31:Tb27,html`

${summary_stats.codons} genomic codon loci analyzed	${summary_stats.polymorphic} loci with observed variants	${summary_stats.evo} loci with evolutionary predictions
${summary_stats.var_stats[0] + ":" + summary_stats.var_stats[1]} Median (95%) observed codon variants per locus	${summary_stats.pred_stats[0] + ":" + summary_stats.pred_stats[1]} Median (95%) predicted codon variants per locus	${summary_stats.diff} Median (predicted-observed) count difference per locus
${summary_stats.pred} Loci with all variants at ≥ 0.1% predicted	${summary_stats.unpred} Loci with unpredicted variants at ≥ 0.01%	${summary_stats.perfect} Sites with perfectly predicted minority variants at 0.01% threshold
${floatFormat(summary_stats.rho)} Spearkman rank correlation between observed and predicted site entropies	${floatFormat(FMIN[0])} Fraction of variable loci where the top minority codon was correctly predicted	${floatFormat(summary_stats.bits)} Prediction bit-score compared to a matched complexity random model (0.01% threshold)

`32:T8d5,chart = { const width = 600, height = 600; const nf = d3.format (".5p"); const links = data.links.map(d => Object.create(d)); const nodes = data.nodes.map(d => Object.create(d)); const max_r = width*0.1; const simulation = d3.forceSimulation(nodes) .force("link", d3.forceLink(links).id(d => d.id)) .force("charge", d3.forceCollide(max_r)) .force("center", d3.forceCenter(width / 2, height / 2)); const svg = d3.create("svg").attr ("width", width).attr ("height", height) .attr("viewBox", [0, 0, width, height]); const link = svg.append("g") .attr("stroke", "#999") .attr("stroke-opacity", 0.6) .selectAll("line") .data(links) .join("line") .attr("stroke-width", d => d.value == 1 ? 1.5 : 0); const size = (d)=>{ return Math.max (Math.sqrt(d.obs) * max_r, 1); } const size_pred = (d)=>{ return Math.max (Math.sqrt(d.pred) * max_r, 1); } const opac = (d)=>{ return 1;//Math.max (d.pred, 0.05); } const textfill = (d)=>{ if (d.obs > 0 && d.pred < 1e-8) return "red"; return "black"; } const node_elements = svg.append("g") .selectAll("g") .data(nodes) .join("g").call(drag(simulation)); node_elements.append ("circle").attr("r", (d)=>size (d)) .attr("stroke", "#000") .attr("stroke-width", 1) .style("opacity", (d)=>opac (d)) .attr("fill", color); node_elements.append ("circle").attr("r", (d)=>size_pred (d)) .attr("fill", "none") .attr("stroke", "#000") .attr("stroke-width", 2) node_elements.append ("text").text((d)=>d.id + "(" + d.aa + ")") .attr("fill", "black").attr("dy","-0.5em").style("text-anchor","middle").style ("fill", (d)=>textfill (d)); node_elements.append("title") .text(d => d.id + " OBS = " + nf(d.obs) + " PRED = " + nf(d.pred)); simulation.on("tick", () => { link .attr("x1", d => d.source.x) .attr("y1", d => d.source.y) .attr("x2", d => d.target.x) .attr("y2", d => d.target.y); node_elements .attr("transform", d => "translate("+d.x+","+d.y+")") .attr("cy", d => d.y); }); invalidation.then(() => simulation.stop()); return svg.node(); }33:T577,preset_mapping = ({ "Empty list" : [], "501Y metasignature" : pad_array(["nsp2/85","nsp2/501","nsp3/183","nsp3/370","nsp3/837","nsp3/890","nsp6/107","nsp6/260","exonuclease/496","helicase/47","helicase/599","E/71","S/18","S/26","S/69","S/98","S/138","S/144","S/215","S/243","S/417","S/484","S/501","S/655","S/681","S/701","S/716","S/1027","S/1118","S/1176","S/1264","ORF3a/57","ORF3a/171","N/205","N/235"]), "alpha (B.1.1.7)" : pad_array (["913","ORF1a/1001","ORF1a/1708","5986","ORF1a/2230","ORF1a/3675","14676","15279","16176","S/69","S/144","S/501","S/570","S/681","S/716","S/982","S/1118","ORF8/27","ORF8/52","ORF8/73","N/3","N/235"]), "beta (B.1.351)" : pad_array (["nsp2/85","ORF1ab/1655","ORF1ab/3353","S/80","S/215","S/484","S/501","S/701","ORF3a/57","ORF3a/171","E/71","N/205","22280","11287"]), "gamma (P.1)" : pad_array (["733","2749","ORF1a/1118","ORF1a/1795","11287","12778","13860","S/20","S/26","S/417","S/484","S/501","S/1027","ORF8/92","N/80"]), "delta (B.1.617.2)" : pad_array (["S/19","S/142","S/452","S/478","S/681","S/950","ORF3a/26","M/82","ORF7a/82","ORF7a/120","N/63","N/203","N/377"]), "mu (B.1.621)" : pad_array (["3037","ORF1a/1055","ORF1a/1538","6037","ORF1a/3255","ORF1a/3729","13057","ORF1b/314","ORF1b/1342","18877","19035","20148","S/95","S/145","S/346","S/484","S/501","S/614","S/681","S/950","ORF3a/57","ORF8/11","ORF8/38","ORF8/67","N/205"]), })34:T480,siteHistory = { let merged_info = {}; let coverage_by_site = []; _.each (obs_data, (vars, index)=> { let packaged = {}; let myN = 0; const i = +index; if ((i in site_equiv) || ((i + 1) in site_equiv)) { packaged.loc = null; if (site_equiv[i]) packaged.loc = _.map (site_equiv[i], (se)=>se[0]); if (site_equiv[i+1]) packaged.loc = _.map (site_equiv[i|+1], (se)=>se[0]); _.each (vars, v => { packaged[v.SCV] = [0, v.Proportion]; myN += v.Count; }); coverage_by_site.push (myN); let pred = pred_data[index]; if (pred && pred[1]) { _.each (pred[1], (freq, c)=> { if (c in packaged) { packaged[c][0] = freq; } else { packaged[c] = [freq,0]; } }); } else { packaged.inv = true; } merged_info[index] = packaged; } }); return {'info' : merged_info, 'median_N': d3.median (coverage_by_site)}; }35:T5a1,summary_stats = { const info_array = _.map (siteHistory.info); const var_per_site = _.map (info_array, (d)=>_.map (_.filter (codons_labeling (d), (dd)=>dd[1] >= minFreq), (d)=>d[1])); //return var_per_site; const pred_per_site = _.map (info_array, (d)=>_.map (_.filter (codons_labeling (d), (dd)=>dd[0] > 0.), (d)=>d[0])); return { 'codons' : info_array.length, 'evo' : _.filter (info_array, (d)=>!d.inv).length, 'polymorphic' : _.filter (var_per_site, (d)=>d.length > 1).length, 'var_stats' : [d3.median (var_per_site, (d)=>d.length), d3.quantile (var_per_site, 0.975, (d)=>d.length)], 'pred_stats' : [d3.median (pred_per_site, (d)=>d.length), d3.quantile (var_per_site, 0.975, (d)=>d.length)], 'diff' : d3.median (_.map (pred_per_site, (d,i) => d.length - var_per_site[i].length)), 'unpred' : _.filter (info_array, (d)=>!d.inv && _.filter (codons_labeling (d,true), (dd)=>dd[1] >= 1/10000 && dd[0] < 1e-8).length > 0).length, 'pred' : _.filter (info_array, (d)=>_.size (d) > 3 && _.filter (codons_labeling (d,true), (dd)=>dd[1] >= 1./1000 && dd[0] < 1./1000 || d[1] < 1./1000 && dd[0] >= 1./1000 ).length == 0).length, 'rho' : ss.sampleRankCorrelation (_.map (annotated_sites, (d)=>d.entropy_observed), _.map (annotated_sites, (d)=>d.entropy_predicted)), 'bits' : d3.sum (annotated_sites, (s)=>s.bit_score), 'perfect' : d3.sum (annotated_sites, (s)=>s.perfect) } } 36:T937,annotated_sites = { let as = []; const l2 = Math.log (2); const compute_coi2 = (v)=> { if (v && v.length > 1) return _.reduce (v, (s,d)=>s+d[0]*d[1], 0); return null; } const compute_coi = (v,i)=> { if (v && v.length > 1) return _.reduce (v, (s,d)=>s+d[i]*d[i], 0); return null; } const compute_bit_score = (v1, thresh) => { thresh = thresh || 0.0001; let observed_min_states = _.filter (v1, (d)=>d[1] >= thresh && d[2] != '---').length - 1; let predicted_min_states = _.filter (v1, (d)=>d[0] >= thresh).length - 1; let correctly_predicted = _.filter (v1, (d,i)=>i > 0 && d[1] >= thresh && d[0] >= thresh && d[2] != '---').length; if (observed_min_states == 0) return null; return [-Math.log (hgm_pdf (correctly_predicted,60,observed_min_states,predicted_min_states))/l2, observed_min_states, predicted_min_states, correctly_predicted]; } _.each (siteHistory["info"], (s,d)=> { d = parseInt (d); const freqs = _.sortBy (codons_labeling(s,null,true),(d)=>-d[1]); let predF = null; if (!s.inv) { predF = _.sortBy (freqs, (d)=>-d[0]); } let bs = predF ? compute_bit_score (freqs) : null; let s_info = { 'Codon_start' : d, 'Label' : site_equiv[d] ? site_equiv[d][0][0] : (site_equiv[d+1] ? site_equiv[d+1][0][0] : "N/A"), 'consensus' : freqs[0][2], 'pred_cons' : predF ? predF[0][2] : null, 'minority' : freqs.length > 1 ? freqs[1][2] : null, 'pred_min' : predF && predF.length > 1 ? predF[1][2] : null, 'rho' : rho_obs(freqs), 'entropy_observed' : predF ? h_ent (_.map (freqs, (d)=>d[1])) : null, 'entropy_predicted' : predF ? h_ent (_.map (freqs, (d)=>d[0])) : null, 'unexpected' : _.filter (freqs, (d)=> d[1] >= 0.0005 && d[0] < 1e-6).length, 'rel_coi' : compute_coi2 (freqs)/compute_coi (freqs,0), 'bit_score' : bs ? bs[0] : null, 'perfect' : bs ? bs[1] == bs[2] && bs[2] == bs[3] : null //'kappa' : fleiss_kappa (categorize_codons_labeling(_.mapValues (s, (dd)=>dd[0])), categorize_codons_labeling(_.mapValues (s, (dd)=>dd[1]))) } as.push (s_info); }); return as; }37:Tc29,html` `38:[] 2f:{"title":"Comparing predicted vs observed evolution in SARS-CoV-2 genomes enabled by data from ","cells":[{"id":0,"value":"md`# Comparing predicted vs observed evolution in SARS-CoV-2 genomes enabled by data from $\"gisaid-logo\"$
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1137,"value":"For a given set of SARS-CoV-2 genomic sites compare predicted probabilities of finding specific codons at given genomic sites (based on the [evolutionary analysis of closely related animal sarbecoviruses](https://www.biorxiv.org/content/10.1101/2021.01.22.427830v3)) vs observed variation with a median of **${siteHistory.median_N}** consensus genomes per codon of SARS-CoV-2 from GISAID. \n\n[Download .JSON data](https://github.com/spond/SARS-CoV-2-variation)","pinned":false,"hidden":false,"mode":"md"},{"id":2104,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":2088,"value":"**Figure 1**. Predicted vs observed (in at least ${minFreq} frequency) codon frequencies at **${preset || \"N/A\"} **. Node = codon. Color = amino-acid that the codon translates to. Area of solid circle ~ fraction of GISAID sequences with this codon. Area of black outlined circle ~ predicted probability of observing the codon based on nCOV projections. Codons are connected if they are one nucleotide different from one another. Codons shown in red are those that occur in GISAID but are **not** predicted based on nCOV data.","pinned":false,"hidden":false,"mode":"md"},{"id":1192,"value":"viewof preset = autoSelect ({\n options : for_site_list,\n description: \"Select one of the sites to display\",\n value: \"Site S/501\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1866,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":2598,"value":"vegalite({\n \"width\" : 800,\n \"height\" : 200,\n \"data\": {\"values\": head2head},\n \"mark\": {\n \"type\": \"boxplot\",\n \"extent\": 1.5,\n \"ticks\" : true,\n \"outliers\" : false,\n },\n \"encoding\": {\n \"y\": {\"field\": \"type\", \"type\": \"nominal\", \"scale\" : {\"domain\" : [\"0.5-1.0\",\"0.005-0.5\",\"0.00005-0.005\", \"<0.00005\"]}},\n \"x\": {\n \"field\": \"pred\",\n \"type\": \"quantitative\",\n \"scale\": {\"zero\": false, \"type\" : \"sqrt\"}\n }\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2410,"value":"md`**Table 1.** Predicted/observed codon distributions for **${preset}**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2382,"value":"Inputs.table(site_table, {\n format : {\n Observed : (x)=>html`${x.toFixed (6)}`,\n Predicted: (x)=>html`${x.toFixed (6)}`\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1378,"value":"md`**Table 2.** Explore the underlying data for all sites`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1368,"value":"viewof wrangled = Wrangler(annotated_sites)","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"md`### Libraries and data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1194,"value":"import {select,autoSelect} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":12,"value":"vegalite = require (\"vega-embed\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"_ = require (\"lodash\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":22,"value":"import {gene_coordinates} from \"@spond/revised-sars-cov-2-analytics-page\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1365,"value":"import {Wrangler, op} from \"@observablehq/data-wrangler\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":692,"value":"site_equiv = _.groupBy (_.toPairs (jsonCodonLookup), (d)=>d[1])","pinned":false,"hidden":false,"mode":"ojs"},{"id":49,"value":"import {jsonCodonLookup} from \"@spond/sars_cov_2_sites\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1209,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1439,"value":"all_times = _.toPairs (_.groupBy (_.filter (siteHistory, (d)=>d.alpha < d.beta), (d)=>d.loc))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1402,"value":"pad_array = (arr)=>_.map (arr, (d)=>\"Site \" + d.toUpperCase())","pinned":false,"hidden":false,"mode":"ojs"},{"id":1477,"value":"selectable_sites = _.keys(jsonCodonLookup)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2098,"value":"min_count_ignore = Math.round (siteHistory.N * 0.00001)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2483,"value":"pred_data = d3.json (\"https://raw.githubusercontent.com/spond/SARS-CoV-2-variation/master/ncov-predictions.json\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2487,"value":"obs_data = {\n let nucs = new Set (\"ACGT-\");\n return _.mapValues (_.groupBy (await d3.tsv (\"https://raw.githubusercontent.com/spond/SARS-CoV-2-variation/master/variation/gisaid-codon.tsv\", d3.autoType), (d)=>d.Coordinate), (d)=>_.filter (d, (c)=> {\n if (_.every (c.SCV, (ch)=>nucs.has (ch))) {\n if (_.some (c.SCV, (ch)=>ch == '-')) {\n return c.SCV == \"---\";\n }\n return true;\n }\n return false;\n}))\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2581,"value":"head2head = {\n let h2h = [];\n const classify = (v)=> {\n if (v>=0.5) return \"0.5-1.0\";\n if (v>=0.005) return \"0.005-0.5\";\n if (v>=0.00005) return \"0.00005-0.005\";\n return \"<0.00005\";\n }\n _.each (siteHistory.info, (d,s)=> {\n _.each (d, (v, codon)=> {\n if (codon.toUpperCase () == codon && codon != '---') {\n h2h.push ({'nuc' : +s,'codon' : codon, 'pred' : v[0], 'obs' : v[1],\n 'type' : classify (v[1])\n });\n }\n });\n \n });\n return h2h;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2482,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":2541,"value":"minFreq = 1/100000","pinned":false,"hidden":false,"mode":"ojs"},{"id":2123,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":2336,"value":"hgm = import('https://cdn.skypack.dev/@stdlib/stats-base-dists-hypergeometric-pmf@0.0.5?min')","pinned":false,"hidden":false,"mode":"ojs"},{"id":2345,"value":"hgm_pdf = hgm.default","pinned":false,"hidden":false,"mode":"ojs"},{"id":2394,"value":"site_table = {\n let t1 = jsonCodonLookup[preset.split (\" \")[1]];\n if (t1 in siteHistory[\"info\"]) t1 = siteHistory[\"info\"][t1];\n else t1 = siteHistory[\"info\"][t1-1];\n return _.map (codons_labeling(t1,null,true), (d)=>({\n 'Codon' : d[2],\n 'Aminoacid' : translation_table [d[2]],\n 'Observed' : d[1],\n 'Predicted' : d[0]\n }));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1874,"value":"gen_data = (site)=>{\n let nodes = [];\n let codons = [];\n _.each (siteHistory[\"info\"][site], (info, codon) => {\n let o = {};\n if (codon == codon.toUpperCase() && info[1] >= minFreq) {\n o.id = codon;\n o.aa = translation_table[codon];\n o.obs = info[1];\n o.pred = info[0];\n codons.push (codon);\n nodes.push (o);\n }\n });\n let links = [];\n for (let c1 = 0; c1 < codons.length; c1++) {\n for (let c2 = c1+1; c2 < codons.length; c2++) {\n const diff = _.reduce (codons[c1], (s,v,i)=> {return s + (v != codons[c2][i]);}, 0);\n \n links.push ({\n 'source' : codons[c1],\n 'target' : codons[c2],\n 'value' : diff\n });\n \n }\n } \n return {'nodes' : nodes, 'links' : links};\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1876,"value":"data = {\n let t1 = jsonCodonLookup[preset.split (\" \")[1]];\n if (t1 in siteHistory[\"info\"]) return gen_data (t1);\n return gen_data (t1-1);\n}\n ","pinned":false,"hidden":false,"mode":"ojs"},{"id":2257,"value":"FMIN = {\n let T = 0, F = 0;\n \n _.each (siteHistory[\"info\"], (d)=> {\n let pairs = codons_labeling (d);\n if (pairs && pairs.length > 1) {\n //coincidence_idx.push (compute_coi2 (pairs));\n const wi = _.map (pairs, (d,i)=> {let dc = _.clone (d); dc.push (i); return dc;});\n let s1 = _.sortBy (wi, (s)=>-s[0]);\n let s2 = _.sortBy (wi, (s)=>-s[1]);\n //if (s1[1][0] < 0.001 || s2[1][1] < 0.001) return;\n if (s1[0][2] == s2[0][2] && s1[1][2] == s2[1][2]) T++;\n else F++;\n } else {\n //coincidence_idx.push (null);\n return null;\n }\n });\n return [T/(T+F)];\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":1769,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":1259,"value":"nucToCodons = _.chain (_.toPairs (jsonCodonLookup)).groupBy ((d)=>d[1]).mapValues ((d)=>_.map (d, (c)=>c[0])).value()","pinned":false,"hidden":false,"mode":"ojs"},{"id":596,"value":"parseSiteList = (site_list) => {\n let labels = {};\n site_list = _.map (site_list, (d)=>d.split (\"/\"));\n \n let sites = new Set (_.map (site_list, (d)=>{\n if (+d == d) {\n for (let i = d; i > d-3; i--) {\n if (i in nucToCodons) {\n d = nucToCodons[i][0].split (\"/\");\n break;\n }\n }\n \n }\n if (d[0].substr (0,4) == \"ORF1\") {\n let eq = null;\n _.each (site_equiv[jsonCodonLookup [d[0]+\"/\"+d[1]]], (tr)=> {\n if (tr[0].substr (0,4) != \"ORF1\") {\n eq = tr[0];\n labels[eq] = d[0] + \"/\" + d[1];\n }\n });\n return eq;\n }\n return d[0] + \"/\" + d[1];\n}));\n return [sites, labels]\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1341,"value":"params = new URLSearchParams(location.search)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1628,"value":"categorize_codons_labeling = (labeling)=> {\n return _.chain (labeling).mapValues ((d)=> {\n if (d >= 0.5) return \"Consensus\";\n if (d >= 0.01) return \"Common\";\n if (d >= 0.0001) return \"Rare\";\n if (d >= 1e-10) return \"Possible\"\n return \"Not seen\";\n }).toPairs ().filter ((d)=>d[0] == d[0].toUpperCase()).fromPairs().value();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1752,"value":"codons_labeling = (labeling, nodel, codon)=> {\n if (labeling.inv && !codon) return null;\n const categorize = (dd)=> {\n if (codon) {\n return dd[1].concat (dd[0]);\n }\n return dd[1];\n /*return _.map (dd, (d)=>{\n if (d > 0.5) return 5;\n if (d > 0.01) return 4;\n if (d > 0.0001) return 3;\n if (d > 0) return 2;\n return 1;\n });*/\n };\n return _.chain (labeling).toPairs ().filter ((d)=>d[0] == d[0].toUpperCase() && (!nodel || d[0] != '---')) .map((d)=>categorize(d)).value();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1344,"value":"sitelist = params.get(\"sites\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1599,"value":"h_ent = (v)=> {\n const l2 = Math.log (2);\n return -_.reduce (v, (h,e)=>h + (e != .0 ? e * Math.log (e)/l2 : 0.), 0);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1729,"value":"rho_obs = (s) => {\n if (!s) return null;\n if (s.length == 1) return 1; \n return ss.sampleRankCorrelation (_.map (s, (d)=>(d[0])), _.map (s, (d)=>(d[1])))\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2002,"value":"for_site_list = _.map (_.filter (selectable_sites, (d)=>{\n const m1 = ((+jsonCodonLookup[d]) - 1);\n return ((jsonCodonLookup[d] in siteHistory[\"info\"]) && !siteHistory[\"info\"][jsonCodonLookup[d]].inv) || ((m1 in siteHistory[\"info\"]) && !siteHistory[\"info\"][m1].inv)})\n , (d)=> \"Site \" + d)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1592,"value":"fleiss_kappa = (ranking1, ranking2) => {\n let subjects = _.union (_.keys (ranking1), _.keys (ranking2));\n let classes = _.union (_.values (ranking1), _.values (ranking2));\n let c2i = {};\n _.each (classes, (d,i)=>c2i[d]=i);\n let k = classes.length;\n let N = subjects.length;\n let n = 2;\n \n let by_subject = [];\n let Pj = 0;\n _.each (subjects, (d)=> {\n let rankings = _.fill (new Array (k), 0);\n let r1 = c2i[ranking1[d]];\n rankings[r1] += 1;\n let r2 = c2i[ranking2[d]];\n rankings[r2] += 1;\n Pj += (_.reduce (rankings, (s,dd)=>s+dd*dd, 0) - n) * 0.5;\n by_subject.push (rankings);\n });\n //return by_subject;\n let p_class = _.fill (new Array (k), 0);\n for (let i = 0; i < k; i++) {\n for (let j = 0; j < N; j++) {\n p_class[i] += by_subject [j][i];\n }\n }\n let Pe = _.reduce (_.map (p_class, (d)=>d/(n*N)),(s,dd)=>s+dd*dd, 0);\n \n return (Pj / N - Pe) / (1 - Pe);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1726,"value":"ss = require (\"simple-statistics\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1870,"value":"import {translation_table} from \"@spond/usual-vs-unusual-mutation-analyses\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1853,"value":"drag = simulation => {\n \n function dragstarted(event) {\n if (!event.active) simulation.alphaTarget(0.3).restart();\n event.subject.fx = event.subject.x;\n event.subject.fy = event.subject.y;\n }\n \n function dragged(event) {\n event.subject.fx = event.x;\n event.subject.fy = event.y;\n }\n \n function dragended(event) {\n if (!event.active) simulation.alphaTarget(0);\n event.subject.fx = null;\n event.subject.fy = null;\n }\n \n return d3.drag()\n .on(\"start\", dragstarted)\n .on(\"drag\", dragged)\n .on(\"end\", dragended);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1863,"value":"color = {\n const scale = d3.scaleOrdinal(d3.schemeCategory10);\n return d => scale(d.aa);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2106,"value":"floatFormat = d3.format (\".3g\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2375,"value":"import {SummaryTable} from \"@observablehq/summary-table\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2419,"value":"qual_scale = d3.scaleThreshold()\n .domain([1e-6,1e-4,1e-2,0.5])\n .range([\"red\", \"orange\", \"brown\",\"teal\",\"green\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":2112,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":2108,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W38","stdlib":"1","titling":"auto"}