31:T5d4,{ var data = [...Array(12).keys()].map(month_no=>{ return { y:relative_disp[month_no], x:[...Array(24).keys()], mode: 'lines', line: {shape:'spline',color: d3.quantize(d3.interpolateHcl("#d66000", "#a9a9b4"), 12)[month_no],width:2}, fill:'', type: 'scatter', hoverinfo: 'name', showlegend:true, name: month_names[month_no] } }) var layout = { height: 250, width: width-30, font: font, hovermode:'closest', showlegend: true, legend: {orientation: 'h', xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside', dtick:1, }, yaxis: { title: 'Output', range: [0,1.19], showgrid: true, zeroline: false, tickformat: '%.0f', ticks:'outside' } }; const div=container('Hourly plant output relative to installed capacity by calendar month. For an undegraded plant.') plt.newPlot(div, data, layout,{displayModeBar: false}) div.on('plotly_hover', d => { var curveNumber = d.points[0].curveNumber var vals = div.data.map((_, i) => (i === curveNumber) ? 1 : 0.2) var vals2 = div.data.map((_, i) => (i === curveNumber) ? 'tozeroy' : '') plt.restyle(div, 'opacity', vals) plt.restyle(div, 'fill', vals2) }) div.on('plotly_unhover', d => { var vals = div.data.map((_, i) => 1) var vals2 = div.data.map((_, i) => '') plt.restyle(div, 'opacity', vals) plt.restyle(div, 'fill', vals2) }) return div }32:T5da,{ var data = [ { y: daily.map(d=>d.price_max).concat(daily.map(d=>d.price_min).reverse()), x: daily.map(d=>d.date).concat(daily.map(d=>d.date).reverse()), mode: 'lines', line: { width: 0}, fill:'toself', fillcolor:'rgba(100, 149, 237,0.3)', type: 'scatter', hoverinfo: 'none', showlegend:false,name: '', } , { y: daily.map(d=>d.price_weight_av), x: daily.map(d=>d.date), mode: 'lines', line: {shape: 'spline', color:'grey', width: 1.5}, type: 'scatter', hoverinfo: 'none', showlegend:false,name: '', }, { y: movingAverage(daily.map(d=>d.price_weight_av),14), x: daily.map(d=>d.date), mode: 'lines', line: {shape: 'spline', color:d3.schemeCategory10[3], width: 1.5}, type: 'scatter', hoverinfo: 'x,y', showlegend:false,name: '', }, ] var layout = { height: 250, width: width-30, font: font, showlegend: true, legend: {xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 5,t: 0}, xaxis: { rangeslider: {}, range:years_range, showgrid: true, zeroline: false, ticks:'outside', }, yaxis: { title: '$/MWh', range: [0,320], showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('SAPP DAM weighted average daily price. 14 day moving average in red. Blue area is the daily maximum and minimum prices.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }33:T451,{ var data = [ { y: daily_price.map(d=>d[1].price), x: daily_price.map(d=>d[0]), mode: 'lines', line: {shape: '', color:col, width: 1.5}, type: 'scatter', hoverinfo: 'x+y', showlegend:false,name: '', }, { y: movingAverage(daily_price.map(d=>d[1].price),14), x: daily_price.map(d=>d[0]), mode: 'lines', line: {shape: 'spline', color:'#566573', width: 1.5}, type: 'scatter', hoverinfo: 'none', showlegend:false,name: '', }, ] var layout = { height: 250, width: width-30, font: font, showlegend: true, legend: {xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 5,t: 0}, xaxis: { rangeslider: {}, range: years_range, showgrid: true, zeroline: false, ticks: 'outside', }, yaxis: { title: `$/MWh`, showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('APP Solar Benchmark price calculated daily from 2016 to date. Grey line: 14 day moving average.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }34:T4f2,{ var data = [{ y: annual_mean.map(v => v[1]), x: annual_mean.map(v => v[0]), text: annual_mean.map(v => d3.format('.1f')(v[1])), name: 'Yearly mean', marker: {color:make_trans(col,0.55)}, type: 'bar', hoverinfo: 'y', showlegend:true, mode:'markers+text', textposition:'outside' }, { y: means.map(v => v.mean), x: means.map(v => v.year), text: means.map(v => d3.format('.1f')(v.mean)),name: 'Expanding average from 2025 back', line: {color:col}, type: 'scatter', hoverinfo: 'y', showlegend:true, mode:'markers+text+lines', textposition:'top' }] var layout = { height: 250, width: width *.5, font: font, barmode:'stack', showlegend: true, legend: {orientation:'h',xanchor: 'left', x:0, y: 1}, margin: {l: 65,r: 10,b: 30,t: 0}, xaxis: { showgrid: false, zeroline: false, ticks:'outside', dtick:1}, yaxis: { title: '$/MWh', showgrid: true, zeroline: true, tickformat: ',.0f', ticks:'outside', range: [40,160] }}; const div=container('Annual average APP Solar Benchmark price (bars) and the expanding average from 2025 back (line).') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }35:T66b,{ function series(dat, col, nam) { let p90 = dat[Math.floor(dat.length*0.1)] let p50 = dat[Math.floor(dat.length*0.5)] return [{ y: dat.map(v=>v.price), x: dat.map(v=>v.exceedance), mode: 'lines', line: {shape: 'spline', color: col, width:3}, fill:'', type: 'scatter', hoverinfo: 'x,y', name: nam, }, { y: [p90.price], x: [p90.exceedance], text: [d3.format('$.0f')(p90.price)], mode: 'markers+text', marker: {color: col, size:10}, textposition:'top-right', type: 'scatter', hoverinfo: 'none', name: nam, showlegend:false }, { y: [p50.price], x: [p50.exceedance], text: [d3.format('$.0f')(p50.price)], mode: 'markers+text', marker: {color: col, size:10}, textposition:'top-right', type: 'scatter', hoverinfo: 'none', name: nam, showlegend:false } ] } var data = [] data=data.concat(series(solar_pdc,col, '2016 to date')) data=data.concat(series(solar_pdc_recent,make_trans(col,0.5), '2021 to date')) var layout = { height: 270, width: width-30, font: font, showlegend: true, legend: {orientation:'h',xanchor: 'right', x:1, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks:'outside', tickformat: '%.0f' }, yaxis: { title: `$/MWh`, showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Solar benchmark price exceedance curves. Median (P50) and P90 prices highlighted.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }36:T75d,solar_chart = function(showDAM){ var data = [ { y: monthly_solar_price.map(d=>d[1].price), x: monthly_solar_price.map(d=>d[1].start_date), mode: 'lines+markers', line: {shape: '', color: col, width: 1.5}, type: 'scatter', showlegend:showDAM, fill:'tozeroy', name:'APP Solar Benchmark', hovertemplate: "%{x}
" + "%{y:,.0f} %{yaxis.title.text}" + "", } , { y: annual_mean.map(d=>d[1]), text: annual_mean.map(d=>'$'+d3.format(',.0f')(d[1])+'/MWh'), x: annual_mean.map(d=>DateTime.fromObject({year:d[0],month:6,day:1}).toJSDate()), mode: 'markers+text', marker: { color: '#444', size: 8},textposition:'topright', type: 'scatter', showlegend:false, name:'', hoverinfo:'text' } ] if (showDAM) { data.push({ y: monthly.map(d=>d.price_mean), x: monthly.map(d=>d.date), mode: 'lines+markers', line: {shape: '', color: '#444', width: 1}, marker:{size:2}, type: 'scatter', showlegend:true, fill:'', name:'SAPP DAM Average Price', hovertemplate: "%{x}
" + "%{y:,.0f} %{yaxis.title.text}" + "", }) } var layout = { height: 220, width: width-30, font: font, showlegend: true, legend: {orientation: 'h',xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks: 'outside', range:[d3.min(monthly_solar_price, d=>d[1].start_date),d3.max(monthly_solar_price, d=>d[1].start_date)] }, yaxis: { range: [20,230], title: `$/MWh`, showgrid: true, zeroline: false, tickformat: '.0f', ticks:'outside' } }; const div=container('Monthly average APP Solar Benchmark price from 2016 to date.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }37:T44a,solar_variance_chart=function(){ var data = [ { y: monthly_solar_variance.map(d=>d.variance), x: monthly_solar_variance.map(d=>d.start_date), marker: {color: make_trans(col,0.7)}, type: 'bar', showlegend:false, fill:'tozeroy', name:'APP Wind Benchmark', hovertemplate: "%{x}
" + "%{y:%.0f} %{yaxis.title.text}" + "", } ] var layout = { height: 200, width: width-30, font: font, showlegend: true, legend: {orientation: 'h',xanchor: 'left', x:0, y: 1}, margin: {l: 50,r: 5,b: 30,t: 0}, xaxis: { showgrid: true, zeroline: false, ticks: 'outside', range:[d3.min(monthly_solar_price, d=>d[1].start_date),d3.max(monthly_solar_price, d=>d[1].start_date)+20] }, yaxis: { title: ``, range:[-0.32,0.32], showgrid: true, zeroline: false, tickformat: '%.0f', ticks:'outside' } }; const div=container('Relative APP Solar Benchmark price compared to the average SAPP DAM Price.') plt.newPlot(div, data, layout,{displayModeBar: false}); return div }38:[{"id":"f97fdeb7341d3f752a7271f84a46608de7c558c00d96c577adeee0965b8603a8c97870615569a9fa16d0c6fa12d7e7b4892657bad77501f966f08b8e13e8e6b9","href":"https://static.observableusercontent.com/files/f97fdeb7341d3f752a7271f84a46608de7c558c00d96c577adeee0965b8603a8c97870615569a9fa16d0c6fa12d7e7b4892657bad77501f966f08b8e13e8e6b9","name":"APP logo.png","type":"image/png","lastModified":1660249420064.245,"size":92844}] 2f:{"title":"APP Solar Benchmark","cells":[{"id":0,"value":"html`

APP Solar Benchmark

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":6825,"value":"

\n\n

\n The APP Solar Benchmark is a reference price (in $/MWh) that would be achieved by a nominal grid-connected solar PV plant that sells all of its energy on the Southern African Power Pool (SAPP) Day-ahead Market (DAM).\n

\n The APP Solar Benchmark is calculated by combining the hourly settlement price of the SAPP DAM market with the hourly relative output of a solar PV plant located in Western Zambia.\n

\n
\n

","pinned":false,"hidden":false,"mode":"html"},{"id":7068,"value":"solar_chart(false)","pinned":false,"hidden":false,"mode":"ojs"},{"id":7072,"value":"solar_variance_chart()","pinned":false,"hidden":false,"mode":"ojs"},{"id":6968,"value":"

Calculating the benchamrk

","pinned":false,"hidden":false,"mode":"html"},{"id":5584,"value":"html`\n

Solar plant

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":6851,"value":"html`\nPlant: Ground-mounted, single-axis tracking mounting system, with bifacial mono-crystalline PV modules and string inverters.
\nLocation: 17°S, 23°E
\nAnnual GHI: 2,215kWh/m2
\nUndegraded Gross Yield: ${d3.format(',.0f')(specific_yield)} MWh/MWac/year
\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":6895,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":5593,"value":"html`\n

Market Price

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":6536,"value":"html`\nThe price on the Southern African Power Pool (SAPP) day-ahead market (DAM) varies hour by hour and average daily prices vary over time. On average prices are lowest at night (and on Sundays and public holidays) and there are two peak periods in during weekday mornings and evenings. An intermediate price occurs during the day on weekdays. \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1924,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":6517,"value":"{ \n var selected_months=['Mar','Jun', 'Aug', 'Dec']\n let data = selected_months.map((m, i)=>{\n const ser = month_profiles[month_dict[m].index]\n return {\n y: [...Array(24).keys()].map(h=>ser.get(h)),\n mode: 'lines', line: {shape: 'spline', color: d3.schemeDark2[month_dict[m].group], width:3}, fill:'',\n type: 'scatter', hoverinfo: 'x,y', name: m,\n }\n })\n \n var layout = {\n height: 250, width: width-30,\n font: font, \n showlegend: true, legend: {xanchor: 'left', x:1,y: 1},\n margin: {l: 50,r: 5,b: 30,t: 0},\n xaxis: {\n title: '',\n showgrid: true, zeroline: false, dtick: 1, ticks:'outside',\n },\n yaxis: {\n range: [20,240],\n title: `$/MWh`,\n showgrid: false, zeroline: false, tickformat: '.0f', ticks:'outside'\n }\n };\n\n const div=container('Average price on the SAPP DAM market by time-of-day for selected months for 2020 to date.')\n plt.newPlot(div, data, layout,{displayModeBar: false});\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5599,"value":"html`\n

Solar benchmark price

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":6541,"value":"html`\nThe price achieved by a solar plant selling onto the SAPP DAM market over the historic period is a product of the solar yield curves and the historic market prices.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5332,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":7030,"value":"solar_chart()","pinned":false,"hidden":false,"mode":"ojs"},{"id":7078,"value":"solar_variance_chart()","pinned":false,"hidden":false,"mode":"ojs"},{"id":6584,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":6974,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":6006,"value":"html`\n

\n

Setup

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":6942,"value":"col='#d68910'","pinned":false,"hidden":false,"mode":"ojs"},{"id":6913,"value":"container = function(captionText) {\n const div=DOM.element('div')\n div.setAttribute(\"class\",\"border m-2 p-2\")\n const caption=DOM.element('figcaption')\n caption.append(captionText)\n div.append(caption)\n return div\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5466,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":7026,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":7084,"value":"monthly_solar_variance = monthly_solar_price.map((d,month_no)=>\n{\n return {\n lux_date: d[1].lux_date,\n start_date: d[1].start_date,\n variance:(d[1].price-monthly[month_no].price_mean)/monthly[month_no].price_mean\n}\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1469,"value":"md `\n## Solar\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":6862,"value":"specific_yield = yield_total/capacity","pinned":false,"hidden":false,"mode":"ojs"},{"id":6860,"value":"capacity = 25","pinned":false,"hidden":false,"mode":"ojs"},{"id":6857,"value":"yield_total = d3.sum(monthly_yield_totals)","pinned":false,"hidden":false,"mode":"ojs"},{"id":6979,"value":"solar_pdc_recent={\n let out={}\n\n let prices=JSON.parse(JSON.stringify(daily_price.filter(a=>a[1].lux_date.year>2020))).sort((a,b)=>a[1]['price']-b[1]['price'])\n out=prices.map((d,i)=>{\n return {\n exceedance: 1-(i/prices.length),\n price: d[1]['price']?d[1]['price']:0\n }\n })\n return out\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6976,"value":"solar_pdc={\n let out={}\n let prices=JSON.parse(JSON.stringify(daily_price)).sort((a,b)=>a[1]['price']-b[1]['price'])\n out=prices.map((d,i)=>{\n return {\n exceedance: 1-(i/prices.length),\n price: d[1]['price']?d[1]['price']:0\n }\n })\n return out\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6469,"value":"monthly_yield_totals = {\n return monthly_hours_p50.map(m => {\n let obs=Object.entries(m)\n obs.pop()\n return d3.sum(obs, v => v[1])\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6057,"value":"relative_yield_profiles = {\n return monthly_hours_p50.map((m, i) => {\n let tmp = Object.keys(m).map(h=>{\n return m[h]/monthly_yield_totals[i]\n })\n tmp.pop()\n return tmp\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6878,"value":"relative_disp = monthly_hours_p50.map((m,i) => Object.values(m).slice(0,24).map(h=>h))","pinned":false,"hidden":false,"mode":"ojs"},{"id":6031,"value":"md `\n### SAPP DAM\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":6522,"value":"month_profiles=[...Array(12).keys()].map(m=>month_profile_price(m+1))","pinned":false,"hidden":false,"mode":"ojs"},{"id":6524,"value":"function month_profile_price(month_no) {\n return d3.rollup(calmonthly_hours.filter(m=>m.month==month_no),v=>d3.mean(v,m=>m.price),t=>t.hour)\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3491,"value":"years_range= [monthly[0].lux_date.plus({month:-0.5}).toJSDate(),monthly[monthly.length-1].lux_date.plus({month:0.5}).toJSDate()]","pinned":false,"hidden":false,"mode":"ojs"},{"id":6681,"value":"means={\n return annual_mean.map(v => { return {year: v[0], mean: d3.mean(annual_mean.filter(m => m[0] >= v[0]), y => y[1])}})\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6588,"value":"annual_mean = d3.rollups(monthly_solar_price, v => d3.mean(v, d => d[1].price), v => DateTime.fromJSDate(v[1].start_date).year)","pinned":false,"hidden":false,"mode":"ojs"},{"id":5341,"value":"monthly_solar_price = {\n \n let ret = d3.rollups(\n daily_price.map(v => v[1]),\n v => {\n return {\n lux_date: v[0].lux_date,\n start_date: d3.min(v, d => d.date),\n weekday_price: d3.mean(v.filter(p => p.lux_date.weekday < 6), d => d.price),\n saturday_price: d3.mean(v.filter(p => p.lux_date.weekday == 6), d => d.price),\n sunday_price: d3.mean(v.filter(p => p.lux_date.weekday == 7), d => d.price),\n price: d3.mean(v, d => d.price),\n }\n },\n v => v.lux_date.year + '_' + v.lux_date.month\n )\n return ret\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5294,"value":"daily_price = d3.rollups(hourly_weighted_price, v => { \n return {\n date: d3.max(v, v=>v.date),\n lux_date: d3.max(v, v=>DateTime.fromJSDate(v.date)),\n price: d3.sum(v, d => d.weighted_price)\n }\n}, v => v.date)","pinned":false,"hidden":false,"mode":"ojs"},{"id":5273,"value":"hourly_weighted_price={\n return hourly.map(h => {\n return {\n date: h.date,\n hour: h.hour,\n weighted_price: h.price * relative_yield_profiles[h.lux_date.month-1][h.hour],\n }\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4499,"value":"md `\n### Data Load\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":6526,"value":"calmonthly_hours = {\n const raw = (await d3.csv(base_url + market_ref + \"_calmonthly_hours.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n \n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5278,"value":"hourly = {\n const raw = (await d3.csv(base_url + market_ref + \"_hourly_slim.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n raw.forEach(r=>{\n r.lux_date=DateTime.fromJSDate(r.date)\n }) \n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5090,"value":"monthly_hours_p50=d3.groups(monthly_hours_p50_raw,v=>v.month).map(v=>v[1].map(l=>l.medianHourlyCapFactor))","pinned":false,"hidden":false,"mode":"ojs"},{"id":7116,"value":"monthly_hours_p50_raw = {\n const raw = (await d3.csv(base_url + \"/pv/iluteCalmonthlyHours.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2118,"value":"monthly = {\n const raw = (await d3.csv(base_url + market_ref + \"_monthly.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n raw.map(r=>{\n r.lux_date=DateTime.fromObject({year: r.year, month: r.month, day: 1})\n r.date=r.lux_date.toJSDate()\n })\n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1918,"value":"daily = {\n const raw = (await d3.csv(base_url + market_ref + \"_daily.csv?\" + Math.floor(Math.random() * 1000),d3.autoType))\n raw.forEach(r=>{\n r.lux_date=DateTime.fromObject({year: r.year, month: r.month, day: r.day})\n r.date=r.lux_date.toJSDate()\n })\n return raw\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":6028,"value":"md `\n### Setup\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4509,"value":"base_url='https://data.africanpowerprojects.com/sapp/output/'","pinned":false,"hidden":false,"mode":"ojs"},{"id":4512,"value":"market_ref='dam'","pinned":false,"hidden":false,"mode":"ojs"},{"id":5103,"value":"function month_days(i) {\n if(i === 4 || i === 6 || i === 9 || i === 11) {\n\t\treturn 30\n\t} else if(i === 2) {\n\t\treturn 28\n\t} else {\n\t\treturn 31\n\t}\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5108,"value":"month_names={\n return {\n 0:'Jan',\n 1:'Feb',\n 2:'Mar',\n 3:'Apr',\n 4:'May',\n 5:'Jun',\n 6:'Jul',\n 7:'Aug',\n 8:'Sep',\n 9:'Oct',\n 10:'Nov',\n 11:'Dec',\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5152,"value":"month_dict={\n return {\n 'Jan':{index: 0, days: 31},\n 'Feb':{index: 1, days: 28},\n 'Mar':{index: 2, days: 31},\n 'Apr':{index: 3, days: 30},\n 'May':{index: 4, days: 31},\n 'Jun':{index: 5, days: 30},\n 'Jul':{index: 6, days: 31},\n 'Aug':{index: 7, days: 31},\n 'Sep':{index: 8, days: 30},\n 'Oct':{index: 9, days: 31},\n 'Nov':{index: 10, days: 30},\n 'Dec':{index: 11, days: 31},\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":964,"value":"md `\n### Imports\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4702,"value":"function catCol(index) {\n return d3.schemeCategory10[index]\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3027,"value":"make_trans=function(col,opacity) {\n let c=d3.color(col)\n c.opacity=opacity\n return c.formatRgb()\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4690,"value":"function movingAverage(values, N) {\n let i = 0;\n let sum = 0;\n const means = new Float64Array(values.length).fill(NaN);\n for (let n = Math.min(N - 1, values.length); i < n; ++i) {\n sum += values[i];\n }\n for (let n = values.length; i < n; ++i) {\n sum += values[i];\n means[i - Math.floor(N / 2)] = sum / N;\n sum -= values[i - N + 1];\n }\n return means;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":14,"value":"font = ({\n family: 'Courier New, monospace',\n size: 13,\n color: '#6f6f6f'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":16,"value":"d3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":18,"value":"plt = require(\"https://cdn.plot.ly/plotly-latest.min.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":5957,"value":"html`\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5117,"value":"html`\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1201,"value":"ss = require('simple-statistics')","pinned":false,"hidden":false,"mode":"ojs"},{"id":4407,"value":"style=html`css\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2137,"value":"luxon = require(\"luxon@1/build/amd/luxon.js\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2140,"value":"DateTime = luxon.DateTime","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W38","stdlib":"1","titling":"auto"}