30:T44a,intro = md`Based on the likelihood ratio test, there **are ${results_json["test results"]["positive test results"]}** branches with evidence of _episodic diversifying selection_ in this dataset (p≤${floatFormat(results_json["test results"]["P-value threshold"] || 0.05)}).
aBSREL analysis (${results_json.analysis.version}) was performed on the alignment from ${results_json.input["file name"]} using HyPhy v${results_json.runtime}.
This analysis ** ${srv_rate_classes > 0 ? "included" : "did not include"}** site-to-site synonymous rate variation. ${_.size (mh_rates['DH']) == 0 ? "" : (_.size (mh_rates['TH'] == 0) ? "Double nucleotide substitutions were included in the model." : "Double and triple nucleotide substitutions were included in the model.")}${+results_json.analysis.version < 2.5 ? "

Some of the visualizations are not available for BUSTED analyses before v2.5" : ""} `31:T930,summary_table = html`

${results_json.input["number of sequences"]} sequences in the alignment	${results_json.input["number of sites"]} codon sites in the alignment	${results_json.input["partition count"]} partitions
${tested_branch_count} median branches/partition used for testing	${d3.extent (omega_rate_classes).join ("-")} ω rate classes per branch	${srv_rate_classes ? "" + srv_rate_classes + " classes" : "None"} synonymous rate variation
${results_json["test results"]["positive test results"]} branches with evidence of selection	${floatFmt(d3.mean (_.map (_.filter (distributionTable, (r)=>r.tested == "Yes"), (d)=>d.sites))\|\|0)} Sites/tested branch with ER≥${pv} for positive selection	${_.size(mh_rates['DH']) ? floatFmt (d3.median (_.map (mh_rates['DH']))) : "N/A"}:${_.size(mh_rates['TH']) ?floatFmt (d3.median (_.map (mh_rates['TH']))) : "N/A"} Median multiple hit rates (2H:3H)

`32:T7b6,distributionTable = { let table = []; let site_er = posteriorsPerBranchSite (true, pv); _.each (results_json["branch attributes"][0], (info, b)=> { let row = {'branch' : b}; const is_tested = results_json["tested"][0][b] == "test"; if (is_tested) { row['tested'] = 'Yes'; row['p-value'] = info["Corrected P-value"]; row['sites'] = site_er[b] || 0; } else { row['tested'] = 'No'; row['p-value'] = null; row['sites'] = null; } row['rates'] = info['Rate classes']; row ['dist'] = ["ω",test_omega(b),""]; row['plot'] = ["",row['dist'][1]]; table.push (row); }); /*_.each (["Unconstrained model", "Constrained model"], (m)=> { if (!_.get (results_json,["fits",m])) return; let record = {'Model' : m}; record['LogL'] = _.get (results_json,["fits",m,"Log Likelihood"]); record['AICc'] = _.get (results_json,["fits",m,"AIC-c"]); record['p'] = _.get (results_json,["fits",m,"estimated parameters"]); record['dist'] = ["Tested", getRateDistribution (["fits",m,"Rate Distributions","Test"]),"ω",m + " ω tested"]; record['plot'] = ["ω",record['dist'][1]]; table.push (record); if (has_background) { record = {'Model' : m}; record['dist'] = ["Background", getRateDistribution (["fits",m,"Rate Distributions","Background"]),"ω",m + " ω background" ]; record['plot'] = ["ω",record['dist'][1]]; table.push (record); } if (srv_rate_classes) { record = {'Model' : m}; record['dist'] = ["Synonymous rates", getRateDistribution (["fits",m,"Rate Distributions","Synonymous site-to-site rates"], ["rate", "proportion"]),"",m + " syn rates" ]; record['plot'] = ["",record['dist'][1]]; table.push (record); } });*/ return table; //getRateDistribution (["fits","Unconstrained model","Rate Distributions","Test"]) }33:T6db,siteTableData = { let site_info = []; let index = 0; let bySite = _.groupBy (profileBranchSites, (d)=>d.site); _.each (results_json["data partitions"], (pinfo, partition)=> { _.each (pinfo["coverage"][0], (ignore, i)=> { let site_record = { //'Partition' : siteIndexPartitionCodon[index][0], 'Codon' : siteIndexPartitionCodon[index][1], }; const sll = _.get (results_json, ["Site Log Likelihood",'unconstrained',0,index]); if (sll) { site_record['LogL'] = sll; } if (srv_distribution) { let site_srv = []; _.each (srv_distribution, (d,i)=> { site_srv.push ({'value' : d.value, 'weight' : results_json["Synonymous site-posteriors"][i][index]}); }); site_record['SRV posterior mean'] = distMean (site_srv); } site_record ["Subs"] = d3.sum (bySite[i+1], (d)=>d.subs); site_record ["ER"] = _.filter (bySite[i+1], (d)=>d.ER >= pv).length; site_info.push (site_record); index++; }) }); return [site_info, { 'Codon' : html`Codon`, 'SRV posterior mean' : html`E_post[α]`, 'LogL' : html`log(L)`, 'Subs' : html`Subs`, 'ER' : html`ER Branch`, }]; }34:T47e,profileBranchSites = { let results = []; const unc = _.get (results_json, ["Site Log Likelihood","unconstrained","0"]); const subs = _.get (results_json, ["substitutions","0"]); if (unc) { _.each (unc, (ll, i)=> { const subs_at_site = generateNodeLabels (tree_objects[0], subs[i]); _.each (subs_at_site, (info, node)=> { if (node != 'root') { const bs_ll = _.get (results_json, ["Site Log Likelihood","tested",node,0,i]); if (bs_ll) { let bit = {}; bit.Key = node + "|" + (i+1); bit.branch = node; bit.site = i+1; bit.ER = Math.exp (unc[i]-bs_ll); bit.subs = info[3]; bit.from = info[2]; bit.to = info[0]; let sub_count = subs_for_pair (bit.from, bit.to); bit.syn_subs = sub_count[0]; bit.nonsyn_subs = sub_count[1]; results.push (bit); } } }); }); } return results; }35:T790,posteriorsPerBranchSite = (do_counts, er)=> { let results = do_counts ? {} : []; let offset = 0; const subs = _.get (results_json, ["substitutions","0"]); _.each (results_json["branch attributes"], (data, partition) => { let partition_size = 0; let subs_per_site = {}; _.each (data, (per_branch, branch)=> { if (per_branch ["posterior"]) { const prior_d = test_omega (branch); let prior_odds = prior_d[prior_d.length-1].weight; const rate_class = prior_d.length-1; if (prior_odds < 1 && prior_odds > 0) { prior_odds = prior_odds / (1-prior_odds); _.each (per_branch ["posterior"][rate_class], (p,i)=> { if (! do_counts && (i in subs_per_site) == false) { subs_per_site[i] = generateNodeLabels (tree_objects[0], subs[i]) } if (do_counts) { results[branch] = (results[branch] ? results[branch] : 0) + ((p/(1-p))/prior_odds >= er); } else { const info = subs_per_site [i][branch]; let sub_count = subs_for_pair (info[2], info[0]); results.push ({'Key' : branch + "|" + (i + offset + 1), 'Posterior' : p, 'ER' : (p/(1-p))/prior_odds, 'subs' : info[3], 'from': info[2], 'to' : info[0], 'syn_subs' : sub_count[0], 'nonsyn_subs' : sub_count[1] }); } }); } partition_size = per_branch ["posterior"][rate_class].length; } }); offset += partition_size; }); return results; }36:T7c1,characterPlot = (data, data2)=> { const branch_order = treeNodeOrdering (0, true); let N = results_json.input["number of sequences"]; let box_size = 10; let font_size = 12; console.log (data2); let spec = { "width": {"step": 2.5*font_size}, "height" : {"step" : font_size}, "data" : {"values" : data, }, "transform" : [ {"calculate" : "parseInt (split(datum.Key, '|')[1])", "as" : "Codon"}, {"calculate" : "split(datum.Key, '|')[0]", "as" : "Branch"}, {"filter": {"param": "character_view", "empty" : true}}, { "lookup": "Key", "from": { "data": {"values" : data2}, "key": "Key", "fields": ["ER"] } } ], "encoding": { "x": { "field": "Codon", "type" : "ordinal", "scale": {"domain": {"selection": "character_view", "encoding": "x"}}, "axis": font_size ? {"grid" : false, "titleFontSize" : 14, "title" : "Codon", "labelFontSize" : font_size} : null }, "y": { "field": "Branch", "scale": {"domain": {"selection": "character_view", "encoding": "y"}}, "type" : "ordinal", "axis": font_size ? {"grid" : false, "titleFontSize" : 14, "title" : "Branch", "labelFontSize" : font_size} : null } }, "layer": [ { "mark": {"type": "text", "clip" : true, "fontSize" : font_size, "font" : "monospace", "fontWeight" : "bold", "color" : "#444", "opacity" : 1.0, "tooltip" : true}, "encoding": { "text" : {"field" : "value"}, "color": { "field" : "aa" //"field": "ER", //"type" : "quantitative", //"legend" : {"orient" : "top"}, //"sort": "ascending", //"scale" : {"scheme" : "lightgreyred"} } } } ] }; return spec; }37:T4f7,subs_for_pair = (from, to) => { if (from == 'NNN' || to == 'NNN') { return [0,0]; } let diffs = []; _.each (from, (c,i)=> { if (c != to[i]) { diffs.push (i); } }); switch (diffs.length) { case 0: return [0,0]; case 1: if (translate_ambiguous_codon (from) == translate_ambiguous_codon(to)) { return [1,0]; } return [0,1]; case 2: { let res = path_diff (from,to,[diffs[0],diffs[1]]); _.each (path_diff (from,to,[diffs[1],diffs[0]]), (d,i) => {res[i] += d;}); return _.map (res, (d)=>0.5*d); } case 3: { let res = path_diff (from,to,[diffs[0],diffs[1],diffs[2]]); _.each (path_diff (from,to,[diffs[0],diffs[2],diffs[1]]), (d,i) => {res[i] += d;}); _.each (path_diff (from,to,[diffs[1],diffs[0],diffs[2]]), (d,i) => {res[i] += d;}); _.each (path_diff (from,to,[diffs[1],diffs[2],diffs[0]]), (d,i) => {res[i] += d;}); _.each (path_diff (from,to,[diffs[2],diffs[0],diffs[1]]), (d,i) => {res[i] += d;}); _.each (path_diff (from,to,[diffs[2],diffs[1],diffs[0]]), (d,i) => {res[i] += d;}); return _.map (res, (d)=>d/6); } } }38:T40c,generateNodeLabels = (T, labels)=> { let L = {}; T.traverse_and_compute (function (n) { if (n.data.name in labels) { L[n.data.name] = [labels[n.data.name], translate_ambiguous_codon (labels[n.data.name]),'',0]; if (n.parent) { L[n.data.name][2] = L[n.parent.data.name][0]; _.each (L[n.data.name][0], (c,i)=> { const c2 = L[n.data.name][2][i]; if (c2 != c && c != '-' && c2 != '-' && c != 'N' && c2 != 'N') { L[n.data.name][3] ++; } }); } } else { if (n.parent) { L[n.data.name] = _.clone (L[n.parent.data.name]); L[n.data.name][2] = L[n.data.name][0]; L[n.data.name][3] = 0; } else { L['root'] = [labels["root"], translate_ambiguous_codon (labels["root"]), "", 0]; } } L[n.data.name][4] = !_.isUndefined (n.children); },"pre-order"); return L; }39:T7aa,ERPlot = (data, from, step, key)=> { let scale = d3.extent (data, (d)=>d[key]); scale[1] = Math.min (dyn_range_cap,Math.max (scale[1], pv)); scale = d3.nice (scale[0], scale[1], 10); return { "width": {"step": 12}, "data" : {"values" : _.map ( _.filter (data, (d,i)=>i+1 >= from && i< from + step - 1), (d)=> { let dd = _.clone (d); _.each ([key], (f)=> { dd[f] = Math.min (dyn_range_cap, dd[f]); }); return dd; })}, "encoding": { "x": { "field": "Codon", "type" : "nominal", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Codon"} } }, "layer": [ { "mark": {"stroke": "black", "type": "line", "size" : 2, "interpolate" : "step", "color" : "lightgrey", "opacity" : 0.5}, "encoding": { "y": { "field": key, "type" : "quantitative", "scale" : {"type" : "symlog", "domain" : scale}, "axis" : {"grid" : false} } } }, { "mark": { "stroke": "black", "type": "point", "size" : 100, "filled" : true, "color" : "lightgrey", "tooltip" : {"contents" : "data"}, "opacity" : 1.}, "encoding": { "y": { "field": key, "type" : "quantitative", }, "color" : {"condition": {"test": "datum['" + key + "'] > " + pv, "value": "firebrick"}, "value": "lightgrey" } } }, { "mark" : {"opacity": 0.5, "type": "line", "color": "steelblue"}, "encoding" : { "y": { "datum": {"expr": "" + pv}, "type": "quantitative", "scale" : {"domain" : scale} }, "size": {"value": 2}, } } ] }; }3a:T72e,SRVPlot = (data, from, step, key, key2)=> { let spec = { "width": {"step": 12}, "data" : {"values" : _.map ( _.filter (data, (d,i)=>i+1 >= from && i<= from + step), (d)=> { let dd = _.clone (d); _.each ([key], (f)=> { dd[f] = Math.min (dyn_range_cap, dd[f]); }); return dd; })}, "encoding": { "x": { "field": "Codon", "type" : "nominal", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Codon"} } }, "layer": [ { "mark": {"type": "line", "size" : 2, "color" : "lightgrey", "opacity" : 0.5, "interpolate" : "step"}, "encoding": { "y": { "field": key, "type" : "quantitative", } } }, { "mark": {"stroke": null, "type": "point", "size" : 100, "filled" : true, "color" : "lightgrey", "tooltip" : {"contents" : "data"}, "opacity" : 1}, "encoding": { "y": { "field": key, "type" : "quantitative", "scale" : {"type" : "symlog"}, "axis" : {"grid" : false} }, //"color" : results_json["Evidence Ratios"]["constrained"] ? {"field" : "ER (constrained)", "type" : "quantitative", "scale" : {"type" : "log", "scheme": "turbo"}, "legend" : {"orient" : "top"}} : null } } ] }; if (key2) { spec.layer.push ( { "mark": {"type": "line", "size" : 4, "color" : "lightgrey", "opacity" : 0.5, "interpolate" : "step", "color" : "firebrick"}, "encoding": { "y": { "field": key2, "type" : "quantitative", } } } ); } return spec; }3b:Tb23,BSPosteriorPlot = (data, from, step)=> { const selected_branches = new Set (_.map (rate_table, (d)=>d.branch)); const branch_order = _.filter (treeNodeOrdering (0), (d)=>profilable_branches.has (d) && selected_branches.has (d)); let N = tested_branch_count; let box_size = 10; let font_size = 8; var size_field = "subs"; switch (fig1_controls) { case "Syn subs": size_field = "syn_subs"; break; case "Nonsyn subs": size_field = "nonsyn_subs"; break; } if (N > 50) { if (N <= 100) {box_size = 8; font_size = 6;} else if (N <= 200) {box_size = 5; font_size = 5;} else {box_size = 4; font_size = 0;} } let spec = { "width": {"step": box_size}, "height" : {"step" : box_size}, "data" : {"values" : _.filter (data, (d)=>selected_branches.has (d.Key.split ("|")[0])) }, "transform" : [ {"calculate" : "parseInt (split(datum.Key, '|')[1])", "as" : "Codon"}, {"calculate" : "split(datum.Key, '|')[0]", "as" : "Branch"}, {"filter" : {"field" : "Codon", "range" : [from, from+step-1]}} ], /*"params": [{ "name": "character_view", "select": { "type": "interval", "encodings": ["x","y"], "nearest" : "true", "mark" : {"stroke" : "#444", "strokeWidth" : 3}, }, "value": {"x": _.range (20), "y": branch_order} }],*/ "encoding": { "x": { "field": "Codon", "type" : "nominal", "axis": font_size ? {"grid" : false, "titleFontSize" : 14, "title" : "Codon", "labelFontSize" : font_size} : null }, "y": { "field": "Branch", "scale" : {"domain" : branch_order}, "type" : "nominal", "axis": font_size ? {"grid" : false, "titleFontSize" : 14, "title" : "Branch", "labelFontSize" : font_size} : null } }, "layer": [ { "mark": {"type": "rect", "size" : 2, "color" : "lightgrey", "opacity" : 1.0, "tooltip" : {"contents" : "data"}}, "encoding": { "color": { "field": "ER", "type" : "quantitative", "legend" : {"orient" : "top", "title" : "EBF"}, "sort": "descending", "scale" : {"type" : "log", "scheme" : "redyellowblue", "domainMid" : 1} } } }, { "mark": {"type": "circle", "size" : 2, "stroke" : "black", "strokeWidth" : 0.5, "color" : null, "opacity" : 1.0}, "encoding": { "color" : {"value" : null}, "size": { "field": size_field, "type" : "quantitative", "legend" : {"orient" : "top", "title" : "# substitutions"} } } } ] }; return spec; }3c:T9e1,ERPosteriorPlot = (data, from, step)=> { const selected_branches = new Set (_.map (rate_table, (d)=>d.branch)); const branch_order = _.filter (treeNodeOrdering (0), (d)=>profilable_branches.has (d) && selected_branches.has (d)); let N = tested_branch_count; let box_size = 10; let font_size = 8; var size_field = "subs"; switch (fig1_controls) { case "Syn subs": size_field = "syn_subs"; break; case "Nonsyn subs": size_field = "nonsyn_subs"; break; } if (N > 50) { if (N <= 100) {box_size = 8; font_size = 6;} else if (N <= 200) {box_size = 5; font_size = 5;} else {box_size = 4; font_size = 0;} } let spec = { "width": {"step": box_size}, "height" : {"step" : box_size}, "data" : {"values" : _.filter (data, (d)=>selected_branches.has (d.Key.split ("|")[0])) }, "transform" : [ {"calculate" : "parseInt (split(datum.Key, '|')[1])", "as" : "Codon"}, {"calculate" : "split(datum.Key, '|')[0]", "as" : "Branch"}, {"filter" : {"field" : "Codon", "range" : [from, from+step-1]}}, ], "encoding": { "x": { "field": "Codon", "type" : "nominal", "axis": font_size ? {"grid" : false, "titleFontSize" : 14, "title" : "Codon", "labelFontSize" : font_size} : null }, "y": { "field": "Branch", "scale" : {"domain" : branch_order}, "type" : "nominal", "axis": font_size ? {"grid" : false, "titleFontSize" : 14, "title" : "Branch", "labelFontSize" : font_size} : null } }, "layer": [ { "mark": {"type": "rect", "size" : 2, "color" : "lightgrey", "opacity" : 0.8, "tooltip": {"content": "data"}}, "encoding": { "color": { "field": "ER", "type" : "quantitative", "legend" : {"orient" : "top"}, "sort": "descending", "scale" : {"type" : "log", "scheme" : "redyellowblue", "domainMid" : 1} } } }, { "mark": {"type": "circle", "size" : 2, "stroke" : "black", "strokeWidth" : 0.5, "color" : null, "opacity" : 1.0}, "encoding": { "color" : {"value" : null}, "size": { "field": size_field, "type" : "quantitative", "legend" : {"orient" : "top", "title" : "# substitutions"} } } } ] }; return spec; }3d:T1ce9,display_tree = (index, T, options) => { let dim = treeDim.length ? _.map (treeDim.split ("x"), (d)=>+d) : null; T.branch_length_accessor = (n)=>(n.data.name in results_json["branch attributes"][index] ? results_json["branch attributes"][index][n.data.name][branch_length] : 0) || 0; let alignTips = treeLabels.indexOf ("align tips") >= 0; var t = T.render({ height: dim && dim[0], width : dim && dim[1], 'align-tips' : alignTips, 'selectable' : false, 'show-scale' : true, 'is-radial' : false, 'left-right-spacing': 'fit-to-size', 'top-bottom-spacing': 'fit-to-size', 'node_circle_size' : (n)=>0, 'internal-names' : treeLabels.indexOf ("show internal") >= 0 } ); add_svg_defs (t.svg); 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) => { e.selectAll ("text").style ("font-family", "ui-monospace"); if (n.children && n.children.length) return; e.selectAll ("title").data ([n.data.name]).join ("title").text ((d)=>d); }); if (color_branches == "Tested") { t.style_edges ((e,n) => { const is_tested = results_json["tested"][index][n.target.data.name] == "test"; if (is_tested) { e.style ("stroke", "firebrick"); } else { e.style ("stroke", null); } }); } else if (color_branches == "Support for selection") { let branch_values = {}; t.svg.selectAll ("defs").selectAll ("linearGradient").remove(); let branch_gradients = {}; T.traverse_and_compute ( (n)=> { let test_omegas = test_omega (n.data.name); if (test_omegas) { let rate_class = test_omegas.length - 1 ; branch_values[n.data.name] = test_omegas[rate_class].value; } } ); let color_scale = d3.scaleDivergingLog([1e-4,1,Math.min(1000,d3.max (_.map (branch_values, (d)=>d)))],["rgb(0,0,255)","rgb(128,128,128)","rgb(255,0,0)"]); let bID = 0; let max_omega_by_branch = {}; T.traverse_and_compute ( (n)=> { let test_omegas = test_omega (n.data.name); if (test_omegas) { let rate_class = test_omegas.length - 1 ; branch_gradients [n.data.name] = "hyphy_phylo_branch_gradient_" + bID; let gradient_def = t.svg.selectAll ("defs").append ("linearGradient").attr ("id", branch_gradients [n.data.name]); bID += 1; let current_frac = 0; _.each (test_omegas, (t)=> { gradient_def.append ("stop").attr ("offset", "" + current_frac*100.0 + "%").style ("stop-color", color_scale (t.value)); current_frac += t.weight; gradient_def.append ("stop").attr ("offset", "" + current_frac*100.0 + "%").style ("stop-color", color_scale (t.value)); }); max_omega_by_branch [n.data.name] = test_omegas[test_omegas.length-1]; } } ); t.color_scale = color_scale; t.color_scale_title = "ω"; t.style_edges ((e,n) => { const is_tested = results_json["tested"][index][n.target.data.name] == "test"; let t_string = n.target.data.name + " "; let b_string = ""; if (is_tested) { let pv_l = test_pv (n.target.data.name); t_string += "(p = " + pv_l.toFixed (3) + ")"; pv_l = -Math.floor(Math.log10(Math.max (pv_l,1e-6))); let mxo = max_omega_by_branch[n.target.data.name].value; if (mxo > 1) { mxo = mxo > 1000. ? ">1000" : mxo.toFixed (2); b_string = mxo + "/" + (max_omega_by_branch[n.target.data.name].weight*100).toFixed (2) + "%"; } e.style ("stroke", "url('#" + branch_gradients[n.target.data.name] + "')").style ("stroke-width", 2 + pv_l); } else { t_string += "(not tested)"; e.style ("stroke", "url('#" + branch_gradients[n.target.data.name] + "')").style ("stroke-width", "2").style ("opacity","0.5"); } t_string += " max ω = " + branch_values[n.target.data.name].toFixed (2); e.style ("stroke-linejoin", "round").style("stroke-linecap", "round"); e.selectAll ("title").data ([t_string]).join ("title").text ((d)=>d); if (options["branch-labels"]) { add_branch_label (e, b_string, t.font_size, t.svg.selectAll (".phylotree-container")); } }); } else { let labels; switch (color_branches) { case "Substitutions" : { labels = subs_by_branch (index); t.color_scale_title = "Min # of nucleotide substitutions"; } break; case "2-hit rate" : { labels = _.mapValues (mh_rates["DH"], d=>d.toFixed (2)); t.color_scale_title = "Double-nucleotide relative substitution rate (δ)"; } break; case "3-hit rate" : { labels = _.mapValues (mh_rates["TH"], d=>d.toFixed (2)); t.color_scale_title = "Three-nucleotide relative substitution rate (ψ)"; } break; } //console.log (labels, options["branch-labels"]); let color_scale = d3.scaleSequential(d3.extent (_.map (labels, d=>d)), d3.interpolateTurbo); t.color_scale = color_scale; t.svg.selectAll (".absrel-branch-labels").remove(); t.style_edges ((e,n) => { const is_tested = labels[n.target.data.name]; if (options["branch-labels"]) { add_branch_label (e, is_tested, t.font_size, t.svg.selectAll (".phylotree-container")); } e.style ("stroke", color_scale(is_tested)).style ("stroke-width", "4").style ("opacity",1.0); e.selectAll ("title").data ([is_tested]).join ("title").text ((d)=>d); e.style ("stroke-linejoin", "round").style("stroke-linecap", "round"); }); } t.placenodes(); t.update(); //label_color_scale.domain (labelDomain); return t; } 3e:T435,display_tree_handle_neighbors = (index, s, node_labels, T, options, results, site_count)=> { let extended_labels = {}; if (options["neighbors"]) { const si = (+s)-1; //const site_count = partition_sizes[index]; let joint_labels = []; for (let idx = si-4; idx <= si+4; idx++) { if (idx >= 0 && idx < site_count) { if (idx != si) { joint_labels.push (generateNodeLabels (T, results["substitutions"][index][idx])); } else { joint_labels.push (_.mapValues (node_labels, (d)=> { return ["·" + d[0] + "·", "·" + d[1] + "·"] })); } } } _.each (node_labels, (d,k)=> { extended_labels [k] = [_.map (joint_labels, (slc)=> { return slc[k][0]; }).join ("|"),_.map (joint_labels, (slc)=> { return slc[k][1]; }).join ("|")]; }); } return extended_labels; } 3f:T2062,display_tree_site = (index, T,s,options) => { let dim = treeDim.length ? _.map (treeDim.split ("x"), (d)=>+d) : null; T.branch_length_accessor = (n)=>results_json["branch attributes"][index][n.data.name][branch_length] || 0; let node_labels = generateNodeLabels (T, results_json["substitutions"][index][(+s)-1]); let extended_labels = {}; let labelDomain = new Set(); let showAA = treeLabels.indexOf ("amino-acids") >= 0; let showCodons = treeLabels.indexOf ("codons") >= 0; let showSeqNames = treeLabels.indexOf ("sequence names") >= 0; let showOnlyMH = treeLabels.indexOf ("show only multiple hits") >= 0; let showOnlyNS = treeLabels.indexOf ("show only non-synonymous changes") >= 0; let alignTips = treeLabels.indexOf ("align tips") >= 0; var t = T.render({ height:dim && dim[0], width:dim && dim[1], 'show-scale' : true, 'is-radial' : false, 'align-tips' : alignTips, 'left-right-spacing': 'fit-to-size', 'top-bottom-spacing': 'fit-to-size', 'node_circle_size' : (n)=>0, 'selectable' : false, 'internal-names' : treeLabels.indexOf ("show internal") >= 0 } ); add_svg_defs (t.svg); extended_labels = display_tree_handle_neighbors (index,s,node_labels,T,options,results_json, partition_sizes[index]); t.nodeLabel ((n)=> { if (!n._display_me) { return ""; } let label = ""; let has_extended_label = extended_labels[n.data.name] || node_labels[n.data.name]; n.data.color_on = ""; if (showCodons) { label = has_extended_label[0]; n.data.color_on = node_labels[n.data.name][0]; if (showAA) label += "/"; } if (showAA) { label += has_extended_label[1]; n.data.color_on = node_labels[n.data.name][1]; } labelDomain.add ( n.data.color_on); if (showSeqNames) label += ":" + n.data.name; return label; }); 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); }); } T.traverse_and_compute (function (n) { n._display_me = ! (showOnlyMH || showOnlyNS); if (!n._display_me) { if (node_labels[n.data.name]) { if (showOnlyMH && node_labels[n.data.name][3] > 1) n._display_me = true; if (! n._display_me) { if (showOnlyNS) { if (n.parent) { const my_aa = node_labels[n.data.name][1]; const parent_aa = node_labels[n.parent.data.name][1]; if (my_aa != parent_aa && my_aa != '-' && parent_aa != '-') { n._display_me = true; if (showOnlyMH) n._display_me = node_labels[n.data.name][3] > 1; } else { n._display_me = false; } } } } } } if (n._display_me && n.parent) { n.parent._display_me = true; } },"pre-order"); sort_nodes (true); t.style_nodes ((e,n) => { e.selectAll ("text").style ("fill", label_color_scale(n.data.color_on)); e.selectAll ("title").data ([n.data.name]).join ("title").text ((d)=>d); e.selectAll ("text").style ("font-family", "ui-monospace"); }); if (color_branches == "Tested") { t.style_edges ((e,n) => { const is_tested = results_json["tested"][index][n.target.data.name] == "test"; if (is_tested) { e.style ("stroke", "firebrick"); } else { e.style ("stroke", null); } }); } else if (color_branches == "Support for selection") { let branch_values = {}; T.traverse_and_compute ( (n)=> { let posteriors = results_json["branch attributes"][index][n.data.name]; if (posteriors && posteriors["posterior"]) { let test_omegas = test_omega (n.data.name); let rate_class = test_omegas.length - 1 ; let prior = test_omegas[test_omegas.length-1].weight; prior = prior / (1-prior); posteriors = posteriors["posterior"][rate_class][s-1]; branch_values [n.data.name] = posteriors/(1-posteriors)/prior; if (branch_values [n.data.name] < 1) branch_values [n.data.name] = null; } }); let color_scale = d3.scaleSequentialLog(d3.extent (_.map (branch_values, (d)=>d)),d3.interpolateTurbo); t.color_scale = color_scale; t.color_scale_title = "Empirical Bayes Factor"; t.style_edges ((e,n) => { const is_tested = branch_values[n.target.data.name]; if (is_tested) { e.style ("stroke", color_scale(is_tested)).style ("stroke-width", "5").style ("opacity",null); e.selectAll ("title").data ([is_tested]).join ("title").text ((d)=>d); } else { e.style ("stroke", null); } }); } else { let labels, color_scale = null; switch (color_branches) { case "Substitutions" : { color_scale = d3.scaleOrdinal([0,1,2,3], d3.schemePuOr[4]); labels = node_labels; t.color_scale_title = "Min # of nucleotide substitutions"; } break; case "2-hit rate" : { labels = _.mapValues (mh_rates["DH"], (d)=>d.toFixed(2)); t.color_scale_title = "Double-nucleotide relative substitution rate (δ)"; } break; case "3-hit rate" : { labels = _.mapValues (mh_rates["TH"], (d)=>d.toFixed(2)); t.color_scale_title = "Three-nucleotide relative substitution rate (ψ)"; } break; } t.color_scale = color_scale || d3.scaleSequential(d3.extent (_.map (labels, d=>d)), d3.interpolateTurbo); t.style_edges ((e,n) => { const is_tested = labels[n.target.data.name]; if (color_branches == "Substitutions") { if (is_tested && is_tested[3]) { e.style ("stroke", t.color_scale(is_tested[3])).style ("stroke-width", "5").style ("opacity","1"); const ts = is_tested[2] + "→" + is_tested[0] + "(" + is_tested[3] + ")" e.selectAll ("title").data ([ts]).join ("title").text ((d)=>d); if (options["branch-labels"]) { add_branch_label (e, ts, t.font_size, t.svg.selectAll (".phylotree-container")); } } else { e.style ("stroke", null); } } else { e.style ("stroke", t.color_scale(is_tested)).style ("stroke-width", "4").style ("opacity","1"); if (options["branch-labels"]) { add_branch_label (e, is_tested, t.font_size, t.svg.selectAll (".phylotree-container")); } } }); } t.placenodes(); t.update(); label_color_scale.domain (labelDomain); return t; }40:Tc3a,bustedStyle = html` `41:[] 2e:{"title":"aBSREL analysis result visualization","cells":[{"id":0,"value":"md`# aBSREL analysis result visualization`","pinned":false,"hidden":false,"mode":"ojs"},{"id":89,"value":"viewof results_file = params.get (\"url\") ? localFileInput({accept: [\".json\",\".gz\"], value : params.get (\"url\"), disable: true}) : localFileInput({accept: [\".json\",\".gz\"]}) ","pinned":false,"hidden":false,"mode":"ojs"},{"id":1302,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":1673,"value":"citation = md`

**Suggested citation**

${results_json.analysis[\"citation\"]}

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1119,"value":"viewof pv = Inputs.text({label: html`Evidence ratio threshold`, value: \"100\", submit: \"Update\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":592,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":2362,"value":"alignmentHeader = md`#### Branch-by-branch results`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2417,"value":"viewof rate_table = Inputs.table (distributionTable, {\n header : {'LogL' : \"Log (L)\", \"AICc\" : \"AIC-c\", \"p\" : html`Params.!_.isUndefined (results_json[\"branch attributes\"][0][d][\"Corrected P-value\"]) && results_json[\"branch attributes\"][0][d][\"Corrected P-value\"]<=0.05 ? html`${d}` : d,\n 'LogL' : d3.format (\"2g\"),\n 'AICc' : d3.format (\"2g\"),\n 'dist' : (d)=>{\n return html`\n ${_.map (d[1], (c,i)=> floatFormat(c.value) + \" (\" + proportionFormat (c.weight) + \") \")}\n Mean = ${floatFormat (distMean (d[1]))}, CoV = ${floatFormat (Math.sqrt (distVar (d[1]))/distMean (d[1]))}`},\n 'plot' : (d)=>d[1].length > 1 ? renderDiscreteDistribution (d[1],{\"height\" : 40, \"width\" : 150, \"ticks\" : 2, \"scale\" : \"log\", \"ref\" : [1]}) : ''\n },\n layout: \"auto\",\n sort: \"p-value\",\n height: 300\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2647,"value":"distComparisonPlot = rate_table.length == 2 ? renderTwoDiscreteDistributions (rate_table[0].dist[1],rate_table[1].dist[1],{\"label\" : {\"chart\" : rate_table[0].plot[0], \"series\" : [rate_table[0].dist[3],rate_table[1].dist[3]]}, \"width\" : 700, \"height\" : 120, \"scale\" : \"sqrt\", \"margin\" : {top: 5, right: 250, bottom: 30, left: 20}}) : md`Select exactly two distributions to plot a side-by-side comparison`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1549,"value":"viewof tree_id = autoSelect ({options:treeViewOptions, size : 10, title: html`Tree to view`, placeholder : \"Select something to view\", \"value\" : \"Alignment-wide tree\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2881,"value":"viewof branch_length = Inputs.select(_.chain (results_json[\"branch attributes\"][\"attributes\"]).toPairs().filter (d=>d[1][\"attribute type\"] == \"branch length\").map (d=>d[0]).value(),{value: \"Baseline MG94xREV\", label: html`Branch length `})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3086,"value":"viewof treeLabels = Inputs.checkbox(\n [\"amino-acids\",\"codons\",\"show internal\",\"show only multiple hits\",\"show only non-synonymous changes\",\"sequence names\",\"align tips\",\"include neighboring codons\",\"show branch labels\"],{\"value\" : [\"amino-acids\",\"align tips\"], label: html`Tree labels` }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3095,"value":"viewof color_branches = Inputs.select(tree_color_options,{value: \"Support for selection\", label: html`Color branches `})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1793,"value":"viewof treeDim = Inputs.text({placeholder : \"1024 x 800\", label: \"H x W (pixels)\", submit: \"Resize\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3447,"value":"{\n tree_id;\n if (figure2 && figure2.color_scale) {\n return Legend(figure2.color_scale, {\n title: figure2.color_scale_title,\n tickFormat: \"f\",\n marginLeft: \"10\",\n marginRight: \"10\"\n });\n }\n return html``;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3453,"value":"{\n if (figure2) {\n return figure2.show();\n }\n return md`### Select a tree option from the menu above to view`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":538,"value":"fig1caption=md`**Figure 1**. ${plot_type ? plot_legends[plot_type] : \"No plotting options available\"}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1646,"value":"viewof plot_type = Inputs.select(_.map (_.filter (plot_options, (d)=>d[1](results_json)), d=>d[0]),{label: html`Plot type`, value : 'Evidence ratio alignment profile'})","pinned":false,"hidden":false,"mode":"ojs"},{"id":4761,"value":"viewof fig1_controls = plot_extras[plot_type] || Inputs.text({label: \"Plot options\", value: \"None\", disabled: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1328,"value":"figure1 = plot_type ? vegalite (plot_specs[plot_type]) : md``","pinned":false,"hidden":false,"mode":"ojs"},{"id":1111,"value":"table1caption=md`**Table 2**. Detailed site-by-site results from the aBSREL analysis`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1114,"value":"viewof table1 = Inputs.table (sites_table[1], {\n rows : 10,\n format: sites_table[0],\n layout: \"auto\",\n header: sites_table[2]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":5101,"value":"table3caption=md`**Table 3**. Detailed site-by-site results for a specific branch in the aBSREL analysis`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5104,"value":"viewof which_branch = multiAutoSelect({\n options: _.map ([...profilable_branches], (d)=>d),\n placeholder: \"Select some branches\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":5132,"value":"viewof table3 = Inputs.table (table3_data,\n {\n rows : 10,\n layout: \"auto\",\n columns : [\"branch\", \"site\", \"from\", \"to\", \"subs\", \"ER\", \"EBF\"],\n header: {\n \"branch\" : \"Branch\",\n \"site\" : \"Site\",\n \"ER\": \"Evidence ratio\",\n \"from\" : \"Parent\",\n \"to\" : \"Codon\",\n \"subs\" : \"# substitutions\",\n \"EBF\" : \"Empirical BF\",\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"html`\n \n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2225,"value":"#### Code, data, and libraries","pinned":false,"hidden":false,"mode":"md"},{"id":1565,"value":"figure2 = {\n let toDisplay = tree_id.split (\" \");\n if (toDisplay.length > 1) {\n let tree_options = { \n 'branch-labels' : treeLabels.indexOf (\"show branch labels\") >= 0,\n 'neighbors' : treeLabels.indexOf (\"include neighboring codons\") >= 0\n };\n \n if (toDisplay[0] == \"Codon\") { \n const codon_index = (+toDisplay[1]);\n let partition_id = siteIndexPartitionCodon [codon_index-1][0]-1;\n let TT = display_tree_site (partition_id, tree_objects[0], codon_index, tree_options);\n return TT;\n } \n let TT = display_tree(0, tree_objects[0], tree_options);\n return TT;\n }\n return null;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":638,"value":"floatFormat = d3.format (\".4g\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":3791,"value":"percentageFormat = d3.format (\".2p\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2518,"value":"proportionFormat = d3.format (\".5p\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2257,"value":"results_json = await get_json (params.get (\"url\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":99,"value":"get_json = async (json_source)=> {\n if (json_source) {\n if (json_source.substr (json_source.length-3) == \".gz\") {\n return JSON.parse (pako.inflate(await d3.buffer(json_source), { to: 'string' }));\n }\n return d3.json (json_source);\n try {\n return d3.json (json_source);\n } catch {}\n //results_file.json();\n }\n try {\n return JSON.parse (pako.inflate(await results_file.arrayBuffer(), { to: 'string' }));//results_file.json();\n } catch {}\n return results_file.json();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1638,"value":"fig1data = {\n let in_set = new Set (_.map (table1, (d)=>d.Codon));\n return _.filter (siteTableData[0], (x)=>in_set.has (x.Codon));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1653,"value":"plot_legends = ({\n \"Synonymous rates\" : \"Posterior means for synonymous site-level substitution rates (α). \",\n \"Support for positive selection\" : \"Empirical Bayes Factors for ω>1 at a particular branch and site (only tested branches with 2 or more rate classes are included).\",\n \"Evidence ratio alignment profile\" : \"Evidence ratios for for ω>1 at a particular branch and site (only tested branches with an ω>1 distribution component are included). Mouse over for more information\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":4757,"value":"plot_extras = ({\n 'Evidence ratio alignment profile' : Inputs.select(['Total subs', 'Syn subs', 'Non-syn subs'], {'label' : 'Circle size'} ),\n 'Support for positive selection' : Inputs.select(['Total subs', 'Syn subs', 'Non-syn subs'], {'label' : 'Circle size'} )\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1758,"value":"plot_options = [\n [\"Synonymous rates\", (d)=>srv_rate_classes > 0 && srv_distribution], \n [\"Support for positive selection\", (d)=>bsPositiveSelection.length > 0],\n [\"Evidence ratio alignment profile\", (d)=>profileBranchSites.length > 0]\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1665,"value":"plot_specs = (\n { \"Synonymous rates\" : {\n \"width\": 800, \"height\": 150, \n \"vconcat\" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> {\n return SRVPlot (fig1data, d, 70, \"SRV posterior mean\", null)\n })},\n \"Support for positive selection\" : {\n //\"autosize\": {\"resize\" : true},\n \"vconcat\" : _.map (_.range (1, results_json.input[\"number of sites\"], er_step_size()), (d)=> {\n return BSPosteriorPlot (bsPositiveSelection, d, er_step_size())\n })},\n \"Evidence ratio alignment profile\" : {\n //\"autosize\": {\"resize\" : true},\n \"vconcat\" : _.map (_.range (1, results_json.input[\"number of sites\"], er_step_size()), (d)=> {\n return ERPosteriorPlot (profileBranchSites, d, er_step_size())\n })}\n}\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":4732,"value":"profilable_branches = new Set (_.chain (_.get (results_json, [\"Site Log Likelihood\",\"tested\"])).keys().value())","pinned":false,"hidden":false,"mode":"ojs"},{"id":1214,"value":"tested_branch_count = d3.median (_.chain (results_json.tested).map ().map((d)=>_.filter (_.map (d), (d)=>d==\"test\").length).value())","pinned":false,"hidden":false,"mode":"ojs"},{"id":1314,"value":"srv_rate_classes = results_json[\"Synonymous site-posteriors\"] ? results_json[\"Synonymous site-posteriors\"].length: 0","pinned":false,"hidden":false,"mode":"ojs"},{"id":2798,"value":"srv_distribution = getRateDistribution ([\"fits\",\"Full adaptive model\",\"Rate Distributions\",\"Synonymous site-to-site rates\"], [\"rate\",\"proportion\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":2325,"value":"omega_rate_classes = _.map (results_json[\"branch attributes\"][\"0\"], (d)=>d[\"Rate classes\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":2584,"value":"mh_rates = ({\n 'DH' : _.chain(_.map (results_json[\"branch attributes\"][0], (d,k) => [k,_.get (d, ['rate at which 2 nucleotides are changed instantly within a single codon'])])).filter (d=>!_.isUndefined(d[1])).fromPairs().value(),\n 'TH' : _.chain(_.map (results_json[\"branch attributes\"][0], (d,k) => [k,_.get (d, ['rate at which 3 nucleotides are changed instantly within a single codon'])])).filter (d=>!_.isUndefined(d[1])).fromPairs().value()\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2554,"value":"test_omega = (branch)=> getRateDistribution ([\"branch attributes\",\"0\",branch,\"Rate Distributions\"],[\"0\",\"1\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":4541,"value":"test_pv = (branch)=> _.get (results_json,[\"branch attributes\",\"0\",branch,\"Corrected P-value\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":2419,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":2369,"value":"getRateDistribution = (keys, tags)=> {\n tags = tags || [\"omega\", \"proportion\"];\n const rate_info = _.get (results_json, keys);\n if (rate_info) {\n\n \n return _.sortBy (_.map (rate_info, (d)=>({\n \"value\" : d[tags[0]],\n \"weight\" : d[tags[1]]\n })), (d)=>d.rate);\n }\n return null;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5007,"value":"partition_sizes = {\n return _.chain (results_json['data partitions']).map ((d,k)=>(d['coverage'][0].length)).value();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2676,"value":"siteIndexPartitionCodon = {\n return _.chain (results_json['data partitions']).map ((d,k)=>_.map (d['coverage'][0], (site)=>[+k+1,site+1])).flatten().value();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1449,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1097,"value":"sites_table = {\n return [{}, siteTableData[0], siteTableData[1]];\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4636,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":2911,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":3925,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":5154,"value":"table3_data = {\n let rc = _.keyBy (_.filter (profileBranchSites, (d)=>which_branch.indexOf (d.branch)>=0), (d)=>d.Key);\n _.each (bsPositiveSelection, (d)=> {\n if (d.Key in rc) {\n rc[d.Key].EBF = d.ER;\n }\n });\n return _.values (rc);\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2931,"value":"bsPositiveSelection = {\n return posteriorsPerBranchSite ();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4683,"value":"path_diff = (from,to,path)=> {\n let res = [0,0];\n let curr = _.map (from),\n next = _.clone (curr);\n \n next [path[0]] = to[path[0]];\n const is_syn = translate_ambiguous_codon (curr.join (\"\")) == translate_ambiguous_codon(next.join (\"\"));\n res[is_syn ? 0 : 1] += 1;\n for (let i = 1; i < path.length; i++) {\n curr = _.clone (next);\n next [path[i]] = to[path[i]];\n const is_syn = translate_ambiguous_codon (curr.join (\"\")) == translate_ambiguous_codon(next.join (\"\"));\n res[is_syn ? 0 : 1] += 1;\n }\n \n return res;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4663,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":2983,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":3112,"value":"treeNodeOrdering = (index, root)=> {\n let order = [];\n if (root) {order.push ('root');}\n const T = tree_objects[index];\n function sort_nodes (asc) {\n T.traverse_and_compute (function (n) {\n var d = 1;\n if (n.children && n.children.length) {\n d += d3.max (n.children, function (d) { return d[\"count_depth\"];});\n } \n\n n[\"count_depth\"] = d;\n });\n T.resortChildren (function (a,b) {\n return (a[\"count_depth\"] - b[\"count_depth\"]) * (asc ? 1 : -1);\n });\n }\n sort_nodes (true);\n T.traverse_and_compute (function (n) {\n if (results_json.tested[index][n.data.name] == \"test\") {\n order.push (n.data.name);\n }\n });\n return order;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2631,"value":"distMean = (d)=> {\n let m = 0;\n _.each (d, (r)=> {\n m += r['value'] * r['weight'];\n });\n return m;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2789,"value":"distVar = (d)=> {\n let m2 = 0, m = distMean (d);\n _.each (d, (r)=> {\n m2 += r['value']*r['value'] * r['weight'];\n });\n return m2 - m*m;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3502,"value":"er_step_size = ()=> {\n let N = results_json.input[\"number of sequences\"];\n if (N < 100) return 70;\n if (N < 200) return 140;\n return 600;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"d3 = require (\"d3\")","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":1748,"value":"dyn_range_cap = 10000","pinned":false,"hidden":false,"mode":"ojs"},{"id":2738,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":2821,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":2948,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":4728,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":13,"value":"_=require(\"lodash\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":692,"value":"tree_objects = _.map (results_json.input.trees, (tree,i)=> {\n let T = new phylotree.phylotree (tree);\n T.branch_length_accessor = (n)=>results_json[\"branch attributes\"][i][n.data.name][\"Global MG94xREV\"];\n return T;\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":3395,"value":"subs_by_branch = (i)=>{\n let counts = {};\n _.each (results_json.substitutions[i], (states, site)=> {\n _.each (states, (state, branch)=> {\n if (branch != \"root\") {\n if (state != '---') {\n counts[branch] = 1 + (counts[branch] ? counts[branch] : 0);\n }\n }\n });\n });\n return counts;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3409,"value":"site_support_by_branch = (i, key, er)=> {\n let counts = {};\n _.each (results_json[\"branch attributes\"][i], (attribs, branch)=> {\n if (key in attribs) {\n _.each (attribs[key], (d)=> {\n if (d[1] >= er) {\n counts[branch] = 1 + (counts[branch] ? counts[branch] : 0);\n }\n });\n }});\n return counts;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3132,"value":"label_color_scale = d3.scaleOrdinal([], d3.schemeCategory10)","pinned":false,"hidden":false,"mode":"ojs"},{"id":756,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":5061,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":3006,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"id":4938,"value":"add_branch_label = (e, text, font_size, container) => {\n const where2 = _.get (parse_svg.default(e.attr(\"d\")),[\"1\"]);\n //console.log (text);\n if (where2 && (text.length || _.isNumber (text))) {\n let my_id = e.attr (\"id\");\n if (!e.attr (\"id\")) {\n my_id = DOM.uid (\"absrel_tree\").id;\n e.attr (\"id\", my_id);\n }\n //t.svg.selectAll (\".phylotree-container\")\n let branch_label = container.selectAll (\"text[label-for='\" + my_id + \"']\").data ([text]).join (\"text\").attr (\"label-for\", my_id).text ((d)=>d).classed (\"absrel-branch-labels\",true).attr (\"x\", where2[1]).attr (\"y\", where2[2]).attr (\"font-size\", font_size * 0.8).attr (\"dx\",\"0.5em\").attr (\"dy\", \"-0.4em\").style (\"font-family\", \"ui-monospace\");\n branch_label.attr (\"filter\",\"url(#tree_branchlabel_bgfill)\");\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4972,"value":"add_svg_defs = (svg)=> {\n let filter = svg.selectAll (\"defs\").append (\"filter\").attr (\"x\", 0).attr (\"y\", 0).attr (\"width\", 1).attr (\"height\", 1).attr (\"id\", \"tree_branchlabel_bgfill\");\n filter.append (\"feFlood\").attr (\"flood-color\", \"lightgray\");\n filter.append (\"feComposite\").attr (\"in\", \"SourceGraphic\").attr (\"operator\", \"atop\");\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3441,"value":"treeViewOptions = {\n let opts = [\"Alignment-wide tree\"];\n if (results_json.substitutions) {\n opts = opts.concat(_.map (_.range (1,results_json.input[\"number of sites\"]+1), (d)=>\"Codon \" + d));\n }\n return opts;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3257,"value":"tree_color_options = {\n let options = [\"Tested\"];\n if (results_json.substitutions) {\n options.push (\"Support for selection\");\n options.push (\"Substitutions\");\n }\n if (_.size (mh_rates['DH'])) {\n options.push (\"2-hit rate\");\n }\n if (_.size (mh_rates['TH'])) {\n options.push (\"3-hit rate\");\n }\n \n return options;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":78,"value":"phylotree=require (\"phylotree\")","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":2230,"value":"params = new URLSearchParams(location.search)","pinned":false,"hidden":false,"mode":"ojs"},{"id":350,"value":"import {legend} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2277,"value":"import {localFileInput} from \"@mbostock/localfile\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2531,"value":"import {renderDiscreteDistribution,renderNDiscreteDistributions, renderTwoDiscreteDistributions} from \"@spond/omega-plots\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2997,"value":"import {translate_ambiguous_codon} from \"@spond/usual-vs-unusual-mutation-analyses\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3107,"value":"import {Legend, Swatches} from \"@d3/color-legend\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":4465,"value":"import {cdsQuant} from \"@spond/busted\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":5123,"value":"import {multiAutoSelect} from \"@john-guerra/multi-auto-select\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":4174,"value":"pako = require('pako@2.1.0/dist/pako.min.js')","pinned":false,"hidden":false,"mode":"ojs"},{"id":4884,"value":"parse_svg = import(/* webpackIgnore: true */ 'https://cdn.skypack.dev/parse-svg-path@0.1.2?min')","pinned":false,"hidden":false,"mode":"ojs"},{"id":587,"value":"$40","pinned":false,"hidden":false,"mode":"ojs"},{"id":598,"value":"simpleIcons = html``","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W41","stdlib":"1","titling":"auto"}