2c:null 2d:["$","title",null,{"children":"Field Service Scheduling: Planning Monthly Recurring Customer Visits | NB.AI Solutions Team | Observable"}] 32:T716,## Introduction In the field force and field service industry, managing recurring customer visits on a weekly or monthly basis has become a standard practice. Field executives are often required to visit customer locations once, twice, or even thrice a week, depending on the service requirements. Efficiently planning and optimizing these schedules is crucial to ensure seamless operations, maintain customer satisfaction, and maximize resource utilization. Mapfusion's powerful Route Optimization API offers a comprehensive solution to address these challenges by leveraging multiple configurable parameters. This innovative tool simplifies the complex task of managing field service operations, enabling organizations to achieve: * Balanced Task Distribution: Avoid overburdening specific agents while ensuring equitable distribution of jobs. * Weekly and Monthly Scheduling: Create effective schedules that align with customer requirements and agent availability. * Dynamic Job Allocation: Allocate jobs to agents intelligently based on factors such as proximity, availability, and service priority. * Efficient Resource Utilization: Minimize idle time, reduce travel costs, and optimize the use of field agents. By addressing these common pain points, Mapfusion helps organizations overcome key operational challenges, such as managing last-minute schedule changes, ensuring on-time visits, and reducing travel redundancies. Whether you're dealing with daily task planning, recurring service appointments, or strategic field force deployment, Mapfusion empowers your team to operate more effectively. This approach not only enhances operational efficiency but also drives customer satisfaction by ensuring reliable and timely service delivery—a critical factor in the competitive field service industry.33:T477,function generateDatesForDay(dayName, year, month) { const daysOfWeek = [ "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday" ]; const monthNames = [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec" ]; // Convert month name to index (0-based) const monthIndex = monthNames.indexOf(month); if (monthIndex === -1) throw new Error(`Invalid month: ${month}`); const dates = []; const firstDayOfMonth = new Date(Date.UTC(year, monthIndex, 1)); // Use UTC for accuracy // Iterate through the days of the month for (let i = 0; i < 31; i++) { const currentDate = new Date(Date.UTC(year, monthIndex, i + 1)); if (currentDate.getUTCMonth() !== monthIndex) break; // Stop when month changes if (daysOfWeek[currentDate.getUTCDay()] === dayName) { // Format as DD-MM-YYYY const formattedDate = currentDate .toISOString() .split("T")[0] .split("-") .reverse() .join("-"); dates.push(formattedDate); } } return dates; }34:T77b,function processYearMonth(data) { const processedRows = []; let jobId = 1; data.forEach((row) => { const { year, month, visit_frequency: frequency } = row; const yearInt = parseInt(year, 10); const frequencyInt = parseInt(frequency, 10); if (isNaN(yearInt)) throw new Error(`Invalid year: ${year}`); if (isNaN(frequencyInt)) throw new Error(`Invalid frequency: ${frequency}`); // Generate all dates for the given month and year const daysInMonth = new Date( yearInt, new Date(Date.parse(`${month} 1, ${yearInt}`)).getMonth() + 1, 0 ).getDate(); const dates = Array.from({ length: daysInMonth }, (_, i) => { const day = (i + 1).toString().padStart(2, "0"); return `${day}-${month}-${year}`; }); if (frequencyInt === 1) { // For frequency 1, use all dates const newRow = { ...row, jobId: jobId++ }; dates.forEach((date, index) => { newRow[`date_${index + 1}`] = date; }); processedRows.push(newRow); } else { // Split dates into chunks based on frequency let chunkSize; if (frequencyInt === 2) { chunkSize = 15; // First 15 days and remaining } else if (frequencyInt === 3) { chunkSize = 10; // First 10 days, next 10, and remaining } else if (frequencyInt === 4) { chunkSize = 7; // First 7 days, next 7, and so on } for (let i = 0; i < frequencyInt; i++) { const chunk = dates.slice(i * chunkSize, (i + 1) * chunkSize); const newRow = { ...row, jobId: jobId++ }; chunk.forEach((date, index) => { newRow[`date_${index + 1}`] = date; }); // Fill remaining `date_*` keys with empty strings for (let j = chunk.length; j < 31; j++) { newRow[`date_${j + 1}`] = ""; } processedRows.push(newRow); } } }); return processedRows; }35:T40a,function generateWeekdaysOfMonth(year, month) { const daysOfWeek = [ "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday" ]; const monthNames = [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec" ]; // Convert month name to index (0-based) const monthIndex = monthNames.indexOf(month); if (monthIndex === -1) throw new Error(`Invalid month: ${month}`); const weekdays = []; for (let i = 1; i <= 31; i++) { const currentDate = new Date(Date.UTC(year, monthIndex, i)); if (currentDate.getUTCMonth() !== monthIndex) break; // Stop when month changes const dayOfWeek = daysOfWeek[currentDate.getUTCDay()]; // If it's a weekday (Monday to Friday), add it to the list if (dayOfWeek !== "Saturday" && dayOfWeek !== "Sunday") { weekdays.push( currentDate.toISOString().split("T")[0].split("-").reverse().join("-") ); } } return weekdays; }36:Te1c,tempVRPProblem = { let loc_index = 0; let jobs = []; let job = {}; let vehicle = {}; let vehicles = []; let locations = []; let relations = []; const startTime = "10:00:00"; const endTime = "19:00:00"; let jobClusters = {}; // Object to group jobs by id and frequency // Group jobs and populate jobClusters customers_visit_schedule.forEach((row) => { locations.push(`${row.latitude},${row.longitude}`); // Prepare time windows let timeWindows = []; const dateFields = Array.from({ length: 31 }, (_, i) => `date_${i + 1}`); dateFields.forEach((dateField) => { const dateStr = row[dateField]; if (dateStr) { const [day, month, year] = dateStr.split("-"); const formattedDate = `${year}-${month.padStart(2, "0")}-${day.padStart( 2, "0" )}`; const startTimeString = `${formattedDate} ${startTime}`; const endTimeString = `${formattedDate} ${endTime}`; const startSchedule = Math.floor( new Date(startTimeString).getTime() / 1000 ); const endSchedule = Math.floor( new Date(endTimeString).getTime() / 1000 ); timeWindows.push([startSchedule, endSchedule]); } }); // Initialize job object job = { id: row.jobId.toString(), description: row.outlet_cd, metadata: { frequency: row.visit_frequency, group_id: row.id // Add the grouping id }, location_index: loc_index++ }; if (timeWindows.length > 0) { job.time_windows = timeWindows; } jobs.push(job); // Group jobs by id and frequency const clusterKey = `${row.id}_${row.visit_frequency}`; if (!jobClusters[clusterKey]) { jobClusters[clusterKey] = []; } jobClusters[clusterKey].push(job); }); // Log the jobClusters console.log("Job Clusters:", JSON.stringify(jobClusters, null, 2)); // Create precedence relations only for jobs with the same id and frequency > 1 Object.values(jobClusters).forEach((group) => { if (group.length > 1 && group[0].metadata.frequency > 1) { for (let i = 0; i < group.length - 1; i++) { const frequency = group[i].metadata.frequency; let min_duration = frequency === 2 ? 10 * 24 * 60 * 60 : frequency === 3 ? 7 * 24 * 60 * 60 : frequency === 4 ? 5 * 24 * 60 * 60 : 0; relations.push({ type: "precedence", min_duration: min_duration, steps: [ { type: "job", id: group[i].id }, { type: "job", id: group[i + 1].id } ] }); } } }); // Process vehicles agents_visit_schedule.forEach((row) => { locations.push(`${row.Latitude},${row.Longitude}`); const [day, month, year] = row.date.split("-"); const formattedDate = `${year}-${month.padStart(2, "0")}-${day.padStart( 2, "0" )}`; const startTimeString = `${formattedDate} ${row.Start_time}`; const endTimeString = `${formattedDate} ${row.End_time}`; const startSchedule = Math.floor( new Date(startTimeString).getTime() / 1000 ); const endSchedule = Math.floor(new Date(endTimeString).getTime() / 1000); vehicle = { id: row.unique_id, description: row.Name, start_index: loc_index, time_window: [startSchedule, endSchedule] }; vehicles.push(vehicle); }); return { locations: locations, location_index: loc_index, jobs: jobs, vehicles: vehicles, relations: relations }; }37:T6ad,async function mvrp( data, options = { objective: { minimise_num_depots: false, travel_cost: "distance" } } ) { let url = ""; if (0) { url = `https://api.nextbillion.io/optimization/v2`; } else { url = `https://api.nextbillion.io/optimise-mvrp`; } //url = `https://api.nextbillion.io/optimization/v2`; const optimizeRequest = await axios .post(`${url}` + `?key=opensesame`, { ...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=opensesame`, 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 }; return result.data; } attempts++; await new Promise((resolve) => setTimeout(resolve, 5 * 1000)); } }38:T4c3,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 checkStatus.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; }39:[{"id":"9a2c568b05f7447271be34d60385a4328df22b444ba4da22a2ecb03cf06b18eed9a8e2c25fce13a2bf9d9ceb160655016500e0a27a3834cc81166c1d563f1120","href":"https://static.observableusercontent.com/files/9a2c568b05f7447271be34d60385a4328df22b444ba4da22a2ecb03cf06b18eed9a8e2c25fce13a2bf9d9ceb160655016500e0a27a3834cc81166c1d563f1120","name":"agents@1.csv","type":"text/csv","lastModified":1736146353985.677,"size":434},{"id":"c1f4186a915760600e3fe8215d8ff07fb23521677be4af2a72a5b6b5d5520e11a4591331ae5200d7be952173afa6f641653dfc4ff442cdbbf29472c6f983a8f4","href":"https://static.observableusercontent.com/files/c1f4186a915760600e3fe8215d8ff07fb23521677be4af2a72a5b6b5d5520e11a4591331ae5200d7be952173afa6f641653dfc4ff442cdbbf29472c6f983a8f4","name":"OUTLETS@1.csv","type":"text/csv","lastModified":1736146345633.257,"size":8768}] 30:{"id":"90963f6639d1e003","slug":"field-service-scheduling-planning-monthly-recurrin","title":"Field Service Scheduling: Planning Monthly Recurring Customer Visits","thumbnail":"6c0c525aae8cdc19d6082db01a33724fc03811f5be2e6bcdba9ae75367122bf7","access_level":"unlisted","create_time":"$D2024-12-30T06:41:13.329Z","update_time":"$D2025-02-12T07:39:11.933Z","view_time":null,"first_public_time":"$D2025-01-06T09:49:30.968Z","fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"05baed136f9134c1","login":"nbai","name":"NB.AI Solutions Team","avatar_url":"https://avatars-next.observableusercontent.com/7a25d1b5deb9c5290b12e4e0914aacdbd4196bb5c3a94c63a0fda0fe706d28b2","tier":"starter_2024"},"version":1404,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"53e0d2bd94fa8912","login":"dhivya-s","name":"Dhivya Selvarajan","avatar_url":"https://avatars-next.observableusercontent.com/c2ddc5b79da0e63c190a3f3ac91670f23f26b4bc0b1be30699fe67082ccb4f0b","tier":null}],"members":[{"type":"team","id":"05baed136f9134c1","login":"nbai","name":"NB.AI Solutions Team","avatar_url":"https://avatars-next.observableusercontent.com/7a25d1b5deb9c5290b12e4e0914aacdbd4196bb5c3a94c63a0fda0fe706d28b2","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Field Service Scheduling: Planning Monthly Recurring Customer Visits","cells":[{"id":0,"value":"# Field Service Scheduling: Planning Monthly Recurring Customer Visits","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":8,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":19,"value":"viewof apiKey = secret2(\"apiKey\", {\n title: \"NextBillion.ai API Key\",\n submit: \"Set\",\n description:\n \"Contact support@nextbillion.ai to get your API key\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":21,"value":"import { nextbillion, cities, secret2 } from \"@yvannbai/nextbillion-ai-helpers\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":75,"value":"outlets = await FileAttachment(\"OUTLETS@1.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":180,"value":"visits = outlets","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":735,"value":"## Generate Customer Visit Schedule","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1005,"value":"In this step, the visit schedule for each customer is generated. Here, you will plan for a specific month. Based on your selection (e.g., January 2025), this step will generate visit dates for all days within the selected month. These visit dates will then be sent to the optimizer. The API will choose the most suitable date from the list of Fridays to optimize the route plan effectively.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":557,"value":"import { retainSelection } from \"@observablehq/fabians-toolbox\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":525,"value":"viewof beat_plan = Inputs.form(inputs, {\n template: (v) => htl.html`\n

\n ${v.month}\n ${v.year}\n

\n\n`\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":585,"value":"dayyearmonth = visits.map((row) => ({\n ...row,\n year: beat_plan.year,\n month: beat_plan.month\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":612,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":615,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1007,"value":"### Jobs with Visit Schedule","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":620,"value":"customers_visit_schedule = processYearMonth(dayyearmonth)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":731,"value":"## Generate Agents Schedule","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1009,"value":"In this step, we will load the agents who will be assigned to fulfill the jobs. The agents' work schedules for the selected month will be generated, excluding all weekends. This ensures that the planning aligns with the agents' availability and non-working days.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":641,"value":"agents_schedule = await FileAttachment(\"agents@1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":690,"value":"agents = agents_schedule.map((row) => ({\n ...row,\n year: beat_plan.year,\n month: beat_plan.month\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":646,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":649,"value":"function processAgentsData(data, year, month) {\n const processedRows = [];\n const weekdays = generateWeekdaysOfMonth(year, month);\n let uniqueId = 1;\n\n data.forEach((row) => {\n const { Name, Latitude, Longitude, Start_time, End_time } = row;\n\n // Create a new row for each generated weekday\n weekdays.forEach((date) => {\n processedRows.push({\n unique_id: `${uniqueId++}_${Name}_${date}`, // Increment unique ID for each row\n Name,\n Latitude,\n Longitude,\n Start_time,\n End_time,\n year,\n month,\n date // Add the generated date\n });\n });\n });\n\n return processedRows;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":652,"value":"agents_visit_schedule = processAgentsData(\n agents,\n beat_plan.year,\n beat_plan.month\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":728,"value":"## Route Optimisation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":1016,"value":"In this stage, the Route Optimization API is utilized to plan routes efficiently. The job and agent records are input into the API, where they are mapped with relevant parameters such as IDs, time windows, descriptions, and other necessary details.\n\n### Managing Multiple Visits\nFor customers requiring two visits per month, this scenario is addressed using the Relations Precedence parameter of the API. Jobs with multiple visits are included in the precedence configuration, and the min_duration parameter is set to 5 days (in seconds). This ensures that the second visit in the precedence sequence occurs only after a minimum interval of 5 days, maintaining compliance with the scheduling requirements.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":742,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":790,"value":"vrpID = {\n let locations = Object({});\n\n let url = `https://api.nextbillion.io/optimization/v2?key=${apiKey}`;\n\n let bodyData = {\n description: `MultiDay Route Planning_Beat Plan}`,\n locations: {\n id: 2,\n location: tempVRPProblem.locations\n },\n // set vehicles information\n vehicles: tempVRPProblem.vehicles,\n jobs: tempVRPProblem.jobs,\n relations: tempVRPProblem.relations,\n options: {\n travel_cost: \"duration\",\n custom: {\n type: \"min\",\n value: \"vehicles\"\n }\n }\n };\n console.log(\"Request Body Data:\", JSON.stringify(bodyData, null, 2));\n\n const response = await fetch(url, {\n method: \"POST\",\n headers: {\n \"Content-Type\": \"application/json\"\n },\n body: JSON.stringify(bodyData)\n });\n const jsonResponse = await response.json(); // Parse the response to JSON\n return jsonResponse;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":839,"value":"html`

If you get a status message indicating Job still processing just wait a few seconds and press this button.

${viewof requestResult}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":892,"value":"checkStatus = {\n vrpID;\n requestResult;\n let url = `https://api.nextbillion.io/optimization/v2/result?key=${apiKey}&id=${vrpID.id}`;\n let retval = null;\n\n yield Promises.delay(5000).then(() => {\n retval = fetch(url).then((response) => response.json());\n return retval;\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1330,"value":"viewof routeSelector = Inputs.select(\n routesGeoJson.features.filter((f) => f.geometry.type === \"LineString\"),\n {\n multiple: false,\n label: \"Select route to view\",\n format: (x) => x.properties.vin,\n value: (t) => t.properties.vin\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1355,"value":"{\n return geoviewer(routesFiltered, {\n width: 800,\n height: 500,\n col: \"#8058a0\",\n fillOpacity: 0.9,\n lineWidth: 3,\n style: \"nbai\",\n apiKey: \"opensesame\"\n });\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1269,"value":"resul = checkStatus.result.routes","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1368,"value":"Plot.plot({\n marks: [\n Plot.auto(resul, {x: \"vehicle\", y: \"cost\", fx: \"profile\", fy: \"waiting_time\"})\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":863,"value":" \n ","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined"},{"id":909,"value":"ncc_url = {\n const result = await axios({\n url: `https://api.nextbillion.io/optimization/v2/create_shared_result?key=${apiKey}&id=${vrpID.id}`,\n method: \"GET\",\n headers: {}\n });\n return `console.nextbillion.ai/route-planner-viewer?id=${vrpID.id}&host=api.nextbillion.io`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":86,"value":"## Dependencies","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":89,"value":"clusters = [\"2\", \"3\", \"4\", \"5\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":91,"value":"inputs = {\n const inputs = {\n clusters: Inputs.select(clusters, {\n label: \"No of Clusters\",\n value: \"5\"\n }),\n day: Inputs.select(day, {\n label: \"select the day\",\n value: \"Monday\"\n }),\n month: Inputs.select(month, {\n label: \"select the month\",\n value: \"Jan\"\n }),\n year: Inputs.select(year, {\n label: \"select the year\",\n value: \"2025\"\n })\n };\n return inputs;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":100,"value":"mutable inputData_a = null","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":102,"value":"mutable run_optimization = false","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":159,"value":"html`${viewof requestResult}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":163,"value":"viewof requestResult = Inputs.button(\"Request Result\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":114,"value":"axios = {\n let axios = await require(\"axios\");\n return axios;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":519,"value":"year = [\"2025\", \"2026\", \"2027\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":517,"value":"month = [\n \"Jan\",\n \"Feb\",\n \"Mar\",\n \"Apr\",\n \"May\",\n \"Jun\",\n \"Jul\",\n \"Aug\",\n \"Sep\",\n \"Oct\",\n \"Nov\",\n \"Dec\"\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":510,"value":"day = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":548,"value":"// A `template` callback for Inputs.form() that displays all inputs inline.\nfunction filterBar(inputs) {\n const scope = DOM.uid().id;\n return htl.html`

${Object.values(inputs)}\n