2a:null 2b:["$","title",null,{"children":"Untitled | kanocztomas | Observable"}] 30:T44b,{ let pipe = fns => arg => fns.reduce((f1, f2) => f2(f1), arg) let map = fn => arr => arr.map(fn) let sort = arr => arr.slice().sort() // let groupeByMonths = d3.group(ketoMojoGKI, d => d.Date.getMonth()) let order = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"] // let usedMonths = Array.from(d3.union(ketoMojoGKI.map(d => d.Date.getMonth()))).sort().map(d => order[d]) const usedMonths = pipe([ map(d => d.Date.getMonth()), d3.union, Array.from, sort, map(d => order[d]) ]) (ketoMojoGKI) return Plot.plot({ width: `${width}`, // height: '300', y: { grid: true, label: "GKI", }, fy: { domain: usedMonths }, marks: [ // Plot.frame(), Plot.text(ketoMojoGKI, {filter: d => d.value === d3.median(ketoMojoGKI, d => d.value), x: "value", text: "value", y: 0, label: null}), Plot.boxX(ketoMojoGKI, {x: "value", y: "count", fy: perMonth ? d => d.Date.toLocaleString('default', {month: 'short'}) : null},), Plot.crosshair(ketoMojoGKI, {x: "value", y: "count"}) ] }) }31:T569,{ let pipe = fns => arg => fns.reduce((f1, f2) => f2(f1), arg) let map = fn => arr => arr.map(fn) let sort = arr => arr.slice().sort() // let groupeByMonths = d3.group(ketoMojoGKI, d => d.Date.getMonth()) let order = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"] // let usedMonths = Array.from(d3.union(ketoMojoGKI.map(d => d.Date.getMonth()))).sort().map(d => order[d]) const usedMonths = pipe([ map(d => d.Date.getMonth()), d3.union, Array.from, sort, map(d => order[d]) ]) (ketoMojoGlucose) return Plot.plot({ width: '1200', height: '300', y: { grid: true, label: "Glucose", }, fy: { domain: usedMonths }, marks: [ // Plot.frame(), Plot.boxX(ketoMojoGlucose, {x: "value", y: "count", fy: perMonthGlucose ? d => d.Date.toLocaleString('default', {month: 'short'}) : null}), Plot.crosshair(ketoMojoGlucose, {x: "value", y: "count"}) ] }) } // Plot.plot({ // width: '1200', // height: '300', // y: { // grid: true, // label: "Glucose", // }, // marks: [ // Plot.boxX(ketoMojoGlucose.filter(d => d.Date >= dateStart && d.Date <= dateEnd), {x: "value", y: "count", sort: {x: "y"}}), // Plot.crosshair(ketoMojoGlucose.filter(d => d.Date >= dateStart && d.Date <= dateEnd), {x: "value", y: "count", sort: {x: "y"}}) // ] // })32:T569,{ let pipe = fns => arg => fns.reduce((f1, f2) => f2(f1), arg) let map = fn => arr => arr.map(fn) let sort = arr => arr.slice().sort() // let groupeByMonths = d3.group(ketoMojoGKI, d => d.Date.getMonth()) let order = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"] // let usedMonths = Array.from(d3.union(ketoMojoGKI.map(d => d.Date.getMonth()))).sort().map(d => order[d]) const usedMonths = pipe([ map(d => d.Date.getMonth()), d3.union, Array.from, sort, map(d => order[d]) ]) (ketoMojoKetones) return Plot.plot({ width: '1200', height: '300', y: { grid: true, label: "Ketones", }, fy: { domain: usedMonths }, marks: [ // Plot.frame(), Plot.boxX(ketoMojoKetones, {x: "value", y: "count", fy: perMonthKetones ? d => d.Date.toLocaleString('default', {month: 'short'}) : null}), Plot.crosshair(ketoMojoKetones, {x: "value", y: "count"}) ] }) } // Plot.plot({ // width: '1200', // height: '300', // y: { // grid: true, // label: "Ketones", // }, // marks: [ // Plot.boxX(ketoMojoKetones.filter(d => d.Date >= dateStart && d.Date <= dateEnd), {x: "value", y: "count", sort: {x: "y"}}), // Plot.crosshair(ketoMojoKetones.filter(d => d.Date >= dateStart && d.Date <= dateEnd), {x: "value", y: "count", sort: {x: "y"}}) // ] // })33:T608,{ let d_type = selectType let data = data_playground.filter(d => d.type === d_type) const accessor = d => d.value let Q1 = d3.quantile(data, 0.25, accessor) let median = d3.quantile(data, 0.5, accessor) let Q3 = d3.quantile(data, 0.75, accessor) let IQR = Q3 - Q1 let outlierBelow = Q1 - 1.5 * IQR let outlierAbove = Q3 + 1.5 * IQR let outliers = data.filter(d => d.value < outlierBelow || d.value > outlierAbove) let normalData = data.filter(d => d.value >= outlierBelow && d.value <= outlierAbove) let min = data => d3.min(data) let max = data => d3.max(data) console.log(outliers) return Plot.plot({ width: `${width}`, // title: `${min}, ${Q1}, ${median}, ${Q3}, ${max}, IQR: ${IQR}`, x: { transform: d => parseFloat(d) }, fy: { interval: "month" }, color: { legend: true }, // height: 70, // y: { // domain: [0,1], // interval: 0.5 // }, marks: [ Plot.ruleY(normalData, Plot.groupZ({x1: "min", x2: "max"}, {x: "value", y: null, stroke: "currentColor", fy: "Date"})), Plot.barX(normalData, Plot.groupZ({x1: "p25", x2: "p75"},{x: "value", fill: true ? "Date" :'#ccc', stroke: "currentColor", fy: "Date", z: null})), Plot.tickX(normalData, Plot.groupZ({x: "min"}, {x: "value", fy: "Date"})), Plot.tickX(normalData, Plot.groupZ({x: "p50"}, {x: "value", fy: "Date"})), Plot.tickX(normalData, Plot.groupZ({x: "max"}, {x: "value", fy: "Date"})), Plot.dot(outliers, {x: "value", fy: "Date"}) ] }) }34:T5ed,{ const data = data_playground.filter(d => d.type === "ketone") const mean = d3.mean(data, d => d.value) const variance = d3.variance(data, d => d.value) const sd = d3.deviation(data, d => d.value) return Plot.plot({ width: `${width}`, title: "Visualising which measurements are withim 1 standard deviation", marginRight: 50, y: {transform: parseFloat}, x: {label: "Date"}, color: {legend: true}, // x: {domain: [data[data.length - 1].Date, data[0].Date]}, marks: [ Plot.ruleY([0]), // Plot.axisX({ticks: "month"}), Plot.ruleX(data, Plot.binX({y1: d => mean, y2: "mean"}, {x: "Date", interval: "day", y: "value"})), Plot.dotY(data, Plot.binX({y: "mean"}, {x: "Date", interval: "day", y: "value", stroke: "type"})), Plot.dotY(data, Plot.select({y: (I, V) => I.filter( i => V[i] > mean + sd || V[i] < mean - sd)} ,Plot.binX({y: "mean"}, {x: "Date", interval: "day", y: "value", stroke: "red"}))), Plot.ruleY([mean], {stroke: "red"}), // Plot.text(data,Plot.selectLast(Plot.binX({y: "mean"}, {x: "Date", interval: "day", y: "value", stroke: "type", text: "mean", textAnchor: "start", dx: 3}))), Plot.ruleY([mean + sd], {stroke: "green"}), Plot.ruleY([mean - sd], {stroke: "green"}), Plot.axisY({anchor: "left", labelAnchor: "center", label: "average daily BHB in mmol/L"}), Plot.text(data, Plot.selectFirst({x: "Date", interval: "day", y: d => mean, text: d => "mean", dx: 5, textAnchor: "start"})), ] }) }35:T93c,{ let ketonesOnly = data_playground.filter(d => d.type === "ketone").map(d => ({date: d.date, ketones: d.value})) let crono = cronometerData.map(d => ({date: d.Date, 'Fat (g)': d['Fat (g)']})) let data = ketonesOnly.concat(crono) // return Inputs.table(data) const dates = Array.from(d3.union(data.map(d => d.date))) .map(d => new Date(d)) .sort((a, b) => a.getTime() - b.getTime()) .map(d => d.toISOString() .split("T")[0]) let byDate = dates.reduce ((acc, date) => { if(acc[date] === undefined) acc[date] = {} acc[date] = { ketones: ketonesOnly.filter(d => d.date === date).map(d => d.ketones).map(parseFloat), 'Fat (g)': crono.filter(d => d.date === date).map(d => d['Fat (g)']).map(parseFloat) } return acc }, {}) byDate = Object.keys(byDate).map(date => [date, byDate[date].ketones, byDate[date]['Fat (g)'], byDate[date]]) .filter(d => d[2].length !== 0 && d[1].length !== 0) .map(d => ({date: d[0], ketones: d[1], 'Fat (g)': d[2]})) .map(d => ({date: d.date, ketones: d3.mean(d.ketones), 'Fat (g)': d3.mean(d['Fat (g)'])})) //I need to see the ketones and the previous day's fat, so I will get the arrays and shift fat be 1 to the left. let newDates = byDate.map(d => d.date) let ketones = byDate.map(d => d.ketones) const fat = byDate.map(d => d['Fat (g)']) fat.shift() //we shift to the left ketones = ketones.slice(0, ketones.length - 1) //we ignore last ketone value, is it has no fat pair newDates = newDates.slice(0, newDates.length - 1) //the same as above let dataToPlot = newDates.map((date, i) => ({date, ketones: ketones[i], 'Fat (g)': fat[i]})) let medianKetones = d3.median(dataToPlot, d => d.ketones) let medianFat = d3.median(dataToPlot, d => d['Fat (g)']) return Plot.plot({ title: "Ketones influenced by previous day's fat intake", width: `${width}`, marginRight: 300, // x: {transform: d => new Date(d)}, y: {type: "sqrt"}, marks: [ // Plot.dotY(dataToPlot, {x: "date", y: "ketones"}), // Plot.dotY(dataToPlot, {x: "date", y: "Fat (g)"}) Plot.dotY(dataToPlot, {x: "Fat (g)", y: "ketones"}), // Plot.linearRegressionY(dataToPlot, {x: "Fat (g)", y: "ketones", stroke: "red"}), Plot.ruleX([medianFat],{stroke: "red"}), Plot.ruleY([medianKetones], {stroke: "red"}) ] }) }36:[{"id":"213c6565f38b58c543d0bec1bb9d1cebc68916386ed587d6301e3cc32baf4e4b9dea7f21aa3c80f6088fc93e82af6bc5a72430a99335708181119cdd53573229","href":"https://static.observableusercontent.com/files/213c6565f38b58c543d0bec1bb9d1cebc68916386ed587d6301e3cc32baf4e4b9dea7f21aa3c80f6088fc93e82af6bc5a72430a99335708181119cdd53573229","name":"readings.csv","type":"text/csv","lastModified":1696697001496.807,"size":107433},{"id":"0bc274a5da13d970db92c6dd650ebe63c07ff28da2f73c283f9a36342f2acd66853f21925ec1a25e9b1ebbeb97d4ba5b5f73b8f0df46aacf67cd5737af756832","href":"https://static.observableusercontent.com/files/0bc274a5da13d970db92c6dd650ebe63c07ff28da2f73c283f9a36342f2acd66853f21925ec1a25e9b1ebbeb97d4ba5b5f73b8f0df46aacf67cd5737af756832","name":"dailysummary.csv","type":"text/csv","lastModified":1696190413677.931,"size":62094},{"id":"547f0ee7e85a2c95b474838dc24bd1978e5bc10267a054e1497c1099c1acb170676620937ec4374d66a02bdf139ad2f00bbf9f5b650b6311da59fcf516b04897","href":"https://static.observableusercontent.com/files/547f0ee7e85a2c95b474838dc24bd1978e5bc10267a054e1497c1099c1acb170676620937ec4374d66a02bdf139ad2f00bbf9f5b650b6311da59fcf516b04897","name":"biometrics.csv","type":"text/csv","lastModified":1696190662690.26,"size":200018}] 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Table","owner":{"id":"f35c755083683fe5","login":"observablehq","name":"Observable"}},"stars":0,"starred":false,"owner":{"type":"team","id":"a349e3a19a29f0fe","login":"kanocztomas","name":"","avatar_url":"https://avatars-next.observableusercontent.com/9218c1f3de790129980fc927edc780149531178d52593afbd07580cdb8cb55b1","tier":"starter_2024"},"version":1968,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"f227a2811f1f4d07","login":"kanocztomas","name":"","avatar_url":"https://avatars-next.observableusercontent.com/9218c1f3de790129980fc927edc780149531178d52593afbd07580cdb8cb55b1","tier":null}],"members":[{"type":"individual","id":"f227a2811f1f4d07","login":"kanocztomas","name":"","avatar_url":"https://avatars-next.observableusercontent.com/9218c1f3de790129980fc927edc780149531178d52593afbd07580cdb8cb55b1","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"","cells":[{"id":1950,"value":"allData = {\n return {\n biometrics: FileAttachment(\"biometrics.csv\"),\n dailysummary: FileAttachment(\"dailysummary.csv\"),\n readings: FileAttachment(\"readings.csv\")\n }\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":725,"value":"# GKI PLAYGROUND","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4,"value":"data = Inputs.attachment(","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":138,"value":"data = FileAttachment(\"biometrics.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":149,"value":"blood = data.filter(d => d.Metric.includes(\"Keto-Mojo\")).map(d => d.Metric.includes(\"Glucose\") ? {...d, Amount: (parseFloat(d.Amount/18).toFixed(1)), Unit: \"mmol/L\"} : d)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":159,"value":"data1 = blood","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"data1","database":"$undefined"},{"id":174,"value":"Plot.plot({\n color: {\n legend: true\n },\n marks: [\n Plot.auto(data1, {x: \"Day\", y: \"Amount\", color: \"Metric\"})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":193,"value":"ketoMojoRaw = FileAttachment(\"readings.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":199,"value":"ketoMojo = ketoMojoRaw.map(d => ({...d, Date: new Date(`${d.date}T${d.time}${d.timeZone}`)})).map (d => {\n d.Date.setHours(d.Date.getHours() - 2) // GMT is -2 and I further shift by 2. So we get a shift of 4 hours\n return {\n ...d,\n Date: d.Date\n }\n}) ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":217,"value":"ketoMojoData = ketoMojo","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"ketoMojoData","database":"$undefined"},{"id":227,"value":"Plot.plot({\n color: {\n legend: true\n },\n marks: [\n Plot.auto(ketoMojoData, {x: \"Date\", y: \"value\", color: \"type\"})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":238,"value":"Plot.plot({\n width: width,\n color: {\n legend: true\n },\n marks: [\n Plot.auto(ketoMojoData, {x: \"value\", y: {reduce: \"count\"}, fx: \"type\", color: \"type\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":254,"value":"ketoMojoGKI = ketoMojoData","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"ketoMojoGKI","database":"$undefined"},{"id":357,"value":"ketoMojoGlucose = ketoMojoData","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"ketoMojoGlucose","database":"$undefined"},{"id":371,"value":"ketoMojoKetones = ketoMojoData","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"ketoMojoKetones","database":"$undefined"},{"id":551,"value":"viewof dateStart = Inputs.date({label: \"Start date\", value: \"2023-04-12\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":553,"value":"viewof dateEnd = Inputs.date({label: \"End date\", value: Date.now()})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":285,"value":"GKIstats = {\n let data = ketoMojoGKI.filter(d => d.Date >= dateStart && d.Date <= dateEnd)\n return [\n {label: \"Median\", value: d3.median(data.map(d => d.value))},\n {label: \"Minimum\", value: d3.min(data.map(d => d.value))},\n {label: \"Maximum\", value: d3.max(data.map(d => d.value))},\n {label: \"1st Quartile\", value: d3.quantile(data.map(d => d.value), 0.25)},\n {label: \"3rd Quartile\", value: d3.quantile(data.map(d => d.value), 0.75)},\n {label: \"4th Quartile\", value: d3.quantile(data.map(d => d.value), 1)}\n ].map(d => ({...d, value: parseFloat(d.value.toFixed(2))}))\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":361,"value":"GlucoseStats = {\n let data = ketoMojoGlucose.filter(d => d.Date >= dateStart && d.Date <= dateEnd)\n return [\n {label: \"Median\", value: d3.median(data.map(d => d.value))},\n {label: \"Minimum\", value: d3.min(data.map(d => d.value))},\n {label: \"Maximum\", value: d3.max(data.map(d => d.value))},\n {label: \"1st Quartile\", value: d3.quantile(data.map(d => d.value), 0.25)},\n {label: \"3rd Quartile\", value: d3.quantile(data.map(d => d.value), 0.75)},\n {label: \"4th Quartile\", value: d3.quantile(data.map(d => d.value), 1)}\n ];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":369,"value":"KetoneStats = {\n let data = ketoMojoKetones.filter(d => d.Date >= dateStart && d.Date <= dateEnd)\n return [\n {label: \"Median\", value: d3.median(data.map(d => d.value))},\n {label: \"Minimum\", value: d3.min(data.map(d => d.value))},\n {label: \"Maximum\", value: d3.max(data.map(d => d.value))},\n {label: \"1st Quartile\", value: d3.quantile(data.map(d => d.value), 0.25)},\n {label: \"3rd Quartile\", value: d3.quantile(data.map(d => d.value), 0.75)},\n {label: \"4th Quartile\", value: d3.quantile(data.map(d => d.value), 1)}\n ];\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":295,"value":"| GKI | Quantity |\n| ------ | -------- |\n| Median | ${GKIstats[0].value} |\n| Minimum | ${GKIstats[1].value} |\n| Maximum | ${GKIstats[2].value} |\n| 1st Quartile| ${GKIstats[3].value} |\n| 3rd Quartile| ${GKIstats[4].value}|","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":704,"value":"viewof perMonth = Inputs.toggle({label: \"Bin GKI per month?\", value: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":270,"value":"{\n let pipe = fns => arg => fns.reduce((f1, f2) => f2(f1), arg)\n let map = fn => arr => arr.map(fn)\n let sort = arr => arr.slice().sort()\n // let groupeByMonths = d3.group(ketoMojoGKI, d => d.Date.getMonth())\n let order = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n \n // let usedMonths = Array.from(d3.union(ketoMojoGKI.map(d => d.Date.getMonth()))).sort().map(d => order[d])\n const usedMonths = pipe([\n map(d => d.Date.getMonth()),\n d3.union,\n Array.from,\n sort,\n map(d => order[d])\n ]) (ketoMojoGKI)\n return Plot.plot({\n width: `${width}`,\n // height: '300',\n y: {\n grid: true,\n label: \"GKI\",\n },\n fy: {\n domain: usedMonths\n },\n color: {legend: true},\n marks: [\n // Plot.frame(),\n Plot.boxX(ketoMojoGKI, {x: \"value\", y: \"count\", fy: perMonth ? d => d.Date.toLocaleString('default', {month: 'short'}) : null}),\n Plot.crosshair(ketoMojoGKI, {x: \"value\", y: \"count\"})\n ]\n})\n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":724,"value":"$30","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":364,"value":"| Glucose | Quantity |\n| ------ | -------- |\n| Median | ${GlucoseStats[0].value} |\n| Minimum | ${GlucoseStats[1].value} |\n| Maximum | ${GlucoseStats[2].value} |\n| 1st Quartile| ${GlucoseStats[3].value} |\n| 3rd Quartile| ${GlucoseStats[4].value}|","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":714,"value":"viewof perMonthGlucose = Inputs.toggle({label: \"Bin Glucose per month?\", value: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":366,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":379,"value":"| Ketones | Quantity |\n| ------ | -------- |\n| Median | ${KetoneStats[0].value} |\n| Minimum | ${KetoneStats[1].value} |\n| Maximum | ${KetoneStats[2].value} |\n| 1st Quartile| ${KetoneStats[3].value} |\n| 3rd Quartile| ${KetoneStats[4].value}|","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":720,"value":"viewof perMonthKetones = Inputs.toggle({label: \"Bin Ketones per month?\", value: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":381,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":386,"value":"sleepDataRaw = FileAttachment(\"biometrics.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":390,"value":"sleepData = sleepDataRaw","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"sleepData","database":"$undefined"},{"id":395,"value":"ketoMojoGlucoseSleepData = ketoMojoData","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":398,"value":"ketoMojoGlucoseData = ketoMojoRaw","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"ketoMojoGlucoseData","database":"$undefined"},{"id":406,"value":"ketoMojoGlucoseDataPerDay = {\n let byDateArray = ketoMojoGlucoseData.reduce((acc, elem) => {\n let date = `${elem.date.toISOString().split(\"T\")[0]}`\n if(acc[date] === undefined){\n acc[date] = []\n }\n acc[date].push(elem.value)\n return acc\n }, {})\n\n return Object.keys(byDateArray)\n .map(d => ({ date: d, value: byDateArray[d]}))\n .map(d => ({ \n date: d.date,\n value: parseFloat((d.value.reduce((acc, elem) => acc + elem, 0)/d.value.length).toFixed(2)),\n type: \"Glucose\"\n })\n )\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":475,"value":"sleepDataPerDay = {\n let byDateArray = sleepData.reduce((acc, elem) => {\n let date = `${elem.date.toISOString().split(\"T\")[0]}`\n if(acc[date] === undefined){\n acc[date] = []\n }\n acc[date].push(elem.value)\n return acc\n }, {})\n\n return Object.keys(byDateArray)\n .map(d => ({ date: d, value: byDateArray[d]}))\n .map(d => ({ \n date: d.date,\n value: parseFloat((d.value.reduce((acc, elem) => acc + elem, 0)/d.value.length).toFixed(2)),\n type: \"Sleep\"\n })\n )\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":434,"value":"concatSleepData = {\n let dates = sleepDataPerDay.reduce( (acc, elem) => acc.concat(elem.date),[]).map(d => new Date(d))\n return dates.reduce((acc, date) => {\n date = date.toISOString().split(\"T\")[0]\n let sleep = sleepDataPerDay.filter(d => d.date === date)\n let glucose = ketoMojoGlucoseDataPerDay.filter(d => d.date === date)\n acc.push({date, sleep: sleep[0] ? sleep[0].value : undefined, glucose: glucose[0] ? glucose[0].value : undefined})\n return acc\n }\n , [])\n .filter(d => d.glucose)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":544,"value":"viewof glucoseCutOff = Inputs.range([3, 7], {label: \"Glucose\", step: 0.1, value: 4})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":523,"value":"concatSleepDataWithoutOutliars = {\n //we filter out outliars\n let data = concatSleepData.map(d => d.sleep)\n let q1 = d3.quantile(data, 0.25);\n let q3 = d3.quantile(data, 0.75);\n let iqr = q3 - q1;\n let range = [q1 - 1.5 * iqr, q3 + 1.5 * iqr];\n \n return {\n q1,\n q3,\n iqr,\n range,\n data: concatSleepData.filter(d => d.sleep >= range[0] && d.sleep <= range[1])//filtering out sleep outliars\n .filter(d => d.glucose >= glucoseCutOff) //filtering out fasting - 4 is just arbitrary\n }.data //doing it like this for debugging purposes\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":503,"value":"Plot.plot({\n width: '1200',\n marks: [\n Plot.dot(concatSleepDataWithoutOutliars, {x: \"glucose\", y: \"sleep\", tip: false}),\n Plot.linearRegressionY(concatSleepDataWithoutOutliars, {x: \"glucose\", y: \"sleep\", stroke: \"red\", tip: true}),\n Plot.gridX({interval: 0.1, stroke: \"black\", strokeOpacity: 0.5}),\n Plot.gridY({interval: 0.5, stroke: \"black\", strokeOpacity: 0.5})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":792,"value":"// Plot.plot({\n// width: `${width}`,\n// // aspectRatio: 1,\n// marks: [\n// Plot.boxX(ketoMojoData, {x: \"value\", y: \"count\", sort: {y: \"x\"}, fx: \"type\", fy: d => d.Date.toLocaleString('default', {month: 'short'}), tip: true})\n// ]\n// })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":815,"value":"data_playground = ketoMojoRaw.map(d => ({Date: new Date(`${d.date}T${d.time}${d.timeZone}`), ...d}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":817,"value":"Plot.plot({\n x: {\n // interval: \"week\",\n // type: \"time\",\n // transform: d => {let a = `${d.date}${d.time}${d.timeZone}${d}`; console.log(a);return new Date(a)}\n },\n fx: {\n interval: \"month\"\n },\n color: {\n legend: true\n },\n marks: [\n Plot.boxX(data_playground, {filter: d => d.type === \"ketone\",x: \"value\", fx: \"Date\", tip: true})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":881,"value":"alphabet_ = alphabet","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":889,"value":"Plot.plot({\n marginTop: 50,\n y: {\n percent: true,\n tickFormat: d => `${d} %`,\n domain: [0, d3.max(alphabet, d => d.frequency)*100+1]\n },\n marks: [\n Plot.barY(alphabet, {x: \"letter\", y: \"frequency\", sort: {x: \"-y\"}, tip: true}),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":912,"value":"Plot.plot({\n y: {percent: true},\n marks: [\n Plot.rectY(\n olympians,\n Plot.normalizeY(\n \"sum\", // normalize each series by the sum per series\n Plot.binX(\n {y2: \"count\"}, // disable implicit stack transform\n {x: \"weight\", fill: \"sex\", mixBlendMode: \"multiply\"}\n )\n )\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":927,"value":"viewof search = Inputs.table(data_playground)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":978,"value":"Plot.plot({\n width: `${width}`,\n x: {\n // interval: 0.1 // change interval to 0.1\n },\n color: {\n type: \"diverging\",\n scheme: \"BuRd\"\n },\n marks: [\n Plot.rectY(data_playground, Plot.binX({y: \"count\", interval:0.1, fill: \"count\"}, {filter: d => d.type === \"ketone\", x: \"value\", tip: true})),\n \n Plot.ruleY([0])\n ]\n})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1046,"value":"Plot.plot({\n width: `${width}`,\n // height: 300,\n fy: {\n interval: \"month\",\n },\n x: {\n // tranform: d => parseFloat(d)\n // interval: \"month\" \n },\n marks: [\n Plot.boxX(data_playground.filter(d => d.type === \"ketone\").map(d => ({...d, value: parseFloat(d.value)})), {x: \"value\", y: \"count\", opacity: 0.5, fill: \"red\"}),\n Plot.boxX(data_playground.filter(d => d.type === \"ketone\").map(d => ({...d, value: parseFloat(d.value)})), {x: \"value\", y: \"count\", fy: \"Date\", tip: true, mixBlendMode: \"none\", fillOpacity: 0.7})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1250,"value":"viewof selectType = Inputs.select(Array.from(d3.union(data_playground.map(d => d.type))), {label: \"Select measurement type\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1091,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1273,"value":"{\n let d_type = selectType\n let data = data_playground.filter(d => d.type === d_type)\n return Plot.plot({\n width: `${width}`,\n // title: `${min}, ${Q1}, ${median}, ${Q3}, ${max}, IQR: ${IQR}`,\n x: {\n transform: d => parseFloat(d),\n // domain: [0, 60]\n },\n fy: {\n interval: \"month\"\n },\n marks: [\n Plot.boxX(data, {x: \"value\", fy: \"Date\", fill: \"Date\", z: null}),\n ]\n })\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1315,"value":"$34","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1324,"value":"// Plot.plot({\n// y: {transform: parseFloat},\n// marks: [\n// Plot.auto(data_playground, {x: \"Date\", y: \"value\", color: \"type\", mark: \"line\"})\n// ]\n// })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1532,"value":"cronometerData = (FileAttachment(\"dailysummary.csv\").csv({typed: false}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1537,"value":"$35","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W36","stdlib":"1"}} 2f:"$2e:body"