31:T422,vegalite({ data: {values: sim_data}, transform: [{filter: "datum.omega<1 && datum.omega2<1 && datum.call21"}], layer: [{ mark: "point", width, height: 360, width: 400, encoding: { size : {"value": 2}, color : {"value" : "grey"}, x: {field: "omega", type: "quantitative", "axis":{ "title" : "ω [1H]", grid : false, titleFontSize: 20, labelFontSize: 14, format : "r" }}, y: {field: "omega2", type: "quantitative", "axis":{ "title" : "ω [2H]", grid : false, titleFontSize: 20, labelFontSize: 14, format : "r" }}, }}, { "mark": { "type": "line", "interpolate": "monotone", "color" : "black", "strokeWidth" : 5, }, "width" : 800, "height" : 800, "encoding": { "x": { "field": "omega", "type": "quantitative" }, "y": { "field": "omega", "type": "quantitative", } } }], })32:T42d,vegalite({ data: {values: sim_data}, transform: [{filter: "datum.delta2H<1 && datum.delta3H<1 && datum.call32"}], layer: [{ mark: "point", width, height: 360, width: 400, encoding: { size : {"value": 2}, color : {"value" : "grey"}, x: {field: "delta2H", type: "quantitative", "axis":{ "title" : "δ [2H]", grid : false, titleFontSize: 20, labelFontSize: 14, format : "r" }}, y: {field: "delta3H", type: "quantitative", "axis":{ "title" : "δ [3H+]", grid : false, titleFontSize: 20, labelFontSize: 14, format : "r" }}, }}, { "mark": { "type": "line", "interpolate": "monotone", "color" : "black", "strokeWidth" : 5, }, "width" : 800, "height" : 800, "encoding": { "x": { "field": "delta2H", "type": "quantitative" }, "y": { "field": "delta2H", "type": "quantitative", } } }], })33:T66e,vegalite ({ "layer" : [{ "data": {"values": subs2}, "transform" : [ {"filter" : "datum.x < 0.25"} ], "mark": "bar", height: 360, width: 400, "encoding": { "color" : {"value" : "gray"}, "opacity" : {"value" : 0.75}, "x": { "bin": {"maxbins": 100}, "field": "x", "type": "quantitative", "axis":{ "title" : "Fraction of sites with ER > 5", grid : false, titleFontSize: 20, labelFontSize: 14, format : "r" } }, "y": { "aggregate": "count", "type": "quantitative", "scale": {"type": "sqrt"}, "axis":{ "title" : "Alignment Count", grid : false, titleFontSize: 20, labelFontSize: 14, format : "r" } } } }, { "data": {"values": subs3}, "transform" : [ {"filter" : "datum.x < 0.25"} ], "mark": "bar", height: 360, width: 400, "encoding": { "color" : {"value" : "red"}, "opacity" : {"value" : 0.5}, "x": { "bin": {"maxbins": 20}, "field": "y", "type": "quantitative", "axis":{ grid : false, titleFontSize: 20, labelFontSize: 14, format : "r" } }, "y": { "aggregate": "count", "type": "quantitative", "axis":{ "title" : "Alignment Count", grid : false, titleFontSize: 20, labelFontSize: 14, format : "r" } } } }] } ) 34:T941,plot_power_curve = (data, x_title, title) => { title = title || "Power"; let lineType = "linear"; return vegalite ({"data": {values : data}, "layer" : [ { "mark": { "type" : "line", "interpolate" : lineType }, "width": 500, "height":300, "encoding": { "x": { "field": "x", "type": "quantitative", "axis":{ "title" : x_title, grid : false, titleFontSize: 20, labelFontSize: 14, format : "r" } }, "y": { "axis":{ "title" : title, grid : false, titleFontSize: 20, labelFontSize: 14, format : "r" }, "field": "p21", "type": "quantitative", "scale" : { "type" : "linear", } } , "axis": null, } }, { "mark": { "type" : "line", "strokeWidth" : 2, "color" : "red", "interpolate" : lineType }, "encoding": { "x": { "field": "x", "type": "quantitative" }, "y": { "field": "p3si", "type": "quantitative", "scale" : { "type" : "linear", } }, } }, { "mark": { "type" : "line", "strokeWidth" : 2, "color" : "grey", "interpolate" : lineType }, "encoding": { "x": { "field": "x", "type": "quantitative", }, "y": { "field": "p32", "type": "quantitative", "scale" : { "type" : "linear", } }, } }, { "mark": { "type" : "circle", "color" : "#f58518" }, "encoding": { "x": { "field": "x", "type": "quantitative", }, "y": { "field": "p21", "type": "quantitative", }, "size": { "field": "count", "type": "quantitative", "legend" : {"title" : "Samples", "titleFontSize": 20, "orient" : "right", "labelFontSize" : 14}, } } } ], "config": { "view": { "stroke": "transparent" } }}) }35:T446,data2 = {return{ "data": {"values": indel_gaps}, "layer" : [ { height: 200, width: 200, "mark": { type : "errorbar", extent: "iqr" }, "encoding": { "y": { "field": "gaps", "type": "quantitative", "scale": {"zero": false}, "axis":{ "title" : "Fraction gaps/sequence", grid : false, titleFontSize: 14, labelFontSize: 12, format : "r" }, }, "x": {"field": "indel_rate", "type": "ordinal", "axis":{ "title" : "Indel rate", grid : false, titleFontSize: 13, labelFontSize: 12, }} } }, { "mark": { "type": "point", "filled": true, "color": "black" }, "encoding": { "y": { "field": "gaps", "type": "quantitative", "aggregate": "median", "axis": null }, "x": {"field": "indel_rate", "type": "ordinal"} } } ] }}; 36:T66c,panel_1 = (l1,l2,label,label2)=>{ label2 = label2 || "FPR"; return {"layer": [ { height: 200, width: 200, "mark": { type : "errorbar", extent: "iqr" }, "encoding": { "y": { "field": l1, "type": "quantitative", "scale": {"zero": false}, "axis":{ "title" : label, grid : false, titleFontSize: 14, labelFontSize: 12, format : "r" }, }, "x": {"field": "indel_rate", "type": "ordinal", "axis":{ "title" : "Indel rate", grid : false, titleFontSize: 13, labelFontSize: 12, }} } }, { "mark": { "type": "point", "filled": true, "color": "black" }, "encoding": { "y": { "field": l1, "type": "quantitative", "aggregate": "median", "axis": null }, "x": {"field": "indel_rate", "type": "ordinal"} } }, { "mark": { "type": "line", "opacity" : 0.6, "color": "steelblue" }, "encoding": { "y": { "field": l2, "type": "quantitative", "aggregate": "mean", "axis":{ "title" : label2, "titleColor" : "steelblue", grid : false, titleFontSize: 13, labelFontSize: 12, } }, "x": {"field": "indel_rate", "type": "ordinal"} } } ], "resolve": {"scale": {"y": "independent"}} }; }37:T402,compute_metrics = (data, accessor, p_crit, label) => { var false_positives = 0, false_negatives = 0, true_positives = 0, true_negatives = 0; _.each (data, (d) => { var p = accessor (d); var l = label (d); if (l) { if (p <= p_crit) { true_positives++; } else { false_negatives++; } } else { if (p <= p_crit) { false_positives++; } else { true_negatives++; } } }); var N = true_positives + false_positives + true_negatives + false_negatives; return { 'p' : p_crit, 'N' : N, 'tp' : true_positives, 'fp' : false_positives, 'tn' : true_negatives, 'fn' : false_negatives, 'fpr' : false_positives / (true_negatives + false_positives), 'power' : true_positives / (true_positives + false_negatives), 'ppv' : true_positives / (true_positives + false_positives) }; };38:T47a,compute_roc = (data, accessor, step, label) => { var aoc_by_step = [[0,0,0,0]]; var sorted_list = []; var total_positives = 0, total_negatives = 0; _.each (data, (d) => { var p = accessor (d); var l = label (d); if (l) { total_positives ++; } else { total_negatives ++; } sorted_list.push ([p,l]); }); sorted_list.sort ((a,b) => a[0]-b[0]); var index = 0; var true_calls = 0, false_calls = 0; for (var p = step; p <= 1 + (step/2); p+=step) { while (index < sorted_list.length && sorted_list[index][0] <= p) { if (sorted_list[index][1]) { true_calls++; } else { false_calls ++; } index ++; } aoc_by_step.push ([p, true_calls+false_calls,true_calls/total_positives,false_calls/total_negatives, true_calls/(true_calls+false_calls)]); } return _.map (aoc_by_step, (d) => {return {'fp' : d[3], 'power' : d[2], 'ppv' : d[4], "p" : d[0], "b" : total_positives/(total_positives + total_negatives)}}); };39:Te84,plot_prc = (plot_data, labels, cv) => { cv = cv || [[0.01,true],[0.05,true],[0.1,true]]; var merged_data = []; var merged_baseline = []; const fmt = d3.format (".2f"); _.each (plot_data, (d,i) => { merged_baseline.push ({'reference' : d[0]['b'], 'label' : labels[i]}); _.each (d, (e) => { e.label = labels[i]; e.pair = "R=" + fmt(e.power) + ", P=" + fmt (e.ppv); merged_data.push (e); }); }); var crticial_values = _.filter (merged_data, (d)=>d3.min (_.map (cv, (c)=>Math.abs (c[0]-d.p))) < 1e-5); var cv_shown = _.filter (cv, c=>c[1]); var text_to_show = _.filter (merged_data, (d)=>d3.min (_.map (cv_shown, (c)=>Math.abs (c[0]-d.p))) < 1e-5); var baseline = plot_data[0][0]['b']; return vegalite ({ "layer" : [{ "data": {values : merged_data}, "mark": { "type": "line", "interpolate": "monotone", "strokeWidth" : 5 }, "width" : 500, "height" : 500, "encoding": { "x": { "field": "power", "axis":{ "title" : "Recall", grid : false, titleFontSize: 18, labelFontSize: 14, format : "g" }, "type": "quantitative" }, "y": { "field": "ppv", "type": "quantitative", "axis":{ "title" : "Precision", grid : false, titleFontSize: 18, labelFontSize: 14, format : "g" } }, "color" : { "field" : "label", "type" : "nominal", "legend" : {"title" : "Simulation", "titleFontSize": 1, "orient" : "right", "labelFontSize" : 14}, } } }, { "data": {values : crticial_values}, "mark": { "type": "point", "size" : 400, "filled" : true }, "encoding": { "x": { "field": "power", "axis":{ "title" : "Recall", grid : false, titleFontSize: 14, labelFontSize: 12, format : "g" }, "type": "quantitative" }, "y": { "field": "ppv", "type": "quantitative", "axis":{ "title" : "Precision", grid : false, titleFontSize: 16, labelFontSize: 12, format : "g" } }, "color" : { "field" : "label", "type" : "nominal" } } }, { "data" : {"values" : merged_baseline}, "mark": { "type": "rule", "strokeDash" : [5,1] }, "encoding": { "y": { "field": "reference", "type": "quantitative" }, "size": {"value": 3}, "color" : { "field" : "label", "type" : "nominal" } } }, { "data": {values : text_to_show}, "mark": { "type": "text", "dx" : 9, "dy" : 0, "fontSize" : 10, "align" : "left", //"angle" : 90. }, "encoding": { "x": { "field": "power", "axis":{ "title" : "Recall", grid : false, titleFontSize: 14, labelFontSize: 12, format : "g" }, "type": "quantitative" }, "y": { "field": "ppv", "type": "quantitative", "axis":{ "title" : "Precision", grid : false, titleFontSize: 16, labelFontSize: 12, format : "g" } }, /*"color" : { "field" : "label", "type" : "nominal" },*/ "text": { "field" : "pair", } } } ], "config": { "view": { "stroke": "transparent" } }}); }3a:T563,plot_qq = (data, label) => { var color = d3.scaleOrdinal(d3.schemeCategory10); return vegalite ({"layer": _.map (data, (plot_data,i) => {return { "data": {values : plot_data}, "layer" : [{ "mark": { "type": "line", "interpolate": "monotone", "color" : color (i) }, "width" : 300, "height" : 300, "encoding": { "x": { "field": "expected", "axis":{ "title" : "Nominal p-value", grid : false, titleFontSize: 14, labelFontSize: 12, format : "g" }, "type": "quantitative" }, "y": { "field": "observed", "type": "quantitative", "axis":{ "title" : "Observed rejection rate", grid : false, titleFontSize: 16, labelFontSize: 12, format : "g" } }, //"color" : color } }, { "mark": { "type": "line", "interpolate": "monotone", "color" : "grey", "strokeDash" : [3,3] }, "width" : 800, "height" : 800, "encoding": { "x": { "field": "expected", "type": "quantitative" }, "y": { "field": "expected", "type": "quantitative", } } } ], "config": { "view": { "stroke": "transparent" } }}})}); }3b:T44e,by_N = { const p = 0.05; var bin_map = function (n) { if (n <= 8) { return "" + n; } if (n <= 16) { let n2 = 2*Math.floor (n/2.); return "" + n2 + "-" + (n2 + 1); } if (n <= 32) { let n4 = 4*Math.floor (n/4.); return "" + n4 + "-" + (n4 + 3); } if (n <= 64) { let n8 = 8*Math.floor (n/8.); return "" + n8 + "-" + (n8 + 7); } return ">64"; }; var binned = {}; // 0 - TN // 1 - FP // 2 - FN // 3 - TP _.each (power_sims_detectable, (d) => { let k = bin_map (d.subs_test); if (! (k in binned)) { binned [k] = [0,0,0,0,0,0]; } binned [k][2] ++; if (d.p < p) { binned [k][3] ++; } }); _.each (null_sims_detectable, (d) => { let k = bin_map (d.subs_test); if (! (k in binned)) { binned [k] = [0,0,0,0,0,0]; } binned [k][0] ++; if (d.p < p) { binned [k][1] ++; } }); _.each (binned, (d) => {d[4] = d[3]/(d[1]+d[3]); d[5] = d[0]/(d[2]+d[0]);}); return binned; };3c:T43a,bin_detection = (data, field, step, from, to) => { from = from || 0; to = to || d3.max (data, (d)=>d[field]); var values = []; var current_floor = from; for (var upto = step + from; upto < to + step * 0.5; upto += step) { values.push ({'count' : 0, 'x' : upto - step * 0.5, 'p21' : 0, 'p32' : 0, 'p3si' : 0 }); } _.each (data, (record)=> { var index = Math.floor ((record[field]-from)/step); if (index < 0) index = 0; if (index >= values.length) index = values.length - 1; values[index].count++; if (record.call21) { values[index].p21++ }; if (record.call32) { values[index].p32++ }; if (record.call3v3si) { values[index].p3si++ }; }); _.each (values, (record) => { _.each (['p21','p32','p3si'], (l) => { record[l] /= record.count; }); }); return values; }3d:[] 2f:{"title":"Multihit paper empirical results","cells":[{"id":0,"value":"md`# Multihit paper empirical results `","pinned":false,"hidden":false,"mode":"ojs"},{"id":1479,"value":"md`**Table 1** Dataset analysis summary`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1450,"value":"table(\n summary_table,\n {\n enableCSVDownload: true,\n columns: [{ \n key: '0',\n name: 'Study'\n }, \n { \n key: '1',\n name: '2H:1H',\n render: val => d3.format (\".2p\")(val[0]) + \" (\" + d3.format (\".4g\")(val[1]) + \")\"\n },\n { \n key: '3',\n name: '3H+:2H',\n render: val => d3.format (\".2p\")(val[0]) + \" (\" + d3.format (\".4g\")(val[1]) + \")\"\n },\n { \n key: '2',\n name: '3H+:1H',\n render: val => d3.format (\".2p\")(val[0]) + \" (\" + d3.format (\".4g\")(val[1]) + \")\"\n },\n { \n key: '4',\n name: '3H+:3HSI',\n render: val => d3.format (\".2p\")(val[0]) + \" (\" + d3.format (\".4g\")(val[1]) + \")\"\n },\n { \n key: '5',\n name: '3HSI:2H',\n render: val => d3.format (\".2p\")(val[0]) + \" (\" + d3.format (\".4g\")(val[1]) + \")\"\n }\n ]\n }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1482,"value":"md`**Figure 1** MH detection rates as a function of alignment length`","pinned":false,"hidden":false,"mode":"ojs"},{"id":239,"value":"plot_power_curve (bin_detection (_.filter (sim_data, (d)=>true), \"S\",50, 100, 2000), \"Alignment length, codons\", \"Detection rate\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1494,"value":"md`**Figure 2** MH detection rates as a function of sequence count`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1488,"value":"plot_power_curve (bin_detection (_.filter (sim_data, (d)=>true), \"N\", 1, 10, 50), \"Number of sequences\", \" \")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1503,"value":"md`**Figure 3** MH detection rates as a function of tree length`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1499,"value":"plot_power_curve (bin_detection (_.filter (sim_data, (d)=>true), \"T\",0.2,0, 5), \"Tree Length, exp subs\", \"Detection rate\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1514,"value":"md`**Figure 4** MH detection rates as a function of ω`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1516,"value":"plot_power_curve (bin_detection (_.filter (sim_data, (d)=>true), \"omega\",0.025,0,0.5), \"ω\", \" \")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1547,"value":"md`**Figure 5** Scatter-plots of rate estimates under different models, conditioned on a significant test`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1550,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1587,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1648,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":555,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1967,"value":"vegalite ({\n \"data\": {\"values\": indelible},\n \"hconcat\" : \n _.map ([[\"2v1\",\"call21\",\"2H:1H p-value\"],\n [\"3v2\",\"call32\",\"3H+:2H p-value\"],\n [\"3siv2\",\"call3siv2\",\"3HSI:2H p-value\"], \n ], (r)=>panel_1 (r[0],r[1], r[2]))\n\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2033,"value":"vegalite ({\n \"data\": {\"values\": indelible},\n \"hconcat\" : \n _.map ([[\"delta2H\",\"Sites2H\",\"δ\", \"Sites ER>5\"],\n [\"psi\",\"Sites3H\",\"ψ\",\"Sites ER>5\"],\n data2\n ], (r)=>{ if (_.isArray(r)) return panel_1 (r[0],r[1], r[2],r[3]); return r;})\n\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":2067,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":2023,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"md`------`","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"sim_data = {\n let cutoff = 0.01;\n var data = raw_data;\n _.each (raw_data, (d)=>{\n d.call21 = d[\"2v1\"] <= cutoff;\n d.call32 = d[\"3v2\"] <= cutoff;\n d.call3v3si = d[\"3v3si\"] <= cutoff;\n d.call3siv2 = d[\"3siv2\"] <= cutoff; \n d.call3v1 = d[\"3v1\"] <= cutoff;\n d[\"3v2_LRT\"] = d[\"3v2LRT\"];\n });\n return data;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":287,"value":"raw_data = await d3.csv (\"https://dl.dropbox.com/s/scd5ds9x8p6pss2/summary.csv\", d3.autoType)//await d3.csv (\"https://dl.dropbox.com/s/pstq3h9s9o468un/results-with-fel.csv\",d3.autoType);","pinned":false,"hidden":false,"mode":"ojs"},{"id":1954,"value":"indelible = {\n let cutoff = 0.05;\n var data = await d3.csv (\"https://dl.dropbox.com/s/orqltr02u51mgkj/indelible.csv\", d3.autoType);;\n _.each (data, (d)=>{\n d.indel_rate = parseFloat(d.path.split (\"adh_\")[1].split (\"_\")[0]);\n d.call21 = d[\"2v1\"] <= cutoff;\n d.call32 = d[\"3v2\"] <= cutoff;\n d.call3v3si = d[\"3v3si\"] <= cutoff;\n d.call3siv2 = d[\"3siv2\"] <= cutoff; \n d.call3v1 = d[\"3v1\"] <= cutoff;\n d[\"3v2_LRT\"] = d[\"3v2LRT\"];\n });\n return data;\n}\n\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":2061,"value":"indel_gaps = {\n var data = await d3.csv (\"https://dl.dropbox.com/s/029kuoufntf7nfo/gaps.csv\", d3.autoType);\n _.each (data, (d)=>{\n d.indel_rate = parseFloat(d.FILE.split (\"adh_\")[1].split (\"_\")[0]);\n });\n let more = await d3.csv (\"https://dl.dropbox.com/s/8tw1ynxju8i9i81/examples.csv\", d3.autoType);\n _.each (more, (d)=> {\n data.push ({\"indel_rate\" : 'Benchmark', \"gaps\": d.gaps});\n });\n more = await d3.csv (\"https://dl.dropbox.com/s/wfj4j9izx082wzx/petrov.csv\", d3.autoType);\n _.each (more, (d)=> {\n data.push ({\"indel_rate\" : 'Enard et al', \"gaps\": d.gaps});\n });\n more = await d3.csv (\"https://dl.dropbox.com/s/x1oianio8picpn7/selectome.csv\", d3.autoType);\n _.each (more, (d)=> {\n data.push ({\"indel_rate\" : 'Moretti et al', \"gaps\": d.gaps});\n });\n more = await d3.csv (\"https://dl.dropbox.com/s/g6pd3avxujiceiq/immune.csv\", d3.autoType);\n _.each (more, (d)=> {\n data.push ({\"indel_rate\" : 'Shultz and Sackton', \"gaps\": d.gaps});\n });\n\n return data;\n}\n\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":278,"value":"null_sims_detectable = _.filter (sim_data, d=>d.kind == \"SAME\" && (d.alpha_est + d.beta1_est + d.beta2_est))","pinned":false,"hidden":false,"mode":"ojs"},{"id":280,"value":"power_sims_detectable = _.filter (sim_data, d=>d.kind != \"SAME\" && (d.alpha_est + d.beta1_est + d.beta2_est))","pinned":false,"hidden":false,"mode":"ojs"},{"id":137,"value":"p_exp = _.filter (sim_data, d => d.kind == 'SAME' && (d.alpha > 0 || d.beta1 > 0 || d.beta2 >0)).length * binning_step","pinned":false,"hidden":false,"mode":"ojs"},{"id":520,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":300,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":330,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":381,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":6,"value":"_=require ('lodash')","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"d3 = require(\"d3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":140,"value":"binning_step = 0.01","pinned":false,"hidden":false,"mode":"ojs"},{"id":251,"value":"binning_step_power_curve = 0.5","pinned":false,"hidden":false,"mode":"ojs"},{"id":10,"value":"vegalite = require(\"vega-embed@3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":201,"value":"by_sim = {\n const p = 0.05;\n \n var binned = {};\n _.each (sim_data, (d) => {\n if (! (d.Simulation in binned)) {\n binned [d.Simulation] = [0,0,0,d.Simulation];\n }\n if (d.variable && d.beta1 == d.beta2) {\n binned [d.Simulation][0] ++;\n if (d.p < p) {\n binned [d.Simulation][1] ++;\n }\n }\n });\n \n _.each (binned, (d) => d[2] = d[1]/d[0]);\n \n return binned;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":266,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":514,"value":"import { table } from \"@gampleman/table\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":756,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":1043,"value":"float_f = d3.format (\".3g\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1476,"value":"summary_table = {\n let r = _.map (_.groupBy (sim_data, (d)=>{\n let pp = d.path.split ('/');\n return pp.slice (0,-1).join ('/');\n}), (subset, key)=> {\n let result = [key];\n let cutoff = 0.01;\n _.each (['2v1','3v1','3v2','3v3si','3siv2'], (test)=>{\n let passing = _.filter (subset, (d)=>d[test] <= cutoff);\n result.push ([passing.length, subset.length, d3.sum (_.map (passing, (d)=>d[test + \"_LRT\"]))]);\n });\n return result;\n });\n let overall = ['Overall', [0,0,0], [0,0,0], [0,0,0], [0,0,0], [0,0,0]];\n for (let i = 0; i < r.length; i++) {\n for (let j = 1; j < r[i].length; j++) {\n overall[j] = _.map (overall[j], (v,k) => v + r[i][j][k]);\n }\n }\n r.push (overall);\n for (let i = 0; i < r.length; i++) {\n for (let j = 1; j < r[i].length; j++) {\n r[i][j] = [r[i][j][0]/r[i][j][1],r[i][j][2]/r[i][j][0]];\n } \n }\n return r;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1608,"value":"subs2 = _.map (_.filter (sim_data, (d)=>d.call21), (d)=>{ return {\"x\": d.Sites2H/d.S, \"y\": d.Sites3H/d.S};})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1699,"value":"subs3 = _.map (_.filter (sim_data, (d)=>d.call32), (d)=>{ return {\"x\": d.Sites2H/d.S, \"y\": d.Sites3H/d.S};})","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W3d","stdlib":"1","titling":"auto"}