3e:null 3f:["$","title",null,{"children":"Gross Margin Cohorts - Yearly | Homam Hosseini | Observable"}] 45:T1616,chart = { const div = DOM.element('div'); const last = trace => R.pipe(R.zip(trace.x), R.filter(x => !!x[1]), R.last)(trace.y); const YEARS = [2020, 2021, 2022, 2023, 2024, 2025]; const QUARTERS = [ { month: 0, name: 'Q1' }, // Jan 1 { month: 3, name: 'Q2' }, // Apr 1 { month: 6, name: 'Q3' }, // Jul 1 { month: 9, name: 'Q4' } // Oct 1 ]; const YAXES = ['y6', 'y7', 'y8']; // Helper to generate all reference‐line traces const referenceLines = YAXES.flatMap(yaxis => YEARS.flatMap(year => QUARTERS .map(({ month, name }) => { const date = new Date(year, month, 1); return date >= new Date(date_from) ? { x: [date, date], y: [0, 1], mode: 'lines', name: `${name} ${year}`, yaxis, visible: true, line: { color: month === 0 ? 'rgba(170,170,170,0.7)': 'rgba(170,170,170,0.5)', width: 1, dash: month === 0 ? 'line' : 'dot' } } : null; }) .filter(Boolean) ) ); const plot = Plotly.newPlot(div, data .concat(referenceLines) .concat([sales_data, ecpa_data, arpu_7_data, arpu_30_data, arpu_90_data, arpu_180_data, ...revenue_data, {...quarterly_sales_data, yaxis: 'y5'}, ]) // .concat(margin_01012020_lines) , {width: width, height: 1400, title: country, margin: {t: 30}, annotations: (cohort_annotations .concat(breakeven_annotations) .concat([ {xy: last(arpu_7_data), label: 'ARPU 7', color: arpu_7_data.marker.color, yref: 'y3'}, {xy: last(arpu_30_data), label: 'ARPU 30', color: arpu_30_data.marker.color, yref: 'y3'}, {xy: last(arpu_90_data), label: 'ARPU 90', color: arpu_90_data.marker.color, yref: 'y3'}, {xy: last(arpu_180_data), label: 'ARPU 180', color: arpu_180_data.marker.color, yref: 'y3'}, {xy: last(ecpa_data), label: 'eCPA', color: ecpa_data.marker.color, yref: 'y3'} ])) .concat(revenue_annotations) // .concat(margin_01012020_annotations) .filter(a => !!a.xy) .map(({xy, label, color, yref, xanchor, yanchor, visible, showarrow, ax, ay, ...rest}) => ({ ...rest, xref: 'x', yref: yref, x: xy[0], y: xy[1], ay: ay || 0, ax: ax || 0, xanchor: xanchor || 'left', yanchor: yanchor || 'middle', // anchor: 'free', text: label, font: { family: 'Arial', size: 12, color: d35.rgb(color).darker(1).hex() }, showarrow: showarrow || false, arrowhead: 2, visible: visible === false ? false : true, someData: Math.random() })), showlegend: false, grid: { rows: 2, columns: 1, pattern: 'independent', roworder: 'bottom to top'}, yaxis6: { // title: '**', anchor: 'free', position: 0.97, overlaying: 'y', visible: false, side: 'right', // hoverformat: ',.0f', tickformat: ',.2f', domain: [0, 1], range: [0, 1], }, yaxis7: { // title: '**', anchor: 'free', position: 0.97, overlaying: 'y3', visible: false, side: 'right', // hoverformat: ',.0f', tickformat: ',.2f', domain: [0, 1], range: [0, 1], }, yaxis8: { // title: '**', anchor: 'free', position: 0.97, overlaying: 'y4', visible: false, side: 'right', // hoverformat: ',.0f', tickformat: ',.2f', domain: [0, 1], range: [0, 1], }, yaxis: { title: 'Sales', anchor: 'free', position: 0, // overlaying: 'y', side: 'left', hoverformat: ',.0f', tickformat: ',s', domain: [0.67, 1], range: [0, d3.max(sales_data.y)], showgrid: false, }, yaxis2: { title: 'Profit till now', anchor: 'free', position: 0.99, overlaying: 'y', side: 'right', tickformat: ',.3s', tickformat: ',.3s', domain: [0.34, 0.66], }, yaxis3: { title: 'ARPU and eCPA', anchor: 'free', position: 0.99, // overlaying: 'y', side: 'right', // hoverformat: ',.0f', tickformat: ',.2f', domain: [0.34, 0.66], range: [0, d3.max(ecpa_data.y.concat(arpu_90_data.y))], }, yaxis4: { title: 'Revenue USD', anchor: 'free', position: 0.99, // overlaying: 'y5', side: 'right', // hoverformat: ',.0f', tickformat: ',.3s', domain: [0, 0.33], range: [0, d3.max(R.chain(revenue_data.map(r => r.y)))], }, yaxis5: { title: 'Sales', // anchor: 'free', position: 0, overlaying: 'y3', side: 'left', hoverformat: ',.0f', tickformat: ',s', domain: [0.34, 0.66], range: [0, d3.max(quarterly_sales_data.y)], showgrid: false, }, }) .then(gd => gd.on('plotly_restyle', d => { console.log(d); div.value = d; div.dispatchEvent(new CustomEvent('input')); console.log('d[0].visibile[0]]', d[0].visible[0]) // toggling the annotation opacity is easiest var astr = 'annotations[' + d[1][0] + '].visible'; var val = (d[0].visible[0] === 'legendonly') ? false : true; // call relayout and don't forget to attach this handler again console.log(astr, val) Plotly.relayout(div, astr, val) // toggling the annotation opacity is easiest var astr = 'annotations[' + (d[1][0] + cohort_annotations.length) + '].visible'; var val = (d[0].visible[0] === 'legendonly') ? false : true; // call relayout and don't forget to attach this handler again console.log(astr, val) Plotly.relayout(div, astr, val) })) div.value = null; return div }46:T474,data = R.pipe( // R.filter(x => resolution === 'quarter' || new Date(x.quarter).getMonth() % (quarterly_or_biannular === 'quarterly' ? 3 : 6) === 0) x => x , R.groupBy(x => x.quarter) , R.map(data => ({ key: data[0].quarter, values: R.map(x => [new Date(x.day), as_if === '1' ? x.sum_gross_margin_for_quarter_cohort : x.sum_gross_margin] )(data.filter(x => new Date(x.day).valueOf() < new Date(data[0].quarter).valueOf() + months_after_sale * 30.5 * 24 * 3600 * 1000) ) })) , R.toPairs , R.sortBy(x => x[0]) , R.map(x => x[1]) , xs => R.addIndex(R.map)(({key, values}, i) => { const name = formatQuarterYear(new Date(key)); // d35.timeFormat("%b %Y")(new Date(key)); return { x: values.map(x => x[0]), y: movingAverage(31, values.map(x => Math.round(x[1]))).result, mode: 'lines', key: key, name, yaxis: 'y2', visible: showTrace(name) /* i === (xs.length - 1) || (i % Math.ceil(xs.length/5)) === 0*/ ? true : 'legendonly', marker: { color: arpu_cohorts_colors(key) } }})(xs) )(raw_data)47:T71f,// not used cohort_annotations_1 = { function find_cohort_annotations(collisionMap, recur) { const cohort_annotations_initial_positions = data.map(trace => { const indexToExclude = collisionMap.get(trace.key) const index = R.pipe( R.zip(trace.x), R.filter(x => x[1] === 0 || !!x[1]), ds => collisionMap.has(trace.key) ? indexToExclude - 20 : d35.leastIndex(ds, (a, b) => d35.ascending(a[1], b[1])) )(trace.y) return {...trace, xy: [trace.x[index], trace.y[index]], xyIndex: index} }) const xf = chart._fullLayout.xaxis.l2p const yf = chart._fullLayout.yaxis.l2p const collisions = R.pipe( R.map(c => ({...c, near: cohort_annotations_initial_positions.find(o => (o.key != c.key) && Math.sqrt( Math.pow(xf(o.xy[0].valueOf()) - xf(c.xy[0].valueOf()), 2) + Math.pow(yf(o.xy[1]) - yf(c.xy[1]), 2) ) < 200) }) ) , R.filter(x => x.near) , R.uniqBy(x => [x.key, x.near.key].sort().join(',')) , R.map(R.pick(['key', 'xy', 'xyIndex'])) , R.reduce((m, a) => m.set(a.key, a.xyIndex) , new Map()) )(cohort_annotations_initial_positions) // return collisions console.log(collisions) return (collisions.size === 0 || recur == 3) ? cohort_annotations_initial_positions : find_cohort_annotations(collisions, recur+1) } return find_cohort_annotations(new Map(),0).map(trace => ({ label: trace.name, key: trace.key, color: trace.marker.color, yref: 'y2', yanchor: 'top', xanchor: 'left', // xshift: 40 - Math.random() * 20, showarrow: !true, // ax: 25 - Math.random() * 50, visible: trace.visible === true })) }48:T4d4,margin_01012020_annotations = { const margin_annotations = data.filter(d => d.name === 'Jan 2019') .map(trace => { const xy = R.pipe( R.zip(trace.x), R.find(x => x[0].toJSON() === new Date('2020-01-01').toJSON()) )(trace.y) return { xy: xy, label: !!xy ? `${d3.format(',.3s')(Math.round(xy[1]))}` : '', color: trace.marker.color, yref: 'y2', ax: -60, ay: 60, yanchor: 'bottom', xanchor: 'middle', visible: !xy ? false : trace.visible === true, showarrow: !xy ? false : true, }} ) .filter(a => a.xy != null); const revenue_annotations = revenue_data.filter(d => d.name === 'Jan 2019') .map(trace => { const xy = R.pipe( R.zip(trace.x), R.find(x => x[0].toJSON() === new Date('2020-01-01').toJSON()) )(trace.y) return { xy: xy, label: !!xy ? `${d3.format(',.3s')(Math.round(xy[1]))}` : '', color: trace.marker.color, yref: 'y4', ax: -60, yanchor: 'bottom', xanchor: 'middle', visible: !xy ? false : trace.visible === true, showarrow: !xy ? false : true, }} ) .filter(a => a.xy != null) return [...margin_annotations, ...revenue_annotations] }49:T469,margin_01012020_lines = { const margin_annotations = data.filter(d => d.name === 'Jan 2019') .map(trace => { const xy = R.pipe( R.zip(trace.x), R.find(x => x[0].toJSON() === new Date('2020-01-01').toJSON()) )(trace.y) return !xy ? null : { x: [xy[0], new Date(date_to)], y: [xy[1], xy[1]], mode: 'lines', line: { dash: 'dash' }, key: 'Margin_', name: 'Margin_', yaxis: 'y2', visible: true, marker: { color: trace.marker.color } }} ) const revenue_annotations = revenue_data.filter(d => d.name === 'Jan 2019') .map(trace => { const xy = R.pipe( R.zip(trace.x), R.find(x => x[0].toJSON() === new Date('2020-01-01').toJSON()) )(trace.y) return !xy ? null: { x: [xy[0], new Date(date_to)], y: [xy[1], xy[1]], mode: 'lines', line: { dash: 'dash' }, key: 'Revenue_', name: 'Revenue_', yaxis: 'y4', visible: true, marker: { color: trace.marker.color } }} ) return [...margin_annotations, ...revenue_annotations].filter(x => !!x) }4a:T484,revenue_data = R.pipe( // R.filter(x => resolution === 'quarter' || new Date(x.quarter).getMonth() % (quarterly_or_biannular === 'quarterly' ? 3 : 6) === 0) x => x , R.groupBy(x => x.quarter) , R.map(data => ({ key: data[0].quarter, values: R.map(x => [new Date(x.day), /*as_if === '1'*/ true ? x.sum_revenue_for_quarter_cohort : x.sum_revenue] )(data.filter(x => new Date(x.day).valueOf() < new Date(data[0].quarter).valueOf() + months_after_sale * 30.5 * 24 * 3600 * 1000) ) })) , R.toPairs , R.sortBy(x => x[0]) , R.map(x => x[1]) , xs => R.addIndex(R.map)(({key, values}, i) => { const name = formatQuarterYear(new Date(key)); // d35.timeFormat("%b %Y")(new Date(key)); return { x: values.map(x => x[0]), y: movingAverage(31, values.map(x => x[1])).result, mode: 'lines', key: key, name, yaxis: 'y4', visible: true, // /*i === (xs.length - 1) || (i % Math.ceil(xs.length/5)) === 0*/ /Jan|Jul|Apr|Oct/.exec(name) ? true : 'legendonly', marker: { color: arpu_cohorts_colors(key) } }})(xs) )(raw_data)4b:T53b,{ const div = DOM.element('div'); const last = trace => R.pipe(R.zip(trace.x), R.filter(x => !!x[1]), R.last)(trace.y) const plot = Plotly.newPlot(div, [{ x: extra.x, y: extra.y, name: 'real' } , { x: R.range(0, R.last(extra.x) + 183), y: R.range(0, R.last(extra.x) + 183).map(x => extrapolation.predict(x)[1]), name: 'Prediction' } ,{ name: 'd1', x: extra_d1.map(d => d.x), y: extra_d1.map(d => d.y), yaxis: 'y2' } ,{ name: 'd1 ma', x: extra_d1_ma.map(d => d[0].x), y: extra_d1_ma.map(d => d[1]), yaxis: 'y2' }, { x: extra_d2.map(d => d.x), y: extra_d2.map(d => d.y), name: 'd2', yaxis: 'y3' }, { x: extra_d2_ma.map(d => d[0].x), y: extra_d2_ma.map(d => d[1]), name: 'd2 ma', yaxis: 'y3' }], {width: width, height: 800, yaxis: { anchor: 'free', position: 0, // overlaying: 'y', side: 'left', hoverformat: ',.0f', tickformat: ',s', domain: [0.66, 1], // range: [0, d3.max(sales_data.y.slice(-360))], showgrid: false, }, yaxis2: { position: 0.1, side: 'left', domain: [0.33, 0.66], }, yaxis3: { position: 0.1, side: 'left', domain: [0.00, 0.33], }} ) div.value = null; return div }4c:[] 43:{"id":"5d1187acb2f4022b","slug":null,"title":"Gross Margin Cohorts - Yearly","thumbnail":"f4938d81da33ce6b492efa47721c845e7a58404cb5f334bed9ae659f1f4cfa17","access_level":"public","create_time":"$D2021-02-02T13:44:43.644Z","update_time":"$D2025-07-05T13:04:39.134Z","view_time":null,"first_public_time":"$D2023-08-16T13:14:28.898Z","has_unpublished_edits":null,"fork":{"id":"7a60a45e20968387","slug":"gross-margin-cohorts","title":"Gross Margin 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\"BE\", \"BH\", \"CI\", \"DE\", \"EE\", \"EG\", \"ES\", \"GH\", \"GR\", \"ID\", \"IE\", \"IQ\", \"KW\", \"LT\", \"MY\", \"NL\", \"NO\", \"OM\", \"PT\", \"QA\", \"SE\", \"TH\", \"TR\", \"ZA\", \"!SA\", \"LT\", 'CH', 'SG', 'PR', 'FR', 'PL'].sort((a,b) => a > b),\n value: searchParams.get('country') || \"OM\",\n submit: 'Set'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":215,"value":"viewof operator_code = select({\n title: \"Operator\",\n description: \"\",\n options: operators,\n value: \"-\",\n submit: 'Set',\n multiple: true\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":234,"value":"viewof gateway = select({\n title: \"Gateway\",\n description: \"\",\n options: gateways,\n value: \"-\",\n submit: 'Set',\n multiple: true\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":265,"value":"viewof affiliate = select({\n title: \"Affiliate\",\n description: \"\",\n options: [{label: 'All DMB', value: '*DMB'}].concat( affiliates.map(x => ({label: x.name || '-', value: x.id || '-'}))),\n value: searchParams.get('affiliate') || \"-\",\n submit: 'Set',\n multiple: true\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"viewof date_from = date({ \n title: \"From\", \n submit: 'Set',\n value: searchParams.get('date-from') || '2023-01-01'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":706,"value":"viewof date_to = date({ \n title: \"To\", \n submit: 'Set',\n value: searchParams.get('date-to') || '2025-07-01'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1018,"value":"viewof resolution = radio({\n title: '',\n description: '',\n options: [\n { label: 'Quarterly', value: 'quarter' },\n { label: 'Monthly', value: 'month' },\n ],\n value: searchParams.get(\"resolution\") || 'quarter',\n submit: 'Set'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":68,"value":"md`---`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":142,"value":"md`\\`\\`\\`\n Country: ${country}, \n Date rane: ${date_from} ... ${date_to}\n Operators: ${operator_code.join(', ')},\n Gateways: ${gateway.join(', ')}, \n Affiliates: ${affiliate.join(', ')}, \n Resolution: ${resolution}, \n \\`\\`\\`\n\n[Shareable Link](https://observablehq.com/@homam/gross-margin-cohorts?country=${country}&affiliate=${affiliate}&date-from=${date_from}&date-to=${date_to}&asif=${as_if}&resolution=${resolution}&months-after-sale=${months_after_sale})\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":886,"value":"viewof as_if = checkbox({\n description: `If checked, the graph shows gross profit generated by the cohorts alone, otherwise it shows the gross profit as if the company was started at the time of the cohort till ${date_to}`,\n options: [{ value: '1', label: `Sum gross margin as if we stopped new acquisitions at the end of the ${resolution} of each cohort` }],\n value: searchParams.get('asif') || '1'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2353,"value":"{\n const button = DOM.element(\"button\")\n button.innerText = 'Save'\n button.onclick = function() {\n Plotly.toImage(chart, {format: 'png', width: 1200, height: 1600}).then(function(dataUrl) {\n var data = atob( dataUrl.replace(/^.*?base64,/, '') ),\n asArray = new Uint8Array(data.length);\n\n for( var i = 0, len = data.length; i < len; ++i ) {\n asArray[i] = data.charCodeAt(i); \n }\n\n var blob = new Blob( [ asArray.buffer ], {type: \"image/png\"} );\n\n saveAs(blob, 'mypng.png');\n })\n\n };\n return button\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":880,"value":"viewof months_after_sale = slider({\n min: 1, \n max: 60, \n step: 1, \n value: parseInt(searchParams.get('months-after-sale')) || 6, \n title: \"Months after Sale\", \n description: \"Draw gorss margin cohort lines up to this month after the startint date of the cohort\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":714,"value":"$45","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2499,"value":"function showTrace(name) {\n // return !!/2023|2022 Q[342]/.exec(name)\n return true\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1082,"value":"html`Cumulative Profit Curve Predictor
(using AVG of ARPUs for the past 30 days and AVG of eCPAs and Sales for the past 7 days)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":473,"value":"md`---- \n### Math`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1401,"value":"chart","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":720,"value":"$46","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1421,"value":"cohort_annotations = \ndata.map(trace => ({\n xy: R.pipe(\n R.zip(trace.x), \n R.filter(x => x[1] === 0 || !!x[1]), \n // R.head,\n ds => d35.least(ds, (a, b) => d35.ascending(a[1], b[1]))\n )(trace.y),\n // xy: trace.xy,\n label: trace.name, \n key: trace.key,\n color: trace.marker.color, \n yref: 'y2',\n yanchor: 'top',\n xanchor: 'left',\n // xshift: 40 - Math.random() * 20,\n showarrow: !true,\n // ax: 25 - Math.random() * 50,\n visible: trace.visible === true\n })\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1618,"value":"$47","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1882,"value":"revenue_annotations = \nrevenue_data.map(trace => ({\n xy: R.pipe(\n R.zip(trace.x), \n R.filter(x => x[1] === 0 || !!x[1]), \n // R.head,\n ds => ds.length > 45 ? ds[56] : R.last(ds) // d35.least(ds, (a, b) => d35.ascending(a[1], b[1]))\n )(trace.y),\n // xy: trace.xy,\n label: trace.name, \n key: trace.key,\n color: trace.marker.color, \n yref: 'y4',\n yanchor: 'top',\n xanchor: 'left',\n // xshift: -7,\n showarrow: !true,\n // ax: 25 - Math.random() * 50,\n visible: trace.visible === true\n })\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1552,"value":"breakeven_annotations = data\n .map(trace => {\n const xy = R.pipe(\n R.zip(trace.x), \n R.filter(x => x[1] === 0 || !!x[1]), \n R.reduce(({before, result}, a) => \n a[1] > 0 && before < 0 \n ? {before: a[1], result: a} \n : {before: a[1], result} \n , {before: 0, result: null}\n ),\n x => x.result\n )(trace.y)\n return {\n xy: xy || [trace.x[0], 0],\n label: !!xy ? `${Math.round((xy[0] - trace.x[0].valueOf()) / (24 * 3600 * 1000))} days` : '', \n color: trace.marker.color, \n yref: 'y2',\n ay: -10 + Math.random() * -80,\n yanchor: 'bottom',\n xanchor: 'middle',\n visible: !xy ? false : trace.visible === true,\n showarrow: !xy ? false : true,\n }}\n)\n.filter(a => a.xy != null)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2304,"value":"$48","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2334,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":772,"value":"sales_data = R.pipe(\n R.groupBy(x => x.day)\n, R.map(xs => xs[0].sales)\n, obj => ({\n x: R.keys(obj).map(x => new Date(x)),\n y: movingAverage(7, R.values(obj)).result,\n type: 'bar',\n name: 'Sales',\n marker: {\n color: '#e2e2e2aa'\n },\n })\n)(raw_data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1819,"value":"quarterly_sales_data = R.pipe(\n R.groupBy(x => x.day)\n, R.map(R.pipe( R.map(x => x.sum_sales_for_quarter_cohort), R.last))\n, obj => ({\n x: R.keys(obj).map(x => new Date(x)),\n y: R.values(obj),\n type: 'bar',\n name: 'Sales',\n marker: {\n color: '#e2e2e266'\n },\n })\n)(raw_data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":850,"value":"ecpa_data = R.pipe(\n R.groupBy(x => x.day)\n, R.map(xs => xs[0].cost / xs[0].sales)\n, obj => ({\n x: R.keys(obj).map(x => new Date(x)),\n y: movingAverage(31, R.values(obj)).result,\n type: 'line',\n name: 'eCPA',\n marker: {\n color: '#FF3B6888'\n },\n line: {\n dash: 'dash'\n },\n yaxis: 'y3',\n })\n)(raw_data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":995,"value":"arpu_7_data = R.pipe(\n R.groupBy(x => x.day)\n, R.map(xs => xs[0].revenue_7 / xs[0].sales_7)\n, obj => ({\n x: R.keys(obj).map(x => new Date(x)),\n y: movingAverage(31, R.values(obj)).result,\n type: 'line',\n name: 'ARPU 7',\n marker: {\n color: '#1feeb4b4'\n },\n\n yaxis: 'y3',\n })\n)(raw_data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1000,"value":"arpu_30_data = R.pipe(\n R.groupBy(x => x.day)\n, R.map(xs => xs[0].revenue_30 / xs[0].sales_30)\n, obj => ({\n x: R.keys(obj).map(x => new Date(x)),\n y: movingAverage(31, R.values(obj)).result,\n type: 'line',\n name: 'ARPU 30',\n marker: {\n color: '#1D9A6C44'\n },\n\n yaxis: 'y3',\n })\n)(raw_data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1042,"value":"arpu_60_data = R.pipe(\n R.groupBy(x => x.day)\n, R.map(xs => xs[0].revenue_60 / xs[0].sales_60)\n, obj => ({\n x: R.keys(obj).map(x => new Date(x)),\n y: movingAverage(31, R.values(obj)).result,\n type: 'line',\n name: 'ARPU 60',\n marker: {\n color: '#1D9A6C44'\n },\n\n yaxis: 'y3',\n })\n)(raw_data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":984,"value":"arpu_90_data = R.pipe(\n R.groupBy(x => x.day)\n, R.map(xs => xs[0].revenue_90 / xs[0].sales_90)\n, obj => ({\n x: R.keys(obj).map(x => new Date(x)),\n y: movingAverage(31, R.values(obj)).result,\n type: 'line',\n name: 'ARPU 90',\n marker: {\n color: '#13717744'\n },\n\n yaxis: 'y3',\n })\n)(raw_data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2531,"value":"arpu_180_data = R.pipe(\n R.groupBy(x => x.day)\n, R.map(xs => xs[0].revenue_180 / xs[0].sales_180)\n, obj => ({\n x: R.keys(obj).map(x => new Date(x)),\n y: movingAverage(31, R.values(obj)).result,\n type: 'line',\n name: 'ARPU 180',\n marker: {\n color: '#00317744'\n },\n\n yaxis: 'y3',\n })\n)(raw_data)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1775,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":477,"value":"colors = d35.scaleOrdinal(d35.schemePastel1.reverse())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1037,"value":"arpu_list = R.map(R.pipe(R.prop('y'), R.reverse(), R.filter(x => !!x), R.head, d3.format('0.2f')))([arpu_30_data, arpu_60_data, arpu_90_data, arpu_180_data]).join('+')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1066,"value":"last_ecpa = d3.format('0.2f')(R.pipe(R.prop('y'), R.reverse(), R.filter(x => !!x), R.head)(ecpa_data))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1074,"value":"last_sales = d3.format('0.0f')(R.pipe(R.prop('y'), R.reverse(), R.filter(x => !!x), R.head)(sales_data))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1045,"value":"gross_mardin_prediction_link = `http://pipend.sam-media.com:8095/?arpu_arpu_list=value&arpu_arpu_list=${arpu_list}&cpc_arpu_list=value&cpc_arpu_list=${arpu_list}&cpc_ecpa=value&cpc_ecpa=${last_ecpa}&cpc_sales=value&cpc_sales=${last_sales}&cpc_delay=value&cpc_delay=3&cpc_calculate=n_clicks&cpc_calculate=2`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2484,"value":"function formatQuarterYear(date) {\n const year = date.getFullYear();\n const quarter = Math.floor((date.getMonth() / 3) + 1); // Calculate the quarter\n return `${year} Q${quarter}`;\n}\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1122,"value":"data","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":480,"value":"styles = html`\n `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"md`---`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"md`### Fetch`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"PROJECT_ID = new URL(location.href).searchParams.get('project-id')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":199,"value":"get_dimension_values = dimension => fetch(`https://pipe.sam-media.com/apis/projects/${PROJECT_ID}/documents/rDVUH68/versions/11/execute/7200/query?from=${-77}&dimension=${dimension}&countries=${country}&operators=-&gateways=-&affiliates=-&services1=-`).then(x => x.json()).then(xs => ['-'].concat(xs.map(x => x.dimension)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"affiliates = get_dimension_values('affiliate_id').then(xs => xs.map(x => {\n const [name, id] = x.split(':')\n return {name, id}\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":206,"value":"operators = get_dimension_values('operator_code')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":231,"value":"gateways = get_dimension_values('gateway')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"raw_data = fetch(raw_data_url).then(x => x.json())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1157,"value":"raw_data_url = `https://pipe.sam-media.com/apis/projects/${PROJECT_ID}/documents/rN7GhNp/execute/7200/query?from=${date_from}&to=${date_to}&countries=${country}&gateways=${gateway.join(',')}&affiliates=${affiliate.join(',')}&operators=${operator_code.join(',')}&service1=-&pages=-&resolution=${resolution}&bc=-`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1165,"value":"searchParams = new URL(document.location.href).searchParams","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1508,"value":"arpu_cohorts_colors = d3.scale.category10().range(d35.schemeSet2)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":210,"value":"md`---- \n### Imports`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2,"value":"import {select, input, slider, text, number, date, checkbox, radio} from \"@jashkenas/inputs\"\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":73,"value":"Plotly = require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2359,"value":"saveAs = require(\"https://cdnjs.cloudflare.com/ajax/libs/FileSaver.js/1.3.8/FileSaver.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":77,"value":"R = require('ramda@0.25.0/dist/ramda.min.js')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":176,"value":"d3 = Plotly.d3","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":468,"value":"d35 = require(\"d3@5\", \"d3-sankey@0.7\", \"d3-scale-chromatic\", \"d3-color\", \"d3-scale-chromatic\", \"d3-time-format\", \"d3-array\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2197,"value":"md`## fun`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":963,"value":"movingAverage = (size, arr) => arr.reduce((acc, a) => {\n let currentWindow = acc.currentWindow.concat([a])\n currentWindow = currentWindow.length > size\n ? R.drop(1, currentWindow)\n : currentWindow\n return {\n currentWindow,\n result: currentWindow.length == size\n ? acc.result.concat([R.sum(currentWindow) / size])\n : acc.result.concat([R.sum(currentWindow) / currentWindow.length])\n }\n}, {currentWindow: [], result: []})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2140,"value":"movingAverage1 = (sum, size, arr) => arr.reduce((acc, a) => {\n let currentWindow = acc.currentWindow.concat([a])\n currentWindow = currentWindow.length > size\n ? R.drop(1, currentWindow)\n : currentWindow\n return {\n currentWindow,\n result: currentWindow.length == size\n ? acc.result.concat([[a, sum(currentWindow) / size]])\n : acc.result.concat([[a, sum(currentWindow) / currentWindow.length]])\n }\n}, {currentWindow: [], result: []})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2143,"value":"derivative = ({x, y}) => R.pipe(\n R.scan(\n (acc, a) => ({x: x(a), y: y(a) - acc.last_value, last_value: y(a)}), {last_value: 0}\n )\n, R.drop(2)\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1905,"value":"regression = require('regression@2.0.1/dist/regression.js')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1956,"value":"extra = {\n const t = revenue_data[16] \n return {x: t.x.slice(30).map((_, i) => i), y: t.y.slice(30) }\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2146,"value":"extra_d1 = derivative({x: d => d[0], y: d => d[1]})(R.zip(extra.x, extra.y))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2171,"value":"extra_d1_ma = movingAverage1(R.pipe(R.map(d => d.y), R.sum), 31, extra_d1).result","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2182,"value":"extra_d2 = derivative({x: d => d[0].x, y: d => d[1]})(extra_d1_ma)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2189,"value":"extra_d2_ma = movingAverage1(R.pipe(R.map(d => d.y), R.sum), 31, extra_d2).result","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1916,"value":"extrapolation = regression.polynomial( R.zip(extra.x, extra.y), {order: 4, precision:10})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1933,"value":"$4b","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":162,"value":"md`----`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2417,"value":"{\n const t = revenue_data.find(r => r.key === '2023-01-01T00:00:00.000Z')\n return R.zip(t.x, t.y)\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4c","stdlib":"1"}} 44:"$43:body"