30:[{"id":"6b1776f5a0a0e76e6428805c0074a8f262e3f34b1b50944da27903e014b409958dc29b03a1c9cc331949d6a2a404c19dfd0d9d36d9c32274e6ffbc07c11350ee","href":"https://static.observableusercontent.com/files/6b1776f5a0a0e76e6428805c0074a8f262e3f34b1b50944da27903e014b409958dc29b03a1c9cc331949d6a2a404c19dfd0d9d36d9c32274e6ffbc07c11350ee","name":"counties-albers-10m.json","type":"application/octet-stream","lastModified":1669922420805.067,"size":795407},{"id":"d8256bbe693088f3ae4b374ffa0c2c28e4b11faf0af9a4ab52e79915e7475cf19eab137ed6b233db5a967c04c70da546007899eff1dcec6fbcdea0b2d586fb05","href":"https://static.observableusercontent.com/files/d8256bbe693088f3ae4b374ffa0c2c28e4b11faf0af9a4ab52e79915e7475cf19eab137ed6b233db5a967c04c70da546007899eff1dcec6fbcdea0b2d586fb05","name":"amtrack_july_2022.json","type":"application/json","lastModified":1669924051530.906,"size":14459165},{"id":"63e0b213fe4eda308733691b0fb4284f001486d42175ce647037197137283a497fea8f48f619a77ec19d17e5379d632e5394e5330721d709286a88d17445efb8","href":"https://static.observableusercontent.com/files/63e0b213fe4eda308733691b0fb4284f001486d42175ce647037197137283a497fea8f48f619a77ec19d17e5379d632e5394e5330721d709286a88d17445efb8","name":"amtrack_july_2022@1.json","type":"application/json","lastModified":1669924999009.826,"size":27126308}] 2e:{"title":"D3 U.S. Map","cells":[{"id":0,"value":"md`# D3 U.S. Map`","pinned":false,"hidden":false,"mode":"ojs"},{"id":313,"value":"## notes\nfull_data was a flat array, but needs to be an array of arrays. So I split it into arbitrarily sized chunks (full_data_chunked). The problem now is that I don't think the groups are accurate... ie the chunks are not together by the correct times. So in the Python code, need to just group the correct time items together so that our \"big data array\" looks something like the full_data_chunked array, then we scrub through that. The animation is pretty chaotic now because of the incorrect grouping, only showing the structure","pinned":false,"hidden":false,"mode":"md"},{"id":213,"value":"viewof data = Scrubber(full_data_chunked, {\n delay: 16,\n autoplay: false\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":29,"value":"map = svg``","pinned":true,"hidden":false,"mode":"ojs"},{"id":179,"value":"getLat = (d) => {\n console.log(d)\n let loc = projection([+d.longitude, +d.latitude])\n return `${loc && loc[0]}`\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":207,"value":"getLng = (d) => {\n let loc = projection([+d.longitude, +d.latitude])\n return `${loc && loc[1]}`\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":215,"value":"// datafiles = [\n// 'amtrak-2022-12-01-130550',\n// 'amtrak-2022-12-01-131040',\n// 'amtrak-2022-12-01-133959',\n// 'amtrak-2022-12-01-135156',\n// 'amtrak-2022-12-01-141433',\n// 'amtrak-2022-12-01-143026',\n// 'amtrak-2022-12-01-144524',\n// 'amtrak-2022-12-01-145533',\n// 'amtrak-2022-12-01-151723',\n// 'amtrak-2022-12-01-153421',\n// 'amtrak-2022-12-01-154610',\n// 'amtrak-2022-12-01-155630',\n// 'amtrak-2022-12-01-162700',\n// 'amtrak-2022-12-01-165036',\n// 'amtrak-2022-12-01-171409',\n// 'amtrak-2022-12-01-173026',\n// 'amtrak-2022-12-01-174521',\n// 'amtrak-2022-12-01-175538'\n// ]","pinned":false,"hidden":false,"mode":"ojs"},{"id":220,"value":"// endpoint = `https://raw.githubusercontent.com/codecreative/train-tester/main/data/${datafile}`","pinned":true,"hidden":false,"mode":"ojs"},{"id":116,"value":"// dataold = fetch(endpoint)\n// .then((response) => response.json())\n// .then((json) => json.filter((d) => d.location))","pinned":true,"hidden":false,"mode":"ojs"},{"id":255,"value":"full_data = FileAttachment(\"amtrack_july_2022@1.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":310,"value":"full_data_chunked = R.splitEvery(60)(full_data)","pinned":true,"hidden":false,"mode":"ojs"},{"id":37,"value":"projection = d3.geoAlbersUsa().scale(1300).translate([487.5, 305])","pinned":true,"hidden":false,"mode":"ojs"},{"id":47,"value":"path = d3.geoPath()","pinned":true,"hidden":false,"mode":"ojs"},{"id":104,"value":"states = topojson.mesh(us, us.objects.states, (a, b) => a !== b)","pinned":true,"hidden":false,"mode":"ojs"},{"id":108,"value":"nation = topojson.feature(us, us.objects.nation)","pinned":true,"hidden":false,"mode":"ojs"},{"id":101,"value":"counties = topojson.mesh(us, us.objects.counties, (a, b) => a !== b && (a.id / 1000 | 0) === (b.id / 1000 | 0))","pinned":true,"hidden":false,"mode":"ojs"},{"id":13,"value":"us = FileAttachment(\"counties-albers-10m.json\").json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":10,"value":"topojson = require(\"topojson-client@3\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":7,"value":"d3 = require(\"d3-geo@2\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":210,"value":"import { Scrubber } from \"@mbostock/scrubber\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":308,"value":"R = import(\"https://cdn.skypack.dev/ramda\")","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W30","stdlib":"1","titling":"auto"}