diff --git a/.gitignore b/.gitignore index 17adea81b3..f425602443 100644 --- a/.gitignore +++ b/.gitignore @@ -8,6 +8,12 @@ stochss-model-editor.html stochss-workflow-manager.html stochss-workflow-selection.html stochss-project-manager.html +multiple-plots-page.html +stochss-domain-editor.html +stochss-loading-page.html +stochss-project-browser.html +stochss-quick-start.html +stochss-user-home.html *.swp *.swo package-lock.json diff --git a/__version__.py b/__version__.py index 8df94791cf..03724dc3d0 100644 --- a/__version__.py +++ b/__version__.py @@ -5,7 +5,7 @@ # @website https://github.com/stochss/stochss # ============================================================================= -__version__ = '2.3' +__version__ = '2.3.2' __title__ = 'StochSS' __description__ = 'StochSS is an integrated development environment (IDE) \ for simulation of biochemical networks.' diff --git a/client/pages/domain-editor.js b/client/pages/domain-editor.js index 7eb2eb1275..4160ab3d44 100644 --- a/client/pages/domain-editor.js +++ b/client/pages/domain-editor.js @@ -669,19 +669,28 @@ let DomainEditor = PageView.extend({ let projBC = $(this.queryByHook("grandparent-breadcrumb")); projBC.text(breadData.project.name); projBC.prop("href", breadData.project.href); + $(this.queryByHook("return-to-project")).prop("href", breadData.project.href); let mdlBC = $(this.queryByHook("parent-two-breadcrumb")); mdlBC.text(breadData.model.name); mdlBC.prop("href", breadData.model.href); + $(this.queryByHook("return-to-model")).prop("href", breadData.model.href); }else if(breadData.model || breadData.project) { $(this.queryByHook("one-parent-breadcrumb-links")).css("display", "block"); let breadcrumb = $(this.queryByHook("parent-one-breadcrumb")); if(breadData.project) { breadcrumb.text(breadData.project.name) breadcrumb.prop("href", breadData.project.href); + $(this.queryByHook("return-to-project")).prop("href", breadData.project.href); + $(this.queryByHook("return-to-model")).css("display", "none"); }else { breadcrumb.text(breadData.model.name) breadcrumb.prop("href", breadData.model.href); + $(this.queryByHook("return-to-project")).css("display", "none"); + $(this.queryByHook("return-to-model")).prop("href", breadData.model.href); } + }else{ + $(this.queryByHook("return-to-model")).css("display", "none"); + $(this.queryByHook("return-to-project")).css("display", "none"); } this.renderDomainProperties(); this.renderDomainLimitations(); diff --git a/client/pages/file-browser.js b/client/pages/file-browser.js index 3ebc068764..94dc7d5c75 100644 --- a/client/pages/file-browser.js +++ b/client/pages/file-browser.js @@ -44,6 +44,7 @@ let FileBrowser = PageView.extend({ 'click [data-hook=file-browser-help]' : function () { let modal = $(modals.operationInfoModalHtml('file-browser')).modal(); }, + 'click [data-hook=empty-trash]' : 'emptyTrash' }, initialize: function (attrs, options) { PageView.prototype.initialize.apply(this, arguments) @@ -78,7 +79,10 @@ let FileBrowser = PageView.extend({ if(op === 'move_node' && more && more.ref && more.ref.type && !(more.ref.type == 'folder' || more.ref.type == 'root')){ return false } - if(op === 'move_node' && more && more.ref && more.ref.type && more.ref.type === 'folder'){ + if(op === 'move_node' && more && more.ref && more.ref.original && path.dirname(more.ref.original._path).split("/").includes("trash")){ + return false + } + if(op === 'move_node' && more && more.ref && more.ref.type && more.ref.type === 'folder' && more.ref.text !== "trash"){ if(!more.ref.state.loaded){ return false } @@ -110,6 +114,11 @@ let FileBrowser = PageView.extend({ node.original._path = path.join(newDir, file); if(node.type === "folder") { $('#models-jstree').jstree().refresh_node(node); + }else if(newDir.endsWith("trash")) { + $(self.queryByHook('empty-trash')).prop('disabled', false); + $('#models-jstree').jstree().refresh_node(par); + }else if(oldPath.split("/").includes("trash")) { + $('#models-jstree').jstree().refresh_node(par); } }, error: function (err, response, body) { @@ -639,16 +648,9 @@ let FileBrowser = PageView.extend({ var endpoint = path.join(app.getApiPath(), "project/new-project")+"?path="+projectPath app.getXHR(endpoint, { success: function (err, response, body) { - if(o){//directory was created with context menu option - var node = $('#models-jstree').jstree().get_node(o); - if(node.type === "root"){ - self.refreshJSTree(); - }else{ - $('#models-jstree').jstree().refresh_node(node); - } - }else{//directory was created with create directory button - self.refreshJSTree(); - } + let queryStr = "?path=" + body.path; + let endpoint = path.join(app.getBasePath(), 'stochss/project/manager') + queryStr; + window.location.href = endpoint; }, error: function (err, response, body) { let errorModel = $(modals.newProjectOrWorkflowGroupErrorHtml(body.Reason, body.Message)).modal(); @@ -770,7 +772,6 @@ let FileBrowser = PageView.extend({ if(o && o.original && o.original.type !== "root"){ parentPath = o.original._path } - console.log(parentPath) if(isModel) { let ext = isSpatial ? ".smdl" : ".mdl" let modelName = o && o.type === "project" ? input.value.trim().split("/").pop() + ext : input.value.trim() + ext; @@ -860,6 +861,43 @@ let FileBrowser = PageView.extend({ } }); }, + moveToTrash: function (o) { + if(document.querySelector('#moveToTrashConfirmModal')) { + document.querySelector('#moveToTrashConfirmModal').remove(); + } + let self = this; + let modal = $(modals.moveToTrashConfirmHtml("model")).modal(); + let yesBtn = document.querySelector('#moveToTrashConfirmModal .yes-modal-btn'); + yesBtn.addEventListener('click', function (e) { + modal.modal('hide'); + let queryStr = "?srcPath=" + o.original._path + "&dstPath=" + path.join("trash", o.text) + let endpoint = path.join(app.getApiPath(), "file/move") + queryStr + app.getXHR(endpoint, { + always: function (err, response, body) { + $(self.queryByHook('empty-trash')).prop('disabled', false); + $('#models-jstree').jstree().refresh(); + } + }); + }); + }, + emptyTrash: function (e) { + if(document.querySelector("#emptyTrashConfirmModal")) { + document.querySelector("#emptyTrashConfirmModal").remove() + } + let self = this; + let modal = $(modals.emptyTrashConfirmHtml()).modal(); + let yesBtn = document.querySelector('#emptyTrashConfirmModal .yes-modal-btn'); + yesBtn.addEventListener('click', function (e) { + modal.modal('hide'); + let endpoint = path.join(app.getApiPath(), "file/empty-trash") + "?path=trash"; + app.getXHR(endpoint, { + success: function (err, response, body) { + self.refreshJSTree(); + $(self.queryByHook('empty-trash')).prop('disabled', true); + } + }); + }); + }, setupJstree: function () { var self = this; $.jstree.defaults.contextmenu.items = (o, cb) => { @@ -905,6 +943,17 @@ let FileBrowser = PageView.extend({ self.duplicateFileOrDirectory(o, null) } }, + "MoveToTrash" : { + "label" : "Move To Trash", + "_disabled" : false, + "separator_before" : false, + "separator_after" : false, + "action" : function (data) { + self.moveToTrash(o); + } + } + } + let delete_node = { "Delete" : { "label" : "Delete", "_disabled" : false, @@ -1268,6 +1317,12 @@ let FileBrowser = PageView.extend({ if (o.type === 'root'){ return folder } + if (o.text === "trash") { + return {"Refresh": folder.Refresh} + } + if (o.original._path.split("/")[0] === "trash") { + return delete_node + } if (o.type === 'folder') { return $.extend(folder, common) } @@ -1326,26 +1381,28 @@ let FileBrowser = PageView.extend({ var file = e.target.text var node = $('#models-jstree').jstree().get_node(e.target) var _path = node.original._path; - if(file.endsWith('.mdl') || file.endsWith('.smdl')){ - window.location.href = path.join(app.getBasePath(), "stochss/models/edit")+"?path="+_path; - }else if(file.endsWith('.ipynb')){ - var notebookPath = path.join(app.getBasePath(), "notebooks", _path) - window.open(notebookPath, '_blank') - }else if(file.endsWith('.sbml')){ - var openPath = path.join(app.getBasePath(), "edit", _path) - window.open(openPath, '_blank') - }else if(file.endsWith('.proj')){ - window.location.href = path.join(app.getBasePath(), "stochss/project/manager")+"?path="+_path; - }else if(file.endsWith('.wkfl')){ - window.location.href = path.join(app.getBasePath(), "stochss/workflow/edit")+"?path="+_path+"&type=none"; - }else if(file.endsWith('.domn')) { - let queryStr = "?domainPath=" + _path - window.location.href = path.join(app.getBasePath(), "stochss/domain/edit") + queryStr - }else if(node.type === "folder" && $('#models-jstree').jstree().is_open(node) && $('#models-jstree').jstree().is_loaded(node)){ - $('#models-jstree').jstree().refresh_node(node) - }else if(node.type === "other"){ - var openPath = path.join(app.getBasePath(), "view", _path); - window.open(openPath, "_blank"); + if(!(_path.split("/")[0] === "trash")) { + if(file.endsWith('.mdl') || file.endsWith('.smdl')){ + window.location.href = path.join(app.getBasePath(), "stochss/models/edit")+"?path="+_path; + }else if(file.endsWith('.ipynb')){ + var notebookPath = path.join(app.getBasePath(), "notebooks", _path) + window.open(notebookPath, '_blank') + }else if(file.endsWith('.sbml')){ + var openPath = path.join(app.getBasePath(), "edit", _path) + window.open(openPath, '_blank') + }else if(file.endsWith('.proj')){ + window.location.href = path.join(app.getBasePath(), "stochss/project/manager")+"?path="+_path; + }else if(file.endsWith('.wkfl')){ + window.location.href = path.join(app.getBasePath(), "stochss/workflow/edit")+"?path="+_path+"&type=none"; + }else if(file.endsWith('.domn')) { + let queryStr = "?domainPath=" + _path + window.location.href = path.join(app.getBasePath(), "stochss/domain/edit") + queryStr + }else if(node.type === "folder" && $('#models-jstree').jstree().is_open(node) && $('#models-jstree').jstree().is_loaded(node)){ + $('#models-jstree').jstree().refresh_node(node) + }else if(node.type === "other"){ + var openPath = path.join(app.getBasePath(), "view", _path); + window.open(openPath, "_blank"); + } } }); } diff --git a/client/pages/multiple-plots.js b/client/pages/multiple-plots.js new file mode 100644 index 0000000000..ad54c5b373 --- /dev/null +++ b/client/pages/multiple-plots.js @@ -0,0 +1,61 @@ +/* +StochSS is a platform for simulating biochemical systems +Copyright (C) 2019-2020 StochSS developers. + +This program is free software: you can redistribute it and/or modify +it under the terms of the GNU General Public License as published by +the Free Software Foundation, either version 3 of the License, or +(at your option) any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU General Public License for more details. + +You should have received a copy of the GNU General Public License +along with this program. If not, see . +*/ + +let path = require('path'); +//support files +let app = require('../app'); +let Plotly = require('../lib/plotly'); +//views +var PageView = require('../pages/base'); +//templates +let template = require("../templates/pages/multiplePlots.pug"); + +import initPage from './page.js'; + +let ModelEditor = PageView.extend({ + template: template, + initialize: function (attrs, options) { + PageView.prototype.initialize.apply(this, arguments); + let urlParams = new URLSearchParams(window.location.search); + this.workflow = urlParams.get("wkfl"); + this.job = urlParams.get("job"); + this.path = urlParams.get("path"); + this.data = urlParams.get("data"); + }, + render: function (attrs, options) { + PageView.prototype.render.apply(this, arguments); + let queryStr = "?path=" + this.path + "&data=" + this.data; + let endpoint = path.join(app.getApiPath(), "workflow/plot-results") + queryStr; + let self = this; + app.getXHR(endpoint, { + success: function (err, response, body) { + self.plotFigure(body); + }, + error: function (err, response, body) { + console.log(body) + } + }); + }, + plotFigure: function (figure) { + let el = this.queryByHook("figures"); + let config = {"responsive":true}; + Plotly.newPlot(el, figure, config); + } +}); + +initPage(ModelEditor); \ No newline at end of file diff --git a/client/pages/project-manager.js b/client/pages/project-manager.js index fd7b4625ba..67f45073ba 100644 --- a/client/pages/project-manager.js +++ b/client/pages/project-manager.js @@ -229,7 +229,7 @@ let ProjectManager = PageView.extend({ let yesBtn = document.querySelector('#emptyTrashConfirmModal .yes-modal-btn'); yesBtn.addEventListener('click', function (e) { modal.modal('hide'); - let endpoint = path.join(app.getApiPath(), "project/empty-trash")+"?path="+path.join(self.model.directory, "trash"); + let endpoint = path.join(app.getApiPath(), "file/empty-trash")+"?path="+path.join(self.model.directory, "trash"); app.getXHR(endpoint, { success: function (err, response, body) { $(self.queryByHook('empty-project-trash')).prop('disabled', true); diff --git a/client/templates/includes/gillespyResultsEnsemble.pug b/client/templates/includes/gillespyResultsEnsemble.pug index ada2db9858..48186f5fb7 100644 --- a/client/templates/includes/gillespyResultsEnsemble.pug +++ b/client/templates/includes/gillespyResultsEnsemble.pug @@ -83,6 +83,8 @@ div#workflow-results.card.card-body button.btn.btn-primary.box-shadow(data-hook="trajectories-edit-plot" data-target="edit-plot" disabled) Edit Plot + button.btn.btn-primary.box-shadow(data-hook="multiple-plots", data-type="mltplplt" disabled) Multiple Plots + button.btn.btn-primary.box-shadow( data-hook="trajectories-download-png-custom" data-target="download-png-custom" diff --git a/client/templates/includes/parameterScanResults.pug b/client/templates/includes/parameterScanResults.pug index 4b76a7a150..2f6efea14b 100644 --- a/client/templates/includes/parameterScanResults.pug +++ b/client/templates/includes/parameterScanResults.pug @@ -50,6 +50,8 @@ div#workflow-results.card.card-body button.btn.btn-primary.box-shadow(data-hook="ts-psweep-edit-plot" data-target="edit-plot" disabled) Edit Plot + button.btn.btn-primary.box-shadow(data-hook="multiple-plots" data-type="ts-psweep-mp" style="display: none;" disabled) Multiple Plots + button.btn.btn-primary.box-shadow( data-hook="ts-psweep-download-png-custom" data-target="download-png-custom" diff --git a/client/templates/includes/parameterSweepResults.pug b/client/templates/includes/parameterSweepResults.pug index 135f7f9121..3df6edbb08 100644 --- a/client/templates/includes/parameterSweepResults.pug +++ b/client/templates/includes/parameterSweepResults.pug @@ -64,6 +64,8 @@ div#workflow-results.card.card-body button.btn.btn-primary.box-shadow(data-hook="ts-psweep-edit-plot" data-target="edit-plot" disabled) Edit Plot + button.btn.btn-primary.box-shadow(data-hook="multiple-plots" data-type="ts-psweep-mp" style="display: none;" disabled) Multiple Plots + button.btn.btn-primary.box-shadow( data-hook="ts-psweep-download-png-custom" data-target="download-png-custom" diff --git a/client/templates/pages/domainEditor.pug b/client/templates/pages/domainEditor.pug index d6d4b90f17..b300cb47a1 100644 --- a/client/templates/pages/domainEditor.pug +++ b/client/templates/pages/domainEditor.pug @@ -319,6 +319,10 @@ section.page li.dropdown-item(data-hook="save-to-model") to Model li.dropdown-item(data-hook="save-to-file") to File (.domn) + a.btn.btn-primary.box-shadow.text-break(role="button" data-hook="return-to-project") Return to Project + + a.btn.btn-primary.box-shadow.text-break(role="button" data-hook="return-to-model") Return to Model + div.mdl-edit-btn.saving-status.inline(data-hook="sd-in-progress") div.spinner-grow.mr-2 diff --git a/client/templates/pages/fileBrowser.pug b/client/templates/pages/fileBrowser.pug index 6446a44c6e..ea87593238 100644 --- a/client/templates/pages/fileBrowser.pug +++ b/client/templates/pages/fileBrowser.pug @@ -35,4 +35,6 @@ section.page button.btn.btn-primary.box-shadow(id="options-for-node" data-hook="options-for-node" disabled) Actions - button.btn.btn-primary.inline.box-shadow(id="refresh-jstree" data-hook="refresh-jstree") Refresh \ No newline at end of file + button.btn.btn-primary.inline.box-shadow(id="refresh-jstree" data-hook="refresh-jstree") Refresh + + button.btn.btn-primary.inline.box-shadow(id="empty-trash" data-hook="empty-trash") Empty Trash \ No newline at end of file diff --git a/client/templates/pages/multiplePlots.pug b/client/templates/pages/multiplePlots.pug new file mode 100644 index 0000000000..ce59115284 --- /dev/null +++ b/client/templates/pages/multiplePlots.pug @@ -0,0 +1,11 @@ +section.page + + h3="Workflow: "+this.workflow + + h4="Job: "+this.job + + div.card.card-body + + h5 Plot Trajectories: Multiple Graphs + + div(data-hook="figures") \ No newline at end of file diff --git a/client/views/edit-project.js b/client/views/edit-project.js index a0662fccaf..042dddc754 100644 --- a/client/views/edit-project.js +++ b/client/views/edit-project.js @@ -29,7 +29,7 @@ let template = require('../templates/includes/editProject.pug'); module.exports = View.extend({ template: template, events: { - 'click [data-hook=remove-project-btn]' : 'handleRemoveProjectClick' + 'click [data-hook=remove-project-btn]' : 'handleMoveToTrash' }, initialize: function (attrs, options) { View.prototype.initialize.apply(this, arguments); @@ -37,16 +37,17 @@ module.exports = View.extend({ render: function (attrs, options) { View.prototype.render.apply(this, arguments); }, - handleRemoveProjectClick: function (e) { - if(document.querySelector('#deleteFileModal')) { - document.querySelector('#deleteFileModal').remove(); + handleMoveToTrash: function (e) { + if(document.querySelector('#moveToTrashConfirmModal')) { + document.querySelector('#moveToTrashConfirmModal').remove(); } let self = this; - let endpoint = path.join(app.getApiPath(), "file/delete")+"?path="+this.model.directory; - let modal = $(modals.deleteFileHtml("project")).modal(); - let yesBtn = document.querySelector('#deleteFileModal .yes-modal-btn'); + let modal = $(modals.moveToTrashConfirmHtml("model")).modal(); + let yesBtn = document.querySelector('#moveToTrashConfirmModal .yes-modal-btn'); yesBtn.addEventListener('click', function (e) { modal.modal('hide'); + let queryStr = "?srcPath=" + self.model.directory + "&dstPath=" + path.join("trash", self.model.directory.split("/").pop()); + let endpoint = path.join(app.getApiPath(), "file/move") + queryStr app.getXHR(endpoint, { success: function (err, response, body) { self.model.collection.remove(self.model); diff --git a/client/views/file-browser-view.js b/client/views/file-browser-view.js index 6a317fbb74..de8ef0e6d2 100644 --- a/client/views/file-browser-view.js +++ b/client/views/file-browser-view.js @@ -1379,7 +1379,7 @@ module.exports = View.extend({ if (o.text === "trash"){ return refresh } - if (o.original._path.includes("trash")) { + if (o.original._path.includes(".proj/trash/")) { return {"Delete": common.Delete} } if (o.type === 'folder') { @@ -1441,26 +1441,28 @@ module.exports = View.extend({ var file = e.target.text var node = $('#models-jstree-view').jstree().get_node(e.target) var _path = node.original._path; - if(file.endsWith('.mdl') || file.endsWith('.smdl')){ - window.location.href = path.join(app.getBasePath(), "stochss/models/edit")+"?path="+_path; - }else if(file.endsWith('.ipynb')){ - var notebookPath = path.join(app.getBasePath(), "notebooks", _path) - window.open(notebookPath, '_blank') - }else if(file.endsWith('.sbml')){ - var openPath = path.join(app.getBasePath(), "edit", _path) - window.open(openPath, '_blank') - }else if(file.endsWith('.proj')){ - window.location.href = path.join(app.getBasePath(), "stochss/project/manager")+"?path="+_path; - }else if(file.endsWith('.wkfl')){ - window.location.href = path.join(app.getBasePath(), "stochss/workflow/edit")+"?path="+_path+"&type=none"; - }else if(file.endsWith('.domn')) { - let queryStr = "?domainPath=" + _path - window.location.href = path.join(app.getBasePath(), "stochss/domain/edit") + queryStr - }else if(node.type === "folder" && $('#models-jstree-view').jstree().is_open(node) && $('#models-jstree-view').jstree().is_loaded(node)){ - $('#models-jstree-view').jstree().refresh_node(node) - }else if(node.type === "other"){ - var openPath = path.join(app.getBasePath(), "view", _path); - window.open(openPath, "_blank"); + if(!_path.includes(".proj/trash/")){ + if(file.endsWith('.mdl') || file.endsWith('.smdl')){ + window.location.href = path.join(app.getBasePath(), "stochss/models/edit")+"?path="+_path; + }else if(file.endsWith('.ipynb')){ + var notebookPath = path.join(app.getBasePath(), "notebooks", _path) + window.open(notebookPath, '_blank') + }else if(file.endsWith('.sbml')){ + var openPath = path.join(app.getBasePath(), "edit", _path) + window.open(openPath, '_blank') + }else if(file.endsWith('.proj')){ + window.location.href = path.join(app.getBasePath(), "stochss/project/manager")+"?path="+_path; + }else if(file.endsWith('.wkfl')){ + window.location.href = path.join(app.getBasePath(), "stochss/workflow/edit")+"?path="+_path+"&type=none"; + }else if(file.endsWith('.domn')) { + let queryStr = "?domainPath=" + _path + window.location.href = path.join(app.getBasePath(), "stochss/domain/edit") + queryStr + }else if(node.type === "folder" && $('#models-jstree-view').jstree().is_open(node) && $('#models-jstree-view').jstree().is_loaded(node)){ + $('#models-jstree-view').jstree().refresh_node(node) + }else if(node.type === "other"){ + var openPath = path.join(app.getBasePath(), "view", _path); + window.open(openPath, "_blank"); + } } }); }) diff --git a/client/views/reactions-editor.js b/client/views/reactions-editor.js index 10647b431e..c54ef00484 100644 --- a/client/views/reactions-editor.js +++ b/client/views/reactions-editor.js @@ -99,6 +99,7 @@ module.exports = View.extend({ this.openReactionsContainer(); } this.toggleProcessError() + $(this.queryByHook('massaction-message')).prop('hidden', this.collection.parent.parameters.length > 0); }, update: function () { }, diff --git a/client/views/workflow-results.js b/client/views/workflow-results.js index 7bcf2fe2f7..adc075079f 100644 --- a/client/views/workflow-results.js +++ b/client/views/workflow-results.js @@ -45,6 +45,7 @@ module.exports = View.extend({ 'click [data-hook=collapse-results-btn]' : 'changeCollapseButtonText', 'click [data-trigger=collapse-plot-container]' : 'handleCollapsePlotContainerClick', 'click [data-target=edit-plot]' : 'openPlotArgsSection', + 'click [data-hook=multiple-plots]' : 'plotMultiplePlots', 'click [data-target=download-png-custom]' : 'handleDownloadPNGClick', 'click [data-target=download-json]' : 'handleDownloadJSONClick', 'click [data-hook=convert-to-notebook]' : 'handleConvertToNotebookClick', @@ -95,28 +96,22 @@ module.exports = View.extend({ changeCollapseButtonText: function (e) { app.changeCollapseButtonText(this, e); }, - getPlot: function (type) { - let self = this; + cleanupPlotContainer: function (type) { let el = this.queryByHook(type + "-plot"); Plotly.purge(el); $(this.queryByHook(type + "-plot")).empty(); - $(this.queryByHook(type + "-plot-spinner")).css("display", "block"); - let data = {}; - if(type === 'psweep'){ - let key = this.getPsweepKey(); - data['plt_key'] = key; - }else if(type === "ts-psweep") { - data['plt_type'] = this.tsPlotData['type']; - let key = this.getTSPsweepKey() - data['plt_key'] = key; - }else{ - data['plt_key'] = type; - } - if(Object.keys(this.plotArgs).length){ - data['plt_data'] = this.plotArgs; - }else{ - data['plt_data'] = null; + if(type === "ts-psweep" || type === "psweep"){ + $(this.queryByHook(type + "-edit-plot")).prop("disabled", true); + $(this.queryByHook(type + "-download-png-custom")).prop("disabled", true); + $(this.queryByHook(type + "-download-json")).prop("disabled", true); + $(this.queryByHook("multiple-plots")).prop("disabled", true); } + $(this.queryByHook(type + "-plot-spinner")).css("display", "block"); + }, + getPlot: function (type) { + let self = this; + this.cleanupPlotContainer(type); + let data = this.getPlotData(type); if(type === "psweep" && Boolean(this.plots[data.plt_key])) { this.plotFigure(this.plots[data.plt_key], type); }else if(type === "ts-psweep" && Boolean(this.plots[data.plt_type + data.plt_key])) { @@ -143,6 +138,32 @@ module.exports = View.extend({ }); } }, + getPlotData: function (type) { + let data = {}; + if(type === 'psweep'){ + let key = this.getPsweepKey(); + data['plt_key'] = key; + }else if(type === "ts-psweep" || type === "ts-psweep-mp") { + if(type === "ts-psweep-mp"){ + data['plt_type'] = "mltplplt"; + }else{ + data['plt_type'] = this.tsPlotData['type']; + } + let key = this.getTSPsweepKey() + data['plt_key'] = key; + }else if(type === "mltplplt"){ + data['plt_type'] = type; + data['plt_key'] = null; + }else{ + data['plt_key'] = type; + } + if(Object.keys(this.plotArgs).length){ + data['plt_data'] = this.plotArgs; + }else{ + data['plt_data'] = null; + } + return data + }, getPlotForEnsembleAggragator: function (e) { this.model.settings.resultsSettings.reducer = e.target.value; this.getPlot('psweep') @@ -172,6 +193,8 @@ module.exports = View.extend({ }, getTSPlotForType: function (e) { this.tsPlotData['type'] = e.target.value; + let display = this.tsPlotData['type'] === "trajectories" ? "inline-block" : "none"; + $(this.queryByHook("multiple-plots")).css("display", display); this.getPlot("ts-psweep"); }, getTSPsweepKey: function () { @@ -250,6 +273,17 @@ module.exports = View.extend({ $(this.queryByHook(type + "-edit-plot")).prop("disabled", false); $(this.queryByHook(type + "-download-png-custom")).prop("disabled", false); $(this.queryByHook(type + "-download-json")).prop("disabled", false); + if(type === "trajectories" || (this.tsPlotData && this.tsPlotData.type === "trajectories")) { + $(this.queryByHook("multiple-plots")).prop("disabled", false); + } + }, + plotMultiplePlots: function (e) { + let type = e.target.dataset.type; + let data = this.getPlotData(type); + var queryStr = "?path=" + this.model.directory + "&wkfl=" + this.parent.model.name; + queryStr += "&job=" + this.model.name + "&data=" + JSON.stringify(data); + let endpoint = path.join(app.getBasePath(), "stochss/multiple-plots") + queryStr; + window.open(endpoint); }, renderEnsembleAggragatorView: function () { let ensembleAggragators = [ diff --git a/public_models/3D_Cylinder/3D_cylinder.ipynb b/public_models/3D_Cylinder/3D_cylinder.ipynb new file mode 100644 index 0000000000..b9d21b09ff --- /dev/null +++ b/public_models/3D_Cylinder/3D_cylinder.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2\n","\n","import numpy as np\n","import spatialpy\n","from spatialpy import Model, Species, Parameter, Reaction, Mesh,\\\n"," PlaceInitialCondition, \\\n"," UniformInitialCondition, \\\n"," ScatterInitialCondition"]},{"cell_type":"markdown","metadata":{},"source":["# 3D_cylinder"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["class M3Dcylinder(Model):\n"," def __init__(self):\n"," Model.__init__(self, name=\"3D_cylinder\")\n","\n"," # Domain\n"," mesh = Mesh.read_stochss_domain('3D_cylinder.smdl')\n"," self.add_mesh(mesh)\n","\n"," self.staticDomain = True\n","\n"," # Variables\n"," A = Species(name=\"A\", diffusion_constant=0.1)\n"," B = Species(name=\"B\", diffusion_constant=0.1)\n"," self.add_species([A, B])\n","\n"," self.restrict(A, [1, 2])\n"," self.restrict(B, [1, 3])\n","\n"," # Parameters\n"," k_react = Parameter(name=\"k_react\", expression=\"1\")\n"," k_creat1 = Parameter(name=\"k_creat1\", expression=\"196.38595510240296\")\n"," k_creat2 = Parameter(name=\"k_creat2\", expression=\"197.70475491603773\")\n"," self.add_parameter([k_react, k_creat1, k_creat2])\n","\n"," # Reactions\n"," r1 = Reaction(name=\"r1\", restrict_to=[2], reactants={}, products={B: 1}, rate=self.listOfParameters[\"k_creat1\"])\n"," r2 = Reaction(name=\"r2\", restrict_to=[3], reactants={}, products={B: 1}, rate=self.listOfParameters[\"k_creat2\"])\n"," r3 = Reaction(name=\"r3\", reactants={B: 2}, products={}, rate=self.listOfParameters[\"k_react\"])\n"," self.add_reaction([r1, r2, r3])\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 500, 1), timestep_size=1)"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["model = M3Dcylinder()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," kwargs = {\n"," # \"number_of_trajectories\":1,\n"," # \"seed\":None,\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plot_species('A', animated=True, width=None, height=None)"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Brusselator/Brusselator.ipynb b/public_models/Brusselator/Brusselator.ipynb index d9ae8c91c9..5f0e2539dd 100644 --- a/public_models/Brusselator/Brusselator.ipynb +++ b/public_models/Brusselator/Brusselator.ipynb @@ -1,37128 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Brusselator" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n", - "from gillespy2 import EventAssignment, EventTrigger, Event\n", - "from gillespy2 import SSACSolver\n", - "# from gillespy2 import VariableSSACSolver\n", - "# from gillespy2 import BasicTauLeapingSolver\n", - "# from gillespy2 import BasicTauHybridSolver\n", - "# from gillespy2 import BasicODESolver" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class Brusselator(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Brusselator\")\n", - " self.volume = 1000\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"rate1\", expression=5000))\n", - " self.add_parameter(Parameter(name=\"rate2\", expression=50))\n", - " self.add_parameter(Parameter(name=\"rate3\", expression=0.00005))\n", - " self.add_parameter(Parameter(name=\"rate4\", expression=5))\n", - "\n", - " # Species\n", - " self.add_species(Species(name=\"A\", initial_value=100000, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"B\", initial_value=100000, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"C\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"D\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"X\", initial_value=2000, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Y\", initial_value=1000, mode=\"discrete\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"reaction1\", reactants={'A': 1}, products={'X': 1, 'A': 1}, propensity_function=\"rate1\"))\n", - " self.add_reaction(Reaction(name=\"reaction2\", reactants={'B': 1, 'X': 1}, products={'Y': 1, 'C': 1, 'B': 1}, propensity_function=\"rate2*X\"))\n", - " self.add_reaction(Reaction(name=\"reaction3\", reactants={'X': 2, 'Y': 1}, products={'X': 3}, propensity_function=\"rate3*Y*X*(X-1)/2\"))\n", - " self.add_reaction(Reaction(name=\"reaction4\", reactants={'X': 1}, products={'D': 1}, propensity_function=\"rate4*X\"))\n", - "\n", - " # Timespan\n", - " self.timespan(np.linspace(0, 30, 3001))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = Brusselator()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "solver = SSACSolver(model=model)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " \"solver\":solver,\n", - " \"number_of_trajectories\":1,\n", - " \"seed\":None,\n", - " # \"tau_tol\":0.03,\n", - " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06}\n", - " }\n", - " return kwargs" - ] - 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"cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Degradation" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n", - "from gillespy2 import EventAssignment, EventTrigger, Event\n", - "from gillespy2 import SSACSolver\n", - "# from gillespy2 import VariableSSACSolver\n", - "# from gillespy2 import BasicTauLeapingSolver\n", - "# from gillespy2 import BasicTauHybridSolver\n", - "# from gillespy2 import BasicODESolver" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class Degradation(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Degradation\")\n", - " self.volume = 1\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"decayrate\", expression=0.05))\n", - 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"cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Dimerization" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n", - "from gillespy2 import EventAssignment, EventTrigger, Event\n", - "from gillespy2 import SSACSolver\n", - "# from gillespy2 import VariableSSACSolver\n", - "# from gillespy2 import BasicTauLeapingSolver\n", - "# from gillespy2 import BasicTauHybridSolver\n", - "# from gillespy2 import BasicODESolver" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class Dimerization(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Dimerization\")\n", - " self.volume = 1\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"k_c\", expression=0.005))\n", - " self.add_parameter(Parameter(name=\"k_d\", expression=0.08))\n", - "\n", - " # Species\n", - " self.add_species(Species(name=\"Monomer\", initial_value=30, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Dimer\", initial_value=0, mode=\"discrete\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"r_creation\", reactants={'Monomer': 2}, products={'Dimer': 1}, rate=self.listOfParameters[\"k_c\"]))\n", - " self.add_reaction(Reaction(name=\"r_dissociation\", reactants={'Dimer': 1}, products={'Monomer': 2}, rate=self.listOfParameters[\"k_d\"]))\n", - "\n", - " # Timespan\n", - " self.timespan(np.linspace(0, 100, 101))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = Dimerization()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "solver = SSACSolver(model=model)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "results.plotplotly()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} +{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Dimerization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class Dimerization(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Dimerization\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"k_c\", expression=\"0.005\"))\n"," self.add_parameter(Parameter(name=\"k_d\", expression=\"0.08\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"Monomer\", initial_value=30, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Dimer\", initial_value=0, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"r_creation\", reactants={'Monomer': 2}, products={'Dimer': 1}, rate=self.listOfParameters[\"k_c\"]))\n"," self.add_reaction(Reaction(name=\"r_dissociation\", reactants={'Dimer': 1}, products={'Monomer': 2}, rate=self.listOfParameters[\"k_d\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 100, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = Dimerization()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," # solver = SSACSolver(model=model)\n"," kwargs = {\n"," # \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Gene_Regulatory_Network/.ipynb_checkpoints/GeneRegulatoryNetwork1-checkpoint.ipynb b/public_models/Gene_Regulatory_Network/.ipynb_checkpoints/GeneRegulatoryNetwork1-checkpoint.ipynb new file mode 100644 index 0000000000..27696ed63f --- /dev/null +++ b/public_models/Gene_Regulatory_Network/.ipynb_checkpoints/GeneRegulatoryNetwork1-checkpoint.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import spatialpy\n", + "from spatialpy import Model, Species, Parameter, Reaction, Mesh,\\\n", + " PlaceInitialCondition, \\\n", + " UniformInitialCondition, \\\n", + " ScatterInitialCondition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GeneRegulatoryNetwork1" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class GeneRegulatoryNetwork1(Model):\n", + " def __init__(self):\n", + " Model.__init__(self, name=\"GeneRegulatoryNetwork1\")\n", + "\n", + " # Domain\n", + " mesh = Mesh.read_stochss_domain('GeneRegulatoryNetwork1.smdl')\n", + " self.add_mesh(mesh)\n", + "\n", + " self.staticDomain = True\n", + "\n", + " # Variables\n", + " G_F = Species(name=\"G_F\", diffusion_constant=1e-12)\n", + " G_O = Species(name=\"G_O\", diffusion_constant=1e-12)\n", + " P = Species(name=\"P\", diffusion_constant=1e-12)\n", + " mRNA = Species(name=\"mRNA\", diffusion_constant=1e-12)\n", + " self.add_species([G_F, G_O, P, mRNA])\n", + "\n", + " self.restrict(G_F, [])\n", + " self.restrict(G_O, [])\n", + " self.restrict(P, [1, 2])\n", + " self.restrict(mRNA, [1, 2])\n", + "\n", + " # Initial Conditions\n", + " self.add_initial_condition(PlaceInitialCondition(species=G_F, count=1, location=[0, 0, 0]))\n", + "\n", + " # Parameters\n", + " mu = Parameter(name=\"mu\", expression=\"0.002\")\n", + " kappa = Parameter(name=\"kappa\", expression=\"0.015\")\n", + " ka = Parameter(name=\"ka\", expression=\"10000000\")\n", + " kd = Parameter(name=\"kd\", expression=\"0.01\")\n", + " gamma_m = Parameter(name=\"gamma_m\", expression=\"0.0006\")\n", + " gamma_p = Parameter(name=\"gamma_p\", expression=\"0.0003\")\n", + " self.add_parameter([mu, kappa, ka, kd, gamma_m, gamma_p])\n", + "\n", + " # Reactions\n", + " r1 = Reaction(name=\"r1\", reactants={G_F: 1}, products={G_F: 1, mRNA: 1}, rate=self.listOfParameters[\"mu\"])\n", + " r2 = Reaction(name=\"r2\", reactants={G_O: 1}, products={G_F: 1, P: 1}, rate=self.listOfParameters[\"ka\"])\n", + " r3 = Reaction(name=\"r3\", reactants={G_F: 1, P: 1}, products={G_O: 1}, rate=self.listOfParameters[\"kd\"])\n", + " r4 = Reaction(name=\"r4\", restrict_to=[2], reactants={mRNA: 1}, products={mRNA: 1, P: 1}, rate=self.listOfParameters[\"kappa\"])\n", + " r5 = Reaction(name=\"r5\", reactants={mRNA: 1}, products={}, rate=self.listOfParameters[\"gamma_m\"])\n", + " r6 = Reaction(name=\"r6\", reactants={P: 1}, products={}, rate=self.listOfParameters[\"gamma_p\"])\n", + " self.add_reaction([r1, r2, r3, r4, r5, r6])\n", + "\n", + " # Timespan\n", + " self.timespan(np.arange(0, 1000, 10), timestep_size=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "model = GeneRegulatoryNetwork1()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulation Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"number_of_trajectories\":1,\n", + " # \"seed\":None,\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + 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\ No newline at end of file diff --git a/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.ipynb b/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.ipynb new file mode 100644 index 0000000000..27696ed63f --- /dev/null +++ b/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import spatialpy\n", + "from spatialpy import Model, Species, Parameter, Reaction, Mesh,\\\n", + " PlaceInitialCondition, \\\n", + " UniformInitialCondition, \\\n", + " ScatterInitialCondition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GeneRegulatoryNetwork1" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class GeneRegulatoryNetwork1(Model):\n", + " def __init__(self):\n", + " Model.__init__(self, name=\"GeneRegulatoryNetwork1\")\n", + "\n", + " # Domain\n", + " mesh = Mesh.read_stochss_domain('GeneRegulatoryNetwork1.smdl')\n", + " self.add_mesh(mesh)\n", + "\n", + " self.staticDomain = True\n", + "\n", + " # Variables\n", + " G_F = Species(name=\"G_F\", diffusion_constant=1e-12)\n", + " G_O = Species(name=\"G_O\", diffusion_constant=1e-12)\n", + " P = Species(name=\"P\", diffusion_constant=1e-12)\n", + " mRNA = Species(name=\"mRNA\", diffusion_constant=1e-12)\n", + " self.add_species([G_F, G_O, P, mRNA])\n", + "\n", + " self.restrict(G_F, [])\n", + " self.restrict(G_O, [])\n", + " self.restrict(P, [1, 2])\n", + " self.restrict(mRNA, [1, 2])\n", + "\n", + " # Initial Conditions\n", + " self.add_initial_condition(PlaceInitialCondition(species=G_F, count=1, location=[0, 0, 0]))\n", + "\n", + " # Parameters\n", + " mu = Parameter(name=\"mu\", expression=\"0.002\")\n", + " kappa = Parameter(name=\"kappa\", expression=\"0.015\")\n", + " ka = Parameter(name=\"ka\", expression=\"10000000\")\n", + " kd = Parameter(name=\"kd\", expression=\"0.01\")\n", + " gamma_m = Parameter(name=\"gamma_m\", expression=\"0.0006\")\n", + " gamma_p = Parameter(name=\"gamma_p\", expression=\"0.0003\")\n", + " self.add_parameter([mu, kappa, ka, kd, gamma_m, gamma_p])\n", + "\n", + " # Reactions\n", + " r1 = Reaction(name=\"r1\", reactants={G_F: 1}, products={G_F: 1, mRNA: 1}, rate=self.listOfParameters[\"mu\"])\n", + " r2 = Reaction(name=\"r2\", reactants={G_O: 1}, products={G_F: 1, P: 1}, rate=self.listOfParameters[\"ka\"])\n", + " r3 = Reaction(name=\"r3\", reactants={G_F: 1, P: 1}, products={G_O: 1}, rate=self.listOfParameters[\"kd\"])\n", + " r4 = Reaction(name=\"r4\", restrict_to=[2], reactants={mRNA: 1}, products={mRNA: 1, P: 1}, rate=self.listOfParameters[\"kappa\"])\n", + " r5 = Reaction(name=\"r5\", reactants={mRNA: 1}, products={}, rate=self.listOfParameters[\"gamma_m\"])\n", + " r6 = Reaction(name=\"r6\", reactants={P: 1}, products={}, rate=self.listOfParameters[\"gamma_p\"])\n", + " self.add_reaction([r1, r2, r3, r4, r5, r6])\n", + "\n", + " # Timespan\n", + " self.timespan(np.arange(0, 1000, 10), timestep_size=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "model = GeneRegulatoryNetwork1()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulation Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"number_of_trajectories\":1,\n", + " # \"seed\":None,\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plot_species('G_F', animated=True, width=None, height=None)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.smdl b/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.smdl new file mode 100644 index 0000000000..a46cd7fabd --- /dev/null +++ b/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork1.smdl @@ -0,0 +1 @@ +{"is_spatial":true,"defaultID":18,"defaultMode":"discrete","annotation":"This model uses the hes1 mesh 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\ No newline at end of file diff --git a/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.ipynb b/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.ipynb new file mode 100644 index 0000000000..e93d8b2df8 --- /dev/null +++ b/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.ipynb @@ -0,0 +1,156 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import spatialpy\n", + "from spatialpy import Model, Species, Parameter, Reaction, Mesh,\\\n", + " PlaceInitialCondition, \\\n", + " UniformInitialCondition, \\\n", + " ScatterInitialCondition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GeneRegulatoryNetwork2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class GeneRegulatoryNetwork2(Model):\n", + " def __init__(self):\n", + " Model.__init__(self, name=\"GeneRegulatoryNetwork2\")\n", + "\n", + " # Domain\n", + " mesh = Mesh.read_stochss_domain('GeneRegulatoryNetwork2.smdl')\n", + " self.add_mesh(mesh)\n", + "\n", + " self.staticDomain = True\n", + "\n", + " # Variables\n", + " G_F = Species(name=\"G_F\", diffusion_constant=1e-12)\n", + " G_O = Species(name=\"G_O\", diffusion_constant=1e-12)\n", + " P = Species(name=\"P\", diffusion_constant=1e-12)\n", + " mRNA = Species(name=\"mRNA\", diffusion_constant=1e-12)\n", + " self.add_species([G_F, G_O, P, mRNA])\n", + "\n", + " self.restrict(G_F, [])\n", + " self.restrict(G_O, [])\n", + "\n", + " # Initial Conditions\n", + " self.add_initial_condition(PlaceInitialCondition(species=G_F, count=1, location=[0, 0, 0]))\n", + "\n", + " # Parameters\n", + " mu = Parameter(name=\"mu\", expression=\"0.002\")\n", + " kappa = Parameter(name=\"kappa\", expression=\"0.015\")\n", + " ka = Parameter(name=\"ka\", expression=\"10000000\")\n", + " kd = Parameter(name=\"kd\", expression=\"0.01\")\n", + " gamma_m = Parameter(name=\"gamma_m\", expression=\"0.0006\")\n", + " gamma_p = Parameter(name=\"gamma_p\", expression=\"0.0003\")\n", + " self.add_parameter([mu, kappa, ka, kd, gamma_m, gamma_p])\n", + "\n", + " # Reactions\n", + " r1 = Reaction(name=\"r1\", reactants={G_F: 1}, products={G_F: 1, mRNA: 1}, rate=self.listOfParameters[\"mu\"])\n", + " r2 = Reaction(name=\"r2\", reactants={G_O: 1}, products={G_F: 1, P: 1}, rate=self.listOfParameters[\"ka\"])\n", + " r3 = Reaction(name=\"r3\", reactants={G_F: 1, P: 1}, products={G_O: 1}, rate=self.listOfParameters[\"kd\"])\n", + " r4 = Reaction(name=\"r4\", restrict_to=[2], reactants={mRNA: 1}, products={mRNA: 1, P: 1}, rate=self.listOfParameters[\"kappa\"])\n", + " r5 = Reaction(name=\"r5\", reactants={mRNA: 1}, products={}, rate=self.listOfParameters[\"gamma_m\"])\n", + " r6 = Reaction(name=\"r6\", reactants={P: 1}, products={}, rate=self.listOfParameters[\"gamma_p\"])\n", + " self.add_reaction([r1, r2, r3, r4, r5, r6])\n", + "\n", + " # Timespan\n", + " self.timespan(np.arange(0, 1000, 10), timestep_size=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "model = GeneRegulatoryNetwork2()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulation Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"number_of_trajectories\":1,\n", + " # \"seed\":None,\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plot_species('G_F', animated=True, width=None, height=None)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.smdl b/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.smdl new file mode 100644 index 0000000000..e7654e1077 --- /dev/null +++ b/public_models/Gene_Regulatory_Network/GeneRegulatoryNetwork2.smdl @@ -0,0 +1 @@ +{"is_spatial":true,"defaultID":17,"defaultMode":"","annotation":"This model uses the cell_coarse mesh (slide 65)\n - This mesh contains 2 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b/public_models/Lotkavolterra_Oscillator/Lotkavolterra_Oscillator.ipynb index 3b16d1335d..d36610645c 100644 --- a/public_models/Lotkavolterra_Oscillator/Lotkavolterra_Oscillator.ipynb +++ b/public_models/Lotkavolterra_Oscillator/Lotkavolterra_Oscillator.ipynb @@ -1,37057 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Lotkavolterra_Oscillator" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n", - "from gillespy2 import EventAssignment, EventTrigger, Event\n", - "# To run a simulation using the SSA Solver simply omit the solver argument from model.run().\n", - "# from gillespy2 import VariableSSACSolver\n", - "# from gillespy2 import BasicTauLeapingSolver\n", - "# from gillespy2 import BasicTauHybridSolver\n", - "from gillespy2 import BasicODESolver" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class Lotkavolterra_Oscillator(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Lotkavolterra_Oscillator\")\n", - " self.volume = 1\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"k1\", expression=0.004))\n", - " self.add_parameter(Parameter(name=\"k2\", expression=0.5))\n", - " self.add_parameter(Parameter(name=\"k3\", expression=0.0045))\n", - "\n", - " # Species\n", - " self.add_species(Species(name=\"R\", initial_value=0.0095, mode=\"continuous\"))\n", - " self.add_species(Species(name=\"W\", initial_value=0.008, mode=\"continuous\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"r1\", reactants={'R': 1}, products={'R': 2}, rate=self.listOfParameters[\"k1\"]))\n", - " self.add_reaction(Reaction(name=\"r2\", reactants={'R': 1, 'W': 1}, products={'W': 2}, rate=self.listOfParameters[\"k2\"]))\n", - " self.add_reaction(Reaction(name=\"r3\", reactants={'W': 1}, products={}, rate=self.listOfParameters[\"k3\"]))\n", - "\n", - " # Timespan\n", - " self.timespan(np.linspace(0, 9000, 9001))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = Lotkavolterra_Oscillator()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " \"solver\":BasicODESolver,\n", - " # \"number_of_trajectories\":1,\n", - " # \"seed\":None,\n", - " # \"tau_tol\":0.03,\n", - " \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06}\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = configure_simulation()\n", - "results = model.run(**kwargs)" - ] - }, - { - "cell_type": "code", - 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"cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Michaelis_Menten" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n", - "from gillespy2 import EventAssignment, EventTrigger, Event\n", - "from gillespy2 import SSACSolver\n", - "# from gillespy2 import VariableSSACSolver\n", - "# from gillespy2 import BasicTauLeapingSolver\n", - "# from gillespy2 import BasicTauHybridSolver\n", - "# from gillespy2 import BasicODESolver" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class Michaelis_Menten(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Michaelis_Menten\")\n", - " self.volume = 1\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"rate1\", expression=0.0017))\n", - " self.add_parameter(Parameter(name=\"rate2\", expression=0.5))\n", - " self.add_parameter(Parameter(name=\"rate3\", expression=0.1))\n", - "\n", - " # Species\n", - " self.add_species(Species(name=\"A\", initial_value=301, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"B\", initial_value=120, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"C\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"D\", initial_value=0, mode=\"discrete\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"r1\", reactants={'A': 1, 'B': 1}, products={'C': 1}, rate=self.listOfParameters[\"rate1\"]))\n", - " self.add_reaction(Reaction(name=\"r2\", reactants={'C': 1}, products={'A': 1, 'B': 1}, rate=self.listOfParameters[\"rate2\"]))\n", - " self.add_reaction(Reaction(name=\"r3\", reactants={'C': 1}, products={'B': 1, 'D': 1}, rate=self.listOfParameters[\"rate3\"]))\n", - "\n", - " # Timespan\n", - " self.timespan(np.linspace(0, 100, 101))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = Michaelis_Menten()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "solver = SSACSolver(model=model)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " \"solver\":solver,\n", - " \"number_of_trajectories\":1,\n", - " \"seed\":None,\n", - " # \"tau_tol\":0.03,\n", - " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06}\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = configure_simulation()\n", - "results = model.run(**kwargs)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - 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"cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Opioid" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n", - "from gillespy2 import EventAssignment, EventTrigger, Event\n", - "from gillespy2 import SSACSolver\n", - "# from gillespy2 import VariableSSACSolver\n", - "# from gillespy2 import BasicTauLeapingSolver\n", - "# from gillespy2 import BasicTauHybridSolver\n", - "# from gillespy2 import BasicODESolver" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class Opioid(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Opioid\")\n", - " self.volume = 1\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"alpha\", expression=0.15))\n", - " self.add_parameter(Parameter(name=\"epsilon\", expression=0.8))\n", - " self.add_parameter(Parameter(name=\"beta_p\", expression=0.00266))\n", - " self.add_parameter(Parameter(name=\"beta_a\", expression=0.00094))\n", - " self.add_parameter(Parameter(name=\"gamma\", expression=0.00744))\n", - " self.add_parameter(Parameter(name=\"zeta\", expression=0.2))\n", - " self.add_parameter(Parameter(name=\"delta\", expression=0.1))\n", - " self.add_parameter(Parameter(name=\"sigma\", expression=0.9))\n", - " self.add_parameter(Parameter(name=\"mu\", expression=0.00729))\n", - " self.add_parameter(Parameter(name=\"mu_prime\", expression=0.01159))\n", - "\n", - " # Species\n", - " self.add_species(Species(name=\"Susceptible\", initial_value=200, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Prescribed_User\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Addicted\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Rehab\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Natural_Deaths\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Addicted_Deaths\", initial_value=0, mode=\"discrete\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"SP\", reactants={'Susceptible': 1}, products={'Prescribed_User': 1}, rate=self.listOfParameters[\"alpha\"]))\n", - " self.add_reaction(Reaction(name=\"SA_a\", reactants={'Susceptible': 1}, products={'Addicted': 1}, rate=self.listOfParameters[\"beta_a\"]))\n", - " self.add_reaction(Reaction(name=\"SA_p\", reactants={'Susceptible': 1}, products={'Addicted': 1}, rate=self.listOfParameters[\"beta_p\"]))\n", - " self.add_reaction(Reaction(name=\"PA\", reactants={'Prescribed_User': 1}, products={'Addicted': 1}, rate=self.listOfParameters[\"gamma\"]))\n", - " self.add_reaction(Reaction(name=\"PS\", reactants={'Prescribed_User': 1}, products={'Susceptible': 1}, rate=self.listOfParameters[\"epsilon\"]))\n", - " self.add_reaction(Reaction(name=\"AR\", reactants={'Addicted': 1}, products={'Rehab': 1}, rate=self.listOfParameters[\"zeta\"]))\n", - " self.add_reaction(Reaction(name=\"RA\", reactants={'Rehab': 1}, products={'Addicted': 1}, rate=self.listOfParameters[\"delta\"]))\n", - " self.add_reaction(Reaction(name=\"RS\", reactants={'Rehab': 1}, products={'Susceptible': 1}, rate=self.listOfParameters[\"sigma\"]))\n", - " self.add_reaction(Reaction(name=\"mu_S\", reactants={'Susceptible': 1}, products={'Susceptible': 1, 'Natural_Deaths': 1}, rate=self.listOfParameters[\"mu\"]))\n", - " self.add_reaction(Reaction(name=\"mu_P\", reactants={'Prescribed_User': 1}, products={'Susceptible': 1, 'Natural_Deaths': 1}, rate=self.listOfParameters[\"mu\"]))\n", - " self.add_reaction(Reaction(name=\"mu_R\", reactants={'Rehab': 1}, products={'Susceptible': 1, 'Natural_Deaths': 1}, rate=self.listOfParameters[\"mu\"]))\n", - " self.add_reaction(Reaction(name=\"mu_prime_A\", reactants={'Addicted': 1}, products={'Susceptible': 1, 'Addicted_Deaths': 1}, rate=self.listOfParameters[\"mu_prime\"]))\n", - "\n", - " # Timespan\n", - " self.timespan(np.linspace(0, 200, 201))" - 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TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Opioid"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class Opioid(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Opioid\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha\", expression=\"0.15\"))\n"," self.add_parameter(Parameter(name=\"epsilon\", expression=\"0.8\"))\n"," self.add_parameter(Parameter(name=\"beta_p\", expression=\"0.00266\"))\n"," self.add_parameter(Parameter(name=\"beta_a\", expression=\"0.00094\"))\n"," self.add_parameter(Parameter(name=\"gamma\", expression=\"0.00744\"))\n"," self.add_parameter(Parameter(name=\"zeta\", expression=\"0.2\"))\n"," self.add_parameter(Parameter(name=\"delta\", expression=\"0.1\"))\n"," self.add_parameter(Parameter(name=\"sigma\", expression=\"0.9\"))\n"," self.add_parameter(Parameter(name=\"mu\", expression=\"0.00729\"))\n"," self.add_parameter(Parameter(name=\"mu_prime\", expression=\"0.01159\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"Susceptible\", initial_value=200, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Prescribed_User\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Addicted\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Rehab\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Natural_Deaths\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Addicted_Deaths\", initial_value=0, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"SP\", reactants={'Susceptible': 1}, products={'Prescribed_User': 1}, rate=self.listOfParameters[\"alpha\"]))\n"," self.add_reaction(Reaction(name=\"SA_a\", reactants={'Susceptible': 1}, products={'Addicted': 1}, rate=self.listOfParameters[\"beta_a\"]))\n"," self.add_reaction(Reaction(name=\"SA_p\", reactants={'Susceptible': 1}, products={'Addicted': 1}, rate=self.listOfParameters[\"beta_p\"]))\n"," self.add_reaction(Reaction(name=\"PA\", reactants={'Prescribed_User': 1}, products={'Addicted': 1}, rate=self.listOfParameters[\"gamma\"]))\n"," self.add_reaction(Reaction(name=\"PS\", reactants={'Prescribed_User': 1}, products={'Susceptible': 1}, rate=self.listOfParameters[\"epsilon\"]))\n"," self.add_reaction(Reaction(name=\"AR\", reactants={'Addicted': 1}, products={'Rehab': 1}, rate=self.listOfParameters[\"zeta\"]))\n"," self.add_reaction(Reaction(name=\"RA\", reactants={'Rehab': 1}, products={'Addicted': 1}, rate=self.listOfParameters[\"delta\"]))\n"," self.add_reaction(Reaction(name=\"RS\", reactants={'Rehab': 1}, products={'Susceptible': 1}, rate=self.listOfParameters[\"sigma\"]))\n"," self.add_reaction(Reaction(name=\"mu_S\", reactants={'Susceptible': 1}, products={'Susceptible': 1, 'Natural_Deaths': 1}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"mu_P\", reactants={'Prescribed_User': 1}, products={'Susceptible': 1, 'Natural_Deaths': 1}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"mu_R\", reactants={'Rehab': 1}, products={'Susceptible': 1, 'Natural_Deaths': 1}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"mu_prime_A\", reactants={'Addicted': 1}, products={'Susceptible': 1, 'Addicted_Deaths': 1}, rate=self.listOfParameters[\"mu_prime\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 200, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = Opioid()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," # solver = SSACSolver(model=model)\n"," kwargs = {\n"," # \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Oregonator/Oregonator.ipynb b/public_models/Oregonator/Oregonator.ipynb index 6f60bb7a57..02563dae00 100644 --- a/public_models/Oregonator/Oregonator.ipynb +++ b/public_models/Oregonator/Oregonator.ipynb @@ -1,1606 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Oregonator" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n", - "from gillespy2 import EventAssignment, EventTrigger, Event\n", - "# To run a simulation using the SSA Solver simply omit the solver argument from model.run().\n", - "# from gillespy2 import VariableSSACSolver\n", - "# from gillespy2 import BasicTauLeapingSolver\n", - "# from gillespy2 import BasicTauHybridSolver\n", - "from gillespy2 import BasicODESolver" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class Oregonator(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Oregonator\")\n", - " self.volume = 1\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"k1\", expression=2))\n", - " self.add_parameter(Parameter(name=\"k2\", expression=0.1))\n", - " self.add_parameter(Parameter(name=\"k3\", expression=104))\n", - " self.add_parameter(Parameter(name=\"k4\", expression=4e-7))\n", - " self.add_parameter(Parameter(name=\"k5\", expression=26))\n", - "\n", - " # Species\n", - " self.add_species(Species(name=\"F\", initial_value=2, mode=\"continuous\"))\n", - " self.add_species(Species(name=\"A\", initial_value=250, mode=\"continuous\"))\n", - " self.add_species(Species(name=\"B\", initial_value=500, mode=\"continuous\"))\n", - " self.add_species(Species(name=\"C\", initial_value=1000, mode=\"continuous\"))\n", - " self.add_species(Species(name=\"P\", initial_value=0, mode=\"continuous\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"reaction1\", reactants={'B': 1, 'F': 1}, products={'A': 1, 'F': 1}, rate=self.listOfParameters[\"k1\"]))\n", - " self.add_reaction(Reaction(name=\"reaction2\", reactants={'A': 1, 'B': 1}, products={'P': 1}, rate=self.listOfParameters[\"k2\"]))\n", - " self.add_reaction(Reaction(name=\"reaction3\", reactants={'A': 1, 'F': 1}, products={'A': 2, 'C': 1, 'F': 1}, rate=self.listOfParameters[\"k3\"]))\n", - " self.add_reaction(Reaction(name=\"reaction4\", reactants={'A': 2}, products={'P': 1}, rate=self.listOfParameters[\"k4\"]))\n", - " self.add_reaction(Reaction(name=\"reaction5\", reactants={'C': 1, 'F': 1}, products={'B': 1, 'F': 1}, rate=self.listOfParameters[\"k5\"]))\n", - "\n", - " # Timespan\n", - " self.timespan(np.linspace(0, 5, 51))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = Oregonator()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " \"solver\":BasicODESolver,\n", - " # \"number_of_trajectories\":1,\n", - " # \"seed\":None,\n", - " # \"tau_tol\":0.03,\n", - " \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06}\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - 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TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Oregonator"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class Oregonator(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Oregonator\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"k1\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"k2\", expression=\"0.1\"))\n"," self.add_parameter(Parameter(name=\"k3\", expression=\"104\"))\n"," self.add_parameter(Parameter(name=\"k4\", expression=\"4e-07\"))\n"," self.add_parameter(Parameter(name=\"k5\", expression=\"26\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"F\", initial_value=2, mode=\"continuous\"))\n"," self.add_species(Species(name=\"A\", initial_value=250, mode=\"continuous\"))\n"," self.add_species(Species(name=\"B\", initial_value=500, mode=\"continuous\"))\n"," self.add_species(Species(name=\"C\", initial_value=1000, mode=\"continuous\"))\n"," self.add_species(Species(name=\"P\", initial_value=0, mode=\"continuous\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"reaction1\", reactants={'B': 1, 'F': 1}, products={'A': 1, 'F': 1}, rate=self.listOfParameters[\"k1\"]))\n"," self.add_reaction(Reaction(name=\"reaction2\", reactants={'A': 1, 'B': 1}, products={'P': 1}, rate=self.listOfParameters[\"k2\"]))\n"," self.add_reaction(Reaction(name=\"reaction3\", reactants={'A': 1, 'F': 1}, products={'A': 2, 'C': 1, 'F': 1}, rate=self.listOfParameters[\"k3\"]))\n"," self.add_reaction(Reaction(name=\"reaction4\", reactants={'A': 2}, products={'P': 1}, rate=self.listOfParameters[\"k4\"]))\n"," self.add_reaction(Reaction(name=\"reaction5\", reactants={'C': 1, 'F': 1}, products={'B': 1, 'F': 1}, rate=self.listOfParameters[\"k5\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 5, 0.1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = Oregonator()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," kwargs = {\n"," # \"solver\":TauHybridSolver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Polarization_In_Yeast/.ipynb_checkpoints/PolarizationInYeast-checkpoint.ipynb b/public_models/Polarization_In_Yeast/.ipynb_checkpoints/PolarizationInYeast-checkpoint.ipynb new file mode 100644 index 0000000000..669f32b3ae --- /dev/null +++ b/public_models/Polarization_In_Yeast/.ipynb_checkpoints/PolarizationInYeast-checkpoint.ipynb @@ -0,0 +1,1263 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import spatialpy\n", + "from spatialpy import Model, Species, Parameter, Reaction, Mesh,\\\n", + " PlaceInitialCondition, \\\n", + " UniformInitialCondition, \\\n", + " ScatterInitialCondition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PolarizationInYeast" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "class PolarizationInYeast(Model):\n", + " def __init__(self):\n", + " Model.__init__(self, name=\"PolarizationInYeast\")\n", + "\n", + " # Domain\n", + " mesh = Mesh.read_stochss_domain('PolarizationInYeast.smdl')\n", + " self.add_mesh(mesh)\n", + "\n", + " self.staticDomain = True\n", + "\n", + " # Variables\n", + " Cdc42c = Species(name=\"Cdc42c\", diffusion_constant=10)\n", + " Cdc42m = Species(name=\"Cdc42m\", diffusion_constant=0.0053)\n", + " self.add_species([Cdc42c, Cdc42m])\n", + "\n", + " self.restrict(Cdc42m, [2])\n", + "\n", + " # Initial Conditions\n", + " self.add_initial_condition(ScatterInitialCondition(species=Cdc42c, count=300, types=[1]))\n", + " self.add_initial_condition(ScatterInitialCondition(species=Cdc42m, count=100, types=[2]))\n", + "\n", + " # Parameters\n", + " kon = Parameter(name=\"kon\", expression=\"1.6666666667e-05\")\n", + " koff = Parameter(name=\"koff\", expression=\"0.15\")\n", + " kfb = Parameter(name=\"kfb\", expression=\"0.166666666666667\")\n", + " self.add_parameter([kon, koff, kfb])\n", + "\n", + " # Reactions\n", + " r1 = Reaction(name=\"r1\", reactants={Cdc42c: 1}, products={Cdc42m: 1}, rate=self.listOfParameters[\"kon\"])\n", + " r2 = Reaction(name=\"r2\", reactants={Cdc42m: 1}, products={Cdc42c: 1}, rate=self.listOfParameters[\"koff\"])\n", + " r3 = Reaction(name=\"r3\", restrict_to=[2], reactants={Cdc42c: 1, Cdc42m: 1}, products={Cdc42m: 2}, rate=self.listOfParameters[\"kfb\"])\n", + " self.add_reaction([r1, r2, r3])\n", + "\n", + " # Timespan\n", + " self.timespan(np.arange(0, 5000, 10), timestep_size=10)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "model = PolarizationInYeast()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulation Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"number_of_trajectories\":1,\n", + " # \"seed\":None,\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plot_species('Cdc42c', animated=True, width=None, height=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PolarizationInYeast\n", + "\n", + "**********\n", + "Species\n", + "**********\n", + "\n", + "Cdc42c: 10\n", + "Cdc42m: 0.0053\n", + "\n", + "**********\n", + "Initial Conditions\n", + "**********\n", + "\n", + "Cdc42c: 300, Scatter over: [1]\n", + "Cdc42m: 100, Scatter over: [2]\n", + "\n", + "**********\n", + "Diffusion Restrictions\n", + "**********\n", + "\n", + "Cdc42m is restricted to: [2]\n", + "\n", + "**********\n", + "Parameters\n", + "**********\n", + "\n", + "kon: 1.6666666667e-05\n", + "koff: 0.15\n", + "kfb: 0.166666666666667\n", + "\n", + "**********\n", + "Reactions\n", + "**********\n", + "\n", + "r1, Active in: None\n", + "\tReactants\n", + "\t\tCdc42c: 1\n", + "\tProducts\n", + "\t\tCdc42m: 1\n", + "\tPropensity Function: kon*Cdc42c\n", + "r2, Active in: None\n", + "\tReactants\n", + "\t\tCdc42m: 1\n", + "\tProducts\n", + "\t\tCdc42c: 1\n", + "\tPropensity Function: koff*Cdc42m\n", + "r3, Active in: [2]\n", + "\tReactants\n", + "\t\tCdc42c: 1\n", + "\t\tCdc42m: 1\n", + "\tProducts\n", + "\t\tCdc42m: 2\n", + "\tPropensity Function: kfb*Cdc42c*Cdc42m/vol\n", + "\n", + "**********\n", + "Domain\n", + "**********\n", + "\n", + "Domain Attributes\n", + "\n", + " On Boundary: None\n", + " Mesh Size: None\n", + " RHO_0: 2\n", + " C_0: 10\n", + " P_0: 10\n", + " Gravity: [0, 0, 0]\n", + " X Limit: (-0.9985541604477006, 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Fixed: False\n" + ] + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Polarization_In_Yeast/PolarizationInYeast.ipynb b/public_models/Polarization_In_Yeast/PolarizationInYeast.ipynb new file mode 100644 index 0000000000..76d542b1ee --- /dev/null +++ b/public_models/Polarization_In_Yeast/PolarizationInYeast.ipynb @@ -0,0 +1,130 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import spatialpy\n", + "from spatialpy import Model, Species, Parameter, Reaction, Mesh,\\\n", + " PlaceInitialCondition, \\\n", + " UniformInitialCondition, \\\n", + " ScatterInitialCondition" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PolarizationInYeast" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class PolarizationInYeast(Model):\n", + " def __init__(self):\n", + " Model.__init__(self, name=\"PolarizationInYeast\")\n", + "\n", + " # Domain\n", + " mesh = Mesh.read_stochss_domain('PolarizationInYeast.smdl')\n", + " self.add_mesh(mesh)\n", + "\n", + " self.staticDomain = True\n", + "\n", + " # Variables\n", + " Cdc42c = Species(name=\"Cdc42c\", diffusion_constant=10)\n", + " Cdc42m = Species(name=\"Cdc42m\", diffusion_constant=0.0053)\n", + " self.add_species([Cdc42c, Cdc42m])\n", + "\n", + " self.restrict(Cdc42m, [2])\n", + "\n", + " # Initial Conditions\n", + " self.add_initial_condition(ScatterInitialCondition(species=Cdc42c, count=300, types=[1]))\n", + " self.add_initial_condition(ScatterInitialCondition(species=Cdc42m, count=100, types=[2]))\n", + "\n", + " # Parameters\n", + " kon = Parameter(name=\"kon\", expression=\"1.6666666667e-05\")\n", + " koff = Parameter(name=\"koff\", expression=\"0.15\")\n", + " kfb = Parameter(name=\"kfb\", expression=\"0.166666666666667\")\n", + " self.add_parameter([kon, koff, kfb])\n", + "\n", + " # Reactions\n", + " r1 = Reaction(name=\"r1\", reactants={Cdc42c: 1}, products={Cdc42m: 1}, rate=self.listOfParameters[\"kon\"])\n", + " r2 = Reaction(name=\"r2\", reactants={Cdc42m: 1}, products={Cdc42c: 1}, rate=self.listOfParameters[\"koff\"])\n", + " r3 = Reaction(name=\"r3\", restrict_to=[2], reactants={Cdc42c: 1, Cdc42m: 1}, products={Cdc42m: 2}, rate=self.listOfParameters[\"kfb\"])\n", + " self.add_reaction([r1, r2, r3])\n", + "\n", + " # Timespan\n", + " self.timespan(np.arange(0, 5000, 10), timestep_size=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "model = PolarizationInYeast()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulation Parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def configure_simulation():\n", + " kwargs = {\n", + " # \"number_of_trajectories\":1,\n", + " # \"seed\":None,\n", + " }\n", + " return kwargs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kwargs = configure_simulation()\n", + "results = model.run(**kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Visualization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results.plot_species('Cdc42c', animated=True, width=None, height=None)" + ] + } + ], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/public_models/Polarization_In_Yeast/PolarizationInYeast.smdl b/public_models/Polarization_In_Yeast/PolarizationInYeast.smdl new file mode 100644 index 0000000000..e0e1ffe698 --- /dev/null +++ b/public_models/Polarization_In_Yeast/PolarizationInYeast.smdl @@ -0,0 +1 @@ +{"is_spatial":true,"defaultID":9,"defaultMode":"","annotation":"Reactions could not be completed from the slides (74 - 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\ No newline at end of file diff --git a/public_models/Schlogl/Schlogl.ipynb b/public_models/Schlogl/Schlogl.ipynb index 9a3a7cfaba..7897e5931e 100644 --- a/public_models/Schlogl/Schlogl.ipynb +++ b/public_models/Schlogl/Schlogl.ipynb @@ -1,1894 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Schlogl" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n", - "from gillespy2 import EventAssignment, EventTrigger, Event\n", - "from gillespy2 import SSACSolver\n", - "# from gillespy2 import VariableSSACSolver\n", - "# from gillespy2 import BasicTauLeapingSolver\n", - "# from gillespy2 import BasicTauHybridSolver\n", - "# from gillespy2 import BasicODESolver" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class Schlogl(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Schlogl\")\n", - " self.volume = 1\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"k1\", expression=1))\n", - " self.add_parameter(Parameter(name=\"k2\", expression=1))\n", - "\n", - " # Species\n", - " self.add_species(Species(name=\"s1\", initial_value=300, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"s2\", initial_value=300, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"s3\", initial_value=300, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"s4\", initial_value=300, mode=\"discrete\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"r1\", reactants={'s1': 1, 's4': 1}, products={'s4': 2}, rate=self.listOfParameters[\"k1\"]))\n", - " self.add_reaction(Reaction(name=\"r2\", reactants={'s2': 1, 's4': 1}, products={'s3': 1}, rate=self.listOfParameters[\"k2\"]))\n", - "\n", - " # Timespan\n", - " self.timespan(np.linspace(0, 100000, 101))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = Schlogl()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "solver = SSACSolver(model=model)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " \"solver\":solver,\n", - " \"number_of_trajectories\":1,\n", - " \"seed\":None,\n", - " # \"tau_tol\":0.03,\n", - " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06}\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = configure_simulation()\n", - "results = model.run(**kwargs)" - ] - }, - { - "cell_type": "code", - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "results.plotplotly()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} +{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Schlogl"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class Schlogl(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Schlogl\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"k1\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"k2\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"s1\", initial_value=300, mode=\"discrete\"))\n"," self.add_species(Species(name=\"s2\", initial_value=300, mode=\"discrete\"))\n"," self.add_species(Species(name=\"s3\", initial_value=300, mode=\"discrete\"))\n"," self.add_species(Species(name=\"s4\", initial_value=300, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"r1\", reactants={'s1': 1, 's4': 1}, products={'s4': 2}, rate=self.listOfParameters[\"k1\"]))\n"," self.add_reaction(Reaction(name=\"r2\", reactants={'s2': 1, 's4': 1}, products={'s3': 1}, rate=self.listOfParameters[\"k2\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 100000, 1000))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = Schlogl()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," # solver = SSACSolver(model=model)\n"," kwargs = {\n"," # \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Toggle_Switch/Toggle_Switch.ipynb b/public_models/Toggle_Switch/Toggle_Switch.ipynb index 2847f5fafb..11eba3b4fc 100644 --- a/public_models/Toggle_Switch/Toggle_Switch.ipynb +++ b/public_models/Toggle_Switch/Toggle_Switch.ipynb @@ -1,2069 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Toggle_Switch" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n", - "from gillespy2 import EventAssignment, EventTrigger, Event\n", - "from gillespy2 import SSACSolver\n", - "# from gillespy2 import VariableSSACSolver\n", - "# from gillespy2 import BasicTauLeapingSolver\n", - "# from gillespy2 import BasicTauHybridSolver\n", - "# from gillespy2 import BasicODESolver" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class Toggle_Switch(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Toggle_Switch\")\n", - " self.volume = 1\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"alpha1\", expression=10))\n", - " self.add_parameter(Parameter(name=\"alpha2\", expression=10))\n", - " self.add_parameter(Parameter(name=\"beta\", expression=2))\n", - " self.add_parameter(Parameter(name=\"gamma\", expression=2))\n", - " self.add_parameter(Parameter(name=\"mu\", expression=1))\n", - "\n", - " # Species\n", - " self.add_species(Species(name=\"A\", initial_value=2, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"B\", initial_value=2, mode=\"discrete\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"cu\", reactants={}, products={'A': 1}, propensity_function=\"alpha1/(1+pow(B, beta))\"))\n", - " self.add_reaction(Reaction(name=\"cv\", reactants={}, products={'B': 1}, propensity_function=\"alpha2/(1+pow(A, gamma))\"))\n", - " self.add_reaction(Reaction(name=\"du\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n", - " self.add_reaction(Reaction(name=\"dv\", reactants={'B': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n", - "\n", - " # Timespan\n", - " self.timespan(np.linspace(0, 250, 251))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = Toggle_Switch()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "solver = SSACSolver(model=model)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " \"solver\":solver,\n", - " \"number_of_trajectories\":1,\n", - " \"seed\":None,\n", - " # \"tau_tol\":0.03,\n", - " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06}\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = configure_simulation()\n", - "results = model.run(**kwargs)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - 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"cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Toggle_Switch" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n", - "from gillespy2 import EventAssignment, EventTrigger, Event\n", - "# To run a simulation using the SSA Solver simply omit the solver argument from model.run().\n", - "from gillespy2 import VariableSSACSolver\n", - "# from gillespy2 import BasicTauLeapingSolver\n", - "# from gillespy2 import BasicTauHybridSolver\n", - "# from gillespy2 import BasicODESolver" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class Toggle_Switch(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Toggle_Switch\")\n", - " self.volume = 1\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"alpha1\", expression=10))\n", - " self.add_parameter(Parameter(name=\"alpha2\", expression=10))\n", - " self.add_parameter(Parameter(name=\"beta\", expression=2))\n", - " self.add_parameter(Parameter(name=\"gamma\", expression=2))\n", - " self.add_parameter(Parameter(name=\"mu\", expression=1))\n", - "\n", - " # Species\n", - " self.add_species(Species(name=\"A\", initial_value=2, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"B\", initial_value=2, mode=\"discrete\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"cu\", reactants={}, products={'A': 1}, propensity_function=\"alpha1/(1+pow(B, beta))\"))\n", - " self.add_reaction(Reaction(name=\"cv\", reactants={}, products={'B': 1}, propensity_function=\"alpha2/(1+pow(A, gamma))\"))\n", - " self.add_reaction(Reaction(name=\"du\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n", - " self.add_reaction(Reaction(name=\"dv\", reactants={'B': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n", - "\n", - " # Timespan\n", - " self.timespan(np.linspace(0, 250, 251))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = Toggle_Switch()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "solver = VariableSSACSolver(model=model)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " \"solver\":solver,\n", - " \"number_of_trajectories\":10,\n", - " \"seed\":None,\n", - " # \"tau_tol\":0.03,\n", - " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06}\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Feature extraction function. What value(s) do you want to extract\n", - "# from the simulation trajectory\n", - "\n", - "def population_at_last_timepoint(c,res):\n", - " if c.verbose: print('population_at_last_timepoint {0}={1}'.format(c.species_of_interest,res[c.species_of_interest][-1]))\n", - " return res[c.species_of_interest][-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Aggregation function, How to we combine the values from multiple \n", - "# trajectores\n", - "\n", - "def mean_std_of_ensemble(c,data):\n", - " a=np.average(data)\n", - " s=np.std(data)\n", - " if c.verbose: print('mean_std_of_ensemble m:{0} s:{1}'.format(a,s))\n", - " return (a,s)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "class ParameterSweep1D():\n", - " \n", - " def run(c, kwargs, verbose=False):\n", - " c.verbose = verbose\n", - " fn = c.feature_extraction\n", - " ag = c.ensemble_aggragator\n", - " data = np.zeros((len(c.p1_range),2)) # mean and std\n", - " for i,v1 in enumerate(c.p1_range):\n", - " if verbose: print(\"running {0}={1}\".format(c.p1,v1))\n", - " #if verbose: print(\"\t{0}\".format([\"{0}={1},\".format(k,v.value) for k,v in tmp_model.listOfParameters.items()]))\n", - " if(c.number_of_trajectories > 1):\n", - " tmp_results = model.run(**kwargs, variables={c.p1:v1})\n", - " (m,s) = ag([fn(x) for x in tmp_results])\n", - " data[i,0] = m\n", - " data[i,1] = s\n", - " else:\n", - " tmp_result = model.run(**kwargs, variables={c.p1:v1})\n", - " data[i,0] = c.feature_extraction(tmp_result)\n", - " c.data = data\n", - "\n", - " def plot(c):\n", - " from matplotlib import pyplot as plt\n", - " from mpl_toolkits.axes_grid1 import make_axes_locatable\n", - " import numpy\n", - " fig, ax = plt.subplots(figsize=(8,8))\n", - " plt.title(\"Parameter Sweep - Species:{0}\".format(c.species_of_interest))\n", - " plt.errorbar(c.p1_range,c.data[:,0],c.data[:,1])\n", - " plt.xlabel(c.p1, fontsize=16, fontweight='bold')\n", - " plt.ylabel(\"Population\", fontsize=16, fontweight='bold')\n", - "\n", - "\n", - " def plotplotly(c, return_plotly_figure=False, species_of_interest=None):\n", - " from plotly.offline import iplot\n", - " import plotly.graph_objs as go\n", - "\n", - " data = c.data\n", - " visible = c.number_of_trajectories > 1\n", - " error_y = dict(type='data', array=data[:,1], visible=visible)\n", - "\n", - " trace_list = [go.Scatter(x=c.p1_range, y=data[:,0], error_y=error_y)]\n", - "\n", - " title = dict(text=\"Parameter Sweep - Species: {0}\".format(c.species_of_interest), x=0.5)\n", - " yaxis_label = dict(title=\"Population\")\n", - " xaxis_label = dict(title=\"{0}\".format(c.p1))\n", - "\n", - " layout = go.Layout(title=title, xaxis=xaxis_label, yaxis=yaxis_label)\n", - "\n", - " fig = dict(data=trace_list, layout=layout)\n", - "\n", - " if return_plotly_figure:\n", - " return fig\n", - " else:\n", - " iplot(fig)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Configuration for the Parameter Sweep\n", - "class ParameterSweepConfig(ParameterSweep1D):\n", - " # What class defines the GillesPy2 model\n", - " ps_class = Toggle_Switch\n", - " model = ps_class()\n", - " p1 = \"alpha1\" # ENTER PARAMETER 1 HERE\n", - " p1_min = 0.5 * float(eval(model.get_parameter(p1).expression)) # ENTER START VALUE FOR P1 RANGE HERE\n", - " p1_max = 1.5 * float(eval(model.get_parameter(p1).expression)) # ENTER END VALUE FOR P1 RANGE HERE\n", - " p1_steps = 11 # ENTER THE NUMBER OF STEPS FOR P1 HERE\n", - " p1_range = np.linspace(p1_min,p1_max,p1_steps)\n", - " species_of_interest = \"A\" # ENTER SPECIES OF INTEREST HERE\n", - " number_of_trajectories = 1\n", - " # What feature of the simulation are we examining\n", - " feature_extraction = population_at_last_timepoint\n", - " # for number_of_trajectories > 1: how do we aggreggate the values\n", - " ensemble_aggragator = mean_std_of_ensemble\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 56.6 ms, sys: 21.1 ms, total: 77.6 ms\n", - "Wall time: 207 ms\n" - ] - } - ], - "source": [ - "kwargs = configure_simulation()\n", - "ps = ParameterSweepConfig()\n", - "ps.number_of_trajectories = kwargs['number_of_trajectories']\n", - "%time ps.run(kwargs)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ps.plotplotly()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/public_models/Toggle_Switch/Toggle_Switch2dParamSweep.ipynb b/public_models/Toggle_Switch/Toggle_Switch2dParamSweep.ipynb deleted file mode 100644 index 5e615baa7e..0000000000 --- a/public_models/Toggle_Switch/Toggle_Switch2dParamSweep.ipynb +++ /dev/null @@ -1,1432 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Toggle_Switch" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n", - "from gillespy2 import EventAssignment, EventTrigger, Event\n", - "# To run a simulation using the SSA Solver simply omit the solver argument from model.run().\n", - "from gillespy2 import VariableSSACSolver\n", - "# from gillespy2 import BasicTauLeapingSolver\n", - "# from gillespy2 import BasicTauHybridSolver\n", - "# from gillespy2 import BasicODESolver" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class Toggle_Switch(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Toggle_Switch\")\n", - " self.volume = 1\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"alpha1\", expression=10))\n", - " self.add_parameter(Parameter(name=\"alpha2\", expression=10))\n", - " self.add_parameter(Parameter(name=\"beta\", expression=2))\n", - " self.add_parameter(Parameter(name=\"gamma\", expression=2))\n", - " self.add_parameter(Parameter(name=\"mu\", expression=1))\n", - "\n", - " # Species\n", - " self.add_species(Species(name=\"A\", initial_value=2, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"B\", initial_value=2, mode=\"discrete\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"cu\", reactants={}, products={'A': 1}, propensity_function=\"alpha1/(1+pow(B, beta))\"))\n", - " self.add_reaction(Reaction(name=\"cv\", reactants={}, products={'B': 1}, propensity_function=\"alpha2/(1+pow(A, gamma))\"))\n", - " self.add_reaction(Reaction(name=\"du\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n", - " self.add_reaction(Reaction(name=\"dv\", reactants={'B': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n", - "\n", - " # Timespan\n", - " self.timespan(np.linspace(0, 250, 251))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = Toggle_Switch()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "solver = VariableSSACSolver(model=model)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " \"solver\":solver,\n", - " \"number_of_trajectories\":10,\n", - " \"seed\":None,\n", - " # \"tau_tol\":0.03,\n", - " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06}\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# Feature extraction function. What value(s) do you want to extract\n", - "# from the simulation trajectory\n", - "\n", - "def population_at_last_timepoint(c,res):\n", - " if c.verbose: print('population_at_last_timepoint {0}={1}'.format(c.species_of_interest,res[c.species_of_interest][-1]))\n", - " return res[c.species_of_interest][-1]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# Aggregation function, How to we combine the values from multiple \n", - "# trajectores\n", - "\n", - "def average_of_ensemble(c,data):\n", - " a=np.average(data)\n", - " if c.verbose: print('average_of_ensemble = {0}'.format(a))\n", - " return a" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "class ParameterSweep2D():\n", - " \n", - " def run(c, kwargs, verbose=False):\n", - " c.verbose = verbose\n", - " fn = c.feature_extraction\n", - " ag = c.ensemble_aggragator\n", - " data = np.zeros((len(c.p1_range),len(c.p2_range)))\n", - " for i,v1 in enumerate(c.p1_range):\n", - " for j,v2 in enumerate(c.p2_range):\n", - " if verbose: print(\"running {0}={1}, {2}={3}\".format(c.p1,v1,c.p2,v2))\n", - " #if verbose: print(\"\t{0}\".format([\"{0}={1}, \".format(k,v.value) for k,v in tmp_model.listOfParameters.items()]))\n", - " if(c.number_of_trajectories > 1):\n", - " tmp_results = model.run(**kwargs, variables={c.p1:v1, c.p2:v2})\n", - " data[i,j] = ag([fn(x) for x in tmp_results])\n", - " else:\n", - " tmp_result = model.run(**kwargs, variables={c.p1:v1, c.p2:v2})\n", - " data[i,j] = c.feature_extraction(tmp_result)\n", - " c.data = data\n", - "\n", - " def plot(c):\n", - " from matplotlib import pyplot as plt\n", - " from mpl_toolkits.axes_grid1 import make_axes_locatable\n", - " import numpy\n", - " fig, ax = plt.subplots(figsize=(8,8))\n", - " plt.imshow(c.data)\n", - " ax.set_xticks(numpy.arange(c.data.shape[1])+0.5, minor=False)\n", - " ax.set_yticks(numpy.arange(c.data.shape[0])+0.5, minor=False)\n", - " plt.title(\"Parameter Sweep - Species: {0}\".format(c.species_of_interest))\n", - " ax.set_xticklabels(c.p1_range, minor=False, rotation=90)\n", - " ax.set_yticklabels(c.p2_range, minor=False)\n", - " ax.set_xlabel(c.p1, fontsize=16, fontweight='bold')\n", - " ax.set_ylabel(c.p2, fontsize=16, fontweight='bold')\n", - " divider = make_axes_locatable(ax)\n", - " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.2)\n", - " _ = plt.colorbar(ax=ax, cax=cax)\n", - "\n", - "\n", - " def plotplotly(c, return_plotly_figure=False):\n", - " from plotly.offline import init_notebook_mode, iplot\n", - " import plotly.graph_objs as go\n", - " \n", - " xaxis_ticks = c.p1_range\n", - " yaxis_ticks = c.p2_range\n", - " data = c.data\n", - "\n", - " trace_list = [go.Heatmap(z=data, x=xaxis_ticks, y=yaxis_ticks)]\n", - "\n", - " title = dict(text=\"Parameter Sweep - Species: {0}\".format(c.species_of_interest), x=0.5)\n", - " xaxis_label = dict(title=\"{0}\".format(c.p1))\n", - " yaxis_label = dict(title=\"{0}\".format(c.p2))\n", - "\n", - " layout = go.Layout(title=title, xaxis=xaxis_label, yaxis=yaxis_label)\n", - "\n", - " fig = dict(data=trace_list, layout=layout)\n", - "\n", - " if return_plotly_figure:\n", - " return fig\n", - " else:\n", - " iplot(fig)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# Configuration for the Parameter Sweep\n", - "class ParameterSweepConfig(ParameterSweep2D):\n", - " # What class defines the GillesPy2 model\n", - " ps_class = Toggle_Switch\n", - " model = ps_class()\n", - " p1 = \"alpha1\" # ENTER PARAMETER 1 HERE\n", - " p2 = \"alpha2\" # ENTER PARAMETER 2 HERE\n", - " p1_min = 0.5 * float(eval(model.get_parameter(p1).expression)) # ENTER START VALUE FOR P1 RANGE HERE\n", - " p1_max = 1.5 * float(eval(model.get_parameter(p1).expression)) # ENTER END VALUE FOR P1 RANGE HERE\n", - " p1_steps = 11 # ENTER THE NUMBER OF STEPS FOR P1 HERE\n", - " p1_range = np.linspace(p1_min,p1_max,p1_steps)\n", - " p2_min = 0.5 * float(eval(model.get_parameter(p2).expression)) # ENTER START VALUE FOR P2 RANGE HERE\n", - " p2_max = 1.5 * float(eval(model.get_parameter(p2).expression)) # ENTER END VALUE FOR P2 RANGE HERE\n", - " p2_steps = 11 # ENTER THE NUMBER OF STEPS FOR P2 HERE\n", - " p2_range = np.linspace(p2_min,p2_max,p2_steps)\n", - " species_of_interest = \"A\" # ENTER SPECIES OF INTEREST HERE\n", - " number_of_trajectories = 1\n", - " # What feature of the simulation are we examining\n", - " feature_extraction = population_at_last_timepoint\n", - " # for number_of_trajectories > 1: how do we aggreggate the values\n", - " ensemble_aggragator = average_of_ensemble\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 508 ms, sys: 180 ms, total: 688 ms\n", - "Wall time: 2.05 s\n" - ] - } - ], - "source": [ - "kwargs = configure_simulation()\n", - "ps = ParameterSweepConfig()\n", - "ps.number_of_trajectories = kwargs['number_of_trajectories']\n", - "%time ps.run(kwargs)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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Parameters\n"," self.add_parameter(Parameter(name=\"alpha1\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"alpha2\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"beta\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"gamma\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"mu\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"A\", initial_value=2, mode=\"discrete\"))\n"," self.add_species(Species(name=\"B\", initial_value=2, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"cu\", reactants={}, products={'A': 1}, propensity_function=\"alpha1/(1+pow(B, beta))\"))\n"," self.add_reaction(Reaction(name=\"cv\", reactants={}, products={'B': 1}, propensity_function=\"alpha2/(1+pow(A, gamma))\"))\n"," self.add_reaction(Reaction(name=\"du\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"dv\", reactants={'B': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 250, 1))"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["model = ToggleSwitch()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," solver = VariableSSACSolver(model=model)\n"," kwargs = {\n"," \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"markdown","metadata":{},"source":["# Model Exploration"]},{"cell_type":"markdown","metadata":{},"source":["## Define simulator function (using gillespy2 wrapper)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["from sciope.utilities.gillespy2 import wrapper\n","settings = configure_simulation()\n","simulator = wrapper.get_simulator(gillespy_model=model, run_settings=settings, species_of_interest=['A', 'B'])\n","expression_array = wrapper.get_parameter_expression_array(model)"]},{"cell_type":"markdown","metadata":{},"source":["## Start local cluster using dask client"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":["from dask.distributed import Client\n","\n","c = Client()"]},{"cell_type":"markdown","metadata":{},"source":["## Define parameter sampler/design and summary statistics"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["from sciope.designs import latin_hypercube_sampling\n","from sciope.utilities.summarystats.auto_tsfresh import SummariesTSFRESH\n","\n","lhc = latin_hypercube_sampling.LatinHypercube(xmin=expression_array, xmax=expression_array*3)\n","lhc.generate_array(1000) # creates a LHD of size 1000\n","\n","# will use default minimal set of features\n","summary_stats = SummariesTSFRESH()"]},{"cell_type":"markdown","metadata":{},"source":["## Initiate StochMET"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":["from sciope.stochmet.stochmet import StochMET\n","\n","met = StochMET(simulator, lhc, summary_stats)"]},{"cell_type":"markdown","metadata":{},"source":["## Run parameter sweep"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[],"source":["met.compute(n_points=500, chunk_size=10)"]},{"cell_type":"markdown","metadata":{},"source":["## Explore the result"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[],"source":["# First lets add some appropiate information about the model and features\n","met.data.configurations['listOfParameters'] = list(model.listOfParameters.keys())\n","met.data.configurations['listOfSpecies'] = list(model.listOfSpecies.keys())\n","met.data.configurations['listOfSummaries'] = met.summaries.features\n","met.data.configurations['timepoints'] = model.tspan"]},{"cell_type":"code","execution_count":13,"metadata":{"scrolled":false},"outputs":[{"data":{"application/javascript":["/* Put everything inside the global mpl namespace */\n","/* global mpl */\n","window.mpl = {};\n","\n","mpl.get_websocket_type = function () {\n"," if (typeof WebSocket !== 'undefined') {\n"," return WebSocket;\n"," } else if (typeof MozWebSocket !== 'undefined') {\n"," return MozWebSocket;\n"," } else {\n"," alert(\n"," 'Your browser does not have WebSocket support. ' +\n"," 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n"," 'Firefox 4 and 5 are also supported but you ' +\n"," 'have to enable WebSockets in about:config.'\n"," );\n"," }\n","};\n","\n","mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n"," this.id = figure_id;\n","\n"," this.ws = websocket;\n","\n"," this.supports_binary = this.ws.binaryType !== undefined;\n","\n"," if (!this.supports_binary) {\n"," var warnings = document.getElementById('mpl-warnings');\n"," if (warnings) {\n"," warnings.style.display = 'block';\n"," warnings.textContent =\n"," 'This browser does not support binary websocket messages. ' +\n"," 'Performance may be slow.';\n"," }\n"," }\n","\n"," this.imageObj = new Image();\n","\n"," this.context = undefined;\n"," this.message = undefined;\n"," this.canvas = undefined;\n"," this.rubberband_canvas = undefined;\n"," this.rubberband_context = undefined;\n"," this.format_dropdown = undefined;\n","\n"," this.image_mode = 'full';\n","\n"," this.root = document.createElement('div');\n"," this.root.setAttribute('style', 'display: inline-block');\n"," this._root_extra_style(this.root);\n","\n"," parent_element.appendChild(this.root);\n","\n"," this._init_header(this);\n"," this._init_canvas(this);\n"," this._init_toolbar(this);\n","\n"," var fig = this;\n","\n"," this.waiting = false;\n","\n"," this.ws.onopen = function () {\n"," fig.send_message('supports_binary', { value: fig.supports_binary });\n"," fig.send_message('send_image_mode', {});\n"," if (fig.ratio !== 1) {\n"," fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n"," }\n"," fig.send_message('refresh', {});\n"," };\n","\n"," this.imageObj.onload = function () {\n"," if (fig.image_mode === 'full') {\n"," // Full images could contain transparency (where diff images\n"," // almost always do), so we need to clear the canvas so that\n"," // there is no ghosting.\n"," fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n"," }\n"," fig.context.drawImage(fig.imageObj, 0, 0);\n"," };\n","\n"," this.imageObj.onunload = function () {\n"," fig.ws.close();\n"," };\n","\n"," this.ws.onmessage = this._make_on_message_function(this);\n","\n"," this.ondownload = ondownload;\n","};\n","\n","mpl.figure.prototype._init_header = function () {\n"," var titlebar = document.createElement('div');\n"," titlebar.classList =\n"," 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n"," var titletext = document.createElement('div');\n"," titletext.classList = 'ui-dialog-title';\n"," titletext.setAttribute(\n"," 'style',\n"," 'width: 100%; text-align: center; padding: 3px;'\n"," );\n"," titlebar.appendChild(titletext);\n"," this.root.appendChild(titlebar);\n"," this.header = titletext;\n","};\n","\n","mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n","\n","mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n","\n","mpl.figure.prototype._init_canvas = function () {\n"," var fig = this;\n","\n"," var canvas_div = (this.canvas_div = document.createElement('div'));\n"," canvas_div.setAttribute(\n"," 'style',\n"," 'border: 1px solid #ddd;' +\n"," 'box-sizing: content-box;' +\n"," 'clear: both;' +\n"," 'min-height: 1px;' +\n"," 'min-width: 1px;' +\n"," 'outline: 0;' +\n"," 'overflow: hidden;' +\n"," 'position: relative;' +\n"," 'resize: both;'\n"," );\n","\n"," function on_keyboard_event_closure(name) {\n"," return function (event) {\n"," return fig.key_event(event, name);\n"," };\n"," }\n","\n"," canvas_div.addEventListener(\n"," 'keydown',\n"," on_keyboard_event_closure('key_press')\n"," );\n"," canvas_div.addEventListener(\n"," 'keyup',\n"," on_keyboard_event_closure('key_release')\n"," );\n","\n"," this._canvas_extra_style(canvas_div);\n"," this.root.appendChild(canvas_div);\n","\n"," var canvas = (this.canvas = document.createElement('canvas'));\n"," canvas.classList.add('mpl-canvas');\n"," canvas.setAttribute('style', 'box-sizing: content-box;');\n","\n"," this.context = canvas.getContext('2d');\n","\n"," var backingStore =\n"," this.context.backingStorePixelRatio ||\n"," this.context.webkitBackingStorePixelRatio ||\n"," this.context.mozBackingStorePixelRatio ||\n"," this.context.msBackingStorePixelRatio ||\n"," this.context.oBackingStorePixelRatio ||\n"," this.context.backingStorePixelRatio ||\n"," 1;\n","\n"," this.ratio = (window.devicePixelRatio || 1) / backingStore;\n","\n"," var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n"," 'canvas'\n"," ));\n"," rubberband_canvas.setAttribute(\n"," 'style',\n"," 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n"," );\n","\n"," // Apply a ponyfill if ResizeObserver is not implemented by browser.\n"," if (this.ResizeObserver === undefined) {\n"," if (window.ResizeObserver !== undefined) {\n"," this.ResizeObserver = window.ResizeObserver;\n"," } else {\n"," var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n"," this.ResizeObserver = obs.ResizeObserver;\n"," }\n"," }\n","\n"," this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n"," var nentries = entries.length;\n"," for (var i = 0; i < nentries; i++) {\n"," var entry = entries[i];\n"," var width, height;\n"," if (entry.contentBoxSize) {\n"," if (entry.contentBoxSize instanceof Array) {\n"," // Chrome 84 implements new version of spec.\n"," width = entry.contentBoxSize[0].inlineSize;\n"," height = entry.contentBoxSize[0].blockSize;\n"," } else {\n"," // Firefox implements old version of spec.\n"," width = entry.contentBoxSize.inlineSize;\n"," height = entry.contentBoxSize.blockSize;\n"," }\n"," } else {\n"," // Chrome <84 implements even older version of spec.\n"," width = entry.contentRect.width;\n"," height = entry.contentRect.height;\n"," }\n","\n"," // Keep the size of the canvas and rubber band canvas in sync with\n"," // the canvas container.\n"," if (entry.devicePixelContentBoxSize) {\n"," // Chrome 84 implements new version of spec.\n"," canvas.setAttribute(\n"," 'width',\n"," entry.devicePixelContentBoxSize[0].inlineSize\n"," );\n"," canvas.setAttribute(\n"," 'height',\n"," entry.devicePixelContentBoxSize[0].blockSize\n"," );\n"," } else {\n"," canvas.setAttribute('width', width * fig.ratio);\n"," canvas.setAttribute('height', height * fig.ratio);\n"," }\n"," canvas.setAttribute(\n"," 'style',\n"," 'width: ' + width + 'px; height: ' + height + 'px;'\n"," );\n","\n"," rubberband_canvas.setAttribute('width', width);\n"," rubberband_canvas.setAttribute('height', height);\n","\n"," // And update the size in Python. We ignore the initial 0/0 size\n"," // that occurs as the element is placed into the DOM, which should\n"," // otherwise not happen due to the minimum size styling.\n"," if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n"," fig.request_resize(width, height);\n"," }\n"," }\n"," });\n"," this.resizeObserverInstance.observe(canvas_div);\n","\n"," function on_mouse_event_closure(name) {\n"," return function (event) {\n"," return fig.mouse_event(event, name);\n"," };\n"," }\n","\n"," rubberband_canvas.addEventListener(\n"," 'mousedown',\n"," on_mouse_event_closure('button_press')\n"," );\n"," rubberband_canvas.addEventListener(\n"," 'mouseup',\n"," on_mouse_event_closure('button_release')\n"," );\n"," // Throttle sequential mouse events to 1 every 20ms.\n"," rubberband_canvas.addEventListener(\n"," 'mousemove',\n"," on_mouse_event_closure('motion_notify')\n"," );\n","\n"," rubberband_canvas.addEventListener(\n"," 'mouseenter',\n"," on_mouse_event_closure('figure_enter')\n"," );\n"," rubberband_canvas.addEventListener(\n"," 'mouseleave',\n"," on_mouse_event_closure('figure_leave')\n"," );\n","\n"," canvas_div.addEventListener('wheel', function (event) {\n"," if (event.deltaY < 0) {\n"," event.step = 1;\n"," } else {\n"," event.step = -1;\n"," }\n"," on_mouse_event_closure('scroll')(event);\n"," });\n","\n"," canvas_div.appendChild(canvas);\n"," canvas_div.appendChild(rubberband_canvas);\n","\n"," this.rubberband_context = rubberband_canvas.getContext('2d');\n"," this.rubberband_context.strokeStyle = '#000000';\n","\n"," this._resize_canvas = function (width, height, forward) {\n"," if (forward) {\n"," canvas_div.style.width = width + 'px';\n"," canvas_div.style.height = height + 'px';\n"," }\n"," };\n","\n"," // Disable right mouse context menu.\n"," this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n"," event.preventDefault();\n"," return false;\n"," });\n","\n"," function set_focus() {\n"," canvas.focus();\n"," canvas_div.focus();\n"," }\n","\n"," window.setTimeout(set_focus, 100);\n","};\n","\n","mpl.figure.prototype._init_toolbar = function () {\n"," var fig = this;\n","\n"," var toolbar = document.createElement('div');\n"," toolbar.classList = 'mpl-toolbar';\n"," this.root.appendChild(toolbar);\n","\n"," function on_click_closure(name) {\n"," return function (_event) {\n"," return fig.toolbar_button_onclick(name);\n"," };\n"," }\n","\n"," function on_mouseover_closure(tooltip) {\n"," return function (event) {\n"," if (!event.currentTarget.disabled) {\n"," return fig.toolbar_button_onmouseover(tooltip);\n"," }\n"," };\n"," }\n","\n"," fig.buttons = {};\n"," var buttonGroup = document.createElement('div');\n"," buttonGroup.classList = 'mpl-button-group';\n"," for (var toolbar_ind in mpl.toolbar_items) {\n"," var name = mpl.toolbar_items[toolbar_ind][0];\n"," var tooltip = mpl.toolbar_items[toolbar_ind][1];\n"," var image = mpl.toolbar_items[toolbar_ind][2];\n"," var method_name = mpl.toolbar_items[toolbar_ind][3];\n","\n"," if (!name) {\n"," /* Instead of a spacer, we start a new button group. */\n"," if (buttonGroup.hasChildNodes()) {\n"," toolbar.appendChild(buttonGroup);\n"," }\n"," buttonGroup = document.createElement('div');\n"," buttonGroup.classList = 'mpl-button-group';\n"," continue;\n"," }\n","\n"," var button = (fig.buttons[name] = document.createElement('button'));\n"," button.classList = 'mpl-widget';\n"," button.setAttribute('role', 'button');\n"," button.setAttribute('aria-disabled', 'false');\n"," button.addEventListener('click', on_click_closure(method_name));\n"," button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n","\n"," var icon_img = document.createElement('img');\n"," icon_img.src = '_images/' + image + '.png';\n"," icon_img.srcset = '_images/' + image + '_large.png 2x';\n"," icon_img.alt = tooltip;\n"," button.appendChild(icon_img);\n","\n"," buttonGroup.appendChild(button);\n"," }\n","\n"," if (buttonGroup.hasChildNodes()) {\n"," toolbar.appendChild(buttonGroup);\n"," }\n","\n"," var fmt_picker = document.createElement('select');\n"," fmt_picker.classList = 'mpl-widget';\n"," toolbar.appendChild(fmt_picker);\n"," this.format_dropdown = fmt_picker;\n","\n"," for (var ind in mpl.extensions) {\n"," var fmt = mpl.extensions[ind];\n"," var option = document.createElement('option');\n"," option.selected = fmt === mpl.default_extension;\n"," option.innerHTML = fmt;\n"," fmt_picker.appendChild(option);\n"," }\n","\n"," var status_bar = document.createElement('span');\n"," status_bar.classList = 'mpl-message';\n"," toolbar.appendChild(status_bar);\n"," this.message = status_bar;\n","};\n","\n","mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n"," // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n"," // which will in turn request a refresh of the image.\n"," this.send_message('resize', { width: x_pixels, height: y_pixels });\n","};\n","\n","mpl.figure.prototype.send_message = function (type, properties) {\n"," properties['type'] = type;\n"," properties['figure_id'] = this.id;\n"," this.ws.send(JSON.stringify(properties));\n","};\n","\n","mpl.figure.prototype.send_draw_message = function () {\n"," if (!this.waiting) {\n"," this.waiting = true;\n"," this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n"," }\n","};\n","\n","mpl.figure.prototype.handle_save = function (fig, _msg) {\n"," var format_dropdown = fig.format_dropdown;\n"," var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n"," fig.ondownload(fig, format);\n","};\n","\n","mpl.figure.prototype.handle_resize = function (fig, msg) {\n"," var size = msg['size'];\n"," if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n"," fig._resize_canvas(size[0], size[1], msg['forward']);\n"," fig.send_message('refresh', {});\n"," }\n","};\n","\n","mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n"," var x0 = msg['x0'] / fig.ratio;\n"," var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n"," var x1 = msg['x1'] / fig.ratio;\n"," var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n"," x0 = Math.floor(x0) + 0.5;\n"," y0 = Math.floor(y0) + 0.5;\n"," x1 = Math.floor(x1) + 0.5;\n"," y1 = Math.floor(y1) + 0.5;\n"," var min_x = Math.min(x0, x1);\n"," var min_y = Math.min(y0, y1);\n"," var width = Math.abs(x1 - x0);\n"," var height = Math.abs(y1 - y0);\n","\n"," fig.rubberband_context.clearRect(\n"," 0,\n"," 0,\n"," fig.canvas.width / fig.ratio,\n"," fig.canvas.height / fig.ratio\n"," );\n","\n"," fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n","};\n","\n","mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n"," // Updates the figure title.\n"," fig.header.textContent = msg['label'];\n","};\n","\n","mpl.figure.prototype.handle_cursor = function (fig, msg) {\n"," var cursor = msg['cursor'];\n"," switch (cursor) {\n"," case 0:\n"," cursor = 'pointer';\n"," break;\n"," case 1:\n"," cursor = 'default';\n"," break;\n"," case 2:\n"," cursor = 'crosshair';\n"," break;\n"," case 3:\n"," cursor = 'move';\n"," break;\n"," }\n"," fig.rubberband_canvas.style.cursor = cursor;\n","};\n","\n","mpl.figure.prototype.handle_message = function (fig, msg) {\n"," fig.message.textContent = msg['message'];\n","};\n","\n","mpl.figure.prototype.handle_draw = function (fig, _msg) {\n"," // Request the server to send over a new figure.\n"," fig.send_draw_message();\n","};\n","\n","mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n"," fig.image_mode = msg['mode'];\n","};\n","\n","mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n"," for (var key in msg) {\n"," if (!(key in fig.buttons)) {\n"," continue;\n"," }\n"," fig.buttons[key].disabled = !msg[key];\n"," fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n"," }\n","};\n","\n","mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n"," if (msg['mode'] === 'PAN') {\n"," fig.buttons['Pan'].classList.add('active');\n"," fig.buttons['Zoom'].classList.remove('active');\n"," } else if (msg['mode'] === 'ZOOM') {\n"," fig.buttons['Pan'].classList.remove('active');\n"," fig.buttons['Zoom'].classList.add('active');\n"," } else {\n"," fig.buttons['Pan'].classList.remove('active');\n"," fig.buttons['Zoom'].classList.remove('active');\n"," }\n","};\n","\n","mpl.figure.prototype.updated_canvas_event = function () {\n"," // Called whenever the canvas gets updated.\n"," this.send_message('ack', {});\n","};\n","\n","// A function to construct a web socket function for onmessage handling.\n","// Called in the figure constructor.\n","mpl.figure.prototype._make_on_message_function = function (fig) {\n"," return function socket_on_message(evt) {\n"," if (evt.data instanceof Blob) {\n"," /* FIXME: We get \"Resource interpreted as Image but\n"," * transferred with MIME type text/plain:\" errors on\n"," * Chrome. But how to set the MIME type? It doesn't seem\n"," * to be part of the websocket stream */\n"," evt.data.type = 'image/png';\n","\n"," /* Free the memory for the previous frames */\n"," if (fig.imageObj.src) {\n"," (window.URL || window.webkitURL).revokeObjectURL(\n"," fig.imageObj.src\n"," );\n"," }\n","\n"," fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n"," evt.data\n"," );\n"," fig.updated_canvas_event();\n"," fig.waiting = false;\n"," return;\n"," } else if (\n"," typeof evt.data === 'string' &&\n"," evt.data.slice(0, 21) === 'data:image/png;base64'\n"," ) {\n"," fig.imageObj.src = evt.data;\n"," fig.updated_canvas_event();\n"," fig.waiting = false;\n"," return;\n"," }\n","\n"," var msg = JSON.parse(evt.data);\n"," var msg_type = msg['type'];\n","\n"," // Call the \"handle_{type}\" callback, which takes\n"," // the figure and JSON message as its only arguments.\n"," try {\n"," var callback = fig['handle_' + msg_type];\n"," } catch (e) {\n"," console.log(\n"," \"No handler for the '\" + msg_type + \"' message type: \",\n"," msg\n"," );\n"," return;\n"," }\n","\n"," if (callback) {\n"," try {\n"," // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n"," callback(fig, msg);\n"," } catch (e) {\n"," console.log(\n"," \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n"," e,\n"," e.stack,\n"," msg\n"," );\n"," }\n"," }\n"," };\n","};\n","\n","// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n","mpl.findpos = function (e) {\n"," //this section is from http://www.quirksmode.org/js/events_properties.html\n"," var targ;\n"," if (!e) {\n"," e = window.event;\n"," }\n"," if (e.target) {\n"," targ = e.target;\n"," } else if (e.srcElement) {\n"," targ = e.srcElement;\n"," }\n"," if (targ.nodeType === 3) {\n"," // defeat Safari bug\n"," targ = targ.parentNode;\n"," }\n","\n"," // pageX,Y are the mouse positions relative to the document\n"," var boundingRect = targ.getBoundingClientRect();\n"," var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n"," var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n","\n"," return { x: x, y: y };\n","};\n","\n","/*\n"," * return a copy of an object with only non-object keys\n"," * we need this to avoid circular references\n"," * http://stackoverflow.com/a/24161582/3208463\n"," */\n","function simpleKeys(original) {\n"," return Object.keys(original).reduce(function (obj, key) {\n"," if (typeof original[key] !== 'object') {\n"," obj[key] = original[key];\n"," }\n"," return obj;\n"," }, {});\n","}\n","\n","mpl.figure.prototype.mouse_event = function (event, name) {\n"," var canvas_pos = mpl.findpos(event);\n","\n"," if (name === 'button_press') {\n"," this.canvas.focus();\n"," this.canvas_div.focus();\n"," }\n","\n"," var x = canvas_pos.x * this.ratio;\n"," var y = canvas_pos.y * this.ratio;\n","\n"," this.send_message(name, {\n"," x: x,\n"," y: y,\n"," button: event.button,\n"," step: event.step,\n"," guiEvent: simpleKeys(event),\n"," });\n","\n"," /* This prevents the web browser from automatically changing to\n"," * the text insertion cursor when the button is pressed. We want\n"," * to control all of the cursor setting manually through the\n"," * 'cursor' event from matplotlib */\n"," event.preventDefault();\n"," return false;\n","};\n","\n","mpl.figure.prototype._key_event_extra = function (_event, _name) {\n"," // Handle any extra behaviour associated with a key event\n","};\n","\n","mpl.figure.prototype.key_event = function (event, name) {\n"," // Prevent repeat events\n"," if (name === 'key_press') {\n"," if (event.which === this._key) {\n"," return;\n"," } else {\n"," this._key = event.which;\n"," }\n"," }\n"," if (name === 'key_release') {\n"," this._key = null;\n"," }\n","\n"," var value = '';\n"," if (event.ctrlKey && event.which !== 17) {\n"," value += 'ctrl+';\n"," }\n"," if (event.altKey && event.which !== 18) {\n"," value += 'alt+';\n"," }\n"," if (event.shiftKey && event.which !== 16) {\n"," value += 'shift+';\n"," }\n","\n"," value += 'k';\n"," value += event.which.toString();\n","\n"," this._key_event_extra(event, name);\n","\n"," this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n"," return false;\n","};\n","\n","mpl.figure.prototype.toolbar_button_onclick = function (name) {\n"," if (name === 'download') {\n"," this.handle_save(this, null);\n"," } else {\n"," this.send_message('toolbar_button', { name: name });\n"," }\n","};\n","\n","mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n"," this.message.textContent = tooltip;\n","};\n","\n","///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n","// prettier-ignore\n","var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n","mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n","\n","mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n","\n","mpl.default_extension = \"png\";/* global mpl */\n","\n","var comm_websocket_adapter = function (comm) {\n"," // Create a \"websocket\"-like object which calls the given IPython comm\n"," // object with the appropriate methods. Currently this is a non binary\n"," // socket, so there is still some room for performance tuning.\n"," var ws = {};\n","\n"," ws.close = function () {\n"," comm.close();\n"," };\n"," ws.send = function (m) {\n"," //console.log('sending', m);\n"," comm.send(m);\n"," };\n"," // Register the callback with on_msg.\n"," comm.on_msg(function (msg) {\n"," //console.log('receiving', msg['content']['data'], msg);\n"," // Pass the mpl event to the overridden (by mpl) onmessage function.\n"," ws.onmessage(msg['content']['data']);\n"," });\n"," return ws;\n","};\n","\n","mpl.mpl_figure_comm = function (comm, msg) {\n"," // This is the function which gets called when the mpl process\n"," // starts-up an IPython Comm through the \"matplotlib\" channel.\n","\n"," var id = msg.content.data.id;\n"," // Get hold of the div created by the display call when the Comm\n"," // socket was opened in Python.\n"," var element = document.getElementById(id);\n"," var ws_proxy = comm_websocket_adapter(comm);\n","\n"," function ondownload(figure, _format) {\n"," window.open(figure.canvas.toDataURL());\n"," }\n","\n"," var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n","\n"," // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n"," // web socket which is closed, not our websocket->open comm proxy.\n"," ws_proxy.onopen();\n","\n"," fig.parent_element = element;\n"," fig.cell_info = mpl.find_output_cell(\"
\");\n"," if (!fig.cell_info) {\n"," console.error('Failed to find cell for figure', id, fig);\n"," return;\n"," }\n"," fig.cell_info[0].output_area.element.on(\n"," 'cleared',\n"," { fig: fig },\n"," fig._remove_fig_handler\n"," );\n","};\n","\n","mpl.figure.prototype.handle_close = function (fig, msg) {\n"," var width = fig.canvas.width / fig.ratio;\n"," fig.cell_info[0].output_area.element.off(\n"," 'cleared',\n"," fig._remove_fig_handler\n"," );\n"," fig.resizeObserverInstance.unobserve(fig.canvas_div);\n","\n"," // Update the output cell to use the data from the current canvas.\n"," fig.push_to_output();\n"," var dataURL = fig.canvas.toDataURL();\n"," // Re-enable the keyboard manager in IPython - without this line, in FF,\n"," // the notebook keyboard shortcuts fail.\n"," IPython.keyboard_manager.enable();\n"," fig.parent_element.innerHTML =\n"," '';\n"," fig.close_ws(fig, msg);\n","};\n","\n","mpl.figure.prototype.close_ws = function (fig, msg) {\n"," fig.send_message('closing', msg);\n"," // fig.ws.close()\n","};\n","\n","mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n"," // Turn the data on the canvas into data in the output cell.\n"," var width = this.canvas.width / this.ratio;\n"," var dataURL = this.canvas.toDataURL();\n"," this.cell_info[1]['text/html'] =\n"," '';\n","};\n","\n","mpl.figure.prototype.updated_canvas_event = function () {\n"," // Tell IPython that the notebook contents must change.\n"," IPython.notebook.set_dirty(true);\n"," this.send_message('ack', {});\n"," var fig = this;\n"," // Wait a second, then push the new image to the DOM so\n"," // that it is saved nicely (might be nice to debounce this).\n"," setTimeout(function () {\n"," fig.push_to_output();\n"," }, 1000);\n","};\n","\n","mpl.figure.prototype._init_toolbar = function () {\n"," var fig = this;\n","\n"," var toolbar = document.createElement('div');\n"," toolbar.classList = 'btn-toolbar';\n"," this.root.appendChild(toolbar);\n","\n"," function on_click_closure(name) {\n"," return function (_event) {\n"," return fig.toolbar_button_onclick(name);\n"," };\n"," }\n","\n"," function on_mouseover_closure(tooltip) {\n"," return function (event) {\n"," if (!event.currentTarget.disabled) {\n"," return fig.toolbar_button_onmouseover(tooltip);\n"," }\n"," };\n"," }\n","\n"," fig.buttons = {};\n"," var buttonGroup = document.createElement('div');\n"," buttonGroup.classList = 'btn-group';\n"," var button;\n"," for (var toolbar_ind in mpl.toolbar_items) {\n"," var name = mpl.toolbar_items[toolbar_ind][0];\n"," var tooltip = mpl.toolbar_items[toolbar_ind][1];\n"," var image = mpl.toolbar_items[toolbar_ind][2];\n"," var method_name = mpl.toolbar_items[toolbar_ind][3];\n","\n"," if (!name) {\n"," /* Instead of a spacer, we start a new button group. */\n"," if (buttonGroup.hasChildNodes()) {\n"," toolbar.appendChild(buttonGroup);\n"," }\n"," buttonGroup = document.createElement('div');\n"," buttonGroup.classList = 'btn-group';\n"," continue;\n"," }\n","\n"," button = fig.buttons[name] = document.createElement('button');\n"," button.classList = 'btn btn-default';\n"," button.href = '#';\n"," button.title = name;\n"," button.innerHTML = '';\n"," button.addEventListener('click', on_click_closure(method_name));\n"," button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n"," buttonGroup.appendChild(button);\n"," }\n","\n"," if (buttonGroup.hasChildNodes()) {\n"," toolbar.appendChild(buttonGroup);\n"," }\n","\n"," // Add the status bar.\n"," var status_bar = document.createElement('span');\n"," status_bar.classList = 'mpl-message pull-right';\n"," toolbar.appendChild(status_bar);\n"," this.message = status_bar;\n","\n"," // Add the close button to the window.\n"," var buttongrp = document.createElement('div');\n"," buttongrp.classList = 'btn-group inline pull-right';\n"," button = document.createElement('button');\n"," button.classList = 'btn btn-mini btn-primary';\n"," button.href = '#';\n"," button.title = 'Stop Interaction';\n"," button.innerHTML = '';\n"," button.addEventListener('click', function (_evt) {\n"," fig.handle_close(fig, {});\n"," });\n"," button.addEventListener(\n"," 'mouseover',\n"," on_mouseover_closure('Stop Interaction')\n"," );\n"," buttongrp.appendChild(button);\n"," var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n"," titlebar.insertBefore(buttongrp, titlebar.firstChild);\n","};\n","\n","mpl.figure.prototype._remove_fig_handler = function (event) {\n"," var fig = event.data.fig;\n"," if (event.target !== this) {\n"," // Ignore bubbled events from children.\n"," return;\n"," }\n"," fig.close_ws(fig, {});\n","};\n","\n","mpl.figure.prototype._root_extra_style = function (el) {\n"," el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n","};\n","\n","mpl.figure.prototype._canvas_extra_style = function (el) {\n"," // this is important to make the div 'focusable\n"," el.setAttribute('tabindex', 0);\n"," // reach out to IPython and tell the keyboard manager to turn it's self\n"," // off when our div gets focus\n","\n"," // location in version 3\n"," if (IPython.notebook.keyboard_manager) {\n"," IPython.notebook.keyboard_manager.register_events(el);\n"," } else {\n"," // location in version 2\n"," IPython.keyboard_manager.register_events(el);\n"," }\n","};\n","\n","mpl.figure.prototype._key_event_extra = function (event, _name) {\n"," var manager = IPython.notebook.keyboard_manager;\n"," if (!manager) {\n"," manager = IPython.keyboard_manager;\n"," }\n","\n"," // Check for shift+enter\n"," if (event.shiftKey && event.which === 13) {\n"," this.canvas_div.blur();\n"," // select the cell after this one\n"," var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n"," IPython.notebook.select(index + 1);\n"," }\n","};\n","\n","mpl.figure.prototype.handle_save = function (fig, _msg) {\n"," fig.ondownload(fig, null);\n","};\n","\n","mpl.find_output_cell = function (html_output) {\n"," // Return the cell and output element which can be found *uniquely* in the notebook.\n"," // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n"," // IPython event is triggered only after the cells have been serialised, which for\n"," // our purposes (turning an active figure into a static one), is too late.\n"," var cells = IPython.notebook.get_cells();\n"," var ncells = cells.length;\n"," for (var i = 0; i < ncells; i++) {\n"," var cell = cells[i];\n"," if (cell.cell_type === 'code') {\n"," for (var j = 0; j < cell.output_area.outputs.length; j++) {\n"," var data = cell.output_area.outputs[j];\n"," if (data.data) {\n"," // IPython >= 3 moved mimebundle to data attribute of output\n"," data = data.data;\n"," }\n"," if (data['text/html'] === html_output) {\n"," return [cell, data, j];\n"," }\n"," }\n"," }\n"," }\n","};\n","\n","// Register the function which deals with the matplotlib target/channel.\n","// The kernel may be null if the page has been refreshed.\n","if (IPython.notebook.kernel !== null) {\n"," IPython.notebook.kernel.comm_manager.register_target(\n"," 'matplotlib',\n"," mpl.mpl_figure_comm\n"," );\n","}\n"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[""],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6d7bc03497a54ae8a9d9759b01f10d0d","version_major":2,"version_minor":0},"text/plain":["Button(description='Clear all', style=ButtonStyle())"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ffbcacb3d05e4a0595e9c2ce041b13c2","version_major":2,"version_minor":0},"text/plain":["Button(description='Submit', style=ButtonStyle())"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3f3642ccc85146dcb85af47a540906e2","version_major":2,"version_minor":0},"text/plain":["IntSlider(value=0, max=10, min=-1)"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ce21f6cc6f03467eb7b8a2f0d2e489c3","version_major":2,"version_minor":0},"text/plain":["Dropdown(description='Species plot 1', options={'A': 0, 'B': 1}, value=0)"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f4fd2c9c572147d683f08cb972e1b3c4","version_major":2,"version_minor":0},"text/plain":["Dropdown(description='Species plot 2', index=1, options={'A': 0, 'B': 1}, value=1)"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["
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alpha1alpha2betagammamu
011.44144111.4414412.9009014.8388392.469469
114.56456514.5645653.6056064.2862862.583584
215.72572612.5025035.4674673.4734731.782783
326.57657723.3533532.3923925.0110111.590591
419.62963022.8528534.5905915.9439442.837838
..................
49512.84284312.8428435.5355362.3043042.393393
49626.37637628.7587595.0430432.4164161.520521
49711.52152213.1231234.2662664.9349352.515516
49815.14514515.1451454.0340343.2052051.088088
49927.41741724.1941945.2112112.5845852.845846
\n","

500 rows × 5 columns

\n","
"],"text/plain":[" alpha1 alpha2 beta gamma mu\n","0 11.441441 11.441441 2.900901 4.838839 2.469469\n","1 14.564565 14.564565 3.605606 4.286286 2.583584\n","2 15.725726 12.502503 5.467467 3.473473 1.782783\n","3 26.576577 23.353353 2.392392 5.011011 1.590591\n","4 19.629630 22.852853 4.590591 5.943944 2.837838\n",".. ... ... ... ... ...\n","495 12.842843 12.842843 5.535536 2.304304 2.393393\n","496 26.376376 28.758759 5.043043 2.416416 1.520521\n","497 11.521522 13.123123 4.266266 4.934935 2.515516\n","498 15.145145 15.145145 4.034034 3.205205 1.088088\n","499 27.417417 24.194194 5.211211 2.584585 2.845846\n","\n","[500 rows x 5 columns]"]},"metadata":{},"output_type":"display_data"}],"source":["# Here we use UMAP for dimension reduction\n","met.explore(dr_method='umap')"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["basinhopping step 0: f 0.0001\n","basinhopping step 1: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 2: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 3: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 4: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 5: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 6: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 7: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 8: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 9: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 10: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 11: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 12: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 13: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 14: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 15: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 16: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 17: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 18: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 19: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 20: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 21: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 22: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 23: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 24: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 25: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 26: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 27: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 28: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 29: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 30: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 31: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 32: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 33: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 34: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 35: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 36: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 37: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 38: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 39: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 40: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 41: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 42: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 43: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 44: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 45: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 46: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 47: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 48: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","basinhopping step 49: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n","adaptive stepsize: acceptance rate 0.980000 target 0.500000 new stepsize 0.111111 old stepsize 0.1\n","basinhopping step 50: f 0.0001 trial_f 0.0001 accepted 1 lowest_f 0.0001\n"]}],"source":["from sciope.models.label_propagation import LPModel\n","# here lets use the dimension reduction embedding as input data\n","data = met.dr_model.embedding_\n","\n","model_lp = LPModel()\n","# train using basinhopping\n","model_lp.train(data, met.data.user_labels, min_=0.01, max_=10, niter=50)"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["# just to vislualize the result we will map the label distribution to the user_labels\n","# (will enable us to see the LP model output when using method 'explore')\n","user_labels = np.copy(met.data.user_labels)\n","# takes the label corresponding to index 0\n","met.data.user_labels = model_lp.model.label_distributions_[:, 0]"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"data":{"application/javascript":["/* Put everything inside the global mpl namespace */\n","/* global mpl */\n","window.mpl = {};\n","\n","mpl.get_websocket_type = function () {\n"," if (typeof WebSocket !== 'undefined') {\n"," return WebSocket;\n"," } else if (typeof MozWebSocket !== 'undefined') {\n"," return MozWebSocket;\n"," } else {\n"," alert(\n"," 'Your browser does not have WebSocket support. ' +\n"," 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n"," 'Firefox 4 and 5 are also supported but you ' +\n"," 'have to enable WebSockets in about:config.'\n"," );\n"," }\n","};\n","\n","mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n"," this.id = figure_id;\n","\n"," this.ws = websocket;\n","\n"," this.supports_binary = this.ws.binaryType !== undefined;\n","\n"," if (!this.supports_binary) {\n"," var warnings = document.getElementById('mpl-warnings');\n"," if (warnings) {\n"," warnings.style.display = 'block';\n"," warnings.textContent =\n"," 'This browser does not support binary websocket messages. ' +\n"," 'Performance may be slow.';\n"," }\n"," }\n","\n"," this.imageObj = new Image();\n","\n"," this.context = undefined;\n"," this.message = undefined;\n"," this.canvas = undefined;\n"," this.rubberband_canvas = undefined;\n"," this.rubberband_context = undefined;\n"," this.format_dropdown = undefined;\n","\n"," this.image_mode = 'full';\n","\n"," this.root = document.createElement('div');\n"," this.root.setAttribute('style', 'display: inline-block');\n"," this._root_extra_style(this.root);\n","\n"," parent_element.appendChild(this.root);\n","\n"," this._init_header(this);\n"," this._init_canvas(this);\n"," this._init_toolbar(this);\n","\n"," var fig = this;\n","\n"," this.waiting = false;\n","\n"," this.ws.onopen = function () {\n"," fig.send_message('supports_binary', { value: fig.supports_binary });\n"," fig.send_message('send_image_mode', {});\n"," if (fig.ratio !== 1) {\n"," fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n"," }\n"," fig.send_message('refresh', {});\n"," };\n","\n"," this.imageObj.onload = function () {\n"," if (fig.image_mode === 'full') {\n"," // Full images could contain transparency (where diff images\n"," // almost always do), so we need to clear the canvas so that\n"," // there is no ghosting.\n"," fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n"," }\n"," fig.context.drawImage(fig.imageObj, 0, 0);\n"," };\n","\n"," this.imageObj.onunload = function () {\n"," fig.ws.close();\n"," };\n","\n"," this.ws.onmessage = this._make_on_message_function(this);\n","\n"," this.ondownload = ondownload;\n","};\n","\n","mpl.figure.prototype._init_header = function () {\n"," var titlebar = document.createElement('div');\n"," titlebar.classList =\n"," 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n"," var titletext = document.createElement('div');\n"," titletext.classList = 'ui-dialog-title';\n"," titletext.setAttribute(\n"," 'style',\n"," 'width: 100%; text-align: center; padding: 3px;'\n"," );\n"," titlebar.appendChild(titletext);\n"," this.root.appendChild(titlebar);\n"," this.header = titletext;\n","};\n","\n","mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n","\n","mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n","\n","mpl.figure.prototype._init_canvas = function () {\n"," var fig = this;\n","\n"," var canvas_div = (this.canvas_div = document.createElement('div'));\n"," canvas_div.setAttribute(\n"," 'style',\n"," 'border: 1px solid #ddd;' +\n"," 'box-sizing: content-box;' +\n"," 'clear: both;' +\n"," 'min-height: 1px;' +\n"," 'min-width: 1px;' +\n"," 'outline: 0;' +\n"," 'overflow: hidden;' +\n"," 'position: relative;' +\n"," 'resize: both;'\n"," );\n","\n"," function on_keyboard_event_closure(name) {\n"," return function (event) {\n"," return fig.key_event(event, name);\n"," };\n"," }\n","\n"," canvas_div.addEventListener(\n"," 'keydown',\n"," on_keyboard_event_closure('key_press')\n"," );\n"," canvas_div.addEventListener(\n"," 'keyup',\n"," on_keyboard_event_closure('key_release')\n"," );\n","\n"," this._canvas_extra_style(canvas_div);\n"," this.root.appendChild(canvas_div);\n","\n"," var canvas = (this.canvas = document.createElement('canvas'));\n"," canvas.classList.add('mpl-canvas');\n"," canvas.setAttribute('style', 'box-sizing: content-box;');\n","\n"," this.context = canvas.getContext('2d');\n","\n"," var backingStore =\n"," this.context.backingStorePixelRatio ||\n"," this.context.webkitBackingStorePixelRatio ||\n"," this.context.mozBackingStorePixelRatio ||\n"," this.context.msBackingStorePixelRatio ||\n"," this.context.oBackingStorePixelRatio ||\n"," this.context.backingStorePixelRatio ||\n"," 1;\n","\n"," this.ratio = (window.devicePixelRatio || 1) / backingStore;\n","\n"," var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n"," 'canvas'\n"," ));\n"," rubberband_canvas.setAttribute(\n"," 'style',\n"," 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n"," );\n","\n"," // Apply a ponyfill if ResizeObserver is not implemented by browser.\n"," if (this.ResizeObserver === undefined) {\n"," if (window.ResizeObserver !== undefined) {\n"," this.ResizeObserver = window.ResizeObserver;\n"," } else {\n"," var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n"," this.ResizeObserver = obs.ResizeObserver;\n"," }\n"," }\n","\n"," this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n"," var nentries = entries.length;\n"," for (var i = 0; i < nentries; i++) {\n"," var entry = entries[i];\n"," var width, height;\n"," if (entry.contentBoxSize) {\n"," if (entry.contentBoxSize instanceof Array) {\n"," // Chrome 84 implements new version of spec.\n"," width = entry.contentBoxSize[0].inlineSize;\n"," height = entry.contentBoxSize[0].blockSize;\n"," } else {\n"," // Firefox implements old version of spec.\n"," width = entry.contentBoxSize.inlineSize;\n"," height = entry.contentBoxSize.blockSize;\n"," }\n"," } else {\n"," // Chrome <84 implements even older version of spec.\n"," width = entry.contentRect.width;\n"," height = entry.contentRect.height;\n"," }\n","\n"," // Keep the size of the canvas and rubber band canvas in sync with\n"," // the canvas container.\n"," if (entry.devicePixelContentBoxSize) {\n"," // Chrome 84 implements new version of spec.\n"," canvas.setAttribute(\n"," 'width',\n"," entry.devicePixelContentBoxSize[0].inlineSize\n"," );\n"," canvas.setAttribute(\n"," 'height',\n"," entry.devicePixelContentBoxSize[0].blockSize\n"," );\n"," } else {\n"," canvas.setAttribute('width', width * fig.ratio);\n"," canvas.setAttribute('height', height * fig.ratio);\n"," }\n"," canvas.setAttribute(\n"," 'style',\n"," 'width: ' + width + 'px; height: ' + height + 'px;'\n"," );\n","\n"," rubberband_canvas.setAttribute('width', width);\n"," rubberband_canvas.setAttribute('height', height);\n","\n"," // And update the size in Python. We ignore the initial 0/0 size\n"," // that occurs as the element is placed into the DOM, which should\n"," // otherwise not happen due to the minimum size styling.\n"," if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n"," fig.request_resize(width, height);\n"," }\n"," }\n"," });\n"," this.resizeObserverInstance.observe(canvas_div);\n","\n"," function on_mouse_event_closure(name) {\n"," return function (event) {\n"," return fig.mouse_event(event, name);\n"," };\n"," }\n","\n"," rubberband_canvas.addEventListener(\n"," 'mousedown',\n"," on_mouse_event_closure('button_press')\n"," );\n"," rubberband_canvas.addEventListener(\n"," 'mouseup',\n"," on_mouse_event_closure('button_release')\n"," );\n"," // Throttle sequential mouse events to 1 every 20ms.\n"," rubberband_canvas.addEventListener(\n"," 'mousemove',\n"," on_mouse_event_closure('motion_notify')\n"," );\n","\n"," rubberband_canvas.addEventListener(\n"," 'mouseenter',\n"," on_mouse_event_closure('figure_enter')\n"," );\n"," rubberband_canvas.addEventListener(\n"," 'mouseleave',\n"," on_mouse_event_closure('figure_leave')\n"," );\n","\n"," canvas_div.addEventListener('wheel', function (event) {\n"," if (event.deltaY < 0) {\n"," event.step = 1;\n"," } else {\n"," event.step = -1;\n"," }\n"," on_mouse_event_closure('scroll')(event);\n"," });\n","\n"," canvas_div.appendChild(canvas);\n"," canvas_div.appendChild(rubberband_canvas);\n","\n"," this.rubberband_context = rubberband_canvas.getContext('2d');\n"," this.rubberband_context.strokeStyle = '#000000';\n","\n"," this._resize_canvas = function (width, height, forward) {\n"," if (forward) {\n"," canvas_div.style.width = width + 'px';\n"," canvas_div.style.height = height + 'px';\n"," }\n"," };\n","\n"," // Disable right mouse context menu.\n"," this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n"," event.preventDefault();\n"," return false;\n"," });\n","\n"," function set_focus() {\n"," canvas.focus();\n"," canvas_div.focus();\n"," }\n","\n"," window.setTimeout(set_focus, 100);\n","};\n","\n","mpl.figure.prototype._init_toolbar = function () {\n"," var fig = this;\n","\n"," var toolbar = document.createElement('div');\n"," toolbar.classList = 'mpl-toolbar';\n"," this.root.appendChild(toolbar);\n","\n"," function on_click_closure(name) {\n"," return function (_event) {\n"," return fig.toolbar_button_onclick(name);\n"," };\n"," }\n","\n"," function on_mouseover_closure(tooltip) {\n"," return function (event) {\n"," if (!event.currentTarget.disabled) {\n"," return fig.toolbar_button_onmouseover(tooltip);\n"," }\n"," };\n"," }\n","\n"," fig.buttons = {};\n"," var buttonGroup = document.createElement('div');\n"," buttonGroup.classList = 'mpl-button-group';\n"," for (var toolbar_ind in mpl.toolbar_items) {\n"," var name = mpl.toolbar_items[toolbar_ind][0];\n"," var tooltip = mpl.toolbar_items[toolbar_ind][1];\n"," var image = mpl.toolbar_items[toolbar_ind][2];\n"," var method_name = mpl.toolbar_items[toolbar_ind][3];\n","\n"," if (!name) {\n"," /* Instead of a spacer, we start a new button group. */\n"," if (buttonGroup.hasChildNodes()) {\n"," toolbar.appendChild(buttonGroup);\n"," }\n"," buttonGroup = document.createElement('div');\n"," buttonGroup.classList = 'mpl-button-group';\n"," continue;\n"," }\n","\n"," var button = (fig.buttons[name] = document.createElement('button'));\n"," button.classList = 'mpl-widget';\n"," button.setAttribute('role', 'button');\n"," button.setAttribute('aria-disabled', 'false');\n"," button.addEventListener('click', on_click_closure(method_name));\n"," button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n","\n"," var icon_img = document.createElement('img');\n"," icon_img.src = '_images/' + image + '.png';\n"," icon_img.srcset = '_images/' + image + '_large.png 2x';\n"," icon_img.alt = tooltip;\n"," button.appendChild(icon_img);\n","\n"," buttonGroup.appendChild(button);\n"," }\n","\n"," if (buttonGroup.hasChildNodes()) {\n"," toolbar.appendChild(buttonGroup);\n"," }\n","\n"," var fmt_picker = document.createElement('select');\n"," fmt_picker.classList = 'mpl-widget';\n"," toolbar.appendChild(fmt_picker);\n"," this.format_dropdown = fmt_picker;\n","\n"," for (var ind in mpl.extensions) {\n"," var fmt = mpl.extensions[ind];\n"," var option = document.createElement('option');\n"," option.selected = fmt === mpl.default_extension;\n"," option.innerHTML = fmt;\n"," fmt_picker.appendChild(option);\n"," }\n","\n"," var status_bar = document.createElement('span');\n"," status_bar.classList = 'mpl-message';\n"," toolbar.appendChild(status_bar);\n"," this.message = status_bar;\n","};\n","\n","mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n"," // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n"," // which will in turn request a refresh of the image.\n"," this.send_message('resize', { width: x_pixels, height: y_pixels });\n","};\n","\n","mpl.figure.prototype.send_message = function (type, properties) {\n"," properties['type'] = type;\n"," properties['figure_id'] = this.id;\n"," this.ws.send(JSON.stringify(properties));\n","};\n","\n","mpl.figure.prototype.send_draw_message = function () {\n"," if (!this.waiting) {\n"," this.waiting = true;\n"," this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n"," }\n","};\n","\n","mpl.figure.prototype.handle_save = function (fig, _msg) {\n"," var format_dropdown = fig.format_dropdown;\n"," var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n"," fig.ondownload(fig, format);\n","};\n","\n","mpl.figure.prototype.handle_resize = function (fig, msg) {\n"," var size = msg['size'];\n"," if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n"," fig._resize_canvas(size[0], size[1], msg['forward']);\n"," fig.send_message('refresh', {});\n"," }\n","};\n","\n","mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n"," var x0 = msg['x0'] / fig.ratio;\n"," var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n"," var x1 = msg['x1'] / fig.ratio;\n"," var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n"," x0 = Math.floor(x0) + 0.5;\n"," y0 = Math.floor(y0) + 0.5;\n"," x1 = Math.floor(x1) + 0.5;\n"," y1 = Math.floor(y1) + 0.5;\n"," var min_x = Math.min(x0, x1);\n"," var min_y = Math.min(y0, y1);\n"," var width = Math.abs(x1 - x0);\n"," var height = Math.abs(y1 - y0);\n","\n"," fig.rubberband_context.clearRect(\n"," 0,\n"," 0,\n"," fig.canvas.width / fig.ratio,\n"," fig.canvas.height / fig.ratio\n"," );\n","\n"," fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n","};\n","\n","mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n"," // Updates the figure title.\n"," fig.header.textContent = msg['label'];\n","};\n","\n","mpl.figure.prototype.handle_cursor = function (fig, msg) {\n"," var cursor = msg['cursor'];\n"," switch (cursor) {\n"," case 0:\n"," cursor = 'pointer';\n"," break;\n"," case 1:\n"," cursor = 'default';\n"," break;\n"," case 2:\n"," cursor = 'crosshair';\n"," break;\n"," case 3:\n"," cursor = 'move';\n"," break;\n"," }\n"," fig.rubberband_canvas.style.cursor = cursor;\n","};\n","\n","mpl.figure.prototype.handle_message = function (fig, msg) {\n"," fig.message.textContent = msg['message'];\n","};\n","\n","mpl.figure.prototype.handle_draw = function (fig, _msg) {\n"," // Request the server to send over a new figure.\n"," fig.send_draw_message();\n","};\n","\n","mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n"," fig.image_mode = msg['mode'];\n","};\n","\n","mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n"," for (var key in msg) {\n"," if (!(key in fig.buttons)) {\n"," continue;\n"," }\n"," fig.buttons[key].disabled = !msg[key];\n"," fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n"," }\n","};\n","\n","mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n"," if (msg['mode'] === 'PAN') {\n"," fig.buttons['Pan'].classList.add('active');\n"," fig.buttons['Zoom'].classList.remove('active');\n"," } else if (msg['mode'] === 'ZOOM') {\n"," fig.buttons['Pan'].classList.remove('active');\n"," fig.buttons['Zoom'].classList.add('active');\n"," } else {\n"," fig.buttons['Pan'].classList.remove('active');\n"," fig.buttons['Zoom'].classList.remove('active');\n"," }\n","};\n","\n","mpl.figure.prototype.updated_canvas_event = function () {\n"," // Called whenever the canvas gets updated.\n"," this.send_message('ack', {});\n","};\n","\n","// A function to construct a web socket function for onmessage handling.\n","// Called in the figure constructor.\n","mpl.figure.prototype._make_on_message_function = function (fig) {\n"," return function socket_on_message(evt) {\n"," if (evt.data instanceof Blob) {\n"," /* FIXME: We get \"Resource interpreted as Image but\n"," * transferred with MIME type text/plain:\" errors on\n"," * Chrome. But how to set the MIME type? It doesn't seem\n"," * to be part of the websocket stream */\n"," evt.data.type = 'image/png';\n","\n"," /* Free the memory for the previous frames */\n"," if (fig.imageObj.src) {\n"," (window.URL || window.webkitURL).revokeObjectURL(\n"," fig.imageObj.src\n"," );\n"," }\n","\n"," fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n"," evt.data\n"," );\n"," fig.updated_canvas_event();\n"," fig.waiting = false;\n"," return;\n"," } else if (\n"," typeof evt.data === 'string' &&\n"," evt.data.slice(0, 21) === 'data:image/png;base64'\n"," ) {\n"," fig.imageObj.src = evt.data;\n"," fig.updated_canvas_event();\n"," fig.waiting = false;\n"," return;\n"," }\n","\n"," var msg = JSON.parse(evt.data);\n"," var msg_type = msg['type'];\n","\n"," // Call the \"handle_{type}\" callback, which takes\n"," // the figure and JSON message as its only arguments.\n"," try {\n"," var callback = fig['handle_' + msg_type];\n"," } catch (e) {\n"," console.log(\n"," \"No handler for the '\" + msg_type + \"' message type: \",\n"," msg\n"," );\n"," return;\n"," }\n","\n"," if (callback) {\n"," try {\n"," // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n"," callback(fig, msg);\n"," } catch (e) {\n"," console.log(\n"," \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n"," e,\n"," e.stack,\n"," msg\n"," );\n"," }\n"," }\n"," };\n","};\n","\n","// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n","mpl.findpos = function (e) {\n"," //this section is from http://www.quirksmode.org/js/events_properties.html\n"," var targ;\n"," if (!e) {\n"," e = window.event;\n"," }\n"," if (e.target) {\n"," targ = e.target;\n"," } else if (e.srcElement) {\n"," targ = e.srcElement;\n"," }\n"," if (targ.nodeType === 3) {\n"," // defeat Safari bug\n"," targ = targ.parentNode;\n"," }\n","\n"," // pageX,Y are the mouse positions relative to the document\n"," var boundingRect = targ.getBoundingClientRect();\n"," var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n"," var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n","\n"," return { x: x, y: y };\n","};\n","\n","/*\n"," * return a copy of an object with only non-object keys\n"," * we need this to avoid circular references\n"," * http://stackoverflow.com/a/24161582/3208463\n"," */\n","function simpleKeys(original) {\n"," return Object.keys(original).reduce(function (obj, key) {\n"," if (typeof original[key] !== 'object') {\n"," obj[key] = original[key];\n"," }\n"," return obj;\n"," }, {});\n","}\n","\n","mpl.figure.prototype.mouse_event = function (event, name) {\n"," var canvas_pos = mpl.findpos(event);\n","\n"," if (name === 'button_press') {\n"," this.canvas.focus();\n"," this.canvas_div.focus();\n"," }\n","\n"," var x = canvas_pos.x * this.ratio;\n"," var y = canvas_pos.y * this.ratio;\n","\n"," this.send_message(name, {\n"," x: x,\n"," y: y,\n"," button: event.button,\n"," step: event.step,\n"," guiEvent: simpleKeys(event),\n"," });\n","\n"," /* This prevents the web browser from automatically changing to\n"," * the text insertion cursor when the button is pressed. We want\n"," * to control all of the cursor setting manually through the\n"," * 'cursor' event from matplotlib */\n"," event.preventDefault();\n"," return false;\n","};\n","\n","mpl.figure.prototype._key_event_extra = function (_event, _name) {\n"," // Handle any extra behaviour associated with a key event\n","};\n","\n","mpl.figure.prototype.key_event = function (event, name) {\n"," // Prevent repeat events\n"," if (name === 'key_press') {\n"," if (event.which === this._key) {\n"," return;\n"," } else {\n"," this._key = event.which;\n"," }\n"," }\n"," if (name === 'key_release') {\n"," this._key = null;\n"," }\n","\n"," var value = '';\n"," if (event.ctrlKey && event.which !== 17) {\n"," value += 'ctrl+';\n"," }\n"," if (event.altKey && event.which !== 18) {\n"," value += 'alt+';\n"," }\n"," if (event.shiftKey && event.which !== 16) {\n"," value += 'shift+';\n"," }\n","\n"," value += 'k';\n"," value += event.which.toString();\n","\n"," this._key_event_extra(event, name);\n","\n"," this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n"," return false;\n","};\n","\n","mpl.figure.prototype.toolbar_button_onclick = function (name) {\n"," if (name === 'download') {\n"," this.handle_save(this, null);\n"," } else {\n"," this.send_message('toolbar_button', { name: name });\n"," }\n","};\n","\n","mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n"," this.message.textContent = tooltip;\n","};\n","\n","///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n","// prettier-ignore\n","var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n","mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n","\n","mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n","\n","mpl.default_extension = \"png\";/* global mpl */\n","\n","var comm_websocket_adapter = function (comm) {\n"," // Create a \"websocket\"-like object which calls the given IPython comm\n"," // object with the appropriate methods. Currently this is a non binary\n"," // socket, so there is still some room for performance tuning.\n"," var ws = {};\n","\n"," ws.close = function () {\n"," comm.close();\n"," };\n"," ws.send = function (m) {\n"," //console.log('sending', m);\n"," comm.send(m);\n"," };\n"," // Register the callback with on_msg.\n"," comm.on_msg(function (msg) {\n"," //console.log('receiving', msg['content']['data'], msg);\n"," // Pass the mpl event to the overridden (by mpl) onmessage function.\n"," ws.onmessage(msg['content']['data']);\n"," });\n"," return ws;\n","};\n","\n","mpl.mpl_figure_comm = function (comm, msg) {\n"," // This is the function which gets called when the mpl process\n"," // starts-up an IPython Comm through the \"matplotlib\" channel.\n","\n"," var id = msg.content.data.id;\n"," // Get hold of the div created by the display call when the Comm\n"," // socket was opened in Python.\n"," var element = document.getElementById(id);\n"," var ws_proxy = comm_websocket_adapter(comm);\n","\n"," function ondownload(figure, _format) {\n"," window.open(figure.canvas.toDataURL());\n"," }\n","\n"," var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n","\n"," // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n"," // web socket which is closed, not our websocket->open comm proxy.\n"," ws_proxy.onopen();\n","\n"," fig.parent_element = element;\n"," fig.cell_info = mpl.find_output_cell(\"
\");\n"," if (!fig.cell_info) {\n"," console.error('Failed to find cell for figure', id, fig);\n"," return;\n"," }\n"," fig.cell_info[0].output_area.element.on(\n"," 'cleared',\n"," { fig: fig },\n"," fig._remove_fig_handler\n"," );\n","};\n","\n","mpl.figure.prototype.handle_close = function (fig, msg) {\n"," var width = fig.canvas.width / fig.ratio;\n"," fig.cell_info[0].output_area.element.off(\n"," 'cleared',\n"," fig._remove_fig_handler\n"," );\n"," fig.resizeObserverInstance.unobserve(fig.canvas_div);\n","\n"," // Update the output cell to use the data from the current canvas.\n"," fig.push_to_output();\n"," var dataURL = fig.canvas.toDataURL();\n"," // Re-enable the keyboard manager in IPython - without this line, in FF,\n"," // the notebook keyboard shortcuts fail.\n"," IPython.keyboard_manager.enable();\n"," fig.parent_element.innerHTML =\n"," '';\n"," fig.close_ws(fig, msg);\n","};\n","\n","mpl.figure.prototype.close_ws = function (fig, msg) {\n"," fig.send_message('closing', msg);\n"," // fig.ws.close()\n","};\n","\n","mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n"," // Turn the data on the canvas into data in the output cell.\n"," var width = this.canvas.width / this.ratio;\n"," var dataURL = this.canvas.toDataURL();\n"," this.cell_info[1]['text/html'] =\n"," '';\n","};\n","\n","mpl.figure.prototype.updated_canvas_event = function () {\n"," // Tell IPython that the notebook contents must change.\n"," IPython.notebook.set_dirty(true);\n"," this.send_message('ack', {});\n"," var fig = this;\n"," // Wait a second, then push the new image to the DOM so\n"," // that it is saved nicely (might be nice to debounce this).\n"," setTimeout(function () {\n"," fig.push_to_output();\n"," }, 1000);\n","};\n","\n","mpl.figure.prototype._init_toolbar = function () {\n"," var fig = this;\n","\n"," var toolbar = document.createElement('div');\n"," toolbar.classList = 'btn-toolbar';\n"," this.root.appendChild(toolbar);\n","\n"," function on_click_closure(name) {\n"," return function (_event) {\n"," return fig.toolbar_button_onclick(name);\n"," };\n"," }\n","\n"," function on_mouseover_closure(tooltip) {\n"," return function (event) {\n"," if (!event.currentTarget.disabled) {\n"," return fig.toolbar_button_onmouseover(tooltip);\n"," }\n"," };\n"," }\n","\n"," fig.buttons = {};\n"," var buttonGroup = document.createElement('div');\n"," buttonGroup.classList = 'btn-group';\n"," var button;\n"," for (var toolbar_ind in mpl.toolbar_items) {\n"," var name = mpl.toolbar_items[toolbar_ind][0];\n"," var tooltip = mpl.toolbar_items[toolbar_ind][1];\n"," var image = mpl.toolbar_items[toolbar_ind][2];\n"," var method_name = mpl.toolbar_items[toolbar_ind][3];\n","\n"," if (!name) {\n"," /* Instead of a spacer, we start a new button group. */\n"," if (buttonGroup.hasChildNodes()) {\n"," toolbar.appendChild(buttonGroup);\n"," }\n"," buttonGroup = document.createElement('div');\n"," buttonGroup.classList = 'btn-group';\n"," continue;\n"," }\n","\n"," button = fig.buttons[name] = document.createElement('button');\n"," button.classList = 'btn btn-default';\n"," button.href = '#';\n"," button.title = name;\n"," button.innerHTML = '';\n"," button.addEventListener('click', on_click_closure(method_name));\n"," button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n"," buttonGroup.appendChild(button);\n"," }\n","\n"," if (buttonGroup.hasChildNodes()) {\n"," toolbar.appendChild(buttonGroup);\n"," }\n","\n"," // Add the status bar.\n"," var status_bar = document.createElement('span');\n"," status_bar.classList = 'mpl-message pull-right';\n"," toolbar.appendChild(status_bar);\n"," this.message = status_bar;\n","\n"," // Add the close button to the window.\n"," var buttongrp = document.createElement('div');\n"," buttongrp.classList = 'btn-group inline pull-right';\n"," button = document.createElement('button');\n"," button.classList = 'btn btn-mini btn-primary';\n"," button.href = '#';\n"," button.title = 'Stop Interaction';\n"," button.innerHTML = '';\n"," button.addEventListener('click', function (_evt) {\n"," fig.handle_close(fig, {});\n"," });\n"," button.addEventListener(\n"," 'mouseover',\n"," on_mouseover_closure('Stop Interaction')\n"," );\n"," buttongrp.appendChild(button);\n"," var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n"," titlebar.insertBefore(buttongrp, titlebar.firstChild);\n","};\n","\n","mpl.figure.prototype._remove_fig_handler = function (event) {\n"," var fig = event.data.fig;\n"," if (event.target !== this) {\n"," // Ignore bubbled events from children.\n"," return;\n"," }\n"," fig.close_ws(fig, {});\n","};\n","\n","mpl.figure.prototype._root_extra_style = function (el) {\n"," el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n","};\n","\n","mpl.figure.prototype._canvas_extra_style = function (el) {\n"," // this is important to make the div 'focusable\n"," el.setAttribute('tabindex', 0);\n"," // reach out to IPython and tell the keyboard manager to turn it's self\n"," // off when our div gets focus\n","\n"," // location in version 3\n"," if (IPython.notebook.keyboard_manager) {\n"," IPython.notebook.keyboard_manager.register_events(el);\n"," } else {\n"," // location in version 2\n"," IPython.keyboard_manager.register_events(el);\n"," }\n","};\n","\n","mpl.figure.prototype._key_event_extra = function (event, _name) {\n"," var manager = IPython.notebook.keyboard_manager;\n"," if (!manager) {\n"," manager = IPython.keyboard_manager;\n"," }\n","\n"," // Check for shift+enter\n"," if (event.shiftKey && event.which === 13) {\n"," this.canvas_div.blur();\n"," // select the cell after this one\n"," var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n"," IPython.notebook.select(index + 1);\n"," }\n","};\n","\n","mpl.figure.prototype.handle_save = function (fig, _msg) {\n"," fig.ondownload(fig, null);\n","};\n","\n","mpl.find_output_cell = function (html_output) {\n"," // Return the cell and output element which can be found *uniquely* in the notebook.\n"," // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n"," // IPython event is triggered only after the cells have been serialised, which for\n"," // our purposes (turning an active figure into a static one), is too late.\n"," var cells = IPython.notebook.get_cells();\n"," var ncells = cells.length;\n"," for (var i = 0; i < ncells; i++) {\n"," var cell = cells[i];\n"," if (cell.cell_type === 'code') {\n"," for (var j = 0; j < cell.output_area.outputs.length; j++) {\n"," var data = cell.output_area.outputs[j];\n"," if (data.data) {\n"," // IPython >= 3 moved mimebundle to data attribute of output\n"," data = data.data;\n"," }\n"," if (data['text/html'] === html_output) {\n"," return [cell, data, j];\n"," }\n"," }\n"," }\n"," }\n","};\n","\n","// Register the function which deals with the matplotlib target/channel.\n","// The kernel may be null if the page has been refreshed.\n","if (IPython.notebook.kernel !== null) {\n"," IPython.notebook.kernel.comm_manager.register_target(\n"," 'matplotlib',\n"," mpl.mpl_figure_comm\n"," );\n","}\n"],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":[""],"text/plain":[""]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fc0939117b6041269f471bb3e7f0bb15","version_major":2,"version_minor":0},"text/plain":["Button(description='Clear all', style=ButtonStyle())"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"45115b5ac15345259cce235abbfdb277","version_major":2,"version_minor":0},"text/plain":["Button(description='Submit', style=ButtonStyle())"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"924892819d544e27be446f0a269ded99","version_major":2,"version_minor":0},"text/plain":["IntSlider(value=0, max=10, min=-1)"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f44afedea0454db2b716a74ca160074e","version_major":2,"version_minor":0},"text/plain":["Dropdown(description='Species plot 1', options={'A': 0, 'B': 1}, value=0)"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"eca5fc5601424c1686b7a94ab6cab267","version_major":2,"version_minor":0},"text/plain":["Dropdown(description='Species plot 2', index=1, options={'A': 0, 'B': 1}, value=1)"]},"metadata":{},"output_type":"display_data"},{"data":{"text/html":["
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alpha1alpha2betagammamu
011.44144111.4414412.9009014.8388392.469469
114.56456514.5645653.6056064.2862862.583584
215.72572612.5025035.4674673.4734731.782783
326.57657723.3533532.3923925.0110111.590591
419.62963022.8528534.5905915.9439442.837838
..................
49512.84284312.8428435.5355362.3043042.393393
49626.37637628.7587595.0430432.4164161.520521
49711.52152213.1231234.2662664.9349352.515516
49815.14514515.1451454.0340343.2052051.088088
49927.41741724.1941945.2112112.5845852.845846
\n","

500 rows × 5 columns

\n","
"],"text/plain":[" alpha1 alpha2 beta gamma mu\n","0 11.441441 11.441441 2.900901 4.838839 2.469469\n","1 14.564565 14.564565 3.605606 4.286286 2.583584\n","2 15.725726 12.502503 5.467467 3.473473 1.782783\n","3 26.576577 23.353353 2.392392 5.011011 1.590591\n","4 19.629630 22.852853 4.590591 5.943944 2.837838\n",".. ... ... ... ... ...\n","495 12.842843 12.842843 5.535536 2.304304 2.393393\n","496 26.376376 28.758759 5.043043 2.416416 1.520521\n","497 11.521522 13.123123 4.266266 4.934935 2.515516\n","498 15.145145 15.145145 4.034034 3.205205 1.088088\n","499 27.417417 24.194194 5.211211 2.584585 2.845846\n","\n","[500 rows x 5 columns]"]},"metadata":{},"output_type":"display_data"}],"source":["met.explore(dr_method='umap')"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[],"source":["met.data.user_labels = user_labels"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Toggle_Switch/Toggle_SwitchSciopeModelInference.ipynb b/public_models/Toggle_Switch/Toggle_SwitchSciopeModelInference.ipynb deleted file mode 100644 index 2ec6bf001f..0000000000 --- a/public_models/Toggle_Switch/Toggle_SwitchSciopeModelInference.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","from gillespy2 import VariableSSACSolver\n","# from gillespy2 import TauLeapingSolver\n","# from gillespy2 import TauHybridSolver\n","# from gillespy2 import ODESolver"]},{"cell_type":"markdown","metadata":{},"source":["# Toggle_Switch"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ToggleSwitch(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Toggle_Switch\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha1\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"alpha2\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"beta\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"gamma\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"mu\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"A\", initial_value=2, mode=\"discrete\"))\n"," self.add_species(Species(name=\"B\", initial_value=2, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"cu\", reactants={}, products={'A': 1}, propensity_function=\"alpha1/(1+pow(B, beta))\"))\n"," self.add_reaction(Reaction(name=\"cv\", reactants={}, products={'B': 1}, propensity_function=\"alpha2/(1+pow(A, gamma))\"))\n"," self.add_reaction(Reaction(name=\"du\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"dv\", reactants={'B': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 250, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = ToggleSwitch()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," solver = VariableSSACSolver(model=model)\n"," kwargs = {\n"," \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"markdown","metadata":{},"source":["# Model Inference"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2\n","\n","from tsfresh.feature_extraction.settings import MinimalFCParameters\n","from sciope.utilities.priors import uniform_prior\n","from sciope.utilities.summarystats import auto_tsfresh\n","from sciope.utilities.distancefunctions import naive_squared\n","from sciope.inference.abc_inference import ABC\n","from sklearn.metrics import mean_absolute_error\n","from dask.distributed import Client"]},{"cell_type":"markdown","metadata":{},"source":["## Generate some fixed(observed) data based on default parameters of the model"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","fixed_data = model.run(**kwargs)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Reshape the data and remove timepoints array\n","fixed_data = fixed_data.to_array()\n","fixed_data = np.asarray([x.T for x in fixed_data])\n","fixed_data = fixed_data[:, 1:, :]"]},{"cell_type":"markdown","metadata":{},"source":["## Define prior distribution"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# take default from mode 1 as reference\n","default_param = np.array(list(model.listOfParameters.items()))[:, 1]\n","\n","bound = []\n","for exp in default_param:\n"," bound.append(float(exp.expression))\n","\n","# Set the bounds\n","bound = np.array(bound)\n","dmin = bound * 0.1\n","dmax = bound * 2.0\n","\n","# Here we use uniform prior\n","uni_prior = uniform_prior.UniformPrior(dmin, dmax)"]},{"cell_type":"markdown","metadata":{},"source":["## Define simulator"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def get_variables(params, model):\n"," # params - array, need to have the same order as model.listOfParameters\n"," variables = {}\n"," for e, pname in enumerate(model.listOfParameters.keys()):\n"," variables[pname] = params[e]\n"," return variables\n","\n","# Here we use the GillesPy2 Solver\n","def simulator(params, model):\n"," variables = get_variables(params, model)\n","\n"," res = model.run(**kwargs, variables=variables)\n"," res = res.to_array()\n"," tot_res = np.asarray([x.T for x in res]) # reshape to (N, S, T)\n"," # should not contain timepoints\n"," tot_res = tot_res[:, 1:, :]\n","\n"," return tot_res\n","\n","# Wrapper, simulator function to abc should should only take one argument (the parameter point)\n","def simulator2(x):\n"," return simulator(x, model=model)"]},{"cell_type":"markdown","metadata":{},"source":["## Define summary statistics and distance function"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Function to generate summary statistics\n","summ_func = auto_tsfresh.SummariesTSFRESH()\n","\n","# Distance\n","ns = naive_squared.NaiveSquaredDistance()"]},{"cell_type":"markdown","metadata":{},"source":["## Start local cluster using dask client"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["c = Client()"]},{"cell_type":"markdown","metadata":{},"source":["## Start abc instance"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["abc = ABC(fixed_data, sim=simulator2, prior_function=uni_prior, summaries_function=summ_func.compute, distance_function=ns)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# First compute the fixed(observed) mean\n","abc.compute_fixed_mean(chunk_size=2)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["res = abc.infer(num_samples=100, batch_size=10, chunk_size=2)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["mae_inference = mean_absolute_error(bound, abc.results['inferred_parameters'])"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Toggle_Switch/Toggle_Switch_1D_Parameter_Sweep.ipynb b/public_models/Toggle_Switch/Toggle_Switch_1D_Parameter_Sweep.ipynb new file mode 100644 index 0000000000..9ce97948ff --- /dev/null +++ b/public_models/Toggle_Switch/Toggle_Switch_1D_Parameter_Sweep.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Toggle_Switch"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ToggleSwitch(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Toggle_Switch\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha1\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"alpha2\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"beta\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"gamma\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"mu\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"A\", initial_value=2, mode=\"discrete\"))\n"," self.add_species(Species(name=\"B\", initial_value=2, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"cu\", reactants={}, products={'A': 1}, propensity_function=\"alpha1/(1+pow(B, beta))\"))\n"," self.add_reaction(Reaction(name=\"cv\", reactants={}, products={'B': 1}, propensity_function=\"alpha2/(1+pow(A, gamma))\"))\n"," self.add_reaction(Reaction(name=\"du\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"dv\", reactants={'B': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 250, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = ToggleSwitch()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," solver = SSACSolver(model=model)\n"," kwargs = {\n"," \"solver\":solver,\n"," \"number_of_trajectories\":20,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"markdown","metadata":{},"source":["# Post Processing"]},{"cell_type":"markdown","metadata":{},"source":["## Feature extraction function"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# What value(s) do you want to extract from the simulation trajectory\n","def population_at_last_timepoint(c, res):\n"," if c.verbose:\n"," print(f'population_at_last_timepoint {c.variable_of_interest}={res[c.variable_of_interest][-1]}')\n"," return res[c.variable_of_interest][-1]"]},{"cell_type":"markdown","metadata":{},"source":["## Aggregation function"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# How do we combine the values from multiple trajectores\n","def mean_std_of_ensemble(c, data):\n"," a = np.average(data)\n"," s = np.std(data)\n"," if c.verbose:\n"," print(f'mean_std_of_ensemble m:{a} s:{s}')\n"," return (a, s)"]},{"cell_type":"markdown","metadata":{},"source":["# Parameter Sweep"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ParameterSweep1D():\n","\n"," def run(c, kwargs, verbose=False):\n"," c.verbose = verbose\n"," fn = c.feature_extraction\n"," ag = c.ensemble_aggragator\n"," data = np.zeros((len(c.p1_range), 2)) # mean and std\n"," for i, v1 in enumerate(c.p1_range):\n"," if c.verbose:\n"," print(f'running {c.p1}={v1}')\n"," if(c.number_of_trajectories > 1):\n"," tmp_results = model.run(**kwargs, variables={c.p1:v1})\n"," (m, s) = ag([fn(x) for x in tmp_results])\n"," data[i, 0] = m\n"," data[i, 1] = s\n"," else:\n"," tmp_result = model.run(**kwargs, variables={c.p1:v1})\n"," data[i, 0] = c.feature_extraction(tmp_result)\n"," c.data = data\n","\n","\n"," def plot(c):\n"," from matplotlib import pyplot as plt\n"," from mpl_toolkits.axes_grid1 import make_axes_locatable\n"," fig, ax = plt.subplots(figsize=(8, 8))\n"," plt.title(f'Parameter Sweep - Variable:{c.variable_of_interest}')\n"," plt.errorbar(c.p1_range, c.data[:, 0], c.data[:, 1])\n"," plt.xlabel(c.p1, fontsize=16, fontweight='bold')\n"," plt.ylabel('Population', fontsize=16, fontweight='bold')\n","\n","\n"," def plotplotly(c, return_plotly_figure=False):\n"," from plotly.offline import iplot\n"," import plotly.graph_objs as go\n","\n"," visible = c.number_of_trajectories > 1\n"," error_y = dict(type='data', array=c.data[:, 1], visible=visible)\n","\n"," trace_list = [go.Scatter(x=c.p1_range, y=c.data[:, 0], error_y=error_y)]\n","\n"," title = dict(text=f'Parameter Sweep - Variable: {c.variable_of_interest}', x=0.5)\n"," yaxis_label = dict(title='Population')\n"," xaxis_label = dict(title=f'{c.p1}')\n","\n"," layout = go.Layout(title=title, xaxis=xaxis_label, yaxis=yaxis_label)\n","\n"," fig = dict(data=trace_list, layout=layout)\n","\n"," if return_plotly_figure:\n"," return fig\n"," iplot(fig)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ParameterSweepConfig(ParameterSweep1D):\n"," # What class defines the GillesPy2 model\n"," model = ToggleSwitch()\n"," # ENTER PARAMETER HERE\n"," p1 = 'alpha1'\n"," # ENTER START VALUE FOR P1 RANGE HERE\n"," p1_min = 0.5 * float(eval(model.get_parameter(p1).expression))\n"," # ENTER END VALUE FOR P1 RANGE HERE\n"," p1_max = 1.5 * float(eval(model.get_parameter(p1).expression))\n"," # ENTER THE NUMBER OF STEPS FOR P1 HERE\n"," p1_steps = 11\n"," p1_range = np.linspace(p1_min, p1_max, p1_steps)\n"," # ENTER VARIABLE OF INTEREST HERE\n"," variable_of_interest = 'A'\n"," number_of_trajectories = 20\n"," # What feature of the simulation are we examining\n"," feature_extraction = population_at_last_timepoint\n"," # for ensemble resutls: how do we aggreggate the values\n"," ensemble_aggragator = mean_std_of_ensemble"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","ps = ParameterSweepConfig()\n","%time ps.run(kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["ps.plot()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["ps.plotplotly()"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Toggle_Switch/Toggle_Switch_2D_Parameter_Sweep.ipynb b/public_models/Toggle_Switch/Toggle_Switch_2D_Parameter_Sweep.ipynb new file mode 100644 index 0000000000..c5992dd1cf --- /dev/null +++ b/public_models/Toggle_Switch/Toggle_Switch_2D_Parameter_Sweep.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Toggle_Switch"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ToggleSwitch(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Toggle_Switch\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha1\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"alpha2\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"beta\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"gamma\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"mu\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"A\", initial_value=2, mode=\"discrete\"))\n"," self.add_species(Species(name=\"B\", initial_value=2, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"cu\", reactants={}, products={'A': 1}, propensity_function=\"alpha1/(1+pow(B, beta))\"))\n"," self.add_reaction(Reaction(name=\"cv\", reactants={}, products={'B': 1}, propensity_function=\"alpha2/(1+pow(A, gamma))\"))\n"," self.add_reaction(Reaction(name=\"du\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"dv\", reactants={'B': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 250, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = ToggleSwitch()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," solver = SSACSolver(model=model)\n"," kwargs = {\n"," \"solver\":solver,\n"," \"number_of_trajectories\":20,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"markdown","metadata":{},"source":["# Post Processing"]},{"cell_type":"markdown","metadata":{},"source":["## Feature extraction function"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# What value(s) do you want to extract from the simulation trajectory\n","def population_at_last_timepoint(c, res):\n"," if c.verbose:\n"," print(f'population_at_last_timepoint {c.variable_of_interest}={res[c.variable_of_interest][-1]}')\n"," return res[c.variable_of_interest][-1]"]},{"cell_type":"markdown","metadata":{},"source":["## Aggregation function"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# How do we combine the values from multiple trajectores\n","def average_of_ensemble(c, data):\n"," a = np.average(data)\n"," if c.verbose:\n"," print(f'average_of_ensemble = {a}')\n"," return a"]},{"cell_type":"markdown","metadata":{},"source":["# Parameter Sweep"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ParameterSweep2D():\n","\n"," def run(c, kwargs, verbose=False):\n"," c.verbose = verbose\n"," fn = c.feature_extraction\n"," ag = c.ensemble_aggragator\n"," data = np.zeros((len(c.p1_range), len(c.p2_range)))\n"," for i, v1 in enumerate(c.p1_range):\n"," for j, v2 in enumerate(c.p2_range):\n"," if c.verbose:\n"," print(f'running {c.p1}={v1}, {c.p2}={v2}')\n"," if(c.number_of_trajectories > 1):\n"," tmp_results = model.run(**kwargs, variables={c.p1:v1, c.p2:v2})\n"," data[i, j] = ag([fn(x) for x in tmp_results])\n"," else:\n"," tmp_result = model.run(**kwargs, variables={c.p1:v1, c.p2:v2})\n"," data[i, j] = c.feature_extraction(tmp_result)\n"," c.data = data\n","\n","\n"," def plot(c):\n"," from matplotlib import pyplot as plt\n"," from mpl_toolkits.axes_grid1 import make_axes_locatable\n"," fig, ax = plt.subplots(figsize=(8, 8))\n"," plt.imshow(c.data)\n"," ax.set_xticks(np.arange(c.data.shape[1]) + 0.5, minor=False)\n"," ax.set_yticks(np.arange(c.data.shape[0]) + 0.5, minor=False)\n"," plt.title(f'Parameter Sweep - Variable: {c.variable_of_interest}')\n"," ax.set_xticklabels(c.p1_range, minor=False, rotation=90)\n"," ax.set_yticklabels(c.p2_range, minor=False)\n"," ax.set_xlabel(c.p1, fontsize=16, fontweight='bold')\n"," ax.set_ylabel(c.p2, fontsize=16, fontweight='bold')\n"," divider = make_axes_locatable(ax)\n"," cax = divider.append_axes('right', size='5%', pad=0.2)\n"," _ = plt.colorbar(ax=ax, cax=cax)\n","\n","\n"," def plotplotly(c, return_plotly_figure=False):\n"," from plotly.offline import init_notebook_mode, iplot\n"," import plotly.graph_objs as go\n","\n"," xaxis_ticks = c.p1_range\n"," yaxis_ticks = c.p2_range\n","\n"," trace_list = [go.Heatmap(z=c.data, x=xaxis_ticks, y=yaxis_ticks)]\n"," title = dict(text=f'Parameter Sweep - Variable: {c.variable_of_interest}', x=0.5)\n"," xaxis_label = dict(title=f'{c.p1}')\n"," yaxis_label = dict(title=f'{c.p2}')\n","\n"," layout = go.Layout(title=title, xaxis=xaxis_label, yaxis=yaxis_label)\n","\n"," fig = dict(data=trace_list, layout=layout)\n","\n"," if return_plotly_figure:\n"," return fig\n"," iplot(fig)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ParameterSweepConfig(ParameterSweep2D):\n"," # What class defines the GillesPy2 model\n"," model = ToggleSwitch()\n"," # ENTER PARAMETER 1 HERE\n"," p1 = 'alpha1'\n"," # ENTER PARAMETER 2 HERE\n"," p2 = 'alpha2'\n"," # ENTER START VALUE FOR P1 RANGE HERE\n"," p1_min = 0.5 * float(eval(model.get_parameter(p1).expression))\n"," # ENTER END VALUE FOR P1 RANGE HERE\n"," p1_max = 1.5 * float(eval(model.get_parameter(p1).expression))\n"," # ENTER THE NUMBER OF STEPS FOR P1 HERE\n"," p1_steps = 11\n"," p1_range = np.linspace(p1_min, p1_max, p1_steps)\n"," # ENTER START VALUE FOR P2 RANGE HERE\n"," p2_min = 0.5 * float(eval(model.get_parameter(p2).expression))\n"," # ENTER END VALUE FOR P2 RANGE HERE\n"," p2_max = 1.5 * float(eval(model.get_parameter(p2).expression))\n"," # ENTER THE NUMBER OF STEPS FOR P2 HERE\n"," p2_steps = 11\n"," p2_range = np.linspace(p2_min, p2_max, p2_steps)\n"," # ENTER VARIABLE OF INTEREST HERE\n"," variable_of_interest = 'A'\n"," number_of_trajectories = 20\n"," # What feature of the simulation are we examining\n"," feature_extraction = population_at_last_timepoint\n"," # for ensemble resutls: how do we aggreggate the values\n"," ensemble_aggragator = average_of_ensemble"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","ps = ParameterSweepConfig()\n","%time ps.run(kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["ps.plot()"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["ps.plotplotly()"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Toggle_Switch/Toggle_Switch_SCIOPE_Model_Exploration.ipynb b/public_models/Toggle_Switch/Toggle_Switch_SCIOPE_Model_Exploration.ipynb new file mode 100644 index 0000000000..62f442c900 --- /dev/null +++ b/public_models/Toggle_Switch/Toggle_Switch_SCIOPE_Model_Exploration.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","%matplotlib notebook\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Toggle_Switch"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ToggleSwitch(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Toggle_Switch\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha1\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"alpha2\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"beta\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"gamma\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"mu\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"A\", initial_value=2, mode=\"discrete\"))\n"," self.add_species(Species(name=\"B\", initial_value=2, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"cu\", reactants={}, products={'A': 1}, propensity_function=\"alpha1/(1+pow(B, beta))\"))\n"," self.add_reaction(Reaction(name=\"cv\", reactants={}, products={'B': 1}, propensity_function=\"alpha2/(1+pow(A, gamma))\"))\n"," self.add_reaction(Reaction(name=\"du\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"dv\", reactants={'B': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 250, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = ToggleSwitch()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," solver = SSACSolver(model=model)\n"," kwargs = {\n"," \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"markdown","metadata":{},"source":["# Model Exploration"]},{"cell_type":"markdown","metadata":{},"source":["## Define simulator function (using gillespy2 wrapper)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sciope.utilities.gillespy2 import wrapper\n","settings = configure_simulation()\n","simulator = wrapper.get_simulator(gillespy_model=model, run_settings=settings, species_of_interest=['A', 'B'])\n","expression_array = wrapper.get_parameter_expression_array(model)"]},{"cell_type":"markdown","metadata":{},"source":["## Start local cluster using dask client"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from dask.distributed import Client\n","\n","c = Client()"]},{"cell_type":"markdown","metadata":{},"source":["## Define parameter sampler/design and summary statistics"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sciope.designs import latin_hypercube_sampling\n","from sciope.utilities.summarystats.auto_tsfresh import SummariesTSFRESH\n","\n","lhc = latin_hypercube_sampling.LatinHypercube(xmin=expression_array, xmax=expression_array*3)\n","lhc.generate_array(1000) # creates a LHD of size 1000\n","\n","# will use default minimal set of features\n","summary_stats = SummariesTSFRESH()"]},{"cell_type":"markdown","metadata":{},"source":["## Initiate StochMET"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sciope.stochmet.stochmet import StochMET\n","\n","met = StochMET(simulator, lhc, summary_stats)"]},{"cell_type":"markdown","metadata":{},"source":["## Run parameter sweep"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["met.compute(n_points=500, chunk_size=10)"]},{"cell_type":"markdown","metadata":{},"source":["## Explore the result"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# First lets add some appropiate information about the model and features\n","met.data.configurations['listOfParameters'] = list(model.listOfParameters.keys())\n","met.data.configurations['listOfSpecies'] = list(model.listOfSpecies.keys())\n","met.data.configurations['listOfSummaries'] = met.summaries.features\n","met.data.configurations['timepoints'] = model.tspan"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Here we use UMAP for dimension reduction\n","met.explore(dr_method='umap')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sciope.models.label_propagation import LPModel\n","# here lets use the dimension reduction embedding as input data\n","data = met.dr_model.embedding_\n","\n","model_lp = LPModel()\n","# train using basinhopping\n","model_lp.train(data, met.data.user_labels, min_=0.01, max_=10, niter=50)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# just to vislualize the result we will map the label distribution to the user_labels\n","# (will enable us to see the LP model output when using method 'explore')\n","user_labels = np.copy(met.data.user_labels)\n","# takes the label corresponding to index 0\n","met.data.user_labels = model_lp.model.label_distributions_[:, 0]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["met.explore(dr_method='umap')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["met.data.user_labels = user_labels"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Toggle_Switch/Toggle_Switch_SCIOPE_Model_Inference.ipynb b/public_models/Toggle_Switch/Toggle_Switch_SCIOPE_Model_Inference.ipynb new file mode 100644 index 0000000000..90f880c7cf --- /dev/null +++ b/public_models/Toggle_Switch/Toggle_Switch_SCIOPE_Model_Inference.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Toggle_Switch"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class ToggleSwitch(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Toggle_Switch\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha1\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"alpha2\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"beta\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"gamma\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"mu\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"A\", initial_value=2, mode=\"discrete\"))\n"," self.add_species(Species(name=\"B\", initial_value=2, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"cu\", reactants={}, products={'A': 1}, propensity_function=\"alpha1/(1+pow(B, beta))\"))\n"," self.add_reaction(Reaction(name=\"cv\", reactants={}, products={'B': 1}, propensity_function=\"alpha2/(1+pow(A, gamma))\"))\n"," self.add_reaction(Reaction(name=\"du\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n"," self.add_reaction(Reaction(name=\"dv\", reactants={'B': 1}, products={}, rate=self.listOfParameters[\"mu\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 250, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = ToggleSwitch()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," solver = SSACSolver(model=model)\n"," kwargs = {\n"," \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"markdown","metadata":{},"source":["# Model Inference"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2\n","\n","from tsfresh.feature_extraction.settings import MinimalFCParameters\n","from sciope.utilities.priors import uniform_prior\n","from sciope.utilities.summarystats import auto_tsfresh\n","from sciope.utilities.distancefunctions import naive_squared\n","from sciope.inference.abc_inference import ABC\n","from sklearn.metrics import mean_absolute_error\n","from dask.distributed import Client"]},{"cell_type":"markdown","metadata":{},"source":["## Generate some fixed(observed) data based on default parameters of the model"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","fixed_data = model.run(**kwargs)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Reshape the data and remove timepoints array\n","fixed_data = fixed_data.to_array()\n","fixed_data = np.asarray([x.T for x in fixed_data])\n","fixed_data = fixed_data[:, 1:, :]"]},{"cell_type":"markdown","metadata":{},"source":["## Define prior distribution"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# take default from mode 1 as reference\n","default_param = np.array(list(model.listOfParameters.items()))[:, 1]\n","\n","bound = []\n","for exp in default_param:\n"," bound.append(float(exp.expression))\n","\n","# Set the bounds\n","bound = np.array(bound)\n","dmin = bound * 0.1\n","dmax = bound * 2.0\n","\n","# Here we use uniform prior\n","uni_prior = uniform_prior.UniformPrior(dmin, dmax)"]},{"cell_type":"markdown","metadata":{},"source":["## Define simulator"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def get_variables(params, model):\n"," # params - array, need to have the same order as model.listOfParameters\n"," variables = {}\n"," for e, pname in enumerate(model.listOfParameters.keys()):\n"," variables[pname] = params[e]\n"," return variables\n","\n","# Here we use the GillesPy2 Solver\n","def simulator(params, model):\n"," variables = get_variables(params, model)\n","\n"," res = model.run(**kwargs, variables=variables)\n"," res = res.to_array()\n"," tot_res = np.asarray([x.T for x in res]) # reshape to (N, S, T)\n"," # should not contain timepoints\n"," tot_res = tot_res[:, 1:, :]\n","\n"," return tot_res\n","\n","# Wrapper, simulator function to abc should should only take one argument (the parameter point)\n","def simulator2(x):\n"," return simulator(x, model=model)"]},{"cell_type":"markdown","metadata":{},"source":["## Define summary statistics and distance function"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Function to generate summary statistics\n","summ_func = auto_tsfresh.SummariesTSFRESH()\n","\n","# Distance\n","ns = naive_squared.NaiveSquaredDistance()"]},{"cell_type":"markdown","metadata":{},"source":["## Start local cluster using dask client"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["c = Client()"]},{"cell_type":"markdown","metadata":{},"source":["## Start abc instance"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["abc = ABC(fixed_data, sim=simulator2, prior_function=uni_prior, summaries_function=summ_func.compute, distance_function=ns)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# First compute the fixed(observed) mean\n","abc.compute_fixed_mean(chunk_size=2)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["res = abc.infer(num_samples=100, batch_size=10, chunk_size=2)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["mae_inference = mean_absolute_error(bound, abc.results['inferred_parameters'])"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Tyson_Oscillator/Tyson_Oscillator.ipynb b/public_models/Tyson_Oscillator/Tyson_Oscillator.ipynb index 53ef2f6e1a..67a0db11f3 100644 --- a/public_models/Tyson_Oscillator/Tyson_Oscillator.ipynb +++ b/public_models/Tyson_Oscillator/Tyson_Oscillator.ipynb @@ -1,1472 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tyson_Oscillator" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n", - "from gillespy2 import EventAssignment, EventTrigger, Event\n", - "from gillespy2 import SSACSolver\n", - "# from gillespy2 import VariableSSACSolver\n", - "# from gillespy2 import BasicTauLeapingSolver\n", - "# from gillespy2 import BasicTauHybridSolver\n", - "# from gillespy2 import BasicODESolver" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class Tyson_Oscillator(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Tyson_Oscillator\")\n", - " self.volume = 300\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"P\", expression=2))\n", - " self.add_parameter(Parameter(name=\"kt\", expression=20))\n", - " self.add_parameter(Parameter(name=\"kd\", expression=1))\n", - " self.add_parameter(Parameter(name=\"a0\", expression=0.005))\n", - " self.add_parameter(Parameter(name=\"a1\", expression=0.05))\n", - " self.add_parameter(Parameter(name=\"a2\", expression=0.1))\n", - " self.add_parameter(Parameter(name=\"kdx\", expression=1))\n", - "\n", - " # Species\n", - " self.add_species(Species(name=\"X\", initial_value=197, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Y\", initial_value=255, mode=\"discrete\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"rxn1\", reactants={}, products={'X': 1}, propensity_function=\"vol*1/(1+(Y*Y/((vol*vol))))\"))\n", - " self.add_reaction(Reaction(name=\"rxn2\", reactants={'X': 1}, products={}, rate=self.listOfParameters[\"kdx\"]))\n", - " self.add_reaction(Reaction(name=\"rxn3\", reactants={'X': 1}, products={'X': 1, 'Y': 1}, rate=self.listOfParameters[\"kt\"]))\n", - " self.add_reaction(Reaction(name=\"rxn4\", reactants={'Y': 1}, products={}, rate=self.listOfParameters[\"kd\"]))\n", - " self.add_reaction(Reaction(name=\"rxn5\", reactants={'Y': 1}, products={}, propensity_function=\"Y/(a0 + a1*(Y/vol)+a2*Y*Y/(vol*vol))\"))\n", - "\n", - " # Timespan\n", - " self.timespan(np.linspace(0, 100, 101))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = Tyson_Oscillator()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "solver = SSACSolver(model=model)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " \"solver\":solver,\n", - " \"number_of_trajectories\":1,\n", - " \"seed\":None,\n", - " # \"tau_tol\":0.03,\n", - " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06}\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = configure_simulation()\n", - "results = model.run(**kwargs)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "results.plotplotly()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} +{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Tyson_Oscillator"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class TysonOscillator(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Tyson_Oscillator\")\n"," self.volume = 300\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"P\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"kt\", expression=\"20\"))\n"," self.add_parameter(Parameter(name=\"kd\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"a0\", expression=\"0.005\"))\n"," self.add_parameter(Parameter(name=\"a1\", expression=\"0.05\"))\n"," self.add_parameter(Parameter(name=\"a2\", expression=\"0.1\"))\n"," self.add_parameter(Parameter(name=\"kdx\", expression=\"1\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"X\", initial_value=197, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Y\", initial_value=255, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"rxn1\", reactants={}, products={'X': 1}, propensity_function=\"vol*1/(1+(Y*Y/((vol*vol))))\"))\n"," self.add_reaction(Reaction(name=\"rxn2\", reactants={'X': 1}, products={}, rate=self.listOfParameters[\"kdx\"]))\n"," self.add_reaction(Reaction(name=\"rxn3\", reactants={'X': 1}, products={'X': 1, 'Y': 1}, rate=self.listOfParameters[\"kt\"]))\n"," self.add_reaction(Reaction(name=\"rxn4\", reactants={'Y': 1}, products={}, rate=self.listOfParameters[\"kd\"]))\n"," self.add_reaction(Reaction(name=\"rxn5\", reactants={'Y': 1}, products={}, propensity_function=\"Y/(a0 + a1*(Y/vol)+a2*Y*Y/(vol*vol))\"))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 100, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = TysonOscillator()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," # solver = SSACSolver(model=model)\n"," kwargs = {\n"," # \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Vilar_Oscillator/Vilar_Oscillator.ipynb b/public_models/Vilar_Oscillator/Vilar_Oscillator.ipynb index 7b9774793c..a893cc22ff 100644 --- a/public_models/Vilar_Oscillator/Vilar_Oscillator.ipynb +++ b/public_models/Vilar_Oscillator/Vilar_Oscillator.ipynb @@ -1,4798 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Vilar_Oscillator" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import gillespy2\n", - "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n", - "from gillespy2 import EventAssignment, EventTrigger, Event\n", - "from gillespy2 import SSACSolver\n", - "# from gillespy2 import VariableSSACSolver\n", - "# from gillespy2 import BasicTauLeapingSolver\n", - "# from gillespy2 import BasicTauHybridSolver\n", - "# from gillespy2 import BasicODESolver" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class Vilar_Oscillator(Model):\n", - " def __init__(self, parameter_values=None):\n", - " Model.__init__(self, name=\"Vilar_Oscillator\")\n", - " self.volume = 1\n", - "\n", - " # Parameters\n", - " self.add_parameter(Parameter(name=\"alpha_a\", expression=50))\n", - " self.add_parameter(Parameter(name=\"alpha_a_prime\", expression=500))\n", - " self.add_parameter(Parameter(name=\"alpha_r\", expression=0.01))\n", - " self.add_parameter(Parameter(name=\"alpha_r_prime\", expression=50))\n", - " self.add_parameter(Parameter(name=\"beta_a\", expression=50))\n", - " self.add_parameter(Parameter(name=\"beta_r\", expression=5))\n", - " self.add_parameter(Parameter(name=\"delta_ma\", expression=10))\n", - " self.add_parameter(Parameter(name=\"delta_mr\", expression=0.5))\n", - " self.add_parameter(Parameter(name=\"delta_a\", expression=1))\n", - " self.add_parameter(Parameter(name=\"delta_r\", expression=0.2))\n", - " self.add_parameter(Parameter(name=\"gamma_a\", expression=1))\n", - " self.add_parameter(Parameter(name=\"gamma_r\", expression=1))\n", - " self.add_parameter(Parameter(name=\"gamma_c\", expression=2))\n", - " self.add_parameter(Parameter(name=\"theta_a\", expression=50))\n", - " self.add_parameter(Parameter(name=\"theta_r\", expression=100))\n", - "\n", - " # Species\n", - " self.add_species(Species(name=\"Da\", initial_value=1, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Da_prime\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Ma\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Dr\", initial_value=1, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Dr_prime\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"Mr\", initial_value=0, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"C\", initial_value=10, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"A\", initial_value=10, mode=\"discrete\"))\n", - " self.add_species(Species(name=\"R\", initial_value=10, mode=\"discrete\"))\n", - "\n", - " # Reactions\n", - " self.add_reaction(Reaction(name=\"r1\", reactants={'Da_prime': 1}, products={'Da': 1}, rate=self.listOfParameters[\"theta_a\"]))\n", - " self.add_reaction(Reaction(name=\"r2\", reactants={'Da': 1, 'A': 1}, products={'Da_prime': 1}, rate=self.listOfParameters[\"gamma_a\"]))\n", - " self.add_reaction(Reaction(name=\"r3\", reactants={'Dr_prime': 1}, products={'Dr': 1}, rate=self.listOfParameters[\"theta_r\"]))\n", - " self.add_reaction(Reaction(name=\"r4\", reactants={'Dr': 1, 'A': 1}, products={'Dr_prime': 1}, rate=self.listOfParameters[\"gamma_r\"]))\n", - " self.add_reaction(Reaction(name=\"r5\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'Ma': 1}, rate=self.listOfParameters[\"alpha_a_prime\"]))\n", - " self.add_reaction(Reaction(name=\"r6\", reactants={'Da': 1}, products={'Da': 1, 'Ma': 1}, rate=self.listOfParameters[\"alpha_a\"]))\n", - " self.add_reaction(Reaction(name=\"r7\", reactants={'Ma': 1}, products={}, rate=self.listOfParameters[\"delta_ma\"]))\n", - " self.add_reaction(Reaction(name=\"r8\", reactants={'Ma': 1}, products={'A': 1, 'Ma': 1}, rate=self.listOfParameters[\"beta_a\"]))\n", - " self.add_reaction(Reaction(name=\"r9\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'A': 1}, rate=self.listOfParameters[\"theta_a\"]))\n", - " self.add_reaction(Reaction(name=\"r10\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'A': 1}, rate=self.listOfParameters[\"theta_a\"]))\n", - " self.add_reaction(Reaction(name=\"r11\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"gamma_c\"]))\n", - " self.add_reaction(Reaction(name=\"r12\", reactants={'A': 1, 'R': 1}, products={'C': 1}, rate=self.listOfParameters[\"gamma_c\"]))\n", - " self.add_reaction(Reaction(name=\"r13\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'Mr': 1}, rate=self.listOfParameters[\"alpha_r_prime\"]))\n", - " self.add_reaction(Reaction(name=\"r14\", reactants={'Dr': 1}, products={'Dr': 1, 'Mr': 1}, rate=self.listOfParameters[\"alpha_r\"]))\n", - " self.add_reaction(Reaction(name=\"r15\", reactants={'Mr': 1}, products={}, rate=self.listOfParameters[\"delta_mr\"]))\n", - " self.add_reaction(Reaction(name=\"r16\", reactants={'Mr': 1}, products={'Mr': 1, 'R': 1}, rate=self.listOfParameters[\"beta_r\"]))\n", - " self.add_reaction(Reaction(name=\"r17\", reactants={'R': 1}, products={}, rate=self.listOfParameters[\"delta_r\"]))\n", - " self.add_reaction(Reaction(name=\"r18\", reactants={'C': 1}, products={'R': 1}, rate=self.listOfParameters[\"delta_a\"]))\n", - "\n", - " # Timespan\n", - " self.timespan(np.linspace(0, 200, 201))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "model = Vilar_Oscillator()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "solver = SSACSolver(model=model)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def configure_simulation():\n", - " kwargs = {\n", - " \"solver\":solver,\n", - " \"number_of_trajectories\":1,\n", - " \"seed\":None,\n", - " # \"tau_tol\":0.03,\n", - " # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06}\n", - " }\n", - " return kwargs" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "kwargs = configure_simulation()\n", - "results = model.run(**kwargs)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - 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Parameters\n"," self.add_parameter(Parameter(name=\"alpha_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"alpha_a_prime\", expression=\"500\"))\n"," self.add_parameter(Parameter(name=\"alpha_r\", expression=\"0.01\"))\n"," self.add_parameter(Parameter(name=\"alpha_r_prime\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"beta_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"beta_r\", expression=\"5\"))\n"," self.add_parameter(Parameter(name=\"delta_ma\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"delta_mr\", expression=\"0.5\"))\n"," self.add_parameter(Parameter(name=\"delta_a\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"delta_r\", expression=\"0.2\"))\n"," self.add_parameter(Parameter(name=\"gamma_a\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"gamma_r\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"gamma_c\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"theta_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"theta_r\", expression=\"100\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"Da\", initial_value=1, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Da_prime\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Ma\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Dr\", initial_value=1, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Dr_prime\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Mr\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"C\", initial_value=10, mode=\"discrete\"))\n"," self.add_species(Species(name=\"A\", initial_value=10, mode=\"discrete\"))\n"," self.add_species(Species(name=\"R\", initial_value=10, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"r1\", reactants={'Da_prime': 1}, products={'Da': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r2\", reactants={'Da': 1, 'A': 1}, products={'Da_prime': 1}, rate=self.listOfParameters[\"gamma_a\"]))\n"," self.add_reaction(Reaction(name=\"r3\", reactants={'Dr_prime': 1}, products={'Dr': 1}, rate=self.listOfParameters[\"theta_r\"]))\n"," self.add_reaction(Reaction(name=\"r4\", reactants={'Dr': 1, 'A': 1}, products={'Dr_prime': 1}, rate=self.listOfParameters[\"gamma_r\"]))\n"," self.add_reaction(Reaction(name=\"r5\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'Ma': 1}, rate=self.listOfParameters[\"alpha_a_prime\"]))\n"," self.add_reaction(Reaction(name=\"r6\", reactants={'Da': 1}, products={'Da': 1, 'Ma': 1}, rate=self.listOfParameters[\"alpha_a\"]))\n"," self.add_reaction(Reaction(name=\"r7\", reactants={'Ma': 1}, products={}, rate=self.listOfParameters[\"delta_ma\"]))\n"," self.add_reaction(Reaction(name=\"r8\", reactants={'Ma': 1}, products={'A': 1, 'Ma': 1}, rate=self.listOfParameters[\"beta_a\"]))\n"," self.add_reaction(Reaction(name=\"r9\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'A': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r10\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'A': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r11\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"gamma_c\"]))\n"," self.add_reaction(Reaction(name=\"r12\", reactants={'A': 1, 'R': 1}, products={'C': 1}, rate=self.listOfParameters[\"gamma_c\"]))\n"," self.add_reaction(Reaction(name=\"r13\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'Mr': 1}, rate=self.listOfParameters[\"alpha_r_prime\"]))\n"," self.add_reaction(Reaction(name=\"r14\", reactants={'Dr': 1}, products={'Dr': 1, 'Mr': 1}, rate=self.listOfParameters[\"alpha_r\"]))\n"," self.add_reaction(Reaction(name=\"r15\", reactants={'Mr': 1}, products={}, rate=self.listOfParameters[\"delta_mr\"]))\n"," self.add_reaction(Reaction(name=\"r16\", reactants={'Mr': 1}, products={'Mr': 1, 'R': 1}, rate=self.listOfParameters[\"beta_r\"]))\n"," self.add_reaction(Reaction(name=\"r17\", reactants={'R': 1}, products={}, rate=self.listOfParameters[\"delta_r\"]))\n"," self.add_reaction(Reaction(name=\"r18\", reactants={'C': 1}, products={'R': 1}, rate=self.listOfParameters[\"delta_a\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 200, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = VilarOscillator()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," # solver = SSACSolver(model=model)\n"," kwargs = {\n"," # \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","results = model.run(**kwargs)"]},{"cell_type":"markdown","metadata":{},"source":["# Visualization"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["results.plotplotly()"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Vilar_Oscillator/Vilar_OscillatorSciopeModelExploration.ipynb b/public_models/Vilar_Oscillator/Vilar_OscillatorSciopeModelExploration.ipynb deleted file mode 100644 index 7aa8e36021..0000000000 --- a/public_models/Vilar_Oscillator/Vilar_OscillatorSciopeModelExploration.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import numpy as np\n","%matplotlib notebook\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","from gillespy2 import VariableSSACSolver\n","# from gillespy2 import TauLeapingSolver\n","# from gillespy2 import TauHybridSolver\n","# from gillespy2 import ODESolver"]},{"cell_type":"markdown","metadata":{},"source":["# Vilar_Oscillator"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[],"source":["class VilarOscillator(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Vilar_Oscillator\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"alpha_a_prime\", expression=\"500\"))\n"," self.add_parameter(Parameter(name=\"alpha_r\", expression=\"0.01\"))\n"," self.add_parameter(Parameter(name=\"alpha_r_prime\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"beta_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"beta_r\", expression=\"5\"))\n"," self.add_parameter(Parameter(name=\"delta_ma\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"delta_mr\", expression=\"0.5\"))\n"," self.add_parameter(Parameter(name=\"delta_a\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"delta_r\", expression=\"0.2\"))\n"," self.add_parameter(Parameter(name=\"gamma_a\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"gamma_r\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"gamma_c\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"theta_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"theta_r\", expression=\"100\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"Da\", initial_value=1, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Da_prime\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Ma\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Dr\", initial_value=1, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Dr_prime\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Mr\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"C\", initial_value=10, mode=\"discrete\"))\n"," self.add_species(Species(name=\"A\", initial_value=10, mode=\"discrete\"))\n"," self.add_species(Species(name=\"R\", initial_value=10, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"r1\", reactants={'Da_prime': 1}, products={'Da': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r2\", reactants={'Da': 1, 'A': 1}, products={'Da_prime': 1}, rate=self.listOfParameters[\"gamma_a\"]))\n"," self.add_reaction(Reaction(name=\"r3\", reactants={'Dr_prime': 1}, products={'Dr': 1}, rate=self.listOfParameters[\"theta_r\"]))\n"," self.add_reaction(Reaction(name=\"r4\", reactants={'Dr': 1, 'A': 1}, products={'Dr_prime': 1}, rate=self.listOfParameters[\"gamma_r\"]))\n"," self.add_reaction(Reaction(name=\"r5\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'Ma': 1}, rate=self.listOfParameters[\"alpha_a_prime\"]))\n"," self.add_reaction(Reaction(name=\"r6\", reactants={'Da': 1}, products={'Da': 1, 'Ma': 1}, rate=self.listOfParameters[\"alpha_a\"]))\n"," self.add_reaction(Reaction(name=\"r7\", reactants={'Ma': 1}, products={}, rate=self.listOfParameters[\"delta_ma\"]))\n"," self.add_reaction(Reaction(name=\"r8\", reactants={'Ma': 1}, products={'A': 1, 'Ma': 1}, rate=self.listOfParameters[\"beta_a\"]))\n"," self.add_reaction(Reaction(name=\"r9\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'A': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r10\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'A': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r11\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"gamma_c\"]))\n"," self.add_reaction(Reaction(name=\"r12\", reactants={'A': 1, 'R': 1}, products={'C': 1}, rate=self.listOfParameters[\"gamma_c\"]))\n"," self.add_reaction(Reaction(name=\"r13\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'Mr': 1}, rate=self.listOfParameters[\"alpha_r_prime\"]))\n"," self.add_reaction(Reaction(name=\"r14\", reactants={'Dr': 1}, products={'Dr': 1, 'Mr': 1}, rate=self.listOfParameters[\"alpha_r\"]))\n"," self.add_reaction(Reaction(name=\"r15\", reactants={'Mr': 1}, products={}, rate=self.listOfParameters[\"delta_mr\"]))\n"," self.add_reaction(Reaction(name=\"r16\", reactants={'Mr': 1}, products={'Mr': 1, 'R': 1}, rate=self.listOfParameters[\"beta_r\"]))\n"," self.add_reaction(Reaction(name=\"r17\", reactants={'R': 1}, products={}, rate=self.listOfParameters[\"delta_r\"]))\n"," self.add_reaction(Reaction(name=\"r18\", reactants={'C': 1}, products={'R': 1}, rate=self.listOfParameters[\"delta_a\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 200, 1))"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[],"source":["model = VilarOscillator()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," solver = VariableSSACSolver(model=model)\n"," kwargs = {\n"," \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"markdown","metadata":{},"source":["# Model Exploration"]},{"cell_type":"markdown","metadata":{},"source":["## Define simulator function (using gillespy2 wrapper)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[],"source":["from sciope.utilities.gillespy2 import wrapper\n","settings = configure_simulation()\n","simulator = wrapper.get_simulator(gillespy_model=model, run_settings=settings, species_of_interest=['Da', 'Da_prime', 'Ma', 'Dr', 'Dr_prime', 'Mr', 'C', 'A', 'R'])\n","expression_array = wrapper.get_parameter_expression_array(model)"]},{"cell_type":"markdown","metadata":{},"source":["## Start local cluster using dask client"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":["from dask.distributed import Client\n","\n","c = Client()"]},{"cell_type":"markdown","metadata":{},"source":["## Define parameter sampler/design and summary statistics"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n","distributed.nanny - WARNING - Restarting worker\n","distributed.nanny - WARNING - Worker exceeded 95% memory budget. Restarting\n","distributed.nanny - WARNING - Restarting worker\n"]}],"source":["from sciope.designs import latin_hypercube_sampling\n","from sciope.utilities.summarystats.auto_tsfresh import SummariesTSFRESH\n","\n","lhc = latin_hypercube_sampling.LatinHypercube(xmin=expression_array, xmax=expression_array*3)\n","lhc.generate_array(1000) # creates a LHD of size 1000\n","\n","# will use default minimal set of features\n","summary_stats = SummariesTSFRESH()"]},{"cell_type":"markdown","metadata":{},"source":["## Initiate StochMET"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sciope.stochmet.stochmet import StochMET\n","\n","met = StochMET(simulator, lhc, summary_stats)"]},{"cell_type":"markdown","metadata":{},"source":["## Run parameter sweep"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["met.compute(n_points=500, chunk_size=10)"]},{"cell_type":"markdown","metadata":{},"source":["## Explore the result"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# First lets add some appropiate information about the model and features\n","met.data.configurations['listOfParameters'] = list(model.listOfParameters.keys())\n","met.data.configurations['listOfSpecies'] = list(model.listOfSpecies.keys())\n","met.data.configurations['listOfSummaries'] = met.summaries.features\n","met.data.configurations['timepoints'] = model.tspan"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Here we use UMAP for dimension reduction\n","met.explore(dr_method='umap')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sciope.models.label_propagation import LPModel\n","# here lets use the dimension reduction embedding as input data\n","data = met.dr_model.embedding_\n","\n","model_lp = LPModel()\n","# train using basinhopping\n","model_lp.train(data, met.data.user_labels, min_=0.01, max_=10, niter=50)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# just to vislualize the result we will map the label distribution to the user_labels\n","# (will enable us to see the LP model output when using method 'explore')\n","user_labels = np.copy(met.data.user_labels)\n","# takes the label corresponding to index 0\n","met.data.user_labels = model_lp.model.label_distributions_[:, 0]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["met.explore(dr_method='umap')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["met.data.user_labels = user_labels"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Vilar_Oscillator/Vilar_OscillatorSciopeModelInference.ipynb b/public_models/Vilar_Oscillator/Vilar_OscillatorSciopeModelInference.ipynb deleted file mode 100644 index c580ee7b7a..0000000000 --- a/public_models/Vilar_Oscillator/Vilar_OscillatorSciopeModelInference.ipynb +++ /dev/null @@ -1 +0,0 @@ -{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","# from gillespy2 import SSACSolver\n","from gillespy2 import VariableSSACSolver\n","# from gillespy2 import TauLeapingSolver\n","# from gillespy2 import TauHybridSolver\n","# from gillespy2 import ODESolver"]},{"cell_type":"markdown","metadata":{},"source":["# Vilar_Oscillator"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class VilarOscillator(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Vilar_Oscillator\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"alpha_a_prime\", expression=\"500\"))\n"," self.add_parameter(Parameter(name=\"alpha_r\", expression=\"0.01\"))\n"," self.add_parameter(Parameter(name=\"alpha_r_prime\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"beta_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"beta_r\", expression=\"5\"))\n"," self.add_parameter(Parameter(name=\"delta_ma\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"delta_mr\", expression=\"0.5\"))\n"," self.add_parameter(Parameter(name=\"delta_a\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"delta_r\", expression=\"0.2\"))\n"," self.add_parameter(Parameter(name=\"gamma_a\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"gamma_r\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"gamma_c\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"theta_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"theta_r\", expression=\"100\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"Da\", initial_value=1, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Da_prime\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Ma\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Dr\", initial_value=1, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Dr_prime\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Mr\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"C\", initial_value=10, mode=\"discrete\"))\n"," self.add_species(Species(name=\"A\", initial_value=10, mode=\"discrete\"))\n"," self.add_species(Species(name=\"R\", initial_value=10, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"r1\", reactants={'Da_prime': 1}, products={'Da': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r2\", reactants={'Da': 1, 'A': 1}, products={'Da_prime': 1}, rate=self.listOfParameters[\"gamma_a\"]))\n"," self.add_reaction(Reaction(name=\"r3\", reactants={'Dr_prime': 1}, products={'Dr': 1}, rate=self.listOfParameters[\"theta_r\"]))\n"," self.add_reaction(Reaction(name=\"r4\", reactants={'Dr': 1, 'A': 1}, products={'Dr_prime': 1}, rate=self.listOfParameters[\"gamma_r\"]))\n"," self.add_reaction(Reaction(name=\"r5\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'Ma': 1}, rate=self.listOfParameters[\"alpha_a_prime\"]))\n"," self.add_reaction(Reaction(name=\"r6\", reactants={'Da': 1}, products={'Da': 1, 'Ma': 1}, rate=self.listOfParameters[\"alpha_a\"]))\n"," self.add_reaction(Reaction(name=\"r7\", reactants={'Ma': 1}, products={}, rate=self.listOfParameters[\"delta_ma\"]))\n"," self.add_reaction(Reaction(name=\"r8\", reactants={'Ma': 1}, products={'A': 1, 'Ma': 1}, rate=self.listOfParameters[\"beta_a\"]))\n"," self.add_reaction(Reaction(name=\"r9\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'A': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r10\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'A': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r11\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"gamma_c\"]))\n"," self.add_reaction(Reaction(name=\"r12\", reactants={'A': 1, 'R': 1}, products={'C': 1}, rate=self.listOfParameters[\"gamma_c\"]))\n"," self.add_reaction(Reaction(name=\"r13\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'Mr': 1}, rate=self.listOfParameters[\"alpha_r_prime\"]))\n"," self.add_reaction(Reaction(name=\"r14\", reactants={'Dr': 1}, products={'Dr': 1, 'Mr': 1}, rate=self.listOfParameters[\"alpha_r\"]))\n"," self.add_reaction(Reaction(name=\"r15\", reactants={'Mr': 1}, products={}, rate=self.listOfParameters[\"delta_mr\"]))\n"," self.add_reaction(Reaction(name=\"r16\", reactants={'Mr': 1}, products={'Mr': 1, 'R': 1}, rate=self.listOfParameters[\"beta_r\"]))\n"," self.add_reaction(Reaction(name=\"r17\", reactants={'R': 1}, products={}, rate=self.listOfParameters[\"delta_r\"]))\n"," self.add_reaction(Reaction(name=\"r18\", reactants={'C': 1}, products={'R': 1}, rate=self.listOfParameters[\"delta_a\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 200, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = VilarOscillator()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," solver = VariableSSACSolver(model=model)\n"," kwargs = {\n"," \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"markdown","metadata":{},"source":["# Model Inference"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2\n","\n","from tsfresh.feature_extraction.settings import MinimalFCParameters\n","from sciope.utilities.priors import uniform_prior\n","from sciope.utilities.summarystats import auto_tsfresh\n","from sciope.utilities.distancefunctions import naive_squared\n","from sciope.inference.abc_inference import ABC\n","from sklearn.metrics import mean_absolute_error\n","from dask.distributed import Client"]},{"cell_type":"markdown","metadata":{},"source":["## Generate some fixed(observed) data based on default parameters of the model"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","fixed_data = model.run(**kwargs)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Reshape the data and remove timepoints array\n","fixed_data = fixed_data.to_array()\n","fixed_data = np.asarray([x.T for x in fixed_data])\n","fixed_data = fixed_data[:, 1:, :]"]},{"cell_type":"markdown","metadata":{},"source":["## Define prior distribution"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# take default from mode 1 as reference\n","default_param = np.array(list(model.listOfParameters.items()))[:, 1]\n","\n","bound = []\n","for exp in default_param:\n"," bound.append(float(exp.expression))\n","\n","# Set the bounds\n","bound = np.array(bound)\n","dmin = bound * 0.1\n","dmax = bound * 2.0\n","\n","# Here we use uniform prior\n","uni_prior = uniform_prior.UniformPrior(dmin, dmax)"]},{"cell_type":"markdown","metadata":{},"source":["## Define simulator"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def get_variables(params, model):\n"," # params - array, need to have the same order as model.listOfParameters\n"," variables = {}\n"," for e, pname in enumerate(model.listOfParameters.keys()):\n"," variables[pname] = params[e]\n"," return variables\n","\n","# Here we use the GillesPy2 Solver\n","def simulator(params, model):\n"," variables = get_variables(params, model)\n","\n"," res = model.run(**kwargs, variables=variables)\n"," res = res.to_array()\n"," tot_res = np.asarray([x.T for x in res]) # reshape to (N, S, T)\n"," # should not contain timepoints\n"," tot_res = tot_res[:, 1:, :]\n","\n"," return tot_res\n","\n","# Wrapper, simulator function to abc should should only take one argument (the parameter point)\n","def simulator2(x):\n"," return simulator(x, model=model)"]},{"cell_type":"markdown","metadata":{},"source":["## Define summary statistics and distance function"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Function to generate summary statistics\n","summ_func = auto_tsfresh.SummariesTSFRESH()\n","\n","# Distance\n","ns = naive_squared.NaiveSquaredDistance()"]},{"cell_type":"markdown","metadata":{},"source":["## Start local cluster using dask client"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["c = Client()"]},{"cell_type":"markdown","metadata":{},"source":["## Start abc instance"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["abc = ABC(fixed_data, sim=simulator2, prior_function=uni_prior, summaries_function=summ_func.compute, distance_function=ns)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# First compute the fixed(observed) mean\n","abc.compute_fixed_mean(chunk_size=2)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["res = abc.infer(num_samples=100, batch_size=10, chunk_size=2)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["mae_inference = mean_absolute_error(bound, abc.results['inferred_parameters'])"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.5"}},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Vilar_Oscillator/Vilar_Oscillator_SCIOPE_Model_Exploration.ipynb b/public_models/Vilar_Oscillator/Vilar_Oscillator_SCIOPE_Model_Exploration.ipynb new file mode 100644 index 0000000000..ffdbb77f69 --- /dev/null +++ b/public_models/Vilar_Oscillator/Vilar_Oscillator_SCIOPE_Model_Exploration.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","%matplotlib notebook\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Vilar_Oscillator"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class VilarOscillator(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Vilar_Oscillator\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"alpha_a_prime\", expression=\"500\"))\n"," self.add_parameter(Parameter(name=\"alpha_r\", expression=\"0.01\"))\n"," self.add_parameter(Parameter(name=\"alpha_r_prime\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"beta_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"beta_r\", expression=\"5\"))\n"," self.add_parameter(Parameter(name=\"delta_ma\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"delta_mr\", expression=\"0.5\"))\n"," self.add_parameter(Parameter(name=\"delta_a\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"delta_r\", expression=\"0.2\"))\n"," self.add_parameter(Parameter(name=\"gamma_a\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"gamma_r\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"gamma_c\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"theta_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"theta_r\", expression=\"100\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"Da\", initial_value=1, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Da_prime\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Ma\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Dr\", initial_value=1, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Dr_prime\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Mr\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"C\", initial_value=10, mode=\"discrete\"))\n"," self.add_species(Species(name=\"A\", initial_value=10, mode=\"discrete\"))\n"," self.add_species(Species(name=\"R\", initial_value=10, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"r1\", reactants={'Da_prime': 1}, products={'Da': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r2\", reactants={'Da': 1, 'A': 1}, products={'Da_prime': 1}, rate=self.listOfParameters[\"gamma_a\"]))\n"," self.add_reaction(Reaction(name=\"r3\", reactants={'Dr_prime': 1}, products={'Dr': 1}, rate=self.listOfParameters[\"theta_r\"]))\n"," self.add_reaction(Reaction(name=\"r4\", reactants={'Dr': 1, 'A': 1}, products={'Dr_prime': 1}, rate=self.listOfParameters[\"gamma_r\"]))\n"," self.add_reaction(Reaction(name=\"r5\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'Ma': 1}, rate=self.listOfParameters[\"alpha_a_prime\"]))\n"," self.add_reaction(Reaction(name=\"r6\", reactants={'Da': 1}, products={'Da': 1, 'Ma': 1}, rate=self.listOfParameters[\"alpha_a\"]))\n"," self.add_reaction(Reaction(name=\"r7\", reactants={'Ma': 1}, products={}, rate=self.listOfParameters[\"delta_ma\"]))\n"," self.add_reaction(Reaction(name=\"r8\", reactants={'Ma': 1}, products={'A': 1, 'Ma': 1}, rate=self.listOfParameters[\"beta_a\"]))\n"," self.add_reaction(Reaction(name=\"r9\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'A': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r10\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'A': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r11\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"gamma_c\"]))\n"," self.add_reaction(Reaction(name=\"r12\", reactants={'A': 1, 'R': 1}, products={'C': 1}, rate=self.listOfParameters[\"gamma_c\"]))\n"," self.add_reaction(Reaction(name=\"r13\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'Mr': 1}, rate=self.listOfParameters[\"alpha_r_prime\"]))\n"," self.add_reaction(Reaction(name=\"r14\", reactants={'Dr': 1}, products={'Dr': 1, 'Mr': 1}, rate=self.listOfParameters[\"alpha_r\"]))\n"," self.add_reaction(Reaction(name=\"r15\", reactants={'Mr': 1}, products={}, rate=self.listOfParameters[\"delta_mr\"]))\n"," self.add_reaction(Reaction(name=\"r16\", reactants={'Mr': 1}, products={'Mr': 1, 'R': 1}, rate=self.listOfParameters[\"beta_r\"]))\n"," self.add_reaction(Reaction(name=\"r17\", reactants={'R': 1}, products={}, rate=self.listOfParameters[\"delta_r\"]))\n"," self.add_reaction(Reaction(name=\"r18\", reactants={'C': 1}, products={'R': 1}, rate=self.listOfParameters[\"delta_a\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 200, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = VilarOscillator()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," solver = SSACSolver(model=model)\n"," kwargs = {\n"," \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"markdown","metadata":{},"source":["# Model Exploration"]},{"cell_type":"markdown","metadata":{},"source":["## Define simulator function (using gillespy2 wrapper)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sciope.utilities.gillespy2 import wrapper\n","settings = configure_simulation()\n","simulator = wrapper.get_simulator(gillespy_model=model, run_settings=settings, species_of_interest=['Da', 'Da_prime', 'Ma', 'Dr', 'Dr_prime', 'Mr', 'C', 'A', 'R'])\n","expression_array = wrapper.get_parameter_expression_array(model)"]},{"cell_type":"markdown","metadata":{},"source":["## Start local cluster using dask client"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from dask.distributed import Client\n","\n","c = Client()"]},{"cell_type":"markdown","metadata":{},"source":["## Define parameter sampler/design and summary statistics"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sciope.designs import latin_hypercube_sampling\n","from sciope.utilities.summarystats.auto_tsfresh import SummariesTSFRESH\n","\n","lhc = latin_hypercube_sampling.LatinHypercube(xmin=expression_array, xmax=expression_array*3)\n","lhc.generate_array(1000) # creates a LHD of size 1000\n","\n","# will use default minimal set of features\n","summary_stats = SummariesTSFRESH()"]},{"cell_type":"markdown","metadata":{},"source":["## Initiate StochMET"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sciope.stochmet.stochmet import StochMET\n","\n","met = StochMET(simulator, lhc, summary_stats)"]},{"cell_type":"markdown","metadata":{},"source":["## Run parameter sweep"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["met.compute(n_points=500, chunk_size=10)"]},{"cell_type":"markdown","metadata":{},"source":["## Explore the result"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# First lets add some appropiate information about the model and features\n","met.data.configurations['listOfParameters'] = list(model.listOfParameters.keys())\n","met.data.configurations['listOfSpecies'] = list(model.listOfSpecies.keys())\n","met.data.configurations['listOfSummaries'] = met.summaries.features\n","met.data.configurations['timepoints'] = model.tspan"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Here we use UMAP for dimension reduction\n","met.explore(dr_method='umap')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["from sciope.models.label_propagation import LPModel\n","# here lets use the dimension reduction embedding as input data\n","data = met.dr_model.embedding_\n","\n","model_lp = LPModel()\n","# train using basinhopping\n","model_lp.train(data, met.data.user_labels, min_=0.01, max_=10, niter=50)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# just to vislualize the result we will map the label distribution to the user_labels\n","# (will enable us to see the LP model output when using method 'explore')\n","user_labels = np.copy(met.data.user_labels)\n","# takes the label corresponding to index 0\n","met.data.user_labels = model_lp.model.label_distributions_[:, 0]"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["met.explore(dr_method='umap')"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["met.data.user_labels = user_labels"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/public_models/Vilar_Oscillator/Vilar_Oscillator_SCIOPE_Model_Inference.ipynb b/public_models/Vilar_Oscillator/Vilar_Oscillator_SCIOPE_Model_Inference.ipynb new file mode 100644 index 0000000000..2d512ebf14 --- /dev/null +++ b/public_models/Vilar_Oscillator/Vilar_Oscillator_SCIOPE_Model_Inference.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["import numpy as np\n","import gillespy2\n","from gillespy2 import Model, Species, Parameter, Reaction, Event, \\\n"," EventTrigger, EventAssignment, RateRule, \\\n"," AssignmentRule, FunctionDefinition\n","from gillespy2 import SSACSolver\n","# from gillespy2 import TauLeapingCSolver\n","# from gillespy2 import ODECSolver\n","# from gillespy2 import TauHybridSolver"]},{"cell_type":"markdown","metadata":{},"source":["# Vilar_Oscillator"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["class VilarOscillator(Model):\n"," def __init__(self, parameter_values=None):\n"," Model.__init__(self, name=\"Vilar_Oscillator\")\n"," self.volume = 1\n","\n"," # Parameters\n"," self.add_parameter(Parameter(name=\"alpha_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"alpha_a_prime\", expression=\"500\"))\n"," self.add_parameter(Parameter(name=\"alpha_r\", expression=\"0.01\"))\n"," self.add_parameter(Parameter(name=\"alpha_r_prime\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"beta_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"beta_r\", expression=\"5\"))\n"," self.add_parameter(Parameter(name=\"delta_ma\", expression=\"10\"))\n"," self.add_parameter(Parameter(name=\"delta_mr\", expression=\"0.5\"))\n"," self.add_parameter(Parameter(name=\"delta_a\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"delta_r\", expression=\"0.2\"))\n"," self.add_parameter(Parameter(name=\"gamma_a\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"gamma_r\", expression=\"1\"))\n"," self.add_parameter(Parameter(name=\"gamma_c\", expression=\"2\"))\n"," self.add_parameter(Parameter(name=\"theta_a\", expression=\"50\"))\n"," self.add_parameter(Parameter(name=\"theta_r\", expression=\"100\"))\n","\n"," # Variables\n"," self.add_species(Species(name=\"Da\", initial_value=1, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Da_prime\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Ma\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Dr\", initial_value=1, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Dr_prime\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"Mr\", initial_value=0, mode=\"discrete\"))\n"," self.add_species(Species(name=\"C\", initial_value=10, mode=\"discrete\"))\n"," self.add_species(Species(name=\"A\", initial_value=10, mode=\"discrete\"))\n"," self.add_species(Species(name=\"R\", initial_value=10, mode=\"discrete\"))\n","\n"," # Reactions\n"," self.add_reaction(Reaction(name=\"r1\", reactants={'Da_prime': 1}, products={'Da': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r2\", reactants={'Da': 1, 'A': 1}, products={'Da_prime': 1}, rate=self.listOfParameters[\"gamma_a\"]))\n"," self.add_reaction(Reaction(name=\"r3\", reactants={'Dr_prime': 1}, products={'Dr': 1}, rate=self.listOfParameters[\"theta_r\"]))\n"," self.add_reaction(Reaction(name=\"r4\", reactants={'Dr': 1, 'A': 1}, products={'Dr_prime': 1}, rate=self.listOfParameters[\"gamma_r\"]))\n"," self.add_reaction(Reaction(name=\"r5\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'Ma': 1}, rate=self.listOfParameters[\"alpha_a_prime\"]))\n"," self.add_reaction(Reaction(name=\"r6\", reactants={'Da': 1}, products={'Da': 1, 'Ma': 1}, rate=self.listOfParameters[\"alpha_a\"]))\n"," self.add_reaction(Reaction(name=\"r7\", reactants={'Ma': 1}, products={}, rate=self.listOfParameters[\"delta_ma\"]))\n"," self.add_reaction(Reaction(name=\"r8\", reactants={'Ma': 1}, products={'A': 1, 'Ma': 1}, rate=self.listOfParameters[\"beta_a\"]))\n"," self.add_reaction(Reaction(name=\"r9\", reactants={'Da_prime': 1}, products={'Da_prime': 1, 'A': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r10\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'A': 1}, rate=self.listOfParameters[\"theta_a\"]))\n"," self.add_reaction(Reaction(name=\"r11\", reactants={'A': 1}, products={}, rate=self.listOfParameters[\"gamma_c\"]))\n"," self.add_reaction(Reaction(name=\"r12\", reactants={'A': 1, 'R': 1}, products={'C': 1}, rate=self.listOfParameters[\"gamma_c\"]))\n"," self.add_reaction(Reaction(name=\"r13\", reactants={'Dr_prime': 1}, products={'Dr_prime': 1, 'Mr': 1}, rate=self.listOfParameters[\"alpha_r_prime\"]))\n"," self.add_reaction(Reaction(name=\"r14\", reactants={'Dr': 1}, products={'Dr': 1, 'Mr': 1}, rate=self.listOfParameters[\"alpha_r\"]))\n"," self.add_reaction(Reaction(name=\"r15\", reactants={'Mr': 1}, products={}, rate=self.listOfParameters[\"delta_mr\"]))\n"," self.add_reaction(Reaction(name=\"r16\", reactants={'Mr': 1}, products={'Mr': 1, 'R': 1}, rate=self.listOfParameters[\"beta_r\"]))\n"," self.add_reaction(Reaction(name=\"r17\", reactants={'R': 1}, products={}, rate=self.listOfParameters[\"delta_r\"]))\n"," self.add_reaction(Reaction(name=\"r18\", reactants={'C': 1}, products={'R': 1}, rate=self.listOfParameters[\"delta_a\"]))\n","\n"," # Timespan\n"," self.timespan(np.arange(0, 200, 1))"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["model = VilarOscillator()"]},{"cell_type":"markdown","metadata":{},"source":["# Simulation Parameters"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def configure_simulation():\n"," solver = SSACSolver(model=model)\n"," kwargs = {\n"," \"solver\":solver,\n"," \"number_of_trajectories\":100,\n"," # \"seed\":None,\n"," # \"tau_tol\":0.03,\n"," # \"integrator_options\":{'rtol': 0.001, 'atol': 1e-06},\n"," }\n"," return kwargs"]},{"cell_type":"markdown","metadata":{},"source":["# Model Inference"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["%load_ext autoreload\n","%autoreload 2\n","\n","from tsfresh.feature_extraction.settings import MinimalFCParameters\n","from sciope.utilities.priors import uniform_prior\n","from sciope.utilities.summarystats import auto_tsfresh\n","from sciope.utilities.distancefunctions import naive_squared\n","from sciope.inference.abc_inference import ABC\n","from sklearn.metrics import mean_absolute_error\n","from dask.distributed import Client"]},{"cell_type":"markdown","metadata":{},"source":["## Generate some fixed(observed) data based on default parameters of the model"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["kwargs = configure_simulation()\n","fixed_data = model.run(**kwargs)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Reshape the data and remove timepoints array\n","fixed_data = fixed_data.to_array()\n","fixed_data = np.asarray([x.T for x in fixed_data])\n","fixed_data = fixed_data[:, 1:, :]"]},{"cell_type":"markdown","metadata":{},"source":["## Define prior distribution"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# take default from mode 1 as reference\n","default_param = np.array(list(model.listOfParameters.items()))[:, 1]\n","\n","bound = []\n","for exp in default_param:\n"," bound.append(float(exp.expression))\n","\n","# Set the bounds\n","bound = np.array(bound)\n","dmin = bound * 0.1\n","dmax = bound * 2.0\n","\n","# Here we use uniform prior\n","uni_prior = uniform_prior.UniformPrior(dmin, dmax)"]},{"cell_type":"markdown","metadata":{},"source":["## Define simulator"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["def get_variables(params, model):\n"," # params - array, need to have the same order as model.listOfParameters\n"," variables = {}\n"," for e, pname in enumerate(model.listOfParameters.keys()):\n"," variables[pname] = params[e]\n"," return variables\n","\n","# Here we use the GillesPy2 Solver\n","def simulator(params, model):\n"," variables = get_variables(params, model)\n","\n"," res = model.run(**kwargs, variables=variables)\n"," res = res.to_array()\n"," tot_res = np.asarray([x.T for x in res]) # reshape to (N, S, T)\n"," # should not contain timepoints\n"," tot_res = tot_res[:, 1:, :]\n","\n"," return tot_res\n","\n","# Wrapper, simulator function to abc should should only take one argument (the parameter point)\n","def simulator2(x):\n"," return simulator(x, model=model)"]},{"cell_type":"markdown","metadata":{},"source":["## Define summary statistics and distance function"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# Function to generate summary statistics\n","summ_func = auto_tsfresh.SummariesTSFRESH()\n","\n","# Distance\n","ns = naive_squared.NaiveSquaredDistance()"]},{"cell_type":"markdown","metadata":{},"source":["## Start local cluster using dask client"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["c = Client()"]},{"cell_type":"markdown","metadata":{},"source":["## Start abc instance"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["abc = ABC(fixed_data, sim=simulator2, prior_function=uni_prior, summaries_function=summ_func.compute, distance_function=ns)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["# First compute the fixed(observed) mean\n","abc.compute_fixed_mean(chunk_size=2)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["res = abc.infer(num_samples=100, batch_size=10, chunk_size=2)"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["mae_inference = mean_absolute_error(bound, abc.results['inferred_parameters'])"]}],"metadata":{},"nbformat":4,"nbformat_minor":4} \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 8d78c03802..eb3d418969 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ python-libsbml==5.18.0 python-libsedml==2.0.9 python-libcombine==0.2.7 -gillespy2==1.5.10 +gillespy2==1.5.11 sciope==0.4 pygmsh==5.0.2 meshio==2.3.10 diff --git a/stochss/handlers/__init__.py b/stochss/handlers/__init__.py index f52ecc0747..426635b43e 100644 --- a/stochss/handlers/__init__.py +++ b/stochss/handlers/__init__.py @@ -49,6 +49,7 @@ def get_page_handlers(route_start): (r'/stochss/project/browser\/?', ProjectBrowserHandler), (r'/stochss/project/manager\/?', ProjectManagerHandler), (r'/stochss/loading-page\/?', LoadingPageHandler), + (r'/stochss/multiple-plots\/?', MultiplePlotsHandler), # ## API Handlers # @@ -57,6 +58,7 @@ def get_page_handlers(route_start): (r"/stochss/api/file/upload\/?", UploadFileAPIHandler), (r"/stochss/api/file/upload-from-link\/?", UploadFileFromLinkAPIHandler), (r"/stochss/api/file/move\/?", MoveFileAPIHandler), + (r"/stochss/api/file/empty-trash\/?", EmptyTrashAPIHandler), (r"/stochss/api/file/delete\/?", DeleteFileAPIHandler), (r"/stochss/api/file/rename\/?", RenameAPIHandler), (r"/stochss/api/file/download\/?", DownloadAPIHandler), @@ -87,7 +89,6 @@ def get_page_handlers(route_start): (r"/stochss/api/project/add-existing-model\/?", AddExistingModelAPIHandler), (r"/stochss/api/project/extract-model\/?", ExtractModelAPIHandler), (r"/stochss/api/project/extract-workflow\/?", ExtractWorkflowAPIHandler), - (r"/stochss/api/project/empty-trash\/?", EmptyTrashAPIHandler), (r"/stochss/api/project/export-combine\/?", ExportAsCombineAPIHandler), (r"/stochss/api/project/meta-data\/?", ProjectMetaDataAPIHandler), (r"/stochss/api/project/save-annotation\/?", UpdateAnnotationAPIHandler), diff --git a/stochss/handlers/file_browser.py b/stochss/handlers/file_browser.py index 7d35d52423..50fbbb523f 100644 --- a/stochss/handlers/file_browser.py +++ b/stochss/handlers/file_browser.py @@ -101,6 +101,35 @@ async def get(self): self.finish() +class EmptyTrashAPIHandler(APIHandler): + ''' + ############################################################################## + Handler for a projects trash directory + ############################################################################## + ''' + @web.authenticated + def get(self): + ''' + Empty the trash directory. + + Attributes + ---------- + ''' + self.set_header('Content-Type', 'application/json') + path = self.get_query_argument(name="path") + log.debug("Path to the trash directory: %s", path) + try: + log.info("Emptying the trash") + folder = StochSSFolder(path=path) + resp = folder.empty() + log.debug("Response message: %s", resp) + log.info("Successfully emptied the trash") + self.write(resp) + except StochSSAPIError as err: + report_error(self, log, err) + self.finish() + + class DeleteFileAPIHandler(APIHandler): ''' ################################################################################################ diff --git a/stochss/handlers/models.py b/stochss/handlers/models.py index ee9c9284e1..a55a29a947 100644 --- a/stochss/handlers/models.py +++ b/stochss/handlers/models.py @@ -315,7 +315,8 @@ async def get(self): self.set_header('Content-Type', 'application/json') try: folder = StochSSFolder(path="") - resp = folder.get_file_list(ext=".domn") + test = lambda ext, root, file: bool("trash" in root.split("/")) + resp = folder.get_file_list(ext=".domn", test=test) log.debug("Response: %s", resp) self.write(resp) except StochSSAPIError as err: @@ -340,7 +341,8 @@ async def get(self): self.set_header('Content-Type', 'application/json') try: folder = StochSSFolder(path="") - resp = folder.get_file_list(ext=".txt") + test = lambda ext, root, file: bool("trash" in root.split("/")) + resp = folder.get_file_list(ext=".txt", test=test) log.debug("Response: %s", resp) self.write(resp) except StochSSAPIError as err: diff --git a/stochss/handlers/pages.py b/stochss/handlers/pages.py index babd45840b..23cece06b1 100644 --- a/stochss/handlers/pages.py +++ b/stochss/handlers/pages.py @@ -248,6 +248,23 @@ async def get(self): self.render("stochss-loading-page.html", server_path=self.get_server_path()) +class MultiplePlotsHandler(PageHandler): + ''' + ################################################################################################ + StochSS Multiple Plots Page Handler + ################################################################################################ + ''' + @web.authenticated + async def get(self): + ''' + Render the StochSS multiple plots page. + + Attributes + ---------- + ''' + self.render("multiple-plots-page.html", server_path=self.get_server_path()) + + class UserLogsAPIHandler(APIHandler): ''' ################################################################################################ diff --git a/stochss/handlers/project.py b/stochss/handlers/project.py index 8615d23428..b0b727364a 100644 --- a/stochss/handlers/project.py +++ b/stochss/handlers/project.py @@ -171,7 +171,7 @@ def get(self): folder = StochSSFolder(path="") # file will be excluded if test passes test = lambda ext, root, file: bool(".wkfl" in root or f"{path}" in root or \ - "trash" in root) + "trash" in root.split("/")) data = folder.get_file_list(ext=[".mdl", ".smdl"], test=test) log.debug("List of models: %s", data) self.write(data) @@ -276,35 +276,6 @@ def get(self): self.finish() -class EmptyTrashAPIHandler(APIHandler): - ''' - ############################################################################## - Handler for a projects trash directory - ############################################################################## - ''' - @web.authenticated - def get(self): - ''' - Empty the trash directory. - - Attributes - ---------- - ''' - self.set_header('Content-Type', 'application/json') - path = self.get_query_argument(name="path") - log.debug("Path to the trash directory: %s", path) - try: - log.info("Emptying the trash") - folder = StochSSFolder(path=path) - resp = folder.empty() - log.debug("Response message: %s", resp) - log.info("Successfully emptied the trash") - self.write(resp) - except StochSSAPIError as err: - report_error(self, log, err) - self.finish() - - class ProjectMetaDataAPIHandler(APIHandler): ''' ############################################################################## diff --git a/stochss/handlers/util/parameter_sweep.py b/stochss/handlers/util/parameter_sweep.py index 2b2c1dca85..20635e18f6 100644 --- a/stochss/handlers/util/parameter_sweep.py +++ b/stochss/handlers/util/parameter_sweep.py @@ -33,7 +33,7 @@ from .parameter_sweep_1d import ParameterSweep1D from .parameter_sweep_2d import ParameterSweep2D from .parameter_scan import ParameterScan -from .stochss_errors import StochSSJobResultsError +from .stochss_errors import StochSSJobResultsError, StochSSJobError log = logging.getLogger("stochss") @@ -232,6 +232,9 @@ def run(self, verbose=True): if verbose: log.info("Running the %s", sim_type) job.run(job_id=self.get_file(), verbose=verbose) + if not job.ts_results: + message = "All simulations failed to complete." + raise StochSSJobError(message) if verbose: log.info("The %s has completed", sim_type) log.info("Storing the results as pickle and csv") diff --git a/stochss/handlers/util/scripts/start_job.py b/stochss/handlers/util/scripts/start_job.py index 0b52ebd1d8..fa880431a8 100755 --- a/stochss/handlers/util/scripts/start_job.py +++ b/stochss/handlers/util/scripts/start_job.py @@ -64,7 +64,7 @@ def report_error(err): err : Exception Obj Error caught in the except block ''' - message = "Job errors: %s\n%s", err, traceback.format_exc() + message = f"Job errors: {str(err)}\n{traceback.format_exc()}" gillespy2_log.error(message) log.error(message) open('ERROR', 'w').close() diff --git a/stochss/handlers/util/stochss_base.py b/stochss/handlers/util/stochss_base.py index 8b6b7478fb..c17075e87f 100644 --- a/stochss/handlers/util/stochss_base.py +++ b/stochss/handlers/util/stochss_base.py @@ -97,6 +97,8 @@ def get_new_path(self, dst_path): New path for the file object from the users home directory ''' new_path = os.path.join(self.user_dir, dst_path) + if dst_path.startswith("trash/") and not "trash" in os.listdir(self.user_dir): + os.mkdir(os.path.join(self.user_dir, "trash")) if new_path.split().pop().replace('.', '', 5).isdigit(): return new_path.replace(new_path.split().pop(), "").strip() if "trash/" in new_path and os.path.exists(new_path): diff --git a/stochss/handlers/util/stochss_folder.py b/stochss/handlers/util/stochss_folder.py index 8d9200e65b..d9dbc44d4c 100644 --- a/stochss/handlers/util/stochss_folder.py +++ b/stochss/handlers/util/stochss_folder.py @@ -340,7 +340,8 @@ def get_project_list(self): Attributes ---------- ''' - data = self.get_file_list(ext=".proj", folder=True) + test = lambda ext, root, file: bool("trash" in root.split("/")) + data = self.get_file_list(ext=".proj", folder=True, test=test) projects = [] for file in data['files']: for path in data['paths'][file[0]]: diff --git a/stochss/handlers/util/stochss_job.py b/stochss/handlers/util/stochss_job.py index b3f3b1b7d9..612c513b62 100644 --- a/stochss/handlers/util/stochss_job.py +++ b/stochss/handlers/util/stochss_job.py @@ -339,9 +339,13 @@ def get_plot_from_results(self, plt_key, plt_data, plt_type): try: self.log("info", "Loading the results...") with open(path, "rb") as results_file: - result = pickle.load(results_file)[plt_key] + result = pickle.load(results_file) + if plt_key is not None: + result = result[plt_key] self.log("info", "Generating the plot...") - if plt_type == "stddevran": + if plt_type == "mltplplt": + fig = result.plotplotly(return_plotly_figure=True, multiple_graphs=True) + elif plt_type == "stddevran": fig = result.plotplotly_std_dev_range(return_plotly_figure=True) else: if plt_type == "stddev": @@ -349,7 +353,8 @@ def get_plot_from_results(self, plt_key, plt_data, plt_type): elif plt_type == "avg": result = result.average_ensemble() fig = result.plotplotly(return_plotly_figure=True) - fig["config"] = {"responsive":True} + if plt_type != "mltplplt": + fig["config"] = {"responsive":True} self.log("info", "Loading the plot...") fig = json.loads(json.dumps(fig, cls=plotly.utils.PlotlyJSONEncoder)) return self.get_results_plot(plt_key=None, plt_data=plt_data, fig=fig) diff --git a/stochss/handlers/util/stochss_workflow.py b/stochss/handlers/util/stochss_workflow.py index 8e7237966f..c4646e5304 100644 --- a/stochss/handlers/util/stochss_workflow.py +++ b/stochss/handlers/util/stochss_workflow.py @@ -60,6 +60,8 @@ def __init__(self, path, new=False, mdl_path=None, wkfl_type=None): self.path = unique_path.replace(self.user_dir + '/', "") os.mkdir(unique_path) settings = self.get_settings_template() + if wkfl_type == "Parameter Sweep": + settings['simulationSettings']['realizations'] = 20 if os.path.exists(mdl_path): with open(mdl_path, "r") as mdl_file: timespan_settings = json.load(mdl_file)['modelSettings'] @@ -331,7 +333,7 @@ def load(self): if ".proj" in self.path: if "WorkflowGroup1.wkgp" in self.path: proj = StochSSFolder(path=os.path.dirname(self.get_dir_name(full=True))) - test = lambda ext, root, file: ".wkfl" in root or "trash" in root + test = lambda ext, root, file: ".wkfl" in root or "trash" in root.split("/") models = proj.get_file_list(ext=[".mdl"], test=test) else: wkgp = StochSSFolder(path=self.get_dir_name(full=True)) @@ -344,7 +346,8 @@ def load(self): self.workflow['model'] = None else: root = StochSSFolder(path="") - test = lambda ext, root, file: ".wkfl" in root or ".proj" in root + test = lambda ext, root, file: ".wkfl" in root or ".proj" in root or \ + "trash" in root.split("/") models = root.get_file_list(ext=[".mdl"], test=test) if models is not None: self.workflow['models'] = models diff --git a/webpack.config.js b/webpack.config.js index ca8bf612da..440717e916 100644 --- a/webpack.config.js +++ b/webpack.config.js @@ -13,7 +13,8 @@ module.exports = { workflowEditor: './client/pages/workflow-manager.js', projectBrowser: './client/pages/project-browser.js', projectManager: './client/pages/project-manager.js', - loadingPage: './client/pages/loading-page.js' + loadingPage: './client/pages/loading-page.js', + multiplePlots: './client/pages/multiple-plots.js' }, output: { filename: 'stochss-[name].bundle.js', @@ -90,6 +91,13 @@ module.exports = { template: 'page_template.pug', name: 'loadingPage', inject: false + }), + new HtmlWebpackPlugin({ + title: "StochSS | Multiple Plots Page", + filename: 'multiple-plots-page.html', + template: 'page_template.pug', + name: 'multiplePlots', + inject: false }) ], optimization: {