31:T1162,chart = { // define parameters const width = 1000; const height = 500; const margin = 30; const radius = (height-(margin*2)) / 2; const dotRadius = 4; const axisCircles = 4; const axisLabelFactor = 1.12; const wrapWidth = 60; const radarLine = d3.lineRadial() .curve(d3["curveLinearClosed"]) .radius(d => rScale(d)) .angle((d, i) => -angleSlice*i); const rScale = d3.scaleLinear() .domain([0, maxValue]) .range([0, radius]) const svg = d3.select(DOM.svg(width, height+(margin*2))) .style('preserveAspectRatio', "xMinYMin met"); const containerWidth = width-(margin*2); const containerHeight = height-(margin*2); const container = svg.append('g') .attr("width", containerWidth) .attr("height", containerHeight) .attr('transform', `translate(${(width/3)+margin}, ${(height/2)+margin})`); // draw the grid var axisGrid = container.append("g") .attr("class", "axisWrapper"); axisGrid.selectAll(".levels") .data(d3.range(1,(axisCircles+1)).reverse()) .enter() .append("circle") .attr("class", "gridCircle") .attr("r", (d, i) => radius/axisCircles*d) .style("fill", "#CDCDCD") .style("stroke", "#CDCDCD") .style("fill-opacity", 0.1); // draw scale at circles var gridGraduation = container.append("g") .attr("class", "gridGraduation"); gridGraduation.selectAll(".levels") .data(d3.range(1,(axisCircles+1)).reverse()) .enter() .append("text") .attr("class", "graduation") .style("font-size", "11px") .attr("text-anchor", "middle") .attr("font-family", "monospace") .attr("fill","gray") .attr("dy", "0.35em") .attr("x", (d, i) => 0) .attr("y", (d, i) => 0 - radius/axisCircles*d) .text(d => String(Math.round(d*maxValue/axisCircles*10)/10)); // draw the axes and labels const axis = axisGrid.selectAll(".axis") .data(axesDomain) .enter() .append("g") .attr("class", "axis"); axis.append("line") .attr("x1", 0) .attr("y1", 0) .attr("x2", (d, i) => rScale(maxValue) * Math.cos(-angleSlice*i - Math.PI/2)) .attr("y2", (d, i) => rScale(maxValue) * Math.sin(-angleSlice*i - Math.PI/2)) .attr("class", "line") .style("stroke", "#CDCDCD") .style("stroke-width", "2px"); axis.append("text") .attr("class", "legend") .style("font-size", "11px") .attr("text-anchor", "middle") .attr("font-family", "monospace") .attr("dy", "0.35em") .attr("x", (d, i) => rScale(maxValue * axisLabelFactor) * Math.cos(-angleSlice*i - Math.PI/2)) .attr("y", (d, i) => rScale(maxValue * axisLabelFactor) * Math.sin(-angleSlice*i - Math.PI/2)) .text(d => bias_dimension_labels_to_show[d]); // draw the lines const plots = container.append('g') .selectAll('g') .data(formattedData) .join('g') // .attr("data-name", (d, i) => type(i)) // .attr("fill", "none") // .attr("stroke", "steelblue"); plots.append('path') .attr("d", d => radarLine(d.map(v => v.value))) .data(selected_monitor_sets) .attr("fill", (d, i) => color[d]) .attr("fill-opacity", 0.1) .attr("stroke", (d, i) => color[d]) .attr("stroke-width", 2); // plots.selectAll("circle") // .data(d => d) // .join("circle") // .attr("r", dotRadius) // .attr("cx", (d,i) => rScale(d.value) * Math.cos(-angleSlice*(i)-Math.PI/2)) // .attr("cy", (d,i) => rScale(d.value) * Math.sin(-angleSlice*(i)-Math.PI/2)) // .style("fill-opacity", 0.8); //legend const labels = selected_monitor_sets const maxword = d3.max(labels, d =>d.length) const r = width/110 const x = width/2.3 - 8*r - 4*maxword; const y = -height/2 +4*r ;//cfg.h / 2 + 100; var legend = container.selectAll("mydots") .data(labels) .enter() .append("circle") .attr("cx", x) .attr("cy", function(d,i){ return y + i*25}) // 100 is where the first dot appears. 25 is the distance between dots .attr("r", r) .style("fill", (d,i) => color[d]) // .style("filter", "url(#glow)"); container.selectAll("mydots") .data(labels) .enter() .append("text") .attr("x", x + 15) .attr("y", function(d,i){ return y + i*25}) // 100 is where the first dot appears. 25 is the distance between dots .style("fill", (d,i) => color[d]) .style("font-size", "11px") .text(d => d) .attr("text-anchor", "left") .style("alignment-baseline", "middle"); return svg.node(); }32:T46f,async function createData() { const ids = ["Atlas", "Random50", "Random100", "Random200"]; // Array of ids let bias = {}; const responses = await Promise.all( ids.map(async id => { if (id == "Atlas") { bias[id] = await get_imp_bias(id); // Send request for each id } else { bias[id] = await get_imp_random_bias(id); } }) ); let dataNew = []; for (let i=0; ie.xlabel == bias_dimension_labels_new[bias_dimensions_selection])[0]; const data_to_plot_filtered = {}; data_to_plot_filtered['feature'] = data_to_plot['feature']; data_to_plot_filtered['xlabel'] = data_to_plot['xlabel']; data_to_plot_filtered['ylabel'] = data_to_plot['ylabel']; if (data_to_plot['bars'] !== undefined) { data_to_plot_filtered['bars'] = {}; let filtered_test_data = Object.keys(data_to_plot['bars']).filter(e => selected_monitor_sets_mapped.includes(e)); for (let i=0; i selected_monitor_sets_mapped.includes(e)); for (let i=0; i { for (let i=0; i< Object.keys(colors).length; i++){ if (key.includes(Object.keys(colors)[i])) { selected_colors[Object.keys(colors)[i]] = colors[Object.keys(colors)[i]] } } }); if (data_to_plot_filtered["bars"] !== undefined) { return Plot.plot({ width: 1200, x: {axis: null}, y: {tickFormat: "s", domain: [0, 1]}, color: {domain: Object.keys(selected_colors), range: Object.values(selected_colors), legend: true}, marks: [ Plot.barY(dist_plot_data_formatted_filtered, { x: "AS", y: "value", fill: d => selected_colors[d["AS"]], fx: d => d[bias_dimensions_selection], // show value when hovered in bar plot title: d => d.value, sort: {x: null, fx: "y", order: "descending"},//, reduce: "max"} }), Plot.axisX({ tickFormat: d => d[bias_dimensions_selection], // tickRotate: -45, label: bias_dimensions_selection, }), Plot.ruleY([0]), ], }) } else { return Plot.plot({ width: 1200, marginBottom: 60, color: {domain: Object.keys(selected_colors), range: Object.values(selected_colors), legend: true}, x: { type: "log", tickRotate: -45, tickFormat: (d) => { const exponent = Math.floor(Math.log10(d)); const base = d / 10 ** exponent; return base.toFixed(1) + "e" + exponent; }, // tickFormat: (d, i, ticks) => { // const exponent = Math.floor(Math.log10(d)); // const base = d / 10 ** exponent; // const multiples = [0, 1, 2, 9, 10, 30, 200, 900, 1000, 2000, 9000, 10000, ]; // if (multiples.includes(i)) { // return base.toFixed(1) + "e" + exponent; // } // return null; // }, }, y: { grid: true, label: `↑ ${data_to_plot_filtered['ylabel']}` }, marks: [ Plot.ruleY([0]), Plot.lineY(dist_plot_data_formatted_filtered, {x: data_to_plot_filtered['xlabel'], y: data_to_plot_filtered['ylabel'], z: "AS", stroke: "AS", sort: {x: null, color: null, z: "AS"} }) ] }) } }36:T649,dist_plot_data_formatted = { let data_to_plot = data_fetched_joined.filter(e=>e.xlabel == bias_dimension_labels_new[bias_dimensions_selection])[0]; let dataArr = []; if (data_to_plot["bars"] !== undefined) { let data_to_plot_bars = data_to_plot['bars']; for (let j=0; j { distr[id] = (id.startsWith("My"))? await get_dist_plots_custom(custom_sets_dict[id], set_numbers_type): await get_dist_plots_imps(id) ; // Send request for each id }) ); let dist_plots_fetched_data_custom = distr["My 1st Custom Set"] let dist_plots_fetched_data_custom1 = distr["My 2nd Custom Set"] let dist_plots_fetched_data_custom2 = distr["My 3rd Custom Set"] let dist_plots_fetched_data_all = distr["all"] let dist_plots_fetched_data_ATLAS = distr["Atlas"] for (let i=0; i response.json()); } else if (monitor == 'Random50' || monitor == 'Random100' || monitor == 'Random200') { null } else if (monitor == 'My 1st Custom Set') { (custom_set.length > 0) ? arr = fetch("https://ai4netmon.csd.auth.gr/api/bias/cause/"+mtype+"/?"+mtype+"="+custom_set.replaceAll(",","&"+mtype+"=")).then((response) => response.json()) : null } else if (monitor == 'My 2nd Custom Set') { (custom_set1.length > 0) ? arr = fetch("https://ai4netmon.csd.auth.gr/api/bias/cause/"+mtype+"/?"+mtype+"="+custom_set1.replaceAll(",","&"+mtype+"=")).then((response) => response.json()) : null } else if (monitor == 'My 3rd Custom Set') { (custom_set2.length > 0) ? arr = fetch("https://ai4netmon.csd.auth.gr/api/bias/cause/"+mtype+"/?"+mtype+"="+custom_set2.replaceAll(",","&"+mtype+"=")).then((response) => response.json()) : null } return arr; }39:[] 2f:{"title":"Bias in RIPE Atlas measurements","cells":[{"id":0,"value":"# Bias in RIPE Atlas measurements\n\nThis notebook visualizes the bias in Internet measurement platforms, based on the analyses of the [AI4NetMon](https://sermpezis.github.io/ai4netmon/) project. For an introduction on the bias of Internet measurement platforms see the [RIPE Labs article](https://labs.ripe.net/author/pavlos_sermpezis/bias-in-internet-measurement-infrastructure/).\n\nThe following radar plot shows: \n- the bias; from 0 (low bias, center of the circle) to 1 (high bias, far from center)\n- for each selected set of monitors (lines/areas of different colors)\n- along different bias dimensions (each radius of the circle)\n\nYou can upload any custom set of monitors to compare their bias, and select which sets of monitors / bias dimensions to visualize.","pinned":false,"hidden":false,"mode":"md"},{"id":599,"value":"[Optional] Select a **custom set** of vantage points. In the box below, add a list of ASNs (only numbers, separated with commas, no spaces; e.g., 174,1299,3333)","pinned":false,"hidden":false,"mode":"md"},{"id":758,"value":"viewof set_numbers_type = {\n const types_array = [\"ASNs\", \"probe IDs\"];\n const set_numbers_type = Inputs.radio(types_array, {label: \"Select the type of list of numbers\", value: types_array[0]});\n set_numbers_type.style.width = \"100%\";\n return set_numbers_type;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":601,"value":"viewof custom_set = {\n const custom_set = Inputs.text({label: `Custom Set #1 (${set_numbers_type})`});\n custom_set.style.width = \"100%\";\n return custom_set;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":741,"value":"viewof custom_set1 = {\n const custom_set1 = Inputs.text({label: `Custom Set #2 (${set_numbers_type})`});\n custom_set1.style.width = \"100%\";\n return custom_set1;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":763,"value":"viewof custom_set2 = {\n const custom_set2 = Inputs.text({label: `Custom Set #3 (${set_numbers_type})`});\n custom_set2.style.width = \"100%\";\n return custom_set2;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":592,"value":"Select the sets of vantage points to visualize","pinned":false,"hidden":false,"mode":"md"},{"id":578,"value":"viewof checkboxes_monitor_sets = {\n const checkboxes_monitor_sets = Inputs.checkbox(monitor_sets, {label: \"Vantage point sets\", value: monitor_sets.slice(0,1)});\n checkboxes_monitor_sets.style.width = \"100%\";\n return checkboxes_monitor_sets;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":597,"value":"Select the bias dimensions to visualize","pinned":false,"hidden":false,"mode":"md"},{"id":494,"value":"viewof checkboxes_bias_dimensions = {\n const checkboxes_bias_dimensions = Inputs.checkbox(Object.keys(bias_dimension_labels_new), {label: \"Bias dimensions\", value: Object.keys(bias_dimension_labels_new)});\n checkboxes_bias_dimensions.style.width = \"100%\";\n return checkboxes_bias_dimensions;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1407,"value":"Select a peer and check info about it","pinned":false,"hidden":false,"mode":"md"},{"id":1409,"value":"viewof peer = {\n const peer = Inputs.text({label: `Type ${set_numbers_type.slice(0,-1)}`});\n peer.style.width = \"100%\";\n return peer;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1411,"value":"viewof table_as_info = (peer == \"\") ? html`` : selectedAS_info[0]['Property'] == \"detail\" ? html`Not found` : Inputs.table(selectedAS_info, {\n columns: [\n \"Property\",\n \"Value\"\n ],\n header: {\n \"Property\": \"Property\",\n \"Value\": \"Value\"\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":104,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":164,"value":"### Data","pinned":false,"hidden":false,"mode":"md"},{"id":769,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1038,"value":"function get_imp_bias(imp) {\n return fetch(\"https://ai4netmon.csd.auth.gr/api/bias/\"+imp).then((response) => response.json())\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1338,"value":"function get_imp_random_bias(imp) {\n return fetch(\"https://ai4netmon.csd.auth.gr/api/bias/random/Atlas/\"+imp.split(\"Random\")[1]).then((response) => response.json());\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1040,"value":"function get_custom_bias(arg_custom_set, arg_set_numbers_type) {\n let custom_data;\n if (arg_custom_set.length > 0){\n let mtype = (arg_set_numbers_type === \"ASNs\") ? \"asn\" : \"probe\";\n custom_data = fetch(\"https://ai4netmon.csd.auth.gr/api/bias/\"+mtype+\"/?\"+mtype+\"=\"+arg_custom_set.replaceAll(\",\",\"&\"+mtype+\"=\")).then((response) => response.json());\n }\n return custom_data\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1306,"value":"random50_data = Object({\n \"RIR region\":0.016817882077277267,\"Location (country)\":0.2964722501458393,\"Location (continent)\":0.020085793104748166,\"Customer cone (#ASNs)\":0.02263046663977012,\"Customer cone (#prefixes)\":0.021575853862139147,\"Customer cone (#addresses)\":0.01842750536516135,\"AS hegemony\":0.028501011392096855,\"Country influence (CTI origin)\":0.05477537238604335,\"Country influence (CTI top)\":0.7862659128268614,\"#neighbors (total)\":0.03222018427637231,\"#neighbors (peers)\":0.03485525834520126,\"#neighbors (customers)\":0.026235041577036662,\"#neighbors (providers)\":0.019919627868339604,\"#IXPs (PeeringDB)\":0.10963397343514132,\"#facilities (PeeringDB)\":0.0696487027178324,\"Peering policy (PeeringDB)\":0.04500711540584966,\"ASDB C1L1\":0.10123912734077019,\"ASDB C1L2\":0.2547034189728773,\"Network type (PeeringDB)\":0.15699583236198866,\"Traffic ratio (PeeringDB)\":0.14604844703999992,\"Traffic volume (PeeringDB)\":0.5165263067515692,\"Scope (PeeringDB)\":0.20954259437699685,\"Personal ASN\":0.0037134517537629947\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1308,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1312,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":771,"value":"data_fetched = createData()","pinned":false,"hidden":false,"mode":"ojs"},{"id":773,"value":"formattedData = selected_monitor_sets.map(m => (data_custom_new.filter(e => bias_dimensions.includes(e.tag)).map(e => ({axis:e.tag, value:e[m]}))))","pinned":false,"hidden":false,"mode":"ojs"},{"id":775,"value":"data_custom_new = {\n let x = JSON.parse(JSON.stringify(data_fetched));\n\n const custom_sets_dict = {\"My 1st Custom Set\": custom_set, \n \"My 2nd Custom Set\": custom_set1, \n \"My 3rd Custom Set\": custom_set2\n }; \n const ids = [\"My 1st Custom Set\", \"My 2nd Custom Set\", \"My 3rd Custom Set\"];\n let bias = {};\n const responses = await Promise.all(\n \tids.map(async id => {\n \t\tbias[id] = await get_custom_bias(custom_sets_dict[id], set_numbers_type); // Send request for each id\n \t})\n );\n\n for (let id=0; id 0){ \n for (var i = 0; i < data_fetched.length; i++) {\n let tt = data_fetched[i][\"tag\"];\n let tk = bias_dimension_labels_new[tt];\n let bias_dict;\n bias_dict = bias[current_custom_set][\"bias\"]\n x[i][current_custom_set] = bias_dict[tk]\n } \n }\n }\n \n return x\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":777,"value":"##### Monitor Sets","pinned":false,"hidden":false,"mode":"md"},{"id":779,"value":"monitor_sets = [\"RIPE Atlas\", \"Random50\", \"Random100\", \"Random200\", \"My 1st Custom Set\", \"My 2nd Custom Set\", \"My 3rd Custom Set\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":781,"value":"selected_monitor_sets = monitor_sets.filter(e => checkboxes_monitor_sets.includes(e))","pinned":false,"hidden":false,"mode":"ojs"},{"id":783,"value":"##### Bias dimensions","pinned":false,"hidden":false,"mode":"md"},{"id":258,"value":"bias_dimensions = data_fetched.map(o=>o.tag).filter(e => checkboxes_bias_dimensions.includes(bias_dimension_labels_to_show[e]))","pinned":false,"hidden":false,"mode":"ojs"},{"id":787,"value":"bias_dimension_labels_to_show = ({\n \"RIR region\": \"RIR region\",\n \"Country\": \"Country\",\n \"Continent\": \"Continent\",\n \"Customer cone (#ASNs)\": \"Customer cone (#ASNs)\",\n \"Customer cone (#pfxs)\": \"Customer cone (#pfxs)\",\n \"Customer cone (#addresses)\": \"Customer cone (#addresses)\",\n \"AS Hegemony\": \"AS Hegemony\",\n \"CTI origin\": \"CTI origin\",\n \"CTI top\": \"CTI top\",\n \"#neighbors (total)\": \"#neighbors (total)\",\n \"#peers\": \"#peers\",\n \"#customers\": \"#customers\",\n \"#providers\": \"#providers\",\n \"#IXPs\": \"#IXPs\",\n \"#facilities\": \"#facilities\",\n \"Peering policy\": \"Peering policy\",\n \"ASDB C1L1\": \"ASDB C1L1\",\n \"ASDB C1L2\": \"ASDB C1L2\",\n \"Net. type\": \"Net. type\",\n \"Traffic ratio\": \"Traffic ratio\",\n \"Traffic volume\": \"Traffic volume\",\n \"Scope\": \"Scope\",\n \"Personal ASN\": \"Personal ASN\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":792,"value":"bias_dimension_labels_new = ({\n \"RIR region\": \"RIR region\",\n \"Country\": \"Location (country)\",\n \"Continent\": \"Location (continent)\",\n \"Customer cone (#ASNs)\": \"Customer cone (#ASNs)\",\n \"Customer cone (#pfxs)\": \"Customer cone (#prefixes)\",\n \"Customer cone (#addresses)\": \"Customer cone (#addresses)\",\n \"AS Hegemony\": \"AS hegemony\",\n \"CTI origin\": \"Country influence (CTI origin)\",\n \"CTI top\": \"Country influence (CTI top)\",\n \"#neighbors (total)\": \"#neighbors (total)\",\n \"#peers\": \"#neighbors (peers)\",\n \"#customers\": \"#neighbors (customers)\",\n \"#providers\": \"#neighbors (providers)\",\n \"#IXPs\": \"#IXPs (PeeringDB)\",\n \"#facilities\": \"#facilities (PeeringDB)\",\n \"Peering policy\": \"Peering policy (PeeringDB)\",\n \"ASDB C1L1\": \"ASDB C1L1\",\n \"ASDB C1L2\": \"ASDB C1L2\",\n \"Net. type\": \"Network type (PeeringDB)\",\n \"Traffic ratio\": \"Traffic ratio (PeeringDB)\",\n \"Traffic volume\": \"Traffic volume (PeeringDB)\",\n \"Scope\": \"Scope (PeeringDB)\",\n \"Personal ASN\": \"Personal ASN\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":794,"value":"bias_dimension_labels_chart = {\n const bias_dimension_labels_chart = {};\n for (let i=0; i Math.max(...Object.values(o).filter(v => typeof v === 'number'))))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1399,"value":"##### Information table for ASNs/probe IDs","pinned":false,"hidden":false,"mode":"md"},{"id":1405,"value":"selectedAS_info = {\n let mtype = (set_numbers_type === \"ASNs\") ? \"asn\" : \"probe\";\n if (peer !== \"\") {\n const response = await fetch(\"https://ai4netmon.csd.auth.gr/api/\"+mtype+\"/\"+peer).then((response) => response.json());\n let info_list = [];\n for (const key in response) {\n info_list.push(Object({'Property': key, 'Value': response[key]}))\n }\n return info_list;\n }\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":162,"value":"### Plotting","pinned":false,"hidden":false,"mode":"md"},{"id":270,"value":"##### Plotting functions","pinned":false,"hidden":false,"mode":"md"},{"id":95,"value":"// radarLine = d3.lineRadial()\n// .curve(d3[\"curveLinear\"])\n// .radius(d => rScale(d))\n// .angle((d, i) => -angleSlice*i)","pinned":false,"hidden":false,"mode":"ojs"},{"id":100,"value":"// rScale = d3.scaleLinear()\n// .domain([0, maxValue])\n// .range([0, radius])","pinned":false,"hidden":false,"mode":"ojs"},{"id":102,"value":"// color = d3.scaleOrdinal()\n// .range([\"#EDC951\",\"#CC333F\",\"#00A0B0\",\"#ab3da9\",\"#a6d75b\", \"#115f9a\"])\ncolor = Object({\n 'RIPE Atlas': \"#EDC951\",\n 'Random50': \"#D15A86\",\n 'Random100': \"#42A5B3\",\n 'Random200': \"#BA5F06\",\n 'My 1st Custom Set': \"#ab3da9\",\n 'My 2nd Custom Set': \"#a6d75b\",\n 'My 3rd Custom Set': \"#115f9a\",\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":225,"value":"##### Plotting parameters","pinned":false,"hidden":false,"mode":"md"},{"id":10,"value":"axesDomain = bias_dimensions","pinned":false,"hidden":false,"mode":"ojs"},{"id":30,"value":"// axisLabelFactor = 1.12","pinned":false,"hidden":false,"mode":"ojs"},{"id":29,"value":"// wrapWidth = 60","pinned":false,"hidden":false,"mode":"ojs"},{"id":43,"value":"\nmaxValue = Math.ceil(max_bias*10)/10 \n","pinned":false,"hidden":false,"mode":"ojs"},{"id":97,"value":"angleSlice = 2*Math.PI/bias_dimensions.length","pinned":false,"hidden":false,"mode":"ojs"},{"id":32,"value":"axisCircles = maxValue*10","pinned":false,"hidden":false,"mode":"ojs"},{"id":20,"value":"axesLength = bias_dimensions.length\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":34,"value":"// dotRadius = 4","pinned":false,"hidden":false,"mode":"ojs"},{"id":36,"value":"// radius = (height-(margin*2)) / 2","pinned":false,"hidden":false,"mode":"ojs"},{"id":40,"value":"// height = 600","pinned":false,"hidden":false,"mode":"ojs"},{"id":38,"value":"// margin = 30","pinned":false,"hidden":false,"mode":"ojs"},{"id":840,"value":"### Distribution Plots","pinned":false,"hidden":false,"mode":"md"},{"id":844,"value":"viewof bias_dimensions_selection = {\n // delete Country from bias dimensions list\n const biasArr = Object.keys(bias_dimension_labels_new);\n biasArr.splice(biasArr.findIndex(e => e === \"Country\"),1);\n biasArr.splice(biasArr.findIndex(e => e === \"ASDB C1L2\"),1);\n const select = Inputs.select(biasArr, {label: \"Bias dimensions\", value: biasArr});\n\n select.style.width = \"100%\";\n return select;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":847,"value":"#### Plots","pinned":false,"hidden":false,"mode":"md"},{"id":849,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":852,"value":"#### Data","pinned":false,"hidden":false,"mode":"md"},{"id":1284,"value":"monitor_sets_Obj = {\n const indexObj = {};\n indexObj['RIPE Atlas'] = \"Atlas\";\n indexObj['My 1st Custom Set'] = \"My 1st Custom Set\";\n indexObj['My 2nd Custom Set'] = \"My 2nd Custom Set\";\n indexObj['My 3rd Custom Set'] = \"My 3rd Custom Set\";\n return indexObj;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1286,"value":"selected_monitor_sets_mapped = {\n const newArr = [];\n newArr.push(\"All ASes\");\n for (let i=0; i selected_monitor_sets_mapped.includes(e['AS']))","pinned":false,"hidden":false,"mode":"ojs"},{"id":854,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":858,"value":"##### All Data","pinned":false,"hidden":false,"mode":"md"},{"id":998,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":1001,"value":"async function get_dist_plots_imps(imp){\n const data = await fetch(\"https://ai4netmon.csd.auth.gr/api/distributions/\"+imp).then((response) => response.json());\n return data[0]\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1003,"value":"async function get_dist_plots_custom(arg_custom_set, arg_set_numbers_type){\n if (arg_custom_set.length>0){\n let mtype = (arg_set_numbers_type === \"ASNs\")? \"asn\":\"probe\"\n // console.log(mtype)\n let data = await fetch(\"https://ai4netmon.csd.auth.gr/api/distributions/custom/\"+mtype+\"/?\"+mtype+\"=\"+arg_custom_set.replaceAll(\",\",\"&\"+mtype+\"=\")).then((response) => response.json());\n // console.log(data[0])\n return data[0];\n } else {\n return null; \n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":947,"value":"### Recommendations","pinned":false,"hidden":false,"mode":"md"},{"id":1366,"value":"monitor_sets","pinned":false,"hidden":false,"mode":"ojs"},{"id":1349,"value":"monitors_index = monitor_sets_Obj","pinned":false,"hidden":false,"mode":"ojs"},{"id":966,"value":"viewof monitor_set_selection = {\n const select = Inputs.select(Object.keys(monitors_index), {label: \"Vantage point set\", value: Object.keys(monitors_index)});\n\n select.style.width = \"100%\";\n return select;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":970,"value":"viewof searched_data = {\n const search_field = Inputs.search(data_monitor_set_formatted, {label: 'Filter by: Bias Dimension, Value or Difference'});\n\n search_field.style.width = \"100%\";\n return search_field;\n}\n// viewof searched_data = Inputs.search(data_monitor_set_formatted, {label: 'Filter by: Bias Dimension, Value or Difference'});","pinned":false,"hidden":false,"mode":"ojs"},{"id":1353,"value":"viewof table_monitor_set = Inputs.table(searched_data, {\n \n // sort: \"TOTAL bias (selected)\",\n width: {\n \"Bias Dimension\": \"40%\",\n \"Value\": \"30%\",\n \"Difference\": \"30%\"\n },\n align: {\n \"Bias Dimension\": \"left\",\n \"Value\": \"left\",\n \"Difference\": \"left\"\n },\n \n columns: [\n \"Bias Dimension\",\n \"Value\",\n \"Difference\"\n ],\n \n header: {\n \"Bias Dimension\": \"Bias Dimension\",\n \"Value\": \"Value\",\n \"Difference\": \"Difference %\"\n },\n\n // format: {\n // \"Bias Dimension\": d => d['Bias Dimension'],\n // \"Value\": d => Object.keys(d),\n // \"Difference\": d => Object.values(d),\n // }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":955,"value":"#### Data","pinned":false,"hidden":false,"mode":"md"},{"id":957,"value":"// format data for table input\ndata_monitor_set_formatted = {\n let dataArr = [];\n const data_fetched_table = (monitor_set_selection.includes(\"My\")) ? ((Object.keys(data_fetched_monitor_set).length > 0) ? data_fetched_monitor_set[\"Custom list\"] : 0 ) : monitor_set_selection.includes(\"Random\") ? 0 : data_fetched_monitor_set[monitors_index[monitor_set_selection]];\n for (let i=0; i el['Difference']);","pinned":false,"hidden":false,"mode":"ojs"},{"id":1380,"value":"smallest_diff_widget = {\n if (diff_only_arr.length !== 0) {\n return html`\n

The characteristic ${data_monitor_set_formatted.map(el => el['Difference'] == Math.min(...diff_only_arr) ? el['Bias Dimension'] : '')} with value ${data_monitor_set_formatted.map(el => el['Difference'] == Math.min(...diff_only_arr) ? el['Value'] : '')} is the most underrepresented in the selected set, by a percentage of ${Math.min(...diff_only_arr)} %

\n `\n } else {\n return html`\n

No results

\n `\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1382,"value":"largest_diff_widget = {\n if (diff_only_arr.length !== 0) {\n return html`\n

The characteristic ${data_monitor_set_formatted.map(el => el['Difference'] == Math.max(...diff_only_arr) ? el['Bias Dimension'] : '')} with value ${data_monitor_set_formatted.map(el => el['Difference'] == Math.max(...diff_only_arr) ? el['Value'] : '')} is the most overrepresented in the selected set, by a percentage of ${Math.max(...diff_only_arr)} %

\n `\n } else {\n return html`\n

No results

\n `\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":159,"value":"### Dependencies","pinned":false,"hidden":false,"mode":"md"},{"id":45,"value":"import {select} from \"@jashkenas/inputs\"\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":1413,"value":"import {autoSelect} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":46,"value":"import {curveTypes} from \"@palewire/d3-curves-inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":49,"value":"_ = require(\"underscore\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":51,"value":"d3 = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W39","stdlib":"1","titling":"auto"}