\n \n \n \n \n\n",[],[173,255],"10309f92-f7d5-11f0-be9d-42dde27851f2","favicon.svg","1030a050-f7d5-11f0-be9d-42dde27851f2",7,["D",1769115492011],"\n",[],[173,264],"34515420-f7e4-11f0-a6f6-42dde27851f2","animalHash.ts","f4ca8372-f7e2-11f0-b095-42dde27851f2","f4aa6a42-f7e2-11f0-94ff-42dde27851f2",49,["D",1769121995073],"// Exact implementation of angry-purple-tiger\n// https://github.com/helium/angry-purple-tiger\n// Using blueimp-md5 for correct MD5 hashing\n\nimport md5 from \"https://esm.sh/blueimp-md5@2.19.0\";\n\n// Word lists from the original library (exact order matters!)\nconst ADJECTIVES = [\n \"attractive\", \"bald\", \"beautiful\", \"rare\", \"clean\", \"dazzling\", \"lucky\", \"elegant\",\n \"fancy\", \"fit\", \"fantastic\", \"glamorous\", \"gorgeous\", \"handsome\", \"long\", \"magnificent\",\n \"muscular\", \"plain\", \"able\", \"quaint\", \"scruffy\", \"innocent\", \"short\", \"skinny\",\n \"acrobatic\", \"tall\", \"proper\", \"alert\", \"lone\", \"agreeable\", \"ambitious\", \"brave\",\n \"calm\", \"delightful\", \"eager\", \"faithful\", \"gentle\", \"happy\", \"jolly\", \"kind\",\n \"lively\", \"nice\", \"obedient\", \"polite\", \"proud\", \"silly\", \"thankful\", \"winning\",\n \"witty\", \"wonderful\", \"zealous\", \"expert\", \"amateur\", \"clumsy\", \"amusing\", \"vast\",\n \"fierce\", \"real\", \"helpful\", \"itchy\", \"atomic\", \"basic\", \"mysterious\", \"blurry\",\n \"perfect\", \"best\", \"powerful\", \"interesting\", \"decent\", \"wild\", \"jovial\", \"genuine\",\n \"broad\", \"brisk\", \"brilliant\", \"curved\", \"deep\", \"flat\", \"high\", \"hollow\", \"low\",\n \"narrow\", \"refined\", \"round\", \"shallow\", \"skinny\", \"square\", \"steep\", \"straight\", \"wide\",\n \"big\", \"colossal\", \"clever\", \"gigantic\", \"great\", \"huge\", \"immense\", \"large\",\n \"little\", \"mammoth\", \"massive\", \"micro\", \"mini\", \"petite\", \"puny\", \"scrawny\",\n \"small\", \"polished\", \"teeny\", \"tiny\", \"crazy\", \"dancing\", \"custom\", \"faint\",\n \"harsh\", \"formal\", \"howling\", \"loud\", \"melodic\", \"noisy\", \"upbeat\", \"quiet\",\n \"dandy\", \"raspy\", \"rhythmic\", \"daring\", \"zany\", \"digital\", \"dizzy\", \"exotic\",\n \"fun\", \"furry\", \"hidden\", \"ancient\", \"brief\", \"early\", \"fast\", \"future\", \"late\",\n \"modern\", \"old\", \"prehistoric\", \"zesty\", \"rapid\", \"slow\", \"swift\", \"young\",\n \"acidic\", \"bitter\", \"cool\", \"creamy\", \"keen\", \"tricky\", \"fresh\", \"special\",\n \"unique\", \"hot\", \"magic\", \"main\", \"nutty\", \"pet\", \"mythical\", \"ripe\", \"wobbly\",\n \"salty\", \"savory\", \"sour\", \"spicy\", \"bright\", \"stale\", \"sweet\", \"tangy\", \"tart\",\n \"rich\", \"rural\", \"urban\", \"breezy\", \"bumpy\", \"chilly\", \"cold\", \"cuddly\", \"damaged\",\n \"damp\", \"restless\", \"dry\", \"flaky\", \"fluffy\", \"virtual\", \"merry\", \"icy\", \"shiny\",\n \"melted\", \"joyous\", \"rough\", \"shaggy\", \"sharp\", \"radiant\", \"sticky\", \"strong\",\n \"soft\", \"uneven\", \"warm\", \"feisty\", \"cheery\", \"energetic\", \"abundant\", \"macho\",\n \"glorious\", \"mean\", \"quick\", \"precise\", \"stable\", \"spare\", \"sunny\", \"trendy\",\n \"shambolic\", \"striped\", \"boxy\", \"generous\", \"tame\", \"joyful\", \"festive\", \"bubbly\",\n \"soaring\", \"orbiting\", \"sparkly\", \"smooth\", \"docile\", \"original\", \"electric\",\n \"funny\", \"passive\", \"active\", \"cheesy\", \"blunt\", \"dapper\", \"bent\", \"curly\",\n \"oblong\", \"sneaky\", \"overt\", \"careful\", \"jumpy\", \"bouncy\", \"recumbent\", \"cheerful\",\n \"droll\", \"odd\", \"suave\", \"sleepy\",\n];\n\nconst COLORS = [\n \"white\", \"pearl\", \"alabaster\", \"snowy\", \"ivory\", \"cream\", \"cotton\", \"chiffon\",\n \"lace\", \"coconut\", \"linen\", \"bone\", \"daisy\", \"powder\", \"frost\", \"porcelain\",\n \"parchment\", \"velvet\", \"tan\", \"beige\", \"macaroon\", \"hazel\", \"felt\", \"metal\",\n \"gingham\", \"sand\", \"sepia\", \"latte\", \"vinyl\", \"glass\", \"hazelnut\", \"canvas\",\n \"wool\", \"yellow\", \"golden\", \"daffodil\", \"flaxen\", \"butter\", \"lemon\", \"mustard\",\n \"tartan\", \"blue\", \"cloth\", \"fiery\", \"banana\", \"plastic\", \"dijon\", \"honey\",\n \"blonde\", \"pineapple\", \"orange\", \"tangerine\", \"marigold\", \"cider\", \"rusty\",\n \"ginger\", \"tiger\", \"bronze\", \"fuzzy\", \"opaque\", \"clay\", \"carrot\", \"corduroy\",\n \"ceramic\", \"marmalade\", \"amber\", \"sandstone\", \"concrete\", \"red\", \"cherry\",\n \"hemp\", \"merlot\", \"garnet\", \"crimson\", \"ruby\", \"scarlet\", \"burlap\", \"brick\",\n \"bamboo\", \"mahogany\", \"blood\", \"sangria\", \"berry\", \"currant\", \"blush\", \"candy\",\n \"lipstick\", \"pink\", \"rose\", \"fuchsia\", \"punch\", \"watermelon\", \"rouge\", \"coral\",\n \"peach\", \"strawberry\", \"rosewood\", \"lemonade\", \"taffy\", \"bubblegum\", \"crepe\",\n \"hotpink\", \"purple\", \"mauve\", \"violet\", \"boysenberry\", \"lavender\", \"plum\",\n \"magenta\", \"lilac\", \"grape\", \"eggplant\", \"eggshell\", \"iris\", \"heather\", \"amethyst\",\n \"raisin\", \"orchid\", \"mulberry\", \"carbon\", \"slate\", \"sky\", \"navy\", \"indigo\",\n \"cobalt\", \"cedar\", \"ocean\", \"azure\", \"cerulean\", \"spruce\", \"stone\", \"aegean\",\n \"denim\", \"admiral\", \"sapphire\", \"arctic\", \"green\", \"chartreuse\", \"juniper\",\n \"sage\", \"lime\", \"fern\", \"olive\", \"emerald\", \"pear\", \"mossy\", \"shamrock\",\n \"seafoam\", \"pine\", \"mint\", \"seaweed\", \"pickle\", \"pistachio\", \"basil\", \"brown\",\n \"coffee\", \"chrome\", \"peanut\", \"carob\", \"hickory\", \"wooden\", \"pecan\", \"walnut\",\n \"caramel\", \"gingerbread\", \"syrup\", \"chocolate\", \"tortilla\", \"umber\", \"tawny\",\n \"brunette\", \"cinnamon\", \"glossy\", \"teal\", \"grey\", \"shadow\", \"graphite\", \"iron\",\n \"pewter\", \"cloud\", \"silver\", \"smoke\", \"gauze\", \"ash\", \"foggy\", \"flint\",\n \"charcoal\", \"pebble\", \"lead\", \"tin\", \"fossilized\", \"black\", \"ebony\", \"midnight\",\n \"inky\", \"oily\", \"satin\", \"onyx\", \"nylon\", \"fleece\", \"sable\", \"jetblack\", \"coal\",\n \"mocha\", \"obsidian\", \"jade\", \"cyan\", \"leather\", \"maroon\", \"carmine\", \"aqua\",\n \"chambray\", \"holographic\", \"laurel\", \"licorice\", \"khaki\", \"goldenrod\", \"malachite\",\n \"mandarin\", \"mango\", \"taupe\", \"aquamarine\", \"turquoise\", \"vermilion\", \"saffron\",\n \"cinnabar\", \"myrtle\", \"neon\", \"burgundy\", \"tangelo\", \"topaz\", \"wintergreen\",\n \"viridian\", \"vanilla\", \"paisley\", \"raspberry\", \"tweed\", \"pastel\", \"opal\",\n \"menthol\", \"champagne\", \"gunmetal\", \"infrared\", \"ultraviolet\", \"rainbow\",\n \"mercurial\", \"clear\", \"misty\", \"steel\", \"zinc\", \"citron\", \"cornflower\", \"lava\",\n \"quartz\", \"honeysuckle\", \"chili\",\n];\n\nconst ANIMALS = [\n \"alligator\", \"bee\", \"bird\", \"camel\", \"cat\", \"cheetah\", \"chicken\", \"cow\", \"dog\",\n \"corgi\", \"eagle\", \"elephant\", \"fish\", \"fox\", \"toad\", \"giraffe\", \"hippo\",\n \"kangaroo\", \"kitten\", \"lobster\", \"monkey\", \"octopus\", \"pig\", \"puppy\", \"rabbit\",\n \"rat\", \"scorpion\", \"seal\", \"sheep\", \"snail\", \"spider\", \"tiger\", \"turtle\", \"newt\",\n \"tadpole\", \"frog\", \"tarantula\", \"albatross\", \"blackbird\", \"canary\", \"crow\",\n \"cuckoo\", \"dove\", \"pigeon\", \"falcon\", \"finch\", \"flamingo\", \"goose\", \"seagull\",\n \"hawk\", \"jay\", \"mockingbird\", \"kestrel\", \"kookaburra\", \"mallard\", \"nightingale\",\n \"nuthatch\", \"ostrich\", \"owl\", \"parakeet\", \"parrot\", \"peacock\", \"pelican\",\n \"penguin\", \"pheasant\", \"piranha\", \"raven\", \"robin\", \"rooster\", \"sparrow\",\n \"stork\", \"swallow\", \"swan\", \"swift\", \"turkey\", \"vulture\", \"woodpecker\", \"wren\",\n \"butterfly\", \"barbel\", \"carp\", \"cod\", \"crab\", \"eel\", \"goldfish\", \"haddock\",\n \"halibut\", \"jellyfish\", \"perch\", \"pike\", \"mantaray\", \"salmon\", \"sawfish\",\n \"scallop\", \"shark\", \"shell\", \"shrimp\", \"trout\", \"ant\", \"aphid\", \"beetle\",\n \"caterpillar\", \"dragonfly\", \"cricket\", \"fly\", \"grasshopper\", \"ladybug\",\n \"millipede\", \"moth\", \"wasp\", \"anteater\", \"antelope\", \"armadillo\", \"badger\",\n \"bat\", \"beaver\", \"bull\", \"chimpanzee\", \"dachshund\", \"deer\", \"dolphin\", \"elk\",\n \"moose\", \"gazelle\", \"gerbil\", \"goat\", \"bear\", \"hamster\", \"hare\", \"hedgehog\",\n \"horse\", \"hyena\", \"lion\", \"llama\", \"lynx\", \"mammoth\", \"marmot\", \"mink\", \"mole\",\n \"mongoose\", \"mouse\", \"mule\", \"otter\", \"panda\", \"platypus\", \"pony\", \"porcupine\",\n \"puma\", \"raccoon\", \"reindeer\", \"rhino\", \"skunk\", \"sloth\", \"squirrel\", \"weasel\",\n \"snake\", \"wolf\", \"zebra\", \"boa\", \"chameleon\", \"copperhead\", \"cottonmouth\",\n \"crocodile\", \"rattlesnake\", \"gecko\", \"iguana\", \"lizard\", \"python\", \"salamander\",\n \"sidewinder\", \"whale\", \"tortoise\", \"lemur\", \"rook\", \"koala\", \"donkey\", \"ferret\",\n \"tardigrade\", \"orca\", \"okapi\", \"liger\", \"unicorn\", \"dragon\", \"squid\", \"ape\",\n \"gorilla\", \"baboon\", \"cormorant\", \"mantis\", \"tapir\", \"capybara\", \"pangolin\",\n \"opossum\", \"wombat\", \"aardvark\", \"starfish\", \"shetland\", \"narwhal\", \"worm\",\n \"hornet\", \"viper\", \"stallion\", \"jaguar\", \"panther\", \"bobcat\", \"leopard\",\n \"osprey\", \"cougar\", \"dalmatian\", \"terrier\", \"duck\", \"sealion\", \"chipmunk\",\n \"loris\", \"poodle\", \"orangutan\", \"gibbon\", \"meerkat\", \"huskie\", \"barracuda\",\n \"bison\", \"caribou\", \"chinchilla\", \"coyote\", \"crane\", \"dinosaur\", \"lark\",\n \"griffin\", \"yeti\", \"troll\", \"seahorse\", \"walrus\", \"yak\", \"wolverine\", \"boar\",\n \"alpaca\", \"porpoise\", \"manatee\", \"guppy\", \"condor\", \"cyborg\", \"cobra\", \"locust\",\n \"mandrill\", \"oyster\", \"urchin\", \"quail\", \"sardine\", \"ram\", \"starling\", \"wallaby\",\n \"buffalo\", \"goblin\", \"tuna\", \"mustang\",\n];\n\n// Compress bytes to target length using XOR (exact algorithm from library)\nfunction compress(bytes: number[], target: number): number[] {\n const length = bytes.length;\n const segSize = Math.floor(length / target);\n const segments: number[][] = [];\n\n for (let i = 0; i < segSize * target; i += segSize) {\n segments.push(bytes.slice(i, i + segSize));\n }\n\n // Add remaining bytes to last segment\n const lastSeg = segments[segments.length - 1];\n segments[segments.length - 1] = lastSeg.concat(bytes.slice(target * segSize));\n\n // XOR each segment to get checksums\n return segments.map(seg => seg.reduce((acc, curr) => acc ^ curr, 0));\n}\n\n// Convert hex string to byte array\nfunction hexToBytes(hex: string): number[] {\n const bytes: number[] = [];\n for (let i = 0; i < hex.length; i += 2) {\n bytes.push(parseInt(hex.substring(i, i + 2), 16));\n }\n return bytes;\n}\n\n// Generate animal name from input string (exact angry-purple-tiger algorithm)\nexport function animalHash(input: string): string {\n const hexDigest = md5(input);\n const bytes = hexToBytes(hexDigest);\n const compressed = compress(bytes, 3);\n\n const adjective = ADJECTIVES[compressed[0] % ADJECTIVES.length];\n const color = COLORS[compressed[1] % COLORS.length];\n const animal = ANIMALS[compressed[2] % ANIMALS.length];\n\n return `${adjective}-${color}-${animal}`;\n}\n",[],[173,280,272],"utils","f4aa697a-f7e2-11f0-94ff-42dde27851f2",41,["D",1769121458791],[201],[173,280],"0b2471bc-f8bc-11f0-920b-42dde27851f2","App.tsx","102e094e-f7d5-11f0-88c4-42dde27851f2","0ff6303c-f7d5-11f0-9712-42dde27851f2",124,["D",1769214697281],44,"/** @jsxImportSource https://esm.sh/react@18.2.0 */\nimport { useState, useEffect, useMemo, useCallback, useRef } from \"https://esm.sh/react@18.2.0\";\nimport * as h3 from \"https://esm.sh/h3-js@4.1.0\";\nimport { animalHash } from \"../utils/animalHash.ts\";\n\n// deck.gl types (loaded from CDN script)\ndeclare const deck: any;\n\n// Types\ninterface Hotspot {\n entity_key_str: string;\n lat: number;\n long: number;\n created_at: string;\n first_seen_at: string;\n}\n\ninterface DailySnapshot {\n snapshot_date: string;\n total_count: number;\n new_count: number;\n}\n\ninterface H3Cluster {\n h3Index: string;\n hotspots: Hotspot[];\n center: [number, number];\n newestDate: string;\n count: number;\n}\n\nconst DC_CENTER = { lat: 38.9072, lng: -77.0369 };\nconst H3_RESOLUTION = 8;\n\n// Calculate days between two dates\nfunction daysBetween(date1: string, date2: string): number {\n const d1 = new Date(date1);\n const d2 = new Date(date2);\n return Math.floor((d2.getTime() - d1.getTime()) / (1000 * 60 * 60 * 24));\n}\n\n// Convert kebab-case to Title Case\nfunction toTitleCase(str: string): string {\n return str.split(\"-\").map(word => word.charAt(0).toUpperCase() + word.slice(1)).join(\" \");\n}\n\n// Get color based on hotspot age [R, G, B, A]\nfunction getHotspotColor(createdAt: string, selectedDate: string, isEstablished: boolean): [number, number, number, number] {\n const age = daysBetween(createdAt, selectedDate);\n const alpha = isEstablished ? 128 : 179; // 0.5 or 0.7 * 255\n if (age < 7) return [46, 204, 113, alpha]; // Green - new\n if (age < 30) return [52, 152, 219, alpha]; // Blue - recent\n return [160, 160, 160, alpha]; // Grey - established\n}\n\n// Group hotspots into H3 clusters\nfunction clusterHotspots(hotspots: Hotspot[]): H3Cluster[] {\n const clusters = new Map();\n\n for (const hotspot of hotspots) {\n const h3Index = h3.latLngToCell(hotspot.lat, hotspot.long, H3_RESOLUTION);\n if (!clusters.has(h3Index)) {\n clusters.set(h3Index, []);\n }\n clusters.get(h3Index)!.push(hotspot);\n }\n\n const result: H3Cluster[] = [];\n for (const [h3Index, clusterHotspots] of clusters) {\n const center = h3.cellToLatLng(h3Index) as [number, number];\n const newestDate = clusterHotspots.reduce((newest, h) =>\n h.created_at > newest ? h.created_at : newest,\n clusterHotspots[0].created_at\n );\n\n result.push({\n h3Index,\n hotspots: clusterHotspots,\n center,\n newestDate,\n count: clusterHotspots.length,\n });\n }\n\n return result;\n}\n\n// Calculate circle radius with sqrt easing\nfunction getClusterRadius(count: number): number {\n const minRadius = 8;\n const maxRadius = 35;\n const maxCount = 70;\n\n if (count <= 1) return minRadius;\n if (count >= maxCount) return maxRadius;\n\n const normalizedCount = Math.sqrt(count - 1) / Math.sqrt(maxCount - 1);\n return minRadius + (maxRadius - minRadius) * normalizedCount;\n}\n\nexport function App() {\n const [hotspots, setHotspots] = useState([]);\n const [snapshots, setSnapshots] = useState([]);\n // Use index instead of date string for faster playback\n const [selectedIdx, setSelectedIdx] = useState(-1);\n const [isPlaying, setIsPlaying] = useState(false);\n const [loading, setLoading] = useState(true);\n const [backfilling, setBackfilling] = useState(false);\n const [error, setError] = useState(null);\n const [tooltip, setTooltip] = useState<{ x: number; y: number; cluster: H3Cluster } | null>(null);\n\n const mapContainerRef = useRef(null);\n const deckRef = useRef(null);\n const canvasRef = useRef(null);\n const histogramRef = useRef(null);\n const [isDragging, setIsDragging] = useState(false);\n const animationRef = useRef(null);\n const lastFrameTimeRef = useRef(0);\n\n // Load data on mount\n useEffect(() => {\n async function loadData() {\n try {\n const [hotspotsRes, snapshotsRes] = await Promise.all([\n fetch(\"/api/hotspots\"),\n fetch(\"/api/snapshots\"),\n ]);\n\n if (!hotspotsRes.ok || !snapshotsRes.ok) {\n throw new Error(\"Failed to fetch data\");\n }\n\n const hotspotsData = await hotspotsRes.json();\n const snapshotsData = await snapshotsRes.json();\n\n setHotspots(hotspotsData);\n setSnapshots(snapshotsData);\n\n if (snapshotsData.length > 0) {\n setSelectedIdx(snapshotsData.length - 1);\n }\n } catch (e) {\n console.error(\"Error loading data:\", e);\n setError(String(e));\n } finally {\n setLoading(false);\n }\n }\n loadData();\n }, []);\n\n // Derive selectedDate from index\n const selectedDate = useMemo(() => {\n if (selectedIdx >= 0 && selectedIdx < snapshots.length) {\n return snapshots[selectedIdx].snapshot_date;\n }\n return new Date().toISOString().split(\"T\")[0];\n }, [selectedIdx, snapshots]);\n\n // Pre-compute clusters for all dates (memoized once when data loads)\n // This avoids expensive H3 clustering on every playback frame\n const clustersByDate = useMemo(() => {\n if (hotspots.length === 0 || snapshots.length === 0) return new Map();\n\n const result = new Map();\n\n // Sort hotspots by created_at for efficient incremental building\n const sortedHotspots = [...hotspots].sort((a, b) => a.created_at.localeCompare(b.created_at));\n\n let currentHotspots: Hotspot[] = [];\n let hotspotIdx = 0;\n\n for (const snapshot of snapshots) {\n const date = snapshot.snapshot_date;\n\n // Add hotspots created on or before this date\n while (hotspotIdx < sortedHotspots.length && sortedHotspots[hotspotIdx].created_at <= date) {\n currentHotspots.push(sortedHotspots[hotspotIdx]);\n hotspotIdx++;\n }\n\n // Cluster the current set of hotspots\n result.set(date, clusterHotspots([...currentHotspots]));\n }\n\n return result;\n }, [hotspots, snapshots]);\n\n // Get clusters for current date (O(1) lookup instead of O(n) computation)\n const clusters = useMemo(() => {\n return clustersByDate.get(selectedDate) || [];\n }, [clustersByDate, selectedDate]);\n\n // Filter hotspots for selected date (only needed for stats, not rendering)\n const visibleHotspots = useMemo(() => {\n return hotspots.filter(h => h.created_at <= selectedDate);\n }, [hotspots, selectedDate]);\n\n // Initialize deck.gl\n useEffect(() => {\n if (!mapContainerRef.current || deckRef.current) return;\n\n // Create canvas for deck.gl\n const canvas = document.createElement(\"canvas\");\n canvas.style.position = \"absolute\";\n canvas.style.top = \"0\";\n canvas.style.left = \"0\";\n canvas.style.width = \"100%\";\n canvas.style.height = \"100%\";\n mapContainerRef.current.appendChild(canvas);\n canvasRef.current = canvas;\n\n const deckInstance = new deck.Deck({\n canvas,\n initialViewState: {\n longitude: DC_CENTER.lng,\n latitude: DC_CENTER.lat,\n zoom: 11,\n pitch: 0,\n bearing: 0,\n },\n controller: true,\n layers: [],\n getTooltip: null,\n });\n\n deckRef.current = deckInstance;\n\n // Handle resize\n const resizeObserver = new ResizeObserver(() => {\n if (deckRef.current && mapContainerRef.current) {\n const { width, height } = mapContainerRef.current.getBoundingClientRect();\n canvas.width = width * window.devicePixelRatio;\n canvas.height = height * window.devicePixelRatio;\n canvas.style.width = `${width}px`;\n canvas.style.height = `${height}px`;\n deckRef.current.setProps({ width, height });\n }\n });\n resizeObserver.observe(mapContainerRef.current);\n\n return () => {\n resizeObserver.disconnect();\n if (deckRef.current) {\n deckRef.current.finalize();\n deckRef.current = null;\n }\n if (canvasRef.current) {\n canvasRef.current.remove();\n canvasRef.current = null;\n }\n };\n }, [loading]);\n\n // Update deck.gl layers when clusters change\n useEffect(() => {\n if (!deckRef.current) return;\n\n // Base map tile layer (CartoDB dark)\n const tileLayer = new deck.TileLayer({\n id: \"base-map\",\n data: \"https://basemaps.cartocdn.com/dark_all/{z}/{x}/{y}@2x.png\",\n minZoom: 0,\n maxZoom: 19,\n tileSize: 256,\n renderSubLayers: (props: any) => {\n const { boundingBox } = props.tile;\n return new deck.BitmapLayer(props, {\n data: null,\n image: props.data,\n bounds: [boundingBox[0][0], boundingBox[0][1], boundingBox[1][0], boundingBox[1][1]],\n });\n },\n });\n\n // During playback, completely disable transitions to prevent interpolation bugs\n // that cause circles to disappear when data changes rapidly\n const scatterLayer = new deck.ScatterplotLayer({\n // Use dynamic id during playback to force complete layer recreation,\n // avoiding any lingering transition state that causes circles to disappear\n id: isPlaying ? `hotspot-clusters-${selectedIdx}` : \"hotspot-clusters\",\n data: clusters,\n pickable: !isPlaying, // Disable picking during playback for performance\n stroked: false,\n filled: true,\n radiusUnits: \"pixels\",\n getPosition: (d: H3Cluster) => [d.center[1], d.center[0]],\n getRadius: (d: H3Cluster) => getClusterRadius(d.count),\n getFillColor: (d: H3Cluster) => {\n const age = daysBetween(d.newestDate, selectedDate);\n const isEstablished = age >= 30;\n return getHotspotColor(d.newestDate, selectedDate, isEstablished);\n },\n // Only enable smooth transitions when NOT playing\n // During playback, no transitions = instant updates without interpolation bugs\n transitions: isPlaying ? undefined : {\n getRadius: { duration: 150, easing: (t: number) => t },\n getFillColor: { duration: 150 },\n },\n onClick: ({ object, x, y }: { object: H3Cluster; x: number; y: number }) => {\n if (object) {\n setTooltip({ x, y, cluster: object });\n }\n },\n updateTriggers: {\n getRadius: [selectedDate],\n getFillColor: [selectedDate],\n },\n });\n\n // Text layer for cluster count labels\n const textLayer = new deck.TextLayer({\n // Use dynamic id during playback to match scatterplot layer behavior\n id: isPlaying ? `cluster-labels-${selectedIdx}` : \"cluster-labels\",\n data: clusters.filter((c: H3Cluster) => c.count > 1), // Only show labels for count > 1\n pickable: false,\n getPosition: (d: H3Cluster) => [d.center[1], d.center[0]],\n getText: (d: H3Cluster) => String(d.count),\n getSize: 12,\n getColor: [255, 255, 255, 230],\n getTextAnchor: \"middle\",\n getAlignmentBaseline: \"center\",\n fontFamily: \"Figtree, sans-serif\",\n fontWeight: 300,\n outlineWidth: 0,\n outlineColor: [0, 0, 0, 150],\n billboard: false,\n sizeUnits: \"pixels\",\n updateTriggers: {\n getText: [selectedDate],\n },\n });\n\n deckRef.current.setProps({ layers: [tileLayer, scatterLayer, textLayer] });\n }, [clusters, selectedDate, selectedIdx, isPlaying]);\n\n // Get stats for selected date\n const currentStats = useMemo(() => {\n const snapshot = snapshots.find(s => s.snapshot_date === selectedDate);\n return snapshot || { total_count: visibleHotspots.length, new_count: 0 };\n }, [snapshots, selectedDate, visibleHotspots]);\n\n // Timeline playback using requestAnimationFrame for smooth animation\n useEffect(() => {\n if (!isPlaying || snapshots.length === 0) return;\n\n const FRAME_INTERVAL = 33; // ~30fps for smooth playback (faster than this looks rushed)\n\n const animate = (timestamp: number) => {\n if (timestamp - lastFrameTimeRef.current >= FRAME_INTERVAL) {\n lastFrameTimeRef.current = timestamp;\n\n setSelectedIdx(prev => {\n if (prev >= snapshots.length - 1) {\n setIsPlaying(false);\n return prev;\n }\n return prev + 1;\n });\n }\n\n animationRef.current = requestAnimationFrame(animate);\n };\n\n lastFrameTimeRef.current = performance.now();\n animationRef.current = requestAnimationFrame(animate);\n\n return () => {\n if (animationRef.current !== null) {\n cancelAnimationFrame(animationRef.current);\n animationRef.current = null;\n }\n };\n }, [isPlaying, snapshots.length]);\n\n // Handle histogram scrub/click - calculate which snapshot index based on mouse position\n const getSnapshotIdxFromMouseEvent = useCallback((clientX: number): number => {\n if (!histogramRef.current || snapshots.length === 0) return -1;\n\n const rect = histogramRef.current.getBoundingClientRect();\n const padding = 12; // Match CSS padding\n const x = clientX - rect.left - padding;\n const contentWidth = rect.width - (padding * 2);\n const percentage = Math.max(0, Math.min(1, x / contentWidth));\n return Math.round(percentage * (snapshots.length - 1));\n }, [snapshots.length]);\n\n const handleHistogramMouseDown = useCallback((e: React.MouseEvent) => {\n setIsDragging(true);\n const idx = getSnapshotIdxFromMouseEvent(e.clientX);\n if (idx >= 0) {\n setSelectedIdx(idx);\n setIsPlaying(false);\n }\n }, [getSnapshotIdxFromMouseEvent]);\n\n const handleHistogramMouseMove = useCallback((e: React.MouseEvent) => {\n if (!isDragging) return;\n const idx = getSnapshotIdxFromMouseEvent(e.clientX);\n if (idx >= 0) {\n setSelectedIdx(idx);\n }\n }, [isDragging, getSnapshotIdxFromMouseEvent]);\n\n const handleHistogramMouseUp = useCallback(() => {\n setIsDragging(false);\n }, []);\n\n const handleHistogramMouseLeave = useCallback(() => {\n setIsDragging(false);\n }, []);\n\n // Touch support for mobile\n const handleHistogramTouchStart = useCallback((e: React.TouchEvent) => {\n setIsDragging(true);\n const idx = getSnapshotIdxFromMouseEvent(e.touches[0].clientX);\n if (idx >= 0) {\n setSelectedIdx(idx);\n setIsPlaying(false);\n }\n }, [getSnapshotIdxFromMouseEvent]);\n\n const handleHistogramTouchMove = useCallback((e: React.TouchEvent) => {\n if (!isDragging) return;\n const idx = getSnapshotIdxFromMouseEvent(e.touches[0].clientX);\n if (idx >= 0) {\n setSelectedIdx(idx);\n }\n }, [isDragging, getSnapshotIdxFromMouseEvent]);\n\n const handleHistogramTouchEnd = useCallback(() => {\n setIsDragging(false);\n }, []);\n\n // Handle play button\n const handlePlayClick = useCallback(() => {\n if (isPlaying) {\n setIsPlaying(false);\n } else {\n // Reset to beginning if at end\n if (selectedIdx >= snapshots.length - 1 && snapshots.length > 0) {\n setSelectedIdx(0);\n }\n setIsPlaying(true);\n }\n }, [isPlaying, snapshots.length, selectedIdx]);\n\n // Handle backfill\n const handleBackfill = async () => {\n setBackfilling(true);\n try {\n const res = await fetch(\"/api/admin/backfill\", { method: \"POST\" });\n if (!res.ok) throw new Error(\"Backfill failed\");\n window.location.reload();\n } catch (e) {\n setError(String(e));\n } finally {\n setBackfilling(false);\n }\n };\n\n // Close tooltip when clicking outside\n const handleMapClick = useCallback((e: React.MouseEvent) => {\n if ((e.target as HTMLElement).closest(\".cluster-popup\")) return;\n setTooltip(null);\n }, []);\n\n if (loading) {\n return (\n

Loading Hotspot data...

\n );\n }\n\n if (hotspots.length === 0) {\n return (\n

No Hotspot Data

\n No Hotspots have been loaded yet. Click the button below to fetch\n DC area Hotspots from the Helium API.\n

\n \n {backfilling ? \"Loading Hotspots...\" : \"Load DC Hotspots\"}\n \n {error &&

{error}

}\n

\n );\n }\n\n const currentIdx = selectedIdx >= 0 ? selectedIdx : 0;\n const minDate = snapshots.length > 0 ? snapshots[0].snapshot_date : selectedDate;\n const maxDate = snapshots.length > 0 ? snapshots[snapshots.length - 1].snapshot_date : selectedDate;\n\n return (\n

\n {/* Stats Panel with Legend */}\n

DC Hotspot Growth

Helium Mobile Hotspots in the DC Metro area

\n\n

Total Hotspots

{currentStats.total_count.toLocaleString()}

\n +{currentStats.new_count} new\n

\n\n

\n Showing: {selectedDate}\n

\n\n {/* Integrated Legend */}\n

\n < 7d\n

\n < 30d\n

\n 30d+\n

\n\n {/* Map Container */}\n \n {/* Tooltip/Popup */}\n {tooltip && (\n \n

\n {tooltip.cluster.count} Hotspot{tooltip.cluster.count !== 1 ? \"s\" : \"\"}\n

\n {name}\n ({h.created_at})\n

\n {tooltip.cluster.count > 10 && (\n

...and {tooltip.cluster.count - 10} more

\n )}\n

\n )}\n \n\n {/* Timeline with Histogram */}\n {snapshots.length > 0 && (\n

\n \n {isPlaying ? \"⏸\" : \"▶\"}\n \n\n

\n {/* Interactive Histogram */}\n \n {(() => {\n const cappedMax = 10;\n return snapshots.map((snapshot) => {\n const cappedCount = Math.min(snapshot.new_count, cappedMax);\n const height = (cappedCount / cappedMax) * 100;\n const isCapped = snapshot.new_count > cappedMax;\n return (\n \n );\n });\n })()}\n {/* Playhead indicator - centered on each bar, accounting for padding */}\n \n

\n {minDate}\n {maxDate}\n

\n \n )}\n \n );\n}\n",[],[173,296,287],"components","0ff62f6a-f7d5-11f0-9712-42dde27851f2",2,["D",1769115491600],[199],[173,296],["D",1769115491425],[198,200,202,203,204,205],[173],"f4c69ab4-f7e2-11f0-94ff-42dde27851f2","scraper.ts","e78c46bc-f7d5-11f0-858d-42dde27851f2",42,["D",1769121458973],"// Backfill and daily scraper for DC Hotspot Growth Map\nimport * as db from \"./db.ts\";\nimport * as helium from \"./heliumApi.ts\";\n\ninterface BackfillResult {\n processed: number;\n dcHotspots: number;\n newHotspots: number;\n skipped: number;\n errors: number;\n}\n\n// Run incremental backfill - only adds hotspots not already in database\nexport async function runBackfill(): Promise {\n await db.initDatabase();\n\n const result: BackfillResult = {\n processed: 0,\n dcHotspots: 0,\n newHotspots: 0,\n skipped: 0,\n errors: 0,\n };\n\n const today = new Date().toISOString().split(\"T\")[0];\n\n console.log(\"Starting incremental backfill of DC area Hotspots...\");\n\n // Get existing hotspots to skip\n const existing = await db.getAllHotspots();\n const existingKeys = new Set(existing.map(h => h.entity_key_str));\n console.log(`Found ${existingKeys.size} existing Hotspots in database`);\n\n // Fetch all DC hotspots\n const dcHotspots = await helium.fetchAllDCHotspots((processed, dcCount, cursor) => {\n if (processed % 1000 === 0) {\n console.log(`Processed ${processed} Hotspots, found ${dcCount} in DC area`);\n }\n result.processed = processed;\n });\n\n result.dcHotspots = dcHotspots.length;\n console.log(`Found ${dcHotspots.length} Hotspots in DC area. Fetching details for new ones...`);\n\n // Insert each new DC hotspot\n for (const hotspot of dcHotspots) {\n // Skip if already exists\n if (existingKeys.has(hotspot.entity_key_str)) {\n result.skipped++;\n continue;\n }\n\n try {\n // Try to get created_at from individual endpoint\n let created_at = today;\n try {\n const details = await helium.fetchHotspotDetails(hotspot.key_to_asset_key);\n if (details.created_at) {\n created_at = details.created_at.split(\"T\")[0]; // Extract date part\n }\n } catch (e) {\n // Use today as fallback\n }\n\n await db.upsertHotspot({\n entity_key_str: hotspot.entity_key_str,\n key_to_asset_key: hotspot.key_to_asset_key,\n lat: hotspot.lat!,\n long: hotspot.long!,\n created_at,\n first_seen_at: today,\n is_active: true,\n });\n\n result.newHotspots++;\n\n // Reduced rate limit for faster processing\n await helium.sleep(25);\n\n if (result.newHotspots % 50 === 0) {\n console.log(`Inserted ${result.newHotspots} new DC Hotspots`);\n }\n } catch (e) {\n console.error(`Error processing Hotspot ${hotspot.entity_key_str}:`, e);\n result.errors++;\n }\n }\n\n // Compute historical snapshots if we added new hotspots\n if (result.newHotspots > 0) {\n console.log(\"Computing historical snapshots...\");\n await db.computeHistoricalSnapshots();\n }\n\n console.log(`Backfill complete: ${result.newHotspots} new Hotspots, ${result.skipped} skipped, ${result.errors} errors`);\n return result;\n}\n\n// Run daily incremental scrape\nexport async function runDailyScrape(): Promise<{\n newHotspots: number;\n updated: number;\n removed: number;\n}> {\n await db.initDatabase();\n\n const today = new Date().toISOString().split(\"T\")[0];\n const stats = { newHotspots: 0, updated: 0, removed: 0 };\n\n console.log(`Starting daily scrape for ${today}...`);\n\n // Get existing hotspots for comparison\n const existing = await db.getAllHotspots();\n const existingMap = new Map(existing.map(h => [h.entity_key_str, h]));\n const seenKeys = new Set();\n\n // Fetch all DC hotspots\n const dcHotspots = await helium.fetchAllDCHotspots();\n\n for (const hotspot of dcHotspots) {\n if (!hotspot.lat || !hotspot.long) continue;\n\n seenKeys.add(hotspot.entity_key_str);\n const existingHotspot = existingMap.get(hotspot.entity_key_str);\n\n if (!existingHotspot) {\n // New hotspot\n let created_at = today;\n try {\n const details = await helium.fetchHotspotDetails(hotspot.key_to_asset_key);\n if (details.created_at) {\n created_at = details.created_at.split(\"T\")[0];\n }\n } catch (e) {\n // Use today as fallback\n }\n\n await db.upsertHotspot({\n entity_key_str: hotspot.entity_key_str,\n key_to_asset_key: hotspot.key_to_asset_key,\n lat: hotspot.lat,\n long: hotspot.long,\n created_at,\n first_seen_at: today,\n is_active: true,\n });\n\n stats.newHotspots++;\n await helium.sleep(25);\n } else {\n // Check for location change\n const threshold = 0.0001; // ~11 meters\n if (\n Math.abs(existingHotspot.lat - hotspot.lat) > threshold ||\n Math.abs(existingHotspot.long - hotspot.long) > threshold\n ) {\n await db.recordLocationChange(hotspot.entity_key_str, hotspot.lat, hotspot.long, today);\n await db.upsertHotspot({\n ...existingHotspot,\n lat: hotspot.lat,\n long: hotspot.long,\n });\n stats.updated++;\n }\n }\n }\n\n // Compute historical snapshots to update with any new dates\n await db.computeHistoricalSnapshots();\n\n // Create daily snapshot\n const totalCount = await db.getHotspotCount();\n await db.upsertSnapshot({\n snapshot_date: today,\n total_count: totalCount,\n new_count: stats.newHotspots,\n });\n\n console.log(`Daily scrape complete: ${stats.newHotspots} new, ${stats.updated} updated`);\n return stats;\n}\n",[],[178,306],"19c58cea-f8b9-11f0-801d-42dde27851f2","migrate.ts","19c58d8a-f8b9-11f0-801d-42dde27851f2",119,["D",1769213433340],"// Migration script to copy data from old sqlite to new Turso sqlite\nimport { sqlite as oldSqlite } from \"https://esm.town/v/std/sqlite\";\nimport newSqlite from \"https://esm.town/v/std/sqlite@14-main/main.ts\";\nimport type { Hotspot, DailySnapshot } from \"../shared/types.ts\";\n\n// Read all hotspots from old database\nasync function getOldHotspots(): Promise {\n const result = await oldSqlite.execute(`\n SELECT entity_key_str, key_to_asset_key, lat, long, created_at, first_seen_at, is_active\n FROM hotspots\n ORDER BY created_at ASC\n LIMIT 10000\n `);\n\n const columns = result.columns || [];\n return result.rows.map((row: any) => {\n if (Array.isArray(row)) {\n const obj: any = {};\n columns.forEach((col: string, i: number) => {\n obj[col] = row[i];\n });\n return {\n entity_key_str: obj.entity_key_str as string,\n key_to_asset_key: obj.key_to_asset_key as string,\n lat: obj.lat as number,\n long: obj.long as number,\n created_at: obj.created_at as string,\n first_seen_at: obj.first_seen_at as string,\n is_active: Boolean(obj.is_active),\n };\n }\n return {\n entity_key_str: row.entity_key_str as string,\n key_to_asset_key: row.key_to_asset_key as string,\n lat: row.lat as number,\n long: row.long as number,\n created_at: row.created_at as string,\n first_seen_at: row.first_seen_at as string,\n is_active: Boolean(row.is_active),\n };\n });\n}\n\n// Read all snapshots from old database\nasync function getOldSnapshots(): Promise {\n const result = await oldSqlite.execute(`\n SELECT snapshot_date, total_count, new_count\n FROM daily_snapshots\n ORDER BY snapshot_date ASC\n `);\n\n const columns = result.columns || [];\n return result.rows.map((row: any) => {\n if (Array.isArray(row)) {\n const obj: any = {};\n columns.forEach((col: string, i: number) => {\n obj[col] = row[i];\n });\n return {\n snapshot_date: obj.snapshot_date as string,\n total_count: obj.total_count as number,\n new_count: obj.new_count as number,\n };\n }\n return {\n snapshot_date: row.snapshot_date as string,\n total_count: row.total_count as number,\n new_count: row.new_count as number,\n };\n });\n}\n\n// Initialize new database schema\nasync function initNewDatabase() {\n await newSqlite.execute(`\n CREATE TABLE IF NOT EXISTS hotspots (\n id INTEGER PRIMARY KEY,\n entity_key_str TEXT UNIQUE NOT NULL,\n key_to_asset_key TEXT NOT NULL,\n lat REAL NOT NULL,\n long REAL NOT NULL,\n created_at TEXT NOT NULL,\n first_seen_at TEXT NOT NULL,\n is_active INTEGER DEFAULT 1\n )\n `);\n\n await newSqlite.execute(`\n CREATE TABLE IF NOT EXISTS hotspot_locations (\n id INTEGER PRIMARY KEY,\n entity_key_str TEXT NOT NULL,\n lat REAL NOT NULL,\n long REAL NOT NULL,\n observed_at TEXT NOT NULL\n )\n `);\n\n await newSqlite.execute(`\n CREATE TABLE IF NOT EXISTS daily_snapshots (\n snapshot_date TEXT PRIMARY KEY,\n total_count INTEGER NOT NULL,\n new_count INTEGER NOT NULL\n )\n `);\n\n await newSqlite.execute(`\n CREATE INDEX IF NOT EXISTS idx_hotspots_created ON hotspots(created_at)\n `);\n await newSqlite.execute(`\n CREATE INDEX IF NOT EXISTS idx_snapshots_date ON daily_snapshots(snapshot_date)\n `);\n}\n\n// Insert hotspots into new database\nasync function insertHotspotsToNew(hotspots: Hotspot[]) {\n for (const hotspot of hotspots) {\n await newSqlite.execute({\n sql: `\n INSERT INTO hotspots (entity_key_str, key_to_asset_key, lat, long, created_at, first_seen_at, is_active)\n VALUES (?, ?, ?, ?, ?, ?, ?)\n ON CONFLICT(entity_key_str) DO UPDATE SET\n lat = excluded.lat,\n long = excluded.long,\n is_active = excluded.is_active\n `,\n args: [\n hotspot.entity_key_str,\n hotspot.key_to_asset_key,\n hotspot.lat,\n hotspot.long,\n hotspot.created_at,\n hotspot.first_seen_at,\n hotspot.is_active ? 1 : 0,\n ],\n });\n }\n}\n\n// Insert snapshots into new database\nasync function insertSnapshotsToNew(snapshots: DailySnapshot[]) {\n for (const snapshot of snapshots) {\n await newSqlite.execute({\n sql: `\n INSERT INTO daily_snapshots (snapshot_date, total_count, new_count)\n VALUES (?, ?, ?)\n ON CONFLICT(snapshot_date) DO UPDATE SET\n total_count = excluded.total_count,\n new_count = excluded.new_count\n `,\n args: [snapshot.snapshot_date, snapshot.total_count, snapshot.new_count],\n });\n }\n}\n\n// Get counts from new database to verify\nasync function getNewCounts(): Promise<{ hotspots: number; snapshots: number }> {\n const hotspotsResult = await newSqlite.execute(`SELECT COUNT(*) as count FROM hotspots`);\n const snapshotsResult = await newSqlite.execute(`SELECT COUNT(*) as count FROM daily_snapshots`);\n\n const hotspotRow = hotspotsResult.rows[0];\n const snapshotRow = snapshotsResult.rows[0];\n\n return {\n hotspots: (Array.isArray(hotspotRow) ? hotspotRow[0] : hotspotRow.count) as number,\n snapshots: (Array.isArray(snapshotRow) ? snapshotRow[0] : snapshotRow.count) as number,\n };\n}\n\n// Run the full migration\nexport async function runMigration() {\n console.log(\"Starting migration from old sqlite to new Turso sqlite...\");\n\n // Step 1: Read from old database\n console.log(\"Reading hotspots from old database...\");\n const hotspots = await getOldHotspots();\n console.log(`Found ${hotspots.length} hotspots in old database`);\n\n console.log(\"Reading snapshots from old database...\");\n const snapshots = await getOldSnapshots();\n console.log(`Found ${snapshots.length} snapshots in old database`);\n\n // Step 2: Initialize new database schema\n console.log(\"Initializing new database schema...\");\n await initNewDatabase();\n\n // Step 3: Insert data into new database\n console.log(\"Inserting hotspots into new database...\");\n await insertHotspotsToNew(hotspots);\n\n console.log(\"Inserting snapshots into new database...\");\n await insertSnapshotsToNew(snapshots);\n\n // Step 4: Verify counts\n console.log(\"Verifying migration...\");\n const newCounts = await getNewCounts();\n\n console.log(\"Migration complete!\");\n return {\n success: true,\n old: {\n hotspots: hotspots.length,\n snapshots: snapshots.length,\n },\n new: newCounts,\n verified: newCounts.hotspots === hotspots.length && newCounts.snapshots === snapshots.length,\n };\n}\n",[],[178,314],"0ff3587c-f7d9-11f0-b918-42dde27851f2","heliumApi.ts","e7738db6-f7d5-11f0-b958-42dde27851f2",["D",1769117209587],"// Helium Entity API client\nimport { HELIUM_API_BASE, DC_BOUNDS, type HeliumApiResponse, type HeliumHotspotListItem } from \"../shared/types.ts\";\n\nconst RATE_LIMIT_MS = 100; // 10 requests per second max\n\n// Fetch paginated mobile hotspots\nexport async function fetchMobileHotspots(cursor?: string | null): Promise {\n const url = new URL(`${HELIUM_API_BASE}/hotspots`);\n url.searchParams.set(\"subnetwork\", \"mobile\");\n if (cursor) {\n url.searchParams.set(\"cursor\", cursor);\n }\n\n const response = await fetch(url.toString());\n if (!response.ok) {\n throw new Error(`Helium API error: ${response.status} ${response.statusText}`);\n }\n\n return response.json();\n}\n\n// Fetch individual hotspot details\nexport async function fetchHotspotDetails(keyToAssetKey: string): Promise<{ created_at?: string; info?: any }> {\n const url = `${HELIUM_API_BASE}/hotspot/${keyToAssetKey}`;\n\n const response = await fetch(url);\n if (!response.ok) {\n if (response.status === 404) {\n return {}; // Hotspot details not available\n }\n throw new Error(`Helium API error: ${response.status}`);\n }\n\n const data = await response.json();\n\n // Extract created_at from hotspot_infos.mobile if available\n const mobileInfo = data.hotspot_infos?.mobile;\n if (mobileInfo?.created_at) {\n return { created_at: mobileInfo.created_at, info: data };\n }\n\n return { info: data };\n}\n\n// Check if coordinates are within DC Metro area\nexport function isInDCArea(lat: number | null, long: number | null): boolean {\n if (lat === null || long === null) return false;\n return (\n lat >= DC_BOUNDS.minLat &&\n lat <= DC_BOUNDS.maxLat &&\n long >= DC_BOUNDS.minLong &&\n long <= DC_BOUNDS.maxLong\n );\n}\n\n// Filter hotspots to DC area\nexport function filterDCHotspots(hotspots: HeliumHotspotListItem[]): HeliumHotspotListItem[] {\n return hotspots.filter(h => isInDCArea(h.lat, h.long));\n}\n\n// Sleep utility for rate limiting\nexport function sleep(ms: number): Promise {\n return new Promise(resolve => setTimeout(resolve, ms));\n}\n\n// Fetch all DC area hotspots with pagination\nexport async function fetchAllDCHotspots(\n onProgress?: (processed: number, dcCount: number, cursor: string | null) => void\n): Promise {\n const dcHotspots: HeliumHotspotListItem[] = [];\n let cursor: string | null = null;\n let processed = 0;\n\n while (true) {\n const response = await fetchMobileHotspots(cursor);\n\n for (const hotspot of response.items) {\n processed++;\n if (isInDCArea(hotspot.lat, hotspot.long)) {\n dcHotspots.push(hotspot);\n }\n }\n\n if (onProgress) {\n onProgress(processed, dcHotspots.length, response.cursor);\n }\n\n if (!response.cursor) break;\n cursor = response.cursor;\n\n // Rate limiting\n await sleep(RATE_LIMIT_MS);\n }\n\n return dcHotspots;\n}\n",[],[178,322],"59aad018-f8b9-11f0-ae34-42dde27851f2","db.ts","e75c8c60-f7d5-11f0-84f8-42dde27851f2",121,["D",1769213540536],8,"// Database schema and query functions for DC Hotspot Growth Map\nimport sqlite from \"https://esm.town/v/std/sqlite@14-main/main.ts\";\nimport type { Hotspot, DailySnapshot } from \"../shared/types.ts\";\n\n// Clear all data from database\nexport async function clearDatabase() {\n await sqlite.execute(`DELETE FROM hotspots`);\n await sqlite.execute(`DELETE FROM hotspot_locations`);\n await sqlite.execute(`DELETE FROM daily_snapshots`);\n}\n\n// Initialize database schema\nexport async function initDatabase() {\n // Hotspot records\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS hotspots (\n id INTEGER PRIMARY KEY,\n entity_key_str TEXT UNIQUE NOT NULL,\n key_to_asset_key TEXT NOT NULL,\n lat REAL NOT NULL,\n long REAL NOT NULL,\n created_at TEXT NOT NULL,\n first_seen_at TEXT NOT NULL,\n is_active INTEGER DEFAULT 1\n )\n `);\n\n // Location change history\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS hotspot_locations (\n id INTEGER PRIMARY KEY,\n entity_key_str TEXT NOT NULL,\n lat REAL NOT NULL,\n long REAL NOT NULL,\n observed_at TEXT NOT NULL\n )\n `);\n\n // Pre-computed daily stats\n await sqlite.execute(`\n CREATE TABLE IF NOT EXISTS daily_snapshots (\n snapshot_date TEXT PRIMARY KEY,\n total_count INTEGER NOT NULL,\n new_count INTEGER NOT NULL\n )\n `);\n\n // Create indexes\n await sqlite.execute(`\n CREATE INDEX IF NOT EXISTS idx_hotspots_created ON hotspots(created_at)\n `);\n await sqlite.execute(`\n CREATE INDEX IF NOT EXISTS idx_snapshots_date ON daily_snapshots(snapshot_date)\n `);\n}\n\n// Get all active hotspots\nexport async function getAllHotspots(): Promise {\n const result = await sqlite.execute(`\n SELECT entity_key_str, key_to_asset_key, lat, long, created_at, first_seen_at, is_active\n FROM hotspots\n ORDER BY created_at ASC\n LIMIT 10000\n `);\n\n // Val Town SQLite returns rows as arrays, access by column index\n const columns = result.columns || [];\n return result.rows.map((row: any) => {\n // If row is an array, convert to object using column names\n if (Array.isArray(row)) {\n const obj: any = {};\n columns.forEach((col: string, i: number) => {\n obj[col] = row[i];\n });\n return {\n entity_key_str: obj.entity_key_str as string,\n key_to_asset_key: obj.key_to_asset_key as string,\n lat: obj.lat as number,\n long: obj.long as number,\n created_at: obj.created_at as string,\n first_seen_at: obj.first_seen_at as string,\n is_active: Boolean(obj.is_active),\n };\n }\n // Otherwise assume it's already an object\n return {\n entity_key_str: row.entity_key_str as string,\n key_to_asset_key: row.key_to_asset_key as string,\n lat: row.lat as number,\n long: row.long as number,\n created_at: row.created_at as string,\n first_seen_at: row.first_seen_at as string,\n is_active: Boolean(row.is_active),\n };\n });\n}\n\n// Get all daily snapshots\nexport async function getAllSnapshots(): Promise {\n const result = await sqlite.execute(`\n SELECT snapshot_date, total_count, new_count\n FROM daily_snapshots\n ORDER BY snapshot_date ASC\n `);\n\n // Val Town SQLite returns rows as arrays\n const columns = result.columns || [];\n return result.rows.map((row: any) => {\n if (Array.isArray(row)) {\n const obj: any = {};\n columns.forEach((col: string, i: number) => {\n obj[col] = row[i];\n });\n return {\n snapshot_date: obj.snapshot_date as string,\n total_count: obj.total_count as number,\n new_count: obj.new_count as number,\n };\n }\n return {\n snapshot_date: row.snapshot_date as string,\n total_count: row.total_count as number,\n new_count: row.new_count as number,\n };\n });\n}\n\n// Insert or update a hotspot\nexport async function upsertHotspot(hotspot: {\n entity_key_str: string;\n key_to_asset_key: string;\n lat: number;\n long: number;\n created_at: string;\n first_seen_at: string;\n is_active: boolean;\n}) {\n await sqlite.execute({\n sql: `\n INSERT INTO hotspots (entity_key_str, key_to_asset_key, lat, long, created_at, first_seen_at, is_active)\n VALUES (?, ?, ?, ?, ?, ?, ?)\n ON CONFLICT(entity_key_str) DO UPDATE SET\n lat = excluded.lat,\n long = excluded.long,\n is_active = excluded.is_active\n `,\n args: [\n hotspot.entity_key_str,\n hotspot.key_to_asset_key,\n hotspot.lat,\n hotspot.long,\n hotspot.created_at,\n hotspot.first_seen_at,\n hotspot.is_active ? 1 : 0,\n ],\n });\n}\n\n// Insert a daily snapshot\nexport async function upsertSnapshot(snapshot: DailySnapshot) {\n await sqlite.execute({\n sql: `\n INSERT INTO daily_snapshots (snapshot_date, total_count, new_count)\n VALUES (?, ?, ?)\n ON CONFLICT(snapshot_date) DO UPDATE SET\n total_count = excluded.total_count,\n new_count = excluded.new_count\n `,\n args: [snapshot.snapshot_date, snapshot.total_count, snapshot.new_count],\n });\n}\n\n// Get hotspot count\nexport async function getHotspotCount(): Promise {\n const result = await sqlite.execute(`SELECT COUNT(*) as count FROM hotspots WHERE is_active = 1`);\n const row = result.rows[0];\n return (Array.isArray(row) ? row[0] : row.count) as number;\n}\n\n// Check if a hotspot exists\nexport async function getHotspot(entity_key_str: string): Promise {\n const result = await sqlite.execute({\n sql: `SELECT * FROM hotspots WHERE entity_key_str = ?`,\n args: [entity_key_str],\n });\n\n if (result.rows.length === 0) return null;\n\n const row = result.rows[0];\n return {\n entity_key_str: row.entity_key_str as string,\n key_to_asset_key: row.key_to_asset_key as string,\n lat: row.lat as number,\n long: row.long as number,\n created_at: row.created_at as string,\n first_seen_at: row.first_seen_at as string,\n is_active: Boolean(row.is_active),\n };\n}\n\n// Record location change\nexport async function recordLocationChange(entity_key_str: string, lat: number, long: number, observed_at: string) {\n await sqlite.execute({\n sql: `INSERT INTO hotspot_locations (entity_key_str, lat, long, observed_at) VALUES (?, ?, ?, ?)`,\n args: [entity_key_str, lat, long, observed_at],\n });\n}\n\n// Compute historical snapshots from hotspot created_at dates\nexport async function computeHistoricalSnapshots() {\n // Get date range - Val Town SQLite returns arrays, so access by index\n const dateRange = await sqlite.execute(`\n SELECT MIN(DATE(created_at)) as min_date, MAX(DATE(created_at)) as max_date\n FROM hotspots\n `);\n\n // Access by index (0 = min_date, 1 = max_date)\n const row = dateRange.rows[0];\n const minDateStr = Array.isArray(row) ? row[0] : row.min_date;\n const maxDateStr = Array.isArray(row) ? row[1] : row.max_date;\n\n if (!minDateStr) return;\n\n const minDate = new Date(minDateStr as string);\n const maxDate = new Date(maxDateStr as string);\n\n // Compute snapshot for each day\n for (let d = new Date(minDate); d <= maxDate; d.setDate(d.getDate() + 1)) {\n const dateStr = d.toISOString().split('T')[0];\n\n const totalResult = await sqlite.execute({\n sql: `SELECT COUNT(*) as total FROM hotspots WHERE DATE(created_at) <= ?`,\n args: [dateStr],\n });\n\n const newResult = await sqlite.execute({\n sql: `SELECT COUNT(*) as new_count FROM hotspots WHERE DATE(created_at) = ?`,\n args: [dateStr],\n });\n\n // Access counts by index as well\n const totalRow = totalResult.rows[0];\n const newRow = newResult.rows[0];\n const total = Array.isArray(totalRow) ? totalRow[0] : totalRow.total;\n const newCount = Array.isArray(newRow) ? newRow[0] : newRow.new_count;\n\n await upsertSnapshot({\n snapshot_date: dateStr,\n total_count: total as number,\n new_count: newCount as number,\n });\n 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