31:T54d,html`

${nrm_results_json.input["number of sequences"]} sequences in the alignment	${nrm_results_json.input["number of sites"]} sites in the alignment	${floatFmt(tree_length_by_model[nrm_best_model])} Tree Length (subs/site)
${nrm_best_model} Best model	${floatFmt(getTestResult ("GTR","NREV12"))} p-value non-reversible	${floatFmt(getTestResult ("NREV12","NREV12+F"))} p-value root frequencies

`32:T709,vegalite ({ "data": {"values": nrm_branch_lengths}, "width" : 400, "height" : 400, "layer" : [{ "mark": {type: "point", "opacity" : 0.5, "size" : 36, tooltip : {"content": "data"}, filled : true, clip : true}, "encoding": { "x": {"field": fig1x, "type": "quantitative", "title" : fig1x, "axis" : {"grid" : false, titleFontSize: 18}, }, "y": {"field": fig1y , "title" : fig1y, "axis" : {"grid" : false, titleFontSize: 18}, "type": "quantitative", }, "stroke" : {"value": "black"}, "color" : {"value" : "grey"} } }, { "mark": { "type": "line", "color": "firebrick", "clip": true, }, "transform": [ { "regression": fig1y, "on": fig1x, "method" : "linear" } //{"calculate" : "datum.GTR_dsn*1.284", "as" : "rgr"} ], "encoding": { "x": { "field": fig1x, "type": "quantitative" }, "y": { "field": fig1y, "type": "quantitative" } } }, { "transform": [ { "regression": fig1y, "on": fig1x, "method" : "linear", "params": true }, {"calculate": "'y=' + format(datum.coef[0], '.4f') + '+' + format(datum.coef[1], '.4f') + 'x; R²: '+format(datum.rSquared, '.2f')", "as": "R2"} ], "mark": { "type": "text", "color": "firebrick", "x": "width", "size" : 16, "align": "right", "y": -5 }, "encoding": { "text": {"type": "nominal", "field": "R2"} } } ] })33:T820,function multiAutoSelect(config = {}) { const { value, title, description, disabled, autocomplete = "off", placeholder, size, options, list = "variants" } = Array.isArray(config) ? { options: config } : config; const optionsSet = new Set(options); const form = input({ type: "text", title, description, attributes: { disabled }, action: fm => { const addSelectedOption = d => { const ele = html`${d} `; ele.querySelector("button").addEventListener("click", b => { fm.value.splice(fm.value.indexOf(d), 1); renderSelection(); fm.dispatchEvent(new CustomEvent("input")); fm.input.focus(); }); return ele; }; function renderSelection() { fm.output.innerHTML = ""; for (let o of fm.value) { fm.output.appendChild(addSelectedOption(o)); } } fm.input.value = ""; fm.value = value || []; fm.onsubmit = e => e.preventDefault(); fm.input.oninput = function(e) { e.stopPropagation(); if ( optionsSet.has(fm.input.value) && fm.value.indexOf(fm.input.value) === -1 ) { fm.value.push(fm.input.value); renderSelection(); fm.dispatchEvent(new CustomEvent("input")); fm.input.value = ""; } }; renderSelection(); }, form: html` ` }); form.output.style["margin-left"] = "0px"; return form; }34:Tfa1,display_tree = (i) => { let dim = treeDim.length ? _.map (treeDim.split ("x"), (d)=>+d) : null; let T = tree_objects[i]; var t = T.render({ height:dim && dim[0] || 1024, width:dim && dim[1] || 600, 'align-tips' : treeLabels.indexOf ("align tips") >= 0, 'show-scale' : true, 'is-radial' : false, 'left-right-spacing': 'fit-to-size', 'top-bottom-spacing': 'fit-to-size', 'node_circle_size' : (n)=> 0, 'draw-size-bubbles' : true, 'node-span':(n)=> n.children ? 0.0 : 1, 'internal-names' : treeLabels.indexOf ("show internal") >= 0 } ); let showSeqNames = treeLabels.indexOf ("sequence names") >= 0; let showFrequencies = treeLabels.indexOf ("show frequencies") >= 0; let distanceIndex = availableDistances.indexOf (distanceFunction); t.nodeLabel ((n)=> { let label = ""; if (showSeqNames) label += n.data.name; if (showFrequencies) { try { let distances = nrm_results_json["branch attributes"]["0"][n.data.name][modelForTree + " frequencies"][0]; if (distanceIndex == 0) label += " " + _.map (nrm_results_json["characters"][0], (d,i)=> d + ":" + floatFmt (distances[i])).join (', '); else label += " " + nrm_results_json["characters"][0][distanceIndex-1] + ":" + floatFormat (distances[distanceIndex-1]); } catch {}; } return label; }); let js_max = 0; _.each (T.getTips(), (n)=> { let js = nodeDistance (nrm_results_json["branch attributes"]["0"][n.data.name][modelForTree + " frequencies"][0],nrm_results_json["branch attributes"]["0"][n.parent.data.name][modelForTree + " frequencies"][0]); js_max = js > js_max ? js : js_max; //console.log (n); }); _.each (T.getInternals(), (n)=> { try { if (n.parent) { let js = nodeDistance (nrm_results_json["branch attributes"]["0"][n.data.name][modelForTree + " frequencies"][0],nrm_results_json["branch attributes"]["0"][n.parent.data.name][modelForTree + " frequencies"][0]); js_max = js > js_max ? js : js_max; //console.log (n); } } catch {}; }); function sort_nodes (asc) { T.traverse_and_compute (function (n) { var d = 1; if (n.children && n.children.length) { d += d3.max (n.children, function (d) { return d["count_depth"];}); } n["count_depth"] = d; }); T.resortChildren (function (a,b) { return (a["count_depth"] - b["count_depth"]) * (asc ? 1 : -1); }); } sort_nodes (true); t.style_nodes ((e,n) => { if (n.children && n.children.length) return; let js = nodeDistance (nrm_results_json["branch attributes"]["0"][n.data.name][modelForTree + " frequencies"][0],nrm_results_json["branch attributes"]["0"][n.data.name]["Observed frequencies"][0]); e.selectAll ("circle").style ("fill", t.color_scale (js)); e.selectAll ("title").data ([n.data.name]).join ("title").text ((d)=>d); }); t.color_scale = d3.scaleSequentialLog([1e-10,js_max],d3.interpolateWarm); //treeMatrixColorScale.domain ([0,js_max]); t.style_edges ((e,n) => { e.style ("stroke", null); if (n.source.parent) { let js = nodeDistance (nrm_results_json["branch attributes"]["0"][n.source.data.name][modelForTree + " frequencies"][0],nrm_results_json["branch attributes"]["0"][n.target.data.name][modelForTree + " frequencies"][0]); e.style ("stroke", t.color_scale (js)); } }); t.placenodes(); t.update(); return t; }35:Tc29,html` `36:[] 2f:{"title":"NRM (non-reversible model) analysis result visualization","cells":[{"id":0,"value":"header1 = md`# NRM (non-reversible model) analysis result visualization`","pinned":false,"hidden":false,"mode":"ojs"},{"id":660,"value":"load_prompt = md`Load an NRM JSON result file `","pinned":false,"hidden":false,"mode":"ojs"},{"id":89,"value":"viewof nrm_results_file = params.get (\"url\") ? localFileInput({accept: \".json\", value : params.get (\"url\"), disable: true}) : localFileInput({accept: \".json\"}) ","pinned":false,"hidden":false,"mode":"ojs"},{"id":592,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1141,"value":"summary=md`Model comparison was run on \\`${nrm_results_json[\"input\"][\"file name\"]}\\`. The best-fitting model (based on AIC-c) is **${nrm_best_model}**. \n\nBased on the comparison of the general reversible (GTR) and non-reversible models (NREV12), there ${report_result(getTestResult (\"GTR\",\"NREV12\"))} for the non-reversibility of the evolutionary process.\n\nBased on the comparison of the non-reversible model which estimate root character frequencies (NREV12+F) and the model which assumes that these equal empirical frequencies (NREV12), there ${report_result(getTestResult (\"NREV12\",\"NREV12+F\"))} for the difference in root character frequencies from the distribution implied by the sequences.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1177,"value":"viewof table1 = Inputs.table (modelTableData, {\n sort: \"AIC-c\",\n rows : 4,\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":538,"value":"table1caption=md`**Table 1**. Summary of model fit, overall tree lengths (subs/site), and corresponding equilibrium frequencies (EF) for each model. Note that the GTR model has the same EF as the base composition of the underlying dataset (empirical frequencies).`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1212,"value":"viewof modelForQ = Inputs.select(_.map (nrm_model_summary, (d)=>d[0]), {value: \"NREV12\", label: \"Substitution model\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1238,"value":"qMatrixColorLegend = Legend(qMatrixColorScale, {\n title: \"Relative rate\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1233,"value":"viewof table2 = Inputs.table (qMatrixTableData, {\n rows : 4,\n format: {\n 'A' : sparkbar(qMaxRate),\n 'C' : sparkbar(qMaxRate),\n 'G' : sparkbar(qMaxRate),\n 'T' : sparkbar(qMaxRate)\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1203,"value":"table2caption=md`**Table 2**. Rate matrix (**Q**) for the substitution model ${modelForQ}. The A⇆G rate is used as the reference (=1.0) for identifiability.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1274,"value":"viewof fig1x = Inputs.select(_.map (nrm_model_summary, (d)=>d[0]), {value: nrm_best_model, label: \"x\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1278,"value":"viewof fig1y = Inputs.select(_.filter (_.map (nrm_model_summary, (d)=>d[0]), (d)=>d!=fig1x), {value: \"GTR\", label: \"y\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":577,"value":"fig1_label=md`**Figure 1**. Compare branch length estimates by model (note that because GTR is not able to properly estimate the relative branch lengths of root descendants, the plot excludes those two branches)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1288,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1531,"value":"fig2label = md`**Figure 2.** Phylogenetic tree renderings show branch lengths under the selected model, and also how the selected measure of differences in base frequencies evolves over the tree. Node bubbles show the difference between the model induced base frequencies and the frequencies observed in the corresponding sequence.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1318,"value":"viewof treeDim = text({placeholder : \"1024 x 800\", description: \"Tree dimension (height x width in pixels), leave blank to auto-scale\", submit: \"Resize\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1346,"value":"viewof treeLabels = Inputs.checkbox(\n [\"show internal\",\"sequence names\",\"align tips\",\"show frequencies\"],{\"value\" : [\"sequence names\",\"show internal\"], label: html`Tree labels` })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1332,"value":"viewof modelForTree = Inputs.select(_.map (nrm_model_summary, (d)=>d[0]), {value: nrm_best_model, label: \"Substitution model\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1517,"value":"viewof distanceFunction = Inputs.select(availableDistances, {value: \"Jensen Shannon\", label: \"Distance\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1391,"value":"treeMatrixColorLegend = Legend(treeMatrixColorScale, {\n title: \"Distance\",\n tickFormat : \"g\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":486,"value":"figure2 = dT.show()","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"html`\n \n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"md`---`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1098,"value":"nrm_model_summary = _.chain (nrm_results_json[\"fits\"]).map ((v,m)=>[m,v [\"AIC-c\"]]).sortBy((d)=>d[1]).value()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1118,"value":"nrm_best_model = nrm_model_summary [0][0]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1292,"value":"nrm_branch_lengths = {\n let bl = [];\n _.each (nrm_results_json[\"branch attributes\"][\"0\"], (d,b)=> {\n if (!rootChildren.has (b)) {\n let r = {'name' : b};\n _.each (nrm_model_summary, (m)=>r[m[0]] = d[m[0]]);\n bl.push (r);\n }\n });\n return bl;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1104,"value":"tree_length_by_model = _.chain (_.keys(nrm_results_json.fits)).map ((d)=>{return [d,d3.sum (_.map (nrm_results_json[\"branch attributes\"][\"0\"], (bv)=>bv[d]))]}).fromPairs().value()","pinned":false,"hidden":false,"mode":"ojs"},{"id":638,"value":"floatFormat = d3.format (\"2g\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1131,"value":"getTestResult = (m1,m2)=> {\n return nrm_results_json[\"test results\"][m1 + \" vs \" + m2][\"Corrected P-value\"];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1180,"value":"modelTableData = {\n return _.chain (tree_length_by_model).map((i,d)=> {\n return {\n 'Model' : d,\n 'AIC-c' : nrm_results_json[\"fits\"][d][\"AIC-c\"],\n 'Tree length' : tree_length_by_model[d],\n 'f(A)' : nrm_results_json[\"fits\"][d][\"Equilibrium frequencies\"][0][0],\n 'f(C)' : nrm_results_json[\"fits\"][d][\"Equilibrium frequencies\"][0][1],\n 'f(G)' : nrm_results_json[\"fits\"][d][\"Equilibrium frequencies\"][0][2],\n 'f(T)' : nrm_results_json[\"fits\"][d][\"Equilibrium frequencies\"][0][3]\n\n };\n }).value();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1264,"value":"qMaxRate = d3.max(_.map (nrm_results_json[\"fits\"][modelForQ][\"Rate Distributions\"], (d)=>d3.max(d)))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1242,"value":"qMatrixColorScale = d3.scaleSequential([0,qMaxRate],d3.interpolateWarm)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1394,"value":"treeMatrixColorScale = dT.color_scale","pinned":false,"hidden":false,"mode":"ojs"},{"id":1221,"value":"qMatrixTableData = {\n let rates = [];\n let rm = nrm_results_json[\"fits\"][modelForQ][\"Rate Distributions\"];\n const nucs = nrm_results_json[\"characters\"][0];\n _.each (nucs, (from, i)=> {\n let row = {'From/To' : from};\n _.each (nucs, (to, j)=> {\n if (i!=j) {\n row[to] = rm[i][j];\n } else {\n row[to] = null;\n }\n });\n rates.push (row);\n });\n return rates;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":99,"value":"nrm_results_json = await get_json (params.get (\"url\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"d3 = require (\"d3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1471,"value":"dT = display_tree(0)","pinned":false,"hidden":false,"mode":"ojs"},{"id":416,"value":"floatFmt = d3.format (\".2g\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":841,"value":"seqNames = (tree)=> {\n let seq_names = [];\n tree.traverse_and_compute (n=>{\n if (n.children && n.children.length) return;\n seq_names.push (n.data.name);\n });\n return seq_names;\n};","pinned":false,"hidden":false,"mode":"ojs"},{"id":1159,"value":"report_result = (p)=> {\n let sup = \"\";\n if (p <= 0.05) {\n sup = \"**is** evidence\";\n } else {\n sup = \"**is no** evidence\";\n }\n return sup + \" (p=\" + floatFmt(p) + \")\";\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":13,"value":"_=require(\"lodash\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":692,"value":"tree_objects = _.map (nrm_results_json.input.trees, (tree,i)=> {\n let T = new phylotree.phylotree (tree);\n T.branch_length_accessor = (n)=>nrm_results_json[\"branch attributes\"][i][n.data.name][modelForTree];\n return T;\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":1565,"value":"rootChildren = {\n let rt = new Set();\n tree_objects[0].traverse_and_compute ((n)=> {\n if (n.parent && !n.parent.parent) {\n rt.add (n.data.name);\n }\n });\n return rt;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":711,"value":"totalTreeLength = (tree)=> {\n let L = 0;\n tree.traverse_and_compute ( (n)=> {\n if (tree.branch_length (n)) {\n L += +tree.branch_length (n);\n }\n });\n return L;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":350,"value":"import {Legend, Swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":78,"value":"phylotree=require (\"phylotree\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":127,"value":"import { table } from \"@gampleman/table\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":432,"value":"vegalite = require (\"vega-embed\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":174,"value":"import {select, text, input, autoSelect} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":833,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1260,"value":"function sparkbar(max) {\n return x => htl.html`

${x.toLocaleString(\"en\")}`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1342,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1366,"value":"get_json = async (json_source)=> {\n if (json_source) {\n return d3.json (json_source);\n }\n return nrm_results_file.json();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1363,"value":"params = new URLSearchParams(location.search)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1381,"value":"entropy = (d)=> {\n return -d3.sum (_.map (d, (e)=>Math.log2 (e)*e));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1376,"value":"jensenShannonDistance = (dist1, dist2)=> {\n let smoothed = _.map (dist1, (d,i)=> 0.5*(d+dist2[i]));\n return entropy (smoothed) - 0.5 * (entropy (dist1) + entropy (dist2));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1548,"value":"availableDistances = [\"Jensen Shannon\", \"|ΔA|\", \"|ΔC|\", \"|ΔG|\", \"|ΔT|\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1503,"value":"nodeDistance = (d1,d2)=> { \n let i = availableDistances.indexOf (distanceFunction);\n if (i == 0) return jensenShannonDistance (d1,d2); \n return Math.abs (d1[i-1]-d2[i-1]);\n}\n \n ","pinned":false,"hidden":false,"mode":"ojs"},{"id":1370,"value":"import {localFileInput} from \"@mbostock/localfile\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":587,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":598,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W36","stdlib":"1","titling":"auto"}