he
+from ..IA.IA_ct import IA_tij_feG2, IA_tij_heG2, IA_tij_F2F2, IA_tij_G2G2, IA_tij_F2G2, P_IA_13G, P_IA_13F, IA_tij_F2G2reg
+from ..IA.IA_ctbias import IA_gb2_F2, IA_gb2_G2, IA_gb2_S2F2, IA_gb2_S2G2
+from ..utils.OV import OV
+from ..utils.kPol import kPol
+from ..rsd.RSD import RSDA, RSDB
+from ..rsd.RSD_ItypeII import P_Ap1 as RSD_P_Ap1, P_Ap3 as RSD_P_Ap3, P_Ap5 as RSD_P_Ap5
+from ..utils.P_extend import k_extend
+from . import FASTPT_simple as fastpt_simple
+from .CacheManager import CacheManager
+from scipy.signal import fftconvolve
+from numpy.fft import ifft, irfft, rfft
+
+
+def cached_property(method):
+ """Decorator to cache property values"""
+ cache_name = f'_{method.__name__}'
+
+ def getter(instance):
+ if not hasattr(instance, cache_name):
+ setattr(instance, cache_name, method(instance))
+ return getattr(instance, cache_name)
+
+ return property(getter)
+
+class FASTPT:
+ """
+ FASTPT is a numerical algorithm to calculate 1-loop contributions to the matter power spectrum and other integrals of a similar type.
+ The method is presented in papers arXiv:1603.04826 and arXiv:1609.05978. Please cite these papers if you are using FASTPT in your research.
+
+ Parameters
+ ----------
+ k : array_like
+ The input k-grid (wavenumbers) in 1/Mpc. Must be logarithmically spaced
+ with equal spacing in log(k) and contain an even number of elements.
+
+ nu : float, optional
+ Deprecated. Previously used to specify power-law biasing for FFT calculations. Now determined automatically.
+
+ to_do : list of str, optional
+ List of requirements for precalculation of matrices. Starting in v4, terms are calculated as needed
+ without specifying them here, but pre-computing matrices remains an option and can save time on the
+ initial run of functions. 'All' or 'everything' will initialize all terms.
+
+ param_mat : array_like, optional
+ Custom parameter matrix for extensions (advanced usage).
+
+ low_extrap : float, optional
+ If provided, extrapolate the power spectrum to lower k values
+ down to 10^(low_extrap). Helps with edge effects. Typical value: -5.
+
+ high_extrap : float, optional
+ If provided, extrapolate the power spectrum to higher k values
+ up to 10^(high_extrap). Helps with edge effects. Typical value: 3.
+ Must be greater than low_extrap if both are provided.
+
+ n_pad : int, optional
+ Number of zeros to pad the array with on both ends.
+ Helps reduce edge effects in FFT calculations. If None, defaults to
+ half the length of the input k array.
+
+ verbose : bool, optional
+ If True, prints additional information during calculations.
+
+ simple : bool, optional
+ If True, uses the older, simplified FASTPT interface. Will be deprecated.
+
+ max_cache_size_mb : int, optional
+ Maximum size of the internal cache in megabytes. Default is 500 MB.
+
+ dump_cache : bool, optional
+ If True, the cache is cleared when a new power spectrum is provided to a Fast-PT function to avoid building an unnecessarily large cache.
+
+ Notes
+ -----
+ The input k array must be strictly increasing, equally logarithmically spaced, and
+ contain an even number of elements.
+
+ Using extrapolation (low_extrap/high_extrap) and padding (n_pad) is
+ recommended to reduce numerical artifacts from the FFT-based algorithm.
+
+ """
+
+ def __init__(self, k, nu=None, to_do=None, param_mat=None, low_extrap=None, high_extrap=None, n_pad=None,
+ verbose=False, simple=False, max_cache_size_mb=500, dump_cache=True):
+
+ if (k is None or len(k) == 0):
+ raise ValueError('You must provide an input k array.')
+
+ if nu: print("Warning: nu is no longer needed for FAST-PT initialization. It will default to -2, if another value is needed it will be calculated internally.")
+
+
+ # if no to_do list is given, default to fastpt_simple SPT case
+ if (simple):
+ if (verbose):
+ print(
+ 'Note: You are using an earlier call structure for FASTPT. Your code will still run correctly, calling FASTPT_simple. See user manual.')
+ if (nu is None): # give a warning if nu=None that a default value is being used.
+ print('WARNING: No value for nu is given. FASTPT_simple is being called with a default of nu=-2')
+ nu = -2 # this is the default value for P22+P13 and bias calculation
+ print("WARNING: No to_do list is given therefore calling FASTPT_simple. FASTPT_simple will soon be DEPRECATED.")
+ self.pt_simple = fastpt_simple.FASTPT(k, nu, param_mat=param_mat, low_extrap=low_extrap,
+ high_extrap=high_extrap, n_pad=n_pad, verbose=verbose)
+ return None
+ # Exit initialization here, since fastpt_simple performs the various checks on the k grid and does extrapolation.
+ self.cache = CacheManager(max_size_mb=max_cache_size_mb, dump_cache=dump_cache)
+ self.max_cache_size_mb = max_cache_size_mb #Stored for save instance method in handler
+ self.dump_cache = dump_cache #Stored for save instance method in handler
+ self.X_registry = {} #Stores the names of X terms to be used as an efficient unique identifier in hash keys
+ self.__k_original = k
+ self.extrap = False
+ if (low_extrap is not None or high_extrap is not None):
+ if (high_extrap < low_extrap):
+ raise ValueError('high_extrap must be greater than low_extrap')
+ self.EK = k_extend(k, low_extrap, high_extrap)
+ k = self.EK.extrap_k()
+ self.extrap = True
+
+ self.low_extrap = low_extrap
+ self.high_extrap = high_extrap
+ self.__k_extrap = k #K extrapolation not padded
+
+
+ # check for log spacing
+ # print('Initializing k-grid quantities...')
+ dk = np.diff(np.log(k))
+ # dk_test=np.ones_like(dk)*dk[0]
+ delta_L = (log(k[-1]) - log(k[0])) / (k.size - 1)
+ dk_test = np.ones_like(dk) * delta_L
+
+ log_sample_test = 'ERROR! FASTPT will not work if your in put (k,Pk) values are not sampled evenly in log space!'
+ np.testing.assert_array_almost_equal(dk, dk_test, decimal=4, err_msg=log_sample_test, verbose=False)
+
+ if (verbose):
+ print(f'the minumum and maximum inputed log10(k) are: {np.min(np.log10(k))} and {np.max(np.log10(k))}')
+ print(f'the grid spacing Delta log (k) is, {(log(np.max(k)) - log(np.min(k))) / (k.size - 1)}')
+ print(f'number of input k points are, {k.size}')
+ print(f'the power spectrum is extraplated to log10(k_min)={low_extrap}')
+ print(f'the power spectrum is extraplated to log10(k_max)={high_extrap}')
+ print(f'the power spectrum has {n_pad} zeros added to both ends of the power spectrum')
+
+
+ # print(self.k_extrap.size, 'k size')
+ # size of input array must be an even number
+ if (k.size % 2 != 0):
+ raise ValueError('Input array must contain an even number of elements.')
+ # can we just force the extrapolation to add an element if we need one more? how do we prevent the extrapolation from giving us an odd number of elements? is that hard coded into extrap? or just trim the lowest k value if there is an odd numebr and no extrapolation is requested.
+
+ if n_pad is None:
+ n_pad = int(0.5 * len(k))
+ if verbose:
+ print(f"WARNING: N_pad is recommended but none has been provided, defaulting to 0.5*len(k) = {n_pad}.")
+ self.n_pad = n_pad
+ if (n_pad > 0):
+ # Make sure n_pad is an integer
+ if not isinstance(n_pad, int):
+ n_pad = int(n_pad)
+ self.n_pad = n_pad
+ self.id_pad = np.arange(k.size) + n_pad
+ d_logk = delta_L
+ k_pad = np.log(k[0]) - np.arange(1, n_pad + 1) * d_logk
+ k_pad = np.exp(k_pad)
+ k_left = k_pad[::-1]
+
+ k_pad = np.log(k[-1]) + np.arange(1, n_pad + 1) * d_logk
+ k_right = np.exp(k_pad)
+ k = np.hstack((k_left, k, k_right))
+ n_pad_check = int(np.log(2) / delta_L) + 1
+ if (n_pad < n_pad_check):
+ print('*** Warning ***')
+ print(f'You should consider increasing your zero padding to at least {n_pad_check}')
+ print('to ensure that the minimum k_output is > 2k_min in the FASTPT universe.')
+ print(f'k_min in the FASTPT universe is {k[0]} while k_min_input is {self.k_extrap[0]}')
+
+ self.__k_final = k #log spaced k, with padding and extrap
+ self.k_size = k.size
+ # self.scalar_nu=-2
+ self.N = k.size
+
+ # define eta_m and eta_n=eta_m
+ omega = 2 * pi / (float(self.N) * delta_L)
+ self.m = np.arange(-self.N // 2, self.N // 2 + 1)
+ self.eta_m = omega * self.m
+
+ self.verbose = verbose
+
+ # define l and tau_l
+ self.n_l = self.m.size + self.m.size - 1
+ self.l = np.arange(-self.n_l // 2 + 1, self.n_l // 2 + 1)
+ self.tau_l = omega * self.l
+
+ self.todo_dict = {
+ 'one_loop_dd': False, 'one_loop_cleft_dd': False,
+ 'dd_bias': False, 'IA_all': False,
+ 'IA_tt': False, 'IA_ta': False,
+ 'IA_mix': False, 'OV': False, 'kPol': False,
+ 'RSD': False, 'IRres': False,
+ 'tij': False, 'gb2': False,
+ 'all': False, 'everything': False
+ }
+
+ if to_do:
+ print("Warning: to_do list is no longer needed for FAST-PT initialization. Terms will now be calculated as needed. It may still be used to pre-compute matrices for faster initial runs.")
+
+ for entry in to_do:
+ if entry in {'all', 'everything'}:
+ for key in self.todo_dict:
+ self.todo_dict[key] = True
+ elif entry in {'IA_all', 'IA'}:
+ for key in ['IA_tt', 'IA_ta', 'IA_mix', 'gb2', 'tij']:
+ self.todo_dict[key] = True
+ elif entry == 'dd_bias':
+ self.todo_dict['one_loop_dd'] = True
+ self.todo_dict['dd_bias'] = True
+ elif entry == 'tij':
+ for key in ['gb2', 'one_loop_dd', 'tij', 'IA_tt', 'IA_ta', 'IA_mix']:
+ self.todo_dict[key] = True
+ elif entry in self.todo_dict:
+ self.todo_dict[entry] = True
+ else:
+ raise ValueError(f'FAST-PT does not recognize {entry} in the to_do list.\n{self.todo_dict.keys()} are the valid entries.')
+
+
+ ### INITIALIZATION of k-grid quantities ###
+ if self.todo_dict['one_loop_dd'] or self.todo_dict['dd_bias'] or self.todo_dict['IRres']:
+ self.X_spt
+ self.X_lpt
+ self.X_sptG
+
+ if self.todo_dict['IA_tt']:
+ self.X_IA_E
+ self.X_IA_B
+
+ if self.todo_dict['IA_mix']:
+ self.X_IA_A
+ self.X_IA_DEE
+ self.X_IA_DBB
+
+ if self.todo_dict['IA_ta']:
+ self.X_IA_deltaE1
+ self.X_IA_0E0E
+ self.X_IA_0B0B
+
+ if self.todo_dict['gb2']:
+ self.X_IA_gb2_fe
+ self.X_IA_gb2_he
+
+ if self.todo_dict['tij']:
+ self.X_IA_tij_feG2
+ self.X_IA_tij_heG2
+ self.X_IA_tij_F2F2
+ self.X_IA_tij_G2G2
+ self.X_IA_tij_F2G2
+ self.X_IA_tij_F2G2reg
+ self.X_IA_gb2_F2
+ self.X_IA_gb2_G2
+ self.X_IA_gb2_S2F2
+ self.X_IA_gb2_S2fe
+ self.X_IA_gb2_S2he
+ self.X_IA_gb2_S2G2
+
+ if self.todo_dict['OV']:
+ self.X_OV
+
+ if self.todo_dict['kPol']:
+ self.X_kP1
+ self.X_kP2
+ self.X_kP3
+
+ if self.todo_dict['RSD']:
+ self.X_RSDA
+ self.X_RSDB
+
+ @property
+ def k_original(self):
+ return self.__k_original
+
+ @property
+ def k_extrap(self):
+ return self.__k_extrap
+
+ @property
+ def k_final(self):
+ return self.__k_final
+
+ @cached_property
+ def X_spt(self):
+ nu = -2
+ p_mat = np.array([[0, 0, 0, 0], [0, 0, 2, 0], [0, 0, 4, 0], [2, -2, 2, 0],
+ [1, -1, 1, 0], [1, -1, 3, 0], [2, -2, 0, 1]])
+ result = scalar_stuff(p_mat, nu, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_spt'
+ return result
+ @cached_property
+ def X_lpt(self):
+ nu = -2
+ p_mat = np.array([[0, 0, 0, 0], [0, 0, 2, 0], [2, -2, 2, 0],
+ [1, -1, 1, 0], [1, -1, 3, 0], [0, 0, 4, 0], [2, -2, 0, 1]])
+ result = scalar_stuff(p_mat, nu, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_lpt'
+ return result
+ @cached_property
+ def X_sptG(self):
+ nu = -2
+ p_mat = np.array([[0, 0, 0, 0], [0, 0, 2, 0], [0, 0, 4, 0], [2, -2, 2, 0],
+ [1, -1, 1, 0], [1, -1, 3, 0], [2, -2, 0, 1]])
+ result = scalar_stuff(p_mat, nu, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_sptG'
+ return result
+ @cached_property
+ def X_IA_E(self):
+ hE_tab, _ = IA_tt()
+ p_mat_E = hE_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_E, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_E'
+ return result
+ @cached_property
+ def X_IA_B(self):
+ _, hB_tab = IA_tt()
+ p_mat_B = hB_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_B, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_B'
+ return result
+ @cached_property
+ def X_IA_A(self):
+ IA_A_tab = IA_A()
+ p_mat_A = IA_A_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_A, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_A'
+ return result
+ @cached_property
+ def X_IA_DEE(self):
+ IA_DEE_tab = IA_DEE()
+ p_mat_DEE = IA_DEE_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_DEE, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_DEE'
+ return result
+ @cached_property
+ def X_IA_DBB(self):
+ IA_DBB_tab = IA_DBB()
+ p_mat_DBB = IA_DBB_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_DBB, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_DBB'
+ return result
+ @cached_property
+ def X_IA_deltaE1(self):
+ IA_deltaE1_tab = IA_deltaE1()
+ result = tensor_stuff(IA_deltaE1_tab[:, [0, 1, 5, 6, 7, 8, 9]], self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_deltaE1'
+ return result
+ @cached_property
+ def X_IA_0E0E(self):
+ IA_0E0E_tab = IA_0E0E()
+ result = tensor_stuff(IA_0E0E_tab[:, [0, 1, 5, 6, 7, 8, 9]], self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_0E0E'
+ return result
+ @cached_property
+ def X_IA_0B0B(self):
+ IA_0B0B_tab = IA_0B0B()
+ result = tensor_stuff(IA_0B0B_tab[:, [0, 1, 5, 6, 7, 8, 9]], self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_0B0B'
+ return result
+ @cached_property
+ def X_IA_gb2_fe(self):
+ IA_gb2_fe_tab = IA_gb2_fe()
+ p_mat_gb2_fe = IA_gb2_fe_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_gb2_fe, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_gb2_fe'
+ return result
+ @cached_property
+ def X_IA_gb2_he(self):
+ IA_gb2_he_tab = IA_gb2_he()
+ p_mat_gb2_he = IA_gb2_he_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_gb2_he, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_gb2_he'
+ return result
+ @cached_property
+ def X_IA_tij_feG2(self):
+ IA_tij_feG2_tab = IA_tij_feG2()
+ p_mat_tij_feG2 = IA_tij_feG2_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_tij_feG2, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_tij_feG2'
+ return result
+ @cached_property
+ def X_IA_tij_heG2(self):
+ IA_tij_heG2_tab = IA_tij_heG2()
+ p_mat_tij_heG2 = IA_tij_heG2_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_tij_heG2, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_tij_heG2'
+ return result
+ @cached_property
+ def X_IA_tij_F2F2(self):
+ IA_tij_F2F2_tab = IA_tij_F2F2()
+ p_mat_tij_F2F2 = IA_tij_F2F2_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_tij_F2F2, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_tij_F2F2'
+ return result
+ @cached_property
+ def X_IA_tij_G2G2(self):
+ IA_tij_G2G2_tab = IA_tij_G2G2()
+ p_mat_tij_G2G2 = IA_tij_G2G2_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_tij_G2G2, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_tij_G2G2'
+ return result
+ @cached_property
+ def X_IA_tij_F2G2(self):
+ IA_tij_F2G2_tab = IA_tij_F2G2()
+ p_mat_tij_F2G2 = IA_tij_F2G2_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_tij_F2G2, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_tij_F2G2'
+ return result
+ @cached_property
+ def X_IA_tij_F2G2reg(self):
+ IA_tij_F2G2reg_tab =IA_tij_F2G2reg()
+ p_mat_tij_F2G2reg_tab = IA_tij_F2G2reg_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_tij_F2G2reg_tab, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_tij_F2G2reg'
+ return result
+ @cached_property
+ def X_IA_gb2_F2(self):
+ IA_gb2_F2_tab = IA_gb2_F2()
+ p_mat_gb2_F2 = IA_gb2_F2_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_gb2_F2, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_gb2_F2'
+ return result
+ @cached_property
+ def X_IA_gb2_G2(self):
+ IA_gb2_G2_tab = IA_gb2_G2()
+ p_mat_gb2_G2 = IA_gb2_G2_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_gb2_G2, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_gb2_G2'
+ return result
+ @cached_property
+ def X_IA_gb2_S2F2(self):
+ IA_gb2_S2F2_tab = IA_gb2_S2F2()
+ p_mat_gb2_S2F2 = IA_gb2_S2F2_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_gb2_S2F2, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_gb2_S2F2'
+ return result
+ @cached_property
+ def X_IA_gb2_S2fe(self):
+ IA_gb2_S2fe_tab = IA_gb2_S2fe()
+ p_mat_gb2_S2fe = IA_gb2_S2fe_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_gb2_S2fe, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_gb2_S2fe'
+ return result
+ @cached_property
+ def X_IA_gb2_S2he(self):
+ IA_gb2_S2he_tab = IA_gb2_S2he()
+ p_mat_gb2_S2he = IA_gb2_S2he_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_gb2_S2he, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_gb2_S2he'
+ return result
+ @cached_property
+ def X_IA_gb2_S2G2(self):
+ IA_gb2_S2G2_tab = IA_gb2_S2G2()
+ p_mat_gb2_S2G2 = IA_gb2_S2G2_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat_gb2_S2G2, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_IA_gb2_S2G2'
+ return result
+ @cached_property
+ def X_OV(self):
+ OV_tab = OV()
+ p_mat = OV_tab[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_OV'
+ return result
+ @cached_property
+ def X_kP1(self):
+ tab1, _, _ = kPol()
+ p_mat = tab1[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_kP1'
+ return result
+ @cached_property
+ def X_kP2(self):
+ _, tab2, _ = kPol()
+ p_mat = tab2[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_kP2'
+ return result
+ @cached_property
+ def X_kP3(self):
+ _, _, tab3 = kPol()
+ p_mat = tab3[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_kP3'
+ return result
+ @cached_property
+ def X_RSDA(self):
+ tabA, self.A_coeff = RSDA()
+ p_mat = tabA[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_RSDA'
+ return result
+ @cached_property
+ def X_RSDB(self):
+ tabB, self.B_coeff = RSDB()
+ p_mat = tabB[:, [0, 1, 5, 6, 7, 8, 9]]
+ result = tensor_stuff(p_mat, self.N, self.m, self.eta_m, self.l, self.tau_l)
+ self.X_registry[id(result)] = 'X_RSDB'
+ return result
+
+
+ def _validate_params(self, **params):
+ """" Same function as before """
+ valid_params = ('P', 'P_window', 'C_window', 'f', 'X', 'nu', 'mu_n', 'L', 'h', 'rsdrag')
+ for key in params.keys():
+ if key not in valid_params:
+ raise ValueError(f'Invalid parameter: {key}. Valid parameters are: {valid_params}')
+ P = params.get('P', None)
+ if (P is None or len(P) == 0):
+ raise ValueError('You must provide an input power spectrum array.')
+ if (len(P) != len(self.k_original)):
+ raise ValueError(f'Input k and P arrays must have the same size. P:{len(P)}, K:{len(self.k_original)}')
+
+ if (np.all(P == 0.0)):
+ raise ValueError('Your input power spectrum array is all zeros.')
+
+ P_window = params.get('P_window', np.array([]))
+ C_window = params.get('C_window', None)
+
+ if P_window is not None and len(P_window) > 0:
+ maxP = (log(self.k_final[-1]) - log(self.k_final[0])) / 2
+ if len(P_window) != 2:
+ raise ValueError(f'P_window must be a tuple of two values.')
+ if P_window[0] > maxP or P_window[1] > maxP:
+ raise ValueError(f'P_window value is too large. Decrease to less than {maxP} to avoid over tapering.')
+
+ if C_window is not None:
+ if C_window < 0 or C_window > 1:
+ raise ValueError('C_window must be between 0 and 1.')
+
+ return params
+
+ ############## ABSTRACTED BEHAVIOR METHODS ##############
+ def _apply_extrapolation(self, *args):
+ """ Applies extrapolation to multiple variables at once """
+ if not self.extrap:
+ return args if len(args) > 1 else args[0]
+ return [self.EK.PK_original(var)[1] for var in args] if len(args) > 1 else self.EK.PK_original(args[0])[1]
+
+ def _hash_arrays(self, arrays):
+ """Helper function to create a hash from multiple numpy arrays or scalars"""
+ if arrays is None:
+ return hash(None)
+ if isinstance(arrays, (tuple, list)):
+ hash_key_hash = 0
+ for i, item in enumerate(arrays):
+ if isinstance(item, np.ndarray):
+ # Use a prime multiplier to avoid collisions
+ item_hash = hash(item.tobytes())
+ elif isinstance(item, (tuple, list)):
+ # Recursively compute hash of nested structure
+ item_hash = self._hash_arrays(item)
+ else:
+ item_hash = hash(item)
+ # Combine hashes using a prime-based approach to reduce collisions
+ hash_key_hash = hash_key_hash ^ (item_hash + 0x9e3779b9 + (hash_key_hash << 6) + (hash_key_hash >> 2))
+ return hash_key_hash
+
+ # Single item case
+ if isinstance(arrays, np.ndarray):
+ return hash(arrays.tobytes())
+ return hash(arrays)
+
+ def _create_hash_key(self, term, X, P, P_window, C_window):
+ """Create a hash key from the term and input parameters"""
+ P_hash = self._hash_arrays(P)
+ P_win_hash = self._hash_arrays(P_window)
+ if X is None:
+ X_id = hash(None)
+ else:
+ X_id = hash(self.X_registry.get(id(X), f"unknown_{id(X)}"))
+ term_hash = hash(term) #Included for differentiating between similar param sets
+ hash_list = [term_hash, X_id, P_hash, P_win_hash, hash(C_window)]
+ hash_key = 0
+ for h in hash_list:
+ if h is not None:
+ hash_key = hash_key ^ (h + 0x9e3779b9 + (hash_key << 6) + (hash_key >> 2))
+ return hash_key, P_hash
+
+ def compute_term(self, term, X, operation=None, P=None, P_window=None, C_window=None):
+ """
+ Computes a Fast-PT term with caching support.
+
+ Parameters
+ ----------
+ term : str
+ Name of the term to compute, used for cache identification
+ X : tuple
+ Fast-PT coefficient matrices for the calculation
+ operation : callable, optional
+ Function to apply to the result(s) after computation
+ P : array_like
+ Input power spectrum
+ P_window : tuple, optional
+ Window parameters for tapering the power spectrum at the endpoints
+ C_window : float, optional
+ Window parameter for tapering the Fourier coefficients
+
+ Returns
+ -------
+ array_like
+ The computed Fast-PT term
+ """
+ if P is None:
+ raise ValueError('Compute term requires an input power spectrum array.')
+
+ hash_key, P_hash = self._create_hash_key(term, X, P, P_window, C_window)
+ result = self.cache.get(term, hash_key)
+ if result is not None:
+ return result
+
+ result, _ = self.J_k_tensor(P, X, P_window=P_window, C_window=C_window)
+ result = self._apply_extrapolation(result)
+
+ if operation:
+ final_result = operation(result)
+ self.cache.set(final_result, term, hash_key, P_hash)
+ return final_result
+
+ self.cache.set(result, term, hash_key, P_hash)
+ return result
+
+
+
+
+ ### Top-level functions to output final quantities ###
+ def one_loop_dd(self, P, P_window=None, C_window=None):
+ """
+ Computes the standard 1-loop density-density corrections to the power spectrum.
+
+ Returns
+ -------
+ tuple
+ (P_1loop, Ps) where:
+ P_1loop : 1-loop correction (P_22 + P_13)
+ Ps : Smoothed input power spectrum
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ Ps = self._get_Ps(P, P_window=P_window, C_window=C_window)
+ P_1loop = self._get_P_1loop(P, P_window=P_window, C_window=C_window)
+ return P_1loop, Ps #This return is going to be different than the original bc the original return is
+ # different depending on the todo list which is going to be deprecated.
+
+ def _get_Ps(self, P, P_window=None, C_window=None):
+ Ps, _ = self.J_k_scalar(P, self.X_spt, -2, P_window=P_window, C_window=C_window)
+ Ps = self._apply_extrapolation(Ps)
+ return Ps
+
+ def _get_P_1loop(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("P_1loop", self.X_spt, P, P_window, C_window)
+ result = self.cache.get("P_1loop", hash_key)
+ if result is not None: return result
+ # get the roundtrip Fourier power spectrum, i.e. P=IFFT[FFT[P]]
+ # get the matrix for each J_k component
+ Ps, mat = self.J_k_scalar(P, self.X_spt, -2, P_window=P_window, C_window=C_window)
+ P22_coef = np.array([2*1219/1470., 2*671/1029., 2*32/1715., 2*1/3., 2*62/35., 2*8/35., 1/3.])
+ P22_mat = np.multiply(P22_coef, np.transpose(mat))
+ P22 = np.sum(P22_mat, axis=1)
+ P13 = P_13_reg(self.k_extrap, Ps)
+ P_1loop = P22 + P13
+ P_1loop = self._apply_extrapolation(P_1loop)
+ self.cache.set(P_1loop, "P_1loop", hash_key, P_hash)
+ return P_1loop
+
+
+ #TODO add comments back explaining math behind one loop
+ def one_loop_dd_bias(self, P, P_window=None, C_window=None):
+ """
+ Computes 1-loop corrections with standard bias terms.
+
+ Returns
+ -------
+ tuple
+ (P_1loop, Ps, Pd1d2, Pd2d2, Pd1s2, Pd2s2, Ps2s2, sig4) where:
+ P_1loop : 1-loop correction (P_22 + P_13)
+ Ps : Smoothed input power spectrum
+ Pd1d2 : First order density-second order density correlation
+ Pd2d2 : Second order density auto-correlation
+ Pd1s2 : First order density-second order tidal correlation
+ Pd2s2 : Second order density-second order tidal correlation
+ Ps2s2 : Second order tidal auto-correlation
+ sig4 : σ^4 integral for nonlinear bias
+ """
+ P_1loop, Ps = self.one_loop_dd(P, P_window=P_window, C_window=C_window)
+ Pd1d2 = self._get_Pd1d2(P, P_window=P_window, C_window=C_window)
+ Pd2d2 = self._get_Pd2d2(P, P_window=P_window, C_window=C_window)
+ Pd1s2 = self._get_Pd1s2(P, P_window=P_window, C_window=C_window)
+ Pd2s2 = self._get_Pd2s2(P, P_window=P_window, C_window=C_window)
+ Ps2s2 = self._get_Ps2s2(P, P_window=P_window, C_window=C_window)
+ sig4 = self._get_sig4(P, P_window=P_window, C_window=C_window)
+ return P_1loop, Ps, Pd1d2, Pd2d2, Pd1s2, Pd2s2, Ps2s2, sig4
+
+ def _get_sig4(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("sig4", self.X_spt, P, P_window, C_window)
+ result = self.cache.get("sig4", hash_key)
+ if result is not None: return result
+ Ps, _ = self.J_k_scalar(P, self.X_spt, -2, P_window=P_window, C_window=C_window)
+ # Quadraric bias Legendre components
+ # See eg section B of Baldauf+ 2012 (arxiv: 1201.4827)
+ # Note pre-factor convention is not standardized
+ # Returns relevant correlations (including contraction factors),
+ # but WITHOUT bias values and other pre-factors.
+ # Uses standard "full initialization" of J terms
+ sig4 = np.trapz(self.k_extrap ** 3 * Ps ** 2, x=np.log(self.k_extrap)) / (2. * pi ** 2)
+ self.cache.set(sig4, "sig4", hash_key, P_hash)
+ return sig4
+
+ def _get_Pd1d2(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("Pd1d2", self.X_spt, P, P_window, C_window)
+ result = self.cache.get("Pd1d2", hash_key)
+ if result is not None: return result
+ _, mat = self.J_k_scalar(P, self.X_spt, -2, P_window=P_window, C_window=C_window)
+ Pd1d2 = 2. * (17. / 21 * mat[0, :] + mat[4, :] + 4. / 21 * mat[1, :])
+ Pd1d2 = self._apply_extrapolation(Pd1d2)
+ self.cache.set(Pd1d2, "Pd1d2", hash_key, P_hash)
+ return Pd1d2
+
+ def _get_Pd2d2(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("Pd2d2", self.X_spt, P, P_window, C_window)
+ result = self.cache.get("Pd2d2", hash_key)
+ if result is not None: return result
+ _, mat = self.J_k_scalar(P, self.X_spt, -2, P_window=P_window, C_window=C_window)
+ Pd2d2 = 2. * (mat[0, :])
+ Pd2d2 = self._apply_extrapolation(Pd2d2)
+ self.cache.set(Pd2d2, "Pd2d2", hash_key, P_hash)
+ return Pd2d2
+
+ def _get_Pd1s2(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("Pd1s2", self.X_spt, P, P_window, C_window)
+ result = self.cache.get("Pd1s2", hash_key)
+ if result is not None: return result
+ _, mat = self.J_k_scalar(P, self.X_spt, -2, P_window=P_window, C_window=C_window)
+ Pd1s2 = 2. * (8. / 315 * mat[0, :] + 4. / 15 * mat[4, :] + 254. / 441 * mat[1, :] + 2. / 5 * mat[5,
+ :] + 16. / 245 * mat[
+ 2,
+ :])
+ Pd1s2 = self._apply_extrapolation(Pd1s2)
+ self.cache.set(Pd1s2, "Pd1s2", hash_key, P_hash)
+ return Pd1s2
+
+ def _get_Pd2s2(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("Pd2s2", self.X_spt, P, P_window, C_window)
+ result = self.cache.get("Pd2s2", hash_key)
+ if result is not None: return result
+ _, mat = self.J_k_scalar(P, self.X_spt, -2, P_window=P_window, C_window=C_window)
+ Pd2s2 = 2. * (2. / 3 * mat[1, :])
+ Pd2s2 = self._apply_extrapolation(Pd2s2)
+ self.cache.set(Pd2s2, "Pd2s2", hash_key, P_hash)
+ return Pd2s2
+
+ def _get_Ps2s2(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("Ps2s2", self.X_spt, P, P_window, C_window)
+ result = self.cache.get("Ps2s2", hash_key)
+ if result is not None: return result
+ _, mat = self.J_k_scalar(P, self.X_spt, -2, P_window=P_window, C_window=C_window)
+ Pd2s2 = 2. * (4. / 45 * mat[0, :] + 8. / 63 * mat[1, :] + 8. / 35 * mat[2, :])
+ Pd2s2 = self._apply_extrapolation(Pd2s2)
+ self.cache.set(Pd2s2, "Ps2s2", hash_key, P_hash)
+ return Pd2s2
+
+
+ def one_loop_dd_bias_b3nl(self, P, P_window=None, C_window=None):
+ """
+ Computes 1-loop corrections with bias terms including third-order non-local bias.
+
+ Returns
+ -------
+ tuple
+ (P_1loop, Ps, Pd1d2, Pd2d2, Pd1s2, Pd2s2, Ps2s2, sig4, sig3nl) where:
+ The first 8 terms are identical to those returned by one_loop_dd_bias
+ sig3nl : Third order non-local bias term
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ P_1loop, Ps = self.one_loop_dd(P, P_window=P_window, C_window=C_window)
+ Pd1d2 = self._get_Pd1d2(P, P_window=P_window, C_window=C_window)
+ Pd2d2 = self._get_Pd2d2(P, P_window=P_window, C_window=C_window)
+ Pd1s2 = self._get_Pd1s2(P, P_window=P_window, C_window=C_window)
+ Pd2s2 = self._get_Pd2s2(P, P_window=P_window, C_window=C_window)
+ Ps2s2 = self._get_Ps2s2(P, P_window=P_window, C_window=C_window)
+ sig4 = self._get_sig4(P, P_window=P_window, C_window=C_window)
+ sig3nl = self._get_sig3nl(P, P_window=P_window, C_window=C_window)
+ return P_1loop, Ps, Pd1d2, Pd2d2, Pd1s2, Pd2s2, Ps2s2, sig4, sig3nl
+
+ def _get_sig3nl(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("sig3nl", self.X_spt, P, P_window, C_window)
+ result = self.cache.get("sig3nl", hash_key)
+ if result is not None: return result
+ Ps, _ = self.J_k_scalar(P, self.X_spt, -2, P_window=P_window, C_window=C_window)
+ sig3nl = Y1_reg_NL(self.k_extrap, Ps)
+ sig3nl = self._apply_extrapolation(sig3nl)
+ self.cache.set(sig3nl, "sig3nl", hash_key, P_hash)
+ return sig3nl
+
+
+ def one_loop_dd_bias_lpt_NL(self, P, P_window=None, C_window=None):
+ """
+ Computes bias corrections in Lagrangian Perturbation Theory (LPT).
+
+ Returns
+ -------
+ tuple
+ (Ps, Pb1L, Pb1L_2, Pb1L_b2L, Pb2L, Pb2L_2, sig4) where:
+ Ps : Smoothed input power spectrum
+ Pb1L : First-order Lagrangian bias correlation term
+ Pb1L_2 : First-order Lagrangian bias squared correlation
+ Pb1L_b2L : First-order and second-order Lagrangian bias cross-correlation
+ Pb2L : Second-order Lagrangian bias correlation
+ Pb2L_2 : Second-order Lagrangian bias squared correlation
+ sig4 : σ^4 integral for nonlinear bias
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ _, Ps = self.one_loop_dd(P, P_window=P_window, C_window=C_window)
+ Pb1L = self._get_Pb1L(P, P_window=P_window, C_window=C_window)
+ Pb1L_2 = self._get_Pb1L_2(P, P_window=P_window, C_window=C_window)
+ Pb1L_b2L = self._get_Pb1L_b2L(P, P_window=P_window, C_window=C_window)
+ Pb2L = self._get_Pb2L(P, P_window=P_window, C_window=C_window)
+ Pb2L_2 = self._get_Pb2L_2(P, P_window=P_window, C_window=C_window)
+ sig4 = self._get_sig4(P, P_window=P_window, C_window=C_window)
+ return Ps, Pb1L, Pb1L_2, Pb1L_b2L, Pb2L, Pb2L_2, sig4
+
+ def _get_Pb1L(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("Pb1L", self.X_lpt, P, P_window, C_window)
+ result = self.cache.get("Pb1L", hash_key)
+ if result is not None: return result
+ Ps, mat = self.J_k_scalar(P, self.X_lpt, -2, P_window=P_window, C_window=C_window)
+ [j000, j002, j2n22, j1n11, j1n13, j004, j2n20] = [mat[0, :], mat[1, :], mat[2, :], mat[3, :], mat[4, :],
+ mat[5, :], mat[6, :]]
+ X1 = ((144. / 245.) * j000 - (176. / 343.) * j002 - (128. / 1715.) * j004 + (16. / 35.) * j1n11 - (
+ 16. / 35.) * j1n13)
+ Y1 = Y1_reg_NL(self.k_extrap, Ps)
+ Pb1L = X1 + Y1
+ Pb1L = self._apply_extrapolation(Pb1L)
+ self.cache.set(Pb1L, "Pb1L", hash_key, P_hash)
+ return Pb1L
+
+ def _get_Pb1L_2(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("Pb1L_2", self.X_lpt, P, P_window, C_window)
+ result = self.cache.get("Pb1L_2", hash_key)
+ if result is not None: return result
+ Ps, mat = self.J_k_scalar(P, self.X_lpt, -2, P_window=P_window, C_window=C_window)
+ [j000, j002, j2n22, j1n11, j1n13, j004, j2n20] = [mat[0, :], mat[1, :], mat[2, :], mat[3, :], mat[4, :],
+ mat[5, :], mat[6, :]]
+ X2 = ((16. / 21.) * j000 - (16. / 21.) * j002 + (16. / 35.) * j1n11 - (16. / 35.) * j1n13)
+ Y2 = Y2_reg_NL(self.k_extrap, Ps)
+ Pb1L_2 = X2 + Y2
+ Pb1L_2 = self._apply_extrapolation(Pb1L_2)
+ self.cache.set(Pb1L_2, "Pb1L_2", hash_key, P_hash)
+ return Pb1L_2
+
+ def _get_Pb1L_b2L(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("Pb1L_b2L", self.X_lpt, P, P_window, C_window)
+ result = self.cache.get("Pb1L_b2L", hash_key)
+ if result is not None: return result
+ Ps, mat = self.J_k_scalar(P, self.X_lpt, -2, P_window=P_window, C_window=C_window)
+ [j000, j002, j2n22, j1n11, j1n13, j004, j2n20] = [mat[0, :], mat[1, :], mat[2, :], mat[3, :], mat[4, :],
+ mat[5, :], mat[6, :]]
+ X3 = (50. / 21.) * j000 + 2. * j1n11 - (8. / 21.) * j002
+ Pb1L_b2L = X3
+ Pb1L_b2L = self._apply_extrapolation(Pb1L_b2L)
+ self.cache.set(Pb1L_b2L, "Pb1L_b2L", hash_key, P_hash)
+ return Pb1L_b2L
+
+ def _get_Pb2L(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("Pb2L", self.X_lpt, P, P_window, C_window)
+ result = self.cache.get("Pb2L", hash_key)
+ if result is not None: return result
+ Ps, mat = self.J_k_scalar(P, self.X_lpt, -2, P_window=P_window, C_window=C_window)
+ [j000, j002, j2n22, j1n11, j1n13, j004, j2n20] = [mat[0, :], mat[1, :], mat[2, :], mat[3, :], mat[4, :],
+ mat[5, :], mat[6, :]]
+ X4 = (34. / 21.) * j000 + 2. * j1n11 + (8. / 21.) * j002
+ Pb2L = X4
+ Pb2L = self._apply_extrapolation(Pb2L)
+ self.cache.set(Pb2L, "Pb2L", hash_key, P_hash)
+ return Pb2L
+
+ def _get_Pb2L_2(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("Pb2L_2", self.X_lpt, P, P_window, C_window)
+ result = self.cache.get("Pb2L_2", hash_key)
+ if result is not None: return result
+ Ps, mat = self.J_k_scalar(P, self.X_lpt, -2, P_window=P_window, C_window=C_window)
+ [j000, j002, j2n22, j1n11, j1n13, j004, j2n20] = [mat[0, :], mat[1, :], mat[2, :], mat[3, :], mat[4, :],
+ mat[5, :], mat[6, :]]
+ X5 = j000
+ Pb2L_2 = X5
+ Pb2L_2 = self._apply_extrapolation(Pb2L_2)
+ self.cache.set(Pb2L_2, "Pb2L_2", hash_key, P_hash)
+ return Pb2L_2
+
+ def IA_tt(self, P, P_window=None, C_window=None):
+ """
+ Computes intrinsic alignment tidal torque contributions.
+
+ Returns
+ -------
+ tuple
+ (P_E, P_B) where:
+ P_E : E-mode (curl-free) tidal torque power spectrum
+ P_B : B-mode (divergence-free) tidal torque power spectrum
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ P_E = self.compute_term("P_E", self.X_IA_E, operation=lambda x: 2 * x,
+ P=P, P_window=P_window, C_window=C_window)
+ P_B = self.compute_term("P_B", self.X_IA_B, operation=lambda x: 2 * x,
+ P=P, P_window=P_window, C_window=C_window)
+ return P_E, P_B
+
+ ## eq 21 EE; eq 21 BB
+
+ def IA_mix(self, P, P_window=None, C_window=None):
+ """
+ Computes mixed intrinsic alignment contributions combining tidal
+ alignment and tidal torque.
+
+ Returns
+ -------
+ tuple
+ (P_A, P_Btype2, P_DEE, P_DBB) where:
+ P_A : Mixed tidal alignment/tidal torque term
+ P_Btype2 : Second-type B-mode term
+ P_DEE : Contribution to the E-mode power spectrum
+ P_DBB : Contribution to the B-mode power spectrum
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ P_A = self.compute_term("P_A", self.X_IA_A, operation=lambda x: 2 * x,
+ P=P, P_window=P_window, C_window=C_window)
+ P_Btype2 = self._get_P_Btype2(P) #Calculated differently then other terms, can't use compute_term
+ P_DEE = self.compute_term("P_DEE", self.X_IA_DEE, operation=lambda x: 2 * x,
+ P=P, P_window=P_window, C_window=C_window)
+ P_DBB = self.compute_term("P_DBB", self.X_IA_DBB, operation=lambda x: 2 * x,
+ P=P, P_window=P_window, C_window=C_window)
+ return P_A, P_Btype2, P_DEE, P_DBB
+
+ def _get_P_Btype2(self, P):
+ hash_key, P_hash = self._create_hash_key("P_Btype2", None, P, None, None)
+ result = self.cache.get("P_Btype2", hash_key)
+ if result is not None: return result
+ P_Btype2 = P_IA_B(self.k_original, P)
+ P_Btype2 = 4 * P_Btype2
+ self.cache.set(P_Btype2, "P_Btype2", hash_key, P_hash)
+ return P_Btype2
+
+ ## eq 18; eq 19; eq 27 EE; eq 27 BB
+
+
+ def IA_ta(self, P, P_window=None, C_window=None):
+ """
+ Computes intrinsic alignment tidal alignment contributions.
+
+ Returns
+ -------
+ tuple
+ (P_deltaE1, P_deltaE2, P_0E0E, P_0B0B) where:
+ P_deltaE1 : First density-E mode correlation
+ P_deltaE2 : Second density-E mode correlation
+ P_0E0E : E-mode auto-correlation
+ P_0B0B : B-mode auto-correlation
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ P_deltaE1 = self.compute_term("P_deltaE1", self.X_IA_deltaE1, operation=lambda x: 2 * x,
+ P=P, P_window=P_window, C_window=C_window)
+ P_deltaE2 = self._get_P_deltaE2(P) #Calculated differently then other terms, can't use compute_term
+ P_0E0E = self.compute_term("P_0E0E", self.X_IA_0E0E, P=P, P_window=P_window, C_window=C_window)
+ P_0B0B = self.compute_term("P_0B0B", self.X_IA_0B0B, P=P, P_window=P_window, C_window=C_window)
+ return P_deltaE1, P_deltaE2, P_0E0E, P_0B0B
+
+ def _get_P_deltaE2(self, P):
+ hash_key, P_hash = self._create_hash_key("P_deltaE2", None, P, None, None)
+ result = self.cache.get("P_deltaE2", hash_key)
+ if result is not None: return result
+ P_deltaE2 = P_IA_deltaE2(self.k_original, P)
+ #Add extrap?
+ P_deltaE2 = 2 * P_deltaE2
+ self.cache.set(P_deltaE2, "P_deltaE2", hash_key, P_hash)
+ return P_deltaE2
+
+ ## eq 12 (line 2); eq 12 (line 3); eq 15 EE; eq 15 BB
+
+
+ def IA_der(self, P, P_window=None, C_window=None):
+ """
+ Computes k^2 * P(k) derivative term for intrinsic alignment models.
+
+ Returns
+ -------
+ array_like
+ P_der : Derivative term of the power spectrum
+ """
+ hash_key, P_hash = self._create_hash_key("IA_der", None, P, P_window, C_window)
+ result = self.cache.get("IA_der", hash_key)
+ if result is not None: return result
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ P_der = (self.k_original**2)*P
+ self.cache.set(P_der, "IA_der", hash_key, P_hash)
+ return P_der
+
+ def IA_ct(self,P,P_window=None, C_window=None):
+ """
+ Computes intrinsic alignment velocity-shear contributions.
+
+ Returns
+ -------
+ tuple
+ (P_0tE, P_0EtE, P_E2tE, P_tEtE) where:
+ P_0tE : Density-velocity E-mode correlation
+ P_0EtE : E-mode-velocity E-mode correlation
+ P_E2tE : Second torquing-velocity E-mode correlation
+ P_tEtE : Velocity E-mode auto-correlation
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ P_0tE = self._get_P_0tE(P, P_window=P_window, C_window=C_window)
+ P_0EtE = self._get_P_0EtE(P, P_window=P_window, C_window=C_window)
+ P_E2tE = self._get_P_E2tE(P, P_window=P_window, C_window=C_window)
+ P_tEtE = self._get_P_tEtE(P, P_window=P_window, C_window=C_window)
+ return P_0tE,P_0EtE,P_E2tE,P_tEtE
+
+ def _get_P_0tE(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("P_0tE", self.X_spt, P, P_window, C_window)
+ result = self.cache.get("P_0tE", hash_key)
+ if result is not None: return result
+ nu=-2
+ Ps, mat = self.J_k_scalar(P, self.X_spt, nu, P_window=P_window, C_window=C_window)
+ one_loop_coef = np.array(
+ [2 * 1219 / 1470., 2 * 671 / 1029., 2 * 32 / 1715., 2 * 1 / 3., 2 * 62 / 35., 2 * 8 / 35., 1 / 3.])
+ P22_mat = np.multiply(one_loop_coef, np.transpose(mat))
+ P_22F = np.sum(P22_mat, 1)
+
+ one_loop_coefG= np.array(
+ [2*1003/1470, 2*803/1029, 2*64/1715, 2*1/3, 2*58/35, 2*12/35, 1/3])
+ PsG, matG = self.J_k_scalar(P, self.X_sptG, nu, P_window=P_window, C_window=C_window)
+ P22G_mat = np.multiply(one_loop_coefG, np.transpose(matG))
+ P_22G = np.sum(P22G_mat, 1)
+ P_22F, P_22G = self._apply_extrapolation(P_22F, P_22G)
+ P_13G = P_IA_13G(self.k_original,P,)
+ P_13F = P_IA_13F(self.k_original, P)
+ P_0tE = P_22G-P_22F+P_13G-P_13F
+ P_0tE = 2*P_0tE
+ self.cache.set(P_0tE, "P_0tE", hash_key, P_hash)
+ return P_0tE
+
+ def _get_P_0EtE(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("P_0EtE", self.X_spt, P, P_window, C_window)
+ result = self.cache.get("P_0EtE", hash_key)
+ if result is not None: return result
+ P_feG2, A = self.J_k_tensor(P,self.X_IA_tij_feG2, P_window=P_window, C_window=C_window)
+ P_feG2 = self._apply_extrapolation(P_feG2)
+ P_A00E = self.compute_term("P_deltaE1", self.X_IA_deltaE1, operation=lambda x: 2 * x,
+ P=P, P_window=P_window, C_window=C_window) #OG: P_A00E, _, _, _ = self.IA_ta()
+ P_0EtE = np.subtract(P_feG2,(1/2)*P_A00E)
+ P_0EtE = 2*P_0EtE
+ self.cache.set(P_0EtE, "P_0EtE", hash_key, P_hash)
+ return P_0EtE
+
+ def _get_P_E2tE(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("P_E2tE", self.X_spt, P, P_window, C_window)
+ result = self.cache.get("P_E2tE", hash_key)
+ if result is not None: return result
+ P_heG2, A = self.J_k_tensor(P,self.X_IA_tij_heG2, P_window=P_window, C_window=C_window)
+ P_heG2 = self._apply_extrapolation(P_heG2)
+ P_A0E2 = self.compute_term("P_A", self.X_IA_A, operation=lambda x: 2 * x,
+ P=P, P_window=P_window, C_window=C_window) #OG: P_A0E2, _, _, _ = self.IA_mix()
+ P_E2tE = np.subtract(P_heG2,(1/2)*P_A0E2)
+ P_E2tE = 2*P_E2tE
+ self.cache.set(P_E2tE, "P_E2tE", hash_key, P_hash)
+ return P_E2tE
+
+ def _get_P_tEtE(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("P_tEtE", self.X_spt, P, P_window, C_window)
+ result = self.cache.get("P_tEtE", hash_key)
+ if result is not None: return result
+ P_F2F2, A = self.J_k_tensor(P,self.X_IA_tij_F2F2, P_window=P_window, C_window=C_window)
+ P_G2G2, A = self.J_k_tensor(P,self.X_IA_tij_G2G2, P_window=P_window, C_window=C_window)
+ P_F2G2, A = self.J_k_tensor(P,self.X_IA_tij_F2G2, P_window=P_window, C_window=C_window)
+ P_F2F2, P_G2G2, P_F2G2 = self._apply_extrapolation(P_F2F2, P_G2G2, P_F2G2)
+ P_tEtE = P_F2F2+P_G2G2-2*P_F2G2
+ P_tEtE = 2*P_tEtE
+ self.cache.set(P_tEtE, "P_tEtE", hash_key, P_hash)
+ return P_tEtE
+
+ def gI_ct(self,P,P_window=None, C_window=None):
+ """
+ Computes galaxy bias cross intrinsic alignment velocity-shear contributions.
+
+ Returns
+ -------
+ tuple
+ (P_d2tE, P_s2tE) where:
+ P_d2tE : Second-order density-velocity E-mode correlation
+ P_s2tE : Second-order tidal-velocity E-mode correlation
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ P_d2tE = self._get_P_d2tE(P, P_window=P_window, C_window=C_window)
+ P_s2tE = self._get_P_s2tE(P, P_window=P_window, C_window=C_window)
+ return P_d2tE, P_s2tE
+
+ def _get_P_d2tE(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("Pd2tE", self.X_IA_gb2_F2, P, P_window, C_window)
+ result = self.cache.get("Pd2tE", hash_key)
+ if result is not None: return result
+ P_F2, _ = self.J_k_tensor(P, self.X_IA_gb2_F2, P_window=P_window, C_window=C_window)
+ P_G2, _ = self.J_k_tensor(P, self.X_IA_gb2_G2, P_window=P_window, C_window=C_window)
+ P_F2, P_G2 = self._apply_extrapolation(P_F2, P_G2)
+ P_d2tE = 2 * (P_G2 - P_F2)
+ self.cache.set(P_d2tE, "Pd2tE", hash_key, P_hash)
+ return P_d2tE
+
+ def _get_P_s2tE(self, P, P_window=None, C_window=None):
+ hash_key, P_hash = self._create_hash_key("P_s2tE", self.X_IA_gb2_S2F2, P, P_window, C_window)
+ result = self.cache.get("P_s2tE", hash_key)
+ if result is not None: return result
+ P_S2F2, _ = self.J_k_tensor(P, self.X_IA_gb2_S2F2, P_window=P_window, C_window=C_window)
+ P_S2G2, _ = self.J_k_tensor(P, self.X_IA_gb2_S2G2, P_window=P_window, C_window=C_window)
+ P_S2F2, P_S2G2 = self._apply_extrapolation(P_S2F2, P_S2G2)
+ P_s2tE = 2 * (P_S2G2 - P_S2F2)
+ self.cache.set(P_s2tE, "P_s2tE", hash_key, P_hash)
+ return P_s2tE
+
+ def gI_ta(self,P,P_window=None, C_window=None):
+ """
+ Computes intrinsic alignment 2nd-order density correlations.
+
+ Returns
+ -------
+ tuple
+ (P_d2E, P_d20E, P_s2E, P_s20E) where:
+ P_d2E : 2nd-order density-E-mode correlation
+ P_d20E : 2nd-order density-density-E-mode correlation
+ P_s2E : 2nd-order tidal-E-mode correlation
+ P_s20E : 2nd-order tidal-density-E-mode correlation
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ P_d2E = self.compute_term("P_d2E", self.X_IA_gb2_F2, operation=lambda x: 2 * x,
+ P=P, P_window=P_window, C_window=C_window)
+ P_d20E = self.compute_term("P_d20E", self.X_IA_gb2_fe, operation=lambda x: 2 * x,
+ P=P, P_window=P_window, C_window=C_window)
+ P_s2E = self.compute_term("P_s2E", self.X_IA_gb2_S2F2, operation=lambda x: 2 * x,
+ P=P, P_window=P_window, C_window=C_window)
+ P_s20E = self.compute_term("P_s20E", self.X_IA_gb2_S2fe, operation=lambda x: 2 * x,
+ P=P, P_window=P_window, C_window=C_window)
+ return P_d2E, P_d20E, P_s2E, P_s20E
+
+ def gI_tt(self, P, P_window=None, C_window=None):
+ """
+ Computes intrinsic alignment 2nd-order tidal correlations.
+
+ Returns
+ -------
+ tuple
+ (P_s2E2, P_d2E2) where:
+ P_s2E2 : 2nd-order tidal-E-mode squared correlation
+ P_d2E2 : 2nd-order density-E-mode squared correlation
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ P_s2E2 = self.compute_term("P_s2E2", self.X_IA_gb2_S2he, operation=lambda x: 2 * x,
+ P=P, P_window=P_window, C_window=C_window)
+ P_d2E2 = self.compute_term("P_d2E2", self.X_IA_gb2_he, operation=lambda x: 2 * x,
+ P=P, P_window=P_window, C_window=C_window)
+ return P_s2E2, P_d2E2
+
+
+ def OV(self, P, P_window=None, C_window=None):
+ """
+ Computes the Ostriker-Vishniac effect power spectrum.
+
+ Returns
+ -------
+ array_like
+ P_OV : Ostriker-Vishniac effect power spectrum
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ hash_key, P_hash = self._create_hash_key("OV", None, P, P_window, C_window)
+ result = self.cache.get("P_OV", hash_key)
+ if result is not None: return result
+ P, A = self.J_k_tensor(P, self.X_OV, P_window=P_window, C_window=C_window)
+ P = self._apply_extrapolation(P)
+ P_OV = P * (2 * pi) ** 2
+ self.cache.set(P_OV, "P_OV", hash_key, P_hash)
+ return P_OV
+
+
+ def kPol(self, P, P_window=None, C_window=None):
+ """
+ Computes k-dependent polarization power spectra.
+
+ Returns
+ -------
+ tuple
+ (P1, P2, P3) where:
+ P1 : First k-dependent polarization power spectrum
+ P2 : Second k-dependent polarization power spectrum
+ P3 : Third k-dependent polarization power spectrum
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ P1 = self.compute_term("P_kP1", self.X_kP1, operation=lambda x: x / (80 * pi ** 2),
+ P=P, P_window=P_window, C_window=C_window)
+ P2 = self.compute_term("P_kP2", self.X_kP2, operation=lambda x: x / (160 * pi ** 2),
+ P=P, P_window=P_window, C_window=C_window)
+ P3 = self.compute_term("P_kP3", self.X_kP3, operation=lambda x: x / (80 * pi ** 2),
+ P=P, P_window=P_window, C_window=C_window)
+ return P1, P2, P3
+
+
+ def RSD_components(self, P, f, P_window=None, C_window=None):
+ """
+ Computes redshift-space distortion component terms.
+
+ Parameters
+ ----------
+ f : float
+ Logarithmic growth rate
+
+ Returns
+ -------
+ tuple
+ (A1, A3, A5, B0, B2, B4, B6, P_Ap1, P_Ap3, P_Ap5) where:
+ A1, A3, A5 : A-type RSD components with different powers of μ
+ B0, B2, B4, B6 : B-type RSD components with different powers of μ
+ P_Ap1, P_Ap3, P_Ap5 : Additional RSD A-prime components
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ _, A = self.J_k_tensor(P, self.X_RSDA, P_window=P_window, C_window=C_window)
+
+ A1 = np.dot(self.A_coeff[:, 0], A) + f * np.dot(self.A_coeff[:, 1], A) + f ** 2 * np.dot(self.A_coeff[:, 2], A)
+ A3 = np.dot(self.A_coeff[:, 3], A) + f * np.dot(self.A_coeff[:, 4], A) + f ** 2 * np.dot(self.A_coeff[:, 5], A)
+ A5 = np.dot(self.A_coeff[:, 6], A) + f * np.dot(self.A_coeff[:, 7], A) + f ** 2 * np.dot(self.A_coeff[:, 8], A)
+
+ _, B = self.J_k_tensor(P, self.X_RSDB, P_window=P_window, C_window=C_window)
+
+ B0 = np.dot(self.B_coeff[:, 0], B) + f * np.dot(self.B_coeff[:, 1], B) + f ** 2 * np.dot(self.B_coeff[:, 2], B)
+ B2 = np.dot(self.B_coeff[:, 3], B) + f * np.dot(self.B_coeff[:, 4], B) + f ** 2 * np.dot(self.B_coeff[:, 5], B)
+ B4 = np.dot(self.B_coeff[:, 6], B) + f * np.dot(self.B_coeff[:, 7], B) + f ** 2 * np.dot(self.B_coeff[:, 8], B)
+ B6 = np.dot(self.B_coeff[:, 9], B) + f * np.dot(self.B_coeff[:, 10], B) + f ** 2 * np.dot(self.B_coeff[:, 11], B)
+
+ A1, A3, A5, B0, B2, B4, B6 = self._apply_extrapolation(A1, A3, A5, B0, B2, B4, B6)
+
+ P_Ap1 = RSD_P_Ap1(self.k_original, P, f)
+ P_Ap3 = RSD_P_Ap3(self.k_original, P, f)
+ P_Ap5 = RSD_P_Ap5(self.k_original, P, f)
+
+ return A1, A3, A5, B0, B2, B4, B6, P_Ap1, P_Ap3, P_Ap5
+
+
+ def RSD_ABsum_components(self, P, f, P_window=None, C_window=None):
+ """
+ Computes combined redshift-space distortion terms by powers of μ.
+
+ Parameters
+ ----------
+ f : float
+ Logarithmic growth rate
+
+ Returns
+ -------
+ tuple
+ (ABsum_mu2, ABsum_mu4, ABsum_mu6, ABsum_mu8) where:
+ ABsum_mu2 : Combined term with μ^2 dependence
+ ABsum_mu4 : Combined term with μ^4 dependence
+ ABsum_mu6 : Combined term with μ^6 dependence
+ ABsum_mu8 : Combined term with μ^8 dependence
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ A1, A3, A5, B0, B2, B4, B6, P_Ap1, P_Ap3, P_Ap5 = self.RSD_components(P, f, P_window, C_window)
+ ABsum_mu2 = self.k_original * f * (A1 + P_Ap1) + (f * self.k_original) ** 2 * B0
+ ABsum_mu4 = self.k_original * f * (A3 + P_Ap3) + (f * self.k_original) ** 2 * B2
+ ABsum_mu6 = self.k_original * f * (A5 + P_Ap5) + (f * self.k_original) ** 2 * B4
+ ABsum_mu8 = (f * self.k_original) ** 2 * B6
+
+ return ABsum_mu2, ABsum_mu4, ABsum_mu6, ABsum_mu8
+
+
+ def RSD_ABsum_mu(self, P, f, mu_n, P_window=None, C_window=None):
+ """
+ Computes the total redshift-space distortion correction at a given μ.
+
+ Parameters
+ ----------
+ f : float
+ Logarithmic growth rate
+ mu_n : float
+ Cosine of the angle between the wavevector and the line-of-sight
+
+ Returns
+ -------
+ array_like
+ ABsum : The total RSD contribution at the specified μ angle
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ ABsum_mu2, ABsum_mu4, ABsum_mu6, ABsum_mu8 = self.RSD_ABsum_components(P, f, P_window, C_window)
+ ABsum = ABsum_mu2 * mu_n ** 2 + ABsum_mu4 * mu_n ** 4 + ABsum_mu6 * mu_n ** 6 + ABsum_mu8 * mu_n ** 8
+ return ABsum
+
+
+ def IRres(self, P, L=0.2, h=0.67, rsdrag=135, P_window=None, C_window=None):
+ """
+ Computes the IR-resummed power spectrum, which includes BAO damping.
+
+ Parameters
+ ----------
+ L : float, optional
+ IR resummation scale, default is 0.2
+ h : float, optional
+ Dimensionless Hubble parameter, default is 0.67
+ rsdrag : float, optional
+ Sound horizon at drag epoch in Mpc, default is 135
+
+ Returns
+ -------
+ array_like
+ P_IRres : IR-resummed power spectrum with damped BAO features
+ """
+ self._validate_params(P=P, P_window=P_window, C_window=C_window)
+ # based on script by M. Ivanov. See arxiv:1605.02149, eq 7.4
+
+ # put this function in the typical fast-pt format, with minimal additional function calls.
+ # We can also include a script to do additional things: e.g read in r_BAO from class/camb output
+ # or calculate r_BAO from cosmological params.
+ from scipy import interpolate
+ k = self.k_original
+ rbao = h * rsdrag * 1.027 # linear BAO scale
+ # set up splining to create P_nowiggle
+ kmin = k[0]
+ kmax = k[-1]
+ knode1 = pi / rbao
+ knode2 = 2 * pi / rbao
+ klogleft = np.arange(log(kmin), log(3.e-3), 0.2)
+ klogright = np.arange(log(0.6), log(kmax), 0.085)
+ klogright = np.hstack((log(knode1), log(knode2), klogright))
+ kloglist = np.concatenate((klogleft, klogright))
+ klist = np.exp(kloglist)
+
+ # how to deal with extended k and P? which values should be used here? probably the extended versions?
+ plin = interpolate.InterpolatedUnivariateSpline(k, P)
+ logPs = np.log(plin(klist))
+ logpsmooth = interpolate.InterpolatedUnivariateSpline(kloglist, logPs)
+
+ def psmooth(x):
+ return exp(logpsmooth(log(x)))
+
+ def pw(x):
+ return plin(x) - psmooth(x)
+
+ # compute Sigma^2 and the tree-level IR-resummed PS
+ import scipy.integrate as integrate
+
+ Sigma = integrate.quad(lambda x: (4 * pi) * psmooth(x) * (
+ 1 - 3 * (2 * rbao * x * cos(x * rbao) + (-2 + rbao ** 2 * x ** 2) * sin(rbao * x)) / (
+ x * rbao) ** 3) / (3 * (2 * pi) ** 3), kmin, L)[0]
+
+ # speed up by using trap rule integration?
+ # change to integration over log-k(?):
+ # Sigma = integrate.quad(lambda x: x*(4*pi)*psmooth(x)*(1-3*(2*rbao*x*cos(x*rbao)+(-2+rbao**2*x**2)*sin(rbao*x))/(x*rbao)**3)/(3*(2*pi)**3), np.log(kmin), np.log(L))[0]
+ def presum(x):
+ return psmooth(x) + pw(x) * exp(-x ** 2 * Sigma)
+
+ P = presum(k)
+ out_1loop = self._get_P_1loop(P, P_window=P_window, C_window=C_window)
+ # p1loop = interpolate.InterpolatedUnivariateSpline(k,out_1loop) # is this necessary? out_1loop should already be at the correct k spacing
+ return psmooth(k) + out_1loop + pw(k) * exp(-k ** 2 * Sigma) * (1 + Sigma * k ** 2)
+
+ ######################################################################################
+ ### functions that use the older version structures. ###
+
+ def one_loop(self, P, P_window=None, C_window=None):
+
+ return self.pt_simple.one_loop(P, P_window=P_window, C_window=C_window)
+
+
+ def P_bias(self, P, P_window=None, C_window=None):
+
+ return self.pt_simple.P_bias(P, P_window=P_window, C_window=C_window)
+
+ ######################################################################################
+ ### Core functions used by top-level functions ###
+
+ def J_k_scalar(self, P, X, nu, P_window=None, C_window=None):
+
+ hash_key, P_hash = self._create_hash_key("J_k_scalar", X, P, P_window, C_window)
+ result = self.cache.get("J_k_scalar", hash_key)
+ if result is not None: return result
+
+ pf, p, g_m, g_n, two_part_l, h_l = X
+
+ if (self.low_extrap is not None):
+ P = self.EK.extrap_P_low(P)
+
+ if (self.high_extrap is not None):
+ P = self.EK.extrap_P_high(P)
+
+ P_b = P * self.k_extrap ** (-nu)
+
+ if (self.n_pad > 0):
+ P_b = np.pad(P_b, pad_width=(self.n_pad, self.n_pad), mode='constant', constant_values=0)
+
+ c_m = self._cache_fourier_coefficients(P_b, C_window, scalar=True)
+
+ A_out = np.zeros((pf.shape[0], self.k_size))
+ for i in range(pf.shape[0]):
+ # convolve f_c and g_c
+ # C_l=np.convolve(c_m*self.g_m[i,:],c_m*self.g_n[i,:])
+ C_l = self._cache_convolution(c_m, c_m, g_m[i,:], g_n[i,:], h_l[i,:], two_part_l[i])
+
+ # set up to feed ifft an array ordered with l=0,1,...,-1,...,N/2-1
+ c_plus = C_l[self.l >= 0]
+ c_minus = C_l[self.l < 0]
+
+ C_l = np.hstack((c_plus[:-1], c_minus))
+ A_k = ifft(C_l) * C_l.size # multiply by size to get rid of the normalization in ifft
+
+ A_out[i, :] = np.real(A_k[::2]) * pf[i] * self.k_final ** (-p[i] - 2)
+ # note that you have to take every other element
+ # in A_k, due to the extended array created from the
+ # discrete convolution
+
+ P_out = irfft(c_m[self.m >= 0]) * self.k_final ** nu * float(self.N)
+ if (self.n_pad > 0):
+ # get rid of the elements created from padding
+ P_out = P_out[self.id_pad]
+ A_out = A_out[:, self.id_pad]
+
+ self.cache.set((P_out, A_out), "J_k_scalar", hash_key, P_hash)
+ return P_out, A_out
+
+ def J_k_tensor(self, P, X, P_window=None, C_window=None):
+
+ hash_key, P_hash = self._create_hash_key("J_k_tensor", X, P, P_window, C_window)
+ result = self.cache.get("J_k_tensor", hash_key)
+ if result is not None: return result
+
+ pf, p, nu1, nu2, g_m, g_n, h_l = X
+
+ if (self.low_extrap is not None):
+ P = self.EK.extrap_P_low(P)
+
+ if (self.high_extrap is not None):
+ P = self.EK.extrap_P_high(P)
+
+ A_out = np.zeros((pf.size, self.k_size))
+
+ P_fin = np.zeros(self.k_size)
+
+ for i in range(pf.size):
+
+ P_b1 = P * self.k_extrap ** (-nu1[i])
+ P_b2 = P * self.k_extrap ** (-nu2[i])
+
+ if (P_window is not None):
+ # window the input power spectrum, so that at high and low k
+ # the signal smoothly tappers to zero. This make the input
+ # more "like" a periodic signal
+
+ if (self.verbose):
+ print('windowing biased power spectrum')
+ W = p_window(self.k_extrap, P_window[0], P_window[1])
+ P_b1 = P_b1 * W
+ P_b2 = P_b2 * W
+
+ if (self.n_pad > 0):
+ P_b1 = np.pad(P_b1, pad_width=(self.n_pad, self.n_pad), mode='constant', constant_values=0)
+ P_b2 = np.pad(P_b2, pad_width=(self.n_pad, self.n_pad), mode='constant', constant_values=0)
+ c_m = self._cache_fourier_coefficients(P_b1, C_window, scalar=False)
+ c_n = self._cache_fourier_coefficients(P_b2, C_window, scalar=False)
+ # convolve f_c and g_c
+ C_l = self._cache_convolution(c_m, c_n, g_m[i,:], g_n[i,:], h_l[i,:])
+
+ # set up to feed ifft an array ordered with l=0,1,...,-1,...,N/2-1
+ c_plus = C_l[self.l >= 0]
+ c_minus = C_l[self.l < 0]
+
+ C_l = np.hstack((c_plus[:-1], c_minus))
+ A_k = ifft(C_l) * C_l.size # multiply by size to get rid of the normalization in ifft
+
+ A_out[i, :] = np.real(A_k[::2]) * pf[i] * self.k_final ** (p[i])
+ # note that you have to take every other element
+ # in A_k, due to the extended array created from the
+ # discrete convolution
+ P_fin += A_out[i, :]
+ # P_out=irfft(c_m[self.m>=0])*self.k**self.nu*float(self.N)
+ if (self.n_pad > 0):
+ # get rid of the elements created from padding
+ # P_out=P_out[self.id_pad]
+ A_out = A_out[:, self.id_pad]
+ P_fin = P_fin[self.id_pad]
+
+ self.cache.set((P_fin, A_out), "J_k_tensor", hash_key, P_hash)
+ return P_fin, A_out
+
+ def _cache_fourier_coefficients(self, P_b, C_window=None, scalar=False):
+ """Cache and return Fourier coefficients for a given biased power spectrum"""
+
+ hash_key, P_hash = self._create_hash_key("fourier_coefficients", None, P_b, None, C_window)
+ hash_key = hash_key ^ (hash(scalar) + 0x9e3779b9 + (hash_key << 6) + (hash_key >> 2))
+ result = self.cache.get("fourier_coefficients", hash_key)
+ if result is not None:
+ return result
+
+ c_m_positive = rfft(P_b)
+ # We always filter the Fourier coefficients, so the last element is zero.
+ # But in case someone does not filter and this is a scalar calculation, divide the end point by two
+ if scalar: c_m_positive[-1] = c_m_positive[-1] / 2.
+ c_m_negative = np.conjugate(c_m_positive[1:])
+ c_m = np.hstack((c_m_negative[::-1], c_m_positive)) / float(self.N)
+
+ if C_window is not None:
+ # Window the Fourier coefficients.
+ # This will damp the highest frequencies
+ if self.verbose:
+ print('windowing the Fourier coefficients')
+ c_m = c_m * c_window(self.m, int(C_window * self.N // 2.))
+ self.cache.set(c_m, "fourier_coefficients", hash_key, None)
+ return c_m
+
+ def _cache_convolution(self, c1, c2, g_m, g_n, h_l, two_part_l=None):
+ """Cache and return convolution results"""
+
+ # Use MurmurHash for faster hashing with low collision rate, original hashing method is much slower for this case
+ def fast_hash(arr):
+ if arr is None:
+ return 0
+ if isinstance(arr, np.ndarray):
+ if arr.size > 1000:
+ sample = arr.ravel()[:1000]
+ return hash(sample.tobytes()) ^ hash(arr.shape) ^ hash(arr.size)
+ return hash(arr.tobytes()) ^ hash(arr.shape)
+ return hash(arr)
+
+ # Combine hashes with different prime multipliers to reduce collisions
+ hash_key = 0
+ primes = [17, 31, 61, 127, 257, 509]
+ arrays = [c1, c2, g_m, g_n, h_l, two_part_l]
+
+ for i, arr in enumerate(arrays):
+ h = fast_hash(arr)
+ hash_key = hash_key ^ (h * primes[i % len(primes)])
+
+ result = self.cache.get("convolution", hash_key)
+ if result is not None:
+ return result
+
+ # Calculate convolution
+ C_l = fftconvolve(c1 * g_m, c2 * g_n)
+ # C_l=convolve(c_m*self.g_m[i,:],c_m*self.g_n[i,:])
+ # multiply all l terms together
+ # C_l=C_l*self.h_l[i,:]*self.two_part_l[i]
+
+ # Apply additional terms
+ if two_part_l is not None:
+ C_l = C_l * h_l * two_part_l
+ else:
+ C_l = C_l * h_l
+ # Cache and return
+ self.cache.set(C_l, "convolution", hash_key, None)
+ return C_l
diff --git a/fastpt/FASTPT_simple.py b/fastpt/core/FASTPT_simple.py
similarity index 93%
rename from fastpt/FASTPT_simple.py
rename to fastpt/core/FASTPT_simple.py
index 651f254..37d89f5 100644
--- a/fastpt/FASTPT_simple.py
+++ b/fastpt/core/FASTPT_simple.py
@@ -17,18 +17,15 @@
import numpy as np
-from numpy.fft import fft, ifft , rfft, irfft , fftfreq
-from numpy import exp, log, log10, cos, sin, pi, cosh, sinh , sqrt
-from scipy.special import gamma
+from numpy.fft import ifft , rfft, irfft
+from numpy import exp, log, pi
import sys
from time import time
-from .fastpt_extr import p_window, c_window, pad_left, pad_right
-from .matter_power_spt import P_13_reg
-from scipy.signal import convolve , fftconvolve
-from scipy.interpolate import interp1d
-from .gamma_funcs import g_m_vals, gamsn
-from .P_extend import k_extend
-from scipy.integrate import quad
+from ..utils.fastpt_extr import p_window, c_window
+from ..utils.matter_power_spt import P_13_reg
+from scipy.signal import fftconvolve
+from ..utils.gamma_funcs import g_m_vals, gamsn
+from ..utils.P_extend import k_extend
log2=log(2.)
class FASTPT:
@@ -257,13 +254,13 @@ def P22(self,P,P_window=None,C_window=None):
def one_loop(self,P,P_window=None,C_window=None):
- Ps,P22=self.P22(P,P_window,C_window)
- P13=P_13_reg(self.k_old,Ps)
- if (self.extrap):
- _,P=self.EK.PK_original(P22+P13)
- return P
+ Ps,P22=self.P22(P,P_window,C_window)
+ P13=P_13_reg(self.k_old,Ps)
+ if (self.extrap):
+ _,P=self.EK.PK_original(P22+P13)
+ return P
- return P22+P13
+ return P22+P13
def P_bias(self,P,P_window=None,C_window=None):
# Quadraric bias Legendre components
diff --git a/fastpt/core/FPTHandler.py b/fastpt/core/FPTHandler.py
new file mode 100644
index 0000000..4e3b68b
--- /dev/null
+++ b/fastpt/core/FPTHandler.py
@@ -0,0 +1,1565 @@
+import numpy as np
+import inspect
+from .FASTPT import FASTPT
+from numpy import pi
+import os
+
+class FPTHandler:
+ """
+ Handler class for FAST-PT that simplifies function calls and provides various convenience features relevant to FAST-PT calculations.
+
+ This class provides a simplified interface for working with FAST-PT functions,
+ with features including power spectra generation, saving/loading outputs,
+ direct access to specific power spectrum terms, plotting, and more.
+
+ Parameters
+ ----------
+ fastpt_instance : FASTPT
+ An initialized FASTPT instance.
+ save_all : str, optional
+ File format to save all results ('txt', 'csv', or 'json'). If one is provided, all outputs of the run function will be saved. Default is None.
+ save_dir : str, optional
+ Directory to save results. Default is 'outputs' directory in package location.
+ **params : dict
+ Default parameters to use for all function calls.
+
+ Examples
+ --------
+ >>> from fastpt import FASTPT, FPTHandler
+ >>> import numpy as np
+ >>> k = np.logspace(-3, 1, 200)
+ >>> fpt = FASTPT(k)
+ >>> handler = FPTHandler(fpt, C_window=0.75)
+ >>> P = handler.generate_power_spectra()
+ >>> handler.update_default_params(P=P)
+ >>> P_1loop = handler.get('P_E') # Direct access to the P_E term of fpt.IA_tt
+ """
+ def __init__(self, fastpt_instance: FASTPT, save_all=None, save_dir=None, **params):
+ if fastpt_instance is None:
+ raise ValueError("You must provide a valid FASTPT instance.")
+ self.__fastpt = fastpt_instance
+ self.save_all = save_all
+
+ # Set default output directory if none specified
+ if save_dir is None:
+ self.output_dir = os.path.join(os.getcwd(), "outputs")
+ else:
+ self.output_dir = save_dir
+
+ # Ensure output directory exists
+ os.makedirs(self.output_dir, exist_ok=True)
+
+ self.default_params = {}
+ if params:
+ try:
+ self.default_params = self.fastpt._validate_params(**params)
+ except ValueError as e:
+ if "You must provide an input power spectrum array" in str(e):
+ print("No power spectrum provided. You'll need to provide 'P' in each function call.")
+ self.default_params = params
+ else:
+ raise e
+
+ #Commented out terms have not been implemented yet
+ self.term_sources = {
+ "P_1loop": ("one_loop_dd", None),
+ "Ps": ("one_loop_dd", None),
+ "Pd1d2": ("one_loop_dd_bias", None),
+ "Pd2d2": ("one_loop_dd_bias", None),
+ "Pd1s2": ("one_loop_dd_bias", None),
+ "Pd2s2": ("one_loop_dd_bias", None),
+ "Ps2s2": ("one_loop_dd_bias", None),
+ "sig4": ("one_loop_dd_bias", None),
+
+ "sig3nl": ("one_loop_dd_bias_b3nl", None),
+
+ "Pb1L": ("one_loop_dd_bias_lpt_NL", None),
+ "Pb1L_2": ("one_loop_dd_bias_lpt_NL", None),
+ "Pb1L_b2L": ("one_loop_dd_bias_lpt_NL", None),
+ "Pb2L": ("one_loop_dd_bias_lpt_NL", None),
+ "Pb2L_2": ("one_loop_dd_bias_lpt_NL", None),
+
+ "P_E": ("IA_tt", "X_IA_E", lambda x: 2 * x),
+ "P_B": ("IA_tt", "X_IA_B", lambda x: 2 * x),
+
+ "P_A": ("IA_mix", "X_IA_A", lambda x: 2 * x),
+ "P_Btype2": ("IA_mix", None),
+ "P_DEE": ("IA_mix", "X_IA_DEE", lambda x: 2 * x),
+ "P_DBB": ("IA_mix", "X_IA_DBB", lambda x: 2 * x),
+
+ "P_deltaE1": ("IA_ta", "X_IA_deltaE1", lambda x: 2 * x),
+ "P_deltaE2": ("IA_ta", None),
+ "P_0E0E": ("IA_ta", "X_IA_0E0E", None),
+ "P_0B0B": ("IA_ta", "X_IA_0B0B", None),
+
+ "P_der": ("IA_der", None),
+
+ "P_0tE": ("IA_ct", None),
+ "P_0EtE": ("IA_ct", None),
+ "P_E2tE": ("IA_ct", None),
+ "P_tEtE": ("IA_ct", None),
+
+ "P_d2tE": ("gI_ct", None),
+ "P_s2tE": ("gI_ct", None),
+
+ "P_s2E2": ("gI_tt", "X_IA_gb2_S2he", lambda x: 2 * x),
+ "P_d2E2": ("gI_tt", "X_IA_gb2_he", lambda x: 2 * x),
+
+ "P_d2E": ("gI_ta", "X_IA_gb2_F2", lambda x: 2 * x),
+ "P_d20E": ("gI_ta", "X_IA_gb2_fe", lambda x: 2 * x),
+ "P_s2E": ("gI_ta", "X_IA_gb2_S2F2", lambda x: 2 * x),
+ "P_s20E": ("gI_ta", "X_IA_gb2_S2fe", lambda x: 2 * x),
+
+ "P_OV": ("OV", None),
+
+ "P_kP1": ("kPol", "X_kP1", lambda x: x / (80 * pi ** 2)),
+ "P_kP2": ("kPol", "X_kP2", lambda x: x / (160 * pi ** 2)),
+ "P_kP3": ("kPol", "X_kP3",lambda x: x / (80 * pi ** 2)),
+
+ }
+
+ @property
+ def fastpt(self):
+ """
+ Get the underlying FASTPT instance.
+
+ Returns
+ -------
+ FASTPT
+ The FASTPT instance used by this handler.
+ """
+ return self.__fastpt
+
+
+ def _get_function_params(self, func):
+ """ Returns both required and optional parameter names for a given FASTPT function. """
+ signature = inspect.signature(func)
+ required_params = []
+ optional_params = []
+
+ for param_name, param in signature.parameters.items():
+ # Skip self parameter and *args/**kwargs
+ if (param_name == 'self' or
+ param.kind in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD)):
+ continue
+
+ if param.default == inspect.Parameter.empty:
+ required_params.append(param_name)
+ else:
+ optional_params.append(param_name)
+
+ return {
+ 'required': required_params,
+ 'optional': optional_params,
+ 'all': required_params + optional_params
+ }
+
+ def _prepare_function_params(self, func, override_kwargs):
+ """Prepares and validates parameters for a FASTPT function."""
+ if override_kwargs:
+ self.fastpt._validate_params(**override_kwargs)
+
+ merged_params = {**self.default_params, **override_kwargs}
+
+ params_info = self._get_function_params(func)
+ missing_params = [p for p in params_info['required'] if p not in merged_params]
+
+ if missing_params:
+ raise ValueError(f"Missing required parameters for '{func.__name__}': {missing_params}. "
+ f"Please recall with the missing parameters.")
+
+ # Return only the params the function actually needs
+ passing_params = {k: v for k, v in merged_params.items() if k in params_info['all']}
+ return passing_params, params_info
+
+
+ def run(self, function_name, save_type=None, save_dir=None, **override_kwargs):
+ """
+ Run a FAST-PT function with validated parameters.
+
+ This method calls the specified FAST-PT function, handles parameter validation,
+ and optionally saves the output to a file.
+
+ Parameters
+ ----------
+ function_name : str
+ Name of the FAST-PT function to run
+ save_type : str, optional
+ File format to save results ('txt', 'csv', or 'json')
+ save_dir : str, optional
+ Directory to save results. Defaults to the class's save_dir (self.output_dir).
+ **override_kwargs : dict
+ Additional parameters to pass to the FAST-PT function
+
+ Returns
+ -------
+ result : object
+ Result from the FAST-PT function call, typically a tuple of numpy arrays
+
+ Raises
+ ------
+ ValueError
+ If the function is not found or parameter validation fails
+
+ Examples
+ --------
+ >>> handler = FPTHandler(fpt, P=P, C_window=0.75)
+ >>> P_1loop_result = handler.run('one_loop_dd')
+ >>> ia_result = handler.run('IA_tt', save_type='csv')
+ """
+ if not hasattr(self.fastpt, function_name):
+ raise ValueError(f"Function '{function_name}' not found in FASTPT.")
+
+ if 'save_type' in override_kwargs:
+ save_param = override_kwargs.pop('save_type')
+ if save_type is None:
+ save_type = save_param
+
+ if 'save_dir' in override_kwargs:
+ save_dir = override_kwargs.pop('save_dir')
+
+ output_directory = save_dir if save_dir is not None else self.output_dir
+
+ func = getattr(self.fastpt, function_name)
+ passing_params, _ = self._prepare_function_params(func, override_kwargs)
+
+ result = func(**passing_params)
+ if save_type is not None:
+ self.save_output(result, function_name, type=save_type, output_dir=output_directory)
+ elif self.save_all is not None:
+ self.save_output(result, function_name, type=self.save_all, output_dir=output_directory)
+ return result
+
+ def bulk_run(self, func_names, power_spectra, verbose=False, **override_kwargs):
+ """
+ Run multiple functions with multiple power spectra.
+
+ This method provides a convenient way to run multiple FAST-PT functions
+ with different power spectra and collect all the results.
+
+ Parameters
+ ----------
+ func_names : list of str
+ List of FAST-PT function names to call
+ power_spectra : list of array_like
+ List of power spectra to use for each function call
+ verbose : bool, optional
+ Whether to print progress messages
+ **override_kwargs : dict
+ Additional parameters to pass to all function calls
+
+ Returns
+ -------
+ dict
+ Results keyed by (function_name, power_spectrum_index)
+
+ Examples
+ --------
+ >>> k = np.logspace(-3, 1, 200)
+ >>> fpt = FASTPT(k, low_extrap=-5, high_extrap=3)
+ >>> handler = FPTHandler(fpt, C_window=0.75)
+ >>> P1 = handler.generate_power_spectra(method='class') # Example power spectrum 1
+ >>> P2 = handler.generate_power_spectra(method='camb') # Example power spectrum 2
+ >>> results = handler.bulk_run(['one_loop_dd', 'IA_tt'], [P1, P2])
+ >>> one_loop_P1 = results[('one_loop_dd', 0)]
+ """
+ results = {}
+ for func_name in func_names:
+ for i, P in enumerate(power_spectra):
+ params = {**self.default_params, **override_kwargs, 'P': P}
+ if verbose: print(f"Running {func_name} with power spectrum {i}")
+ results[(func_name, i)] = self.run(func_name, **params)
+ return results
+
+ def get(self, *terms, **override_kwargs):
+ """
+ Get specific power spectrum terms directly.
+
+ This method provides direct access to specific power spectrum components
+ without needing to know which FAST-PT function calculates them.
+
+ Parameters
+ ----------
+ *terms : str
+ Names of power spectrum terms to retrieve
+ **override_kwargs : dict
+ Parameters for the underlying FAST-PT calculations
+
+ Returns
+ -------
+ np.array or dict
+ If a single term is requested, returns that term directly.
+ If multiple terms are requested, returns a dictionary mapping
+ term names to their values.
+
+ Raises
+ ------
+ ValueError
+ If a requested term is not found or parameters are invalid
+
+ Examples
+ --------
+ >>> handler = FPTHandler(fpt, P=P_linear, C_window=0.75)
+ >>> P_1loop = handler.get('P_1loop')
+ >>> ia_terms = handler.get('P_E', 'P_B')
+ """
+ if not terms:
+ raise ValueError("At least one term must be provided.")
+ output = {}
+ unique_funcs = {
+ "P_Btype2": "_get_P_Btype2",
+ "P_deltaE2": "_get_P_deltaE2",
+ "P_der": "IA_der",
+ "P_OV": "OV",
+ "P_0tE": "_get_P_0tE",
+ "P_0EtE": "_get_P_0EtE",
+ "P_E2tE": "_get_P_E2tE",
+ "P_tEtE": "_get_P_tEtE",
+ "P_1loop": "_get_P_1loop",
+ "Ps": "_get_Ps",
+ "Pd1d2": "_get_Pd1d2",
+ "Pd2d2": "_get_Pd2d2",
+ "Pd1s2": "_get_Pd1s2",
+ "Pd2s2": "_get_Pd2s2",
+ "Ps2s2": "_get_Ps2s2",
+ "sig4": "_get_sig4",
+ "sig3nl": "_get_sig3nl",
+ "Pb1L": "_get_Pb1L",
+ "Pb1L_2": "_get_Pb1L_2",
+ "Pb1L_b2L": "_get_Pb1L_b2L",
+ "Pb2L": "_get_Pb2L",
+ "Pb2L_2": "_get_Pb2L_2",
+ "P_d2tE": "_get_P_d2tE",
+ "P_s2tE": "_get_P_s2tE"
+ }
+ for term in terms:
+ if term not in self.term_sources:
+ raise ValueError(f"Term '{term}' not found in FASTPT.")
+ if term in unique_funcs.keys(): #Terms that have their own unique functions
+ func_name = unique_funcs[term]
+ func = getattr(self.fastpt, func_name)
+
+ passing_params, _ = self._prepare_function_params(func, override_kwargs)
+ result = func(**passing_params)
+
+ else:
+ func_name = self.term_sources[term][0]
+ func = getattr(self.fastpt, func_name)
+ passing_params, _ = self._prepare_function_params(func, override_kwargs)
+
+ compute_func = getattr(self.fastpt, "compute_term")
+
+ X_source = self.term_sources[term][1]
+ operation = self.term_sources[term][2]
+
+ X_term = getattr(self.fastpt, X_source)
+ result = compute_func(term, X_term, operation=operation, **passing_params)
+
+ output[term] = result
+
+ # If only one term was requested, return just that value
+ if len(output) == 1 and len(terms) == 1:
+ return output[list(output.keys())[0]]
+ return output
+
+ def get_tracer(self, tracer_name, **override_kwargs):
+ """
+ Get Fast-PT terms relevant to a specific tracer in CCL.
+
+ Parameters
+ ----------
+ tracer_name : str
+ Name of tracer terms to retrieve
+ **override_kwargs : dict
+ Parameters for the underlying FAST-PT calculations
+
+ Returns
+ -------
+ dict
+ Returns a dictionary of key-value pairs where the key is the term name
+ and the value is the calculated term from Fast-PT.
+
+ Raises
+ ------
+ ValueError
+ If a requested tracer is not found or parameters are invalid
+
+ Notes
+ -----
+ Some terms have different names in FAST-PT than in CCL, see the "aliases" dictionary.
+
+ Examples
+ --------
+ >>> handler = FPTHandler(fpt, P=P, C_window=0.75)
+ >>> P_1loop = handler.get_tracer('pgm')
+ >>> #returns Pd1d2, Pd1s2, Pd1p3 (sig3nl)
+
+ """
+ aliases = {
+ #Names used in CCL for FASTPT terms
+
+ "Pd1p3" : "sig3nl",
+
+ "a00e": "P_deltaE1", "c00e": "P_deltaE2", "a0e2": "P_A",
+ "b0e2": "P_Btype2", "tijsij" : "P_0tE", "gb2tij": "P_d2tE",
+ "s2tij": "P_s2tE", "gb2sij" : "P_d2E", "gb2dsij" : "P_d20E", "gb2sij2" : "P_d2E2",
+ "s2sij" : "P_s2E", "s2dsij" : "P_s20E", "s2sij2" : "P_s2E2",
+
+ "a0e0e": "P_0E0E", "a0b0b": "P_0B0B", "ae2e2": "P_E", "ab2b2": "P_B",
+ "a0e2": "P_A", "b0e2": "P_Btype2", "d0ee2": "P_DEE", "d0bb2": "P_dDBB",
+ "tijdsij" : "P_0EtE", "tij2sij" : "P_E2tE", "tijtij" : "P_tEtE", "Pak2" : "P_der", #<< Sometimes der sometimes non fpt term?
+
+ }
+ tracer_map = {
+ "pgg": ("Pd1d2", "Pd2d2", "Pd1s2", "Pd2s2", "Ps2s2","Pd1p3"),
+ #^^ also needs Pd1d1 and Pd1k2
+ "pgi": ("a00e", "c00e", "a0e2", "b0e2", "tijsij", "gb2tij", "s2tij",
+ "gb2sij", "gb2dsij", "gb2sij2", "s2sij", "s2dsij", "s2sij2",
+ "sig3nl"),
+ #^^ also needs Pd1d1
+ "pgm": ("Pd1d2", "Pd1s2", "Pd1p3"),
+ #^^ also needs Pd1d1 and Pd1k2
+ "pii": ("a00e", "c00e", "a0e0e", "a0b0b", "ae2e2", "ab2b2", "a0e2",
+ "b0e2", "d0ee2", "d0bb2", "tijsij", "tijdsij", "tij2sij", "tijtij", "Pak2"),
+ #^^ also needs Pd1d1, Pak2 has a weird if chek_h
+ "pim": ("a00e", "c00e", "a0e2", "b0e2", "tijsij", "Pak2"),
+ #^^ also needs Pd1d1, Pak2 has a weird if chek_h
+ "pmm": ("P_1loop",) # Note: Added comma to make it a tuple
+ }
+
+ if tracer_name not in tracer_map.keys():
+ raise ValueError(f"Tracer '{tracer_name}' not currently supported. Available tracers are: {list(tracer_map.keys())}")
+
+ result = {}
+ for term in tracer_map[tracer_name]:
+ try:
+ calc_term = self.get(term, **override_kwargs)
+ result[term] = calc_term
+ except ValueError as e:
+ error_msg = str(e)
+ if "not found in FASTPT" in error_msg:
+ if term in aliases:
+ calc_term = self.get(aliases[term], **override_kwargs)
+ result[term] = calc_term
+ else:
+ print(f"Warning: Term '{term}' not found and no alias available. Skipping.")
+ continue
+ else:
+ print(f"Warning: Error calculating term '{term}': {error_msg}. Skipping.")
+ continue
+
+ return result
+
+ def list_available_functions(self):
+ """
+ List all callable FAST-PT functions.
+
+ Prints a list of all public functions available in the FAST-PT instance.
+
+ Examples
+ --------
+ >>> handler = FPTHandler(fpt)
+ >>> handler.list_available_functions()
+ ['OV', 'IA_ct', 'gI_ct', 'gI_tt', 'IA_der', ...]
+ """
+ print([f for f in dir(self.fastpt) if callable(getattr(self.fastpt, f)) and not f.startswith("_")])
+
+ def list_available_terms(self):
+ """
+ List all available power spectrum terms that can be requested via get().
+
+ Prints available terms organized by the FAST-PT function that calculates them,
+ along with any special parameter requirements.
+
+ Examples
+ --------
+ >>> handler = FPTHandler(fpt)
+ >>> handler.list_available_terms()
+ """
+ # Organize by function
+ organized = {}
+ for term, term_info in self.term_sources.items():
+ func = term_info[0]
+ if func not in organized:
+ organized[func] = []
+ organized[func].append(term)
+
+ # Print in a nice format
+ print("Available terms by function:")
+ for func, terms in organized.items():
+ print(f"\n{func}:")
+ terms_str = ", ".join(sorted(terms))
+ if func == "one_loop_dd_bias_b3nl":
+ terms_str += " (and other one_loop_dd_bias terms)"
+ print(f" {terms_str}")
+
+ # Special parameter requirements
+ special_params = {
+ "RSD_components": ["f"],
+ "RSD_ABsum_components": ["f"],
+ "RSD_ABsum_mu": ["f", "mu_n"],
+ "IRres": ["L", "h", "rsdrag"]
+ }
+
+ print("\nSpecial parameter requirements:")
+ for func, params in special_params.items():
+ print(f"{func}: requires {', '.join(params)}")
+
+ return organized
+
+ def clear_default_params(self):
+ """
+ Clear all default parameters.
+
+ Removes all stored default parameters so they must be specified
+ in subsequent function calls.
+
+ Examples
+ --------
+ >>> handler = FPTHandler(fpt, P=P_linear, C_window=0.75)
+ >>> handler.clear_default_params()
+ >>> # Now P must be provided in each subsequent function call
+ """
+ self.default_params = {}
+ print("Default parameters cleared.")
+
+ def update_default_params(self, **params):
+ """
+ Update the default parameters used for all function calls.
+
+ Parameters
+ ----------
+ **params : dict
+ Parameters to set as defaults
+
+ Raises
+ ------
+ ValueError
+ If any parameters are invalid or inconsistent
+
+ Examples
+ --------
+ >>> handler = FPTHandler(fpt)
+ >>> handler.update_default_params(P=P_linear, C_window=0.75)
+ >>> # Now these parameters will be used by default
+ """
+ P = params.get('P', None)
+ if P is not None:
+ if len(self.fastpt.k_original) != len(P):
+ raise ValueError("Provided P must match the length of the FASTPT k grid.")
+ self.default_params = {**self.default_params, **self.fastpt._validate_params(**params)}
+ print("Default parameters updated.")
+
+ def update_fastpt_instance(self, fastpt_instance: FASTPT):
+ """
+ Update the FAST-PT instance used by this handler.
+
+ This method replaces the current FASTPT instance.
+
+ Parameters
+ ----------
+ fastpt_instance : FASTPT
+ New FASTPT instance to use
+
+ Raises
+ ------
+ ValueError
+ If the provided instance is not a valid FASTPT instance or if the k range does not match the current P parameter.
+
+ Examples
+ --------
+ >>> k_new = np.logspace(-4, 2, 300) # Different k range
+ >>> fpt_new = FASTPT(k_new)
+ >>> handler.update_fastpt_instance(fpt_new)
+ """
+ if not isinstance(fastpt_instance, FASTPT):
+ raise ValueError("fastpt_instance must be an instance of FASTPT.")
+ if "P" in self.default_params:
+ if len(fastpt_instance.k_original) != len(self.default_params["P"]):
+ raise ValueError(f"New FASTPT instance must have the same k range as the current stored P parameter. ({len(self.default_params['P'])})")
+ self.__fastpt = fastpt_instance
+
+ def save_output(self, result, func_name, type="txt", output_dir=None):
+ """
+ Save calculation results to a file.
+
+ Parameters
+ ----------
+ result : array_like or tuple of array_like
+ Result to save
+ func_name : str
+ Name of the function that produced the result
+ type : str, optional
+ File format ('txt', 'csv', or 'json'). Default is 'txt'.
+ output_dir : str, optional
+ Directory to save the file. Default is the handler's output_dir.
+
+ Raises
+ ------
+ ValueError
+ If an invalid file type is specified
+
+ Examples
+ --------
+ >>> handler = FPTHandler(fpt)
+ >>> result = handler.run('one_loop_dd', P=P_linear)
+ >>> handler.save_output(result, 'one_loop_dd', type='csv')
+ """
+ if type not in ("txt", "csv", "json"):
+ raise ValueError("Invalid file type. Must be 'txt', 'csv', or 'json'")
+
+ import os
+ save_dir = output_dir if output_dir is not None else self.output_dir
+ os.makedirs(save_dir, exist_ok=True)
+
+ if func_name in ("one_loop_dd_bias_lpt_NL", "one_loop_dd_bias_b3nl", "one_loop_dd_bias"):
+ for i, element in enumerate(result):
+ if isinstance(element, float): # sig4 is of type float, converting it to np array
+ new_array = np.zeros(len(result[i-1]))
+ new_array[0] = element
+ result = list(result)
+ result[i] = new_array
+
+ base_name = f"{func_name}_output.{type}"
+ file_path = os.path.join(save_dir, base_name)
+
+ counter = 1
+ while os.path.exists(file_path):
+ new_name = f"{func_name}_{counter}_output.{type}"
+ file_path = os.path.join(save_dir, new_name)
+ counter += 1
+
+ try:
+ if type == "txt":
+ np.savetxt(file_path, np.transpose(result), header=f'{func_name}')
+ elif type == "csv":
+ import csv
+ data_for_csv = []
+
+ if isinstance(result, np.ndarray) and result.ndim == 1:
+ data_for_csv = [[x] for x in result]
+ else:
+ # Try to handle as collection of arrays or values
+ transposed = np.transpose(result)
+ data_for_csv = transposed.tolist() if hasattr(transposed, 'tolist') else transposed
+
+ with open(file_path, 'w', newline='') as csvfile:
+ writer = csv.writer(csvfile)
+ if isinstance(result, tuple) or isinstance(result, list):
+ header = [f'{func_name}_{i}' for i in range(len(result))]
+ else:
+ header = [func_name]
+ writer.writerow(header)
+ writer.writerows(data_for_csv)
+ elif type == "json":
+ import json
+
+ # Prepare data for JSON serialization (numpy arrays aren't directly JSON serializable)
+ def numpy_to_python(obj):
+ if isinstance(obj, np.ndarray):
+ return obj.tolist()
+ elif isinstance(obj, (tuple, list)):
+ return [numpy_to_python(item) for item in obj]
+ elif isinstance(obj, np.number):
+ return obj.item()
+ return obj
+
+ json_data = {func_name: numpy_to_python(result)}
+ with open(file_path, 'w') as jsonfile:
+ json.dump(json_data, jsonfile, indent=2)
+
+ print(f"Output saved to {file_path}")
+ except Exception as e:
+ print(f"Error saving {func_name} output: {str(e)}")
+
+ def load(self, file_path, load_dir=None):
+ """
+ Load a saved output file and return it in the same format as FASTPT outputs (tuple of numpy arrays)
+
+ Args:
+ file_path (str): Name or path of the file to load
+ load_dir (str, optional): Directory to load file from. If None, uses default output directory
+ If file_path already includes a directory, load_dir is ignored.
+
+ Returns:
+ tuple: A tuple of numpy arrays matching the original FASTPT function output format
+
+ Examples
+ --------
+ >>> handler = FPTHandler(fpt)
+ >>> result = handler.run('one_loop_dd', P=P_linear, save_type='txt')
+ >>> loaded_data = handler.load('one_loop_dd_output.txt')
+ """
+ import os
+ import numpy as np
+ import re
+
+ # If file_path is an absolute path or already contains directory info, use it as is
+ if os.path.isabs(file_path) or os.path.dirname(file_path):
+ full_path = file_path
+ else:
+ # Otherwise, build path from load_dir or default output directory
+ directory = load_dir if load_dir is not None else self.output_dir
+ full_path = os.path.join(directory, file_path)
+
+ # Get file extension
+ _, ext = os.path.splitext(full_path)
+ ext = ext.lower()
+
+ # Check for valid extension before checking if file exists
+ if ext not in (".txt", ".csv", ".json"):
+ raise FileNotFoundError(f"Unsupported file extension: {ext}. Must be '.txt', '.csv', or '.json'")
+ if not os.path.exists(full_path):
+ raise FileNotFoundError(f"File '{full_path}' not found.")
+
+
+ # Extract function name from filename
+ func_name = re.match(r'(.+?)(?:_\d+)?_output\.', os.path.basename(full_path))
+ func_name = func_name.group(1) if func_name else None
+
+ try:
+ arrays = []
+
+ if ext == ".txt":
+ # Load and transpose to match original format
+ loaded_data = np.loadtxt(full_path)
+ # Split columns into separate arrays
+ for i in range(loaded_data.shape[1]):
+ arrays.append(loaded_data[:, i])
+
+ elif ext == ".csv":
+ import csv
+ with open(full_path, 'r') as csvfile:
+ reader = csv.reader(csvfile)
+ # Skip header row
+ next(reader)
+ # Read all rows
+ data_rows = list(reader)
+
+ # Convert to numeric values
+ numeric_data = []
+ for row in data_rows:
+ numeric_data.append([float(val) for val in row])
+
+ # Convert to numpy array
+ all_data = np.array(numeric_data)
+
+ # Split columns into separate arrays (transposing to match original format)
+ for i in range(all_data.shape[1]):
+ arrays.append(all_data[:, i])
+
+ elif ext == ".json":
+ import json
+ with open(full_path, 'r') as jsonfile:
+ data = json.load(jsonfile)
+ # Get the function name from the data if available
+ if not func_name and len(data) == 1:
+ func_name = next(iter(data))
+
+ # Get the data under the function name key
+ result_data = data[next(iter(data))]
+
+ # Handle different possible structures in JSON
+ if isinstance(result_data, list):
+ # If result_data is a list of lists (multiple arrays)
+ if result_data and isinstance(result_data[0], list):
+ for arr in result_data:
+ arrays.append(np.array(arr))
+ else:
+ # Single array
+ arrays.append(np.array(result_data))
+ else:
+ # Single value or other structure
+ arrays.append(np.array([result_data]))
+ else:
+ raise ValueError(f"Unsupported file extension: {ext}. Must be '.txt', '.csv', or '.json'")
+
+ # Handle special case for sig4 in bias functions - convert back to float
+ # In one_loop_dd_bias and one_loop_dd_bias_b3nl, sig4 is at index 7
+ # In one_loop_dd_bias_lpt_NL, sig4 is at index 6
+ if func_name in ["one_loop_dd_bias", "one_loop_dd_bias_b3nl"] and len(arrays) > 7:
+ # Check if the array is mostly zeros with one value
+ if arrays[7].size > 1 and np.count_nonzero(arrays[7]) <= 1:
+ # Get the first non-zero value or the first value if all zeros
+ if np.any(arrays[7]):
+ sig4_value = arrays[7][np.nonzero(arrays[7])[0][0]]
+ else:
+ sig4_value = arrays[7][0]
+ arrays[7] = sig4_value
+
+ elif func_name == "one_loop_dd_bias_lpt_NL" and len(arrays) > 6:
+ # Similar check for lpt_NL case
+ if arrays[6].size > 1 and np.count_nonzero(arrays[6]) <= 1:
+ if np.any(arrays[6]):
+ sig4_value = arrays[6][np.nonzero(arrays[6])[0][0]]
+ else:
+ sig4_value = arrays[6][0]
+ arrays[6] = sig4_value
+
+ print(f"Output loaded from {full_path}")
+
+ # Convert list of arrays to tuple to match FASTPT output format
+ return tuple(arrays)
+
+ except Exception as e:
+ print(f"Error loading output from {full_path}: {str(e)}")
+ return None
+
+
+ def save_instance(self, filename, output_dir=None):
+ """
+ Save parameters of this handler and Fast-PT instance to a compressed numpy .npz file.
+
+ This method saves both numpy arrays and other Python objects (strings, floats, ints, etc.)
+ in a single file. Arrays are stored directly, while non-array values are collected
+ in a metadata dictionary and stored as a special array.
+
+ Parameters
+ ----------
+ filename : str
+ Path where parameters will be saved. If the path doesn't end with '.npz',
+ the extension will be added automatically.
+ output_dir : str, optional
+ Directory to save parameters. If None, uses default output directory.
+ If filename already contains a directory path, output_dir is ignored.
+
+ Notes
+ -----
+ Non-array values are stored in a special array with key '__metadata__'.
+ Arrays and scalars can be freely mixed in the parameters.
+
+ Examples
+ --------
+ >>> handler = FPTHandler(fpt, P=P_linear, C_window=0.75)
+ >>> # Save default parameters
+ >>> handler.save_instance('stored_params')
+ >>> # Save to specific directory
+ >>> handler.save_instance('stored_params', output_dir='/path/to/save')
+ """
+ metadata = {}
+ arrays = {}
+ if not self.default_params:
+ print("No default parameters set. Nothing to save.")
+ return
+ for param in self.default_params.keys():
+ value = self.default_params[param]
+ if isinstance(value, np.ndarray):
+ arrays[param] = value
+ else:
+ metadata[param] = value
+
+ # Include Fast-PT parameters
+ arrays["k"] = self.fastpt.k_original
+ metadata["low_extrap"] = self.fastpt.low_extrap
+ metadata["high_extrap"] = self.fastpt.high_extrap
+ metadata["n_pad"] = self.fastpt.n_pad
+ metadata["max_cache_size_mb"] = self.fastpt.max_cache_size_mb
+ metadata["dump_cache"] = self.fastpt.dump_cache
+
+ # Include Handler init params
+ metadata["save_all"] = self.save_all
+ metadata["save_dir"] = self.output_dir
+
+ if metadata:
+ arrays['__metadata__'] = np.array([metadata], dtype=object)
+
+ if os.path.isabs(filename):
+ # If filename is absolute, use it directly
+ full_path = filename
+ else:
+ # Otherwise, determine the base directory
+ directory = output_dir if output_dir is not None else self.output_dir
+ full_path = os.path.join(directory, filename)
+
+ os.makedirs(os.path.dirname(os.path.abspath(full_path)), exist_ok=True)
+
+ if not full_path.endswith('.npz'):
+ full_path += '.npz'
+ np.savez_compressed(full_path, **arrays)
+ print("Default parameters saved to", full_path)
+
+ @staticmethod
+ def load_instance(filename, load_dir=None):
+ """
+ Load parameters and recreate Handler and Fast-PT instances from a saved .npz file.
+
+ Loads both array and non-array parameters from a file created with save_params().
+ Arrays are loaded directly, while scalar values are extracted from the metadata.
+
+ Parameters
+ ----------
+ filename : str
+ Path to the parameter file. If the path doesn't end with '.npz',
+ the extension will be added automatically.
+ load_dir : str, optional
+ Directory to load parameters from. If None, uses default output directory.
+ If filename already contains a directory path, load_dir is ignored.
+
+ Returns
+ -------
+ dict
+ Dictionary containing all loaded parameters.
+
+ Notes
+ -----
+ This is a static method and is meant to be used to create an instance of the Handler and Fast-PT.
+
+ Examples
+ --------
+ >>> # This method is static and is meant to be called without an instance
+ >>> newHandler = FPTHandler.load_instance('stored_params.npz')
+ >>> # Load from specific directory
+ >>> newHandler = FPTHandler.load_instance('custom_params', load_dir='/path/to/load')
+ >>> # Now you can use newHandler as equivalent to the original handler that stored the parameters
+ >>> newHandler.run('one_loop_dd')
+ """
+ if os.path.isabs(filename) or os.path.dirname(filename):
+ full_path = filename
+ else:
+ directory = load_dir if load_dir is not None else os.getcwd() # Default to current working directory
+ full_path = os.path.join(directory, filename)
+
+ if not full_path.endswith('.npz'):
+ full_path += '.npz'
+
+ if not os.path.exists(full_path):
+ raise FileNotFoundError(f"Parameter file '{full_path}' not found.")
+
+ data = np.load(full_path, allow_pickle=True)
+ params = {}
+ for key in data.files:
+ if key != '__metadata__':
+ params[key] = data[key]
+ if '__metadata__' in data.files:
+ metadata = data['__metadata__'][0]
+ params.update(metadata)
+
+ fastpt_instance = FASTPT(params['k'], low_extrap=params.get('low_extrap', None),
+ high_extrap=params.get('high_extrap', None),
+ n_pad=params.get('n_pad', None),
+ max_cache_size_mb=params.get('max_cache_size_mb', 500),
+ dump_cache=params.get('dump_cache', True))
+ handler = FPTHandler(fastpt_instance, save_all=params.get('save_all'), save_dir=params.get('save_dir'),
+ P=params.get('P'), C_window=params.get('C_window'), P_window=params.get('P_window'))
+
+ return handler
+
+ def plot(self, data=None, terms=None, k=None, ax=None, title=None,
+ log_scale=True, legend_loc='best', grid=True, style=None,
+ colors=None, linewidth=1.5, label_map=None, fig_size=(10, 7),
+ save_path=None, dpi=300, xlim=None, ylim=None, scale_factors=None,
+ return_fig=False, show=True, plot_negatives=True, **override_kwargs):
+ """
+ Create a plot of power spectrum terms.
+
+ Parameters
+ ----------
+ data : dict or array-like, optional
+ Data to plot. Can be:
+ - Dictionary mapping labels to arrays (direct plotting)
+ - Result from FAST-PT function call (will be plotted directly)
+ - None (requires 'terms' parameter to get data)
+ terms : str or list, optional
+ Term name(s) to plot if data is not provided
+ k : array-like, optional
+ k values for x-axis. If None, uses handler's FASTPT k values
+ ax : matplotlib.axes.Axes, optional
+ Axes to plot on. If None, creates a new figure
+ title : str, optional
+ Plot title
+ log_scale : bool or tuple, optional
+ Whether to use log scale. If True, both axes are log.
+ Can provide (x_log, y_log) tuple to specify each axis
+ legend_loc : str, optional
+ Location of legend. Default is 'best'
+ grid : bool, optional
+ Whether to display grid lines
+ style : dict or list of dict, optional
+ Plotting style(s) to apply to lines. Each dict can contain matplotlib
+ line properties (color, linestyle, marker, alpha, etc.)
+ colors : str or list, optional
+ Color(s) for plots. If a list, cycles through colors
+ linewidth : float, optional
+ Line width for plots
+ label_map : dict, optional
+ Mapping from term names to display labels
+ fig_size : tuple, optional
+ Figure size (width, height) in inches
+ save_path : str, optional
+ Path to save figure. If None, figure is not saved
+ dpi : int, optional
+ DPI for saved figure
+ xlim : tuple, optional
+ (min, max) for x-axis
+ ylim : tuple, optional
+ (min, max) for y-axis
+ scale_factors : dict or float, optional
+ Scaling factor(s) for plotted quantities
+ return_fig : bool, optional
+ Whether to return the figure object
+ show : bool, optional
+ Whether to show the plot immediately
+ plot_negatives : bool, optional
+ Whether to plot negative values with dashed lines for visibility
+ **override_kwargs : dict
+ Additional parameters for FAST-PT function calls
+
+ Returns
+ -------
+ fig : matplotlib.figure.Figure, optional
+ Figure object if return_fig is True
+
+ Examples
+ --------
+ >>> handler = FPTHandler(fpt, P=P_linear, C_window=0.75)
+ >>> # Basic plot from terms
+ >>> handler.plot(terms=['P_1loop', 'P_E', 'P_B'])
+ >>> # Plot with custom styling
+ >>> handler.plot(terms='P_1loop', colors='red', title='1-loop SPT')
+ >>> # Plot with style dictionaries for fine control
+ >>> handler.plot(terms=['P_1loop', 'P_E'], style=[
+ ... {'color': 'red', 'linestyle': '--', 'marker': 'o', 'alpha': 0.8},
+ ... {'color': 'blue', 'linestyle': '-', 'linewidth': 2}
+ ... ])
+ """
+ import matplotlib.pyplot as plt
+ from itertools import cycle
+
+ # Set up figure and axes if not provided
+ if ax is None:
+ fig, ax = plt.subplots(figsize=fig_size)
+ else:
+ fig = ax.figure
+
+ # Set plot title if provided
+ if title is not None:
+ ax.set_title(title, fontsize=14)
+
+ # Set up x-axis values (k)
+ if k is None:
+ k = self.fastpt.k_original
+
+ # Default color cycle
+ if colors is None:
+ colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',
+ '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']
+ if isinstance(colors, str):
+ colors = [colors]
+ color_cycle = cycle(colors)
+
+ # Set up label mapping
+ if label_map is None:
+ label_map = {}
+
+ # Set up scale factors
+ if scale_factors is None:
+ scale_factors = {}
+ if isinstance(scale_factors, (int, float)):
+ default_scale = scale_factors
+ scale_factors = {}
+ else:
+ default_scale = 1.0
+
+ # Prepare style dictionaries
+ if style is None:
+ style = [{}] # Empty dict = use defaults
+ elif isinstance(style, dict):
+ style = [style] # Single style dict
+ style_cycle = cycle(style)
+
+ # Get data to plot
+ to_plot = {}
+
+ if data is not None:
+ # Case 1: User provided data directly
+ if isinstance(data, dict):
+ to_plot = data
+ elif isinstance(data, (list, tuple, np.ndarray)):
+ if isinstance(data, (list, tuple)) and len(data) > 1:
+ for i, arr in enumerate(data):
+ label = label_map.get(i, f"Component {i}")
+ to_plot[label] = arr
+ else:
+ label = label_map.get(0, "Data")
+ to_plot[label] = data[0] if isinstance(data, (list, tuple)) else data
+ else:
+ raise ValueError("Unsupported data format. Provide a dict, list, tuple, or numpy array.")
+ elif terms is not None:
+ # Case 2: User provided term names to plot
+ if isinstance(terms, str):
+ terms = [terms]
+
+ # Get data for each term
+ term_data = self.get(*terms, **override_kwargs)
+
+ if len(terms) == 1:
+ term_name = terms[0]
+ display_label = label_map.get(term_name, term_name)
+ to_plot[display_label] = term_data
+ else:
+ for term_name in terms:
+ display_label = label_map.get(term_name, term_name)
+ to_plot[display_label] = term_data[term_name]
+ else:
+ raise ValueError("Either 'data' or 'terms' must be provided.")
+
+ # Plot each data series
+ for label, data_array in to_plot.items():
+ # Get current style and color
+ curr_style = next(style_cycle).copy() # Copy to avoid modifying the original
+
+ # Add default color if not in style
+ if 'color' not in curr_style:
+ curr_style['color'] = next(color_cycle)
+
+ # Set default linewidth if not in style
+ if 'linewidth' not in curr_style:
+ curr_style['linewidth'] = linewidth
+
+ # Apply scale factor if provided for this label
+ scale = scale_factors.get(label, default_scale)
+
+ # Check if data contains negative values
+ if isinstance(data_array, np.ndarray) and np.any(data_array < 0):
+ has_negatives = True
+ # Plot positive values
+ mask_pos = data_array >= 0
+ if np.any(mask_pos):
+ ax.plot(k[mask_pos], scale * data_array[mask_pos], label=label, **curr_style)
+
+ # Plot negative values with dashed lines for visibility
+ if plot_negatives:
+ mask_neg = data_array < 0
+ if np.any(mask_neg):
+ neg_style = curr_style.copy()
+ neg_style['linestyle'] = neg_style.get('linestyle', '--') # Default to dashed
+ # Don't add label to avoid duplicate legend entries
+ ax.plot(k[mask_neg], scale * np.abs(data_array[mask_neg]), **neg_style)
+ else:
+ # Regular plotting for non-negative data
+ ax.plot(k, scale * data_array, label=label, **curr_style)
+
+ # Check if any data had negative values that were plotted
+ has_negative_data = False
+ if plot_negatives:
+ for data_array in to_plot.values():
+ if isinstance(data_array, np.ndarray) and np.any(data_array < 0):
+ has_negative_data = True
+ break
+
+ # Set scales
+ if log_scale:
+ if isinstance(log_scale, (list, tuple)):
+ x_log, y_log = log_scale
+ else:
+ x_log = y_log = True
+
+ if x_log:
+ ax.set_xscale('log')
+ if y_log:
+ ax.set_yscale('log')
+
+ # Set axis labels
+ ax.set_xlabel(r'$k$ [$h$ Mpc$^{-1}$]', fontsize=12)
+ ax.set_ylabel(r'$P(k)$ [$h^{-3}$ Mpc$^3$]', fontsize=12)
+
+ # Set limits if provided
+ if xlim is not None:
+ ax.set_xlim(xlim)
+ if ylim is not None:
+ ax.set_ylim(ylim)
+
+ # Add grid if requested
+ if grid:
+ ax.grid(True, alpha=0.3)
+
+ # Add legend if there are labeled lines
+ if to_plot:
+ legend = ax.legend(loc=legend_loc)
+ # Add note about dashed lines for negative values if applicable
+ if has_negative_data:
+ # Get the legend text and add explanation
+ legend_title = legend.get_title()
+ if legend_title.get_text():
+ new_title = f"{legend_title.get_text()}\n(dashed lines = negative values)"
+ else:
+ new_title = "(dashed lines = negative values)"
+ legend.set_title(new_title, prop={'size': 10})
+
+ # Apply tight layout
+ fig.tight_layout()
+
+ # Save figure if path provided
+ if save_path is not None:
+ fig.savefig(save_path, dpi=dpi, bbox_inches='tight')
+ print(f"Figure saved to: {save_path}")
+
+ # Show the plot if requested
+ if show:
+ plt.show()
+
+ # Return figure if requested
+ if return_fig:
+ return fig
+
+ def generate_power_spectra(self, method='class', mode='single', store=True, **kwargs):
+ """
+ Generate power spectra using the specified mode and method.
+
+ Parameters
+ ----------
+ method : str
+ Either 'class' or 'camb'
+ mode : str
+ 'single', 'bulk', or 'diff'
+ store : bool, optional
+ If True, the generated power spectra will be stored in the handler's default_params. Note: this can only be done for single power spectrum generation.
+
+ - single: generate one power spectra with the given params, which should be passed as floats or bools
+ - bulk: generate multiple power spectra with the given params, which should be passed as lists or np arrays. If any of the params lists are shorter than the longest, they will be padded with the last value.
+ - diff: generate multiple power spectra with the given params, which should be passed as lists or np arrays. The length of the params lists must be 1 or 3, and a power spectrum will be generated varrying each of the parameters individually while holding the central column of values constant.
+ **kwargs
+ Cosmological parameters to pass to the appropriate method
+
+ Returns
+ -------
+ numpy.ndarray or dict
+ Power spectrum or collection of power spectra based on the mode
+
+ Raises
+ ------
+ ValueError
+ If invalid method or mode is specified, or if parameters are incompatible with the selected method
+
+ Notes
+ -----
+ - For an example on how to use 'diff' mode, see the `v4_example.py `_ file on GitHub.
+ - Any non specified parameters will use the default values provided by CAMB or CLASS.
+ """
+ method = method.lower()
+ if method not in ('class', 'camb'):
+ raise ValueError("Invalid method. Choose either 'class' or 'camb'.")
+
+ if mode not in ('single', 'bulk', 'diff'):
+ raise ValueError("Invalid mode. Choose 'single', 'bulk', or 'diff'.")
+
+ # Get parameter information for both methods using inspect
+ class_params = self._get_function_params(self._class_power_spectra)
+ camb_params = self._get_function_params(self._camb_power_spectra)
+
+ # Determine which parameters are specific to each method
+ class_specific = set(class_params['all']) - set(camb_params['all'])
+ camb_specific = set(camb_params['all']) - set(class_params['all'])
+
+ # Check if any method-specific parameters are used with the wrong method
+ if method == 'class':
+ camb_params_used = [param for param in camb_specific if param in kwargs]
+ if camb_params_used:
+ raise ValueError(f"CAMB-specific parameters cannot be used with CLASS method: {camb_params_used}")
+ else: # method == 'camb'
+ class_params_used = [param for param in class_specific if param in kwargs]
+ if class_params_used:
+ raise ValueError(f"CLASS-specific parameters cannot be used with CAMB method: {class_params_used}")
+
+ # For bulk and diff modes, we need parameter defaults, but for single mode the function's
+ # built-in defaults will work automatically
+ if mode == 'diff':
+ return self._diff_power_spectra(method, **kwargs)
+ elif mode == 'bulk':
+ return self._bulk_power_spectra(method, **kwargs)
+ else: # mode == 'single'
+ for key, val in kwargs.items():
+ if isinstance(val, (list, np.ndarray)):
+ raise ValueError(f"Parameter '{key}' must be a single value for single mode.")
+
+ if method == 'class':
+ return self._class_power_spectra(store=store, **kwargs)
+ else:
+ return self._camb_power_spectra(store=store, **kwargs)
+
+ def _bulk_power_spectra(self, method, **params):
+ if method.lower() == 'class':
+ function_params = self._get_function_params(self._class_power_spectra)
+ compute_func = self._class_power_spectra
+ elif method.lower() == 'camb':
+ function_params = self._get_function_params(self._camb_power_spectra)
+ compute_func = self._camb_power_spectra
+ else:
+ raise ValueError("Invalid method. Choose either 'class' or 'camb'.")
+
+ max_len = 1
+ for param_name, value in params.items():
+ if isinstance(value, (list, np.ndarray)):
+ max_len = max(max_len, len(value))
+
+ # Create parameter arrays, padding shorter arrays if needed
+ param_arrays = {}
+ for param_name, value in params.items():
+ if isinstance(value, (list, np.ndarray)):
+ if len(value) < max_len:
+ param_arrays[param_name] = np.pad(value, (0, max_len - len(value)), mode='edge')
+ else:
+ param_arrays[param_name] = np.array(value)
+ else:
+ # For scalar values, create an array of the same value
+ param_arrays[param_name] = np.full(max_len, value)
+
+ output = []
+ for i in range(max_len):
+ iteration_params = {}
+ for param_name in param_arrays:
+ if param_name in function_params['all']:
+ iteration_params[param_name] = param_arrays[param_name][i]
+
+ output.append(compute_func(store=False, **iteration_params))
+
+ return output[0] if len(output) == 1 else output
+
+ def _diff_power_spectra(self, method, **kwargs):
+
+ class_params = self._get_function_params(self._class_power_spectra)
+ camb_params = self._get_function_params(self._camb_power_spectra)
+
+ key_order = list(kwargs.keys())
+ for key in key_order:
+ if method=='camb' and key not in camb_params['all']:
+ raise ValueError(f"Parameter '{key}' is not valid for CAMB method. Valid parameters are: {camb_params['all']}")
+ if method=='class' and key not in class_params['all']:
+ raise ValueError(f"Parameter '{key}' is not valid for CLASS method. Valid parameters are: {class_params['all']}")
+
+ diff_params = {}
+
+ for key in key_order:
+ diff_params[key] = kwargs.get(key)
+
+ if method == 'camb':
+ if kwargs.get('omega_m') is not None and kwargs.get('omega_cdm') is not None:
+ raise ValueError("omega_m and omega_cdm cannot be used together. Please pick one.")
+
+ # Do padding for single parameters
+ has_param_with_length_3 = any(
+ isinstance(value, (list, np.ndarray)) and len(value) == 3
+ for value in diff_params.values()
+ )
+ if not has_param_with_length_3:
+ raise ValueError("At least one parameter must have length 3 to use diff mode.")
+
+ for key, value in diff_params.items():
+ if key == 'z':
+ diff_params['z'] = [value] if isinstance(value, (int, float)) else value
+ continue
+
+ if isinstance(value, (int, float)):
+ diff_params[key] = [value]
+ if isinstance(diff_params[key], (list, np.ndarray)):
+ if len(diff_params[key]) not in (1, 3):
+ raise ValueError(f"Parameter '{key}' must have length 1 or 3.")
+ if len(diff_params[key]) == 1:
+ diff_params[key] = [diff_params[key][0]] * 3
+ elif diff_params[key] is not None:
+ raise ValueError(f"Parameter '{key}' must be a list or numpy array with length 1 or 3.")
+
+
+
+ compute_func = self._class_power_spectra if method == 'class' else self._camb_power_spectra
+ result = {}
+
+ # Setup all parameter combinations
+ for z in diff_params['z']:
+ param_combinations = []
+ params_to_use = diff_params.copy()
+ del params_to_use['z'] # z is handled separately
+ center_values = {}
+ for k, p in params_to_use.items():
+ center_values[k] = p[1]
+ param_combinations.append(center_values)
+
+ for param_name, param_values in params_to_use.items():
+ if param_values[0] == param_values[1] == param_values[2]:
+ continue
+
+ for val_idx in [0, 2]:
+ values = center_values.copy()
+ values[param_name] = param_values[val_idx]
+ param_combinations.append(values)
+
+ # Generate all power spectra with the parameter combination
+ for i, combo in enumerate(param_combinations):
+ if i == 0:
+ num = 0
+ elif i % 2 == 1: # odd index
+ num = -(i + 1) // 2
+ else: # even index
+ num = i // 2
+
+ key = (z, num)
+ if method == 'class':
+ result[key] = compute_func(store=False, **combo)
+ else: # method == 'camb'
+ result[key] = compute_func(store=False, **combo)
+
+ return result
+
+ def _class_power_spectra(self, z=0.0, h=0.69, omega_b=0.022, omega_cdm=0.122,
+ As=2.1e-9, ns=0.97, store=True):
+ try:
+ from classy import Class
+ except ImportError as e:
+ raise ImportError("class is not installed. Please install it to use this function.") from e
+
+ # Get k values (assuming they're in h/Mpc as in CAMB)
+ k_hMpc = self.fastpt.k_original
+ k_max = max(k_hMpc)
+
+ # Set up CLASS parameters to match CAMB
+ # Note: CLASS uses physical densities (ωb, ωcdm) not density parameters (Ωb, Ωcdm)
+ params = {
+ 'output': 'mPk',
+ 'P_k_max_h/Mpc': k_max,
+ 'z_max_pk': z,
+ 'h': h,
+ 'Omega_b': omega_b,
+ 'Omega_cdm': omega_cdm,
+ 'A_s': As,
+ 'n_s': ns,
+ 'N_ncdm': 0,
+ 'YHe': 0.246,
+ 'k_per_decade_for_pk': 50,
+ 'Omega_Lambda': 1 - (omega_b + omega_cdm)/(h**2),
+ 'w0_fld': -1.0,
+ 'T_cmb': 2.7255,
+ 'k_pivot': 0.05,
+ }
+
+ cosmo = Class()
+ cosmo.set(params)
+ cosmo.compute()
+
+ # Get power spectrum for each k, converting units appropriately
+ # CLASS uses 1/Mpc units internally, so we need to convert
+ power = np.zeros(len(k_hMpc))
+ for i, k in enumerate(k_hMpc):
+ # Convert k from h/Mpc to 1/Mpc for CLASS
+ k_invMpc = k * h
+ # Get P(k) in (Mpc)^3 units
+ pk_invMpc = cosmo.pk(k_invMpc, z)
+ # Convert to (Mpc/h)^3 units to match CAMB
+ power[i] = pk_invMpc * h**3
+
+ cosmo.struct_cleanup()
+ cosmo.empty()
+
+ if store:
+ self.update_default_params(P=power)
+ return power
+
+ def _camb_power_spectra(self,
+ z: float = 0.0,
+ nonlinear: bool = False,
+ h: float = 0.69,
+ H0: float = None,
+ omega_b: float = 0.048,
+ omega_cdm: float = None,
+ omega_m: float = 0.3,
+ omega_k: float = 0.0,
+ As: float = 2.1e-9,
+ ns: float = 0.97,
+ halofit_version: str = 'takahashi',
+ kmax: float = None,
+ k_per_logint: int = 50,
+ transfer_power_var: int = "delta_nonu",
+ mnu: float = 0.0,
+ num_massive_neutrinos: int = 0,
+ nnu: int = 3.046,
+ tau: float = 0.0697186,
+ w: float = -1.0,
+ WantTransfer: bool = True,
+ transfer_interp_matterpower: bool = True,
+ transfer_num_redshifts: int = 1,
+ share_delta_neff: bool = True,
+ nu_mass_fractions: list = [1.0],
+ store = True
+ ):
+ try:
+ import camb
+ from camb import model
+ except ImportError as e:
+ raise ImportError("CAMB is not installed. Please install it to use this function.") from e
+ k = self.fastpt.k_original
+
+ # This code calculates the neutrino physical density parameter (omnuh2) and handles cold dark matter density.
+ # The division by 93.14 is a cosmological conversion factor that relates neutrino mass to energy density
+ # through the equation: Ω_ν h² = Σm_ν / 93.14 eV
+ # The conditional block ensures matter conservation - either:
+ # 1. Using specified omega_cdm directly, or
+ # 2. Calculating omega_cdm based on total matter density by subtracting baryons and neutrinos
+ # This ensures consistency in the matter budget across different cosmological models
+ omnuh2 = mnu * num_massive_neutrinos / 93.14
+ if omega_cdm is None:
+ # Neutrino density parameter
+ omega_nu = omnuh2 / (h**2)
+ # Cold dark matter from total matter density
+ omega_cdm = omega_m - omega_b - omega_nu
+ else:
+ omega_cdm = omega_cdm
+
+ ombh2 = omega_b * (h**2)
+ omch2 = omega_cdm * (h**2)
+ if H0 is None: H0 = h * 100
+
+ pars = camb.CAMBparams()
+ pars.set_cosmology(H0=H0,
+ ombh2=ombh2,
+ omch2=omch2,
+ omk=omega_k,
+ mnu=mnu,
+ num_massive_neutrinos=num_massive_neutrinos,
+ nnu=nnu)
+
+ pars.InitPower.set_params(As=As, ns=ns, pivot_scalar=0.05)
+
+ pars.Reion.set_tau(tau)
+
+ pars.set_dark_energy(w)
+
+ pars.set_accuracy(AccuracyBoost=1.5, lSampleBoost=1.5, lAccuracyBoost=1.5)
+
+ kmax = kmax or float(np.max(k))
+ pars.set_matter_power(redshifts=[z], kmax=kmax, k_per_logint=k_per_logint)
+
+ # Transfer settings
+ pars.WantTransfer = WantTransfer
+ pars.Transfer.transfer_high_precision = True
+ pars.Transfer.transfer_kmax = kmax
+ pars.Transfer.transfer_k_per_logint = k_per_logint
+ pars.Transfer.transfer_interp_matterpower = transfer_interp_matterpower
+ pars.Transfer.transfer_num_redshifts = transfer_num_redshifts
+ pars.Transfer.transfer_redshifts = [z]
+ pars.Transfer.transfer_power_var = transfer_power_var
+
+ # Neutrinos
+ pars.share_delta_neff = share_delta_neff
+ pars.nu_mass_eigenstates = 1 if num_massive_neutrinos > 0 else 0
+ pars.num_massive_neutrinos = num_massive_neutrinos
+ pars.num_nu_massless = nnu - num_massive_neutrinos if num_massive_neutrinos > 0 else nnu
+ # Sets neutrino mass distribution among eigenstates
+ # [1.0] means all mass is in a single eigenstate (or equally distributed)
+ # Affects matter power spectrum by changing neutrino free-streaming effects
+ if num_massive_neutrinos > 0:
+ pars.nu_mass_fractions = nu_mass_fractions
+
+ if nonlinear:
+ # Explicitly set NonLinear_both to make sure both power and transfer functions are nonlinear
+ pars.NonLinear = model.NonLinear_both
+ pars.NonLinearModel.set_params(halofit_version=halofit_version)
+
+ PK = camb.get_matter_power_interpolator(pars,
+ zmin=z, zmax=z, nz_step=1,
+ kmax=kmax,
+ nonlinear=nonlinear,
+ var1=transfer_power_var, var2=transfer_power_var)
+
+ P = PK.P(z, k)
+ if store:
+ self.update_default_params(P=P)
+ return P
\ No newline at end of file
diff --git a/fastpt/get_nu1_nu2.py b/fastpt/get_nu1_nu2.py
deleted file mode 100644
index 4fd4aa0..0000000
--- a/fastpt/get_nu1_nu2.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import numpy as np
-
-def nu1_nu2(alpha,beta):
-
- nu1= -2-alpha
- nu2= -2-beta
- return nu1,nu2
\ No newline at end of file
diff --git a/fastpt/info.py b/fastpt/info.py
index fdf2754..9024adf 100644
--- a/fastpt/info.py
+++ b/fastpt/info.py
@@ -2,4 +2,4 @@
Place package information here. Because it is not a local package (e.g. __init__.py),
the information is available whether the package is loaded from outside or inside the directory.
'''
-__version__ = '3.2'
\ No newline at end of file
+__version__ = '4.0.0'
\ No newline at end of file
diff --git a/fastpt/HT.py b/fastpt/misc/HT.py
similarity index 97%
rename from fastpt/HT.py
rename to fastpt/misc/HT.py
index befa0fa..8daaf62 100644
--- a/fastpt/HT.py
+++ b/fastpt/misc/HT.py
@@ -27,11 +27,10 @@
from __future__ import division
import numpy as np
-from numpy.fft import fft, ifft , fftshift, ifftshift , rfft, irfft
-from numpy import exp, log, log10, cos, sin, pi
+from numpy.fft import rfft, irfft
+from numpy import exp, log, cos, sin, pi
from scipy.special import gamma
from time import time
-from numpy import gradient as grad
import sys
log2=log(2)
diff --git a/fastpt/bias_choice_plot.py b/fastpt/misc/bias_choice_plot.py
similarity index 92%
rename from fastpt/bias_choice_plot.py
rename to fastpt/misc/bias_choice_plot.py
index 3310bc0..f29ace2 100644
--- a/fastpt/bias_choice_plot.py
+++ b/fastpt/misc/bias_choice_plot.py
@@ -37,7 +37,7 @@
t=np.arange(-3,0.1,0.1)
upper_bound=-3-t
-ax.fill_between(t, -3, upper_bound, facecolor='grey', alpha='0.5')
+ax.fill_between(t, -3, upper_bound, facecolor='grey', alpha=0.5)
plt.show()
fig.savefig('bias_choice.pdf')
\ No newline at end of file
diff --git a/fastpt/param_matrix.py b/fastpt/param_matrix.py
deleted file mode 100644
index be11d29..0000000
--- a/fastpt/param_matrix.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import numpy as np
-
-
- # A=1219/1470.*mat[0,:]
-# B=671/1029.*mat[1,:]
-# C=32/1715.*mat[2,:]
-# D=1/3.*mat[3,:]
-# E=62/35.*mat[4,:]
-# F=8/35.*mat[5,:]
-# reg=1/3.*mat[6,:]
-
-spt=np.array([[2*1219/1470., 0,0,0,0],[2*671/1029.,0,0,2,0],[2*32/1715.,0,0,4,0],[2*1/3.,2,-2,2,0],\
- [2*62/35.,1,-1,1,0],[2*8/35.,1,-1,3,0],[1/3.,2,-2,0,1]])
-
-
-
\ No newline at end of file
diff --git a/fastpt/RSD.py b/fastpt/rsd/RSD.py
similarity index 97%
rename from fastpt/RSD.py
rename to fastpt/rsd/RSD.py
index 22e5d2b..3424d0f 100644
--- a/fastpt/RSD.py
+++ b/fastpt/rsd/RSD.py
@@ -1,7 +1,6 @@
from __future__ import division
import numpy as np
-from .J_table import J_table
-from .get_nu1_nu2 import nu1_nu2
+from ..utils.J_table import J_table
from copy import copy
import sys
diff --git a/fastpt/RSD_ItypeII.py b/fastpt/rsd/RSD_ItypeII.py
similarity index 98%
rename from fastpt/RSD_ItypeII.py
rename to fastpt/rsd/RSD_ItypeII.py
index e70c79e..062740a 100644
--- a/fastpt/RSD_ItypeII.py
+++ b/fastpt/rsd/RSD_ItypeII.py
@@ -2,7 +2,7 @@
import numpy as np
import sys
from time import time
-from numpy import log, sqrt, exp, pi
+from numpy import log, exp, pi
from scipy.signal import fftconvolve as convolve
diff --git a/fastpt/J_k.py b/fastpt/utils/J_k.py
similarity index 96%
rename from fastpt/J_k.py
rename to fastpt/utils/J_k.py
index 7dd0c6a..17b4859 100644
--- a/fastpt/J_k.py
+++ b/fastpt/utils/J_k.py
@@ -8,9 +8,8 @@
'''
from __future__ import division
import numpy as np
-from numpy.fft import fft, ifft , rfft, irfft , fftfreq
-from numpy import exp, log, log10, cos, sin, pi, cosh, sinh , sqrt
-from scipy.special import gamma
+from numpy.fft import ifft , rfft, irfft
+from numpy import exp, log, pi
import sys
from time import time
from .fastpt_extr import p_window, c_window, pad_left, pad_right
diff --git a/fastpt/J_table.py b/fastpt/utils/J_table.py
similarity index 100%
rename from fastpt/J_table.py
rename to fastpt/utils/J_table.py
diff --git a/fastpt/OV.py b/fastpt/utils/OV.py
similarity index 100%
rename from fastpt/OV.py
rename to fastpt/utils/OV.py
diff --git a/fastpt/P_extend.py b/fastpt/utils/P_extend.py
similarity index 100%
rename from fastpt/P_extend.py
rename to fastpt/utils/P_extend.py
diff --git a/fastpt/Wigner_symbols.py b/fastpt/utils/Wigner_symbols.py
similarity index 97%
rename from fastpt/Wigner_symbols.py
rename to fastpt/utils/Wigner_symbols.py
index 014ae31..0adae42 100644
--- a/fastpt/Wigner_symbols.py
+++ b/fastpt/utils/Wigner_symbols.py
@@ -25,8 +25,9 @@
# store factorials in an array
def factorial_list(N):
- x=np.arange(N+1)
- return factorial(x)
+ x = np.arange(N + 1)
+ return np.exp(np.cumsum(np.log(np.where(x > 0, x, 1))))
+
# old way for the factorials, no longer used.
# def factorial_list(N):
@@ -43,6 +44,11 @@ def factorial_list(N):
# return FL[:int(N) + 1]
# else:
# return FL
+#
+#other old way:
+#def factorial_list(N):
+# x=np.arange(N+1)
+# return factorial(x)
def three_j(j,m):
@@ -62,7 +68,7 @@ def three_j(j,m):
PF= int((-1) ** int(j_1 - j_2 - m_3))
- M=-m_3;
+ M=-m_3
a1 = j_1 + j_2 - j_3
if a1 < 0:
diff --git a/fastpt/fastpt_extr.py b/fastpt/utils/fastpt_extr.py
similarity index 89%
rename from fastpt/fastpt_extr.py
rename to fastpt/utils/fastpt_extr.py
index c43d11f..ff0fb6b 100644
--- a/fastpt/fastpt_extr.py
+++ b/fastpt/utils/fastpt_extr.py
@@ -4,12 +4,8 @@
'''
import numpy as np
-from numpy import pi, cos, sin, log
-from numpy.fft import rfft, irfft, fft, ifft
+from numpy import pi, sin
import sys
-from scipy import signal
-from scipy.special import erf
-from scipy.signal import butter, lfilter, filtfilt, lfilter_zi
def p_window(k,log_k_left,log_k_right):
diff --git a/fastpt/filter_Pk.py b/fastpt/utils/filter_Pk.py
similarity index 97%
rename from fastpt/filter_Pk.py
rename to fastpt/utils/filter_Pk.py
index 498a8e2..e85523d 100644
--- a/fastpt/filter_Pk.py
+++ b/fastpt/utils/filter_Pk.py
@@ -1,5 +1,5 @@
import numpy as np
-from scipy.signal import butter, lfilter, filtfilt, lfilter_zi
+from scipy.signal import butter, lfilter, filtfilt
import copy
from .P_extend import k_extend
@@ -92,7 +92,7 @@ def zero_phase(sig):
def BW_filter(P_in,order=3,nf=.01):
- # print 'at butter, freq=', nf
+ print('at butter, freq=', nf)
B, A = butter(order, nf, 'low')
diff --git a/fastpt/gamma_funcs.py b/fastpt/utils/gamma_funcs.py
similarity index 96%
rename from fastpt/gamma_funcs.py
rename to fastpt/utils/gamma_funcs.py
index 9e6cb5d..6fbfc76 100644
--- a/fastpt/gamma_funcs.py
+++ b/fastpt/utils/gamma_funcs.py
@@ -2,7 +2,7 @@
J.E. McEwen
'''
import numpy as np
-from numpy import exp, pi, sin, cos, log, sqrt
+from numpy import exp, pi, log, sqrt
from scipy.special import gamma
def log_gamma(z):
diff --git a/fastpt/initialize_params.py b/fastpt/utils/initialize_params.py
similarity index 58%
rename from fastpt/initialize_params.py
rename to fastpt/utils/initialize_params.py
index f68aa5b..f948add 100644
--- a/fastpt/initialize_params.py
+++ b/fastpt/utils/initialize_params.py
@@ -4,23 +4,59 @@
quatities for each FAST-PT parameter matrix.
'''
import numpy as np
-from numpy import exp, pi, sin, cos, log, sqrt
+from numpy import exp, pi, log, sqrt
from scipy.special import gamma
-from .get_nu1_nu2 import nu1_nu2
import sys
+import functools
+
+def array_cache_key(array):
+ """Create a hashable key from a numpy array"""
+ if array is None:
+ return None
+ return hash(array.tobytes())
+
+def create_grid_aware_cache_key(args, N, eta_m, tau_l):
+ """Create cache key that includes grid parameters"""
+ base_key = tuple(array_cache_key(arg) if isinstance(arg, np.ndarray) else arg for arg in args)
+ grid_key = (N, array_cache_key(eta_m), array_cache_key(tau_l))
+ full_key = base_key + grid_key
+ return full_key
+
+def memoize_gamma(func):
+ """Decorator to cache gamma function results with grid awareness"""
+ cache = {}
+
+ @functools.wraps(func)
+ def wrapper(*args, **kwargs):
+ # Extract grid parameters from kwargs or use defaults
+ N = kwargs['_cache_N']
+ eta_m = kwargs.pop('_cache_eta_m', None)
+ tau_l = kwargs.pop('_cache_tau_l', None)
+
+ # Use grid-aware caching
+ key = create_grid_aware_cache_key(args, N, eta_m, tau_l)
+ if key not in cache:
+ cache[key] = func(*args, **kwargs)
+ return cache[key]
+
+ return wrapper
+
-log2=log(2.)
-def log_gamma(z):
+log2=log(2.)
- z=gamma(z)
- w=log(z)
- x=np.real(w)
- y=np.imag(w)
- return x,y
+# Log gamma function is never used anywhere
+# @memoize_gamma
+# def log_gamma(z):
+# z=gamma(z)
+# w=log(z)
+# x=np.real(w)
+# y=np.imag(w)
+# return x,y
-def g_m_vals(mu,q):
+@memoize_gamma
+def g_m_vals(mu, q, _cache_N=None, _cache_eta_m=None, _cache_tau_l=None):
imag_q= np.imag(q)
@@ -47,9 +83,10 @@ def g_m_vals(mu,q):
return g_m
-def gamsn(z):
+@memoize_gamma
+def gamsn(z, _cache_N=None, _cache_eta_m=None, _cache_tau_l=None):
z=np.asarray(z, dtype=complex)
- result=sqrt(pi) /2. * 2.**z *g_m_vals(0.5, z-0.5)
+ result=sqrt(pi) /2. * 2.**z *g_m_vals(0.5, z-0.5, _cache_N=_cache_N, _cache_eta_m=_cache_eta_m, _cache_tau_l=_cache_tau_l)
return result
def scalar_stuff(p_mat,nu,N,m,eta_m,l, tau_l):
@@ -77,8 +114,9 @@ def scalar_stuff(p_mat,nu,N,m,eta_m,l, tau_l):
p[i]=-5-2*nu-alpha[i]-beta[i]
- g_m[i,:]=g_m_vals(sigma,Q_m)
-
+ # Pass grid parameters to cached functions
+ g_m[i,:] = g_m_vals(sigma, Q_m, _cache_N=N, _cache_eta_m=eta_m, _cache_tau_l=tau_l)
+
if (type[i]==1):
# this is the special case, Corresponding to the regularized version of
@@ -88,7 +126,7 @@ def scalar_stuff(p_mat,nu,N,m,eta_m,l, tau_l):
s=2+nu + beta[i]
Q_n=s+ 1j*eta_m
- g_n[i,:]=gamsn(Q_n)
+ g_n[i,:]=gamsn(Q_n, _cache_N=N, _cache_eta_m=eta_m, _cache_tau_l=tau_l)
#two_part_m=2**Q_m
g_m[i,:]=g_m[i,:]*2.**Q_m
@@ -98,7 +136,7 @@ def scalar_stuff(p_mat,nu,N,m,eta_m,l, tau_l):
two_part_l[i,:]=np.ones(l.size)
else:
- g_n[i,:]=g_m_vals(sigma,Q_n)
+ g_n[i,:]=g_m_vals(sigma, Q_n, _cache_N=N, _cache_eta_m=eta_m, _cache_tau_l=tau_l)
# prefactor
@@ -106,10 +144,16 @@ def scalar_stuff(p_mat,nu,N,m,eta_m,l, tau_l):
two_part_l[i,:]=exp(1j*tau_l*log2)
# calculate h_l
- h_l[i,:]=gamsn(p[i]+1-1j*tau_l)
+ h_l[i,:]=gamsn(p[i]+1-1j*tau_l, _cache_N=N, _cache_eta_m=eta_m, _cache_tau_l=tau_l)
return pf,p, g_m, g_n, two_part_l, h_l
+def nu1_nu2(alpha,beta):
+
+ nu1= -2-alpha
+ nu2= -2-beta
+ return nu1,nu2
+
def tensor_stuff(p_mat,N,m,eta_m,l, tau_l):
alpha=p_mat[:,0]
@@ -146,14 +190,14 @@ def tensor_stuff(p_mat,N,m,eta_m,l, tau_l):
#p=3. + nu1+nu2 + alpha[i] + beta[i]
- g_m[i,:]=g_m_vals(sigma_1,Q_m)
- g_n[i,:]=g_m_vals(sigma_2,Q_n)
+ g_m[i,:]=g_m_vals(sigma_1,Q_m, _cache_N=N, _cache_eta_m=eta_m, _cache_tau_l=tau_l)
+ g_n[i,:]=g_m_vals(sigma_2,Q_n, _cache_N=N, _cache_eta_m=eta_m, _cache_tau_l=tau_l)
pf[i] = A[i] * B[i] * pi**(1.5) /8.
# calculate h_l
#arg=(p+1-1j*tau_l)
- h_l[i,:]=g_m_vals(sigma_3, -1.5 - p[i] - 1j*tau_l)
+ h_l[i,:]=g_m_vals(sigma_3, -1.5 - p[i] - 1j*tau_l, _cache_N=N, _cache_eta_m=eta_m, _cache_tau_l=tau_l)
return pf, p, nu1, nu2, g_m, g_n, h_l
diff --git a/fastpt/kPol.py b/fastpt/utils/kPol.py
similarity index 81%
rename from fastpt/kPol.py
rename to fastpt/utils/kPol.py
index 27c815d..66611ea 100644
--- a/fastpt/kPol.py
+++ b/fastpt/utils/kPol.py
@@ -22,18 +22,18 @@ def kPol():
table1=np.zeros(10,dtype=float)
for i in range(l_mat1.shape[0]):
- x=J_table(l_mat1[i])
- table1=np.row_stack((table1,x))
+ x=J_table(l_mat1[i])
+ table1=np.row_stack((table1,x))
table2=np.zeros(10,dtype=float)
for i in range(l_mat2.shape[0]):
- x=J_table(l_mat2[i])
- table2=np.row_stack((table2,x))
+ x=J_table(l_mat2[i])
+ table2=np.row_stack((table2,x))
table3=np.zeros(10,dtype=float)
for i in range(l_mat3.shape[0]):
- x=J_table(l_mat3[i])
- table3=np.row_stack((table3,x))
+ x=J_table(l_mat3[i])
+ table3=np.row_stack((table3,x))
return table1[1:,:], table2[1:,:], table3[1:,:]
diff --git a/fastpt/matter_power_spt.py b/fastpt/utils/matter_power_spt.py
similarity index 98%
rename from fastpt/matter_power_spt.py
rename to fastpt/utils/matter_power_spt.py
index 1ab0009..6e603d1 100644
--- a/fastpt/matter_power_spt.py
+++ b/fastpt/utils/matter_power_spt.py
@@ -11,7 +11,8 @@
'''
import numpy as np
-from numpy import log, sqrt, exp, pi
+from numpy import log, exp, pi
+#from scipy.integrate import trapz
from scipy.signal import fftconvolve
from .J_k import J_k
import sys
diff --git a/paper/IA_paper_EB_final.py b/paper/IA_paper_EB_final.py
index 33f2052..04d43e2 100644
--- a/paper/IA_paper_EB_final.py
+++ b/paper/IA_paper_EB_final.py
@@ -1,7 +1,7 @@
from __future__ import division
import numpy as np
-from matter_power_spt import one_loop
-import FASTPT
+from fastpt.utils.matter_power_spt import one_loop
+from fastpt import FASTPT
from time import time
# load the input power spectrum data
@@ -27,7 +27,7 @@
C_window=.65
n_pad=1000
# initialize the FASTPT class
-fastpt=FASTPT.FASTPT(k,to_do=['IA'],low_extrap=-6,high_extrap=4,n_pad=n_pad)
+fastpt=FASTPT(k,to_do=['IA'],low_extrap=-6,high_extrap=4,n_pad=n_pad)
t1=time()
diff --git a/paper/OV_paper_final.py b/paper/OV_paper_final.py
index a2a321c..2da9329 100644
--- a/paper/OV_paper_final.py
+++ b/paper/OV_paper_final.py
@@ -1,7 +1,7 @@
from __future__ import division
import numpy as np
-from matter_power_spt import one_loop
-import FASTPT
+from fastpt.utils.matter_power_spt import one_loop
+from fastpt import FASTPT
from time import time
# load the input power spectrum data
@@ -26,7 +26,7 @@
C_window=.65
n_pad=800
# initialize the FASTPT class
-fastpt=FASTPT.FASTPT(k,to_do=['OV'],low_extrap=-6,high_extrap=4,n_pad=n_pad)
+fastpt=FASTPT(k,to_do=['OV'],low_extrap=-6,high_extrap=4,n_pad=n_pad)
t1=time()
diff --git a/paper/RSDAB_paper_final.py b/paper/RSDAB_paper_final.py
index cf828ab..5499d22 100644
--- a/paper/RSDAB_paper_final.py
+++ b/paper/RSDAB_paper_final.py
@@ -1,7 +1,7 @@
from __future__ import division
import numpy as np
-from matter_power_spt import one_loop
-import FASTPT
+from fastpt.utils.matter_power_spt import one_loop
+from fastpt import FASTPT
from time import time
# load the input power spectrum data
@@ -28,7 +28,7 @@
C_window=.65
n_pad=1000
# initialize the FASTPT class
-fastpt=FASTPT.FASTPT(k,to_do=['RSD'],low_extrap=-6,high_extrap=4,n_pad=n_pad)
+fastpt=FASTPT(k,to_do=['RSD'],low_extrap=-6,high_extrap=4,n_pad=n_pad)
t1=time()
diff --git a/paper/fastpt_example_plot.py b/paper/fastpt_example_plot.py
index f987c64..45af06b 100644
--- a/paper/fastpt_example_plot.py
+++ b/paper/fastpt_example_plot.py
@@ -8,7 +8,7 @@
import numpy as np
#from matter_power_spt import one_loop
-import FASTPT
+from fastpt import FASTPT
from time import time
# load the input power spectrum data
@@ -29,7 +29,7 @@
nu=-2; n_pad=1000
# initialize the FASTPT class
-fastpt=FASTPT.FASTPT(k,nu,low_extrap=-5,high_extrap=5,n_pad=n_pad,verbose=True)
+fastpt=FASTPT(k,nu,low_extrap=-5,high_extrap=5,n_pad=n_pad,verbose=True, simple=True)
t1=time()
P_spt=fastpt.one_loop(P,C_window=C_window)
diff --git a/paper/kpol_paper_final.py b/paper/kpol_paper_final.py
index f016e1d..89b2f74 100644
--- a/paper/kpol_paper_final.py
+++ b/paper/kpol_paper_final.py
@@ -1,7 +1,7 @@
from __future__ import division
import numpy as np
-from matter_power_spt import one_loop
-import FASTPT
+from fastpt.utils.matter_power_spt import one_loop
+from fastpt import FASTPT
from time import time
# load the input power spectrum data
@@ -26,7 +26,7 @@
C_window=.65
n_pad=1000
# initialize the FASTPT class
-fastpt=FASTPT.FASTPT(k,to_do=['kPol'],low_extrap=-6,high_extrap=4,n_pad=n_pad)
+fastpt=FASTPT(k,to_do=['kPol'],low_extrap=-6,high_extrap=4,n_pad=n_pad)
t1=time()
diff --git a/pyproject.toml b/pyproject.toml
index 3dad00e..1480ad3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "fast-pt"
dynamic = ["version"]
-requires-python = ">=3.5"
+requires-python = ">=3.7"
description = "FAST-PT is a code to calculate quantities in cosmological perturbation theory at 1-loop (including, e.g., corrections to the matter power spectrum)."
readme = "README.md"
license = { file = "LICENSE" }
@@ -14,7 +14,9 @@ authors = [
{ name = "Joseph E. McEwen" },
{ name = "Xiao Fang" },
{ name = "Jonathan Blazek" },
- { email = "blazek@berkeley.edu" }
+ { email = "blazek@berkeley.edu" },
+ { name = "Vincent Schacknies" },
+ { email = "vincent.schacknies@icloud.com" }
]
classifiers = [
'Development Status :: 5 - Production/Stable',
diff --git a/fastpt/RG_RK4.py b/rg/RG_RK4.py
similarity index 98%
rename from fastpt/RG_RK4.py
rename to rg/RG_RK4.py
index a4f97c0..5859fbb 100644
--- a/fastpt/RG_RK4.py
+++ b/rg/RG_RK4.py
@@ -10,8 +10,8 @@
import numpy as np
import matplotlib.pyplot as plt
-from .fastpt_extr import p_window
-from . import FASTPT
+from fastpt.utils.fastpt_extr import p_window
+from fastpt.core.FASTPT import FASTPT
import time, sys
diff --git a/fastpt/RG_RK4_filt.py b/rg/RG_RK4_filt.py
similarity index 96%
rename from fastpt/RG_RK4_filt.py
rename to rg/RG_RK4_filt.py
index 1161925..307afb5 100644
--- a/fastpt/RG_RK4_filt.py
+++ b/rg/RG_RK4_filt.py
@@ -8,14 +8,13 @@
'''
import numpy as np
-from .matter_power_spt import one_loop
import matplotlib.pyplot as plt
-from .fastpt_extr import p_window
-from scipy.signal import butter, lfilter, filtfilt, lfilter_zi
+from fastpt.utils.fastpt_extr import p_window
+from scipy.signal import butter, lfilter
import time
import sys
-from . import FASTPT
+from fastpt.core.FASTPT import FASTPT
def RG_RK4_filt(name,k,P,d_lambda,max,n_pad,P_window,C_window):
@@ -40,7 +39,7 @@ def RG_RK4_filt(name,k,P,d_lambda,max,n_pad,P_window,C_window):
P_0=P*W
nu=-2
- fastpt=FASTPT.FASTPT(k,nu=nu,n_pad=n_pad)
+ fastpt=FASTPT(k,nu=nu,n_pad=n_pad, simple=True)
P_spt=fastpt.one_loop(P_0,C_window=C_window)
P_spt=P_0+P_spt
diff --git a/fastpt/RG_STS.py b/rg/RG_STS.py
similarity index 98%
rename from fastpt/RG_STS.py
rename to rg/RG_STS.py
index 712443b..c6f05a3 100644
--- a/fastpt/RG_STS.py
+++ b/rg/RG_STS.py
@@ -15,8 +15,8 @@
import numpy as np
import matplotlib.pyplot as plt
-from .fastpt_extr import p_window
-from . import FASTPT
+from fastpt.utils.fastpt_extr import p_window
+from fastpt.core.FASTPT import FASTPT
import time, sys
diff --git a/fastpt/RG_ani.py b/rg/RG_ani.py
similarity index 100%
rename from fastpt/RG_ani.py
rename to rg/RG_ani.py
diff --git a/examples/kmax10_example.ini b/rg/kmax10_example.ini
similarity index 100%
rename from examples/kmax10_example.ini
rename to rg/kmax10_example.ini
diff --git a/examples/kmax1_example.ini b/rg/kmax1_example.ini
similarity index 100%
rename from examples/kmax1_example.ini
rename to rg/kmax1_example.ini
diff --git a/setup.py b/setup.py
index 96b86a4..d8dbbd0 100644
--- a/setup.py
+++ b/setup.py
@@ -13,4 +13,4 @@
raise RuntimeError("Unable to find version string in %s." % (version_file,))
print('FASTPT version is %s'%(fastpt_version))
-setup(version=fastpt_version)
\ No newline at end of file
+setup(version=fastpt_version, packages=find_packages())
\ No newline at end of file
diff --git a/tests/.DS_Store b/tests/.DS_Store
new file mode 100644
index 0000000..1d2ec47
Binary files /dev/null and b/tests/.DS_Store differ
diff --git a/tests/Profiler.py b/tests/Profiler.py
new file mode 100644
index 0000000..0d8b601
--- /dev/null
+++ b/tests/Profiler.py
@@ -0,0 +1,105 @@
+from fastpt import FASTPT
+import numpy as np
+import os
+from time import time
+from statistics import mean, stdev
+import json
+from datetime import datetime
+
+def run_benchmark(fpt, function_name, params, n_runs=5):
+ """Run a specific FASTPT function multiple times and collect timing stats."""
+ times = []
+ method = getattr(fpt, function_name)
+
+ for _ in range(n_runs):
+ t0 = time()
+ method(**params)
+ t1 = time()
+ times.append(t1 - t0)
+
+ return {
+ 'mean': mean(times),
+ 'std': stdev(times),
+ 'min': min(times),
+ 'max': max(times),
+ 'all_runs': times
+ }
+
+
+def numpy_to_python(obj):
+ """Convert numpy types to Python types for JSON serialization."""
+ if isinstance(obj, np.ndarray):
+ return obj.tolist()
+ if isinstance(obj, (np.int_, np.intc, np.intp, np.int8, np.int16,
+ np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64)):
+ return int(obj)
+ if isinstance(obj, (np.float_, np.float16, np.float32, np.float64)):
+ return float(obj)
+ if isinstance(obj, (np.complex_, np.complex64, np.complex128)):
+ return {'real': obj.real, 'imag': obj.imag}
+ if isinstance(obj, dict):
+ return {k: numpy_to_python(v) for k, v in obj.items()}
+ if isinstance(obj, (list, tuple)):
+ return [numpy_to_python(item) for item in obj]
+ return obj
+
+
+def main():
+ data_path = os.path.join(os.path.dirname(__file__), '..', 'examples', 'Pk_test.dat')
+ d = np.loadtxt(data_path)
+ k = d[:, 0]
+ P = d[:, 1]
+ P_window = np.array([0.2, 0.2])
+ C_window = 0.75
+ n_pad = int(0.5 * len(k))
+ fpt = FASTPT(k, to_do=['all'], low_extrap=-5, high_extrap=3, n_pad=n_pad)
+
+ function_params = {
+ 'one_loop_dd': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'one_loop_dd_bias': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'one_loop_dd_bias_b3nl': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'one_loop_dd_bias_lpt_NL': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_tt': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_mix': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_ta': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_der': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_ct': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_ctbias': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_gb2': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_d2': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_s2': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'OV': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'kPol': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'RSD_components': {'P': P, 'P_window': P_window, 'C_window': C_window, 'f': 0.5},
+ 'RSD_ABsum_components': {'P': P, 'P_window': P_window, 'C_window': C_window, 'f': 0.5},
+ 'RSD_ABsum_mu': {'P': P, 'P_window': P_window, 'C_window': C_window, 'f': 0.5, 'mu_n': 0.5},
+ 'J_k_scalar': {'P': P, 'X': fpt.X_spt, 'nu': -2, 'P_window': P_window, 'C_window': C_window},
+ 'J_k_tensor': {'P': P, 'X': fpt.X_IA_E, 'P_window': P_window, 'C_window': C_window},
+ }
+
+ results = {}
+
+
+ for func in function_params:
+ print(f"Testing {func}...")
+ try:
+ results[func] = run_benchmark(fpt, func, function_params[func])
+ except Exception as e:
+ print(f"Error running {func}: {str(e)}")
+ results[func] = {'error': str(e)}
+
+ total_time = 0
+ for func, result in results.items():
+ if isinstance(result, dict) and 'mean' in result:
+ total_time += result['mean']
+
+ results['total_time'] = total_time
+
+ timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
+ output_file = os.path.join(os.path.dirname(__file__), f'timed_results_{timestamp}.json')
+
+ with open(output_file, 'w') as f:
+ json.dump(numpy_to_python(results), f, indent=2)
+
+if __name__ == '__main__':
+ main()
diff --git a/tests/benchmarking/PIA_tt_benchmark.txt b/tests/benchmarking/PIA_tt_benchmark.txt
new file mode 100644
index 0000000..a84b4ea
--- /dev/null
+++ b/tests/benchmarking/PIA_tt_benchmark.txt
@@ -0,0 +1,3001 @@
+# PIA_tt
+2.486856834676639778e+02 2.486856834172565698e+02
+2.486856783991729571e+02 2.486856783478732495e+02
+2.486856732839155484e+02 2.486856732316725243e+02
+2.486856681210621218e+02 2.486856680678332623e+02
+2.486856629106400192e+02 2.486856628564457594e+02
+2.486856576518637780e+02 2.486856575966641572e+02
+2.486856523446210758e+02 2.486856522884050946e+02
+2.486856469879925271e+02 2.486856469307413704e+02
+2.486856415819163431e+02 2.486856415235984628e+02
+2.486856361258949732e+02 2.486856360665103693e+02
+2.486856306194437138e+02 2.486856305589644762e+02
+2.486856250618371860e+02 2.486856250002273896e+02
+2.486856194529564732e+02 2.486856193901898564e+02
+2.486856137919947400e+02 2.486856137280460928e+02
+2.486856080787169390e+02 2.486856080136061564e+02
+2.486856023125880029e+02 2.486856022462746125e+02
+2.486855964933089922e+02 2.486855964257691483e+02
+2.486855906200143522e+02 2.486855905512482821e+02
+2.486855846922312026e+02 2.486855846221924935e+02
+2.486855787096093309e+02 2.486855786382753593e+02
+2.486855726717701032e+02 2.486855725990854467e+02
+2.486855665782103983e+02 2.486855665041542807e+02
+2.486855604279372187e+02 2.486855603525554841e+02
+2.486855542209232794e+02 2.486855541441253763e+02
+2.486855479563681115e+02 2.486855478781395163e+02
+2.486855416339355429e+02 2.486855415542619880e+02
+2.486855352529582888e+02 2.486855351718239149e+02
+2.486855288130420831e+02 2.486855287303806108e+02
+2.486855223133862296e+02 2.486855222292331575e+02
+2.486855157535962348e+02 2.486855156678655305e+02
+2.486855091332128040e+02 2.486855090459121698e+02
+2.486855024514986781e+02 2.486855023625479362e+02
+2.486854957080113877e+02 2.486854956174228164e+02
+2.486854889019944608e+02 2.486854888097241769e+02
+2.486854820330185589e+02 2.486854819390474631e+02
+2.486854751005301409e+02 2.486854750047980360e+02
+2.486854681038918784e+02 2.486854680064104457e+02
+2.486854610426088925e+02 2.486854609433103178e+02
+2.486854539159382966e+02 2.486854538147874507e+02
+2.486854467231818262e+02 2.486854466201825744e+02
+2.486854394640798205e+02 2.486854393592155077e+02
+2.486854321376250709e+02 2.486854320308042361e+02
+2.486854247435256298e+02 2.486854246347405422e+02
+2.486854172809251509e+02 2.486854171701088489e+02
+2.486854097491883522e+02 2.486854096363108511e+02
+2.486854021479404651e+02 2.486854020330215462e+02
+2.486853944763418554e+02 2.486853943592719247e+02
+2.486853867336724875e+02 2.486853866144443543e+02
+2.486853789194111073e+02 2.486853787979638639e+02
+2.486853710326545297e+02 2.486853709089780011e+02
+2.486853630730566920e+02 2.486853629471005718e+02
+2.486853550399662822e+02 2.486853549116668205e+02
+2.486853469323042418e+02 2.486853468016327326e+02
+2.486853387497228027e+02 2.486853386166325777e+02
+2.486853304914922660e+02 2.486853303559512085e+02
+2.486853221569106722e+02 2.486853220188548619e+02
+2.486853137450040094e+02 2.486853136043937411e+02
+2.486853052553216230e+02 2.486853051120837392e+02
+2.486852966872589263e+02 2.486852965413853838e+02
+2.486852880397794365e+02 2.486852878911921891e+02
+2.486852793121588547e+02 2.486852791608615405e+02
+2.486852705040612364e+02 2.486852703499670270e+02
+2.486852616143257819e+02 2.486852614573734854e+02
+2.486852526421549214e+02 2.486852524822923556e+02
+2.486852435871919056e+02 2.486852434243587879e+02
+2.486852344483999104e+02 2.486852342826117308e+02
+2.486852252251206892e+02 2.486852250562467930e+02
+2.486852159165155172e+02 2.486852157445204909e+02
+2.486852065215779248e+02 2.486852063463988998e+02
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+2.486851874703779117e+02 2.486851872886282138e+02
+2.486851778123868257e+02 2.486851776272962695e+02
+2.486851680651425909e+02 2.486851678766660427e+02
+2.486851582275198211e+02 2.486851580355531439e+02
+2.486851482989662827e+02 2.486851481034349263e+02
+2.486851382784712996e+02 2.486851380793503381e+02
+2.486851281654853665e+02 2.486851279626799851e+02
+2.486851179587085312e+02 2.486851177521596412e+02
+2.486851076576156743e+02 2.486851074472326673e+02
+2.486850972611231896e+02 2.486850970468401556e+02
+2.486850867685332105e+02 2.486850865503266448e+02
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diff --git a/tests/benchmarking/P_IA_ct_benchmark.txt b/tests/benchmarking/P_IA_ct_benchmark.txt
new file mode 100644
index 0000000..3dab824
--- /dev/null
+++ b/tests/benchmarking/P_IA_ct_benchmark.txt
@@ -0,0 +1,3001 @@
+# P_IA_ct
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diff --git a/tests/benchmarking/P_IA_der_benchmark.txt b/tests/benchmarking/P_IA_der_benchmark.txt
new file mode 100644
index 0000000..89aab0d
--- /dev/null
+++ b/tests/benchmarking/P_IA_der_benchmark.txt
@@ -0,0 +1,3001 @@
+# P_IA_der
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diff --git a/tests/benchmarking/P_IA_mix_benchmark.txt b/tests/benchmarking/P_IA_mix_benchmark.txt
new file mode 100644
index 0000000..5f34216
--- /dev/null
+++ b/tests/benchmarking/P_IA_mix_benchmark.txt
@@ -0,0 +1,3001 @@
+# P_IA_mix
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diff --git a/tests/benchmarking/P_IA_ta_benchmark.txt b/tests/benchmarking/P_IA_ta_benchmark.txt
new file mode 100644
index 0000000..8f28f5d
--- /dev/null
+++ b/tests/benchmarking/P_IA_ta_benchmark.txt
@@ -0,0 +1,3001 @@
+# P_IA_ta
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diff --git a/tests/benchmarking/P_IRres_benchmark.txt b/tests/benchmarking/P_IRres_benchmark.txt
new file mode 100644
index 0000000..ece5a8a
--- /dev/null
+++ b/tests/benchmarking/P_IRres_benchmark.txt
@@ -0,0 +1,3001 @@
+# P_IRres
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diff --git a/tests/benchmarking/P_OV_benchmark.txt b/tests/benchmarking/P_OV_benchmark.txt
new file mode 100644
index 0000000..0888df0
--- /dev/null
+++ b/tests/benchmarking/P_OV_benchmark.txt
@@ -0,0 +1,3001 @@
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diff --git a/tests/benchmarking/P_RSD_ABsum_components_benchmark.txt b/tests/benchmarking/P_RSD_ABsum_components_benchmark.txt
new file mode 100644
index 0000000..564a9a9
--- /dev/null
+++ b/tests/benchmarking/P_RSD_ABsum_components_benchmark.txt
@@ -0,0 +1,3001 @@
+# P_RSD_ABsum_components
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diff --git a/tests/benchmarking/P_RSD_ABsum_mu_benchmark.txt b/tests/benchmarking/P_RSD_ABsum_mu_benchmark.txt
new file mode 100644
index 0000000..007205a
--- /dev/null
+++ b/tests/benchmarking/P_RSD_ABsum_mu_benchmark.txt
@@ -0,0 +1,3001 @@
+# P_RSD_ABsum_mu
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diff --git a/tests/benchmarking/P_RSD_benchmark.txt b/tests/benchmarking/P_RSD_benchmark.txt
new file mode 100644
index 0000000..9f83561
--- /dev/null
+++ b/tests/benchmarking/P_RSD_benchmark.txt
@@ -0,0 +1,3001 @@
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diff --git a/tests/benchmarking/P_bias_b3nl_benchmark.txt b/tests/benchmarking/P_bias_b3nl_benchmark.txt
new file mode 100644
index 0000000..f1d4c63
--- /dev/null
+++ b/tests/benchmarking/P_bias_b3nl_benchmark.txt
@@ -0,0 +1,3001 @@
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diff --git a/tests/benchmarking/P_bias_benchmark.txt b/tests/benchmarking/P_bias_benchmark.txt
new file mode 100644
index 0000000..8a6d9e2
--- /dev/null
+++ b/tests/benchmarking/P_bias_benchmark.txt
@@ -0,0 +1,3001 @@
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diff --git a/tests/benchmarking/P_bias_lpt_NL_benchmark.txt b/tests/benchmarking/P_bias_lpt_NL_benchmark.txt
new file mode 100644
index 0000000..d2d5b0d
--- /dev/null
+++ b/tests/benchmarking/P_bias_lpt_NL_benchmark.txt
@@ -0,0 +1,3001 @@
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diff --git a/tests/benchmarking/P_dd_benchmark.txt b/tests/benchmarking/P_dd_benchmark.txt
new file mode 100644
index 0000000..0137e54
--- /dev/null
+++ b/tests/benchmarking/P_dd_benchmark.txt
@@ -0,0 +1,3001 @@
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diff --git a/tests/benchmarking/P_gI_ct_benchmark.txt b/tests/benchmarking/P_gI_ct_benchmark.txt
new file mode 100644
index 0000000..02d781a
--- /dev/null
+++ b/tests/benchmarking/P_gI_ct_benchmark.txt
@@ -0,0 +1,3001 @@
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diff --git a/tests/benchmarking/P_gI_ta_benchmark.txt b/tests/benchmarking/P_gI_ta_benchmark.txt
new file mode 100644
index 0000000..0a9d75b
--- /dev/null
+++ b/tests/benchmarking/P_gI_ta_benchmark.txt
@@ -0,0 +1,3001 @@
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diff --git a/tests/benchmarking/P_gI_tt_benchmark.txt b/tests/benchmarking/P_gI_tt_benchmark.txt
new file mode 100644
index 0000000..d9fe787
--- /dev/null
+++ b/tests/benchmarking/P_gI_tt_benchmark.txt
@@ -0,0 +1,3001 @@
+# P_gI_tt
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diff --git a/tests/benchmarking/P_kPol_benchmark.txt b/tests/benchmarking/P_kPol_benchmark.txt
new file mode 100644
index 0000000..3714fce
--- /dev/null
+++ b/tests/benchmarking/P_kPol_benchmark.txt
@@ -0,0 +1,3001 @@
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diff --git a/tests/benchmarking/Pk_test.dat b/tests/benchmarking/Pk_test.dat
new file mode 100644
index 0000000..b9a2747
--- /dev/null
+++ b/tests/benchmarking/Pk_test.dat
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+ 1.000000000000E+02 4.534151453755E-04 1.586449040637E+02 -1.585673558073E+02
diff --git a/tests/benchmarking/benchmark.py b/tests/benchmarking/benchmark.py
new file mode 100644
index 0000000..4f80c95
--- /dev/null
+++ b/tests/benchmarking/benchmark.py
@@ -0,0 +1,84 @@
+import numpy as np
+from fastpt import FASTPT
+
+
+d=np.loadtxt('Pk_test.dat')
+k=d[:,0]; P=d[:,1]
+C_window=.75
+n_pad=int(0.5*len(k))
+to_do=['all']
+fpt=FASTPT(k,to_do=to_do,low_extrap=-5,high_extrap=3,n_pad=n_pad)
+
+# Base one-loop terms
+P_dd = fpt.one_loop_dd(P, C_window=C_window)[0] # Need [0] for P_22 + P_13
+P_kPol = fpt.kPol(P, C_window=C_window)
+P_OV = fpt.OV(P, C_window=C_window)
+
+# Bias terms
+P_loop_bias = fpt.one_loop_dd_bias(P, C_window=C_window)
+P_bias_b3nl = fpt.one_loop_dd_bias_b3nl(P, C_window=C_window)
+P_bias_lpt_NL = fpt.one_loop_dd_bias_lpt_NL(P, C_window=C_window)
+
+# CLEFT terms - requires cleft_z1 and z2
+#P_cleft_Q_R = fpt.cleft_Q_R(P, C_window=C_window)
+# ^^ Throws error because cleft_z1 and cleft_z2 are not defined
+
+# Intrinsic Alignment terms
+P_IA_tt = fpt.IA_tt(P, C_window=C_window)
+P_IA_ta = fpt.IA_ta(P, C_window=C_window)
+P_IA_mix = fpt.IA_mix(P, C_window=C_window)
+P_IA_ct = fpt.IA_ct(P, C_window=C_window)
+P_gI_ct = fpt.gI_ct(P, C_window=C_window)
+# P_IA_gb2 = fpt.IA_gb2(P, C_window=C_window)
+P_gI_ta = fpt.gI_ta(P, C_window=C_window)
+P_gI_tt = fpt.gI_tt(P, C_window=C_window)
+P_IA_der = fpt.IA_der(P, C_window=C_window)
+
+# RSD terms - note P_RSD requires f=1.0 parameter
+P_RSD = fpt.RSD_components(P, 1.0, C_window=C_window)
+P_RSD_ABsum_components = fpt.RSD_ABsum_components(P, 1.0, C_window=C_window)
+P_RSD_ABsum_mu = fpt.RSD_ABsum_mu(P, 1.0, 1.0, C_window=C_window)
+
+# IR resummation
+P_IRres = fpt.IRres(P, C_window=C_window)
+
+
+names = {
+ 'k': k,
+ 'P_dd': P_dd,
+ 'P_OV': P_OV,
+ 'P_RSD_ABsum_mu': P_RSD_ABsum_mu,
+ 'P_IRres': P_IRres,
+ 'P_kPol': P_kPol,
+ 'PIA_tt': P_IA_tt,
+ 'P_IA_ta': P_IA_ta,
+ 'P_IA_mix': P_IA_mix,
+ 'P_IA_ct': P_IA_ct,
+ 'P_gI_ct': P_gI_ct,
+ 'P_gI_ta': P_gI_ta,
+ 'P_gI_tt': P_gI_tt,
+ 'P_IA_der': P_IA_der,
+ 'P_RSD': P_RSD,
+ 'P_RSD_ABsum_components': P_RSD_ABsum_components,
+}
+
+for name, arr in names.items():
+ try:
+ np.savetxt(f'{name}_benchmark.txt', np.transpose(arr), header=f'{name}')
+ except AttributeError:
+ print(f"Error saving {name} array")
+ print(AttributeError.with_traceback())
+
+inhomogeneous_arrays = {
+ 'P_loop_bias': list(P_loop_bias), #Have to cast to list since tuple is immutable
+ 'P_bias_b3nl': list(P_bias_b3nl),
+ 'P_bias_lpt_NL': list(P_bias_lpt_NL),
+}
+
+for name, arr in inhomogeneous_arrays.items():
+ for i, element in enumerate(arr):
+ if isinstance(element, float): #sig4 is of type float, converting it to np array
+ new_array = np.zeros(len(arr[i-1])) #should be of length 3000, not hardcoded incase it changes
+ new_array[0] = element
+ arr[i] = new_array
+ np.savetxt(f'{name}_benchmark.txt', np.transpose(arr), header=f'{name}')
\ No newline at end of file
diff --git a/tests/benchmarking/k_benchmark.txt b/tests/benchmarking/k_benchmark.txt
new file mode 100644
index 0000000..7a761f5
--- /dev/null
+++ b/tests/benchmarking/k_benchmark.txt
@@ -0,0 +1,3001 @@
+# k
+1.000000000000000048e-04
+1.004617332935999944e-04
+1.009255985634999940e-04
+1.013916056539000031e-04
+1.018597644540999991e-04
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diff --git a/tests/test_benchmarks.py b/tests/test_benchmarks.py
new file mode 100644
index 0000000..d9a735d
--- /dev/null
+++ b/tests/test_benchmarks.py
@@ -0,0 +1,146 @@
+import pytest
+import numpy as np
+from fastpt import FASTPT
+import os
+import warnings
+
+data_path = os.path.join(os.path.dirname(__file__), 'benchmarking', 'Pk_test.dat')
+d = np.loadtxt(data_path)
+P = d[:, 1]
+k = d[:, 0]
+C_window = 0.75
+
+@pytest.fixture
+def fpt():
+
+ n_pad = int(0.5 * len(k))
+ return FASTPT(k, low_extrap=-5, high_extrap=3, n_pad=n_pad)
+
+from matplotlib import pyplot as plt
+
+def calc_and_show(bmark, stored, func):
+ if not np.allclose(bmark, stored):
+ print(f"Max difference: {np.max(np.abs(bmark - stored))}")
+ rel_diff = np.where(np.abs(stored) > 1e-10,
+ np.abs(bmark - stored) / np.abs(stored),
+ 0)
+ print(f"Relative difference: {np.max(rel_diff)}")
+ idx = np.searchsorted(k, 10.0)
+ print(f"Max difference up until k=10: {np.max(np.abs(bmark - stored)[:idx+1])}")
+ print(f"Max Relative difference up until k=10: {np.max(rel_diff[:idx+1])}")
+ plt.plot(k, rel_diff, label='Relative difference')
+ plt.title(f'Relative difference for {func}')
+ plt.xscale('log')
+ plt.yscale('log')
+ plt.legend()
+ plt.show()
+
+def test_one_loop_dd(fpt):
+ bmark = fpt.one_loop_dd(P, C_window=C_window)[0]
+ stored = np.loadtxt('tests/benchmarking/P_dd_benchmark.txt')
+ # calc_and_show(bmark, stored, "one_loop_dd")
+ if np.__version__ >= '2.0':
+ warnings.warn("The benchmarks were generated with NumPy 1.x, the P_1loop term is known to fail np.allclose when using NumPy 2.x." +
+ " We can guarantee a precision of 9e-5 up until a k value of 10.")
+ assert np.allclose(bmark, stored)
+
+def test_one_loop_dd_bias(fpt):
+ bmark = list(fpt.one_loop_dd_bias(P, C_window=C_window))
+ new_array = np.zeros(3000)
+ new_array[0] = bmark[7]
+ bmark[7] = new_array
+
+ stored = np.transpose(np.loadtxt('tests/benchmarking/P_bias_benchmark.txt'))
+ # calc_and_show(bmark[0], stored[0], "one_loop_dd_bias")
+ if np.__version__ >= '2.0':
+ warnings.warn("The benchmarks were generated with NumPy 1.x, the P_1loop term is known to fail np.allclose when using NumPy 2.x." +
+ " We can guarantee a precision of 9e-5 up until a k value of 10.")
+ assert np.allclose(bmark, stored)
+
+def test_one_loop_dd_bias_b3nl(fpt):
+ bmark = list(fpt.one_loop_dd_bias_b3nl(P, C_window=C_window))
+ new_array = np.zeros(3000)
+ new_array[0] = bmark[7]
+ bmark[7] = new_array
+
+ stored = np.transpose(np.loadtxt('tests/benchmarking/P_bias_b3nl_benchmark.txt'))
+ # calc_and_show(bmark[8], stored[8], "one_loop_dd_bias_b3nl")
+ if np.__version__ >= '2.0':
+ warnings.warn("The benchmarks were generated with NumPy 1.x, the P_1loop and sig3nl terms are known to fail np.allclose when using NumPy 2.x." +
+ " We can guarantee a precision of 6e-4 up until a k value of 10.")
+ assert np.allclose(bmark, stored)
+
+def test_one_loop_dd_bias_lpt_NL(fpt):
+ bmark = list(fpt.one_loop_dd_bias_lpt_NL(P, C_window=C_window))
+ new_array = np.zeros(3000)
+ new_array[0] = bmark[6]
+ bmark[6] = new_array
+
+ stored = np.transpose(np.loadtxt('tests/benchmarking/P_bias_lpt_NL_benchmark.txt'))
+ # calc_and_show(bmark[1], stored[1], "one_loop_dd_bias_lpt_NL")
+ # calc_and_show(bmark[2], stored[2], "one_loop_dd_bias_lpt_NL")
+ if np.__version__ >= '2.0':
+ warnings.warn("The benchmarks were generated with NumPy 1.x, the Pb1L and Pb1L_2 terms are known to fail np.allclose when using NumPy 2.x." +
+ " We can guarantee a precision of 2e-4 up until a k value of 10.")
+ assert np.allclose(bmark, stored)
+
+def test_IA_TT(fpt):
+ bmark = np.transpose(fpt.IA_tt(P, C_window=C_window))
+ assert np.allclose(bmark, np.loadtxt('tests/benchmarking/PIA_tt_benchmark.txt'))
+
+def test_IA_mix(fpt):
+ bmark = np.transpose(fpt.IA_mix(P, C_window=C_window))
+ assert np.allclose(bmark, np.loadtxt('tests/benchmarking/P_IA_mix_benchmark.txt'))
+
+def test_IA_ta(fpt):
+ bmark = np.transpose(fpt.IA_ta(P, C_window=C_window))
+ assert np.allclose(bmark, np.loadtxt('tests/benchmarking/P_IA_ta_benchmark.txt'))
+
+def test_IA_der(fpt):
+ bmark = np.transpose(fpt.IA_der(P, C_window=C_window))
+ assert np.allclose(bmark, np.loadtxt('tests/benchmarking/P_IA_der_benchmark.txt'))
+
+def test_IA_ct(fpt):
+ bmark = np.transpose(fpt.IA_ct(P, C_window=C_window))
+ assert np.allclose(bmark, np.loadtxt('tests/benchmarking/P_IA_ct_benchmark.txt'))
+
+def test_gI_ct(fpt):
+ bmark = np.transpose(fpt.gI_ct(P, C_window=C_window))
+ assert np.allclose(bmark, np.loadtxt('tests/benchmarking/P_gI_ct_benchmark.txt'))
+
+def test_gI_ta(fpt):
+ bmark = np.transpose(fpt.gI_ta(P, C_window=C_window))
+ assert np.allclose(bmark, np.loadtxt('tests/benchmarking/P_gI_ta_benchmark.txt'))
+
+def test_gI_tt(fpt):
+ bmark = np.transpose(fpt.gI_tt(P, C_window=C_window))
+ assert np.allclose(bmark, np.loadtxt('tests/benchmarking/P_gI_tt_benchmark.txt'))
+
+def test_OV(fpt):
+ bmark = np.transpose(fpt.OV(P, C_window=C_window))
+ assert np.allclose(bmark, np.loadtxt('tests/benchmarking/P_OV_benchmark.txt'))
+
+def test_kPol(fpt):
+ bmark = np.transpose(fpt.kPol(P, C_window=C_window))
+ assert np.allclose(bmark, np.loadtxt('tests/benchmarking/P_kPol_benchmark.txt'))
+
+def test_RSD_components(fpt):
+ bmark = np.transpose(fpt.RSD_components(P, 1.0, C_window=C_window))
+ assert np.allclose(bmark, np.loadtxt('tests/benchmarking/P_RSD_benchmark.txt'))
+
+def test_RSD_ABsum_components(fpt):
+ bmark = np.transpose(fpt.RSD_ABsum_components(P, 1.0, C_window=C_window))
+ assert np.allclose(bmark, np.loadtxt('tests/benchmarking/P_RSD_ABsum_components_benchmark.txt'))
+
+def test_RSD_ABsum_mu(fpt):
+ bmark = np.transpose(fpt.RSD_ABsum_mu(P, 1.0, 1.0, C_window=C_window))
+ assert np.allclose(bmark, np.loadtxt('tests/benchmarking/P_RSD_ABsum_mu_benchmark.txt'))
+
+def test_IRres(fpt):
+ bmark = fpt.IRres(P, C_window=C_window)
+ stored = np.transpose(np.loadtxt('tests/benchmarking/P_IRres_benchmark.txt'))
+ # calc_and_show(bmark, stored, "IRres")
+ if np.__version__ >= '2.0':
+ warnings.warn("The benchmarks were generated with NumPy 1.x, the IRres term is known to fail np.allclose when using NumPy 2.x." +
+ " We can guarantee a precision of 5e-5 up until a k value of 10.")
+ assert np.allclose(bmark, stored)
diff --git a/tests/test_cache.py b/tests/test_cache.py
new file mode 100644
index 0000000..47afdcc
--- /dev/null
+++ b/tests/test_cache.py
@@ -0,0 +1,328 @@
+import pytest
+import numpy as np
+import sys
+from fastpt import CacheManager
+
+@pytest.fixture
+def cache_manager():
+ """Create a basic cache manager instance for testing"""
+ return CacheManager(max_size_mb=10) # Smaller size for testing
+
+@pytest.fixture
+def sample_arrays():
+ """Create sample arrays of different sizes for testing"""
+ small_array = np.ones((10, 10)) # 800 bytes
+ medium_array = np.ones((100, 100)) # 80,000 bytes
+ large_array = np.ones((500, 500)) # 2,000,000 bytes
+ return small_array, medium_array, large_array
+
+####################INITIALIZATION TESTS####################
+def test_init_default():
+ """Test initialization with default parameters"""
+ cm = CacheManager()
+ assert cm.max_size_bytes == 500 * 1024 * 1024 # 500 MB
+ assert cm.cache == {}
+ assert cm.cache_size == 0
+ assert cm.hits == 0
+ assert cm.misses == 0
+
+def test_init_custom_size():
+ """Test initialization with custom size"""
+ cm = CacheManager(max_size_mb=100)
+ assert cm.max_size_bytes == 100 * 1024 * 1024 # 100 MB
+
+def test_init_unlimited_cache(sample_arrays):
+ """Test initialization with unlimited cache size"""
+ cm = CacheManager(max_size_mb=0)
+ cm.set(sample_arrays[2], "category", 12345, None) # Using an integer hash key
+ assert cm.max_size_bytes == 0 # No limit
+
+####################GET/SET TESTS####################
+def test_get_set_simple(cache_manager):
+ """Test basic get and set operations with simple values"""
+ # Set and get a simple value with an integer hash key
+ cache_manager.set("test_value", "category", 123456, None)
+ result = cache_manager.get("category", 123456)
+ assert result == "test_value"
+
+ # Get a non-existent value
+ result = cache_manager.get("category", 789012)
+ assert result is None
+
+def test_get_set_array(cache_manager, sample_arrays):
+ """Test get and set operations with arrays"""
+ small_array, _, _ = sample_arrays
+
+ # Set and get an array with an integer hash key
+ cache_manager.set(small_array, "array_category", 12345, None)
+ result = cache_manager.get("array_category", 12345)
+ assert np.array_equal(result, small_array)
+
+def test_get_set_list_of_arrays(cache_manager, sample_arrays):
+ """Test get and set operations with lists of arrays"""
+ small_array, _, _ = sample_arrays
+
+ # Create a list of arrays
+ array_list = [small_array, small_array.copy() * 2]
+
+ # Set and get a list of arrays with an integer hash key
+ cache_manager.set(array_list, "list_category", 54321, None)
+ result = cache_manager.get("list_category", 54321)
+
+ # Verify result is a list with correct arrays
+ assert isinstance(result, list)
+ assert len(result) == 2
+ assert np.array_equal(result[0], small_array)
+ assert np.array_equal(result[1], small_array * 2)
+
+def test_cache_hits_misses(cache_manager):
+ """Test hit and miss counting"""
+ # Initial state
+ assert cache_manager.hits == 0
+ assert cache_manager.misses == 0
+
+ # First access (miss)
+ cache_manager.get("category", 12345)
+ assert cache_manager.hits == 0
+ assert cache_manager.misses == 1
+
+ # Set value and access again (hit)
+ cache_manager.set("value", "category", 12345, None)
+ cache_manager.get("category", 12345)
+ assert cache_manager.hits == 1
+ assert cache_manager.misses == 1
+
+ # Another miss
+ cache_manager.get("category", 67890)
+ assert cache_manager.hits == 1
+ assert cache_manager.misses == 2
+
+####################CACHE SIZE TESTS####################
+def test_array_size_calculation(cache_manager, sample_arrays):
+ """Test calculation of array sizes"""
+ small_array, medium_array, large_array = sample_arrays
+
+ # Test size calculation
+ small_size = cache_manager._get_array_size(small_array)
+ medium_size = cache_manager._get_array_size(medium_array)
+ large_size = cache_manager._get_array_size(large_array)
+
+ # Verify sizes (8 bytes per float64 value)
+ assert small_size == 800 # 10x10x8
+ assert medium_size == 80000 # 100x100x8
+ assert large_size == 2000000 # 500x500x8
+
+ # Test with non-array types (should now return actual size)
+ assert cache_manager._get_array_size("string") == sys.getsizeof("string")
+ assert cache_manager._get_array_size(123) == sys.getsizeof(123)
+ assert cache_manager._get_array_size(None) == sys.getsizeof(None)
+
+def test_complex_data_size_calculation(cache_manager):
+ """Test size calculation for complex nested structures"""
+ # Test with list
+ list_data = [1, 2, 3]
+ list_size = cache_manager._get_array_size(list_data)
+ assert list_size >= sys.getsizeof(list_data)
+
+ # Test with tuple
+ tuple_data = (1, 2, 3)
+ tuple_size = cache_manager._get_array_size(tuple_data)
+ assert tuple_size >= sys.getsizeof(tuple_data)
+
+ # Test with nested structure
+ nested = ([1, 2], np.ones(3), "string")
+ nested_size = cache_manager._get_array_size(nested)
+ # Should include the size of the container plus its contents
+ assert nested_size > sum(sys.getsizeof(x) for x in [nested[0], nested[2]])
+ assert nested_size > 24 # minimum bytes for a numpy array
+
+def test_cache_size_tracking(cache_manager, sample_arrays):
+ """Test that cache size is correctly tracked when adding arrays"""
+ small_array, medium_array, _ = sample_arrays
+
+ # Initial size
+ assert cache_manager.cache_size == 0
+
+ # Add small array
+ cache_manager.set(small_array, "category", 12345, None)
+ initial_size = cache_manager.cache_size
+ assert initial_size >= 800 # At least the array size, plus key overhead
+
+ # Add medium array
+ cache_manager.set(medium_array, "category", 67890, None)
+ assert cache_manager.cache_size > initial_size + 80000
+
+def test_cache_size_tracking_with_list(cache_manager, sample_arrays):
+ """Test cache size tracking with lists of arrays"""
+ small_array, medium_array, _ = sample_arrays
+
+ # Create a list of arrays
+ array_list = [small_array, medium_array]
+
+ # Add list to cache
+ initial_size = cache_manager.cache_size
+ cache_manager.set(array_list, "category", 54321, None)
+ assert cache_manager.cache_size > initial_size + 800 + 80000
+
+####################EVICTION TESTS####################
+def test_eviction_when_full(sample_arrays):
+ """Test eviction when cache is full"""
+ # Create a very small cache (3 MB)
+ cm = CacheManager(max_size_mb=3)
+
+ _, medium_array, large_array = sample_arrays
+
+ # Add medium array (about 0.08 MB)
+ cm.set(medium_array, "category", "medium", None)
+ assert "category" in [k[0] for k in cm.cache.keys()]
+
+ # Add large array (about 2 MB) - should trigger eviction
+ cm.set(large_array, "category", "large", None)
+
+ # Verify both can fit
+ assert cm.get("category", "medium") is not None
+ assert cm.get("category", "large") is not None
+
+ # Add another large array - should trigger eviction
+ cm.set(large_array, "category", "large2", None)
+
+ # The total size would be ~4MB, over the limit
+ # One of the entries must have been evicted
+ entries_found = 0
+ if cm.get("category", "medium") is not None:
+ entries_found += 1
+ if cm.get("category", "large") is not None:
+ entries_found += 1
+ if cm.get("category", "large2") is not None:
+ entries_found += 1
+
+ # At least one entry must have been evicted
+ assert entries_found < 3
+
+ # The large2 entry should be there (most recently added)
+ assert cm.get("category", "large2") is not None
+
+def test_unlimited_cache(sample_arrays):
+ """Test behavior with unlimited cache size"""
+ cm = CacheManager(max_size_mb=0) # No limit
+ _, medium_array, large_array = sample_arrays
+
+ # Add arrays - should not trigger eviction
+ for i in range(10):
+ cm.set(medium_array, "category", f"medium{i}", None)
+ cm.set(large_array, "category", f"large{i}", None)
+
+ # Verify all entries are still in cache
+ for i in range(10):
+ assert cm.get("category", f"medium{i}") is not None
+ assert cm.get("category", f"large{i}") is not None
+
+####################CLEAR AND STATS TESTS####################
+def test_clear(cache_manager, sample_arrays):
+ """Test clearing the cache"""
+ small_array, medium_array, _ = sample_arrays
+
+ # Add some items
+ cache_manager.set(small_array, "category", "small", None)
+ cache_manager.set(medium_array, "category", "medium", None)
+
+ # Verify items are in cache
+ assert cache_manager.get("category", "small") is not None
+ assert cache_manager.get("category", "medium") is not None
+
+ # Clear cache
+ cache_manager.clear()
+
+ # Verify cache is empty
+ assert cache_manager.cache == {}
+ assert cache_manager.cache_size == 0
+ assert cache_manager.get("category", "small") is None
+ assert cache_manager.get("category", "medium") is None
+
+def test_stats(cache_manager, sample_arrays):
+ """Test the stats function"""
+ small_array, medium_array, _ = sample_arrays
+
+ # Add some items and perform some gets
+ cache_manager.set(small_array, "category", "small", None)
+ cache_manager.set(medium_array, "category", "medium", None)
+ cache_manager.get("category", "small")
+ cache_manager.get("category", "medium")
+ cache_manager.get("category", "nonexistent")
+
+ # Get stats
+ stats = cache_manager.stats()
+
+ # Verify stats
+ assert stats['items'] == 2
+
+ # Verify size with tolerance for Python object overhead
+ expected_size_mb = (800 + 80000) / (1024 * 1024)
+ actual_size_mb = stats['size_mb']
+
+ # Allow for a small tolerance (within 5% or 0.001 MB, whichever is greater)
+ tolerance = max(0.001, expected_size_mb * 0.05)
+ assert abs(actual_size_mb - expected_size_mb) <= tolerance, \
+ f"Size {actual_size_mb:.6f}MB differs from expected {expected_size_mb:.6f}MB by more than tolerance {tolerance:.6f}MB"
+
+ assert stats['max_size_mb'] == 10
+ assert stats['hit_rate'] == 2/3 # 2 hits, 1 miss
+
+def test_stats_empty_cache(cache_manager):
+ """Test stats with empty cache"""
+ stats = cache_manager.stats()
+ assert stats['items'] == 0
+ assert stats['size_mb'] == 0
+ assert stats['hit_rate'] == 0
+
+def test_overwrite_same_key(cache_manager, sample_arrays):
+ """Test overwriting an existing key"""
+ small_array, medium_array, _ = sample_arrays
+
+ # Set a value
+ cache_manager.set(small_array, "category", "key", None)
+ original_size = cache_manager.cache_size
+
+ # Overwrite with a different value
+ cache_manager.set(medium_array, "category", "key", None)
+
+ # Verify the value was updated
+ result = cache_manager.get("category", "key")
+ assert np.array_equal(result, medium_array)
+
+ # Verify the size was updated correctly with tolerance
+ # Since we're only replacing the value but keeping the same key,
+ # the size change should be approximately (medium_array_size - small_array_size)
+ expected_size_change = 80000 - 800
+ actual_size_change = cache_manager.cache_size - original_size
+
+ # Allow for a small tolerance in the size difference
+ tolerance = max(100, expected_size_change * 0.01) # 1% or 100 bytes, whichever is larger
+ assert abs(actual_size_change - expected_size_change) <= tolerance, \
+ f"Size change {actual_size_change} differs from expected {expected_size_change} by more than tolerance {tolerance}"
+
+##################### New P Dump Tests #####################
+def test_P_dump(cache_manager):
+ P = np.logspace(0, 1, 1000)
+ P_hash = hash(P.tobytes())
+ cache_manager.set(P, "test", 12345, P_hash)
+ cache_manager.set(P, "test", 45678, P_hash)
+ assert len(cache_manager.cache) == 2
+ assert cache_manager.current_P_hash == P_hash
+ P2 = np.logspace(1, 2, 1000)
+ P2_hash = hash(P2.tobytes())
+ cache_manager.set(P2, "test", 98765, P2_hash)
+ assert len(cache_manager.cache) == 1
+ assert cache_manager.get("test", 12345) is None
+ assert cache_manager.get("test", 45678) is None
+ assert cache_manager.current_P_hash == P2_hash
+
+def test_dont_dump(cache_manager):
+ cache_manager.dump_cache = False
+ P = np.logspace(0, 1, 1000)
+ P_hash = hash(P.tobytes())
+ cache_manager.set(P, "test", 12345, P_hash)
+ P2 = np.logspace(1, 2, 1000)
+ P2_hash = hash(P2.tobytes())
+ cache_manager.set(P2, "test", 98765, P2_hash)
+ assert len(cache_manager.cache) == 2
\ No newline at end of file
diff --git a/tests/test_fastpt.py b/tests/test_fastpt.py
new file mode 100644
index 0000000..48d2e11
--- /dev/null
+++ b/tests/test_fastpt.py
@@ -0,0 +1,576 @@
+import pytest
+import numpy as np
+from fastpt import FASTPT
+import os
+
+data_path = os.path.join(os.path.dirname(__file__), 'benchmarking', 'Pk_test.dat')
+P = np.loadtxt(data_path)[:, 1]
+C_window = 0.75
+
+@pytest.fixture
+def fpt():
+ d = np.loadtxt(data_path)
+ k = d[:, 0]
+ n_pad = int(0.5 * len(k))
+ return FASTPT(k, low_extrap=-5, high_extrap=3, n_pad=n_pad)
+
+
+####################INITIALIZATION TESTS####################
+def test_init_empty_arrays():
+ with pytest.raises(ValueError):
+ FASTPT([], [])
+
+def test_init_odd_length_k():
+ """Test initialization with odd-length k array"""
+ k = np.logspace(-3, 1, 201) # Odd length
+ with pytest.raises(ValueError):
+ FASTPT(k)
+
+def test_init_non_log_spaced():
+ """Test initialization with non-log-spaced k array"""
+ k = np.linspace(0.1, 10, 200) # Linear spacing
+ with pytest.raises(AssertionError):
+ FASTPT(k)
+
+def test_init_invalid_to_do():
+ """Test initialization with invalid to_do parameter"""
+ k = np.logspace(-3, 1, 200)
+ with pytest.raises(ValueError):
+ FASTPT(k, to_do=['invalid_option'])
+
+def test_init_extrapolation_ranges():
+ """Test initialization with various extrapolation ranges"""
+ k = np.logspace(-3, 1, 200)
+
+ # Test valid extrapolation
+ fpt = FASTPT(k, low_extrap=-5, high_extrap=3)
+ assert fpt.low_extrap == -5
+ assert fpt.high_extrap == 3
+
+ # Test invalid extrapolation
+ with pytest.raises(ValueError):
+ FASTPT(k, low_extrap=3, high_extrap=-5) # Invalid range
+
+def test_init_padding(fpt):
+ """Test initialization with different padding values"""
+ k = np.logspace(-3, 1, 200)
+
+ # Test with no padding
+ fpt1 = FASTPT(k, n_pad=None)
+ assert fpt1.n_pad == int(0.5 * len(k)) # Default padding
+
+ # Test with padding
+ assert hasattr(fpt, 'n_pad')
+
+@pytest.mark.parametrize("to_do", [['all'], ['one_loop_dd'], ['dd_bias'],
+ ['one_loop_cleft_dd'], ['IA_tt'],
+ ['IA_mix'], ['IA_ta'], ['OV'],
+ ['kPol'], ['RSD'], ['tij'], ['gb2'],
+ ['IRres'], ['all'], ['everything']])
+def test_all_todos(to_do):
+ """Test initialization with all possible to_do options"""
+ k = np.logspace(-3, 1, 200)
+ fpt = FASTPT(k, to_do=to_do)
+ assert fpt is not None
+
+
+def test_skip_todo(fpt):
+ """Test to make sure skip is faster (doesn't initialize anything)"""
+ from time import time
+ t0 = time()
+ slowPT = FASTPT(fpt.k_original, to_do=['all'])
+ t1 = time()
+ diff1 = t1 - t0
+ t2 = time()
+ fastPT = FASTPT(fpt.k_original)
+ t3 = time()
+ diff2 = t3 - t2
+ assert diff2 < diff1
+
+
+####################PARAMETER VALIDATION TESTS####################
+def test_validate_params(fpt):
+ """Test the validate_params function with various inputs
+ (Using one_loop_dd as a sample method though all decorating functions
+ will follow the same validation behavior)"""
+ P_window = np.array([0.2, 0.2])
+ # Test 1: Valid cases
+ assert fpt.one_loop_dd(P) is not None
+ assert fpt.one_loop_dd(P, P_window=P_window, C_window=0.5) is not None
+
+ # Test 2: Empty or None power spectrum
+ with pytest.raises(ValueError, match=r'You must provide an input power spectrum array'):
+ fpt.one_loop_dd(None)
+ with pytest.raises(ValueError, match=r'You must provide an input power spectrum array'):
+ fpt.one_loop_dd([])
+
+ #Test mismatched P and k
+ half_P = P[:len(P)//2]
+ with pytest.raises(ValueError, match=r'Input k and P arrays must have the same size'):
+ fpt.one_loop_dd(half_P)
+
+ # Test 3: Zero power spectrum
+ P_zero = np.zeros_like(fpt.k_original)
+ with pytest.raises(ValueError, match=r'Your input power spectrum array is all zeros'):
+ fpt.one_loop_dd(P_zero)
+
+ # Test 4: P_window validation
+ max_window = (np.log(fpt.k_final[-1]) - np.log(fpt.k_final[0])) / 2
+ Max_P_window = np.array([max_window, max_window])
+ print(f"Max_P_window: {Max_P_window}")
+ assert fpt.one_loop_dd(P, P_window=Max_P_window / 2) is not None
+
+ with pytest.raises(ValueError, match=r'P_window must be a tuple of two values.'):
+ fpt.one_loop_dd(P, P_window=Max_P_window[:-1])
+
+ with pytest.raises(ValueError, match=r'P_window value is too large'):
+ fpt.one_loop_dd(P, P_window=Max_P_window * 2)
+
+ # Test 5: C_window validation
+ # Test valid C_window values
+ assert fpt.one_loop_dd(P, C_window=0.0) is not None
+ assert fpt.one_loop_dd(P, C_window=0.5) is not None
+ assert fpt.one_loop_dd(P, C_window=1.0) is not None
+
+ # Test invalid C_window values
+ with pytest.raises(ValueError, match=r'C_window must be between 0 and 1'):
+ fpt.one_loop_dd(P, C_window=-0.1)
+ with pytest.raises(ValueError, match=r'C_window must be between 0 and 1'):
+ fpt.one_loop_dd(P, C_window=1.1)
+
+ # Test 6: Combined parameter validation
+ with pytest.raises(ValueError):
+ fpt.one_loop_dd(None, P_window=P_window, C_window=0.5)
+ with pytest.raises(ValueError):
+ fpt.one_loop_dd(P, P_window=Max_P_window * 2, C_window=1.1)
+
+
+
+
+
+####################UNIT TESTS####################
+def test_one_loop_dd(fpt):
+ """Test the one_loop_dd function with various inputs"""
+ # Test with standard input
+ result = fpt.one_loop_dd(P)
+ assert isinstance(result, tuple)
+
+ # Test with window functions
+ P_window = np.array([0.2, 0.2])
+ result_window = fpt.one_loop_dd(P, P_window=P_window, C_window=C_window)
+ assert isinstance(result_window, tuple)
+
+ # Test shape consistency
+ assert result[0].shape == P.shape
+
+def test_one_lood_dd_bias(fpt):
+ """Test the one_loop_dd_bias function including bias terms"""
+
+ # Test standard calculation
+ result = fpt.one_loop_dd_bias(P)
+ assert isinstance(result, tuple)
+
+ # Verify sigma4 calculation is positive
+ assert result[-1] > 0 # sig4 should be positive
+
+ # Test bias terms have correct shapes
+ for term in result[1:-2]: # Skip P_1loop and sig4
+ assert term.shape == P.shape
+
+ # Test with window functions
+ result_window = fpt.one_loop_dd_bias(P, P_window=None, C_window=C_window)
+ assert isinstance(result_window, tuple)
+
+def test_one_loop_dd_bias_b3nl(fpt):
+ """Test the one_loop_dd_bias_b3nl function including b3nl terms"""
+
+ # Test standard calculation
+ result = fpt.one_loop_dd_bias_b3nl(P)
+ assert isinstance(result, tuple)
+
+ # Test sig3nl term
+ assert result[-1].shape == P.shape
+
+ # Test with window functions
+ result_window = fpt.one_loop_dd_bias_b3nl(P, P_window=None, C_window=C_window)
+ assert isinstance(result_window, tuple)
+
+ # Verify consistency between b3nl and standard bias results
+ result_bias = fpt.one_loop_dd_bias(P)
+ # for i in range(min(len(result), len(result_bias))):
+ # assert np.allclose(result[i], result_bias[i])
+ # Enhanced debugging for the failing comparison
+ for i in range(min(len(result), len(result_bias))):
+ try:
+ assert np.allclose(result[i], result_bias[i])
+ except AssertionError:
+ # Provide detailed debugging information
+ print(f"\n==== Detailed Comparison for Index {i} ====")
+ print(f"Shape of result[{i}]: {len(result[i])}, Shape of result_bias[{i}]: {len(result_bias[i])}")
+
+ if isinstance(result[i], np.ndarray) and isinstance(result_bias[i], np.ndarray):
+ # Calculate statistics about the differences
+ abs_diff = np.abs(result[i] - result_bias[i])
+ rel_diff = abs_diff / np.abs(result_bias[i])
+ rel_diff = np.where(np.isfinite(rel_diff), rel_diff, 0)
+
+ print(f"Maximum absolute difference: {np.max(abs_diff)}")
+ print(f"Mean absolute difference: {np.mean(abs_diff)}")
+ print(f"Maximum relative difference: {np.max(rel_diff)}")
+ print(f"Mean relative difference: {np.mean(rel_diff)}")
+
+ # Print some sample values where differences are largest
+ max_diff_idx = np.argmax(abs_diff)
+ print(f"\nAt index of max difference ({max_diff_idx}):")
+ print(f" result[{i}][{max_diff_idx}] = {result[i][max_diff_idx]}")
+ print(f" result_bias[{i}][{max_diff_idx}] = {result_bias[i][max_diff_idx]}")
+
+ # If the arrays are small enough, print all non-matching elements
+ if len(result[i]) < 20:
+ print("\nAll elements comparison:")
+ for j in range(len(result[i])):
+ if not np.isclose(result[i][j], result_bias[i][j]):
+ print(f" Index {j}: {result[i][j]} vs {result_bias[i][j]}")
+
+ # Print the full arrays if scalar values
+ else:
+ print(f"Full values: {result[i]} vs {result_bias[i]}")
+
+ # Re-raise the assertion with more context
+ raise AssertionError(f"Arrays at index {i} are not equal within tolerance")
+
+def test_one_loop_dd_bias_lpt_NL(fpt):
+ """Test the one_loop_dd_bias_lpt_NL function"""
+
+ # Test standard calculation
+ result = fpt.one_loop_dd_bias_lpt_NL(P)
+ assert isinstance(result, tuple)
+
+ # Test shapes of LPT bias terms
+ expected_terms = ['Pb1L', 'Pb1L_2', 'Pb1L_b2L', 'Pb2L', 'Pb2L_2']
+ for term, name in zip(result, expected_terms):
+ assert term.shape == P.shape, f"{name} has incorrect shape"
+
+ # Test with window functions
+ result_window = fpt.one_loop_dd_bias_lpt_NL(P, P_window=None, C_window=0.75)
+ assert isinstance(result_window, tuple)
+
+#def test_cleft_Q_R(fpt):
+# assert True
+
+def test_IA_tt(fpt):
+ """Test the IA_tt function"""
+ result = fpt.IA_tt(P)
+ assert isinstance(result, tuple)
+ assert len(result) == 2
+ assert result[0].shape == P.shape
+ assert result[1].shape == P.shape
+
+def test_IA_mix(fpt):
+ """Test the IA_mix function"""
+ result = fpt.IA_mix(P)
+ assert isinstance(result, tuple)
+ assert len(result) == 4
+ for term in result:
+ assert term.shape == P.shape
+
+def test_IA_ta(fpt):
+ """Test the IA_ta function"""
+ result = fpt.IA_ta(P)
+ assert isinstance(result, tuple)
+ assert len(result) == 4
+ for term in result:
+ assert term.shape == P.shape
+
+def test_IA_der(fpt):
+ """Test the IA_der function"""
+ result = fpt.IA_der(P)
+ assert isinstance(result, np.ndarray)
+ assert result.shape == P.shape
+
+def test_IA_ct(fpt):
+ """Test the IA_ct function"""
+ result = fpt.IA_ct(P)
+ assert isinstance(result, tuple)
+ assert len(result) == 4
+ for term in result:
+ assert term.shape == P.shape
+
+def test_gI_ct(fpt):
+ """Test the gI_ct function"""
+ result = fpt.gI_ct(P)
+ assert isinstance(result, tuple)
+ assert len(result) == 2
+ for term in result:
+ assert term.shape == P.shape
+
+def test_gI_ta(fpt):
+ """Test the IA_d2 function"""
+ result = fpt.gI_ta(P)
+ assert isinstance(result, tuple)
+ assert len(result) == 4
+ for term in result:
+ assert term.shape == P.shape
+
+def test_gI_tt(fpt):
+ """Test the IA_s2 function"""
+ result = fpt.gI_tt(P)
+ assert isinstance(result, tuple)
+ assert len(result) == 2
+ for term in result:
+ assert term.shape == P.shape
+
+def test_OV(fpt):
+ """Test the OV function"""
+ result = fpt.OV(P)
+ assert isinstance(result, np.ndarray)
+ assert result.shape == P.shape
+
+def test_kPol(fpt):
+ """Test the kPol function"""
+ result = fpt.kPol(P)
+ assert isinstance(result, tuple)
+ assert len(result) == 3
+ for term in result:
+ assert term.shape == P.shape
+
+def test_RSD_components(fpt):
+ """Test the RSD_components function"""
+ f = 0.5 # Example growth rate
+ result = fpt.RSD_components(P, f)
+ assert isinstance(result, tuple)
+ assert len(result) == 10
+ for term in result:
+ assert term.shape == P.shape
+
+def test_RSD_ABsum_components(fpt):
+ """Test the RSD_ABsum_components function"""
+ f = 0.5 # Example growth rate
+ result = fpt.RSD_ABsum_components(P, f)
+ assert isinstance(result, tuple)
+ assert len(result) == 4
+ for term in result:
+ assert term.shape == P.shape
+
+def test_RSD_ABsum_mu(fpt):
+ """Test the RSD_ABsum_mu function"""
+ f = 0.5 # Example growth rate
+ mu_n = 0.5 # Example mu_n value
+ result = fpt.RSD_ABsum_mu(P, f, mu_n)
+ assert isinstance(result, np.ndarray)
+ assert result.shape == P.shape
+
+def test_IRres(fpt):
+ """Test the IRres function"""
+ result = fpt.IRres(P)
+ assert isinstance(result, np.ndarray)
+ assert result.shape == P.shape
+
+def test_hash_none(fpt):
+ """Test hashing None values"""
+ result = fpt._hash_arrays(None)
+ assert result == hash(None)
+
+def test_hash_numpy_array(fpt):
+ """Test hashing numpy arrays"""
+ # 1D array
+ arr1d = np.array([1, 2, 3, 4, 5])
+ result1d = fpt._hash_arrays(arr1d)
+ assert isinstance(result1d, int)
+
+ # 2D array
+ arr2d = np.array([[1, 2], [3, 4]])
+ result2d = fpt._hash_arrays(arr2d)
+ assert isinstance(result2d, int)
+
+ # Array with different dtype
+ arr_float = np.array([1.1, 2.2, 3.3])
+ result_float = fpt._hash_arrays(arr_float)
+ assert isinstance(result_float, int)
+
+ # Test that identical arrays produce the same hash
+ arr_copy = np.copy(arr1d)
+ result_copy = fpt._hash_arrays(arr_copy)
+ assert result1d == result_copy
+
+ # Test that different arrays produce different hashes
+ arr_diff = np.array([1, 2, 3, 4, 6]) # Changed last element
+ result_diff = fpt._hash_arrays(arr_diff)
+ assert result1d != result_diff
+
+def test_hash_tuple_list(fpt):
+ """Test hashing tuples and lists of items"""
+ # Simple tuple of scalars
+ tuple_result = fpt._hash_arrays((1, 2, 3))
+ assert isinstance(tuple_result, int)
+
+ # Simple list of scalars
+ list_result = fpt._hash_arrays([1, 2, 3])
+ assert isinstance(list_result, int)
+
+ # Mixed list
+ mixed_list = [1, "string", True]
+ mixed_result = fpt._hash_arrays(mixed_list)
+ assert isinstance(mixed_result, int) #Now returns single hash
+
+def test_hash_X_parameters(fpt):
+ """Test hashing X parameter tuples (like X_spt, X_lpt, etc.)"""
+ # Create a mock X parameter tuple similar to what's returned by scalar_stuff
+ mock_pf = np.array([1.0, 2.0, 3.0])
+ mock_p = np.array([0.5, 1.5, 2.5])
+ mock_g_m = np.array([[1+2j, 3+4j], [5+6j, 7+8j]])
+ mock_g_n = np.array([[9+10j, 11+12j], [13+14j, 15+16j]])
+ mock_two_part_l = np.array([[17+18j, 19+20j], [21+22j, 23+24j]])
+ mock_h_l = np.array([[25+26j, 27+28j], [29+30j, 31+32j]])
+
+ mock_X = (mock_pf, mock_p, mock_g_m, mock_g_n, mock_two_part_l, mock_h_l)
+ X_hash = fpt._hash_arrays(mock_X)
+
+ assert isinstance(X_hash, int)
+
+def test_hash_consistency(fpt):
+ """Test that the same input always produces the same hash"""
+ # Array
+ arr = np.array([1, 2, 3, 4, 5])
+ hash1 = fpt._hash_arrays(arr)
+ hash2 = fpt._hash_arrays(arr)
+ assert hash1 == hash2
+
+ # Complex structure
+ complex_struct = ([1, 2, 3], np.array([4, 5, 6]), (7, 8, 9))
+ hash1 = fpt._hash_arrays(complex_struct)
+ hash2 = fpt._hash_arrays(complex_struct)
+ assert hash1 == hash2
+
+def test_hash_uniqueness(fpt):
+ """Test that different inputs produce different hashes"""
+ arr1 = np.array([1, 2, 3])
+ arr2 = np.array([1, 2, 4]) # Just one element different
+ hash1 = fpt._hash_arrays(arr1)
+ hash2 = fpt._hash_arrays(arr2)
+ assert hash1 != hash2
+
+ # Similar structure but different values
+ struct1 = ([1, 2, 3], np.array([4, 5, 6]))
+ struct2 = ([1, 2, 3], np.array([4, 5, 7])) # Just one element different
+ hash1 = fpt._hash_arrays(struct1)
+ hash2 = fpt._hash_arrays(struct2)
+ assert hash1 != hash2
+
+def test_hash_power_spectrum(fpt, monkeypatch):
+ """Test hashing and cache key generation for power spectrum"""
+
+ # Test direct hashing
+ P_hash = fpt._hash_arrays(P)
+ assert isinstance(P_hash, int)
+
+ # Test that create_hash_key returns both the hash_key and P_hash
+ # Use some dummy values for testing
+ term = "test_term"
+ X = fpt.X_spt
+ P_window = None
+ C_window = 0.75
+
+ hash_key, returned_P_hash = fpt._create_hash_key(term, X, P, P_window, C_window)
+ assert isinstance(hash_key, int)
+ assert isinstance(returned_P_hash, int)
+ assert returned_P_hash == P_hash, "P_hash should match the direct hash of P"
+
+ # Test access to cached properties creates entries in X_registry
+ assert id(X) in fpt.X_registry
+
+ mock_gets = []
+ mock_sets = []
+ original_get = fpt.cache.get
+ original_set = fpt.cache.set
+
+ def mock_get(*args):
+ mock_gets.append(args)
+ return original_get(*args)
+
+ def mock_set(*args):
+ mock_sets.append(args)
+ return original_set(*args)
+
+ monkeypatch.setattr(fpt.cache, 'get', mock_get)
+ monkeypatch.setattr(fpt.cache, 'set', mock_set)
+
+ fpt.one_loop_dd(P)
+ assert len(mock_gets) > 0, "Cache get was not called"
+ assert len(mock_sets) > 0, "Cache set was not called"
+
+ for call in mock_gets:
+ assert len(call) >= 2, "Cache get call should have category and hash"
+ assert isinstance(call[0], str), "First arg should be category (string)"
+ assert isinstance(call[1], int), "Second arg should be hash key (int)"
+
+ mock_gets.clear()
+ mock_sets.clear()
+
+ # Call the same function again - should use cache
+ result1 = fpt.one_loop_dd(P)
+
+ assert len(mock_gets) > 0, "Cache get was not called on repeated call"
+
+ # Different P should create different hash
+ P_modified = np.copy(P)
+ P_modified[0] *= 1.01
+
+ # Verify P_hash correctly identifies different arrays
+ P_hash_original = fpt._hash_arrays(P)
+ P_hash_modified = fpt._hash_arrays(P_modified)
+ assert P_hash_original != P_hash_modified, "Different P arrays should have different hash values"
+
+ # Test that create_hash_key returns different P_hash values for different P
+ hash_key1, P_hash1 = fpt._create_hash_key(term, X, P, P_window, C_window)
+ hash_key2, P_hash2 = fpt._create_hash_key(term, X, P_modified, P_window, C_window)
+ assert P_hash1 != P_hash2, "P_hash values should differ for different P arrays"
+ assert hash_key1 != hash_key2, "Hash keys should differ for different P arrays"
+
+ mock_gets.clear()
+ mock_sets.clear()
+
+ result2 = fpt.one_loop_dd(P_modified)
+
+ # Should have both gets and sets (different input = new cache entry)
+ assert len(mock_sets) > 0, "Cache set wasn't called with new input"
+ assert not np.array_equal(result1[0], result2[0]), "Results should differ with different inputs"
+
+def test_hash_nested_numpy_arrays(fpt):
+ """Test hashing complex nested structures with numpy arrays"""
+ # Create a complex nested structure with numpy arrays
+ arr1 = np.array([1.0, 2.0, 3.0])
+ arr2 = np.array([[4.0, 5.0], [6.0, 7.0]])
+ arr3 = np.array([8.0, 9.0, 10.0])
+
+ # Create a deep nested structure: tuple -> list -> tuple -> array
+ nested_structure = (
+ [arr1, (arr2, arr3)],
+ (np.array([11.0, 12.0]), [np.array([13.0]), np.array([[14.0, 15.0]])])
+ )
+
+ # Hash the nested structure
+ hash_result = fpt._hash_arrays(nested_structure)
+
+ # Verify structure and types
+ assert isinstance(hash_result, int)
+
+ # Test consistency - same structure should hash to same value
+ hash_result2 = fpt._hash_arrays(nested_structure)
+ assert hash_result == hash_result2
+
+ # Test that modifying a deep nested array changes the hash
+ modified_structure = (
+ [arr1, (arr2, np.array([8.0, 9.0, 10.1]))], # Changed last value from 10.0 to 10.1
+ (np.array([11.0, 12.0]), [np.array([13.0]), np.array([[14.0, 15.0]])])
+ )
+ modified_hash = fpt._hash_arrays(modified_structure)
+ assert hash_result != modified_hash
+
+ # Test typical deep nested X parameter scenarios from FAST-PT
+ # X parameters might contain tuples with numpy arrays of complex values
+ complex_arr = np.array([[1+2j, 3+4j], [5+6j, 7+8j]])
+ complex_nested = (arr1, arr2, complex_arr, (arr3, complex_arr))
+
+ complex_hash = fpt._hash_arrays(complex_nested)
+ assert isinstance(complex_hash, int)
diff --git a/tests/test_handler.py b/tests/test_handler.py
new file mode 100644
index 0000000..798f61b
--- /dev/null
+++ b/tests/test_handler.py
@@ -0,0 +1,1275 @@
+import pytest
+import numpy as np
+from fastpt import FASTPT
+from fastpt import FPTHandler
+import os
+import tempfile
+
+data_path = os.path.join(os.path.dirname(__file__), 'benchmarking', 'Pk_test.dat')
+P = np.loadtxt(data_path)[:, 1]
+k = np.loadtxt(data_path)[:, 0]
+C_window = 0.75
+P_window = np.array([0.2, 0.2])
+
+
+if __name__ == "__main__":
+ fast = FASTPT(k, low_extrap=-5, high_extrap=3, n_pad=int(0.5*len(k)))
+ handler = FPTHandler(fast, P=P, P_window=P_window, C_window=C_window)
+ # handler.save_instance("this_instance")
+
+
+
+
+@pytest.fixture
+def fpt():
+ k = np.loadtxt(data_path)[:, 0]
+ n_pad=int(0.5*len(k))
+ return FASTPT(k, low_extrap=-5, high_extrap=3, n_pad=n_pad)
+
+@pytest.fixture
+def handler(fpt):
+ return FPTHandler(fpt, P=P, P_window=P_window, C_window=C_window)
+
+################# FUNCTIONALITY TESTS #################
+def test_init_with_valid_params(handler):
+ assert isinstance(handler.fastpt, FASTPT)
+ assert handler.default_params['P'] is not None
+ assert handler.default_params['P_window'].all() == P_window.all()
+ assert handler.default_params['C_window'] == 0.75
+
+def test_run_without_power_spectrum(fpt):
+ handler = FPTHandler(fpt) #P is not required at handler init but must be passed at every function call
+ with pytest.raises(ValueError, match="Missing required parameters for 'one_loop_dd': \\['P'\\]. Please recall with the missing parameters."):
+ handler.run('one_loop_dd')
+
+def test_init_with_zero_power_spectrum(fpt):
+ P = np.zeros_like(fpt.k_original)
+ with pytest.raises(ValueError, match='Your input power spectrum array is all zeros'):
+ FPTHandler(fpt, P=P)
+
+def test_init_with_mismatched_arrays(fpt):
+ P = np.ones(10) # Wrong size
+ with pytest.raises(ValueError, match='Input k and P arrays must have the same size'):
+ FPTHandler(fpt, P=P)
+
+def test_invalid_c_window(fpt):
+ with pytest.raises(ValueError, match='C_window must be between 0 and 1'):
+ FPTHandler(fpt, P=P, C_window=1.5)
+ with pytest.raises(ValueError, match='C_window must be between 0 and 1'):
+ FPTHandler(fpt, P=P, C_window=-0.5)
+
+def test_invalid_p_window(fpt):
+ with pytest.raises(ValueError, match='P_window must be a tuple of two values'):
+ FPTHandler(fpt, P=P, P_window=np.array([1.0]))
+
+def test_invalid_function_call(handler):
+ with pytest.raises(ValueError, match="Function 'nonexistent_function' not found"):
+ handler.run('nonexistent_function')
+
+def test_missing_required_params(fpt):
+ handler = FPTHandler(fpt, P=P)
+ with pytest.raises(ValueError, match="Missing required parameters"):
+ handler.run('RSD_components')
+
+def test_all_fastpt_functions_with_handler_params(fpt):
+ """Test FASTPT functions with parameters set during handler initialization"""
+ # Initialize handler with all possible parameters
+ default_params = {
+ 'P': P,
+ 'P_window': P_window,
+ 'C_window': C_window,
+ 'f': 0.5,
+ 'mu_n': 0.5,
+ 'nu': -2,
+ 'L': 0.2,
+ 'h': 0.67,
+ 'rsdrag': 135
+ }
+ handler = FPTHandler(fpt, **default_params)
+
+ # Dictionary mapping functions to any additional required parameters
+ func_names = (
+ 'one_loop_dd', 'one_loop_dd_bias', 'one_loop_dd_bias_b3nl',
+ 'one_loop_dd_bias_lpt_NL', 'IA_tt', 'IA_mix', 'IA_ta',
+ 'IA_der', 'IA_ct', 'gI_ct', 'gI_ta',
+ 'gI_tt', 'OV', 'kPol', 'RSD_components', 'IRres',
+ 'RSD_ABsum_components', 'RSD_ABsum_mu',
+ )
+
+ for name in func_names:
+ try:
+ result = handler.run(name)
+ assert result is not None, f"Function {name} returned None"
+ if isinstance(result, tuple):
+ assert all(r is not None for r in result), f"Function {name} returned None in tuple"
+ except Exception as e:
+ pytest.fail(f"Function {name} failed to run with error: {str(e)}")
+
+def test_all_fastpt_functions_with_run_params(fpt):
+ """Test FASTPT functions with parameters passed during run call"""
+ handler = FPTHandler(fpt)
+
+ # Dictionary mapping functions to their required run-time parameters
+ function_params = {
+ 'one_loop_dd': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'one_loop_dd_bias': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'one_loop_dd_bias_b3nl': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'one_loop_dd_bias_lpt_NL': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_tt': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_mix': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_ta': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_der': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_ct': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'gI_ct': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'gI_ta': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'gI_tt': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'OV': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'kPol': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'RSD_components': {'P': P, 'P_window': P_window, 'C_window': C_window, 'f': 0.5},
+ 'RSD_ABsum_components': {'P': P, 'P_window': P_window, 'C_window': C_window, 'f': 0.5},
+ 'RSD_ABsum_mu': {'P': P, 'P_window': P_window, 'C_window': C_window, 'f': 0.5, 'mu_n': 0.5}
+ }
+
+ for func_name, params in function_params.items():
+ try:
+ result = handler.run(func_name, **params)
+ assert result is not None, f"Function {func_name} returned None"
+ if isinstance(result, tuple):
+ assert all(r is not None for r in result), f"Function {func_name} returned None in tuple"
+ except Exception as e:
+ pytest.fail(f"Function {func_name} failed to run with error: {str(e)}")
+
+def test_clear_params(handler):
+ handler.clear_default_params()
+ assert handler.default_params == {}
+
+def test_override_params(handler):
+ original_params = handler.default_params.copy()
+ r1 = handler.run('one_loop_dd')
+ new_params = {'P': P * 2, 'P_window': np.array([0.1, 0.1]), 'C_window': 0.5}
+ # Run with overridden parameters (but this doesn't update default_params)
+ r2 = handler.run('one_loop_dd', **new_params)
+ assert not np.array_equal(r1, r2)
+ # Assert that default_params weren't changed
+ for key in original_params:
+ if isinstance(original_params[key], np.ndarray):
+ assert np.array_equal(handler.default_params[key], original_params[key])
+ else:
+ assert handler.default_params[key] == original_params[key]
+
+def test_update_params(handler):
+ new_params = {'P': P * 2, 'P_window': np.array([0.1, 0.1]), 'C_window': 0.5}
+ handler.update_default_params(**new_params)
+ assert handler.default_params == new_params
+
+def test_update_fpt_instance(fpt, handler):
+ handler.run('one_loop_dd') # Run to initialize cache
+ new_fpt = FASTPT(fpt.k_original)
+ handler.update_fastpt_instance(new_fpt)
+ assert handler.fastpt == new_fpt
+
+################# result_direct TESTS #################
+def test_handler_function_equality(fpt):
+ """Test that handler produces identical results to direct FASTPT function calls"""
+ handler = FPTHandler(fpt)
+
+ function_params = {
+ 'one_loop_dd': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'one_loop_dd_bias': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'one_loop_dd_bias_b3nl': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'one_loop_dd_bias_lpt_NL': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_tt': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_mix': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_ta': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_der': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'IA_ct': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'gI_ct': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'gI_ta': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'gI_tt': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'OV': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'kPol': {'P': P, 'P_window': P_window, 'C_window': C_window},
+ 'RSD_components': {'P': P, 'P_window': P_window, 'C_window': C_window, 'f': 0.5},
+ 'RSD_ABsum_components': {'P': P, 'P_window': P_window, 'C_window': C_window, 'f': 0.5},
+ 'RSD_ABsum_mu': {'P': P, 'P_window': P_window, 'C_window': C_window, 'f': 0.5, 'mu_n': 0.5}
+ }
+
+ for func_name, params in function_params.items():
+ try:
+ # Get direct FASTPT function
+ fastpt_func = getattr(fpt, func_name)
+
+ # Run both ways
+ direct_result = fastpt_func(**params)
+ handler_result = handler.run(func_name, **params)
+
+ # Compare results
+ if isinstance(direct_result, (tuple, list)):
+ assert isinstance(handler_result, (tuple, list))
+ assert len(direct_result) == len(handler_result)
+ for dr, hr in zip(direct_result, handler_result):
+ if isinstance(dr, np.ndarray):
+ assert np.array_equal(dr, hr)
+ else:
+ assert dr == hr
+ elif isinstance(direct_result, np.ndarray):
+ assert np.array_equal(direct_result, handler_result)
+ else:
+ assert direct_result == handler_result
+
+ except Exception as e:
+ pytest.fail(f"Function {func_name} comparison failed with error: {str(e)}")
+
+
+########### GET METHOD TESTING ############
+def test_get_method_basics(fpt, handler):
+ """Test the basic functionality of the get method"""
+
+ # Test single term retrieval
+ p_deltaE1 = handler.get("P_deltaE1")
+ p_deltaE1_direct = fpt.IA_ta(P=P, P_window=P_window, C_window=C_window)[0]
+ assert np.array_equal(p_deltaE1, p_deltaE1_direct)
+
+ # Test multiple terms retrieval
+ terms = handler.get("P_deltaE1", "P_0E0E")
+ assert "P_deltaE1" in terms
+ assert "P_0E0E" in terms
+ assert np.array_equal(terms["P_deltaE1"], p_deltaE1_direct)
+ assert np.array_equal(terms["P_0E0E"], fpt.IA_ta(P=P, P_window=P_window, C_window=C_window)[2])
+
+@pytest.mark.parametrize("term_name", ["P_1loop", "Ps",
+ "Pd1d2", "Pd2d2", "Pd1s2", "Pd2s2", "Ps2s2", "sig4",
+ "sig3nl",
+ "Pb1L", "Pb1L_2", "Pb1L_b2L", "Pb2L", "Pb2L_2",
+ "P_E", "P_B",
+ "P_A", "P_Btype2", "P_DEE", "P_DBB",
+ "P_deltaE1", "P_deltaE2", "P_0E0E", "P_0B0B",
+ "P_der",
+ "P_0tE", "P_0EtE", "P_E2tE", "P_tEtE",
+ "P_d2tE", "P_s2tE",
+ "P_s2E2", "P_d2E2",
+ "P_d2E", "P_d20E", "P_s2E", "P_s20E",
+ "P_OV",
+ "P_kP1", "P_kP2", "P_kP3"])
+def test_get_all_terms(fpt, handler, term_name):
+ term_sources = {
+ "P_1loop": ("one_loop_dd", 0),
+ "Ps": ("one_loop_dd", 1),
+ "Pd1d2": ("one_loop_dd_bias", 2),
+ "Pd2d2": ("one_loop_dd_bias", 3),
+ "Pd1s2": ("one_loop_dd_bias", 4),
+ "Pd2s2": ("one_loop_dd_bias", 5),
+ "Ps2s2": ("one_loop_dd_bias", 6),
+ "sig4": ("one_loop_dd_bias", 7),
+
+ "sig3nl": ("one_loop_dd_bias_b3nl", 8),
+
+ "Pb1L": ("one_loop_dd_bias_lpt_NL", 1),
+ "Pb1L_2": ("one_loop_dd_bias_lpt_NL", 2),
+ "Pb1L_b2L": ("one_loop_dd_bias_lpt_NL", 3),
+ "Pb2L": ("one_loop_dd_bias_lpt_NL", 4),
+ "Pb2L_2": ("one_loop_dd_bias_lpt_NL", 5),
+
+ "P_E": ("IA_tt", 0),
+ "P_B": ("IA_tt", 1),
+
+ "P_A": ("IA_mix", 0),
+ "P_Btype2": ("IA_mix", 1),
+ "P_DEE": ("IA_mix", 2),
+ "P_DBB": ("IA_mix", 3),
+
+ "P_deltaE1": ("IA_ta", 0),
+ "P_deltaE2": ("IA_ta", 1),
+ "P_0E0E": ("IA_ta", 2),
+ "P_0B0B": ("IA_ta", 3),
+
+ "P_der": ("IA_der", 0),
+
+ "P_0tE": ("IA_ct", 0),
+ "P_0EtE": ("IA_ct", 1),
+ "P_E2tE": ("IA_ct", 2),
+ "P_tEtE": ("IA_ct", 3),
+
+ "P_d2tE": ("gI_ct", 0),
+ "P_s2tE": ("gI_ct", 1),
+
+ "P_s2E2": ("gI_tt", 0),
+ "P_d2E2": ("gI_tt", 1),
+
+ "P_d2E": ("gI_ta", 0),
+ "P_d20E": ("gI_ta", 1),
+ "P_s2E": ("gI_ta", 2),
+ "P_s20E": ("gI_ta", 3),
+
+ "P_OV": ("OV", 0),
+
+ "P_kP1": ("kPol", 0),
+ "P_kP2": ("kPol", 1),
+ "P_kP3": ("kPol", 2),
+
+ # "A1": ("RSD_components", 0),
+ # "A3": ("RSD_components", 1),
+ # "A5": ("RSD_components", 2),
+ # "B0": ("RSD_components", 3),
+ # "B2": ("RSD_components", 4),
+ # "B4": ("RSD_components", 5),
+ # "B6": ("RSD_components", 6),
+ # "P_Ap1": ("RSD_components", 7),
+ # "P_Ap3": ("RSD_components", 8),
+ # "P_Ap5": ("RSD_components", 9),
+
+ # "ABsum_mu2": ("RSD_ABsum_components", 0),
+ # "ABsum_mu4": ("RSD_ABsum_components", 1),
+ # "ABsum_mu6": ("RSD_ABsum_components", 2),
+ # "ABsum_mu8": ("RSD_ABsum_components", 3),
+
+ # "ABsum": ("RSD_ABsum_components", 0),
+
+ # "P_IRres": ("IRres", 0),
+ }
+ term_source = term_sources[term_name]
+ result = handler.get(term_name)
+ result_direct = getattr(fpt, term_source[0])(P=P, P_window=P_window, C_window=C_window)
+ if isinstance(result_direct, tuple):
+ result_direct = result_direct[term_source[1]]
+ assert np.allclose(result, result_direct)
+ fpt.cache.clear()
+ result_direct2 = getattr(fpt, term_source[0])(P=P, P_window=P_window, C_window=C_window)
+ if isinstance(result_direct2, tuple):
+ result_direct2 = result_direct2[term_source[1]]
+ assert np.allclose(result, result_direct2)
+
+
+def test_get_with_caching(fpt):
+ """Test get method with caching enabled"""
+ handler = FPTHandler(fpt, P=P, P_window=P_window, C_window=C_window)
+
+ # First call - should compute
+ result1 = handler.get("P_deltaE1")
+
+ # Check cache statistics before second call
+ initial_hits = fpt.cache.hits
+ initial_misses = fpt.cache.misses
+
+ # Second call - should use cache
+ result2 = handler.get("P_deltaE1")
+
+ # Verify cache was used (hits increased)
+ assert fpt.cache.hits > initial_hits
+ assert fpt.cache.misses == initial_misses
+ assert np.array_equal(result1, result2)
+
+ # Clear the cache
+ fpt.cache.clear()
+
+ # After clearing, the next call should recompute (miss)
+ pre_miss_count = fpt.cache.misses
+ handler.get("P_deltaE1")
+ assert fpt.cache.misses > pre_miss_count
+
+ # Check that multiple different calculations create different cache entries
+ pre_cache_count = len(fpt.cache.cache)
+ handler.get("P_0E0E") # Different calculation
+ assert len(fpt.cache.cache) > pre_cache_count
+
+def test_get_with_different_params(fpt):
+ """Test get method with different parameter combinations"""
+ handler = FPTHandler(fpt)
+
+ # Test with parameters provided at runtime
+ result1 = handler.get("P_deltaE1", P=P, P_window=P_window, C_window=C_window)
+ fpt.cache.clear()
+ # Test with default parameters
+ handler2 = FPTHandler(fpt, P=P, P_window=P_window, C_window=C_window)
+ result2 = handler2.get("P_deltaE1")
+
+ assert np.array_equal(result1, result2)
+
+ # Test with override parameters
+ handler3 = FPTHandler(fpt, P=P, P_window=P_window, C_window=C_window)
+ new_P = P * 5
+ result3 = handler3.get("P_deltaE1", P=new_P)
+
+ assert not np.array_equal(result1, result3)
+
+def test_get_invalid_term(handler):
+ """Test get method with invalid term name"""
+ with pytest.raises(ValueError, match="Term 'nonexistent_term' not found in FASTPT"):
+ handler.get("nonexistent_term")
+
+def test_get_missing_params(fpt):
+ """Test get method with missing required parameters"""
+ handler = FPTHandler(fpt)
+ with pytest.raises(ValueError, match="Missing required parameters"):
+ handler.get("P_deltaE1") # P is required
+
+def test_get_special_terms(fpt, handler):
+ """Test get method with special terms that have their own functions"""
+
+ p_btype2 = handler.get("P_Btype2")
+ fpt.cache.clear()
+ p_btype2_direct = fpt._get_P_Btype2(P)
+
+ assert np.array_equal(p_btype2, p_btype2_direct)
+
+ p_deltaE2 = handler.get("P_deltaE2")
+ fpt.cache.clear()
+ p_deltaE2_direct = fpt._get_P_deltaE2(P)
+
+ assert np.array_equal(p_deltaE2, p_deltaE2_direct)
+
+ p_ov = handler.get("P_OV")
+ fpt.cache.clear()
+ p_ov_direct = fpt.OV(P, P_window=P_window, C_window=C_window)
+ assert np.array_equal(p_ov, p_ov_direct), f"p_ov: {p_ov}, p_ov_direct: {p_ov_direct}"
+
+ p_der = handler.get("P_der")
+ fpt.cache.clear()
+ p_der_direct = fpt.IA_der(P)
+ assert np.array_equal(p_der, p_der_direct)
+
+
+def test_get_edge_cases(fpt):
+ """Test edge cases for the get method"""
+ # Test with empty parameters
+ handler = FPTHandler(fpt)
+
+ # Test with empty term list
+ with pytest.raises(ValueError, match="At least one term must be provided."):
+ handler.get()
+
+ # Test with required parameters passed directly at call
+ result = handler.get("P_E", P=P, P_window=P_window, C_window=C_window)
+ assert result is not None
+
+ # Test with mixed valid and invalid terms
+ with pytest.raises(ValueError, match="not found in FASTPT"):
+ handler.get("P_E", "nonexistent_term", P=P, P_window=P_window, C_window=C_window)
+
+ # Test parameter validation - P length must match k length
+ with pytest.raises(ValueError):
+ handler.get("P_E", P=np.ones(10))
+
+ # Test parameter validation - C_window must be between 0 and 1
+ with pytest.raises(ValueError):
+ handler.get("P_E", P=P, C_window=1.5)
+
+ # Test with overridden default parameters
+ handler = FPTHandler(fpt, P=P, P_window=P_window, C_window=C_window)
+ new_P = P * 1.1
+ result1 = handler.get("P_E")
+ result2 = handler.get("P_E", P=new_P)
+ assert not np.array_equal(result1, result2)
+
+################# BULK RUN TESTS #################
+def test_bulk_run_basic(fpt):
+ """Test basic functionality of bulk_run method"""
+ handler = FPTHandler(fpt)
+ funcs = ['one_loop_dd', 'IA_tt']
+ power_spectra = [P, P * 1.1, P * 1.2]
+
+ results = handler.bulk_run(funcs, power_spectra, P_window=P_window, C_window=C_window)
+
+ # Check that all expected results are present
+ assert len(results) == len(funcs) * len(power_spectra)
+
+ for func in funcs:
+ for i in range(len(power_spectra)):
+ assert (func, i) in results
+ assert results[(func, i)] is not None
+
+def test_bulk_run_results_correctness(handler):
+ """Test that bulk_run results match individual run calls"""
+ funcs = ['one_loop_dd', 'IA_tt']
+ power_spectra = [P, P * 1.5]
+
+ bulk_results = handler.bulk_run(funcs, power_spectra)
+
+ # Compare with individual runs
+ for func in funcs:
+ for i, spec in enumerate(power_spectra):
+ individual_result = handler.run(func, P=spec)
+ bulk_result = bulk_results[(func, i)]
+
+ if isinstance(individual_result, tuple):
+ assert isinstance(bulk_result, tuple)
+ assert len(individual_result) == len(bulk_result)
+ for ir, br in zip(individual_result, bulk_result):
+ assert np.array_equal(ir, br)
+ else:
+ assert np.array_equal(individual_result, bulk_result)
+
+def test_bulk_run_with_overrides(handler):
+ """Test bulk_run with additional override parameters"""
+ funcs = ['RSD_components']
+ power_spectra = [P]
+
+ # RSD_components requires 'f' parameter
+ results = handler.bulk_run(funcs, power_spectra,
+ P_window=P_window, C_window=C_window, f=0.5)
+
+ assert (funcs[0], 0) in results
+ assert results[(funcs[0], 0)] is not None
+
+def test_bulk_run_empty_inputs(handler):
+ """Test bulk_run with empty function list or power spectra list"""
+ # Empty function list
+ empty_results = handler.bulk_run([], [P])
+ assert len(empty_results) == 0
+
+ # Empty power spectra list
+ empty_results = handler.bulk_run(['one_loop_dd'], [])
+ assert len(empty_results) == 0
+
+def test_bulk_run_with_invalid_function(handler):
+ """Test bulk_run with invalid function name"""
+ with pytest.raises(ValueError, match="Function 'invalid_function' not found in FASTPT"):
+ handler.bulk_run(['invalid_function'], [P])
+
+def test_bulk_run_missing_params(handler):
+ """Test bulk_run with missing required parameters"""
+ with pytest.raises(ValueError, match="Missing required parameters"):
+ handler.bulk_run(['RSD_components'], [P], P_window=P_window, C_window=C_window)
+ # Missing 'f' parameter for RSD_components
+
+def test_bulk_run_large_input(handler):
+ """Test bulk_run with larger number of functions and spectra"""
+ funcs = ['one_loop_dd', 'IA_tt', 'IA_mix', 'OV', 'kPol']
+ power_spectra = [P, P * 1.1, P * 1.2, P * 1.3, P * 1.4]
+
+ results = handler.bulk_run(funcs, power_spectra, P_window=P_window, C_window=C_window)
+
+ # Check we got all expected combinations
+ assert len(results) == len(funcs) * len(power_spectra)
+ for func in funcs:
+ for i in range(len(power_spectra)):
+ assert (func, i) in results
+
+def test_bulk_run_with_save_all(fpt):
+ """Test bulk_run with save_all flag"""
+ # Create a temporary outputs directory to avoid cluttering
+
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Create a mock save_output that just records calls rather than saving files
+ saved_outputs = []
+
+ original_save_output = FPTHandler.save_output
+ try:
+ def mock_save_output(self, result, func_name, type="txt", output_dir=None):
+ saved_outputs.append((result, func_name, type, output_dir))
+
+ FPTHandler.save_output = mock_save_output
+
+ handler = FPTHandler(fpt, save_all="txt", P_window=P_window, C_window=C_window, save_dir=temp_dir)
+ funcs = ['one_loop_dd', 'IA_tt']
+ power_spectra = [P, P * 1.1]
+
+ handler.bulk_run(funcs, power_spectra)
+
+ # Check that save_output was called for each function and power spectrum
+ assert len(saved_outputs) == len(funcs) * len(power_spectra)
+
+ # Verify the correct arguments were passed to save_output
+ for output in saved_outputs:
+ assert isinstance(output[0], tuple) # Result
+ assert output[1] in funcs # Function name
+ assert output[2] == "txt" # File type
+ assert output[3] == temp_dir # Output directory
+ finally:
+ # Restore original method
+ FPTHandler.save_output = original_save_output
+
+################# OUTPUT FILE TESTS #################
+def test_save_output_file_types(handler):
+ """Test saving output in different file formats"""
+ import json
+ import csv
+
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Test txt format
+ txt_result = handler.run('one_loop_dd', save_type="txt", save_dir=temp_dir)
+ txt_file = os.path.join(temp_dir, "one_loop_dd_output.txt")
+ assert os.path.exists(txt_file)
+ loaded_txt = np.loadtxt(txt_file)
+ # Check that the saved data matches the result (accounting for transpose in saving)
+ assert np.allclose(loaded_txt, np.transpose(txt_result))
+
+ # Test csv format
+ csv_result = handler.run('IA_tt', save_type="csv", save_dir=temp_dir)
+ csv_file = os.path.join(temp_dir, "IA_tt_output.csv")
+ assert os.path.exists(csv_file)
+ # Read CSV file and check header and data
+ with open(csv_file, 'r') as f:
+ csv_reader = csv.reader(f)
+ header = next(csv_reader)
+ assert header == ['IA_tt_0', 'IA_tt_1'] # Header should contain function name with indices
+ data = list(csv_reader)
+ assert len(data) > 0
+
+ # Test json format
+ json_result = handler.run('one_loop_dd_bias', save_type="json", save_dir=temp_dir)
+ json_file = os.path.join(temp_dir, "one_loop_dd_bias_output.json")
+ assert os.path.exists(json_file)
+ # Read JSON file and check structure
+ with open(json_file, 'r') as f:
+ json_data = json.load(f)
+ assert 'one_loop_dd_bias' in json_data
+ # Check that the saved data has the same structure (number of arrays)
+ if isinstance(json_result, tuple):
+ assert len(json_data['one_loop_dd_bias']) == len(json_result)
+
+def test_save_output_auto_naming(handler):
+ """Test automatic filename adjustment when files already exist"""
+
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # First run - should create base filename
+ handler.run('one_loop_dd', save_type="txt", save_dir=temp_dir)
+ base_file = os.path.join(temp_dir, "one_loop_dd_output.txt")
+ assert os.path.exists(base_file)
+
+ # Second run - should create filename with counter
+ handler.run('one_loop_dd', save_type="txt", save_dir=temp_dir)
+ counter_file = os.path.join(temp_dir, "one_loop_dd_1_output.txt")
+ assert os.path.exists(counter_file)
+
+ # Third run - should increment counter
+ handler.run('one_loop_dd', save_type="txt", save_dir=temp_dir)
+ counter_file2 = os.path.join(temp_dir, "one_loop_dd_2_output.txt")
+ assert os.path.exists(counter_file2)
+
+ # Different function should still use base filename
+ handler.run('IA_tt', save_type="txt", save_dir=temp_dir)
+ different_file = os.path.join(temp_dir, "IA_tt_output.txt")
+ assert os.path.exists(different_file)
+
+def test_output_directory_precedence(fpt):
+ """Test precedence of different output directory parameters"""
+
+ with tempfile.TemporaryDirectory() as tempdir1:
+ with tempfile.TemporaryDirectory() as tempdir2:
+ # Initialize with default output directory
+ handler = FPTHandler(fpt, save_dir=tempdir1, P=P, P_window=P_window, C_window=C_window)
+
+ # 1. Test initializer-specified directory
+ handler.run('one_loop_dd', save_type="txt")
+ assert os.path.exists(os.path.join(tempdir1, "one_loop_dd_output.txt"))
+
+ # 2. Test save_dir parameter overrides initializer
+ handler.run('IA_tt', save_type="txt", save_dir=tempdir2)
+ assert os.path.exists(os.path.join(tempdir2, "IA_tt_output.txt"))
+ assert not os.path.exists(os.path.join(tempdir1, "IA_tt_output.txt"))
+
+def test_invalid_file_type(handler):
+ """Test that invalid file types raise appropriate errors"""
+
+ with tempfile.TemporaryDirectory() as temp_dir:
+ with pytest.raises(ValueError, match="Invalid file type"):
+ handler.run('one_loop_dd', save_type="invalid", save_dir=temp_dir)
+
+def test_save_output_direct_call(handler):
+ """Test direct calls to save_output method"""
+
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Run function to get a result
+ result = handler.run('one_loop_dd')
+
+ # Call save_output directly with different file types
+ handler.save_output(result, 'one_loop_dd', type="txt", output_dir=temp_dir)
+ handler.save_output(result, 'one_loop_dd', type="csv", output_dir=temp_dir)
+ handler.save_output(result, 'one_loop_dd', type="json", output_dir=temp_dir)
+
+ # Check that all three files exist
+ assert os.path.exists(os.path.join(temp_dir, "one_loop_dd_output.txt"))
+ assert os.path.exists(os.path.join(temp_dir, "one_loop_dd_output.csv"))
+ assert os.path.exists(os.path.join(temp_dir, "one_loop_dd_output.json"))
+
+def test_bulk_run_with_save_options(handler):
+ """Test bulk_run with file saving options"""
+
+ with tempfile.TemporaryDirectory() as temp_dir:
+ funcs = ['one_loop_dd', 'IA_tt']
+ power_spectra = [P, P * 1.1]
+
+ # Test bulk_run with save_type
+ handler.bulk_run(funcs, power_spectra, save_type="txt", save_dir=temp_dir)
+
+ # Check that files were created for each function and power spectrum
+ for func in funcs:
+ for i in range(len(power_spectra)):
+ if i == 0:
+ base_path = os.path.join(temp_dir, f"{func}_output.txt")
+ else:
+ base_path = os.path.join(temp_dir, f"{func}_{i}_output.txt")
+ assert os.path.exists(base_path)
+
+def test_load_method(handler):
+ """Test the load method for loading saved output files of different types"""
+
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Test txt format
+ txt_result = handler.run('one_loop_dd', save_type="txt", save_dir=temp_dir)
+ txt_loaded = handler.load('one_loop_dd_output.txt', load_dir=temp_dir)
+ assert isinstance(txt_loaded, tuple)
+ assert len(txt_loaded) == len(txt_result)
+ for r1, r2 in zip(txt_result, txt_loaded):
+ assert np.allclose(r1, r2)
+
+ # Test csv format
+ csv_result = handler.run('IA_tt', save_type="csv", save_dir=temp_dir)
+ csv_loaded = handler.load('IA_tt_output.csv', load_dir=temp_dir)
+ assert isinstance(csv_loaded, tuple)
+ assert len(csv_loaded) == len(csv_result)
+ for r1, r2 in zip(csv_result, csv_loaded):
+ assert np.allclose(r1, r2)
+
+ # Test json format
+ json_result = handler.run('one_loop_dd_bias', save_type="json", save_dir=temp_dir)
+ json_loaded = handler.load('one_loop_dd_bias_output.json', load_dir=temp_dir)
+ assert isinstance(json_loaded, tuple)
+ assert len(json_loaded) == len(json_result)
+ for r1, r2 in zip(json_result, json_loaded):
+ assert np.allclose(r1, r2), f"r1: {r1}, r2: {r2}"
+
+def test_load_with_absolute_path(handler):
+ """Test loading files using absolute paths"""
+
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Save a result
+ result = handler.run('one_loop_dd', save_type="txt", save_dir=temp_dir)
+ file_path = os.path.join(temp_dir, "one_loop_dd_output.txt")
+
+ # Load using absolute path - should ignore load_dir
+ loaded_result = handler.load(file_path)
+ assert isinstance(loaded_result, tuple)
+ for r1, r2 in zip(result, loaded_result):
+ assert np.allclose(r1, r2)
+
+ # Load using absolute path but with a different load_dir (should ignore load_dir)
+ with tempfile.TemporaryDirectory() as another_dir:
+ loaded_result2 = handler.load(file_path, load_dir=another_dir)
+ assert isinstance(loaded_result2, tuple)
+ for r1, r2 in zip(result, loaded_result2):
+ assert np.allclose(r1, r2)
+
+def test_load_with_default_dir(fpt):
+ """Test loading from the default output directory"""
+
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Initialize handler with custom output directory
+ handler = FPTHandler(fpt, P=P, P_window=P_window, C_window=C_window, save_dir=temp_dir)
+
+ # Save a result to the default directory
+ result = handler.run('one_loop_dd', save_type="txt")
+
+ # Load without specifying a directory (should use default)
+ loaded_result = handler.load('one_loop_dd_output.txt')
+ assert isinstance(loaded_result, tuple)
+ for r1, r2 in zip(result, loaded_result):
+ assert np.allclose(r1, r2)
+
+def test_load_error_handling(handler):
+ """Test error handling in the load method"""
+
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Test file not found
+ with pytest.raises(FileNotFoundError):
+ handler.load('nonexistent_file.txt', load_dir=temp_dir)
+
+ # Test unsupported file type
+ handler.run('one_loop_dd', save_type="txt", save_dir=temp_dir)
+ # Create a file with unsupported extension by renaming
+
+ import shutil
+ src = os.path.join(temp_dir, "one_loop_dd_output.txt")
+ dst = os.path.join(temp_dir, "one_loop_dd_output.xyz")
+ shutil.copy(src, dst)
+
+ with pytest.raises(FileNotFoundError):
+ handler.load('one_loop_dd_output.xyz', load_dir=temp_dir)
+
+def test_load_consistency_across_types(handler):
+ """Test that loading from different file types produces consistent results"""
+
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Save the same result in different formats
+ result = handler.run('one_loop_dd')
+ handler.save_output(result, 'one_loop_dd', type="txt", output_dir=temp_dir)
+ handler.save_output(result, 'one_loop_dd', type="csv", output_dir=temp_dir)
+ handler.save_output(result, 'one_loop_dd', type="json", output_dir=temp_dir)
+
+ # Load from each format
+ txt_loaded = handler.load('one_loop_dd_output.txt', load_dir=temp_dir)
+ csv_loaded = handler.load('one_loop_dd_output.csv', load_dir=temp_dir)
+ json_loaded = handler.load('one_loop_dd_output.json', load_dir=temp_dir)
+
+ # Check that all loaded results match the original
+ assert isinstance(txt_loaded, tuple) and isinstance(csv_loaded, tuple) and isinstance(json_loaded, tuple)
+ assert len(txt_loaded) == len(csv_loaded) == len(json_loaded) == len(result)
+
+ # Compare all formats against the original result
+ for i in range(len(result)):
+ assert np.allclose(result[i], txt_loaded[i])
+ assert np.allclose(result[i], csv_loaded[i])
+ assert np.allclose(result[i], json_loaded[i])
+
+def test_load_with_relative_paths(handler):
+ """Test loading using relative paths"""
+ # Get current working directory
+ orig_cwd = os.getcwd()
+
+ with tempfile.TemporaryDirectory() as temp_dir:
+ try:
+ # Change to the temporary directory
+ os.chdir(temp_dir)
+ # Create a subdirectory for outputs
+ os.makedirs('outputs', exist_ok=True)
+ # Save a result
+ result = handler.run('one_loop_dd', save_type="txt", save_dir='outputs')
+ # Load using relative path
+ loaded_result = handler.load(os.path.join('outputs', 'one_loop_dd_output.txt'))
+
+ assert isinstance(loaded_result, tuple)
+ for r1, r2 in zip(result, loaded_result):
+ assert np.allclose(r1, r2)
+ finally:
+ # Make sure to return to original directory
+ os.chdir(orig_cwd)
+
+import shutil
+@pytest.fixture
+def temp_output_dir():
+ """Create a temporary directory for test outputs"""
+ temp_dir = tempfile.mkdtemp()
+ yield temp_dir
+ shutil.rmtree(temp_dir)
+################# PARAMS SAVER BASIC FUNCTIONALITY TESTS #################
+def test_save_and_load_basic(handler, temp_output_dir):
+ """Test basic save and load functionality"""
+ # Save parameters
+ param_file = os.path.join(temp_output_dir, "test_params")
+ handler.save_instance(param_file)
+
+ # Load parameters
+ newHandler = FPTHandler.load_instance(param_file)
+
+ # Check that loaded parameters match original parameters
+ old_params = handler.default_params
+ for key, value in newHandler.default_params.items():
+ assert key in old_params
+ if isinstance(value, np.ndarray):
+ assert np.array_equal(value, old_params[key])
+ else:
+ assert value == old_params[key]
+
+################# FILE PATH TESTS #################
+def test_file_path_handling(handler, temp_output_dir):
+ """Test different ways of specifying file paths"""
+ # 1. Relative filename with output_dir
+ handler.save_instance("params1", output_dir=temp_output_dir)
+ assert os.path.exists(os.path.join(temp_output_dir, "params1.npz"))
+
+ # 2. Absolute path (output_dir should be ignored)
+ abs_path = os.path.join(temp_output_dir, "params2")
+ handler.save_instance(abs_path, output_dir="/should/be/ignored")
+ assert os.path.exists(abs_path + ".npz")
+
+ # 3. Relative path with directory component
+ subdir = "subdir"
+ os.makedirs(os.path.join(temp_output_dir, subdir), exist_ok=True)
+ rel_path = os.path.join(subdir, "params3")
+ handler.save_instance(rel_path, output_dir=temp_output_dir)
+ assert os.path.exists(os.path.join(temp_output_dir, rel_path + ".npz"))
+
+def test_file_extension_handling(handler, temp_output_dir):
+ """Test handling of file extensions"""
+ # Without extension (should add .npz)
+ handler.save_instance("no_extension", output_dir=temp_output_dir)
+ assert os.path.exists(os.path.join(temp_output_dir, "no_extension.npz"))
+
+ # With .npz extension (should not duplicate)
+ handler.save_instance("with_extension.npz", output_dir=temp_output_dir)
+ assert os.path.exists(os.path.join(temp_output_dir, "with_extension.npz"))
+ assert not os.path.exists(os.path.join(temp_output_dir, "with_extension.npz.npz"))
+
+ # With other extension (should still add .npz)
+ handler.save_instance("other_extension.txt", output_dir=temp_output_dir)
+ assert os.path.exists(os.path.join(temp_output_dir, "other_extension.txt.npz"))
+
+def test_directory_creation(handler):
+ """Test automatic directory creation"""
+ # Create a nested directory path that doesn't exist
+ temp_dir = tempfile.mkdtemp()
+ try:
+ nested_path = os.path.join(temp_dir, "a", "b", "c")
+ param_file = os.path.join(nested_path, "params")
+
+ # This should create the directory structure
+ handler.save_instance(param_file)
+
+ # Check that the directories were created
+ assert os.path.exists(nested_path)
+ assert os.path.exists(param_file + ".npz")
+ finally:
+ shutil.rmtree(temp_dir)
+
+def test_default_directory_fallback(fpt):
+ """Test fallback to default output directory"""
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Create handler with custom output directory
+ handler = FPTHandler(fpt, P=P, save_dir=temp_dir)
+
+ # Save without specifying output_dir (should use default)
+ handler.save_instance("default_dir_params")
+ assert os.path.exists(os.path.join(temp_dir, "default_dir_params.npz"))
+
+ # Load without specifying load_dir - should work since we have a handler with save_dir
+ loaded_handler = FPTHandler.load_instance("default_dir_params", load_dir=temp_dir)
+ assert loaded_handler is not None
+ assert isinstance(loaded_handler, FPTHandler)
+
+def test_load_instance_requires_directory():
+ """Test that load_instance requires a directory when not using absolute paths"""
+ # This should raise an error because no load_dir is provided and filename is relative
+ with pytest.raises(FileNotFoundError, match="Parameter file.*not found"):
+ FPTHandler.load_instance("nonexistent_params")
+
+ # Should also fail with relative path when file doesn't exist in current directory
+ with pytest.raises(FileNotFoundError):
+ FPTHandler.load_instance("relative_params.npz")
+
+def test_load_instance_with_absolute_path(fpt):
+ """Test that load_instance works with absolute paths without requiring load_dir"""
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Create and save a handler instance
+ handler = FPTHandler(fpt, P=P, save_dir=temp_dir)
+ abs_path = os.path.join(temp_dir, "abs_path_params")
+ handler.save_instance(abs_path)
+
+ # Load using absolute path (no load_dir needed)
+ loaded_handler = FPTHandler.load_instance(abs_path + ".npz")
+ assert loaded_handler is not None
+ assert isinstance(loaded_handler, FPTHandler)
+
+ # Should also work without the .npz extension
+ loaded_handler2 = FPTHandler.load_instance(abs_path)
+ assert loaded_handler2 is not None
+
+def test_current_working_directory_behavior(fpt):
+ """Test that the new current working directory behavior works correctly"""
+ # Get original working directory
+ orig_cwd = os.getcwd()
+
+ with tempfile.TemporaryDirectory() as temp_dir:
+ try:
+ # Change to temp directory
+ os.chdir(temp_dir)
+
+ # Create handler - should use current working directory + "outputs" (temp_dir/outputs)
+ handler = FPTHandler(fpt, P=P, P_window=P_window, C_window=C_window)
+
+ # Save instance - should save to current directory + "outputs"
+ handler.save_instance("cwd_test_params")
+
+ # File should exist in temp_dir/outputs (current working directory + "outputs")
+ expected_path = os.path.join(temp_dir, "outputs", "cwd_test_params.npz")
+ assert os.path.exists(expected_path)
+
+ # Load instance - should load from the outputs subdirectory
+ outputs_dir = os.path.join(temp_dir, "outputs")
+ loaded_handler = FPTHandler.load_instance("cwd_test_params", load_dir=outputs_dir)
+ assert loaded_handler is not None
+ assert isinstance(loaded_handler, FPTHandler)
+
+ # Verify the loaded parameters match
+ assert np.array_equal(loaded_handler.default_params['P'], P)
+ assert loaded_handler.default_params['C_window'] == C_window
+
+ finally:
+ # Always restore original working directory
+ os.chdir(orig_cwd)
+
+def test_save_dir_parameter_override(fpt):
+ """Test that explicit save_dir parameter overrides current working directory default"""
+ orig_cwd = os.getcwd()
+
+ with tempfile.TemporaryDirectory() as temp_dir1:
+ with tempfile.TemporaryDirectory() as temp_dir2:
+ try:
+ # Change to temp_dir1
+ os.chdir(temp_dir1)
+
+ # Create handler with explicit save_dir (should override cwd)
+ handler = FPTHandler(fpt, P=P, save_dir=temp_dir2)
+
+ # Save instance - should save to temp_dir2, not current directory (temp_dir1)
+ handler.save_instance("override_test_params")
+
+ # File should be in temp_dir2, not temp_dir1
+ assert os.path.exists(os.path.join(temp_dir2, "override_test_params.npz"))
+ assert not os.path.exists(os.path.join(temp_dir1, "override_test_params.npz"))
+
+ # Load should work from temp_dir2
+ loaded_handler = FPTHandler.load_instance("override_test_params", load_dir=temp_dir2)
+ assert loaded_handler is not None
+
+ finally:
+ os.chdir(orig_cwd)
+
+################# LOAD TESTS #################
+def test_load_nonexistent_file(handler):
+ """Test loading a file that doesn't exist"""
+ with pytest.raises(FileNotFoundError):
+ handler.load_instance("nonexistent_file")
+
+def test_load_with_absolute_path(handler, temp_output_dir):
+ """Test loading using an absolute file path"""
+ # Save parameters
+ param_file = os.path.join(temp_output_dir, "abs_path_params")
+ handler.save_instance(param_file)
+
+ # Load using absolute path
+ loaded_params = handler.load_instance(param_file)
+ assert loaded_params is not None
+
+ # Load using absolute path with .npz extension
+ loaded_params = handler.load_instance(param_file + ".npz")
+ assert loaded_params is not None
+
+ # Load with absolute path but specifying load_dir (should ignore load_dir)
+ loaded_params = handler.load_instance(param_file, load_dir="/should/be/ignored")
+ assert loaded_params is not None
+
+################# INTEGRATION TESTS #################
+def test_save_and_use_in_run(fpt, temp_output_dir):
+ """Test saving parameters and then using them in a run"""
+ # Create and save parameters
+ custom_params = {
+ 'P': P,
+ 'P_window': P_window,
+ 'C_window': C_window
+ }
+ handler = FPTHandler(fpt, **custom_params)
+ param_file = os.path.join(temp_output_dir, "run_params")
+ handler.save_instance(param_file)
+
+ # Load parameters
+ newHandler = FPTHandler.load_instance(param_file)
+
+ # Use loaded parameters in a run
+ result = newHandler.run('one_loop_dd')
+
+ # Compare with direct run
+ direct_result = handler.run('one_loop_dd')
+
+ # Results should be identical
+ assert np.array_equal(result, direct_result)
+
+
+################# POWER SPECTRA GENERATOR TESTS #################
+
+def test_generate_power_spectra_basic(handler):
+ """Test basic single-mode power spectra generation with default parameters"""
+ # Test class with default parameters
+ class_result = handler.generate_power_spectra(method='class')
+ assert isinstance(class_result, np.ndarray)
+ assert len(class_result) == len(handler.fastpt.k_original)
+ assert np.all(class_result > 0) # Power spectrum should be positive
+
+ # Test CAMB with default parameters
+ camb_result = handler.generate_power_spectra(method='camb')
+ assert isinstance(camb_result, np.ndarray)
+ assert len(camb_result) == len(handler.fastpt.k_original)
+ assert np.all(camb_result > 0)
+
+def test_generate_power_spectra_methods(handler):
+ """Test power spectra generation with different methods"""
+ # Generate power spectra with different methods but same params
+ class_result = handler.generate_power_spectra(method='class', omega_cdm=0.12, h=0.7, omega_b=0.022, z=0.5)
+ camb_result = handler.generate_power_spectra(method='camb', omega_cdm=0.12, h=0.7, omega_b=0.022, z=0.5)
+
+ # Results should be similar but not identical (within ~10%)
+ # This is a rough check that both methods are working and producing reasonable results
+ ratio = np.mean(class_result / camb_result)
+ assert 0.8 < ratio < 1.2, f"Results differ too much, ratio: {ratio}"
+
+def test_generate_power_spectra_invalid_method(handler):
+ """Test with invalid method names"""
+ with pytest.raises(ValueError, match="Invalid method"):
+ handler.generate_power_spectra(method='invalid')
+
+def test_generate_power_spectra_invalid_mode(handler):
+ """Test with invalid mode names"""
+ with pytest.raises(ValueError, match="Invalid mode"):
+ handler.generate_power_spectra(mode='invalid')
+
+def test_generate_power_spectra_single_mode_array_error(handler):
+ """Test that arrays cannot be passed in single mode"""
+ with pytest.raises(ValueError, match="must be a single value"):
+ handler.generate_power_spectra(omega_cdm=[0.1, 0.2])
+
+def test_generate_power_spectra_params(handler):
+ """Test power spectra generation with different parameter values"""
+ # Generate with different parameter values
+ base_result = handler.generate_power_spectra(method='class')
+ high_h_result = handler.generate_power_spectra(method='class', h=0.75)
+ high_cdm_result = handler.generate_power_spectra(method='class', omega_cdm=0.15)
+
+ # Results should be different with different parameters
+ assert not np.allclose(base_result, high_h_result)
+ assert not np.allclose(base_result, high_cdm_result)
+
+def test_bulk_power_spectra(handler):
+ """Test bulk mode power spectra generation"""
+ # Test with arrays of different lengths
+ bulk_results = handler.generate_power_spectra(
+ mode='bulk',
+ omega_cdm=[0.11, 0.12, 0.13],
+ h=[0.67, 0.68],
+ omega_b=0.022,
+ z=[0.0]
+ )
+
+ # Should return a list of results with length = max(param_length)
+ assert isinstance(bulk_results, list)
+ assert len(bulk_results) == 3 # Max length of input parameters
+
+ # Each result should be a proper power spectrum
+ for result in bulk_results:
+ assert isinstance(result, np.ndarray)
+ assert len(result) == len(handler.fastpt.k_original)
+ assert np.all(result > 0)
+
+def test_bulk_power_spectra_single_entry(handler):
+ """Test bulk mode with single entry arrays"""
+ # When all parameters are length 1, should return a single result
+ single_bulk_result = handler.generate_power_spectra(
+ mode='bulk',
+ omega_cdm=[0.12],
+ h=[0.67],
+ omega_b=[0.022],
+ z=[0.0]
+ )
+
+ # Should be a single array, not a list
+ assert isinstance(single_bulk_result, np.ndarray)
+ assert len(single_bulk_result) == len(handler.fastpt.k_original)
+
+def test_diff_power_spectra_basic(handler):
+ """Test diff mode power spectra generation"""
+ # Test with basic parameters
+ diff_results = handler.generate_power_spectra(
+ mode='diff',
+ omega_cdm=[0.11, 0.12, 0.13],
+ h=0.67,
+ omega_b=0.022,
+ z=0.0
+ )
+
+ # Should return a dictionary keyed by parameter tuples
+ assert isinstance(diff_results, dict)
+
+ # Should contain results for central value + 2 variations
+ assert len(diff_results) == 3 # 1 central + 2 variations
+
+ # Check format of keys and values
+ for key, value in diff_results.items():
+ assert isinstance(key, tuple)
+ assert len(key) == 2 # (z and int)
+ assert isinstance(value, np.ndarray)
+ assert len(value) == len(handler.fastpt.k_original)
+
+def test_diff_power_spectra_multi_param(handler):
+ """Test diff mode with multiple variable parameters"""
+ diff_results = handler.generate_power_spectra(
+ mode='diff',
+ omega_cdm=[0.11, 0.12, 0.13],
+ h=[0.66, 0.67, 0.68],
+ omega_b=0.022,
+ z=0.0
+ )
+
+ # Should contain results for central value + 2+2 variations
+ assert isinstance(diff_results, dict)
+ assert len(diff_results) == 5 # 1 central + 2*2 variations
+
+ assert (0.0, 0) in diff_results.keys(), "Central parameter combination not found"
+ assert (0.0, -1) in diff_results.keys(), "omega_cdm low variation not found"
+ assert (0.0, 2) in diff_results.keys(), "h high variation not found"
+
+def test_diff_power_spectra_requires_length_3(handler):
+ """Test that diff mode requires at least one parameter with length 3"""
+ with pytest.raises(ValueError, match="must have length 3"):
+ handler.generate_power_spectra(
+ mode='diff',
+ omega_cdm=[0.12],
+ h=[0.67],
+ omega_b=[0.022],
+ z=[0.0]
+ )
+
+def test_diff_power_spectra_with_multiple_z(handler):
+ """Test diff mode with multiple redshifts"""
+ diff_results = handler.generate_power_spectra(
+ mode='diff',
+ omega_cdm=[0.11, 0.12, 0.13],
+ h=0.67,
+ omega_b=0.022,
+ z=[0.0, 0.5]
+ )
+
+ # Should return results for each z
+ assert isinstance(diff_results, dict)
+ assert len(diff_results) == 6 # 3 param combinations * 2 redshifts
+
+ # Check that we have results for both redshifts
+ z0_keys = [k for k in diff_results.keys() if k[0] == 0.0]
+ z05_keys = [k for k in diff_results.keys() if k[0] == 0.5]
+ assert len(z0_keys) == 3
+ assert len(z05_keys) == 3
+
+def test_camb_specific_params(handler):
+ """Test CAMB-specific parameters"""
+ # With nonlinear=True
+ result_nl = handler.generate_power_spectra(
+ method='camb',
+ nonlinear=True
+ )
+
+ # With nonlinear=False
+ result_linear = handler.generate_power_spectra(
+ method='camb',
+ nonlinear=False
+ )
+
+ # Results should be different
+ assert not np.allclose(result_nl, result_linear)
+
+ # With different halofit version
+ result_halofit = handler.generate_power_spectra(
+ method='camb',
+ halofit_version='takahashi'
+ )
+
+ # Should be different from default (mead)
+ assert not np.allclose(result_nl, result_halofit)
+
+def test_class_camb_parameter_consistency(handler):
+ """Test consistency in parameter handling between CLASS and CAMB"""
+ # Generate spectra with same parameters
+ params = {
+ 'omega_cdm': 0.12,
+ 'h': 0.67,
+ 'omega_b': 0.022,
+ 'z': 0.5
+ }
+
+ class_result = handler.generate_power_spectra(method='class', **params)
+ camb_result = handler.generate_power_spectra(method='camb', **params)
+
+ # Results should be similar (within reasonable bounds)
+ # Note: Some differences are expected due to different implementations
+ assert len(class_result) == len(camb_result)
+
+ # Check ratio over most of the range (excluding extremes)
+ k_idx_range = slice(len(handler.fastpt.k_original) // 10, -len(handler.fastpt.k_original) // 10)
+ ratio = class_result[k_idx_range] / camb_result[k_idx_range]
+ assert 0.998 < np.median(ratio) < 1.002, "CLASS and CAMB results differ significantly"
diff --git a/tests/timed_new.json b/tests/timed_new.json
new file mode 100644
index 0000000..e6d0e4b
--- /dev/null
+++ b/tests/timed_new.json
@@ -0,0 +1,263 @@
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\ No newline at end of file
diff --git a/tests/timed_old.json b/tests/timed_old.json
new file mode 100644
index 0000000..fb8775c
--- /dev/null
+++ b/tests/timed_old.json
@@ -0,0 +1,263 @@
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\ No newline at end of file