31:T15ef,matrix = { const svg = d3.select(DOM.svg(width, height)) .style("font", "1rem verdana"); const rects = svg.append("g") .selectAll("g") .data(data) .enter().append("g") .attr("class", (d, i) => `${i} bar`) .selectAll("g") .data(d => categories.map(e => {return {'xval' : d["Episode"], 'yval' : e, 'count': d[e]}})) .enter().append("g") .attr("class", (d, i) => `${i} tile`) rects.append("rect") .attr("x", d => x(d.xval)) .attr("y", d => y(d.yval)) .attr("width", x.bandwidth()) .attr("height", y.bandwidth()) .style("stroke-width", .05) .style("stroke", "grey") .style("fill", d => { if (relativity == "all" && color_categories.includes(d.yval)) { return color_color(d.yval)(d.count) } else if (color_categories.includes(d.yval)) { return color_colors(d.yval)(d.count) } else if (typeof d.count === 'string' || d.count instanceof String) { return string_colors[d.yval](d.count) } else if (relativity == "all" && class_categories.includes(d.yval)) { return color(d.count) } else { return colors[d.yval](d.count) } }); rects.append("text") .attr("dy", ".35em") .attr("x", d => x(d.xval) + x.bandwidth() / 2) .attr("y", d => y(d.yval) + y.bandwidth() / 2) .attr("temp_x", d => d.xval) .attr("temp_y", d => d.yval) .attr("temp_val", d => { if (typeof d.count === 'string' || d.count instanceof String) { return d.count } else { if (class_categories.includes(d.yval)) return rounding(normalizeds[d.yval](d.count)) // else if (color_categories.includes(d.yval)) // return percentage(normalizeds[d.yval](d.count)) else return rounding(normalizeds[d.yval](d.count)) }}) .attr("temp_og_val", d => { if (typeof d.count === 'string' || d.count instanceof String) { return d.count } else if (class_categories.includes(d.yval)) { return rounding(normalized(d.count)) } else if (color_categories.includes(d.yval)) return percentage(d.count/100) else { return rounding(d.count) }}) .text(d => { if (typeof d.count === 'string' || d.count instanceof String) { return d.count } else { if (relativity == "all" && class_categories.includes(d.yval)) return rounding(normalized(d.count)) else if (relativity == "all" && color_categories.includes(d.yval)) return rounding(normalized_color(d.count)) else return rounding(normalizeds[d.yval](d.count)) }}) .style("font-size", "10") .style("opacity", "0") .style("text-anchor", "middle") .on("mouseover", function(d) { // the `function` keyword, instead of arrow function syntax, is required when using the keyword `this` within d3.select(this) .style("opacity", "1"); var cur_x = d3.select(this).attr("temp_x"); var cur_y = d3.select(this).attr("temp_y"); var cur_val = d3.select(this).attr("temp_val"); var cur_og_val = d3.select(this).attr("temp_og_val"); var tgrp = svg.append("g") .attr("id", "info"); tgrp.append("text").attr("x", width-margin.right+20).attr("y", height/2-150).text("Episode: ").style("font-size","20px").style("font-size","18px"); tgrp.append("text").attr("x", width-margin.right+20).attr("y", height/2-120).text(cur_x).style("font-size","28px"); tgrp.append("text").attr("x", width-margin.right+20).attr("y", height/2+-60).text("Category: ").style("font-size","20px").style("font-size","18px"); tgrp.append("text").attr("x", width-margin.right+20).attr("y", height/2-30).text(cur_y).style("font-size","28px"); if (class_categories.includes(cur_y)) tgrp.append("text").attr("x", width-margin.right+20).attr("y", height/2+30).text("Value Relative to All: ").style("font-size","20px").style("font-size","18px"); else if (color_categories.includes(cur_y)) tgrp.append("text").attr("x", width-margin.right+20).attr("y", height/2+30).text("Percent of Episode's Frames: ").style("font-size","20px").style("font-size","18px"); else tgrp.append("text").attr("x", width-margin.right+20).attr("y", height/2+30).text("Original Value: ").style("font-size","20px").style("font-size","18px"); tgrp.append("text").attr("x", width-margin.right+20).attr("y", height/2+60).text(cur_og_val).style("font-size","28px"); if (class_categories.includes(cur_y)) { tgrp.append("text").attr("x", width-margin.right+20).attr("y", height/2+120).text("Value Relative to Tag: ").style("font-size","20px").style("font-size","18px"); tgrp.append("text").attr("x", width-margin.right+20).attr("y", height/2+150).text(cur_val).style("font-size","28px"); } else { // if (typeof cur_og_val === 'number') { tgrp.append("text").attr("x", width-margin.right+20).attr("y", height/2+120).text("Relative Value: ").style("font-size","20px").style("font-size","18px"); tgrp.append("text").attr("x", width-margin.right+20).attr("y", height/2+150).text(cur_val).style("font-size","28px"); } }) .on("mouseout", function(d) { d3.select(this) // see note above .style("opacity", "0"); d3.select("#info").remove(); }); svg.append(text) svg.append("g") .call(xAxis); svg.append("g") .call(yAxis); return svg.node(); }32:Tb90,### Usage Guide: * Select the type of data from Comparisons that you would like to compare. Audio characteristics are data extracted from the audio signal, tag predictions are the predicted amount that an episode is associated with an audio tag, audio and tags is the combination of the prior two, color pallettes is the number of frames containing that color pallette in the episodes, and all will combine all of these features into one. * Data can be assessed by visualizing the saturation of a frame as the relative value of that data point. The exact values of any square, along with the episode and the trait it is associated with, can be seen by hovering over it and looking to the right of the visual. * Tag/Color Density defines whether or not the color of a square is based upon its relative value compared to the exact same category or compared to all other categories of its type. More specifically, the saturation of all audio tags can be either based upon the value of an audio tag for that episode relative to the value of that audio tag for other episodes or relative to the value of all audio tags and the saturation of the colors can be relative to either the number of frames of that color in all episodes relative to the number of frames of all colors in all episodes. Audio traits will always only be compared relative to its own category since it is uncomparable across categories. * Can sort in either the default order, alphabetically, or based upon the relative values of all categories in a given episode. In other words, categories where the chosen episode has a higher relative value compared to other episodes will be higher than another category where it is relatively less. This is a really useful tool to compare episodes as it makes the categories that are most strong in certain episodes but not strong in other episodes very clear and associable. * Colors can be changed by taste. Red and black allow for different styles based on visual needs. Can also show colors as their actual colors or as the same color as the rest of the chart. * There are some categorical data visualizable in the chart. These are not cohesive with the rest of the chart but can be turned on if you'd like to explore them. ### Data Extraction: * Audio features were created using a singular analysis of an entire episode with varying traits extracted in different ways mostly based upon the windowing described in the audio section but over the whole episode instead of real-time. * Audio tags were predicted using a pre-trained machine learning algorithm that predicts the presence of the 50 most popular tags on Discogs in each frame of the audio. Higher values are associated with a larger likelihood of that tag. The mean of these tag values across all frames of the episode results in the final value shown here. * Color pallette values are simply the number of frames in an episode that have that color as one of its three primary colors.33:T453,string_colors = { let colores = {} for (let i=0; i < Object.keys(max_vals).length; i++) { let cur = Object.keys(max_vals)[i] let terms = [] if (!(typeof cur === 'string' || cur instanceof String)) { continue; } // else if (cur.includes()) for (let j=0; j < data.length; j++) { let row = data[j]; if (terms.includes(row[cur])) { terms.push(row[cur]) } } colores[cur] = d3.scaleOrdinal() // .domain([0, max_vals[cur]]) .domain(terms) .range(["#68d4b4","#aaa4de","#c7d955","#f7cc52", "#c4a160","#a6cee3","#b07f99"]) // .range(["#a6cee3","#b2df8a", "#fb9a99", "#fdbf6f","#cab2d6","#ffff99","#b15928"]) // .range(["#1b9e77","#7570b3","#66a61e","#e6ab02","#cab2d6", "#a6761d","#a6cee3","#666666"]) // ["#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c","#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99","#b15928"] // "#d95f02","#e7298a", // .range(["#4e79a7","#f28e2c","#e15759","#76b7b2","#59a14f","#edc949","#af7aa1","#ff9da7","#9c755f","#bab0ab"]) } return colores }34:T4a2,function get_data(input) { let classifications = [] for (let k = 0; k < input.length; k++ ) { let old_dict = input[k] let new_key = Object.keys(old_dict).filter(d => !d.includes("Stdev")); let new_dict = new Object for (let i = 0; i < new_key.length; i++) { let next_key = new_key[i] let up_key = new_key[i].substring(0,1).toUpperCase() + new_key[i].substring(1).slice(0,-5); let up_key_2 = new_key[i].substring(0,1).toUpperCase() + new_key[i].substring(1) if ( (new_key[i] == "" && (!("Episode" in new_key) || old_dict["Episode"].isInteger())) || (new_key[i] == "Episode" && typeof old_dict["Episode"] == 'string') ) new_dict["Episode"] = old_dict[new_key[i]] else if (new_key[i].slice(-4) == "Mean") { new_dict[up_key] = old_dict[new_key[i]] } else if (include_strings === false && (typeof old_dict[new_key[i]] === 'string' || old_dict[new_key[i]] instanceof String)) { continue } else if (new_key[i] != "Season" && new_key[i] != "Episode" ) { new_dict[new_key[i]] = old_dict[new_key[i]] } } classifications.push(new_dict) } return classifications }35:T45c,function comparing_for_sort(a, b) { if (typeof normalizeds[a](data[cur_idx][a]) === 'undefined' && typeof normalizeds[b](data[cur_idx][b]) === 'undefined' ) return 0 else if (typeof normalizeds[b](data[cur_idx][b]) === 'undefined') return 1 else if (typeof normalizeds[a](data[cur_idx][a]) === 'undefined') return -1 var a_val var b_val // if (relativity == "all" && class_categories.includes(d.yval)) { // return color(d.count) if (relativity == "all" && class_categories.includes(a)) a_val = normalized(data[cur_idx][a]) else if (relativity == "all" && color_categories.includes(a)) a_val = normalized_color(data[cur_idx][a]) else a_val = normalizeds[a](data[cur_idx][a]) if (relativity == "all" && class_categories.includes(b)) b_val = normalized(data[cur_idx][b]) else if (relativity == "all" && color_categories.includes(b)) b_val = normalized_color(data[cur_idx][b]) else b_val = normalizeds[b](data[cur_idx][b]) if ( a_val > b_val ) return 1 else if ( a_val == b_val ) return 0 else return -1 }36:[{"id":"518f226719fc11a504161bea196a3abd3c8d27a1923cf4278431112162884044a2ea70787e6c8a46244f1a99e2fc5b6c8cbe9257b9d927e5247502727b3bfda1","href":"https://static.observableusercontent.com/files/518f226719fc11a504161bea196a3abd3c8d27a1923cf4278431112162884044a2ea70787e6c8a46244f1a99e2fc5b6c8cbe9257b9d927e5247502727b3bfda1","name":"colorDistribution@1.csv","type":"text/csv","lastModified":1682560378641.006,"size":1442},{"id":"d88dd5ba52bef35c170fbc487f8a0e5e25949dfe86dff8f02e5a95038074b7f49a2be07d9e1d47899cb880c7fab3620e71021233af6cfae993b3b36ca96fea49","href":"https://static.observableusercontent.com/files/d88dd5ba52bef35c170fbc487f8a0e5e25949dfe86dff8f02e5a95038074b7f49a2be07d9e1d47899cb880c7fab3620e71021233af6cfae993b3b36ca96fea49","name":"colorDistribution_reorder.csv","type":"text/csv","lastModified":1682560928138.631,"size":1442},{"id":"92136c61eb2262952457b72856e5db7e4b238079058ceeca81264b8a0c47f62694be7f58831b7f1ccb664b7a3cd2989bcfc461646fd91456e1b173b7491e62bb","href":"https://static.observableusercontent.com/files/92136c61eb2262952457b72856e5db7e4b238079058ceeca81264b8a0c47f62694be7f58831b7f1ccb664b7a3cd2989bcfc461646fd91456e1b173b7491e62bb","name":"FINAL_episode_audio_layified@4.csv","type":"text/csv","lastModified":1682531538844.448,"size":5514},{"id":"0d9575c97529a5f74b826609f7c16478b749469828101c58c4dd9e642bf21af4d1f1a384abca66d2cd6335e064bea80c6a561a803fe970a75e3cb33a823dc649","href":"https://static.observableusercontent.com/files/0d9575c97529a5f74b826609f7c16478b749469828101c58c4dd9e642bf21af4d1f1a384abca66d2cd6335e064bea80c6a561a803fe970a75e3cb33a823dc649","name":"FINAL_episode_classes.csv","type":"text/csv","lastModified":1682487930871.808,"size":12193}] 2f:{"title":"Connect the Episodes","cells":[{"id":0,"value":"# Connect the Episodes\n## On \"Off the Air\" Part 4","pinned":false,"hidden":false,"mode":"md"},{"id":1552,"value":"viewof comparisons = Inputs.select(new Map([[\"Audio Characteristics\", \"audio\"], [\"Tag Predictions\", \"class\"], [\"Color Pallettes\", \"color\"], [\"Audio & Tags\", \"combo\"],[\"All\", \"both\"]]), {label: \"Comparisons\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1105,"value":"viewof relativity = Inputs.select(options, {label: \"Tag/Color Density\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1832,"value":"viewof sort_choice = Inputs.select(eps.concat(\"Alphabetical\").concat(\"Default\"), {label: \"Sort by\", value: \"Default\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2173,"value":"viewof use_color = Inputs.select(new Map([[\"Yes\", true], [\"No\", false]]), {label: \"Use color for color pallettes? \"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2151,"value":"viewof gimme_color = Inputs.select(new Map([[\"Red\", secondary_color], [\"Gray-scale\", dark_color]]), {label: \"Choose main color: \"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2183,"value":"viewof include_strings = Inputs.select(new Map([[\"Yes\", true], [\"No\", false]]), {label: \"Include categorical values?\", value: false})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1649,"value":"viewof height = slider({\n min: Math.pow(Object.keys(data[0]).length, .62)*48,\n max: 2500,\n step: 10,\n title: \"Height\",\n // value: 800\n value: margin.top + margin.bottom + 100 + Math.pow(Object.keys(data[0]).length, .3)*200\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":418,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":2220,"value":"$32","pinned":false,"hidden":false,"mode":"md"},{"id":2235,"value":"### Now, go watch some Off the Air !\n## [Return to Main Page](https://observablehq.com/d/d01266fb55c1aeeb)","pinned":false,"hidden":false,"mode":"md"},{"id":1425,"value":"## Setup","pinned":false,"hidden":false,"mode":"md"},{"id":1333,"value":"margin = ({top: 5, right: 520, bottom: 25, left: 160})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1334,"value":"width = 1110","pinned":false,"hidden":false,"mode":"ojs"},{"id":1683,"value":"options = get_options()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1689,"value":"function get_options() {\n if (comparisons == \"audio\")\n return new Map([[\"Relative to Same Category\", \"same\"]])\n else\n return new Map([[\"Relative to Same Category\", \"same\"], [\"Relative to All Categories\", \"all\"]])\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":952,"value":"rounding = d3.format(\".3f\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2091,"value":"percentage = d3.format(\".1%\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":423,"value":"x = d3.scaleBand()\n .domain(eps)\n .range([margin.left, width - margin.right])","pinned":false,"hidden":false,"mode":"ojs"},{"id":425,"value":"y = d3.scaleBand()\n .domain(sorted_categories)\n .range([height - margin.bottom, margin.top])","pinned":false,"hidden":false,"mode":"ojs"},{"id":441,"value":"xAxis = g => g\n .attr(\"transform\", `translate(0, ${height - margin.bottom})`)\n .call(d3.axisBottom(x).tickSizeOuter(0))\n .call(g => g.selectAll(\".domain\").remove())\n .selectAll(\"text\")\t\n .style(\"text-anchor\", \"middle\")\n .style(\"dominant-bassline\", \"middle\")\n .style(\"font-size\", d => 8 + 10/(Math.pow(d.length, .75)) + \"px\")\n .style(\"font-weight\", 700)","pinned":false,"hidden":false,"mode":"ojs"},{"id":443,"value":"yAxis = g => g\n .attr(\"transform\", `translate(${margin.left}, 0)`)\n .call(d3.axisLeft(y).ticks(null, \"s\"))\n .call(g => g.selectAll(\".domain\").remove())","pinned":false,"hidden":false,"mode":"ojs"},{"id":435,"value":"color = d3.scaleLinear()\n .domain([min_val, max_val])\n .range([light_color, gimme_color])\n // .range([light_color, secondary_color])","pinned":false,"hidden":false,"mode":"ojs"},{"id":2162,"value":"gimme_color","pinned":false,"hidden":false,"mode":"ojs"},{"id":860,"value":"colors = {\n let colores = {}\n for (let i=0; i < Object.keys(max_vals).length; i++) {\n let cur = Object.keys(max_vals)[i]\n colores[cur] = d3.scaleLinear()\n // .domain([0, max_vals[cur]])\n .domain([min_vals[cur], max_vals[cur]])\n .range([light_color, gimme_color])\n // .range([light_color, secondary_color])\n // .range([light_color, secondary_color])\n }\n return colores\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1491,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1981,"value":"function color_color(in_color) {\n\n var out_color = in_color\n if (!use_color)\n out_color = gimme_color\n else if (in_color == \"lilac\")\n out_color = d3.color(\"#784890\").darker(.6)\n\n else if (in_color == \"brown\")\n out_color = \"#964B00\"\n \n else if (in_color == \"white\")\n out_color = d3.color(\"#FAF9F6\").darker(2)\n \n else if (in_color == \"beige\")\n out_color = d3.color(\"#AC9362\").darker(.7)\n \n else if ( in_color == \"orange\")\n out_color = d3.color(\"#DB4914\").darker(.1)\n \n else if ( in_color == \"pink\")\n out_color = d3.color(\"#AA336A\").darker(.3)\n\n else if (in_color == \"yellow\" )\n out_color = d3.color(\"#EEBC1D\").darker(.3)\n\n else if (in_color == \"grey\")\n out_color = d3.color(\"grey\").darker(.7)\n\n return d3.scaleLinear(in_color)\n .domain([min_color_val, max_color_val]) \n .range([\"white\", out_color])\n // .attr(\"gradientUnits\", \"userSpaceOnUse\")\n}\n // .range([d3.rgb('').brighter(), d3.rgb('blue').brighter()]);","pinned":false,"hidden":false,"mode":"ojs"},{"id":2015,"value":"function color_colors(in_color) {\n // let colores = {}\n // let max = max_vals(in_color)\n // let min = min(in_color)\n var out_color = in_color\n if (!use_color)\n out_color = gimme_color\n else if (in_color == \"lilac\")\n out_color = d3.color(\"#784890\").darker(.6)\n else if (in_color == \"brown\")\n out_color = \"#964B00\"\n else if (in_color == \"white\")\n out_color = d3.color(\"#FAF9F6\").darker(2)\n \n else if (in_color == \"beige\")\n out_color = d3.color(\"#AC9362\").darker(.7)\n \n else if ( in_color == \"orange\")\n out_color = d3.color(\"#DB4914\").darker(.1)\n \n else if ( in_color == \"pink\")\n out_color = d3.color(\"#AA336A\").darker(.3)\n\n else if (in_color == \"yellow\" )\n out_color = d3.color(\"#EEBC1D\").darker(.3)\n\n else if (in_color == \"grey\")\n out_color = d3.color(\"grey\").darker(.7)\n\n return d3.scaleLinear(in_color)\n .domain([min_vals[in_color], max_vals[in_color]])\n .range([\"white\", out_color])\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":488,"value":"max_val = {\n let maxim = 0\n for (let i=0; i < data.length; i++) {\n let row = data[i]\n for (let j=0; j < class_categories.length; j++)\n {\n let category = class_categories[j]\n if (row[category] > maxim)\n maxim = row[category];\n } \n }\n return maxim\n} ","pinned":false,"hidden":false,"mode":"ojs"},{"id":2007,"value":"max_color_val = {\n let maxim = 0\n for (let i=0; i < data.length; i++) {\n let row = data[i]\n for (let j=0; j < color_categories.length; j++)\n {\n let category = color_categories[j]\n if (row[category] > maxim)\n maxim = row[category];\n } \n }\n return maxim\n} ","pinned":false,"hidden":false,"mode":"ojs"},{"id":1123,"value":"min_val = {\n let minim = 1\n for (let i=0; i < data.length; i++) {\n let row = data[i]\n for (let j=0; j < class_categories.length; j++)\n {\n let category = class_categories[j]\n if (row[category] < minim)\n minim = row[category];\n } \n }\n return minim\n} ","pinned":false,"hidden":false,"mode":"ojs"},{"id":2005,"value":"min_color_val = {\n let minim = 1\n for (let i=0; i < data.length; i++) {\n let row = data[i]\n for (let j=0; j < color_categories.length; j++)\n {\n let category = color_categories[j]\n if (row[category] < minim)\n minim = row[category];\n } \n }\n return minim\n} ","pinned":false,"hidden":false,"mode":"ojs"},{"id":862,"value":"max_vals = {\n let maxim = {}\n for (let i=0; i < data.length; i++) {\n let row = data[i]\n let groups = categories\n for (let j=0; j < categories.length; j++)\n {\n let category = groups[j]\n if ( !(category in maxim) || row[category] > maxim[category] )\n maxim[category] = row[category];\n } \n }\n return maxim\n} ","pinned":false,"hidden":false,"mode":"ojs"},{"id":2114,"value":"normalized_color = d3.scaleLinear()\n .domain([min_color_val, max_color_val])\n .range([0, 1])\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":1127,"value":"normalized = d3.scaleLinear()\n .domain([min_val, max_val])\n .range([0, 1])\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":949,"value":"normalizeds = {\n let colores = {}\n for (let i=0; i < Object.keys(max_vals).length; i++) {\n let cur = Object.keys(max_vals)[i]\n colores[cur] = d3.scaleLinear()\n .domain([min_vals[cur], max_vals[cur]])\n .range([0, 1])\n }\n return colores\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":455,"value":"min_vals = {\n let minim = {}\n for (let i=0; i < data.length; i++) {\n let row = data[i]\n // /)/ console.log(data[i]\n // let groups = categories.slice(3)\n let groups = categories\n for (let j=0; j < categories.length; j++)\n {\n let category = groups[j]\n if ( !(category in minim) || row[category] < minim[category] )\n // if ((!(category in Object.keys(minim))) || row[category] < minim[category])\n minim[category] = row[category];\n } \n }\n return minim\n} ","pinned":false,"hidden":false,"mode":"ojs"},{"id":1548,"value":"function choose_data() {\n if (comparisons == \"both\")\n return combo_data\n else if (comparisons == \"audio\")\n return audio_data\n else if (comparisons == \"color\")\n return color_data\n else if (comparisons == \"combo\")\n return audio_combo_data\n else \n return class_data\n}\n// data = audio_data","pinned":false,"hidden":false,"mode":"ojs"},{"id":693,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1572,"value":"data = choose_data()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1616,"value":"combo_data = combine_data(audio_combo_data, color_data)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1956,"value":"audio_combo_data = combine_data(audio_data, class_data)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1610,"value":"function combine_data(data_a, data_b) {\n var combo_data = []\n for (let i=0; i < eps.length; i++) {\n let new_dict = {} \n if (data_a[i][\"Episode\"] != data_b[i][\"Episode\"]) {\n return null\n }\n for (const [key, value] of Object.entries(data_a[i]))\n new_dict[key] = value\n for (const [key, value] of Object.entries(data_b[i]))\n new_dict[key] = value\n combo_data.push(new_dict)\n }\n return combo_data\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1451,"value":"class_data = get_data(input_classes)","pinned":false,"hidden":false,"mode":"ojs"},{"id":691,"value":"audio_data = get_data(input_audio)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1951,"value":"color_data = get_data(input_colors)","pinned":false,"hidden":false,"mode":"ojs"},{"id":677,"value":"input_classes = FileAttachment(\"FINAL_episode_classes.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1447,"value":"// input_audio = FileAttachment(\"FINAL_episode_audio.csv\").csv({ typed: true })\ninput_audio = FileAttachment(\"FINAL_episode_audio_layified@4.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1941,"value":"input_colors = FileAttachment(\"colorDistribution_reorder.csv\").csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":681,"value":"// eps = data.map((d) => d[\"\"]).sort()\neps = audio_data.map((d) => d[\"Episode\"]).sort()","pinned":false,"hidden":false,"mode":"ojs"},{"id":409,"value":"indeces = Object.keys(data)","pinned":false,"hidden":false,"mode":"ojs"},{"id":416,"value":"// categories = Object.keys(data[0]).slice(1).sort(comparing_for_sort).reverse()\ncategories = Object.keys(data[0]).slice(1)\n\n\n// categories = Object.keys(data[0]).slice(3).sort().reverse()\n// categories = Object.keys(data[0]).slice(1)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1782,"value":"sorted_categories = choose_sort()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1698,"value":"class_categories = Object.keys(class_data[0]).slice(1).reverse()//.reverse()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1972,"value":"color_categories = Object.keys(color_data[0]).slice(1).reverse()//.reverse()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1863,"value":"cur_idx = get_sort_index()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1865,"value":"get_sort_index = function () {\n if (sort_choice != \"Alphabetical\" && sort_choice != \"Default\") {\n for (let i=0; i