31:T18ad,Treemap = function Treemap(data, { // data is either tabular (array of objects) or hierarchy (nested objects) path, // as an alternative to id and parentId, returns an array identifier, imputing internal nodes id = Array.isArray(data) ? d => d.id : null, // if tabular data, given a d in data, returns a unique identifier (string) parentId = Array.isArray(data) ? d => d.parentId : null, // if tabular data, given a node d, returns its parent’s identifier children, // if hierarchical data, given a d in data, returns its children value, // given a node d, returns a quantitative value (for area encoding; null for count) sort = (a, b) => d3.descending(a.value, b.value), // how to sort nodes prior to layout label, // given a leaf node d, returns the name to display on the rectangle group, // given a leaf node d, returns a categorical value (for color encoding) title, // given a leaf node d, returns its hover text link, // given a leaf node d, its link (if any) linkTarget = "_blank", // the target attribute for links (if any) tile = d3.treemapBinary, // treemap strategy width = 640, // outer width, in pixels height = 400, // outer height, in pixels margin = 0, // shorthand for margins marginTop = margin, // top margin, in pixels marginRight = margin, // right margin, in pixels marginBottom = margin, // bottom margin, in pixels marginLeft = margin, // left margin, in pixels padding = 1, // shorthand for inner and outer padding paddingInner = padding, // to separate a node from its adjacent siblings paddingOuter = padding, // shorthand for top, right, bottom, and left padding paddingTop = paddingOuter, // to separate a node’s top edge from its children paddingRight = paddingOuter, // to separate a node’s right edge from its children paddingBottom = paddingOuter, // to separate a node’s bottom edge from its children paddingLeft = paddingOuter, // to separate a node’s left edge from its children round = true, // whether to round to exact pixels colors = d3.schemeTableau10, // array of colors zDomain, // array of values for the color scale fill = "#ccc", // fill for node rects (if no group color encoding) fillOpacity = group == null ? null : 0.6, // fill opacity for node rects stroke, // stroke for node rects strokeWidth, // stroke width for node rects strokeOpacity, // stroke opacity for node rects strokeLinejoin, // stroke line join for node rects } = {}) { // If id and parentId options are specified, or the path option, use d3.stratify // to convert tabular data to a hierarchy; otherwise we assume that the data is // specified as an object {children} with nested objects (a.k.a. the “flare.json” // format), and use d3.hierarchy. const root = path != null ? d3.stratify().path(path)(data) : id != null || parentId != null ? d3.stratify().id(id).parentId(parentId)(data) : d3.hierarchy(data, children); // Compute the values of internal nodes by aggregating from the leaves. value == null ? root.count() : root.sum(d => Math.max(0, value(d))); // Prior to sorting, if a group channel is specified, construct an ordinal color scale. const leaves = root.leaves(); const G = group == null ? null : leaves.map(d => group(d.data, d)); if (zDomain === undefined) zDomain = G; zDomain = new d3.InternSet(zDomain); const color = group == null ? null : d3.scaleOrdinal(zDomain, colors); // Compute labels and titles. const L = label == null ? null : leaves.map(d => label(d.data, d)); const T = title === undefined ? L : title == null ? null : leaves.map(d => title(d.data, d)); // Sort the leaves (typically by descending value for a pleasing layout). if (sort != null) root.sort(sort); // Compute the treemap layout. d3.treemap() .tile(tile) .size([width - marginLeft - marginRight, height - marginTop - marginBottom]) .paddingInner(paddingInner) .paddingTop(paddingTop) .paddingRight(paddingRight) .paddingBottom(paddingBottom) .paddingLeft(paddingLeft) .round(round) (root); const svg = d3.create("svg") .attr("viewBox", [-marginLeft, -marginTop, width, height]) .attr("width", width) .attr("height", height) .attr("style", "max-width: 100%; height: auto; height: intrinsic;") .attr("font-family", "Roboto, sans-serif") .attr("font-size", 16) .attr("font-weight", 800) .attr("fill", "#f8fafa"); const node = svg.selectAll("a") .data(leaves) .join("a") .attr("xlink:href", link == null ? null : (d, i) => link(d.data, d)) .attr("target", link == null ? null : linkTarget) .attr("transform", d => `translate(${d.x0},${d.y0})`); node.append("rect") .attr("fill", color ? (d, i) => color(G[i]) : fill) .attr("fill-opacity", fillOpacity) .attr("stroke", stroke) .attr("stroke-width", strokeWidth) .attr("stroke-opacity", strokeOpacity) .attr("stroke-linejoin", strokeLinejoin) // mouseover & mouseout events on leaf hover .on("mouseover", function(d) { d3.select(this).attr("fill-opacity", 0.7) }) .on("mouseout", function(d) { d3.select(this).attr("fill-opacity", 1) d3.select(".leaf-text").style("fill", "#f8fafa") }) // transition effect .transition() .duration(500) .attr("width", d => d.x1 - d.x0) .attr("height", d => d.y1 - d.y0); if (T) { node.append("title").text((d, i) => T[i]) } if (L) { // A unique identifier for clip paths (to avoid conflicts). const uid = `O-${Math.random().toString(16).slice(2)}`; node.append("clipPath") .attr("id", (d, i) => `${uid}-clip-${i}`) .append("rect") .attr("width", d => d.x1 - d.x0) .attr("height", d => d.y1 - d.y0); node.append("text") .attr("clip-path", (d, i) => `url(${new URL(`#${uid}-clip-${i}`, location)})`) .selectAll("tspan") .data((d, i) => `${L[i]}`.split(/\n/g)) .join("tspan") .attr("x", 3) .attr("y", (d, i, D) => `${(i === D.length - 1) * 0.3 + 1.1 + i * 0.9}em`) .attr("fill-opacity", (d, i, D) => i === D.length - 1 ? 0.7 : null) .text(d => d); } return Object.assign(svg.node(), {scales: {color}}); }32:T146e,function zoomableTreemapAll(rendered_data, width, height, colors, lang){ // parameters and needed functions const treemap = data => d3.treemap() .tile(tile) (d3.hierarchy(data) .sum(d => d.value)); // uncomment next line if data need to be sorted // .sort((a, b) => b.value - a.value)); function tile(node, x0, y0, x1, y1) { d3.treemapBinary(node, 0, 0, width, height); for (const child of node.children) { child.x0 = x0 + child.x0 / width * (x1 - x0); child.x1 = x0 + child.x1 / width * (x1 - x0); child.y0 = y0 + child.y0 / height * (y1 - y0); child.y1 = y0 + child.y1 / height * (y1 - y0); } }; const name = d => d.ancestors().reverse().map(d => d.data.topic).join("/"); // create treemap const x = d3.scaleLinear().rangeRound([0, width]); const y = d3.scaleLinear().rangeRound([0, height]); const svg = d3.create("svg") .attr("viewBox", [0.5, -30.5, width, height + 30]) .style("font", "16px Roboto, sans-serif"); let group = svg.append("g") .call(render, treemap(rendered_data)); function render(group, root) { const node = group .selectAll("g") .data(root.children.concat(root)) .join("g"); node.filter(d => d === root ? d.parent : d.children) .attr("cursor", "pointer") .on("click", (event, d) => d === root ? zoomout(root) : zoomin(d)); node.append("title") // .text(d => `${name(d)}\n${format(d.freq)}`); // .text(d => `${d["topic"]}\n${Math.round(d["freq"]*100)}`); .text(d => (d === root ? name(d) : d.data.topic).split(/\n/g).concat(d.data.freq == null ? "" : `${Math.round(d.data.freq*100)}%`)); node.append("rect") .attr("id", d => (d.leafUid = DOM.uid("leaf")).id) // .attr("fill", d => d === root ? "#fff" : d.children ? "#ccc" : "#ddd") .attr("fill", d => d === root ? "#484e4e" : colors[d.data.topic]) .attr("stroke", "#fff") // mouseover & mouseout events on leaf hover .on("mouseover", function(d) { d3.select(this).attr("fill-opacity", 0.7) }) .on("mouseout", function(d) { d3.select(this).attr("fill-opacity", 1) d3.select(".leaf-text").style("fill", "#f8fafa") }); node.append("clipPath") .attr("id", d => (d.clipUid = DOM.uid("clip")).id) .append("use") .attr("xlink:href", d => d.leafUid.href); node.append("text") .attr("clip-path", d => d.clipUid) // .attr("font-weight", d => d === root ? "bold" : null) .selectAll("tspan") // .data(d => (d === root ? name(d) : d.data.topic).split(/(?=[A-Z][^A-Z])/g).concat(format(d.freq))) .data(d => lang == "en" ? (d === root ? d.data.topic == "All Data" ? name(d) : `${name(d)} - Click to return` : d.data.children ? `${d.data.topic}\n\n(Click to expand)` : d.data.topic).split(/\n/g).concat(d.data.freq == null ? "" : `${Math.round(d.data.freq*100)}%`) : (d === root ? d.data.topic == "Όλα τα δεδομένα" ? name(d) : `${name(d)} - Κλικ για επιστροφή` : d.data.children ? `${d.data.topic}\n\n(Κλικ για περαιτέρω)` : d.data.topic).split(/\n/g).concat(d.data.freq == null ? "" : `${Math.round(d.data.freq*100)}%`)) .join("tspan") .attr("x", 3) .attr("y", (d, i, nodes) => `${(i === nodes.length - 1) * 0.3 + 1.2 + i * 0.9}em`) .attr("fill-opacity", (d, i, nodes) => i === nodes.length - 1 ? 0.7 : null) // .attr("font-weight", (d, i, nodes) => i === nodes.length - 1 ? "normal" : null) .attr("font-family", "Roboto, sans-serif") .attr("font-size", 18) .attr("font-weight", 800) .attr("fill", "#f8fafa") .text(d => d); group.call(position, root); } function position(group, root) { group.selectAll("g") .attr("transform", d => d === root ? `translate(0,-30)` : `translate(${x(d.x0)},${y(d.y0)})`) .select("rect") .attr("width", d => d === root ? width : x(d.x1) - x(d.x0)) .attr("height", d => d === root ? 30 : y(d.y1) - y(d.y0)); } // When zooming in, draw the new nodes on top, and fade them in. function zoomin(d) { const group0 = group.attr("pointer-events", "none"); const group1 = group = svg.append("g").call(render, d); x.domain([d.x0, d.x1]); y.domain([d.y0, d.y1]); svg.transition() .duration(750) .call(t => group0.transition(t).remove() .call(position, d.parent)) .call(t => group1.transition(t) .attrTween("opacity", () => d3.interpolate(0, 1)) .call(position, d)); } // When zooming out, draw the old nodes on top, and fade them out. function zoomout(d) { const group0 = group.attr("pointer-events", "none"); const group1 = group = svg.insert("g", "*").call(render, d.parent); x.domain([d.parent.x0, d.parent.x1]); y.domain([d.parent.y0, d.parent.y1]); svg.transition() .duration(750) .call(t => group0.transition(t).remove() .attrTween("opacity", () => d3.interpolate(1, 0)) .call(position, d)) .call(t => group1.transition(t) .call(position, d.parent)); } return svg.node(); }33:T416,async function topics_percentage_categories(pol_button, speech_button, treemap_list, lang) { let perc = {} for (const k of Object.keys(treemap_list)) { let path; if (lang == "en") { path = (speech_button !== "All Speeches") ? gh_path+index_pol_leaders_eng[pol_button]+"/"+speech_button+"/analysis/visuals/"+treemap_list[k]+".json" : gh_path_aggregate+index_pol_leaders_eng[pol_button] + "/" + treemap_list[k] + ".json"; } else { path = (speech_button !== "Όλες οι ομιλίες") ? gh_path+index_pol_leaders_gr[pol_button]+"/"+speech_button+"/analysis/visuals/"+treemap_list[k]+".json" : gh_path_aggregate+index_pol_leaders_gr[pol_button] + "/" + treemap_list[k] + ".json"; } let list_topics = await fetch(path).then((response) => response.json()) perc[k] = list_topics.reduce((accumulator, object) => {return accumulator + object.counts;}, 0) } const sum = Object.values(perc).reduce((a, b) => a + b, 0); for (const k of Object.keys(perc)) { perc[k] = (perc[k] / sum).toFixed(2)*100 } return perc }34:T41f,async function topics_percentage_categories2(pol_button, speech_button, treemap_list, lang) { let perc = {} for (const k of Object.keys(treemap_list)) { let path; if (lang == "en") { path = (speech_button !== "All Speeches") ? gh_path2+index_pol_leaders_eng2[pol_button]+"/"+speech_button+"/analysis/visuals/"+treemap_list[k]+".json" : gh_path_aggregate2+index_pol_leaders_eng2[pol_button] + "/" + treemap_list[k] + ".json"; } else { path = (speech_button !== "Όλες οι ομιλίες") ? gh_path2+index_pol_leaders_gr2[pol_button]+"/"+speech_button+"/analysis/visuals/"+treemap_list[k]+".json" : gh_path_aggregate2+index_pol_leaders_gr2[pol_button] + "/" + treemap_list[k] + ".json"; } let list_topics = await fetch(path).then((response) => response.json()) perc[k] = list_topics.reduce((accumulator, object) => {return accumulator + object.counts;}, 0) } const sum = Object.values(perc).reduce((a, b) => a + b, 0); for (const k of Object.keys(perc)) { perc[k] = (perc[k] / sum).toFixed(2)*100 } return perc }35:T9cd,async function create_zoomable_data_per_speech(pol_button, speech_button, categories_perc, lang){ let path_agenda; let path_criticism; if (lang == "en") { path_agenda = (speech_button !== "All Speeches") ? gh_path+index_pol_leaders_eng[pol_button]+"/"+speech_button+"/analysis/visuals/only_topic_treemap_political_agenda.json" : gh_path_aggregate+index_pol_leaders_eng[pol_button]+"/only_topic_treemap_political_agenda.json"; path_criticism = (speech_button !== "All Speeches") ? gh_path+index_pol_leaders_eng[pol_button]+"/"+speech_button+"/analysis/visuals/only_topic_treemap_criticism.json" : gh_path_aggregate+index_pol_leaders_eng[pol_button]+"/only_topic_treemap_criticism.json"; } else { path_agenda = (speech_button !== "Όλες οι ομιλίες") ? gh_path+index_pol_leaders_gr[pol_button]+"/"+speech_button+"/analysis/visuals/only_topic_el_treemap_political_agenda.json" : gh_path_aggregate+index_pol_leaders_gr[pol_button]+"/only_topic_el_treemap_political_agenda.json"; path_criticism = (speech_button !== "Όλες οι ομιλίες") ? gh_path+index_pol_leaders_gr[pol_button]+"/"+speech_button+"/analysis/visuals/only_topic_el_treemap_criticism.json" : gh_path_aggregate+index_pol_leaders_gr[pol_button]+"/only_topic_el_treemap_criticism.json"; }; const data_github_treemap_agenda = await fetch(path_agenda).then((response) => response.json()); const data_github_treemap_criticism = await fetch(path_criticism).then((response) => response.json()); const newObj = { topic: lang == "en" ? "All Data" : "Όλα τα δεδομένα", freq: null, children: [{ topic: lang == "en" ? "Agenda" : "Ατζέντα", freq: lang == "en" ? categories_perc["Agenda"]/100 : categories_perc["Ατζέντα"]/100, children: data_github_treemap_agenda.map(el => Object({ topic: el.topic, counts: el.counts, freq: el.freq, value: lang == "en" ? el.freq*categories_perc["Agenda"]/100 : el.freq*categories_perc["Ατζέντα"]/100 })) }, { topic: lang == "en" ? "Criticism" : "Κριτική", freq: lang == "en" ? categories_perc["Criticism"]/100 : categories_perc["Κριτική"]/100, children: data_github_treemap_criticism.map(el => Object({ topic: el.topic, counts: el.counts, freq: el.freq, value: lang == "en" ? el.freq*categories_perc["Criticism"]/100 : el.freq*categories_perc["Κριτική"]/100 })) }] }; return newObj; }36:T9de,async function create_zoomable_data_per_speech2(pol_button, speech_button, categories_perc, lang){ let path_agenda; let path_criticism; if (lang == "en") { path_agenda = (speech_button !== "All Speeches") ? gh_path2+index_pol_leaders_eng2[pol_button]+"/"+speech_button+"/analysis/visuals/only_topic_treemap_political_agenda.json" : gh_path_aggregate2+index_pol_leaders_eng2[pol_button]+"/only_topic_treemap_political_agenda.json"; path_criticism = (speech_button !== "All Speeches") ? gh_path2+index_pol_leaders_eng2[pol_button]+"/"+speech_button+"/analysis/visuals/only_topic_treemap_criticism.json" : gh_path_aggregate2+index_pol_leaders_eng2[pol_button]+"/only_topic_treemap_criticism.json"; } else { path_agenda = (speech_button !== "Όλες οι ομιλίες") ? gh_path2+index_pol_leaders_gr2[pol_button]+"/"+speech_button+"/analysis/visuals/only_topic_el_treemap_political_agenda.json" : gh_path_aggregate2+index_pol_leaders_gr2[pol_button]+"/only_topic_el_treemap_political_agenda.json"; path_criticism = (speech_button !== "Όλες οι ομιλίες") ? gh_path2+index_pol_leaders_gr2[pol_button]+"/"+speech_button+"/analysis/visuals/only_topic_el_treemap_criticism.json" : gh_path_aggregate2+index_pol_leaders_gr2[pol_button]+"/only_topic_el_treemap_criticism.json"; }; const data_github_treemap_agenda = await fetch(path_agenda).then((response) => response.json()); const data_github_treemap_criticism = await fetch(path_criticism).then((response) => response.json()); const newObj = { topic: lang == "en" ? "All Data" : "Όλα τα δεδομένα", freq: null, children: [{ topic: lang == "en" ? "Agenda" : "Ατζέντα", freq: lang == "en" ? categories_perc["Agenda"]/100 : categories_perc["Ατζέντα"]/100, children: data_github_treemap_agenda.map(el => Object({ topic: el.topic, counts: el.counts, freq: el.freq, value: lang == "en" ? el.freq*categories_perc["Agenda"]/100 : el.freq*categories_perc["Ατζέντα"]/100 })) }, { topic: lang == "en" ? "Criticism" : "Κριτική", freq: lang == "en" ? categories_perc["Criticism"]/100 : categories_perc["Κριτική"]/100, children: data_github_treemap_criticism.map(el => Object({ topic: el.topic, counts: el.counts, freq: el.freq, value: lang == "en" ? el.freq*categories_perc["Criticism"]/100 : el.freq*categories_perc["Κριτική"]/100 })) }] }; return newObj; }37:Tdc3,// Test function for aggregating treemaps async function create_zoomable_data_all(politician, list_of_speeches, lang) { // Helper function async function get_avg_topics(politician, agenda_or_criticism, list_of_speeches) { let pol_speeches = list_of_speeches[politician] let aggregate_topic_agenda_dict = {} let total_counts = 0 let url; for (let j=0; j response.json()) for (let jj=0; jj obj.topic === 'Other'), 1)[0]) return {'total_counts': total_counts, 'topics': aggregate_topic_agenda_list} } const agenda_dict = await get_avg_topics(politician, "political_agenda", list_of_speeches) const criticism_dict = await get_avg_topics(politician, "criticism", list_of_speeches) let total_counts = agenda_dict["total_counts"] + criticism_dict["total_counts"] let agenda_freq = agenda_dict["total_counts"]/total_counts let criticism_freq = criticism_dict["total_counts"]/total_counts let agenda_topics = agenda_dict["topics"].map(e => Object({'topic': e["topic"], 'counts': e['counts'], 'freq': e['freq'], 'value': e['freq']*agenda_freq })) let criticism_topics = criticism_dict["topics"].map(e => Object({'topic': e["topic"], 'counts': e['counts'], 'freq': e['freq'], 'value': e['freq']*criticism_freq })) return {topic: lang == "en" ? "All Data" : "'Ολα τα δεδομένα", freq: null, children: [{topic: lang == "en" ? "Agenda" : "Ατζέντα", freq: parseFloat(agenda_freq.toFixed(2)), children: agenda_topics}, {topic: lang == "en" ? "Criticism" : "Κριτική", freq: parseFloat(criticism_freq.toFixed(2)), children: criticism_topics}] } }38:T750,// Test function for aggregating treemaps async function create_zoomable_data_all_new(pol_button, categories_perc, lang) { let path_agenda; let path_criticism; if (lang == "en") { path_agenda = gh_path_aggregate+index_pol_leaders_eng[pol_button]+"/only_topic_treemap_political_agenda.json"; path_criticism = gh_path_aggregate+index_pol_leaders_eng[pol_button]+"/only_topic_treemap_criticism.json"; } else { path_agenda = gh_path_aggregate+index_pol_leaders_gr[pol_button]+"/only_topic_el_treemap_political_agenda.json"; path_criticism = gh_path_aggregate+index_pol_leaders_gr[pol_button]+"/only_topic_el_treemap_criticism.json" }; const data_github_treemap_agenda = await fetch(path_agenda).then((response) => response.json()); const data_github_treemap_criticism = await fetch(path_criticism).then((response) => response.json()); const newObj = { topic: lang == "en" ? "All Data" : "Όλα τα δεδομένα", freq: null, children: [{ topic: lang == "en" ? "Agenda" : "Ατζέντα", freq: lang == "en" ? categories_perc["Agenda"]/100 : categories_perc["Ατζέντα"]/100, children: data_github_treemap_agenda.map(el => Object({ topic: el.topic, counts: el.counts, freq: el.freq, value: lang == "en" ? el.freq*categories_perc["Agenda"]/100 : el.freq*categories_perc["Ατζέντα"]/100 })) }, { topic: lang == "en" ? "Criticism" : "Κριτική", freq: lang == "en" ? categories_perc["Criticism"]/100 : categories_perc["Κριτική"]/100, children: data_github_treemap_criticism.map(el => Object({ topic: el.topic, counts: el.counts, freq: el.freq, value: lang == "en" ? el.freq*categories_perc["Criticism"]/100 : el.freq*categories_perc["Κριτική"]/100 })) }] }; return newObj; }39:Td70,function Speedometer(data,config,lang){ const width = "700" const height = Math.min(width, 400) const radius = Math.min(width, 400) / 2; const arc = d3.arc().innerRadius(radius * 0.47).outerRadius(radius - 1); const arc2 = d3.arc().innerRadius(radius).outerRadius(radius+radius/4 - 1); let pie = d3.pie() .startAngle(-110 * (Math.PI/180)) .endAngle(110 * (Math.PI/180)) .sort(null) .value(d => d.value) // set angle based on data values const angle = ((220*data.value)/config.total) // use the following for discrete colors const arcs = pie([{name: "neg", value: 1/3, color: "#ba3a0b"}, {name: "neu", value: 1/3, color: "#b9c0c0"}, {name: "pos", value: 1/3, color: "#768948"}]); const colors = {"color1": "#702307", "color1light": "#ba3a0b", "color2": "#f0a202", "color3light": "#aeab66", "color3": "#768948", "color4": "#2b2f2f", "color5": "#111313"} const arcsnew = pie([{name: "", value: 0, color: ""}, {name: "", value: 1, color: ""}]); const svg = d3.create("svg") .attr("viewBox", [0, 0, width, height]) .attr("text-anchor", "middle") .style("font", "12px sans-serif"); const g = svg.append("g") .attr("transform", `translate(${width / 2},${height / 1.5})`); // create the main arc with colors g.selectAll(".arc") .data(arcs) .join("path") .attr("class", "arc") .attr('fill', (d) => { if(d.data.name != ''){ return d.data.color; }else{ return 'url(#gradient)'; } }) .attr("d", arc) // create a secondary arc with gray color on top of the main g.selectAll(".arc2") .data(arcsnew) .join("path") .attr("class", "arc2") .attr('fill', "#e2e9e9") .attr("d", arc2) // Create the polygon that represents the needle const needle = g .append("polygon") .attr("points", "0,-10 18,0 0,240 -18,0") .attr("fill", colors.color4) .attr("stroke", colors.color4) .attr("transform", "rotate(180)"); // Rotate the needle to the desired angle needle .transition() .duration(750) .attr("transform", `rotate(${angle + 180})`); // create the circle in the middle of the needle const circle = g .append("circle") .attr("cx", 0) .attr("cy", 0) //.attr("dy", 15) .attr("r", 20) .attr("fill", colors.color5) const text = g.selectAll("text") .data(arcs) .join("text") .attr("transform", d => `translate(${arc.centroid(d)})`) .attr("dy", "0.35em"); text.filter(d => (d.endAngle - d.startAngle)).append("tspan") .attr("x", 0) .attr("y", "0.7em") .attr("font-size", "24px") .attr("fill-opacity", 0.7); // set text to show on the bottom of the chart let textToShow = ""; if (data.value > 0.33){ // textToShow = `Positive ${data.value}` textToShow = lang== "en" ? "Positive" : "Θετικό" } else if (data.value>=-0.33 & data.value <= 0.33){ // textToShow = `Neutral ${data.value}` textToShow = lang == "en" ? "Neutral" : "Ουδέτερο" } else { // textToShow = `Negative ${data.value}` textToShow = lang == "en" ? "Negative" : "Αρνητικό" } const p = g .append("text") // .text(`${Math.round((data.value/data.total)*100)}%`) .text(`${textToShow}`) .attr("x", 0) .attr("y", 80) .attr("font-size", "24px") .attr("font-family", "Roboto, sans-serif") return svg.node(); }3a:T609,async function createSpeedChart(pol,speech,lang) { const config_speedometer = ({"total": 2, "average": 0, "minimum": -1, "maximum": 1}); let response; if (lang == "en") { if (speech !== "All Speeches") { response = await fetch(gh_path+pol+"/"+speech+"/analysis/visuals/sentiment.json"); const data_sentiment_speeches = Object({"loc":speech, "value": Math.round((await response.json())*100)/100}); return Speedometer(data_sentiment_speeches,config_speedometer, "en") } else { response = await fetch(gh_path_aggregate+pol+"/sentiment.json"); const data_sentiment_all = Object({"loc": "All Speeches", "value": Math.round((await response.json())*100)/100}); return Speedometer(data_sentiment_all,config_speedometer, "en") } } else { if (speech !== "Όλες οι ομιλίες") { response = await fetch(gh_path+pol+"/"+speech+"/analysis/visuals/sentiment.json"); const data_sentiment_speeches = Object({"loc":speech, "value": Math.round((await response.json())*100)/100}); return Speedometer(data_sentiment_speeches,config_speedometer, "gr") } else { response = await fetch(gh_path_aggregate+pol+"/sentiment.json"); const data_sentiment_all = Object({"loc": "Όλες οι ομιλίες", "value": Math.round((await response.json())*100)/100}); return Speedometer(data_sentiment_all,config_speedometer, "gr") } } }3b:T10b4,function Speedometer2(pol,data,config,lang){ const width = "700" const height = Math.min(width, 400) const radius = Math.min(width, 400) / 2; const arc = d3.arc().innerRadius(radius * 0.47).outerRadius(radius - 1); const arc2 = d3.arc().innerRadius(radius).outerRadius(radius+radius/4 - 1); let pie = d3.pie() .startAngle(-110 * (Math.PI/180)) .endAngle(110 * (Math.PI/180)) .sort(null) .value(d => d.value) // set angle based on data values const angle = ((220*data.value)/config.total) // set angle based on values from phase-1 data const phase1_sent = { Mitsotakis: 0.45557912147741725, Tsipras: -0.03526184932228161, Androulakis: -0.036854712364998626, Koutsoumpas: -0.26738498904303276, Velopoulos: -0.4261423613980005, Varoufakis: -0.2948390919127278 } const angle2 = ((220*phase1_sent[pol])/config.total) // use the following for discrete colors const arcs = pie([{name: "neg", value: 1/3, color: "#ba3a0b"}, {name: "neu", value: 1/3, color: "#b9c0c0"}, {name: "pos", value: 1/3, color: "#768948"}]); const colors = {"color1": "#702307", "color1light": "#ba3a0b", "color2": "#f0a202", "color3light": "#aeab66", "color3": "#768948", "color4": "#2b2f2f", "color5": "#111313"} const arcsnew = pie([{name: "", value: 0, color: ""}, {name: "", value: 1, color: ""}]); const svg = d3.create("svg") .attr("viewBox", [0, 0, width, height]) .attr("text-anchor", "middle") .style("font", "12px sans-serif"); const g = svg.append("g") .attr("transform", `translate(${width / 2},${height / 1.5})`); // create the main arc with colors g.selectAll(".arc") .data(arcs) .join("path") .attr("class", "arc") .attr('fill', (d) => { if(d.data.name != ''){ return d.data.color; }else{ return 'url(#gradient)'; } }) .attr("d", arc) // create a secondary arc with gray color on top of the main g.selectAll(".arc2") .data(arcsnew) .join("path") .attr("class", "arc2") .attr('fill', "#e2e9e9") .attr("d", arc2) // Create the secondary needle with phase-1 data // const needle2 = g // .append("polygon") // .attr("points", "0,-10 18,0 0,240 -18,0") // .attr("fill", "#ffffff") // .attr("opacity", "0.5") // // .attr("stroke", "#d4d4d4") // .attr("stroke", "#2b2f2f") // // .attr("transform", "rotate(180)"); // .attr("transform", `rotate(${angle2 + 180})`); // needle2 // .transition() // .duration(750) // .attr("transform", `rotate(${angle2 + 180})`); // Create the polygon that represents the needle const needle = g .append("polygon") .attr("points", "0,-10 18,0 0,240 -18,0") .attr("fill", colors.color4) .attr("stroke", colors.color4) .attr("transform", "rotate(180)"); // Rotate the needle to the desired angle needle .transition() .duration(750) .attr("transform", `rotate(${angle + 180})`); // create the circle in the middle of the needle const circle = g .append("circle") .attr("cx", 0) .attr("cy", 0) //.attr("dy", 15) .attr("r", 20) .attr("fill", colors.color5) const text = g.selectAll("text") .data(arcs) .join("text") .attr("transform", d => `translate(${arc.centroid(d)})`) .attr("dy", "0.35em"); text.filter(d => (d.endAngle - d.startAngle)).append("tspan") .attr("x", 0) .attr("y", "0.7em") .attr("font-size", "24px") .attr("fill-opacity", 0.7); // set text to show on the bottom of the chart let textToShow = ""; if (data.value > 0.33){ // textToShow = `Positive ${data.value}` textToShow = lang== "en" ? "Positive" : "Θετικό" } else if (data.value>=-0.33 & data.value <= 0.33){ // textToShow = `Neutral ${data.value}` textToShow = lang == "en" ? "Neutral" : "Ουδέτερο" } else { // textToShow = `Negative ${data.value}` textToShow = lang == "en" ? "Negative" : "Αρνητικό" } const p = g .append("text") // .text(`${Math.round((data.value/data.total)*100)}%`) .text(`${textToShow}`) .attr("x", 0) .attr("y", 80) .attr("font-size", "24px") .attr("font-family", "Roboto, sans-serif") return svg.node(); }3c:T622,async function createSpeedChart2(pol,speech,lang) { const config_speedometer = ({"total": 2, "average": 0, "minimum": -1, "maximum": 1}); let response; if (lang == "en") { if (speech !== "All Speeches") { response = await fetch(gh_path2+pol+"/"+speech+"/analysis/visuals/sentiment.json"); const data_sentiment_speeches = Object({"loc":speech, "value": Math.round((await response.json())*100)/100}); return Speedometer2(pol,data_sentiment_speeches,config_speedometer, "en") } else { response = await fetch(gh_path_aggregate2+pol+"/sentiment.json"); const data_sentiment_all = Object({"loc": "All Speeches", "value": Math.round((await response.json())*100)/100}); return Speedometer2(pol,data_sentiment_all,config_speedometer, "en") } } else { if (speech !== "Όλες οι ομιλίες") { response = await fetch(gh_path2+pol+"/"+speech+"/analysis/visuals/sentiment.json"); const data_sentiment_speeches = Object({"loc":speech, "value": Math.round((await response.json())*100)/100}); return Speedometer2(pol,data_sentiment_speeches,config_speedometer, "gr") } else { response = await fetch(gh_path_aggregate2+pol+"/sentiment.json"); const data_sentiment_all = Object({"loc": "Όλες οι ομιλίες", "value": Math.round((await response.json())*100)/100}); return Speedometer2(pol,data_sentiment_all,config_speedometer, "gr") } } }3d:T12fe,RadialDendro = function(data){ // set the container of the chart and chart details function autoBox() { document.body.appendChild(this); const {x, y, width, height} = this.getBBox(); document.body.removeChild(this); return [x, y, width, height]; } // change next 2 parameters to change the density and size of the final plot const width = 1080 const radius = width/2.5 const tree = d3.cluster().size([2*Math.PI, radius - 100]) // setting up colors to use etc. const colorsPalette =["#164457", "#6D1A0D", "#DB8E01", "#725E7F", "#CC9EA6", "#338C83", "#768948", "#206F82", "#BB2418", "#D7AD41", "#B7ADBE", "#CC9EA6", "#97C5C0", "#B9C3A2", "#8FAECB", "#F87E63", "#EDD798", "#E5CED2", "#DCEDEF", "#DCE1D1"] const colorsToUse = colorsPalette.slice(0,data.children.length) const color = d3.scaleOrdinal() .domain(data.children.map(d => d.name)) // .range(d3.schemeCategory10) .range(colorsToUse) // Set the color of each node by recursively inheriting. function setColor(d) { var name = d.data.name; d.color = color.domain().indexOf(name) >= 0 ? color(name) : d.parent ? d.parent.color : null; if (d.children) d.children.forEach(setColor); } // reformating data to calculate each topic circle radius in relation to the frequence of each topic in the text //get the min and max values of the topic word weightings. These values are obtained during the process of topic modeling. So, it is also possible to get the values from python and change the domain parts of CirSize and CircleAlpha manually. function getMinMaxvalues() { let mappedData = data.children.map(d => d.children) var allthedata = [] for (let i = 0; i < mappedData.length; i++) { let j = i * 2; allthedata[j] = d3.min(mappedData[i].map(v => v.value)); allthedata[j+1] = d3.max(mappedData[i].map(v => v.value)); } return allthedata; } //define circle sizes by using getMinMaxvalues(). It is also possible to use the values obtained from python. In this case, it is not needed to define getMinMaxvalues(). const CirSize = d3.scaleSqrt().domain([d3.min(getMinMaxvalues()),d3.max(getMinMaxvalues())]).range([1,8]) //define circle opacities by using getMinMaxvalues() const CircleAlpha = d3.scaleLinear().domain([d3.min(getMinMaxvalues()),d3.max(getMinMaxvalues())]).range([0.3,1]) // the main radial dendogram component const root = tree(d3.hierarchy(data) //Sorting of words. Uncomment the adopted one. //the following line is for sorting the topic words by alphabetical order. //.sort((a, b) => a.data.name.toLowerCase().localeCompare (b.data.name.toLowerCase())) //the following line is for sorting the topic words by their weightings. .sort((a, b) => d3.descending(a.data.value, b.data.value)) ); setColor(root); const svg = d3.create("svg"); svg.append("g") .attr("fill", "none") .attr("stroke", "grey") .attr("stroke-opacity", 0.4) .attr("stroke-width", 1.5) .selectAll("path") .data(root.links()) .join("path") .attr("d", d3.linkRadial() .angle(d => d.x) .radius(d => d.y)) .each(function(d) { d.target.linkNode = this; }) .attr("stroke", d => d.target.color); svg.append("g") .selectAll("circle") .data(root.descendants()) .join("circle") // uncomment next 2 lines to create a transition on circles entering // .transition() // .duration(1500) .attr("transform", d => ` rotate(${d.x * 180 / Math.PI - 90}) translate(${d.y},0) `) .attr("fill", d => d.children ? color(d.data.name) : "#484e4e") // //uncomment the following line to show circles with different opacities .attr("fill-opacity", d => d.children ? 1 : CircleAlpha(d.data.value)) .attr("r", d => d.children ? 4 : CirSize(d.data.value)); svg.append("g") .attr("font-family", "Roboto, sans-serif") .attr("font-size", 12) .attr("font-weight", 400) .attr("fill", "#484e4e") .attr("stroke-linejoin", "round") .attr("stroke-width", 3) .selectAll("text") .data(root.descendants()) .join("text") .attr("transform", d => ` rotate(${d.x * 180 / Math.PI - 90}) translate(${d.y},0) rotate(${d.x >= Math.PI ? 180 : 0}) `) .attr("dy", "0.31em") .attr("x", d => d.x < Math.PI === !d.children ? 6 : -6) .attr("text-anchor", d => d.x < Math.PI === !d.children ? "start" : "end") // exclude the dash if no name is defined - used only for the first title which we want to exclude .text(d => d.data.name == "" ? "" : d.data.children ? `${d.data.name}` : `${d.data.name} (${d.data.value})`) .clone(true).lower() .attr("stroke", "white"); return svg.attr("viewBox", autoBox).node(); }3e:Tb14,function LineChartPlot(data, lang){ let data_bands = []; if (lang == "en") { data_bands = [ {title: "Negative", x: 2, y: -0.7}, {title: "Neutral", x: 2, y: 0}, {title: "Positive", x:2, y: 0.7} ] } else if (lang =="gr") { data_bands = [ {title: "Αρνητικό", x: 2, y: -0.7}, {title: "Ουδέτερο", x: 2, y: 0}, {title: "Θετικό", x:2, y: 0.7} ] }; return Plot.plot({ style: "overflow: visible; background: transparent; font-family: Roboto, sans-serif", height: 450, marginRight: 50, padding: 0, // grid: true, width: 1280, y: { // grid: true, label: lang == "en" ? "↑ Sentiment" : "Συναίσθημα", domain: [-1,1] }, x: { // grid: true, // label: "Speech completion (%)", ticks: [0, 50, 100], tickFormat: x => { switch (x) { case 0: return lang == "en" ? "Speech Start" : "Αρχή Ομιλίας"; case 50: return lang == "en" ? "Speech Middle" : "Μέση Ομιλίας"; case 100: return lang == "en" ? "Speech End" : "Τέλος Ομιλίας"; default: return ""; } } }, marks: [ Plot.text(data_bands, { x: "x", y: "y", text: "title", fontSize: "12px", fontWeight: "600", textAnchor: "start", }), // bg area between -1 and -0.34 Plot.area([{x:0, y1:-1, y2: -0.33},{x:100, y1:-1, y2: -0.33}], { x1: 'x', y1: 'y1', x2: 'x', y2: 'y2', fill: "#ba3a0b", fillOpacity: 0.2 }), // bg area between -0.33 and 0.33 Plot.area([{x:0, y1:-0.33, y2: 0.33},{x:100, y1:-0.33, y2: 0.33}], { x1: 'x', y1: 'y1', x2: 'x', y2: 'y2', fill: "#b9c0c0", fillOpacity: 0.2 }), // bg area between 0.34 and 1.0 Plot.area([{x:0, y1:0.33, y2: 1},{x:100, y1:0.33, y2: 1}], { x1: 'x', y1: 'y1', x2: 'x', y2: 'y2', fill: "#768948", fillOpacity: 0.2 }), // Create a line mark Plot.line(data[0], { x: "perc", y: "weight", order: "speech", // stroke: d => colorData[d.speech.split("_")[0]], stroke: "#0e3354", strokeWidth: 3, // use linear as value alternatively in the following key curve: "catmull-rom" }), Plot.line(data[1], { x: "perc", y: "weight", order: "speech", // stroke: d => colorData[d.speech.split("_")[0]], stroke: "#75b9be", strokeWidth: 3, // use linear as value alternatively in the following key curve: "catmull-rom" }), // Plot.text(data, Plot.selectLast({ // x: "perc", // y: "weight", // z: "speech", // text: "speech", // textAnchor: "start", // dx: 2 // })) ] }) }3f:Tbaa,function LineChartPlotAll(data, lang){ let data_bands = []; if (lang == "en") { data_bands = [ {title: "Negative", x: 2, y: -0.7}, {title: "Neutral", x: 2, y: 0}, {title: "Positive", x:2, y: 0.7} ] } else if (lang =="gr") { data_bands = [ {title: "Αρνητικό", x: 2, y: -0.7}, {title: "Ουδέτερο", x: 2, y: 0}, {title: "Θετικό", x:2, y: 0.7} ] }; return Plot.plot({ style: "overflow: visible; background: transparent; font-family: Roboto, sans-serif", height: 450, marginRight: 80, marginLeft: 80, padding: 0, // grid: true, width: 1280, y: { // grid: true, label: lang == "en" ? "↑ Sentiment" : "Συναίσθημα", domain: [-1,1] }, x: { // grid: true, // label: "Speech completion (%)", ticks: [0, 50, 100], tickFormat: x => { switch (x) { case 0: return lang == "en" ? "Start of pre-election period" : "Αρχή προεκλογικής περιόδου"; case 50: return lang == "en" ? "Middle of pre-election period" : "Μέση προεκλογικής περιόδου"; case 100: return lang == "en" ? "End of pre-election period" : "Τέλος προεκλογικής περιόδου"; default: return ""; } } }, marks: [ Plot.text(data_bands, { x: "x", y: "y", text: "title", fontSize: "12px", fontWeight: "600", textAnchor: "start", }), // bg area between -1 and -0.34 Plot.area([{x:0, y1:-1, y2: -0.33},{x:100, y1:-1, y2: -0.33}], { x1: 'x', y1: 'y1', x2: 'x', y2: 'y2', fill: "#ba3a0b", fillOpacity: 0.2 }), // bg area between -0.33 and 0.33 Plot.area([{x:0, y1:-0.33, y2: 0.33},{x:100, y1:-0.33, y2: 0.33}], { x1: 'x', y1: 'y1', x2: 'x', y2: 'y2', fill: "#b9c0c0", fillOpacity: 0.2 }), // bg area between 0.34 and 1.0 Plot.area([{x:0, y1:0.33, y2: 1},{x:100, y1:0.33, y2: 1}], { x1: 'x', y1: 'y1', x2: 'x', y2: 'y2', fill: "#768948", fillOpacity: 0.2 }), // Create a line mark Plot.line(data[0], { x: "perc", y: "weight", order: "speech", // stroke: d => colorData[d.speech.split("_")[0]], stroke: "#0e3354", strokeWidth: 3, // use linear as value alternatively in the following key curve: "catmull-rom" }), Plot.line(data[1], { x: "perc", y: "weight", order: "speech", // stroke: d => colorData[d.speech.split("_")[0]], stroke: "#75b9be", strokeWidth: 3, // use linear as value alternatively in the following key curve: "catmull-rom" }), // Plot.text(data, Plot.selectLast({ // x: "perc", // y: "weight", // z: "speech", // text: "speech", // textAnchor: "start", // dx: 2 // })) ] }) }40:Te30,function LineChartPlotAllDual(data1, data2, lang){ let data_bands = []; if (lang == "en") { data_bands = [ {title: "Negative", x: 2, y: -0.7}, {title: "Neutral", x: 2, y: 0}, {title: "Positive", x:2, y: 0.7} ] } else if (lang =="gr") { data_bands = [ {title: "Αρνητικό", x: 2, y: -0.7}, {title: "Ουδέτερο", x: 2, y: 0}, {title: "Θετικό", x:2, y: 0.7} ] }; return Plot.plot({ style: "overflow: visible; background: transparent; font-family: Roboto, sans-serif", height: 450, marginRight: 80, marginLeft: 80, padding: 0, // grid: true, width: 1280, y: { // grid: true, label: lang == "en" ? "↑ Sentiment" : "Συναίσθημα", domain: [-1,1] }, x: { // grid: true, // label: "Speech completion (%)", ticks: [0, 50, 100], tickFormat: x => { switch (x) { case 0: return lang == "en" ? "Start of pre-election period" : "Αρχή προεκλογικής περιόδου"; case 50: return lang == "en" ? "Middle of pre-election period" : "Μέση προεκλογικής περιόδου"; case 100: return lang == "en" ? "End of pre-election period" : "Τέλος προεκλογικής περιόδου"; default: return ""; } } }, marks: [ Plot.text(data_bands, { x: "x", y: "y", text: "title", fontSize: "12px", fontWeight: "600", textAnchor: "start", }), // bg area between -1 and -0.34 Plot.area([{x:0, y1:-1, y2: -0.33},{x:100, y1:-1, y2: -0.33}], { x1: 'x', y1: 'y1', x2: 'x', y2: 'y2', fill: "#ba3a0b", fillOpacity: 0.2 }), // bg area between -0.33 and 0.33 Plot.area([{x:0, y1:-0.33, y2: 0.33},{x:100, y1:-0.33, y2: 0.33}], { x1: 'x', y1: 'y1', x2: 'x', y2: 'y2', fill: "#b9c0c0", fillOpacity: 0.2 }), // bg area between 0.34 and 1.0 Plot.area([{x:0, y1:0.33, y2: 1},{x:100, y1:0.33, y2: 1}], { x1: 'x', y1: 'y1', x2: 'x', y2: 'y2', fill: "#768948", fillOpacity: 0.2 }), // Create a line mark Plot.line(data1[0], { x: "perc", y: "weight", order: "speech", // stroke: d => colorData[d.speech.split("_")[0]], stroke: "#0e3354", strokeWidth: 3, // use linear as value alternatively in the following key curve: "catmull-rom" }), Plot.line(data1[1], { x: "perc", y: "weight", order: "speech", // stroke: d => colorData[d.speech.split("_")[0]], stroke: "#75b9be", strokeWidth: 3, // use linear as value alternatively in the following key curve: "catmull-rom" }), Plot.line(data2[0], { x: "perc", y: "weight", order: "speech", // stroke: d => colorData[d.speech.split("_")[0]], stroke: "#0e3354", strokeWidth: 3, strokeOpacity: 0.2, // use linear as value alternatively in the following key curve: "catmull-rom" }), Plot.line(data2[1], { x: "perc", y: "weight", order: "speech", // stroke: d => colorData[d.speech.split("_")[0]], stroke: "#75b9be", strokeWidth: 3, strokeOpacity: 0.2, // use linear as value alternatively in the following key curve: "catmull-rom" }), // Plot.text(data, Plot.selectLast({ // x: "perc", // y: "weight", // z: "speech", // text: "speech", // textAnchor: "start", // dx: 2 // })) ] }) }41:T975,function PolarizationPopulismPlotAllSpeeches(pol, index_pol_leaders, speeches_list, data, property, lang){ // calculate max value of data const maxDataPerc = Math.max(...data.map(e => Object.values(e)[0])); // calculate y axis step // let xTicksArr = []; // for (let i=0; i Object.keys(el)[0]); return Plot.plot({ style: "overflow: visible; background: transparent; font-family: Roboto, sans-serif", height: 600, padding: 0, marginBottom: 150, marginLeft: 100, marginRight: 100, // grid: true, width: 1280, y: { // grid: true, label: lang == "en" ? "% Speech" : "% Λόγου", domain: [0,lang == "en" ? (property == "Moderate/High Polarization" ? (maxDataPerc*100 < 30 ? 40 : maxDataPerc*100+10) : (maxDataPerc*100 < 20 ? 20 : maxDataPerc*100+10)) : (property == "Μέτριο/Υψηλό επίπεδο πόλωσης" ? (maxDataPerc*100 < 30 ? 40 : maxDataPerc*100+10) : (maxDataPerc*100 < 20 ? 20 : maxDataPerc*100+10))], // ticks: xTicksArr, // domain: [0,50], // ticks: [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50] }, x: { // grid: true, // domain: [0, data.length-1], // label: lang== "en" ? "Speeches" : "Ομιλίες", // ticks: d => xLabelsArr[d], // ticks: [0, (data.length-1)/4, (data.length-1)/2, (data.length-1)*3/4, data.length-1], // tickFormat: x => { // switch (x) { // case 0: // return lang == "en" ? "1st Speech" : "1η Ομιλία"; // case data.length-1: // return lang == "en" ? "Latest Speech" : "Πιο πρόσφατη Ομιλία"; // default: // return ""; // } // }, }, marks: [ Plot.axisX({ ticks: xLabelsArr.length, tickFormat: d => xLabelsArr[d], tickRotate: -45, // dy: 2 label: lang== "en" ? "Speeches" : "Ομιλίες", }), // Create a line mark Plot.line(data, { x: d => data.indexOf(d), y: d => Object.values(d)*100, stroke: "#114b5f", strokeWidth: 2, // use linear as value alternatively in the following key curve: "step" }), ] }) }42:Tbdd,function PolarizationPopulismPlotAllSpeeches2(pol, index_pol_leaders, speeches_list, data, phase1_number_of_speeches, property, lang){ // calculate max value of data const maxDataPerc = Math.max(...data.map(e => Object.values(e)[0])); // calculate x axis labels for speeches const xLabelsArr = createXLabels(index_pol_leaders[pol], speeches_list, data); // set text labels to separate the 2 bands of the pre-elections phases let text_bands = []; if (lang == "en") { text_bands = [ {title: "Previous election period ↑", x: phase1_number_of_speeches - 1, y: (property == "Moderate/High Polarization" ? (maxDataPerc*100 < 30 ? 30 : maxDataPerc*100) : (maxDataPerc*100 < 20 ? 15 : maxDataPerc*100))}, {title: "Current election period ↓", x: phase1_number_of_speeches, y: (property == "Moderate/High Polarization" ? (maxDataPerc*100 < 30 ? 30 : maxDataPerc*100) : (maxDataPerc*100 < 20 ? 15 : maxDataPerc*100))}, ] } else { text_bands = [ {title: "Προηγούμενη προεκλογική περίοδος ↑", x: phase1_number_of_speeches - 1, y: (property == "Μέτριο/Υψηλό επίπεδο πόλωσης" ? (maxDataPerc*100 < 30 ? 30 : maxDataPerc*100) : (maxDataPerc*100 < 20 ? 15 : maxDataPerc*100))}, {title: "Τρέχουσα προεκλογική περίοδος ↓", x: phase1_number_of_speeches, y: (property == "Μέτριο/Υψηλό επίπεδο πόλωσης" ? (maxDataPerc*100 < 30 ? 30 : maxDataPerc*100) : (maxDataPerc*100 < 20 ? 15 : maxDataPerc*100))}, ] }; return Plot.plot({ style: "overflow: visible; background: transparent; font-family: Roboto, sans-serif", height: 600, padding: 0, marginBottom: 150, marginLeft: 100, marginRight: 100, width: 1280, y: { label: lang == "en" ? "% Speech" : "% Λόγου", domain: [0,lang == "en" ? (property == "Moderate/High Polarization" ? (maxDataPerc*100 < 30 ? 40 : maxDataPerc*100+10) : (maxDataPerc*100 < 20 ? 20 : maxDataPerc*100+10)) : (property == "Μέτριο/Υψηλό επίπεδο πόλωσης" ? (maxDataPerc*100 < 30 ? 40 : maxDataPerc*100+10) : (maxDataPerc*100 < 20 ? 20 : maxDataPerc*100+10))], }, x: { // domain: [0, data.length-1], // label: lang== "en" ? "Speeches" : "Ομιλίες", }, marks: [ Plot.text(text_bands, { x: "x", y: "y", text: "title", fontSize: "12px", fontWeight: "600", textAnchor: "middle", rotate: -90, fill: "#999999", fontWeight: "200" }), Plot.axisX({ ticks: xLabelsArr.length, tickFormat: d => xLabelsArr[d], tickRotate: -45, // dy: 2, label: lang== "en" ? "Speeches" : "Ομιλίες", }), Plot.ruleX([phase1_number_of_speeches - 0.5], {stroke: "red"}), // Create a line mark Plot.line(data, { x: d => data.indexOf(d), y: d => Object.values(d)*100, stroke: "#114b5f", strokeWidth: 2, // use linear as value alternatively in the following key curve: "step" }), ] }) }43:T7e5,async function HeatMapSentiment(pol_button, data, topics, speeches_list, index_pol_leaders, lang) { // calculate number of ticks in x axis to change width porpotionally - change accordingly for pub notebooks const xLabelsNum = speeches_list[index_pol_leaders[pol_button]].length; const customColorScale = d3.scaleOrdinal() .domain(['positive', 'neutral', 'negative']) .range(['#768948', '#b9c0c0', '#ba3a0b']); const cellWidth = 50; const cellHeight =22; const marginLeft = 120; const marginBottom = 140; return Plot.plot({ style: "overflow: visible; background: transparent; font-family: Roboto, sans-serif", marginLeft: marginLeft, marginBottom: marginBottom, // width: xLabelsNum < 15 ? xLabelsNum * 40 + 150 : width, width: xLabelsNum*cellWidth + marginLeft, height: (topics.length !== 2) ? topics.length*cellHeight + marginBottom : topics.length*2*cellHeight + marginBottom, x: { label: null, domain: speeches_list[index_pol_leaders[pol_button]].map(el => el[0]).reverse(), tickRotate: -45, }, y: { label: null, domain: topics, // reverse: true }, color: { // type: "linear", // scheme: "RdYlGn", type: "ordinal", range: customColorScale.range(), domain: customColorScale.domain() }, marks: [ Plot.cell(data, { x: "speech",//d=> index_speeches[d["speech"]], // x: d=> index_speeches[d["speech"]][0], y: "topic", fill: "sentiment-label", // title: o => o["sentiment"].toFixed(2)+" ("+o["sentiment-label"]+")", // title: o => o["Name 1"]+" - "+o["Name 2"]+" ("+o["Number of followers"]+")" }), // Plot.text(data, { // x: "speech", // y: "topic", // text: o => o["sentiment"].toFixed(2), //o => (Math.round(o["Number of followers"]/100)).toString()+"k", // title: o => o["Name 1"]+" - "+o["Name 2"]+" ("+o["Number of followers"]+")" // }) ] }) } 44:T1015,async function HeatMapPoliticianNER(ner_type, lang) { // ner_type: "Persons" or "Political parties" let plot_data = [] const politicians_list = ["Mitsotakis", "Tsipras", "Androulakis", "Koutsoumpas", "Velopoulos", "Varoufakis"] const parties_list = ["New Democracy", "SYRIZA", "PASOK", "KKE", "Greek Solution", "MeRA25"] let pol_dict = {} pol_dict["gr"]= {"Mitsotakis": "Κ. Μητσοτάκης", "Tsipras": "Α. Τσίπρας", "Androulakis": "Ν. Ανδρουλάκης", "Koutsoumpas": "Δ. Κουτσούμπας", "Velopoulos": "Κ. Βελόπουλος", "Varoufakis": "Γ. Βαρουφάκης"} pol_dict["en"]= {"Mitsotakis": "K. Mitsotakis", "Tsipras": "A. Tsipras", "Androulakis": "N. Androulakis", "Koutsoumpas": "D. Koutsoumpas", "Velopoulos": "K. Velopoulos", "Varoufakis": "Y. Varoufakis"} let party_dict = {} // "New Democracy", "SYRIZA", "PASOK", "KKE", "Greek Solution", "MeRA25" party_dict["gr"]= {"New Democracy": "Νέα Δημοκρατία", "SYRIZA": "ΣΥΡΙΖΑ", "PASOK": "ΠΑΣΟΚ", "KKE": "ΚΚΕ", "Greek Solution": "Ελληνική Λύση", "MeRA25": "ΜέΡΑ25"} party_dict["en"]= {"New Democracy": "New Democracy", "SYRIZA": "SYRIZA", "PASOK": "PASOK", "KKE": "KKE", "Greek Solution": "Greek Solution", "MeRA25": "MeRA25"} let ylabel = {} ylabel['en'] = "Speaker" ylabel['gr'] = "Ομιλητής" let xlabel = {} xlabel['en'] = "Mentioned by speaker" xlabel['gr'] = "Αναφέρθηκε από τον ομιλητή" let ner_list let ner_dict if (ner_type === 'Persons'){ ner_list = politicians_list ner_dict = pol_dict } else { ner_list = parties_list ner_dict = party_dict } for (let i=0; i response.json()) let limited_ner_data = ner_data["children"].filter(e => e["name"] == ner_type)[0]["children"].filter(e => ner_list.includes(e["name"])) for (const ner of limited_ner_data){ plot_data.push({"pol1": politician, "ner": ner["name"], "counts": ner["value"]}) } } const cellWidth = 50; const cellHeight =50; const marginLeft = 100; const marginTop = 80; const customColorRange = ["#FFF5EB", "#E25609"]; return Plot.plot({ style: "overflow: visible; background: transparent; font-family: Roboto, sans-serif", marginLeft: marginLeft, marginTop: marginTop, width: 6*cellWidth + marginLeft, height: 6*cellHeight + marginTop, x: { label: xlabel[lang],//"Mentioned by speaker", domain: ner_list, tickRotate: -45, // reverse: true tickFormat: o => ner_dict[lang][o], axis: "top" }, y: { label: ylabel[lang],// "Speaker", domain: politicians_list, tickFormat: o => pol_dict[lang][o], }, color: { type: "linear", range: customColorRange, // scheme: "Oranges", }, marks: [ // Plot.axisX({ // anchor: "top", // tickRotate: -45, // // label: null // }), // Plot.axisY({ // anchor: "left", // // label: null // }), Plot.cell(plot_data, { x: "ner", y: "pol1", fill: "counts", title: "counts" // title: o => o["Name 1"]+" - "+o["Name 2"]+" ("+o["Number of followers"]+")" }), Plot.text(plot_data, { x: "ner", y: "pol1", text: "counts" }) ] }) }45:Tf32,async function HeatMapPoliticianNER2(ner_type, lang) { // ner_type: "Persons" or "Political parties" let plot_data = [] const politicians_list = ["Mitsotakis", "Tsipras", "Androulakis", "Koutsoumpas", "Velopoulos"] const parties_list = ["New Democracy", "SYRIZA", "PASOK", "KKE", "Greek Solution"] let pol_dict = {} pol_dict["gr"]= {"Mitsotakis": "Κ. Μητσοτάκης", "Tsipras": "Α. Τσίπρας", "Androulakis": "Ν. Ανδρουλάκης", "Koutsoumpas": "Δ. Κουτσούμπας", "Velopoulos": "Κ. Βελόπουλος"} pol_dict["en"]= {"Mitsotakis": "K. Mitsotakis", "Tsipras": "A. Tsipras", "Androulakis": "N. Androulakis", "Koutsoumpas": "D. Koutsoumpas", "Velopoulos": "K. Velopoulos"} let party_dict = {} // "New Democracy", "SYRIZA", "PASOK", "KKE", "Greek Solution", "MeRA25" party_dict["gr"]= {"New Democracy": "Νέα Δημοκρατία", "SYRIZA": "ΣΥΡΙΖΑ", "PASOK": "ΠΑΣΟΚ", "KKE": "ΚΚΕ", "Greek Solution": "Ελληνική Λύση"} party_dict["en"]= {"New Democracy": "New Democracy", "SYRIZA": "SYRIZA", "PASOK": "PASOK", "KKE": "KKE", "Greek Solution": "Greek Solution"} let ylabel = {} ylabel['en'] = "Speaker" ylabel['gr'] = "Ομιλητής" let xlabel = {} xlabel['en'] = "Mentioned by speaker" xlabel['gr'] = "Αναφέρθηκε από τον ομιλητή" let ner_list let ner_dict if (ner_type === 'Persons'){ ner_list = politicians_list ner_dict = pol_dict } else { ner_list = parties_list ner_dict = party_dict } for (let i=0; i response.json()) let limited_ner_data = ner_data["children"].filter(e => e["name"] == ner_type)[0]["children"].filter(e => ner_list.includes(e["name"])) for (const ner of limited_ner_data){ plot_data.push({"pol1": politician, "ner": ner["name"], "counts": ner["value"]}) } } const cellWidth = 50; const cellHeight =50; const marginLeft = 100; const marginTop = 80; const customColorRange = ["#FFF5EB", "#E25609"]; return Plot.plot({ style: "overflow: visible; background: transparent; font-family: Roboto, sans-serif", marginLeft: marginLeft, marginTop: marginTop, width: 6*cellWidth + marginLeft, height: 6*cellHeight + marginTop, x: { label: xlabel[lang],//"Mentioned by speaker", domain: ner_list, tickRotate: -45, // reverse: true tickFormat: o => ner_dict[lang][o], axis: "top" }, y: { label: ylabel[lang],// "Speaker", domain: politicians_list, tickFormat: o => pol_dict[lang][o], }, color: { type: "linear", range: customColorRange, // scheme: "Oranges", }, marks: [ // Plot.axisX({ // anchor: "top", // tickRotate: -45, // // label: null // }), // Plot.axisY({ // anchor: "left", // // label: null // }), Plot.cell(plot_data, { x: "ner", y: "pol1", fill: "counts", title: "counts" // title: o => o["Name 1"]+" - "+o["Name 2"]+" ("+o["Number of followers"]+")" }), Plot.text(plot_data, { x: "ner", y: "pol1", text: "counts" }) ] }) }46:[] 2f:{"title":"Helper functions and data to use in IANUS CS","cells":[{"id":0,"value":"# Helper functions and data to use in IANUS CS","pinned":false,"hidden":false,"mode":"md"},{"id":591,"value":"### Setup speeches titles to use","pinned":false,"hidden":false,"mode":"md"},{"id":670,"value":"import {speeches_names} from \"@skaram82/speeches-list-and-info-ianus\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1698,"value":"import {speeches_names2} from \"@skaram82/elections2-speeches-list-and-info-ianus\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":28,"value":"### Treemap","pinned":false,"hidden":false,"mode":"md"},{"id":25,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":792,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1344,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1751,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1349,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1748,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":1140,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":1367,"value":"#### Treemap - old functions not usable now","pinned":false,"hidden":false,"mode":"md"},{"id":1324,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":1228,"value":"async function calculateDataForAllDataIncl(pol_button, speech_button, list_treemap_perc, lang) {\n if (lang == \"en\") {\n if (speech_button !== \"All Speeches\") {\n let categories_perc_all_data_not_incl = await topics_percentage_categories(pol_button, speech_button, list_treemap_perc, \"en\");\n let zoomable_data_all_data_not_incl = create_zoomable_data_per_speech(pol_button, speech_button, categories_perc_all_data_not_incl, \"en\");\n return zoomable_data_all_data_not_incl;\n } else \n return null;\n } else {\n if (speech_button !== \"Όλες οι ομιλίες\") {\n let categories_perc_all_data_not_incl = await topics_percentage_categories(pol_button, speech_button, list_treemap_perc, \"gr\");\n let zoomable_data_all_data_not_incl = create_zoomable_data_per_speech(pol_button, speech_button, categories_perc_all_data_not_incl, \"gr\");\n return zoomable_data_all_data_not_incl;\n } else \n return null;\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":46,"value":"### Speedometer","pinned":false,"hidden":false,"mode":"md"},{"id":43,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":1374,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":1656,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":1658,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":1386,"value":"#### Speedometer - old functions not usable now","pinned":false,"hidden":false,"mode":"md"},{"id":48,"value":"async function createSpeedChartOld(pol,speech,lang) {\n const response = await fetch(gh_path+pol+\"/\"+speech+\"/analysis/visuals/sentiment.json\");\n const config_speedometer = ({\"total\": 2, \"average\": 0, \"minimum\": -1, \"maximum\": 1});\n const data_sentiment_speeches = Object({\"loc\":speech, \n \"value\": Math.round((await response.json())*100)/100});\n if (lang == \"en\") {\n return Speedometer(data_sentiment_speeches,config_speedometer, \"en\")\n } else {return Speedometer(data_sentiment_speeches,config_speedometer, \"gr\")}\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1053,"value":"async function createSpeedChartAll(pol, speeches_list, lang){\n const sentiment = await get_avg_sentiment(pol, speeches_list);\n const config_speedometer = ({\"total\": 2, \"average\": 0, \"minimum\": -1, \"maximum\": 1});\n const data_sentiment_input = Object({\n \"loc\": \"All Speeches\", \"value\": sentiment})\n if (lang == \"en\") {\n return Speedometer(data_sentiment_input, config_speedometer, \"en\")\n } else if (lang == \"gr\") {\n return Speedometer(data_sentiment_input, config_speedometer, \"gr\")\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":56,"value":"### Radial Dendrogram","pinned":false,"hidden":false,"mode":"md"},{"id":58,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":829,"value":"### Linechart","pinned":false,"hidden":false,"mode":"md"},{"id":831,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":1616,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"id":1905,"value":"$40","pinned":false,"hidden":false,"mode":"ojs"},{"id":1148,"value":"### Stacked Bar Chart","pinned":false,"hidden":false,"mode":"md"},{"id":1150,"value":"function stackedChartSentiment(data, avg_data, lang){\n const colorScheme = Object({\n positive: \"#768948\",\n negative: \"#ba3a0b\",\n neutral: \"#b9c0c0\"\n });\n \n return Plot.plot({\n style: \"overflow: visible; background: transparent; font-size: 16; font-family: Roboto, sans-serif\",\n marginLeft: 140,\n marginBottom: 40,\n width: 1150,\n height: avg_data.length*30,\n x: {\n // grid: true,\n label: lang == \"en\" ? \"Frequency (%)\" : \"Συχνότητα (%)\",\n ticks: [0, 0.2, 0.4, 0.6, 0.8, 1],\n tickFormat: x => `${x*100}%`,\n },\n y: {\n grid: true,\n percent: true,\n },\n facet: {\n data: data,\n y: \"topic\",\n label: null\n },\n marks: [\n Plot.barX(data, Plot.groupZ({x: \"proportion-facet\"}, {fill: d => colorScheme[d[\"sentiment\"]]})),\n // Plot.barX(data, Plot.groupZ({x: \"count\"}, {fill: d => colorScheme[d[\"sentiment\"]]})),\n // Plot.ruleX([0, 1])\n ],\n // color: {legend: true}\n})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1153,"value":"function sumTopicsSentiment(data){\n // uncomment commented lines to calculate average sentiment and counts per topic viewed in speech\n const result = Object.values(\n data.reduce((acc, { topic, sentiment }, index, array) => {\n if (!acc[topic]) {\n acc[topic] = {\n topic,\n // sentiment: 0,\n // count: 0\n };\n }\n \n // acc[topic].sentiment += sentiment;\n // acc[topic].count++;\n\n // if (index === array.length - 1) {\n // Object.values(acc).forEach((obj) => {\n // obj.sentiment /= obj.count;\n // });\n // }\n\n return acc;\n }, {})\n );\n return result;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1155,"value":"function sentiment_score_to_label(data) {\n if (data === null || data === undefined) {\n return null;\n } else if (data < -0.33) {\n return \"negative\";\n } else if (data >= -0.33 && data <= 0.33) {\n return \"neutral\";\n } else if (data > 0.33 && data <= 1) {\n return \"positive\";\n } else {\n return null;\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1401,"value":"### Polarization Linechart","pinned":false,"hidden":false,"mode":"md"},{"id":1411,"value":"function createXLabels(pol, speeches_list, data) {\n const tempSpeechesArr = Object.values(data).map(el => Object.keys(el)[0]);\n let tempArr = [];\n for (let i=0; i response.json());\n for (let i=0; i response.json());\n for (let i=0; i response.json())","pinned":false,"hidden":false,"mode":"ojs"},{"id":1710,"value":"gh_list_of_speeches2 = fetch(\"https://raw.githubusercontent.com/Datalab-AUTH/imedd-political-speech-data-test/main/pre-elections-2/list_of_speeches_new.json\").then((response) => response.json())","pinned":false,"hidden":false,"mode":"ojs"},{"id":371,"value":"gh_list_of_speeches_pub = {\n let list_of_ready_speeches = (await fetch(\"https://raw.githubusercontent.com/Datalab-AUTH/imedd-political-speech-data-test/main/pre-elections/ready_to_viz_speeches.json\").then((response) => response.json()))\n let pub_speeches = {}\n for (const key of Object.keys(gh_list_of_speeches)) {\n const value = gh_list_of_speeches[key]\n pub_speeches[key] = value.filter(e => list_of_ready_speeches.includes(e[1]))\n }\n return pub_speeches\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1712,"value":"gh_list_of_speeches_pub2 = {\n let list_of_ready_speeches = (await fetch(\"https://raw.githubusercontent.com/Datalab-AUTH/imedd-political-speech-data-test/main/pre-elections-2/ready_to_viz_speeches.json\").then((response) => response.json()))\n let pub_speeches = {}\n for (const key of Object.keys(gh_list_of_speeches2)) {\n const value = gh_list_of_speeches2[key]\n pub_speeches[key] = value.filter(e => list_of_ready_speeches.includes(e[1]))\n }\n return pub_speeches\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":11,"value":"function politicalLeaderButton(data){\n let list_of_leaders = Object.keys(data)\n return Inputs.select(list_of_leaders, {value:list_of_leaders[0], label: \"\"})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1095,"value":"function politicalLeaderButtonDual(data){\n let list_of_leaders = Object.keys(data)\n return Inputs.select(list_of_leaders, {value:list_of_leaders[1], label: \"\"})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":202,"value":"index_pol_leaders_eng = Object({\n \"Kyriakos Mitsotakis\": \"Mitsotakis\",\n \"Alexis Tsipras\": \"Tsipras\",\n \"Nikos Androulakis\": \"Androulakis\",\n \"Dimitris Koutsoumpas\": \"Koutsoumpas\",\n \"Kyriakos Velopoulos\": \"Velopoulos\",\n \"Yanis Varoufakis\": \"Varoufakis\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1722,"value":"index_pol_leaders_eng2 = Object({\n \"Kyriakos Mitsotakis\": \"Mitsotakis\",\n \"Alexis Tsipras\": \"Tsipras\",\n \"Nikos Androulakis\": \"Androulakis\",\n \"Dimitris Koutsoumpas\": \"Koutsoumpas\",\n \"Kyriakos Velopoulos\": \"Velopoulos\",\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":239,"value":"index_pol_leaders_gr = Object({\n \"Κυριάκος Μητσοτάκης\": \"Mitsotakis\",\n \"Αλέξης Τσίπρας\": \"Tsipras\",\n \"Νίκος Ανδρουλάκης\": \"Androulakis\",\n \"Δημήτρης Κουτσούμπας\": \"Koutsoumpas\",\n \"Κυριάκος Βελόπουλος\": \"Velopoulos\",\n \"Γιάνης Βαρουφάκης\": \"Varoufakis\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1725,"value":"index_pol_leaders_gr2 = Object({\n \"Κυριάκος Μητσοτάκης\": \"Mitsotakis\",\n \"Αλέξης Τσίπρας\": \"Tsipras\",\n \"Νίκος Ανδρουλάκης\": \"Androulakis\",\n \"Δημήτρης Κουτσούμπας\": \"Koutsoumpas\",\n \"Κυριάκος Βελόπουλος\": \"Velopoulos\",\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":94,"value":"function speechButton(data, leader){\n let list_of_speeches = Object.values(data[leader])\n return list_of_speeches\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"list_of_visualizations = Object({\n // \"Polarization\": \"polarization_sequence\",\n \"Polarization\": \"polarization-definition_sequence\",\n // \"Toxicity\": \"toxicity_sequence\",\n // \"Toxicity Def\": \"toxicity-definition_sequence\",\n // \"Populism\": \"populism_sequence\",\n \"Populism\": \"populism-definition_sequence\",\n // \"Hate Speech\": \"hate speech_sequence\"\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":74,"value":"list_of_visualizations_gr = Object({\n \"Πόλωση\": \"polarization-definition_sequence\",\n \"Λαϊκισμός\": \"populism-definition_sequence\",\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"function visualizationsButton(data){\n return Inputs.select(Object.keys(data), {value: Object.keys(data), label:\"\"})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":639,"value":"list_treemap_views = Object({\n 'Agenda': \"only_topic_treemap_political_agenda\",\n 'Criticism': \"only_topic_treemap_criticism\",\n 'All Data': \"only_topic_treemap\"\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":648,"value":"list_treemap_views_gr = Object({\n 'Ατζέντα': \"only_topic_el_treemap_political_agenda\",\n 'Κριτική': \"only_topic_el_treemap_criticism\",\n 'Όλα τα δεδομένα': \"only_topic_el_treemap\"\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":788,"value":"list_treemap_perc_calc = Object({\n 'Agenda': \"only_topic_treemap_political_agenda\",\n 'Criticism': \"only_topic_treemap_criticism\",\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":797,"value":"list_treemap_perc_calc_gr = Object({\n 'Ατζέντα': \"only_topic_treemap_political_agenda\",\n 'Κριτική': \"only_topic_treemap_criticism\",\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":308,"value":"function index_of_speeches_creation(speeches_list){\n const index_speeches = {};\n for (let i=0; i el.replace(\"ΜΜΕ\", \"MME\"));\n tempArr.push(\"Άλλα\");\n return tempArr;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":769,"value":"async function thaMeterPolLeaders() {\n let data_github_tha_meter = {};\n for (let i=0; i response.json()); \n data_github_tha_meter[politician].push(data_fetched_pol_leader.freq);\n }\n }\n return data_github_tha_meter;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":773,"value":"data_tha_meter_all = thaMeterPolLeaders()","pinned":false,"hidden":false,"mode":"ojs"},{"id":981,"value":"// aggregate sentiment\nasync function get_avg_sentiment(politician, list_of_speeches) {\n let pol_speeches = list_of_speeches[politician]\n let temp_sentiment_list = []\n for (let j=0; j response.json()) \n if (speech_sentiment !== null){\n temp_sentiment_list.push(speech_sentiment) \n }\n }\n return temp_sentiment_list.reduce((a, b) => a + b, 0) / temp_sentiment_list.length\n}","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W46","stdlib":"1","titling":"auto"}