2b:null 2c:["$","title",null,{"children":"Route Optimization Problem - Minimizing Vehicles | NB.AI Solutions Team | Observable"}] 31:T415,kpiTable = { let kpis = []; mvrpID.forEach((sol) => { kpis.push(sol.kpis); }); yield Inputs.table(kpis, { columns: [ "vehiclesInProblem", "nbrRoutes", "unassigned", "totalTime", "totalDistance", "avgStops" ], header: { vehiclesInProblem: "Vehicle Count", nbrRoutes: "Routes", unassigned: "Unassigned", totalTime: "Total Time", totalDistance: "Total Distance", avgStops: "Avg. Stops per Veh" }, width: { vehiclesInProblem: 80, nbrRoutes: 80, unassigned: 80, totalTime: 120, totalDistance: 120, avgStops: 120 }, format: { type: (x) => x.toUpperCase(), arrival: (x) => new Date(x * 1000).toLocaleTimeString([], { hour: "2-digit", minute: "2-digit", hour12: false }), load: (x) => `${x[0]} back ${x[1]} front`, duration: (x) => (x / 60).toFixed(1) + " mn" }, maxWidth: 1100, multiple: false, required: false }); }32:T11f8,mvrpID = { if (inputData_a) { progress.value = 0; let location = []; //V2 API let locationV1 = ""; //V1 API let loc_index = 0; let shipments = []; let jobs = []; let vehicles = []; let locations = []; let bodyData = []; let nbrVehicles = parseInt(inputData_a.vehicles); for (let i = 0; i < 4; i++) { vehicles = []; shipments = []; locations = []; location = []; locationV1 = ""; loc_index = 0; let amount = [1, 0]; for (let j = 0; j < inputData_a.jobs; j++) { if (i == 0) { amount = randomVRPInputs.pointsArray[j].frontSeat ? [0, 1] : [1, 0]; } else if (i == 1) { amount = randomVRPInputs.pointsArray[j].frontSeat ? Math.random() < 0.66 ? [0, 1] : [1, 0] : [1, 0]; } else if (i == 2) { amount = randomVRPInputs.pointsArray[j].frontSeat ? Math.random() < 0.33 ? [0, 1] : [1, 0] : [1, 0]; } else { amount = [1, 0]; } let shipment = { pickup: { id: randomVRPInputs.pointsArray[j].id, description: randomVRPInputs.pointsArray[j].name, location_index: loc_index, service: parseInt(inputData_a.serviceTime) }, delivery: { id: randomVRPInputs.pointsArray[j].id, description: randomVRPInputs.pointsArray[j].name, location_index: loc_index + 1, service: parseInt(inputData_a.serviceTime) }, amount: amount }; shipments.push(shipment); // Location object different based on API version if (inputData_a.apiVersion.value === "V2") { location.push( `${randomVRPInputs.pointsArray[j].pickup_latitude},${randomVRPInputs.pointsArray[j].pickup_longitude}` ); location.push( `${randomVRPInputs.pointsArray[j].dropoff_latitude},${randomVRPInputs.pointsArray[j].dropoff_longitude}` ); } else { locationV1 += `${randomVRPInputs.pointsArray[j].pickup_latitude},${randomVRPInputs.pointsArray[j].pickup_longitude}|${randomVRPInputs.pointsArray[j].dropoff_latitude},${randomVRPInputs.pointsArray[j].dropoff_longitude}|`; } loc_index += 2; } // build vehicle location and inventory // increase number of vehicles at each pass for (let j = 0; j < nbrVehicles; j++) { if (inputData_a.apiVersion.value === "V2") { console.log(`Nbr veh ${nbrVehicles}`); console.log(`${randomVRPInputs.vehicleArray[j]}`); location.push( `${randomVRPInputs.vehicleArray[j].latitude},${randomVRPInputs.vehicleArray[j].longitude}` ); } else { locationV1 += `${randomVRPInputs.vehicleArray[j].latitude},${randomVRPInputs.vehicleArray[j].longitude}|`; } let vehicle = { id: randomVRPInputs.vehicleArray[j].id, description: randomVRPInputs.vehicleArray[j].vin, start_index: loc_index, end_index: loc_index, time_window: [ Date.parse(inputData_a.shiftStart) / 1000, Date.parse(inputData_a.shiftEnd) / 1000 ], capacity: [parseInt(inputData_a.vanCapacity), 1], skills: randomVRPInputs.vehicleArray[j].attr3 }; vehicles.push(vehicle); ++loc_index; } if (inputData_a.apiVersion.value === "V2") { locations = { id: 12, location: location }; } else { locationV1 = locationV1.substring(0, locationV1.length - 1); locations = { id: 12, location: locationV1 }; } bodyData.push({ description: `MVRP Tutorial ${i} ${new Date().toISOString()}|${nbrVehicles}`, locations: locations, // set shipments information shipments: shipments, // set vehicles information vehicles: vehicles, jobs: [], options: { travel_cost: inputs.objectiveFunction.value.value, minimise_num_depots: false } }); nbrVehicles += parseInt(inputData_a.vehicleIncrement); } const result = await Promise.all( bodyData.map(async (data) => { return mvrp(data, { objective: { minimise_num_depots: false, travel_cost: "duration" } }); }) ); progress.value = 0; return result; } }33:T6f0,async function mvrp( data, options = { objective: { minimise_num_depots: false, travel_cost: "distance" } } ) { let url = ""; if (inputData_a.apiVersion.value === "V2") { url = `https://api.nextbillion.io/optimization/v2`; } else { url = `https://api.nextbillion.io/optimise-mvrp`; } const optimizeRequest = await axios .post(`${url}` + `?key=${apiKey}`, { ...data, options }) .then((res) => res.data); const mvrpRequestId = optimizeRequest.id; let attempts = 0; while (attempts < 50) { const result = await axios({ url: `${url}/result?id=${mvrpRequestId}&key=${apiKey}`, method: "GET", headers: {} }); if ( result.data && result.data.status === "Ok" && result.data.message == "" ) { result.data.id = mvrpRequestId; // KPIs let deliveryCount = 0; result.data.result.routes.forEach((rte, rteidx) => { rte.steps.forEach((step, index) => { if (step.id && step.id != 99) { ++deliveryCount; } }); }); result.data.kpis = { totalDistance: `${(result.data.result.summary.distance / 1000).toFixed( 1 )} km`, totalTime: `${(result.data.result.summary.duration / 60).toFixed( 1 )} mn`, nbrRoutes: result.data.result.summary.routes, avgStops: (deliveryCount / result.data.result.summary.routes).toFixed( 1 ), unassigned: result.data.result.summary.unassigned, vehiclesInProblem: result.data.description.split("|")[1] }; return result.data; } attempts++; progress.value += 1; await new Promise((resolve) => setTimeout(resolve, 5 * 1000)); } }34:T83a,inputs = { const offset = new Date().getTimezoneOffset(); let newBase = new Date(new Date().getTime() + 86400 * 1000); let tmrwStart = new Date( `${newBase.toLocaleDateString("en-US")} 10:00:00 GMT${ offset >= 0 ? "+" : "" }${Math.round(offset / 60)}${ Math.abs(offset % 60) ? Math.abs(offset % 60) : "" }` ); let tmrwEnd = new Date( `${newBase.toLocaleDateString("en-US")} 13:00:00 GMT${ offset >= 0 ? "+" : "" }${Math.round(offset / 60)}${ Math.abs(offset % 60) ? Math.abs(offset % 60) : "" }` ); console.log(tmrwEnd.toISOString().slice(0, -5)); const inputs = { city: Inputs.select(cities, { label: "Select city", format: (x) => x.name, value: cities.find((t) => t.name === "Los Angeles") }), jobs: Inputs.select(jobs, { label: "Number of Jobs", value: "25" }), vehicles: Inputs.text({ label: "Starting Nbr of Vehicles", value: "10", max: 100, maxlength: 3 }), vehicleIncrement: Inputs.text({ label: "Vehicle Increment", value: "10" }), shiftStart: Inputs.datetime({ label: "Shift Start", value: tmrwStart.toISOString().slice(0, -1) }), shiftEnd: Inputs.datetime({ label: "Shift End", value: `${tmrwEnd.toISOString().slice(0, -1)}` }), useCase: Inputs.select(useCases, { label: "Use Case", format: (x) => x.name, value: useCases.find((t) => t.name === "service") }), objectiveFunction: Inputs.select(objectives, { label: "Objective Function", format: (x) => x.name, value: objectives.find((t) => t.name === "Minimize Time") }), serviceTime: Inputs.text({ label: "Service Time at Each Stop", value: "300" }), vanCapacity: Inputs.select(backseatCapacity, { label: "SeatingCapacity", value: "3" }), apiVersion: Inputs.select( [ { name: "V1", value: "V1" }, { name: "V2", value: "V2" } ], { label: "API Version", format: (x) => x.name } ) }; return inputs; }35:T5f3,routesGeoJson = { let routesGeoJson = { type: "FeatureCollection", features: [] }; // we create a geojson object to send to our mapview elements // It will be a feature collection of all the route polylines let bestRoute = -1; mvrpID.forEach((sol, idx) => { if (mvrpID[idx].result.summary.unassigned === 0) { if (bestRoute === -1) { bestRoute = idx; } } }); if (bestRoute === -1) { // no routes with 0 unassigned - just show the first route bestRoute = 0; } mvrpID[bestRoute].result.routes.forEach((rte, idx) => { //console.log(rte); let feature = { type: "Feature", properties: { id: rte.vehicle, vin: rte.description }, geometry: { coordinates: nextbillion.utils.polyline .decode(rte.geometry, 5) .map((c) => c.reverse()), type: "LineString" } }; routesGeoJson.features.push(feature); rte.steps.forEach((st, stIdx) => { let stop = { type: "Feature", properties: { id: rte.vehicle, vin: rte.description, description: st.description, stop_idx: stIdx, arrival: st.arrival, type: st.type, pointType: (st.type == "start") | (st.type == "end") ? 16 : 255 }, geometry: { coordinates: [st.location[1], st.location[0]], type: "Point" } }; routesGeoJson.features.push(stop); }); }); return routesGeoJson; }36:[] 2f:{"id":"c8f9c8ec0954a1fe","slug":"route-optimization-problem-minimizing-vehicles","title":"Route Optimization Problem - Minimizing Vehicles","thumbnail":"062f80af8b188a91149da44144c5122a03ea8b8ced238765db26c9e9285ff628","access_level":"public","create_time":"$D2023-07-10T13:55:38.760Z","update_time":"$D2023-08-07T13:19:47.817Z","view_time":null,"first_public_time":"$D2023-07-11T18:40:53.775Z","fork":{"id":"54f2e8fc42f99717","slug":"example-vehicle-routing-problem","title":"Example Route Optimization Problem","owner":{"id":"05baed136f9134c1","login":"nbai","name":"NB.AI Solutions Team"}},"stars":0,"starred":false,"owner":{"type":"team","id":"05baed136f9134c1","login":"nbai","name":"NB.AI Solutions Team","avatar_url":"https://avatars-next.observableusercontent.com/7a25d1b5deb9c5290b12e4e0914aacdbd4196bb5c3a94c63a0fda0fe706d28b2","tier":"starter_2024"},"version":2746,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"bcb6b2e256659128","login":"jim-welch","name":"Jim Welch","avatar_url":"https://avatars-next.observableusercontent.com/6334ed2ccd004bd8ab34e9f05c047bf4ef291dea914767f26079d7756d073607","tier":null}],"members":[{"type":"individual","id":"bcb6b2e256659128","login":"jim-welch","name":"Jim Welch","avatar_url":"https://avatars-next.observableusercontent.com/6334ed2ccd004bd8ab34e9f05c047bf4ef291dea914767f26079d7756d073607","tier":null,"role":"editor"},{"type":"team","id":"05baed136f9134c1","login":"nbai","name":"NB.AI Solutions Team","avatar_url":"https://avatars-next.observableusercontent.com/7a25d1b5deb9c5290b12e4e0914aacdbd4196bb5c3a94c63a0fda0fe706d28b2","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"Route Optimization Problem - Minimizing Vehicles","cells":[{"id":0,"value":"![text](https://d12qcqjlhp2ahm.cloudfront.net/maps-logo.svg) \n# Route Optimization Problem - Minimizing Vehicles","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1935,"value":"toc(\"h2, h3\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1189,"value":"viewof apiKey = secret2(\"apiKey\", {\n title: \"NextBillion.ai API Key\",\n submit: \"Set\",\n description: \"Contact support@nextbillion.ai to get your API key\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":508,"value":"### [Background](https://nextbillion.ai/developers/notebooks/route-optimization-minimizing-vehicles)\n[NextBillion's Route Optimization API](https://docs.nextbillion.ai) provides an interface for managing a list of orders/jobs/tasks with locations and creating the optimal, most efficient routes.\n

\nIn this tutorial, we'll guide you through a sequence of iterative steps to find an optimal solution that meets a customer's requirement to minimize the number of vehicles being used. \n\nThe main objective function for routing optimization is as follows:\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2563,"value":"\\text{Assume } {N}\\ldots \\text{ is a number of customers (1,2,\\ldots{n}) that need to be serviced}\\newline\n\n{c_{ij}}\\ldots\\text{ the transportation cost for the customer from \\textit{i} to \\textit{j.}}\\text{ Cost is measured as either travel time or travel distance}\\newline\\newline\n{V}\\ldots\\text{the set of all available vehicles } {k,k}\\in{V}\\newline\n{d}\\ldots\\text{the demand of the customer going from }{i} \\text{ to } {j}\\newline\n{q}\\ldots\\text{represents the demand or capacity required at location {i}}\\newline \n{Q_{k}}\\ldots\\text{denotes the maximum capacity of vehicle } \\textit{k} \\text{ for } \\textit{k}\\in{V}\\newline\n\\sum_{i\\in{N}}\\sum_{j\\in{N}}{c_{ij}}+\\sum_{k\\in{V}}\\lgroup\\sum_{i\\in{N}}\\sum_{j\\in{N}}{q_{i}}-{Q_{k}}\\rgroup{}\\rightarrow{min}\n","pinned":false,"hidden":false,"mode":"tex","author":"$undefined","summary":"$undefined"},{"id":2569,"value":"For this scenario we want to start with some number of vehicles and run 4 routing optimization solutions where each solution increments the number of vehicles by some value. Using the results from a first run, the planner can hone in on the optimal/minimum number of vehicles for the solution.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1934,"value":"{\n nextbillion.setApiKey(apiKey);\n return html`Set API key
nextbillion.setApiKey(${apiKey.substr(0,4)}....)`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":525,"value":"### Initial Setup\nIn this section we will set some initial parameters that are used to formulate the inputs to the MVRP solver. The `NB.ai API KEY` is necessary to interact with the MVRP services and it should be entered first to activate the tutorial tasks.\n

\nNext modify the other parameters according to the problem that you want to create: City Selection, Number of Jobs, Use Case and the Shift definitions.\n - City - creates the mock data in a defined geographic area based on city selection\n - Number of Jobs - the number of random pickup/delivery jobs to consider \n - Starting Number of Vehicles - the number of vehicles to consider for the first pass solution\n - Vehicle Increment - how many vehicles to add for each subsequent solution\n - Shift Start - date/time of the vehicle shift commencement\n - Shift End - date/time of the vehicle shift completion\n - Seating Capacity - how much seating capacity is available in each vehicle","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1234,"value":"---\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1451,"value":"viewof panel = Inputs.form(inputs, {\n template: (v) => htl.html`\n

\n ${v.city}\n

\n\n`\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2361,"value":"viewof panel2 = Inputs.form(inputs, {\n template: (v) => htl.html`\n

\n ${v.jobs}\n ${v.vehicles}\n ${v.vehicleIncrement}\n ${v.shiftStart}\n ${v.shiftEnd}\n ${v.serviceTime}\n ${v.vanCapacity}\n ${v.apiVersion}\n

\n\n`\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1566,"value":"{\n const button = html` `;\n button.onclick = () => {\n mutable inputData_a = panel2;\n button.disabled = true;\n };\n return html`${button}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":348,"value":"viewof jobID = htl.html`

Route Optimization Request IDs: ${\n mvrpID ? `${mvrpID[0].id}/${mvrpID[1].id}/${mvrpID[2].id}` : mvrpID\n}

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2435,"value":"progress = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2496,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":221,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2218,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":605,"value":"---\n### Render Solution on a Map (showing first solution with 0 unassigned)\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1261,"value":"viewof routeSelector = Inputs.select(routesGeoJson.features.filter(f => f.geometry.type === 'LineString'), {multiple: false, label: \"Select route to view\", format: x => x.properties.vin, value: t => t.properties.vin})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2674,"value":"stoptimeline = {\n const wrapper = DOM.element(\"div\");\n const width = 1000;\n //const height = 500;\n wrapper.style.width = width + \"px\";\n\n const m = DOM.element(\"div\");\n m.id = \"timeline\";\n m.style.width = 1 * width + \"px\";\n\n m.style.margin = \"auto\";\n // needs padding otherwise label optimization will fail\n m.style.padding = \"1px\";\n wrapper.appendChild(m);\n let items = [];\n selectedRoute[0].steps.forEach((step) => {\n items.push({\n year: step.arrival,\n title: step.description\n ? `${step.type}-${step.description}`\n : `${step.type}`,\n type: step.type\n });\n });\n console.log(items);\n // without the timeout observable isn't able to do the label optimization properly\n setTimeout(() => {\n const ms = milestones(m)\n .mapping({\n timestamp: \"year\",\n text: \"title\"\n })\n .parseTime(\"%s\")\n .labelFormat(\"%H:%M\")\n\n .optimize(true)\n .distribution(\"top-bottom\")\n .render(items);\n }, 0);\n\n return wrapper;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":997,"value":"{\n return geoviewer(routesFiltered, {\n width: 500,\n height: 500,\n col: \"#8058a0\",\n fillOpacity: 0.9,\n lineWidth: 3,\n style: \"nbai\",\n apiKey: apiKey\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1862,"value":"Inputs.table(selectedRoute[0].steps, {\n columns: [\"type\", \"arrival\", \"load\", \"description\", \"duration\"],\n header: {\n type: \"Task\",\n arrival: \"Arrival Time\",\n load: \"Nbr Loaded\",\n description: \"Passenger\",\n duration: \"Duration\"\n },\n width: {\n type: 80,\n arrival: 120,\n load: 100,\n duration: 100\n },\n format: {\n type: (x) => x.toUpperCase(),\n arrival: (x) =>\n new Date(x * 1000).toLocaleTimeString([], {\n hour: \"2-digit\",\n minute: \"2-digit\",\n hour12: false\n }),\n load: (x) => `${x[0]} back ${x[1]} front`,\n duration: (x) => (x / 60).toFixed(1) + \" mn\"\n },\n rows: 10,\n maxWidth: 600,\n multiple: false,\n required: false\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1898,"value":"selectedRoute = {\n let bestRoute = -1;\n mvrpID.forEach((sol, idx) => {\n if (mvrpID[idx].result.summary.unassigned === 0) {\n if (bestRoute === -1) {\n bestRoute = idx;\n }\n }\n });\n\n if (bestRoute === -1) {\n // no routes with 0 unassigned - just show the first route\n bestRoute = 0;\n }\n const theRoute = mvrpID[bestRoute].result.routes.filter(\n (f) => f.description === routeSelector.properties.vin\n );\n return theRoute;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":341,"value":"### Other Dependencies","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":2672,"value":"milestones = require(\"d3-milestones\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2678,"value":"stylesheet = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1139,"value":"import { nextbillion, cities, secret2 } from \"@nbai/nextbillion-ai-dependencies\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1912,"value":"randomVRPInputs = {\n let retval = null;\n retval = await fetch(\n `https://m4aqpzp5ah6n7ilzunwyo5ma2u0ezqcc.lambda-url.us-east-2.on.aws/?region=${panel.city.value}&number=1500&vehicles=100&type=darp`\n ).then((response) => response.json());\n\n // randomize whether passenger can sit in front seat preference\n retval.pointsArray = retval.pointsArray.map((pt) => {\n let frontSeat = Math.random() < 0.5;\n return { ...pt, frontSeat: frontSeat };\n });\n return retval;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":352,"value":"styling = html`\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":849,"value":"geoviewer = require(\"https://d12qcqjlhp2ahm.cloudfront.net/nb_geoverview2.js\").then((f) => f.view)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1686,"value":"mutable inputData_a = null","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2339,"value":"backseatCapacity = [\"2\", \"3\", \"4\", \"5\"];","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1111,"value":"vehicles = [\"1\", \"2\", \"5\", \"10\", \"15\", \"20\", \"50\", \"100\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1163,"value":"useCases = [\n {name: \"Delivery\", value: \"delivery\"},\n {name: \"Service Worker\", value: \"service\"}\n ]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1175,"value":"objectives = [\n {name: \"Minimize Time\", value: \"duration\"},\n {name: \"Minimize Distance\", value: \"distance\"}\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2581,"value":"jobs = [\"5\", \"10\", \"25\", \"50\", \"100\", \"200\", \"500\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1523,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":924,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1279,"value":"routesFiltered = Object({\n type: 'FeatureCollection',\n features: routesGeoJson.features.filter(f => f.properties.vin === routeSelector.properties.vin)\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1931,"value":"import { toc } from \"@nebrius/indented-toc\" // This component generates the Table of Contents","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2191,"value":"axios = {\n let axios = await require(\"axios\");\n return axios;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2268,"value":"turf = require(\"https://unpkg.com/@turf/turf@6/turf.min.js\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2370,"value":"import { progressBar } from \"@kasivisu4/progress-bar\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W36","stdlib":"1","titling":"auto"}} 30:"$2f:body"