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Smokescreen documentation master file, created by + sphinx-quickstart on Thu Aug 1 17:44:01 2024. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +******************************************************************** +**Smokescreen** -- DESC Library for concealing cosmological results +******************************************************************** + +.. image:: _static/Smokescreen_Banner.png + :align: center + +`Smokescreen `_ (currently under development) contains the modules for data concealment (blinding) at the following levels of the analysis: + +* Data-vector measurements + +* Posterior distribution [not yet developed] + +* (TBC) Catalogues + +.. attention:: + The term "blinding" is used in the context of data concealment for scientific analysis. We understand this is an outdated term and we are working to update it to a more appropriate term. If you have any suggestions, please let us know. + +Smokescreen's data-vector blinding methodis based on the `Muir et al. (2021) `_ and was developed to be used as part of DESC measurements and inference pipelines such as `TXPipe `_ . + +.. toctree:: + :maxdepth: 2 + :caption: Contents + + installation + usage + reference + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/_sources/installation.rst.txt b/_sources/installation.rst.txt new file mode 100644 index 0000000..64b6076 --- /dev/null +++ b/_sources/installation.rst.txt @@ -0,0 +1,70 @@ +Installation +============ + +Regular Installation +-------------------- + +.. note:: + If you have both `Firecrown `_ and `pyccl `_ installed in your environment, you can skip the installation of the dependencies in the ``environment.yml`` file and simply install ``Smokescreen`` using ``pip``: + + .. code-block:: bash + + python -m pip install smokescreen + +If you do not have *Firecrown* and *pyccl* installed, you can install the dependencies using conda: + +.. code-block:: bash + + conda install -c conda-forge lsstdesc-smokescreen + + +Developer Installation +----------------------- +If you want to install the package in development mode (or from source to get the latest version), Follow these instructions below. + +Creating a new environment: +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +You can create a new conda environment with the required packages using the `environment.yml` file: + +.. code-block:: bash + + conda env create -f environment.yml + +This will create a new environment called `desc_smokescreen` with the required packages. You can activate the environment with: + +.. code-block:: bash + + conda activate desc_smokescreen + +Using an existing environment +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +If you want to install the `Smokescreen` package in an existing environment, you can install it using: + +.. code-block:: bash + + conda activate myenv + conda env update --name myenv --file environment.yml --prune + +After installing the dependencies from `environment.yml`, you can install the `Smokescreen` package using: + +Normal Usage Installation +~~~~~~~~~~~~~~~~~~~~~~~~~ + +After installing the dependencies from ``environment.yml``, you can install the ``Smokescreen`` package using: + +.. code-block:: bash + + python -m pip install [-e] . + +The `-e` flag is optional and installs the package in editable mode (useful for development). + +Testing the installation +------------------------ + +You can test the developer installation by running the unit tests from the Smokescreen directory: + +.. code-block:: bash + + pytest . \ No newline at end of file diff --git a/_sources/reference.rst.txt b/_sources/reference.rst.txt new file mode 100644 index 0000000..44c292f --- /dev/null +++ b/_sources/reference.rst.txt @@ -0,0 +1,6 @@ +API References +=============== + +.. automodule:: smokescreen.datavector +.. automodule:: smokescreen.param_shifts +.. automodule:: smokescreen.utils diff --git a/_sources/usage.rst.txt b/_sources/usage.rst.txt new file mode 100644 index 0000000..d4a9512 --- /dev/null +++ b/_sources/usage.rst.txt @@ -0,0 +1,139 @@ +Usage +====== + +Currently, only data vector concealment is implemented in Smokescreen. Posterior-level concealment is under development. + +Data Vector Concealment (blinding) +----------------------------------- + +The `Smokescreen` library provides a method for blinding data vectors. This method is based on the `Muir et al. (2021) `_ data-vector blinding method. + +To conceal a data-vector you need the following elements: + +* A CCL cosmology object + +* A dictionary of the nuisance parameters used in the likelihood (soon to be deprecated) + +* A Firecrown Likelihood, which takes a SACC data-vector (see more below). It can be either a path to the python file containing the likelihood or the module itself. + +* A dictionary of cosmological parameters to be shifted in the format: + + .. code-block:: python + + # for a random uniform parameter shift: + {'PARAM_Y': (Y_MIN, Y_MAX), 'PARAM_Z': (Z_MIN, Z_MAX)} + # or for a determinist shift (used for debugging): + {'PARAM_Y': Y_VALUE, 'PARAM_Z': Z_VALUE} + +* A SACC data-vector + +* A random seed as int or string + +.. attention:: + **Likelihood Requirements** + + The blinding module requires the Firecrown likelihood to be built with certain requirements. First, we must be able to build the likelihood by providing a `sacc `_ object with the measurements for the data-vector: + + .. code-block:: python + + def build_likelihood(build_parameters): + """ + This is a generic likelihood theory model + for a generic data vector. + """ + sacc_data = build_parameters['sacc_data'] + + This is simular to what is currently done in `TXPipe `_. + + The likelihood module also must have a method ``.compute_theory_vector(ModellingTools)`` which calls for the calculation of the theory vector inside the likelihood object. + +.. danger:: + **Likelihoods with hardcoded sacc files:** + + If you provide a Firecrown likelihood with a hardcoded path to a sacc file as the data-vector, **Smokescreen will conceal the hardcoded sacc file and not the one you provided**. This is because the likelihood is built with the hardcoded path. Firecrown currently has not checks to avoid a hardcoded sacc file in the ``build_likelihood(...)`` function. To avoid this, please build the likelihood as described above. + +The likelihood can be provided either as a path to the python file containing the ``build_likelihood`` function or as a python module. In the latter case, the module must be imported. + +TL;DR: Check the `Smokescreen notebooks folder `_ for a couple of examples. + +From the command line +~~~~~~~~~~~~~~~~~~~~~~ +The blinding module can be used to blind the data-vector measurements. The module can be used as follows: + +.. code-block:: bash + + python -m smokescreen --config configuration_file.yaml + +You can find an example of a configuration file here: + +.. code-block:: yaml + + path_to_sacc: "./cosmicshear_sacc.fits" + likelihood_path: "./cosmicshear_likelihood.py" + systematics: + trc1_delta_z: 0.1 + trc0_delta_z: 0.1 + shifts_dict: + Omega_c: [0.20, 0.42] + sigma8: [0.67, 0.92] + seed: 2112 + # only needed if you want a different reference cosmology + # than ccl.VanillaLCDM + reference_cosmology: + sigma8: 0.85 + +Or you can use the following command to create a template configuration file: + +.. code-block:: bash + + python -m smokescreen --print_config > template_config.yaml + +Note that the `reference_cosmology` is optional. If not provided, the CCL `VanillaLCDM` reference cosmology will be the one used to compute the data vector. + +From a notebook/your code +~~~~~~~~~~~~~~~~~~~~~~~~~ + +The smokescreen module can be used to blind the data-vector measurements. The module can be used as follows: + +.. code-block:: python + + # import the module + import pyccl as ccl + from smokescreen import ConcealDataVector + # import the likelihood that contains the model and data vector + [...] + import my_likelihood + + # create the cosmology ccl object + cosmo = ccl.Cosmology(Omega_c=0.27, + Omega_b=0.045, + h=0.67, + sigma8=0.8, + n_s=0.96, + transfer_function='bbks') + # load a sacc object with the data vector [FIXME: this is a placeholder, the sacc object should be loaded from the likelihood] + sacc_data = sacc.Sacc.load_fits('path/to/data_vector.sacc') + # create a dictionary of the necessary firecrown nuisance parameters + syst_dict = { + "ia_a_1": 1.0, + "ia_a_2": 0.5, + "ia_a_d": 0.5, + "lens0_bias": 2.0, + "lens0_b_2": 1.0, + "lens0_b_s": 1.0, + "lens0_mag_bias": 1.0, + "src0_delta_z": 0.000, + "lens0_delta_z": 0.000,} + # create the smokescreen object + smoke = ConcealDataVector(cosmo, syst_dict, sacc_data, my_likelihood, + {'Omega_c': (0.22, 0.32), 'sigma8': (0.7, 0.9)}) + # conceals (blinds) the data vector + smoke.calculate_concealing_factor() + concealed_dv = smoke.apply_concealing_to_likelihood_datavec() + +Posterior Concealment (blinding) +--------------------------------- + +.. warning:: + + **UNDER DEVELOPMENT** \ No newline at end of file diff --git a/_static/Smokescreen_Banner.png b/_static/Smokescreen_Banner.png new file mode 100644 index 0000000..b0a102f Binary files /dev/null and b/_static/Smokescreen_Banner.png differ diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 0000000..8141580 --- /dev/null +++ b/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. 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a/_static/doctools.js b/_static/doctools.js new file mode 100644 index 0000000..4d67807 --- /dev/null +++ b/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + 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 = _( + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${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]. +// 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.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]. + 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], + ]); + } + } + } + + // 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], + ]; + 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]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + 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/_static/sphinx_highlight.js b/_static/sphinx_highlight.js new file mode 100644 index 0000000..8a96c69 --- /dev/null +++ b/_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. 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+ + +
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+

© Copyright LSST Dark Energy Science Collaboration .

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/index.html b/index.html new file mode 100644 index 0000000..2f6702e --- /dev/null +++ b/index.html @@ -0,0 +1,157 @@ + + + + + + + Smokescreen – DESC Library for concealing cosmological results — Smokescreen documentation + + + + + + + + + + + + + + + + + + + + + +
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+

Smokescreen – DESC Library for concealing cosmological results

+_images/Smokescreen_Banner.png +

Smokescreen (currently under development) contains the modules for data concealment (blinding) at the following levels of the analysis:

+
    +
  • Data-vector measurements

  • +
  • Posterior distribution [not yet developed]

  • +
  • (TBC) Catalogues

  • +
+
+

Attention

+

The term “blinding” is used in the context of data concealment for scientific analysis. We understand this is an outdated term and we are working to update it to a more appropriate term. If you have any suggestions, please let us know.

+
+

Smokescreen’s data-vector blinding methodis based on the Muir et al. (2021) and was developed to be used as part of DESC measurements and inference pipelines such as TXPipe .

+ +
+

Indices and tables

+ +
+
+ + +
+
+
+ +
+ +
+

© Copyright LSST Dark Energy Science Collaboration .

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/installation.html b/installation.html new file mode 100644 index 0000000..80ce410 --- /dev/null +++ b/installation.html @@ -0,0 +1,179 @@ + + + + + + + Installation — Smokescreen documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Installation

+
+

Regular Installation

+
+

Note

+

If you have both Firecrown and pyccl installed in your environment, you can skip the installation of the dependencies in the environment.yml file and simply install Smokescreen using pip:

+
python -m pip install smokescreen
+
+
+
+

If you do not have Firecrown and pyccl installed, you can install the dependencies using conda:

+
conda install -c conda-forge lsstdesc-smokescreen
+
+
+
+
+

Developer Installation

+

If you want to install the package in development mode (or from source to get the latest version), Follow these instructions below.

+
+

Creating a new environment:

+

You can create a new conda environment with the required packages using the environment.yml file:

+
conda env create -f environment.yml
+
+
+

This will create a new environment called desc_smokescreen with the required packages. You can activate the environment with:

+
conda activate desc_smokescreen
+
+
+
+
+

Using an existing environment

+

If you want to install the Smokescreen package in an existing environment, you can install it using:

+
conda activate myenv
+conda env update --name myenv --file environment.yml --prune
+
+
+

After installing the dependencies from environment.yml, you can install the Smokescreen package using:

+
+
+

Normal Usage Installation

+

After installing the dependencies from environment.yml, you can install the Smokescreen package using:

+
python -m pip install [-e] .
+
+
+

The -e flag is optional and installs the package in editable mode (useful for development).

+
+
+
+

Testing the installation

+

You can test the developer installation by running the unit tests from the Smokescreen directory:

+
pytest .
+
+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/objects.inv b/objects.inv new file mode 100644 index 0000000..34852c8 --- /dev/null +++ b/objects.inv @@ -0,0 +1,6 @@ +# Sphinx inventory version 2 +# Project: Smokescreen +# Version: +# The remainder of this file is compressed using zlib. +xڭSN0+,\**j ؑg’~:uӤ 5%̼9&"K oh$D7MR+ 4tեZp.!?tY]d;^;4W$W`avorts + N+BFs*W7/m_J[E8XeqgpGKVϕS®U E/3R/!b_ ;[|W,IՈp,64N>vKث*Pcr.=;N,@V'>"%_<#_scVblӧO]n7i`hS 8}{ =C \ No newline at end of file diff --git a/py-modindex.html b/py-modindex.html new file mode 100644 index 0000000..9ad138b --- /dev/null +++ b/py-modindex.html @@ -0,0 +1,142 @@ + + + + + + Python Module Index — Smokescreen documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
  • + +
  • +
  • +
+
+
+
+
+ + +

Python Module Index

+ +
+ s +
+ + + + + + + + + + + + + + + + +
 
+ s
+ smokescreen +
    + smokescreen.datavector +
    + smokescreen.param_shifts +
    + smokescreen.utils +
+ + +
+
+
+ +
+ +
+

© Copyright LSST Dark Energy Science Collaboration .

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/reference.html b/reference.html new file mode 100644 index 0000000..7b46891 --- /dev/null +++ b/reference.html @@ -0,0 +1,350 @@ + + + + + + + API References — Smokescreen documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

API References

+
+

Conceal Data Vector (smokescreen.datavector)

+

The smokescreen.datavector module provides functionalities +to conceal data vectors in the context of cosmological analysis.

+
+

Conceal Data Vector Class

+
+
+class smokescreen.datavector.ConcealDataVector(cosmo, systm_dict, likelihood, shifts_dict, sacc_data, seed='2112', **kwargs)
+

Class for calling a smokescreen on the measured data-vector.

+

FIXME: Only cosmological parameters are supported for now for the shifts

+
+
Parameters:
+
    +
  • cosmo (pyccl.Cosmology) – Cosmology object from CCL with a fiducial cosmology.

  • +
  • systm_dict (dict) – Dictionary of systematics names and corresponding fiducial values.

  • +
  • likelihood (str or module) – path to the likelihood or a module containing the likelihood +must contain both build_likelihood and compute_theory_vector methods

  • +
  • shifts_dict (dict) – Dictionary of parameter names and corresponding shift widths. If the +shifts are single values, the dictionary values should be the shift +widths. If the shifts are tuples of values, the dictionary values +should be the (lower, upper) bounds of the shift widths.

  • +
  • sacc_data (sacc.sacc.Sacc) – Data-vector to be concealed (blinded). +If None, the data-vector will be loaded from the likelihood.

  • +
  • seed (int or str) – Random seed.

  • +
+
+
Keyword Arguments:
+
    +
  • shift_type (str) – Type of shift to be applied. Default is “flat”. +FIXME: Only flat shifts are implemented for now.

  • +
  • debug (bool) – If True, prints debug information. Default is False.

  • +
+
+
+
+
+apply_concealing_to_likelihood_datavec()
+

Applies the concealing (blinding) factor to the data-vector.

+
+ +
+
+calculate_concealing_factor(factor_type='add')
+
+
Calculates the concealing (blinding) factor for the data-vector,

according to Muir et al. 2019:

+
+
+
+
Parameters:
+

factor_type (str) – Type of concealing (blinding) factor to be calculated. Default is add.

+
+
Returns:
+

.__concealing_factor – Concealing factor.

+
+
Return type:
+

np.ndarray

+
+
+

Notes

+
+
type=”add”:
+\[f^{\rm add} = d(\theta_{\rm blind}) - d(\theta_{\rm fid})\]
+
+
type=”mult”:
+\[f^{\rm mult} = d(\theta_{\rm blind}) / d(\theta_{\rm fid})\]
+
+
+
+ +
+
+save_concealed_datavector(path_to_save, file_root, return_sacc=False)
+

Saves the concealed (blinded) data-vector to a file.

+

Saves the blinded data-vector to a file with the name: +{path_to_save}/{file_root}_blinded_data_vector.fits

+
+
Parameters:
+
    +
  • path_to_save (str) – Path to save the blinded data-vector.

  • +
  • file_root (str) – Root of the file name.

  • +
  • return_sacc (bool) – If True, returns the sacc object with the blinded data-vector.

  • +
+
+
+
+ +
+ +
+
+
+

Parameter Shifts (smokescreen.param_shifts)

+

The smokescreen.param_shifts module provides modules +to perform shifts in the cosmological parameters.

+
+

Parameter Shifts

+
+
+smokescreen.param_shifts.draw_flat_param_shifts(shift_dict, seed)
+

Draw flat parameter shifts from a dictionary of parameter names and +corresponding shift widths.

+
+
Parameters:
+
    +
  • shift_dict (dict) – Dictionary of parameter names and corresponding shift widths. If the +shifts are single values, the dictionary values should be the shift +widths. If the shifts are tuples of values, the dictionary values +should be the (lower, upper) bounds of the shift widths.

  • +
  • seed (int or str) – Random seed.

  • +
+
+
Returns:
+

Dictionary of parameter names and corresponding flat parameter shifts.

+
+
Return type:
+

dict

+
+
+
+ +
+
+smokescreen.param_shifts.draw_flat_or_deterministic_param_shifts(cosmo, shifts_dict, seed)
+

Draw flat or deterministic parameter shifts from a dictionary of parameter +names and corresponding shift widths.

+
+
Parameters:
+
    +
  • cosmo (pyccl.Cosmology) – Cosmology object.

  • +
  • shift_dict (dict) – Dictionary of parameter names and corresponding shift. If the +shifts are single values, it does a deterministic shift: PARAM = FIDUCIAL + SHIFT +If the shifts are tuples of values, the dictionary values +should be the (lower, upper) bounds of the shift widths: PARAM = U(a, b)

  • +
  • seed (int or str) – Random seed.

  • +
+
+
Returns:
+

Dictionary of parameter names and corresponding flat or deterministic +parameter shifts.

+
+
Return type:
+

dict

+
+
+
+ +
+
+
+

Utils (smokescreen.utils)

+

The smokescreen.utils module provides utility functions +for the smokescreen package.

+
+

Smokescreen Utils

+
+
+smokescreen.utils.load_cosmology_from_partial_dict(cosmo_dict)
+

Given a partial dictionary with cosmological parameters, return a Cosmology +object setting the unspecified parameters to their default values.

+
+
Parameters:
+

cosmo_dict (dict) – Dictionary with the cosmological parameters.

+
+
Returns:
+

Cosmology object with the specified parameters.

+
+
Return type:
+

Cosmology

+
+
+
+ +
+
+smokescreen.utils.load_module_from_path(path)
+

Load a module from a given path.

+
+
Parameters:
+

path (str) – Path to the module to load.

+
+
Returns:
+

Module loaded from the given path.

+
+
Return type:
+

module

+
+
+
+ +
+
+smokescreen.utils.string_to_seed(seedstring)
+

Convert a string to a random seed.

+
+
Parameters:
+

string (str) – String to convert to a random seed.

+
+
Returns:
+

Random seed.

+
+
Return type:
+

int

+
+
+
+ +
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/search.html b/search.html new file mode 100644 index 0000000..b1b6cad --- /dev/null +++ b/search.html @@ -0,0 +1,127 @@ + + + + + + Search — Smokescreen documentation + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
  • + +
  • +
  • +
+
+
+
+
+ + + + +
+ +
+ +
+
+
+ +
+ +
+

© Copyright LSST Dark Energy Science Collaboration .

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/searchindex.js b/searchindex.js new file mode 100644 index 0000000..a7faf81 --- /dev/null +++ b/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"alltitles": {"API References": [[2, null]], "Conceal Data Vector (smokescreen.datavector)": [[2, "conceal-data-vector-smokescreen-datavector"]], "Conceal Data Vector Class": [[2, "conceal-data-vector-class"]], "Contents": [[0, null]], "Creating a new environment:": [[1, "creating-a-new-environment"]], "Data Vector Concealment (blinding)": [[3, "data-vector-concealment-blinding"]], "Developer Installation": [[1, "developer-installation"]], "From a notebook/your code": [[3, "from-a-notebook-your-code"]], "From the command line": [[3, "from-the-command-line"]], "Indices and tables": [[0, "indices-and-tables"]], "Installation": [[1, null]], "Normal Usage Installation": [[1, "normal-usage-installation"]], "Parameter Shifts": [[2, "parameter-shifts"]], "Parameter Shifts 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"environ": 1, "exist": 1, "from": 3, "indic": 0, "instal": 1, "librari": 0, "line": 3, "new": 1, "normal": 1, "notebook": 3, "param_shift": 2, "paramet": 2, "posterior": 3, "refer": 2, "regular": 1, "result": 0, "shift": 2, "smokescreen": [0, 2], "tabl": 0, "test": 1, "us": 1, "usag": [1, 3], "util": 2, "vector": [2, 3], "your": 3}}) \ No newline at end of file diff --git a/usage.html b/usage.html new file mode 100644 index 0000000..b568972 --- /dev/null +++ b/usage.html @@ -0,0 +1,245 @@ + + + + + + + Usage — Smokescreen documentation + + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Usage

+

Currently, only data vector concealment is implemented in Smokescreen. Posterior-level concealment is under development.

+
+

Data Vector Concealment (blinding)

+

The Smokescreen library provides a method for blinding data vectors. This method is based on the Muir et al. (2021) data-vector blinding method.

+

To conceal a data-vector you need the following elements:

+
    +
  • A CCL cosmology object

  • +
  • A dictionary of the nuisance parameters used in the likelihood (soon to be deprecated)

  • +
  • A Firecrown Likelihood, which takes a SACC data-vector (see more below). It can be either a path to the python file containing the likelihood or the module itself.

  • +
  • A dictionary of cosmological parameters to be shifted in the format:

    +
    +
    # for a random uniform parameter shift:
    +{'PARAM_Y': (Y_MIN, Y_MAX), 'PARAM_Z': (Z_MIN, Z_MAX)}
    +# or for a determinist shift (used for debugging):
    +{'PARAM_Y': Y_VALUE, 'PARAM_Z': Z_VALUE}
    +
    +
    +
    +
  • +
  • A SACC data-vector

  • +
  • A random seed as int or string

  • +
+
+

Attention

+

Likelihood Requirements

+

The blinding module requires the Firecrown likelihood to be built with certain requirements. First, we must be able to build the likelihood by providing a sacc object with the measurements for the data-vector:

+
+
def build_likelihood(build_parameters):
+    """
+    This is a generic likelihood theory model
+    for a generic data vector.
+    """
+    sacc_data = build_parameters['sacc_data']
+
+
+

This is simular to what is currently done in TXPipe.

+

The likelihood module also must have a method .compute_theory_vector(ModellingTools) which calls for the calculation of the theory vector inside the likelihood object.

+
+
+
+

Danger

+

Likelihoods with hardcoded sacc files:

+

If you provide a Firecrown likelihood with a hardcoded path to a sacc file as the data-vector, Smokescreen will conceal the hardcoded sacc file and not the one you provided. This is because the likelihood is built with the hardcoded path. Firecrown currently has not checks to avoid a hardcoded sacc file in the build_likelihood(...) function. To avoid this, please build the likelihood as described above.

+
+

The likelihood can be provided either as a path to the python file containing the build_likelihood function or as a python module. In the latter case, the module must be imported.

+

TL;DR: Check the Smokescreen notebooks folder for a couple of examples.

+
+

From the command line

+

The blinding module can be used to blind the data-vector measurements. The module can be used as follows:

+
python -m smokescreen --config configuration_file.yaml
+
+
+

You can find an example of a configuration file here:

+
path_to_sacc: "./cosmicshear_sacc.fits"
+likelihood_path: "./cosmicshear_likelihood.py"
+systematics:
+    trc1_delta_z: 0.1
+    trc0_delta_z: 0.1
+shifts_dict:
+    Omega_c: [0.20, 0.42]
+    sigma8: [0.67, 0.92]
+seed: 2112
+# only needed if you want a different reference cosmology
+# than ccl.VanillaLCDM
+reference_cosmology:
+    sigma8: 0.85
+
+
+

Or you can use the following command to create a template configuration file:

+
python -m smokescreen --print_config > template_config.yaml
+
+
+

Note that the reference_cosmology is optional. If not provided, the CCL VanillaLCDM reference cosmology will be the one used to compute the data vector.

+
+
+

From a notebook/your code

+

The smokescreen module can be used to blind the data-vector measurements. The module can be used as follows:

+
# import the module
+import pyccl as ccl
+from smokescreen import ConcealDataVector
+# import the likelihood that contains the model and data vector
+[...]
+import my_likelihood
+
+# create the cosmology ccl object
+cosmo = ccl.Cosmology(Omega_c=0.27,
+                      Omega_b=0.045,
+                      h=0.67,
+                      sigma8=0.8,
+                      n_s=0.96,
+                      transfer_function='bbks')
+# load a sacc object with the data vector [FIXME: this is a placeholder, the sacc object should be loaded from the likelihood]
+sacc_data = sacc.Sacc.load_fits('path/to/data_vector.sacc')
+# create a dictionary of the necessary firecrown nuisance parameters
+syst_dict = {
+            "ia_a_1": 1.0,
+            "ia_a_2": 0.5,
+            "ia_a_d": 0.5,
+            "lens0_bias": 2.0,
+            "lens0_b_2": 1.0,
+            "lens0_b_s": 1.0,
+            "lens0_mag_bias": 1.0,
+            "src0_delta_z": 0.000,
+            "lens0_delta_z": 0.000,}
+# create the smokescreen object
+smoke = ConcealDataVector(cosmo, syst_dict, sacc_data, my_likelihood,
+                          {'Omega_c': (0.22, 0.32), 'sigma8': (0.7, 0.9)})
+# conceals (blinds) the data vector
+smoke.calculate_concealing_factor()
+concealed_dv = smoke.apply_concealing_to_likelihood_datavec()
+
+
+
+
+
+

Posterior Concealment (blinding)

+
+

Warning

+

UNDER DEVELOPMENT

+
+
+
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file