background.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %cd ~/src/Sunrise-Demo/\n",
    "from __future__ import annotations\n",
    "try:\n",
    "    from mediocreatbest import auto, run\n",
    "except ImportError:\n",
    "    %pip install --quiet --upgrade pip\n",
    "    %pip install --upgrade --force-reinstall \\\n",
    "        mediocreatbest@git+https://gist.github.com/player1537/3457b026ed6ef6696d758517f55a58df.git\n",
    "from mediocreatbest import auto, run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "SPECIES = \"0000143\"\n",
    "TIXEL_Z = 21\n",
    "ZOOM_RESOLUTION = 5\n",
    "SHELVE_PATH = auto.pathlib.Path.cwd() / 'background-shelve' / 'Sunrise.shelve'\n",
    "\n",
    "DO_SAVE = True\n",
    "TERRAIN_QUALITY = (\n",
    "    0\n",
    "    # 1\n",
    "    # 2\n",
    ")\n",
    "VIDEO_QUALITY = (\n",
    "#     0\n",
    "#     1\n",
    "    2\n",
    ")\n",
    "\n",
    "# # Zoom level 13\n",
    "# RLO=824832\n",
    "# RHI=827647\n",
    "RLO=823296\n",
    "RHI=829438\n",
    "# CLO=559104\n",
    "CLO=558080\n",
    "# CHI=564479\n",
    "CHI=566270\n",
    "\n",
    "# # Zoom level 15\n",
    "# RLO=3300096\n",
    "# RHI=3310591\n",
    "# CLO=2236928\n",
    "# CHI=2259455"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%scope\n",
    "def string_function(arr):\n",
    "    name = arr.__class__.__name__\n",
    "    if arr.size < 1024:\n",
    "        values = auto.numpy.array_repr(auto.numpy.ravel(arr))\n",
    "    else:\n",
    "        values = '...'\n",
    "        \n",
    "    shape = auto.numpy.shape(arr)\n",
    "    dtype = arr.dtype\n",
    "    return f'{name}({values}, {shape=!r}, {dtype=!r})'\n",
    "auto.numpy.set_string_function(string_function, repr=False)\n",
    "auto.numpy.set_string_function(string_function, repr=True)\n",
    "\n",
    "auto.numpy.set_printoptions(\n",
    "    threshold=4,\n",
    "    edgeitems=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Typing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Degree = auto.typing.NewType('Degree', float)\n",
    "Radian = auto.typing.NewType('Radian', float)\n",
    "Meter = auto.typing.NewType('Meter', float)\n",
    "Kilometer = auto.typing.NewType('Kilometer', float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Location (lat, lng, alt) to spatial (x, y, z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_location_to_spatial(\n",
    "    *,\n",
    "    lat: Degree,\n",
    "    lng: Degree,\n",
    "    alt: Meter,\n",
    "    math=auto.math,\n",
    ") -> Tuple[Meter, Meter, Meter]:\n",
    "    # Thanks https://gis.stackexchange.com/a/4148\n",
    "    \n",
    "    #> Note that \"Lat/Lon/Alt\" is just another name for spherical coordinates, and \n",
    "    #> phi/theta/rho are just another name for latitude, longitude, and altitude.\n",
    "    #> :) (A minor difference: altitude is usually measured from the surface of the \n",
    "    #> sphere; rho is measured from the center -- to convert, just add/subtract the \n",
    "    #> radius of the sphere.)\n",
    "    phi: Radian = math.radians(lat)\n",
    "    theta: Radian = math.radians(lng)\n",
    "    \n",
    "    # Thanks https://en.wikipedia.org/wiki/Earth_radius\n",
    "    #> A globally-average value is usually considered to be 6,371 kilometres (3,959 mi)\n",
    "    rho: Meter = 6_371_000 + alt\n",
    "    \n",
    "    #> x = math.cos(phi) * math.cos(theta) * rho\n",
    "    x: Meter = math.cos(phi) * math.cos(theta) * rho\n",
    "    \n",
    "    #> y = math.cos(phi) * math.sin(theta) * rho\n",
    "    y: Meter = math.cos(phi) * math.sin(theta) * rho\n",
    "\n",
    "    #> z = math.sin(phi) * rho # z is 'up'\n",
    "    z: Meter = math.sin(phi) * rho\n",
    "    \n",
    "    #> (Note there's some slightly arbitrary choices here in what each axis means...\n",
    "    #> you might want 'y' to point at the north pole instead of 'z', for example.)\n",
    "    \n",
    "    # I do :)\n",
    "    y, z = z, y\n",
    "    \n",
    "    return x, y, z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "@auto.mediocreatbest.doctest\n",
    "def convert_tile_coordinates_to_latlng(\n",
    "    *,\n",
    "    z: int,\n",
    "    x: int,\n",
    "    y: int,\n",
    "    math=auto.numpy,\n",
    "    return_google_maps_url: bool=False,\n",
    ") -> Tuple[Degree, Degree]:\n",
    "    # r\"\"\"\n",
    "\n",
    "    # >>> convert_tile_coordinates_to_latlng(z=15, x=8739, y=12925, return_google_maps_url=True)\n",
    "    # 'https://www.google.com/maps/@35.48751102385376,-83.990478515625,15.79z?entry=ttu'\n",
    "\n",
    "    # \"\"\"\n",
    "    \n",
    "    # Thanks https://gis.stackexchange.com/a/133535\n",
    "    \n",
    "    #> n = 2 ^ zoom\n",
    "    n = 2 ** z\n",
    "    \n",
    "    #> lon_deg = xtile / n * 360.0 - 180.0\n",
    "    lon_deg = x / n * 360.0 - 180.0\n",
    "\n",
    "    print(\"LATLNG\")\n",
    "    \n",
    "    #> lat_rad = arctan(sinh(π * (1 - 2 * ytile / n)))\n",
    "    lat_rad = math.arctan(math.sinh(math.pi * (1.0 - 2.0 * y / n)))\n",
    "    \n",
    "    #> lat_deg = lat_rad * 180.0 / π\n",
    "    lat_deg = lat_rad * 180.0 / math.pi\n",
    "    \n",
    "    if return_google_maps_url:\n",
    "        return f\"\"\"https://www.google.com/maps/@{lat_deg},{lon_deg},15.79z?entry=ttu\"\"\"\n",
    "    \n",
    "    return lat_deg, lon_deg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataclasses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "@auto.dataclasses.dataclass(eq=True, order=True, frozen=True)\n",
    "class Coord:\n",
    "    \"\"\"Tile image coordinates for mapbox/etc services\"\"\"\n",
    "    z: int\n",
    "    x: int\n",
    "    y: int\n",
    "\n",
    "    @property\n",
    "    def nw(self) -> Location:\n",
    "        \"\"\"North-West lat/lng of current tile\"\"\"\n",
    "        lat, lng = tile_coordinates_to_latlng(\n",
    "            z=self.z,\n",
    "            x=self.y,\n",
    "            y=self.x,\n",
    "            # z=self.z,\n",
    "            # x=self.x,\n",
    "            # y=self.y,\n",
    "        )\n",
    "        return Location(lat=lat, lng=lng)\n",
    "\n",
    "    @property\n",
    "    def se(self) -> Location:\n",
    "        \"\"\"South-East lat/lng of current tile\"\"\"\n",
    "        lat, lng = tile_coordinates_to_latlng(\n",
    "            z=self.z,\n",
    "            x=1+self.y,\n",
    "            y=1+self.x,\n",
    "            # z=self.z,\n",
    "            # x=1+self.x,\n",
    "            # y=1+self.y,\n",
    "        )\n",
    "        return Location(lat=lat, lng=lng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "@auto.dataclasses.dataclass(eq=True, frozen=True)\n",
    "class Domain:\n",
    "    \"\"\"Lower (inclusive) and upper (inclusive) bounds of latitude/longitude\"\"\"\n",
    "    lo: Coord\n",
    "    hi: Coord\n",
    "        \n",
    "    @property\n",
    "    def coords(self) -> List[Coord]:\n",
    "        \"\"\"All coordinates within lower/upper bounds\"\"\"\n",
    "        coords = []\n",
    "        for z, x, y in auto.itertools.product(\n",
    "            range(self.lo.z, 1+self.hi.z),\n",
    "            range(self.lo.x, 1+self.hi.x),\n",
    "            range(self.lo.y, 1+self.hi.y),\n",
    "        ):\n",
    "            coords.append(Coord(\n",
    "                z=z,\n",
    "                x=x,\n",
    "                y=y,\n",
    "            ))\n",
    "        \n",
    "        return coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "@auto.dataclasses.dataclass(eq=True, order=True, frozen=True)\n",
    "class Location:\n",
    "    \"\"\"Geographical latitude/longitude\"\"\"\n",
    "    lat: Degree\n",
    "    lng: Degree\n",
    "    alt: Optional[Meter] = auto.dataclasses.field(default=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "@auto.dataclasses.dataclass(eq=True, order=True, frozen=True)\n",
    "class Point:\n",
    "    x: Kilometer\n",
    "    y: Kilometer\n",
    "    z: Kilometer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @dispatch\n",
    "@auto.dataclasses.dataclass(eq=True, order=True, frozen=True)\n",
    "class Tixel:\n",
    "    y: int\n",
    "    x: int\n",
    "        \n",
    "    # z: ClassVar[int] = 23\n",
    "    z: ClassVar[int] = TIXEL_Z\n",
    "        \n",
    "    @property\n",
    "    def location(self) -> Location:\n",
    "        \"\"\"Center lat/lng of current tixel\"\"\"\n",
    "        lat, lng = tile_coordinates_to_latlng(\n",
    "            z=1+self.z,\n",
    "            x=1+self.x,\n",
    "            y=1+self.y,\n",
    "        )\n",
    "        return Location(lat=lat, lng=lng)\n",
    "\n",
    "# @Tixel.register\n",
    "# def __tixel_from_coord(coord: Coord, /, *, res: int):\n",
    "#     dz = int(auto.math.log2(Tixel.z - coord.z))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "@auto.dataclasses.dataclass\n",
    "class TileURL:\n",
    "    scheme: str\n",
    "    netloc: str\n",
    "    prefix: str\n",
    "    z: int\n",
    "    x: int\n",
    "    y: int\n",
    "    suffix: str\n",
    "    query: Dict[str, str]\n",
    "    fragment: str\n",
    "\n",
    "    @classmethod\n",
    "    @auto.mediocreatbest.doctest\n",
    "    def decode(cls, url: str) -> Self:\n",
    "        r\"\"\"\n",
    "        \n",
    "        >>> decode(dict, 'https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/549/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg')\n",
    "        ... # doctest: +NORMALIZE_WHITESPACE\n",
    "        {'scheme': 'https',\n",
    "         'netloc': 'atlas-stg.geoplatform.gov',\n",
    "         'prefix': ['', 'styles', 'v1', 'atlas-user', 'ck58pyquo009v01p99xebegr9', 'tiles', '256'],\n",
    "         'z': 11,\n",
    "         'x': 549,\n",
    "         'y': 806,\n",
    "         'suffix': '@2x',\n",
    "         'query': [('access_token',\n",
    "                    'pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg')],\n",
    "         'fragment': ''}\n",
    "        >>> decode(dict, 'https://atlas-stg.geoplatform.gov:443/v4/atlas-user.0000214_pink/11/549/806.png?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg')\n",
    "        ... # doctest: +NORMALIZE_WHITESPACE\n",
    "        {'scheme': 'https',\n",
    "         'netloc': 'atlas-stg.geoplatform.gov:443',\n",
    "         'prefix': ['', 'v4', 'atlas-user.0000214_pink'],\n",
    "         'z': 11,\n",
    "         'x': 549,\n",
    "         'y': 806,\n",
    "         'suffix': '.png',\n",
    "         'query': [('access_token',\n",
    "                    'pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg')],\n",
    "         'fragment': ''}\n",
    "        \n",
    "        \"\"\"\n",
    "        \n",
    "        scheme, netloc, path, query, fragment = auto.urllib.parse.urlsplit(\n",
    "            url,\n",
    "        )\n",
    "        path = path.split('/')\n",
    "        *prefix, z, x, suffix = path\n",
    "        assert (match := auto.re.match(r'^(\\d+)(.*)$', suffix)) is not None, \\\n",
    "            f'{suffix = !r}'\n",
    "        y, suffix = match.groups()\n",
    "            \n",
    "        \n",
    "        z, x, y = map(int, (z, x, y))\n",
    "        query = auto.urllib.parse.parse_qsl(\n",
    "            query,\n",
    "        )\n",
    "\n",
    "        return cls(\n",
    "            scheme=scheme,\n",
    "            netloc=netloc,\n",
    "            prefix=prefix,\n",
    "            z=z,\n",
    "            x=x,\n",
    "            y=y,\n",
    "            suffix=suffix,\n",
    "            query=query,\n",
    "            fragment=fragment,\n",
    "        )\n",
    "\n",
    "    def encode(self) -> str:\n",
    "        query = auto.urllib.parse.urlencode(\n",
    "            self.query,\n",
    "            doseq=True,\n",
    "        )\n",
    "        \n",
    "        z, x, y = map(str, (self.z, self.x, self.y))\n",
    "\n",
    "        path = '/'.join([\n",
    "            *self.prefix,\n",
    "            z,\n",
    "            x,\n",
    "            y + self.suffix,\n",
    "        ])\n",
    "\n",
    "        return auto.urllib.parse.urlunsplit((\n",
    "            self.scheme,\n",
    "            self.netloc,\n",
    "            path,\n",
    "            query,\n",
    "            self.fragment,\n",
    "        ))\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## HTTP Request Caching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "@run(once=True)\n",
    "def cache():\n",
    "    path = SHELVE_PATH\n",
    "    # path = auto.pathlib.Path.cwd() / 'background' / 'Sunrise.shelve'\n",
    "    return auto.shelve.open(str(path), 'c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "@run\n",
    "def fetch(cache, /):\n",
    "    def fetch(url: str, *, tqdm=None) -> bytes:\n",
    "        key = url\n",
    "        if key not in cache:\n",
    "            if tqdm is not None:\n",
    "                tqdm.set_description(f'Cache Miss: {url}')\n",
    "\n",
    "            # with auto.requests.get(url) as r:\n",
    "            #     r.raise_for_status()\n",
    "            #     cache[key] = r.content\n",
    "            try:\n",
    "                r = auto.requests.get(url)\n",
    "                r.raise_for_status()\n",
    "                content = r.content\n",
    "            except auto.requests.exceptions.HTTPError:\n",
    "                content = 404\n",
    "            \n",
    "            cache[key] = content\n",
    "            auto.time.sleep(1)\n",
    "        \n",
    "        else:\n",
    "            if tqdm is not None:\n",
    "                tqdm.set_description('Cache Hit: {url}')\n",
    "            \n",
    "            content = cache[key]\n",
    "        \n",
    "        if isinstance(content, int):\n",
    "            # raise auto.requests.exceptions.HTTPError(content)\n",
    "            return None\n",
    "        \n",
    "        return content\n",
    "\n",
    "    return fetch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def __stitch_tiles(tiles, /, size: int=128, flip=False):\n",
    "    auto.PIL; from PIL import Image\n",
    "    \n",
    "    xs = set(tile.coord.x for tile in tiles)\n",
    "    ys = set(tile.coord.y for tile in tiles)\n",
    "    \n",
    "    xs = sorted(xs)\n",
    "    ys = sorted(ys)\n",
    "    \n",
    "    nx = len(xs)\n",
    "    ny = len(ys)\n",
    "    \n",
    "    sz = size\n",
    "    \n",
    "    composite = Image.new('RGB', (nx*sz, ny*sz))\n",
    "    \n",
    "    for tile in tiles:\n",
    "        xi = xs.index(tile.coord.x)\n",
    "        yi = ys.index(tile.coord.y)\n",
    "        \n",
    "        with auto.io.BytesIO(tile.image) as f:\n",
    "            try:\n",
    "                image = Image.open(f)\n",
    "            except auto.PIL.UnidentifiedImageError:\n",
    "                continue\n",
    "\n",
    "            image.thumbnail((sz, sz))\n",
    "\n",
    "        # composite.paste(image, (yi*sz, xi*sz))\n",
    "        if flip==True:\n",
    "            composite.paste(image, (yi*sz, xi*sz))\n",
    "        else:\n",
    "            composite.paste(image, (xi*sz, yi*sz))\n",
    "            \n",
    "    \n",
    "    return composite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "## @Tile.from_.register(Location)\n",
    "@auto.mediocreatbest.doctest\n",
    "def convert_location_to_tile(*, lat: Degree, lng: Degree, zoom: int, math=auto.math) -> tuple[int, int]:\n",
    "    r\"\"\"\n",
    "    \n",
    "    >>> convert_location_to_tile(lat=35.6, lng=-83.52, zoom=11)\n",
    "    (807, 548)\n",
    "    >>> convert_location_to_tile(lat=35.0, lng=-84.5, zoom=15)\n",
    "    (12979, 8692)\n",
    "    >>> convert_location_to_tile(lat=36.25, lng=-82.5, zoom=15)\n",
    "    (12839, 8874)\n",
    "    \n",
    "    \"\"\"\n",
    "    # Thanks https://stackoverflow.com/a/72476578\n",
    "    \n",
    "    #> lat_rad = math.radians(lat_deg)\n",
    "    lat_rad = math.radians(lat)\n",
    "    \n",
    "    #> n = 2.0 ** zoom\n",
    "    n = 2.0 ** zoom\n",
    "    \n",
    "    #> xtile = int((lon_deg + 180.0) / 360.0 * n)\n",
    "    col = int((lng + 180.0) / 360.0 * n)\n",
    "    \n",
    "    #> ytile = int((1.0 - math.asinh(math.tan(lat_rad)) / math.pi) / 2.0 * n)\n",
    "    row = int((1.0 - math.asinh(math.tan(lat_rad)) / math.pi) / 2.0 * n)\n",
    "    \n",
    "    #> return (xtile, ytile)\n",
    "    return row, col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Domain(lo=Coord(z=11, x=545, y=804), hi=Coord(z=11, x=552, y=809))\n"
     ]
    }
   ],
   "source": [
    "@run(after=auto.pprint.pp, scope='background')\n",
    "def domain():\n",
    "    from bs4 import BeautifulSoup\n",
    "    \n",
    "    images = ''\n",
    "    template = '<img alt=\"\" role=\"presentation\" src=\"https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{x}/{y}\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 512px; height: 512px; transform: translate3d(439px, -19px, 0px); opacity: 1;\">'\n",
    "    zoom = ZOOM_RESOLUTION\n",
    "    # for x in range((2 ** zoom)-1):\n",
    "    #     for y in range((2 ** zoom)-10):\n",
    "    #         imgurl = template.format(z = zoom, x = x, y = y)\n",
    "    #         images = images + imgurl\n",
    "    #         print(imgurl)\n",
    "    # soup = BeautifulSoup(images, 'html.parser')\n",
    "    \n",
    "    soup = BeautifulSoup(r'''\n",
    "        <img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/548/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(439px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/549/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(695px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/548/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(439px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/549/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(695px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/547/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(183px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/550/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(951px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/547/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(183px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/550/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(951px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/546/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(-73px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/551/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(1207px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/546/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(-73px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/551/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(1207px, 237px, 0px); opacity: 1;\">\n",
    "        <img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/548/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(439px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/549/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(695px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/548/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(439px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/549/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(695px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/547/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(183px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/550/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(951px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/547/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(183px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/550/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(951px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/546/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(-73px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/551/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(1207px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/546/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(-73px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/551/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(1207px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/548/805@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(439px, -275px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/549/805@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(695px, -275px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/547/805@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(183px, -275px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/550/805@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(951px, -275px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/546/805@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(-73px, -275px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/551/805@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(1207px, -275px, 0px); opacity: 1;\">\n",
    "        <img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/548/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(439px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/549/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(695px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/548/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(439px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/549/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(695px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/547/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(183px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/550/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(951px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/547/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(183px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/550/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(951px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/546/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(-73px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/551/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(1207px, -19px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/546/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(-73px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/551/807@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(1207px, 237px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/548/805@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(439px, -275px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/549/805@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(695px, -275px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/547/805@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(183px, -275px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/550/805@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(951px, -275px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/546/805@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(-73px, -275px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/551/805@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(1207px, -275px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/548/808@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(439px, 493px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/549/808@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(695px, 493px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/547/808@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(183px, 493px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/550/808@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(951px, 493px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/546/808@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(-73px, 493px, 0px); opacity: 1;\"><img alt=\"\" role=\"presentation\" src=\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/551/808@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\" class=\"leaflet-tile leaflet-tile-loaded\" style=\"width: 256px; height: 256px; transform: translate3d(1207px, 493px, 0px); opacity: 1;\">\n",
    "    ''', 'html.parser')\n",
    "    \n",
    "    \n",
    "    urls = []\n",
    "    for img in soup('img'):\n",
    "        src = img['src']\n",
    "        url = TileURL.decode(src)\n",
    "        urls.append(url)\n",
    "\n",
    "    return Domain(\n",
    "        lo=Coord(z=11, x=546-1, y=805-1),\n",
    "        hi=Coord(z=11, x=551+1, y=808+1),\n",
    "    )\n",
    "    \n",
    "    # return Domain(\n",
    "    #     lo=Coord(\n",
    "    #         z=min(url.z for url in urls),\n",
    "    #         x=min(url.x for url in urls),\n",
    "    #         y=min(url.y for url in urls),\n",
    "    #     ),\n",
    "    #     hi=Coord(\n",
    "    #         z=max(url.z for url in urls),\n",
    "    #         x=max(url.x for url in urls),\n",
    "    #         y=max(url.y for url in urls),\n",
    "    #     ),\n",
    "    # )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Cache Hit: {url}: 100%|██████████| 48/48 [00:00<00:00, 1186.11it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "@auto.dataclasses.dataclass(eq=True, frozen=True)\n",
    "class Tile:\n",
    "    coord: Coord\n",
    "    url: str = auto.dataclasses.field(repr=False)\n",
    "    image: bytes = auto.dataclasses.field(repr=False)\n",
    "\n",
    "@run(scope='background', after=lambda x: print(len(x)))\n",
    "def tiles(domain, fetch, /):\n",
    "    def make_url(coord: Coord, /, *, url=TileURL.decode(\n",
    "        # r\"\"\"https://atlas-stg.geoplatform.gov/styles/v1/atlas-user/ck58pyquo009v01p99xebegr9/tiles/256/11/549/806@2x?access_token=pk.eyJ1IjoiYXRsYXMtdXNlciIsImEiOiJjazFmdGx2bjQwMDAwMG5wZmYwbmJwbmE2In0.lWXK2UexpXuyVitesLdwUg\"\"\"\n",
    "        r\"\"\"https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/0/0/0\"\"\"\n",
    "    )):\n",
    "        return auto.dataclasses.replace(\n",
    "            url,\n",
    "            z=coord.z,\n",
    "            # x=coord.x,\n",
    "            # y=coord.y,\n",
    "            x=coord.y,\n",
    "            y=coord.x,\n",
    "        )\n",
    "    \n",
    "    tiles = []\n",
    "    for coord in (tqdm := auto.tqdm.tqdm(domain.coords)):\n",
    "        url = make_url(coord).encode()\n",
    "        # print(f'url: {url}')\n",
    "        # print(f'coord: {coord.x}, {coord.y}')\n",
    "        # print(f'url: {url}')\n",
    "        image = fetch(url, tqdm=tqdm)\n",
    "\n",
    "        tiles.append(Tile(\n",
    "            coord=coord,\n",
    "            url=url,\n",
    "            image=image,\n",
    "        ))\n",
    "\n",
    "    return tiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=256x192>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "__stitch_tiles(run.g['background__tiles'], size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len(ys)=6 len(xs)=8\n",
      "ys: points[0][[0, -1]]=ndarray(array([823296., 829436.]), shape=(2,), dtype=dtype('float64'))\n",
      "xs: points[1][[0, -1]]=ndarray(array([558080., 566268.]), shape=(2,), dtype=dtype('float64'))\n",
      "values.shape=(1536, 2048, 3) values.dtype=dtype('float32')\n"
     ]
    }
   ],
   "source": [
    "@run(scope='background')\n",
    "def interpolator(tiles, /):\n",
    "    # res = 512\n",
    "    res = 256\n",
    "    \n",
    "    zs = sorted(set(tile.coord.z for tile in tiles))\n",
    "    assert len(zs) == 1\n",
    "    ys = sorted(set(tile.coord.y for tile in tiles))\n",
    "    xs = sorted(set(tile.coord.x for tile in tiles))\n",
    "    \n",
    "    print(f\"\"\"{len(ys)=!r} {len(xs)=!r}\"\"\")\n",
    "    \n",
    "    # This is cursed.\n",
    "    points = (\n",
    "        2 ** (Tixel.z - auto.math.log2(res) - zs[0]) * (auto.numpy.repeat(res * auto.numpy.array(ys)[:, None], res, axis=1) + auto.numpy.arange(res)[None, :]).ravel(),\n",
    "        2 ** (Tixel.z - auto.math.log2(res) - zs[0]) * (auto.numpy.repeat(res * auto.numpy.array(xs)[:, None], res, axis=1) + auto.numpy.arange(res)[None, :]).ravel(),\n",
    "    )\n",
    "    \n",
    "    print(f\"\"\"ys: {points[0][[0, -1]]=!r}\n",
    "xs: {points[1][[0, -1]]=!r}\"\"\")\n",
    "    \n",
    "    values = auto.numpy.zeros(\n",
    "        shape=(len(points[0]), len(points[1]), 3),\n",
    "        dtype=auto.numpy.float32,\n",
    "    )\n",
    "    print(f\"\"\"{values.shape=!r} {values.dtype=!r}\"\"\")\n",
    "\n",
    "    for tile in tiles:\n",
    "        yi = ys.index(tile.coord.y)\n",
    "        xi = xs.index(tile.coord.x)\n",
    "        \n",
    "        with auto.io.BytesIO(tile.image) as f:\n",
    "            image = auto.PIL.Image.open(f)\n",
    "            image.load()\n",
    "            image = auto.PIL.ImageOps.flip(image)\n",
    "            \n",
    "        image = image.convert('RGB')\n",
    "        image = auto.numpy.array(image, dtype=auto.numpy.float32)\n",
    "        image = image[::-1,:,:]\n",
    "        assert image.shape == (res, res, 3), image.shape\n",
    "        assert image.dtype == auto.numpy.float32\n",
    "        assert 1.0 < image.max() <= 255.0\n",
    "        \n",
    "        image /= 255.0\n",
    "        assert 0.0 < image.max() <= 1.0\n",
    "        \n",
    "#         import numpy.lib.recfunctions\n",
    "#         image = auto.numpy.lib.recfunctions.unstructured_to_structured(\n",
    "#             image,\n",
    "#             dtype=values.dtype,\n",
    "#         )\n",
    "#         assert image.shape == (res, res), \\\n",
    "#             f\"\"\"Wrong shape: {image.shape=!r}\"\"\"\n",
    "#         assert image.dtype == values.dtype\n",
    "        \n",
    "        values[\n",
    "            slice(\n",
    "                (yi+0)*res,\n",
    "                (yi+1)*res,\n",
    "            ),\n",
    "            slice(\n",
    "                (xi+0)*res,\n",
    "                (xi+1)*res,\n",
    "            ),\n",
    "        ] = image\n",
    "               \n",
    "    \n",
    "    return auto.scipy.interpolate.RegularGridInterpolator(\n",
    "        points,\n",
    "        values,\n",
    "        bounds_error=False,\n",
    "        fill_value=0,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote 25,284,608 bytes to /home/raustin9/src/Sunrise-Demo-Dev/background-shelve/Sunrise.shelve\n"
     ]
    }
   ],
   "source": [
    "@run\n",
    "def __save(background__interpolator, /):\n",
    "    def save(\n",
    "        *,\n",
    "        name: str,\n",
    "        interpolator: auto.scipy.interpolate.RegularGridInterpolator,\n",
    "    ):\n",
    "        # path = auto.pathlib.Path.cwd() / 'temp' / f'park-{name}.npz'\n",
    "        path = SHELVE_PATH\n",
    "        auto.numpy.savez_compressed(\n",
    "            path,\n",
    "            points0=interpolator.grid[0],\n",
    "            points1=interpolator.grid[1],\n",
    "            values=interpolator.values,\n",
    "        )\n",
    "        \n",
    "        print(f'Wrote {path.stat().st_size :,d} bytes to {path}')\n",
    "    \n",
    "    save(\n",
    "        name='background',\n",
    "        interpolator=background__interpolator,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wrote 402,653,196 bytes to /home/raustin9/src/Sunrise-Demo-Dev/data/pink0/OSPTexture.texture2d.data.vec3f.bin\n",
      "Wrote 402,653,196 bytes to /home/raustin9/src/Sunrise-Demo-Dev/data/pink1/OSPTexture.texture2d.data.vec3f.bin\n",
      "Wrote 402,653,196 bytes to /home/raustin9/src/Sunrise-Demo-Dev/data/pink2/OSPTexture.texture2d.data.vec3f.bin\n",
      "Wrote 402,653,196 bytes to /home/raustin9/src/Sunrise-Demo-Dev/data/pink3/OSPTexture.texture2d.data.vec3f.bin\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "@run(scope='render', after=print)\n",
    "def colormap(background__interpolator, /):\n",
    "    def Data(array: auto.numpy.ndarray, /, *, type: lib.OSPDataType) -> lib.OSPData:\n",
    "        if len(array.shape) == 0:\n",
    "            array = array[None, None, None]\n",
    "\n",
    "        elif len(array.shape) == 1:\n",
    "            array = array[:, None, None]\n",
    "\n",
    "        elif len(array.shape) == 2:\n",
    "            array = array[:, :, None]\n",
    "            \n",
    "        elif len(array.shape) == 3:\n",
    "            array = array[:, :, :]\n",
    "\n",
    "        else:\n",
    "            raise NotImplementedError()\n",
    "\n",
    "        src = lib.ospNewSharedData(\n",
    "            array.ctypes.data, type,\n",
    "            array.shape[0], array.strides[0],\n",
    "            array.shape[1], array.strides[1],\n",
    "            array.shape[2], array.strides[2],\n",
    "        )\n",
    "        lib.ospCommit(src)\n",
    "\n",
    "        dst = lib.ospNewData(type, *array.shape)\n",
    "        lib.ospCopyData(src, dst, 0, 0, 0)\n",
    "        lib.ospCommit(dst)\n",
    "\n",
    "        lib.ospRelease(src)\n",
    "        return dst\n",
    "\n",
    "    # Thanks https://species.atlasofthesmokies.org/style.css\n",
    "    pinks = auto.numpy.array([\n",
    "        # Black, obviously.\n",
    "        # Represents 0-25%\n",
    "        [0, 0, 0],\n",
    "\n",
    "        #> #legend-pink-50 { background-color: #f069a3; }\n",
    "        # Represents 25-50%\n",
    "        [247, 105, 164],\n",
    "\n",
    "        #> #legend-pink-75 { background-color: #c31d8e; }\n",
    "        # Represents 50-75%\n",
    "        [196, 28, 142],\n",
    "\n",
    "        #> #legend-pink-100 { background-color: #752879; }\n",
    "        # Represents 75-100%\n",
    "        [121, 0, 124],\n",
    "    ], dtype=auto.numpy.uint8)\n",
    "\n",
    "    \n",
    "    # rlo = interpolate.observation.grid[0].min()\n",
    "    # rhi = interpolate.observation.grid[0].max()\n",
    "    # clo = interpolate.observation.grid[1].min()\n",
    "    # chi = interpolate.observation.grid[1].max()\n",
    "\n",
    "    # print(f'elevation RLO: {interpolate.elevation.grid[0].min()}')\n",
    "    # print(f'elevation CHI: {interpolate.elevation.grid[0].max()}')\n",
    "    # print(f'elevation CLO: {interpolate.elevation.grid[1].min()}')\n",
    "    # print(f'elevation CHI: {interpolate.elevation.grid[1].max()}')\n",
    "    # print(f'background RLO: {interpolate.background.grid[0].min()}')\n",
    "    # print(f'background CHI: {interpolate.background.grid[0].max()}')\n",
    "    # print(f'background CLO: {interpolate.background.grid[1].min()}')\n",
    "    # print(f'background CHI: {interpolate.background.grid[1].max()}')\n",
    "    # print(f'observation RLO: {interpolate.observation.grid[0].min()}')\n",
    "    # print(f'observation CHI: {interpolate.observation.grid[0].max()}')\n",
    "    # print(f'observation CLO: {interpolate.observation.grid[1].min()}')\n",
    "    # print(f'observation CHI: {interpolate.observation.grid[1].max()}')\n",
    "\n",
    "    rlo = RLO\n",
    "    rhi = RHI\n",
    "    clo = CLO\n",
    "    chi = CHI\n",
    "    # assert rlo >= real_rlo, \\\n",
    "    #     f'{rlo=!r} < {real_rlo=!r}'\n",
    "    # assert rhi <= real_rhi, \\\n",
    "    #     f'{rhi=!r} > {real_rhi=!r}'\n",
    "    # assert clo >= real_clo, \\\n",
    "    #     f'{clo=!r} < {real_clo=!r}'\n",
    "    # assert chi <= real_chi, \\\n",
    "    #     f'{chi=!r} > {real_chi=!r}'\n",
    "    rs = auto.numpy.linspace(rlo, rhi, num=1024 * 4)\n",
    "    rs = rs[::-1]  # Flip Y\n",
    "    cs = auto.numpy.linspace(clo, chi, num=2048 * 4)\n",
    "    # rs = auto.numpy.linspace(RLO, RHI, num=1024 * 4)\n",
    "    # rs = rs[::-1]  # Flip Y\n",
    "    # cs = auto.numpy.linspace(CLO, CHI, num=2048 * 4)\n",
    "    \n",
    "    @auto.mediocreatbest.immediate\n",
    "    @auto.mediocreatbest.coroutine\n",
    "    def display():\n",
    "        handle = auto.IPython.display.display(None, display_id=True)\n",
    "        \n",
    "        while True:\n",
    "            obj = yield\n",
    "            handle.update(obj)\n",
    "    \n",
    "    def Colormap(pi: int, /) -> lib.OSPTexture:\n",
    "        rgb = auto.numpy.zeros(\n",
    "            shape=(len(rs), len(cs)),\n",
    "            dtype=[\n",
    "                ('r', 'f4'),\n",
    "                ('g', 'f4'),\n",
    "                ('b', 'f4'),\n",
    "            ],\n",
    "        )\n",
    "        \n",
    "        for ri, r in enumerate(rs):\n",
    "            grid = auto.numpy.hstack(tuple(\n",
    "                v.reshape((-1, 1))\n",
    "                for v in auto.numpy.meshgrid(\n",
    "                    [r],\n",
    "                    cs,\n",
    "                    indexing='ij',\n",
    "                )\n",
    "            ))\n",
    "            assert grid.shape == (len(cs), 2), \\\n",
    "                f\"\"\"Wrong shape: {grid.shape=!r}\"\"\"\n",
    "#             print(f\"\"\"{grid[:, 0].min()=!r} ;; {grid[:, 0].max()=!r}\n",
    "# {grid[:, 1].min()=!r} ;; {grid[:, 1].max()=!r}\"\"\")\n",
    "            \n",
    "            # rgbs = interpolate.background(\n",
    "            #     grid,\n",
    "            #     method='linear',\n",
    "            # )\n",
    "            rgbs = background__interpolator(\n",
    "                grid,\n",
    "                method='linear',\n",
    "            )\n",
    "            assert rgbs.shape == (len(cs), 3), \\\n",
    "                f\"\"\"Wrong shape: {rgbs.shape=!r}\"\"\"\n",
    "            \n",
    "            def composite(background: 'RGB', foreground: 'RGB', *, alpha: float):\n",
    "                return background * (1.0 - alpha) + foreground * alpha\n",
    "            \n",
    "            pink = pinks[None, pi, :]\n",
    "            pink = pink.astype(auto.numpy.float32)\n",
    "            pink /= 255.0\n",
    "            \n",
    "            if pi == 0:\n",
    "                alpha = 0.0\n",
    "            else:\n",
    "                alpha = 0.5\n",
    "            \n",
    "            rgbs = composite(rgbs, pink, alpha=alpha)\n",
    "            assert rgbs.shape == (len(cs), 3), \\\n",
    "                f\"\"\"Wrong shape: {rgbs.shape=!r}\"\"\"\n",
    "            \n",
    "            rgb['r'][ri, :] = rgbs[:, 0]\n",
    "            rgb['g'][ri, :] = rgbs[:, 1]\n",
    "            rgb['b'][ri, :] = rgbs[:, 2]\n",
    "\n",
    "#         import numpy.lib.recfunctions\n",
    "#         display.send(auto.PIL.Image.fromarray(\n",
    "#             (255.0 * auto.numpy.lib.recfunctions.structured_to_unstructured(rgb)).astype(auto.numpy.uint8),\n",
    "#             mode='RGB',\n",
    "#         ))\n",
    "\n",
    "\n",
    "        #--- Save to Disk\n",
    "\n",
    "        def Save(data: auto.numpy.NDArray, /, *, path: auto.pathlib.Path):\n",
    "            if not DO_SAVE:\n",
    "                return\n",
    "            \n",
    "            path.parent.mkdir(parents=True, exist_ok=True)\n",
    "            with open(path, 'wb') as f:\n",
    "                f.write(auto.struct.pack('I', len(data.shape)))\n",
    "                f.write(auto.struct.pack(f'{len(data.shape)}I', *data.shape))\n",
    "                data.tofile(f)\n",
    "            print(f\"Wrote {path.stat().st_size :,d} bytes to {path}\")\n",
    "\n",
    "        Save(\n",
    "            rgb,\n",
    "            path=auto.pathlib.Path.cwd().joinpath(\n",
    "                f\"data\",\n",
    "                f\"pink{pi}\",\n",
    "                f\"OSPTexture.texture2d.data.vec3f.bin\",\n",
    "            ),\n",
    "        )\n",
    "    \n",
    "    for pi in range(len(pinks)):\n",
    "        Colormap(pi)"
   ]
  }
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